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The ability to reflect is considered an essential element of Education for Sustainable Development (ESD) and a key competence for learners and educators in ESD (UNECE Strategy for ESD, 2012). In contrast to its high importance, little is known about how reflective thinking can be identified, influenced or increased in the classroom. Therefore, the objective of this study is to address this need by developing an empirical multi-stage model designed to help educators diagnose different levels of reflective thinking and to identify factors that influence students’ reflective thinking about sustainability. Based on a 4–8-week project with grade 10 and 11 students studying sustainability, reflective thinking performance using weblogs as reflective journals was analysed. In addition, qualitative semi-structured interviews were conducted with the teachers to comprehend the learning environment and the personal value they assigned to ESD in their geography class. To determine the levels of reflective thinking achieved by the students, the study built on the work of Dewey (1933) and pre-existing multi-stage models of reflective thinking (Bain, Ballantyne, & Packer, 1999; Chen, Wei, Wu, & Uden, 2009). Using a qualitative, iterative data analysis, the study adapted the stage models to be applicable in ESD and found great differences in the students’ reflection levels. Furthermore, the study identified eight factors that influence students’ reflective thinking about sustainability. The outcomes of this study may be valuable for educators in high school and higher education, who seek to diagnose their students’ reflective thinking performance and facilitate reflection about sustainability.
Geophysical data acquisition in oceanic domains is challenging, implying measurements with low and/or nonhomogeneous spatial resolution. The evolution of satellite gravimetry and altimetry techniques allows testing 3-D density models of the lithosphere, taking advantage of the high spatial resolution and homogeneous coverage of satellites. However, it is not trivial to discretise the source of the gravity field at different depths. Here, we propose a new method for inferring tectonic boundaries at the crustal level. As a novelty, instead of modeling the gravity anomalies and assuming a flat Earth approximation, we model the vertical gravity gradients (VGG) in spherical coordinates, which are especially sensitive to density contrasts in the upper layers of the Earth. To validate the methodology, the complex oceanic domain of the Caribbean region is studied, which includes different crustal domains with a tectonic history since Late Jurassic time. After defining a lithospheric starting model constrained by up-to-date geophysical data sets, we tested several a-priory density distributions and selected the model with the minimum misfits with respect to the VGG calculated from the EIGEN-6C4 data set. Additionally, the density of the crystalline crust was inferred by inverting the VGG field. Our methodology enabled us not only to refine, confirm, and/or propose tectonic boundaries in the study area but also to identify a new anomalous buoyant body, located in the South Lesser Antilles subduction zone, and high-density bodies along the Greater, Lesser, and Leeward Antilles forearcs.
Remote Sensing technologies allow to map biophysical, biochemical, and earth surface parameters of the land surface. Of especial interest for various applications in environmental and urban sciences is the combination of spectral and 3D elevation information. However, those two data streams are provided separately by different instruments, namely airborne laser scanner (ALS) for elevation and a hyperspectral imager (HSI) for high spectral resolution data. The fusion of ALS and HSI data can thus lead to a single data entity consistently featuring rich structural and spectral information. In this study, we present the application of fusing the first pulse return information from ALS data at a sub-decimeter spatial resolution with the lower-spatial resolution hyperspectral information available from the HSI into a hyperspectral point cloud (HSPC). During the processing, a plausible hyperspectral spectrum is assigned to every first-return ALS point. We show that the complementary implementation of spectral and 3D information at the point-cloud scale improves object-based classification and information extraction schemes. This improvements have great potential for numerous land cover mapping and environmental applications.
Channel transmission losses in drylands take place normally in extensive alluvial channels or streambeds underlain by fractured rocks. They can play an important role in streamflow rates, groundwater recharge, freshwater supply and channel-associated ecosystems. We aim to develop a process-oriented, semi-distributed channel transmission losses model, using process formulations which are suitable for data-scarce dryland environments and applicable to both hydraulically disconnected losing streams and hydraulically connected losing(/gaining) streams. This approach should be able to cover a large variation in climate and hydro-geologic controls, which are typically found in dryland regions of the Earth. Our model was first evaluated for a losing/gaining, hydraulically connected 30 km reach of the Middle Jaguaribe River (MJR), Ceara, Brazil, which drains a catchment area of 20 000 km(2). Secondly, we applied it to a small losing, hydraulically disconnected 1.5 km channel reach in the Walnut Gulch Experimental Watershed (WGEW), Arizona, USA. The model was able to predict reliably the streamflow volume and peak for both case studies without using any parameter calibration procedure. We have shown that the evaluation of the hypotheses on the dominant hydrological processes was fundamental for reducing structural model uncertainties and improving the streamflow prediction. For instance, in the case of the large river reach (MJR), it was shown that both lateral stream-aquifer water fluxes and groundwater flow in the underlying alluvium parallel to the river course are necessary to predict streamflow volume and channel transmission losses, the former process being more relevant than the latter. Regarding model uncertainty, it was shown that the approaches, which were applied for the unsaturated zone processes (highly nonlinear with elaborate numerical solutions), are much more sensitive to parameter variability than those approaches which were used for the saturated zone (mathematically simple water budgeting in aquifer columns, including backwater effects). In case of the MJR-application, we have seen that structural uncertainties due to the limited knowledge of the subsurface saturated system interactions (i.e. groundwater coupling with channel water; possible groundwater flow parallel to the river) were more relevant than those related to the subsurface parameter variability. In case of the WEGW application we have seen that the non-linearity involved in the unsaturated flow processes in disconnected dryland river systems (controlled by the unsaturated zone) generally contain far more model uncertainties than do connected systems controlled by the saturated flow. Therefore, the degree of aridity of a dryland river may be an indicator of potential model uncertainty and subsequent attainable predictability of the system.
Air pollution is a pressing issue that is associated with adverse effects on human health, ecosystems, and climate. Despite many years of effort to improve air quality, nitrogen dioxide (NO2) limit values are still regularly exceeded in Europe, particularly in cities and along streets. This study explores how concentrations of nitrogen oxides (NOx = NO + NO2) in European urban areas have changed over the last decades and how this relates to changes in emissions. To do so, the incremental approach was used, comparing urban increments (i.e. urban background minus rural concentrations) to total emissions, and roadside increments (i.e. urban roadside concentrations minus urban background concentrations) to traffic emissions. In total, nine European cities were assessed. The study revealed that potentially confounding factors like the impact of urban pollution at rural monitoring sites through atmospheric transport are generally negligible for NOx. The approach proves therefore particularly useful for this pollutant. The estimated urban increments all showed downward trends, and for the majority of the cities the trends aligned well with the total emissions. However, it was found that factors like a very densely populated surrounding or local emission sources in the rural area such as shipping traffic on inland waterways restrict the application of the approach for some cities. The roadside increments showed an overall very diverse picture in their absolute values and trends and also in their relation to traffic emissions. This variability and the discrepancies between roadside increments and emissions could be attributed to a combination of local influencing factors at the street level and different aspects introducing inaccuracies to the trends of the emis-sion inventories used, including deficient emission factors. Applying the incremental approach was evaluated as useful for long-term pan-European studies, but at the same time it was found to be restricted to certain regions and cities due to data availability issues. The results also highlight that using emission inventories for the prediction of future health impacts and compliance with limit values needs to consider the distinct variability in the concentrations not only across but also within cities.
The quantification of spatial propagation of extreme precipitation events is vital in water resources planning and disaster mitigation. However, quantifying these extreme events has always been challenging as many traditional methods are insufficient to capture the nonlinear interrelationships between extreme event time series. Therefore, it is crucial to develop suitable methods for analyzing the dynamics of extreme events over a river basin with a diverse climate and complicated topography. Over the last decade, complex network analysis emerged as a powerful tool to study the intricate spatiotemporal relationship between many variables in a compact way. In this study, we employ two nonlinear concepts of event synchronization and edit distance to investigate the extreme precipitation pattern in the Ganga river basin. We use the network degree to understand the spatial synchronization pattern of extreme rainfall and identify essential sites in the river basin with respect to potential prediction skills. The study also attempts to quantify the influence of precipitation seasonality and topography on extreme events. The findings of the study reveal that (1) the network degree is decreased in the southwest to northwest direction, (2) the timing of 50th percentile precipitation within a year influences the spatial distribution of degree, (3) the timing is inversely related to elevation, and (4) the lower elevation greatly influences connectivity of the sites. The study highlights that edit distance could be a promising alternative to analyze event-like data by incorporating event time and amplitude and constructing complex networks of climate extremes.
Over the past decades, natural hazards, many of which are aggravated by climate change and reveal an increasing trend in frequency and intensity, have caused significant human and economic losses and pose a considerable obstacle to sustainable development. Hence, dedicated action toward disaster risk reduction is needed to understand the underlying drivers and create efficient risk mitigation plans. Such action is requested by the Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR), a global agreement launched in 2015 that establishes stating priorities for action, e.g. an improved understanding of disaster risk. Turkey is one of the SFDRR contracting countries and has been severely affected by many natural hazards, in particular earthquakes and floods. However, disproportionately little is known about flood hazards and risks in Turkey. Therefore, this thesis aims to carry out a comprehensive analysis of flood hazards for the first time in Turkey from triggering drivers to impacts. It is intended to contribute to a better understanding of flood risks, improvements of flood risk mitigation and the facilitated monitoring of progress and achievements while implementing the SFDRR.
In order to investigate the occurrence and severity of flooding in comparison to other natural hazards in Turkey and provide an overview of the temporal and spatial distribution of flood losses, the Turkey Disaster Database (TABB) was examined for the years 1960-2014. The TABB database was reviewed through comparison with the Emergency Events Database (EM-DAT), the Dartmouth Flood Observatory database, the scientific literature and news archives. In addition, data on the most severe flood events between 1960 and 2014 were retrieved. These served as a basis for analyzing triggering mechanisms (i.e. atmospheric circulation and precipitation amounts) and aggravating pathways (i.e. topographic features, catchment size, land use types and soil properties). For this, a new approach was developed and the events were classified using hierarchical cluster analyses to identify the main influencing factor per event and provide additional information about the dominant flood pathways for severe floods. The main idea of the study was to start with the event impacts based on a bottom-up approach and identify the causes that created damaging events, instead of applying a model chain with long-term series as input and searching for potentially impacting events as model outcomes. However, within the frequency analysis of the flood-triggering circulation pattern types, it was discovered that events in terms of heavy precipitation were not included in the list of most severe floods, i.e. their impacts were not recorded in national and international loss databases but were mentioned in news archives and reported by the Turkish State Meteorological Service. This finding challenges bottom-up modelling approaches and underlines the urgent need for consistent event and loss documentation. Therefore, as a next step, the aim was to enhance the flood loss documentation by calibrating, validating and applying the United Nations Office for Disaster Risk Reduction (UNDRR) loss estimation method for the recent severe flood events (2015-2020). This provided, a consistent flood loss estimation model for Turkey, allowing governments to estimate losses as quickly as possible after events, e.g. to better coordinate financial aid.
This thesis reveals that, after earthquakes, floods have the second most destructive effects in Turkey in terms of human and economic impacts, with over 800 fatalities and US$ 885.7 million in economic losses between 1960 and 2020, and that more attention should be paid on the national scale. The clustering results of the dominant flood-producing mechanisms (e.g. circulation pattern types, extreme rainfall, sudden snowmelt) present crucial information regarding the source and pathway identification, which can be used as base information for hazard identification in the preliminary risk assessment process. The implementation of the UNDRR loss estimation model shows that the model with country-specific parameters, calibrated damage ratios and sufficient event documentation (i.e. physically damaged units) can be recommended in order to provide first estimates of the magnitude of direct economic losses, even shortly after events have occurred, since it performed well when estimates were compared to documented losses.
The presented results can contribute to improving the national disaster loss database in Turkey and thus enable a better monitoring of the national progress and achievements with regard to the targets stated by the SFDRR. In addition, the outcomes can be used to better characterize and classify flood events. Information on the main underlying factors and aggravating flood pathways further supports the selection of suitable risk reduction policies.
All input variables used in this thesis were obtained from publicly available data. The results are openly accessible and can be used for further research.
As an overall conclusion, it can be stated that consistent loss data collection and better event documentation should gain more attention for a reliable monitoring of the implementation of the SFDRR. Better event documentation should be established according to a globally accepted standard for disaster classification and loss estimation in Turkey. Ultimately, this enables stakeholders to create better risk mitigation actions based on clear hazard definitions, flood event classification and consistent loss estimations.
The improvement of process representations in hydrological models is often only driven by the modelers' knowledge and data availability. We present a comprehensive comparison between two hydrological models of different complexity that is developed to support (1) the understanding of the differences between model structures and (2) the identification of the observations needed for model assessment and improvement. The comparison is conducted on both space and time and by aggregating the outputs at different spatiotemporal scales. In the present study, mHM, a process‐based hydrological model, and ParFlow‐CLM, an integrated subsurface‐surface hydrological model, are used. The models are applied in a mesoscale catchment in Germany. Both models agree in the simulated river discharge at the outlet and the surface soil moisture dynamics, lending their supports for some model applications (drought monitoring). Different model sensitivities are, however, found when comparing evapotranspiration and soil moisture at different soil depths. The analysis supports the need of observations within the catchment for model assessment, but it indicates that different strategies should be considered for the different variables. Evapotranspiration measurements are needed at daily resolution across several locations, while highly resolved spatially distributed observations with lower temporal frequency are required for soil moisture. Finally, the results show the impact of the shallow groundwater system simulated by ParFlow‐CLM and the need to account for the related soil moisture redistribution. Our comparison strategy can be applied to other models types and environmental conditions to strengthen the dialog between modelers and experimentalists for improving process representations in Earth system models.
A conundrum of trends
(2022)
This comment is meant to reiterate two warnings: One applies to the uncritical use of ready-made (openly available) program packages, and one to the estimation of trends in serially correlated time series. Both warnings apply to the recent publication of Lischeid et al. about lake-level trends in Germany.
Transverse dispersion, or tracer spreading orthogonal to the mean flow direction, which is relevant e.g, for quantifying bio-degradation of contaminant plumes or mixing of reactive solutes, has been studied in the literature less than the longitudinal one. Inferring transverse dispersion coefficients from field experiments is a difficult and error-prone task, requiring a spatial resolution of solute plumes which is not easily achievable in applications. In absence of field data, it is a questionable common practice to set transverse dispersivities as a fraction of the longitudinal one, with the ratio 1/10 being the most prevalent. We collected estimates of field-scale transverse dispersivities from existing publications and explored possible scale relationships as guidance criteria for applications. Our investigation showed that a large number of estimates available in the literature are of low reliability and should be discarded from further analysis. The remaining reliable estimates are formation-specific, span three orders of magnitude and do not show any clear scale-dependence on the plume traveled distance. The ratios with the longitudinal dispersivity are also site specific and vary widely. The reliability of transverse dispersivities depends significantly on the type of field experiment and method of data analysis. In applications where transverse dispersion plays a significant role, inference of transverse dispersivities should be part of site characterization with the transverse dispersivity estimated as an independent parameter rather than related heuristically to longitudinal dispersivity.
Rainfall erosivities as defined by the R factor from the universal soil loss equation were determined for all events during a two-year period at the station La Cuenca in western Amazonia. Three methods based on a power relationship between rainfall amount and erosivity were then applied to estimate event and daily rainfall erosivities from the respective rainfall amounts. A test of the resulting regression equations against an independent data set proved all three methods equally adequate in predicting rainfall erosivity from daily rainfall amount. We recommend the Richardson model for testing in the Amazon Basin, and its use with the coefficient from La Cuenca in western Amazonia.
A New Efficient Method to Solve the Stream Power Law Model Taking Into Account Sediment Deposition
(2019)
The stream power law model has been widely used to represent erosion by rivers but does not take into account the role played by sediment in modulating erosion and deposition rates. Davy and Lague (2009, ) provide an approach to address this issue, but it is computationally demanding because the local balance between erosion and deposition depends on sediment flux resulting from net upstream erosion. Here, we propose an efficient (i.e., O(N) and implicit) method to solve their equation. This means that, unlike other methods used to study the complete dynamics of fluvial systems (e.g., including the transition from detachment-limited to transport-limited behavior), our method is unconditionally stable even when large time steps are used. We demonstrate its applicability by performing a range of simulations based on a simple setup composed of an uplifting region adjacent to a stable foreland basin. As uplift and erosion progress, the mean elevations of the uplifting relief and the foreland increase, together with the average slope in the foreland. Sediments aggrade in the foreland and prograde to reach the base level where sediments are allowed to leave the system. We show how the topography of the uplifting relief and the stratigraphy of the foreland basin are controlled by the efficiency of river erosion and the efficiency of sediment transport by rivers. We observe the formation of a steady-state geometry in the uplifting region, and a dynamic steady state (i.e., autocyclic aggradation and incision) in the foreland, with aggradation and incision thicknesses up to tens of meters.
Quantifying the extremeness of heavy precipitation allows for the comparison of events. Conventional quantitative indices, however, typically neglect the spatial extent or the duration, while both are important to understand potential impacts. In 2014, the weather extremity index (WEI) was suggested to quantify the extremeness of an event and to identify the spatial and temporal scale at which the event was most extreme. However, the WEI does not account for the fact that one event can be extreme at various spatial and temporal scales. To better understand and detect the compound nature of precipitation events, we suggest complementing the original WEI with a “cross-scale weather extremity index” (xWEI), which integrates extremeness over relevant scales instead of determining its maximum.
Based on a set of 101 extreme precipitation events in Germany, we outline and demonstrate the computation of both WEI and xWEI. We find that the choice of the index can lead to considerable differences in the assessment of past events but that the most extreme events are ranked consistently, independently of the index. Even then, the xWEI can reveal cross-scale properties which would otherwise remain hidden. This also applies to the disastrous event from July 2021, which clearly outranks all other analyzed events with regard to both WEI and xWEI.
While demonstrating the added value of xWEI, we also identify various methodological challenges along the required computational workflow: these include the parameter estimation for the extreme value distributions, the definition of maximum spatial extent and temporal duration, and the weighting of extremeness at different scales. These challenges, however, also represent opportunities to adjust the retrieval of WEI and xWEI to specific user requirements and application scenarios.
Quantifying the extremeness of heavy precipitation allows for the comparison of events. Conventional quantitative indices, however, typically neglect the spatial extent or the duration, while both are important to understand potential impacts. In 2014, the weather extremity index (WEI) was suggested to quantify the extremeness of an event and to identify the spatial and temporal scale at which the event was most extreme. However, the WEI does not account for the fact that one event can be extreme at various spatial and temporal scales. To better understand and detect the compound nature of precipitation events, we suggest complementing the original WEI with a “cross-scale weather extremity index” (xWEI), which integrates extremeness over relevant scales instead of determining its maximum.
Based on a set of 101 extreme precipitation events in Germany, we outline and demonstrate the computation of both WEI and xWEI. We find that the choice of the index can lead to considerable differences in the assessment of past events but that the most extreme events are ranked consistently, independently of the index. Even then, the xWEI can reveal cross-scale properties which would otherwise remain hidden. This also applies to the disastrous event from July 2021, which clearly outranks all other analyzed events with regard to both WEI and xWEI.
While demonstrating the added value of xWEI, we also identify various methodological challenges along the required computational workflow: these include the parameter estimation for the extreme value distributions, the definition of maximum spatial extent and temporal duration, and the weighting of extremeness at different scales. These challenges, however, also represent opportunities to adjust the retrieval of WEI and xWEI to specific user requirements and application scenarios.
The precise and accurate assessment of carbon dioxide (CO2) exchange is crucial to identify terrestrial carbon (C) sources and sinks and for evaluating their role within the global C budget. The substantial uncertainty in disentangling the management and soil impact on measured CO2 fluxes are largely ignored especially in cropland. The reasons for this lies in the limitation of the widely used eddy covariance as well as manual and automatic chamber systems, which either account for short-term temporal variability or small-scale spatial heterogeneity, but barely both. To address this issue, we developed a novel robotic chamber system allowing for dozens of spatial measurement repetitions, thus enabling CO2 exchange measurements in a sufficient temporal and high small-scale spatial resolution. The system was tested from 08th July to 09th September 2019 at a heterogeneous field (100 m x 16 m), located within the hummocky ground moraine landscape of northeastern Germany (CarboZALF-D). The field is foreseen for a longer-term block trial manipulation experiment extending over three erosion induced soil types and was covered with spring barley. Measured fluxes of nighttime ecosystem respiration (R-eco) and daytime net ecosystem exchange (NEE) showed distinct temporal patterns influenced by crop phenology, weather conditions and management practices. Similarly, we found clear small-scale spatial differences in cumulated (gap-filled) R-eco, gross primary productivity (GPP) and NEE fluxes affected by the three distinct soil types. Additionally, spatial patterns induced by former management practices and characterized by differences in soil pH and nutrition status (P and K) were also revealed between plots within each of the three soil types, which allowed compensating for prior to the foreseen block trial manipulation experiment. The results underline the great potential of the novel robotic chamber system, which not only detects short-term temporal CO2 flux dynamics but also reflects the impact of small-scale spatial heterogeneity.
Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils' SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R-2) = 0.91; root mean square error (RMSE) = 0.11% and R-2 = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R-2 = 0.88, RMSE = 0.07%; R-2 = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.
The tropical peat swamp forests of South-East Asia are being rapidly converted to agricultural plantations of oil palm and Acacia creating a significant global “hot-spot” for CO2 emissions. However, the effect of this major perturbation has yet to be quantified in terms of global warming potential (GWP) and the Earth's radiative budget. We used a GWP analysis and an impulse-response model of radiative forcing to quantify the climate forcing of this shift from a long-term carbon sink to a net source of greenhouse gases (CO2 and CH4). In the GWP analysis, five tropical peatlands were sinks in terms of their CO2 equivalent fluxes while they remained undisturbed. However, their drainage and conversion to oil palm and Acacia plantations produced a dramatic shift to very strong net CO2-equivalent sources. The induced losses of peat carbon are ~20× greater than the natural CO2 sequestration rates. In contrast, a radiative forcing model indicates that the magnitude of this shift from a net cooling to warming effect is ultimately related to the size of an individual peatland's carbon pool. The continuous accumulation of carbon in pristine tropical peatlands produced a progressively negative radiative forcing (i.e., cooling) that ranged from −2.1 to −6.7 nW/m2 per hectare peatland by 2010 CE, referenced to zero at the time of peat initiation. Peatland conversion to plantations leads to an immediate shift from negative to positive trend in radiative forcing (i.e., warming). If drainage persists, peak warming ranges from +3.3 to +8.7 nW/m2 per hectare of drained peatland. More importantly, this net warming impact on the Earth's radiation budget will persist for centuries to millennia after all the peat has been oxidized to CO2. This previously unreported and undesirable impact on the Earth's radiative balance provides a scientific rationale for conserving tropical peatlands in their pristine state.
Currently, Southeast Europe (SEE) is witnessing a boom in hydropower plant (HPP) construction, which has not even spared protected areas. As SEE includes global hotspots of aquatic biodiversity, it is expected that this boom will result in a more severe impact on biodiversity than that of other regions. A more detailed assessment of the environmental risks resulting from HPP construction would have to rely on the existence of nearby hydrological and biological monitoring stations.
For this reason, we review the distribution and trends of HPPs in the area, as well as the availability of hydrological and biological monitoring data from national institutions useable for environmental impact assessment. Our analysis samples tributary rivers of the Danube in Slovenia, Croatia, Bosnia and Herzegovina, Serbia, and Montenegro, referred to hereafter as TRD rivers.
Currently, 636 HPPs are operating along the course of TRD rivers, most of which are small (<1 MW). An additional 1315 HPPs are currently planned to be built, mostly in Serbia and in Bosnia and Herzegovina. As official monitoring stations near HPPs are rare, the impact of those HPPs on river flow, fish and macro-invertebrates is difficult to assess.
This manuscript represents the first regional review of hydropower use and of available data sources on its environmental impact for an area outside of the Alps. We conclude that current hydrological and biological monitoring in TRD rivers is insufficient for an assessment of the ecological impacts of HPPs. This data gap also prevents an adequate assessment of the ecological impacts of planned HP projects, as well as the identification of appropriate measures to mitigate the environmental effects of existing HPPs.
A digital filter is introduced which treats the problem of predictability versus time averaging in a continuous, seamless manner. This seamless filter (SF) is characterized by a unique smoothing rule that determines the strength of smoothing in dependence on lead time. The rule needs to be specified beforehand, either by expert knowledge or by user demand. As a result, skill curves are obtained that allow a predictability assessment across a whole range of time-scales, from daily to seasonal, in a uniform manner. The SF is applied to downscaled SEAS5 ensemble forecasts for two focus regions in or near the tropical belt, the river basins of the Karun in Iran and the Sao Francisco in Brazil. Both are characterized by strong seasonality and semi-aridity, so that predictability across various time-scales is in high demand. Among other things, it is found that from the start of the water year (autumn), areal precipitation is predictable with good skill for the Karun basin two and a half months ahead; for the Sao Francisco it is only one month, longer-term prediction skill is just above the critical level.
A digital filter is introduced which treats the problem of predictability versus time averaging in a continuous, seamless manner. This seamless filter (SF) is characterized by a unique smoothing rule that determines the strength of smoothing in dependence on lead time. The rule needs to be specified beforehand, either by expert knowledge or by user demand. As a result, skill curves are obtained that allow a predictability assessment across a whole range of time-scales, from daily to seasonal, in a uniform manner. The SF is applied to downscaled SEAS5 ensemble forecasts for two focus regions in or near the tropical belt, the river basins of the Karun in Iran and the Sao Francisco in Brazil. Both are characterized by strong seasonality and semi-aridity, so that predictability across various time-scales is in high demand. Among other things, it is found that from the start of the water year (autumn), areal precipitation is predictable with good skill for the Karun basin two and a half months ahead; for the Sao Francisco it is only one month, longer-term prediction skill is just above the critical level.
Pollen records from Siberia are mostly absent in global or Northern Hemisphere synthesis works. Here we present a taxonomically harmonized and temporally standardized pollen dataset that was synthesized using 173 palynological records from Siberia and adjacent areas (northeastern Asia, 42-75 degrees N, 50-180 degrees E). Pollen data were taxonomically harmonized, i.e. the original 437 taxa were assigned to 106 combined pollen taxa. Age-depth models for all records were revised by applying a constant Bayesian age-depth modelling routine. The pollen dataset is available as count data and percentage data in a table format (taxa vs. samples), with age information for each sample. The dataset has relatively few sites covering the last glacial period between 40 and 11.5 ka (calibrated thousands of years before 1950 CE) particularly from the central and western part of the study area. In the Holocene period, the dataset has many sites from most of the area, with the exception of the central part of Siberia. Of the 173 pollen records, 81 % of pollen counts were downloaded from open databases (GPD, EPD, PANGAEA) and 10 % were contributions by the original data gatherers, while a few were digitized from publications. Most of the pollen records originate from peatlands (48 %) and lake sediments (33 %). Most of the records (83 %) have >= 3 dates, allowing the establishment of reliable chronologies. The dataset can be used for various purposes, including pollen data mapping (example maps for Larix at selected time slices are shown) as well as quantitative climate and vegetation reconstructions. The datasets for pollen counts and pollen percentages are available at https://doi.org/10.1594/PANGAEA.898616 (Cao et al., 2019a), also including the site information, data source, original publication, dating data, and the plant functional type for each pollen taxa.
Pollen records from Siberia are mostly absent in global or Northern Hemisphere synthesis works. Here we present a taxonomically harmonized and temporally standardized pollen dataset that was synthesized using 173 palynological records from Siberia and adjacent areas (northeastern Asia, 42-75 degrees N, 50-180 degrees E). Pollen data were taxonomically harmonized, i.e. the original 437 taxa were assigned to 106 combined pollen taxa. Age-depth models for all records were revised by applying a constant Bayesian age-depth modelling routine. The pollen dataset is available as count data and percentage data in a table format (taxa vs. samples), with age information for each sample. The dataset has relatively few sites covering the last glacial period between 40 and 11.5 ka (calibrated thousands of years before 1950 CE) particularly from the central and western part of the study area. In the Holocene period, the dataset has many sites from most of the area, with the exception of the central part of Siberia. Of the 173 pollen records, 81 % of pollen counts were downloaded from open databases (GPD, EPD, PANGAEA) and 10 % were contributions by the original data gatherers, while a few were digitized from publications. Most of the pollen records originate from peatlands (48 %) and lake sediments (33 %). Most of the records (83 %) have >= 3 dates, allowing the establishment of reliable chronologies. The dataset can be used for various purposes, including pollen data mapping (example maps for Larix at selected time slices are shown) as well as quantitative climate and vegetation reconstructions. The datasets for pollen counts and pollen percentages are available at https://doi.org/10.1594/PANGAEA.898616 (Cao et al., 2019a), also including the site information, data source, original publication, dating data, and the plant functional type for each pollen taxa.
In this study transmission X-ray microscopy (TXM) was tested as a method to investigate the chemistry and structure of corroded silicate glasses at the nanometer scale. Three different silicate glasses were altered in static corrosion experiments for 1-336 hours at temperatures between 60 degrees C and 85 degrees C using a 25% HCl solution. Thin lamellas were cut perpendicular to the surface of corroded glass monoliths and were analysed with conventional TEM as well as with TXM. By recording optical density profiles at photon energies around the Na and O K-edges, the shape of the corrosion rim/pristine glass interfaces and the thickness of the corrosion rims has been determined. Na and O near-edge X-ray absorption fine-structure spectra (NEXAFS) were obtained without inducing irradiation damage and have been used to detect chemical changes in the corrosion rims. Spatially resolved NEXAFS spectra at the O K-edge provided insight to structural changes in the corrosion layer on the atomic scale. By comparison to O K-edge spectra of silicate minerals and (hydrous) albite glass as well as to O K-edge NEXAFS of model structures simulated with ab initio calculations, evidence is provided that changes of the fine structure at the O K-edge are assigned to the formation of siloxane groups in the corrosion rim.
A water quality model for shallow river-lake systems and its application in river basin management
(2007)
This work documents the development and application of a new model for simulating mass transport and turnover in rivers and shallow lakes. The simulation tool called 'TRAM' is intended to complement mesoscale eco-hydrological catchment models in studies on river basin management. TRAM aims at describing the water quality of individual water bodies, using problem- and scale-adequate approaches for representing their hydrological and ecological characteristics. The need for such flexible water quality analysis and prediction tools is expected to further increase during the implementation of the European Water Framework Directive (WFD) as well as in the context of climate change research. The developed simulation tool consists of a transport and a reaction module with the latter being highly flexible with respect to the description of turnover processes in the aquatic environment. Therefore, simulation approaches of different complexity can easily be tested and model formulations can be chosen in consideration of the problem at hand, knowledge of process functioning, and data availability. Consequently, TRAM is suitable for both heavily simplified engineering applications as well as scientific ecosystem studies involving a large number of state variables, interactions, and boundary conditions. TRAM can easily be linked to catchment models off-line and it requires the use of external hydrodynamic simulation software. Parametrization of the model and visualization of simulation results are facilitated by the use of geographical information systems as well as specific pre- and post-processors. TRAM has been developed within the research project 'Management Options for the Havel River Basin' funded by the German Ministry of Education and Research. The project focused on the analysis of different options for reducing the nutrient load of surface waters. It was intended to support the implementation of the WFD in the lowland catchment of the Havel River located in North-East Germany. Within the above-mentioned study TRAM was applied with two goals in mind. In a first step, the model was used for identifying the magnitude as well as spatial and temporal patterns of nitrogen retention and sediment phosphorus release in a 100~km stretch of the highly eutrophic Lower Havel River. From the system analysis, strongly simplified conceptual approaches for modeling N-retention and P-remobilization in the studied river-lake system were obtained. In a second step, the impact of reduced external nutrient loading on the nitrogen and phosphorus concentrations of the Havel River was simulated (scenario analysis) taking into account internal retention/release. The boundary conditions for the scenario analysis such as runoff and nutrient emissions from river basins were computed by project partners using the catchment models SWIM and ArcEGMO-Urban. Based on the output of TRAM, the considered options of emission control could finally be evaluated using a site-specific assessment scale which is compatible with the requirements of the WFD. Uncertainties in the model predictions were also examined. According to simulation results, the target of the WFD -- with respect to total phosphorus concentrations in the Lower Havel River -- could be achieved in the medium-term, if the full potential for reducing point and non-point emissions was tapped. Furthermore, model results suggest that internal phosphorus loading will ease off noticeably until 2015 due to a declining pool of sedimentary mobile phosphate. Mass balance calculations revealed that the lakes of the Lower Havel River are an important nitrogen sink. This natural retention effect contributes significantly to the efforts aimed at reducing the river's nitrogen load. If a sustainable improvement of the river system's water quality is to be achieved, enhanced measures to further reduce the immissions of both phosphorus and nitrogen are required.
Continental rift systems form by propagation of isolated rift segments that interact, and eventually evolve into continuous zones of deformation. This process impacts many aspects of rifting including rift morphology at breakup, and eventual ocean-ridge segmentation. Yet, rift segment growth and interaction remain enigmatic. Here we present geological data from the poorly documented Ririba rift (South Ethiopia) that reveals how two major sectors of the East African rift, the Kenyan and Ethiopian rifts, interact. We show that the Ririba rift formed from the southward propagation of the Ethiopian rift during the Pliocene but this propagation was short-lived and aborted close to the Pliocene-Pleistocene boundary. Seismicity data support the abandonment of laterally offset, overlapping tips of the Ethiopian and Kenyan rifts. Integration with new numerical models indicates that rift abandonment resulted from progressive focusing of the tectonic and magmatic activity into an oblique, throughgoing rift zone of near pure extension directly connecting the rift sectors.
The Alborz range of N Iran provides key information on the spatiotemporal evolution and characteristics of the Arabia-Eurasia continental collision zone. The southwestern Alborz range constitutes a transpressional duplex, which accommodates oblique shortening between Central Iran and the South Caspian Basin. The duplex comprises NW-striking frontal ramps that are kinematically linked to inherited E-W-striking, right-stepping lateral to obliquely oriented ramps. New zircon and apatite (U-Th)/He data provide a high-resolution framework to unravel the evolution of collisional tectonics in this region. Our data record two pulses of fast cooling associated with SW-directed thrusting across the frontal ramps at similar to 18-14 and 9.5-7.5 Ma, resulting in the tectonic repetition of a fossil zircon partial retention zone and a cooling pattern with a half U-shaped geometry. Uniform cooling ages of similar to 7-6 Ma along the southernmost E-W striking oblique ramp and across its associated NW-striking frontal ramps suggests that the ramp was reactivated as a master throughgoing, N-dipping thrust. We interpret this major change in fault kinematics and deformation style to be related to a change in the shortening direction from NE to N/NNE. The reduction in the obliquity of thrusting may indicate the termination of strike-slip faulting (and possibly thrusting) across the Iranian Plateau, which could have been triggered by an increase in elevation. Furthermore, we suggest that similar to 7-6-m.y.-old S-directed thrusting predated inception of the westward motion of the South Caspian Basin. Citation: Ballato, P., D. F. Stockli, M. R. Ghassemi, A. Landgraf, M. R. Strecker, J. Hassanzadeh, A. Friedrich, and S. H. Tabatabaei (2012), Accommodation of transpressional strain in the Arabia-Eurasia collision zone: new constraints from (U-Th)/He thermochronology in the Alborz mountains.
Digital terrain models (DTMs) are a fundamental source of information in Earth sciences. DTM-based studies, however, can contain remarkable biases if limitations and inaccuracies in these models are disregarded. In this work, four freely available datasets, including Shuttle Radar Topography Mission C-Band Synthetic Aperture Radar (SRTM C-SAR V3 DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Map (ASTER GDEM V2), and two nationwide airborne light detection and ranging (LiDAR)-derived DTMs (at 5-m and 1-m spatial resolution, respectively) were analysed in three geomorphologically contrasting, small (3–5 km2) catchments located in Mediterranean landscapes under intensive human influence (Mallorca Island, Spain). Vertical accuracy as well as the influence of each dataset’s characteristics on hydrological and geomorphological modelling applicability were assessed by using ground-truth data, classic geometric and morphometric parameters, and a recently proposed index of sediment connectivity. Overall vertical accuracy—expressed as the root mean squared error (RMSE) and normalised median deviation (NMAD)—revealed the highest accuracy for the 1-m (RMSE = 1.55 m; NMAD = 0.44 m) and 5-m LiDAR DTMs (RMSE = 1.73 m; NMAD = 0.84 m). Vertical accuracy of the SRTM data was lower (RMSE = 6.98 m; NMAD = 5.27 m), but considerably higher than for the ASTER data (RMSE = 16.10 m; NMAD = 11.23 m). All datasets were affected by systematic distortions. Propagation of these errors and coarse horizontal resolution caused negative impacts on flow routing, stream network, and catchment delineation, and to a lower extent, on the distribution of slope values. These limitations should be carefully considered when applying DTMs for catchment hydrogeomorphological modelling.
The Maghreb region (from Tunisia to Gibraltar) is a key area in the western Mediterranean to study the active tectonics and stress pattern across the Africa-Eurasia convergent plate boundary. In the present study, we compile comprehensive data set of well-constrained crustal stress indicators (from single focal mechanism solutions, formal inversion of focal mechanism solutions, and young geologic fault slip data) based on our and published data analyses. Stress inversion of focal mechanisms reveals a first-order transpression-compatible stress field and a second-order spatial variation of tectonic regime across the Maghreb region, with a relatively stable S-Hmax orientation from east to west. Therefore, the present-day active contraction of the western Africa-Eurasia plate boundary is accommodated by (1) E-W strike-slip faulting with reverse component along the Eastern Tell and Saharan-Tunisian Atlas, (2) a predominantly NE trending thrust faulting with strike-slip component in the Western Tell part, and (3) a conjugate strike-slip faulting regime with normal component in the Alboran/Rif domain. This spatial variation of the present-day stress field and faulting regime is relatively in agreement with the inferred stress information from neotectonic features. According to existing and newly proposed structural models, we highlight the role of main geometrically complex shear zones in the present-day stress pattern of the Maghreb region. Then, different geometries of these major inherited strike-slip faults and its related fractures (V-shaped conjugate fractures, horsetail splays faults, and Riedel fractures) impose their component on the second- and third-order stress regimes. Neotectonic and smoothed present-day stress map (mean S-Hmax orientation) reveal that plate boundary forces acting on the Africa-Eurasia collisional plates control the long wavelength of the stress field pattern in the Maghreb. The current tectonic deformations and the upper crustal stress field in the study area are governed by the interplay of the oblique plate convergence (i.e., Africa-Eurasia), lithosphere-mantle interaction, and preexisting tectonic weakness zones.
Flood loss modeling is an important component within flood risk assessments. Traditionally, stage-damage functions are used for the estimation of direct monetary damage to buildings. Although it is known that such functions are governed by large uncertainties, they are commonly applied - even in different geographical regions - without further validation, mainly due to the lack of real damage data. Until now, little research has been done to investigate the applicability and transferability of such damage models to other regions. In this study, the last severe flood event in the Austrian Lech Valley in 2005 was simulated to test the performance of various damage functions from different geographical regions in Central Europe for the residential sector. In addition to common stage-damage curves, new functions were derived from empirical flood loss data collected in the aftermath of recent flood events in neighboring Germany. Furthermore, a multi-parameter flood loss model for the residential sector was adapted to the study area and also evaluated with official damage data. The analysis reveals that flood loss functions derived from related and more similar regions perform considerably better than those from more heterogeneous data sets of different regions and flood events. While former loss functions estimate the observed damage well, the latter overestimate the reported loss clearly. To illustrate the effect of model choice on the resulting uncertainty of damage estimates, the current flood risk for residential areas was calculated. In the case of extreme events like the 300 yr flood, for example, the range of losses to residential buildings between the highest and the lowest estimates amounts to a factor of 18, in contrast to properly validated models with a factor of 2.3. Even if the risk analysis is only performed for residential areas, our results reveal evidently that a carefree model transfer in other geographical regions might be critical. Therefore, we conclude that loss models should at least be selected or derived from related regions with similar flood and building characteristics, as far as no model validation is possible. To further increase the general reliability of flood loss assessment in the future, more loss data and more comprehensive loss data for model development and validation are needed.
Flood risk management in Germany follows an integrative approach in which both private households and businesses can make an important contribution to reducing flood damage by implementing property-level adaptation measures. While the flood adaptation behavior of private households has already been widely researched, comparatively less attention has been paid to the adaptation strategies of businesses. However, their ability to cope with flood risk plays an important role in the social and economic development of a flood-prone region. Therefore, using quantitative survey data, this study aims to identify different strategies and adaptation drivers of 557 businesses damaged by a riverine flood in 2013 and 104 businesses damaged by pluvial or flash floods between 2014 and 2017. Our results indicate that a low perceived self-efficacy may be an important factor that can reduce the motivation of businesses to adapt to flood risk. Furthermore, property-owners tended to act more proactively than tenants. In addition, high experience with previous flood events and low perceived response costs could strengthen proactive adaptation behavior. These findings should be considered in business-tailored risk communication.
Flood risk management in Germany follows an integrative approach in which both private households and businesses can make an important contribution to reducing flood damage by implementing property-level adaptation measures. While the flood adaptation behavior of private households has already been widely researched, comparatively less attention has been paid to the adaptation strategies of businesses. However, their ability to cope with flood risk plays an important role in the social and economic development of a flood-prone region. Therefore, using quantitative survey data, this study aims to identify different strategies and adaptation drivers of 557 businesses damaged by a riverine flood in 2013 and 104 businesses damaged by pluvial or flash floods between 2014 and 2017. Our results indicate that a low perceived self-efficacy may be an important factor that can reduce the motivation of businesses to adapt to flood risk. Furthermore, property-owners tended to act more proactively than tenants. In addition, high experience with previous flood events and low perceived response costs could strengthen proactive adaptation behavior. These findings should be considered in business-tailored risk communication.
Advances in Flood Research
(2002)
Precipitation forecasting has an important place in everyday life – during the day we may have tens of small talks discussing the likelihood that it will rain this evening or weekend. Should you take an umbrella for a walk? Or should you invite your friends for a barbecue? It will certainly depend on what your weather application shows.
While for years people were guided by the precipitation forecasts issued for a particular region or city several times a day, the widespread availability of weather radars allowed us to obtain forecasts at much higher spatiotemporal resolution of minutes in time and hundreds of meters in space. Hence, radar-based precipitation nowcasting, that is, very-short-range forecasting (typically up to 1–3 h), has become an essential technique, also in various professional application contexts, e.g., early warning, sewage control, or agriculture.
There are two major components comprising a system for precipitation nowcasting: radar-based precipitation estimates, and models to extrapolate that precipitation to the imminent future. While acknowledging the fundamental importance of radar-based precipitation retrieval for precipitation nowcasts, this thesis focuses only on the model development: the establishment of open and competitive benchmark models, the investigation of the potential of deep learning, and the development of procedures for nowcast errors diagnosis and isolation that can guide model development.
The present landscape of computational models for precipitation nowcasting still struggles with the availability of open software implementations that could serve as benchmarks for measuring progress. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. We distribute the corresponding set of models as a software library, rainymotion, which is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion). That way, the library acts as a tool for providing fast, open, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing.
One of the promising directions for model development is to challenge the potential of deep learning – a subfield of machine learning that refers to artificial neural networks with deep architectures, which may consist of many computational layers. Deep learning showed promising results in many fields of computer science, such as image and speech recognition, or natural language processing, where it started to dramatically outperform reference methods.
The high benefit of using "big data" for training is among the main reasons for that. Hence, the emerging interest in deep learning in atmospheric sciences is also caused and concerted with the increasing availability of data – both observational and model-based. The large archives of weather radar data provide a solid basis for investigation of deep learning potential in precipitation nowcasting: one year of national 5-min composites for Germany comprises around 85 billion data points.
To this aim, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. RainNet was trained to predict continuous precipitation intensities at a lead time of 5 min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900 km x 900 km and has a resolution of 1 km in space and 5 min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In these experiments, RainNet was applied recursively in order to achieve lead times of up to 1 h. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the previously developed rainymotion library.
RainNet significantly outperformed the benchmark models at all lead times up to 60 min for the routine verification metrics mean absolute error (MAE) and critical success index (CSI) at intensity thresholds of 0.125, 1, and 5 mm/h. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15 mm/h). The limited ability of RainNet to predict high rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5 min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16 km and below.
Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5 min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5 min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research on model development for precipitation nowcasting, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance.
The model development together with the verification experiments for both conventional and deep learning model predictions also revealed the need to better understand the source of forecast errors. Understanding the dominant sources of error in specific situations should help in guiding further model improvement. The total error of a precipitation nowcast consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow to isolate the location error, making it difficult to specifically improve nowcast models with regard to location prediction.
To fill this gap, we introduced a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time ahead of the forecast time corresponds to the Euclidean distance between the observed and the predicted feature location at the corresponding lead time.
Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the DWD. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion; and the remaining two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear and Semi-Lagrangian extrapolation.
For all competing models, the mean location error exceeds a distance of 5 km after 60 min, and 10 km after 110 min. At least 25% of all forecasts exceed an error of 5 km after 50 min, and of 10 km after 90 min. Even for the best models in our experiment, at least 5 percent of the forecasts will have a location error of more than 10 km after 45 min. When we relate such errors to application scenarios that are typically suggested for precipitation nowcasting, e.g., early warning, it becomes obvious that location errors matter: the order of magnitude of these errors is about the same as the typical extent of a convective cell. Hence, the uncertainty of precipitation nowcasts at such length scales – just as a result of locational errors – can be substantial already at lead times of less than 1 h. Being able to quantify the location error should hence guide any model development that is targeted towards its minimization. To that aim, we also consider the high potential of using deep learning architectures specific to the assimilation of sequential (track) data.
Last but not least, the thesis demonstrates the benefits of a general movement towards open science for model development in the field of precipitation nowcasting. All the presented models and frameworks are distributed as open repositories, thus enhancing transparency and reproducibility of the methodological approach. Furthermore, they are readily available to be used for further research studies, as well as for practical applications.
Aerial and surface rivers
(2018)
The abundant evapotranspiration provided by the Amazon forests is an important component of the hydrological cycle, both regionally and globally. Since the last century, deforestation and expanding agricultural activities have been changing the ecosystem and its provision of moisture to the atmosphere. However, it remains uncertain how the ongoing land use change will influence rainfall, runoff, and water availability as findings from previous studies differ. Using moisture tracking experiments based on observational data, we provide a spatially detailed analysis recognizing potential teleconnection between source and sink regions of atmospheric moisture. We apply land use scenarios in upwind moisture sources and quantify the corresponding rainfall and runoff changes in downwind moisture sinks. We find spatially varying responses of water regimes to land use changes, which may explain the diverse results from previous studies. Parts of the Peruvian Amazon and western Bolivia are identified as the sink areas most sensitive to land use change in the Amazon and we highlight the current water stress by Amazonian land use change on these areas in terms of the water availability. Furthermore, we also identify the influential source areas where land use change may considerably reduce a given target sink's water reception (from our example of the Ucayali River basin outlet, rainfall by 5–12 % and runoff by 19–50 % according to scenarios). Sensitive sinks and influential sources are therefore suggested as hotspots for achieving sustainable land–water management.
Aerosol emissions from human activities are extensive and changing rapidly over Asia. Model simulations and satellite observations indicate a dipole pattern in aerosol emissions and loading between South Asia and East Asia, two of the most heavily polluted regions of the world. We examine the previously unexplored diverging trends in the existing dipole pattern of aerosols between East and South Asia using the high quality, two-decade long ground-based time series of observations of aerosol properties from the Aerosol Robotic Network (AERONET), from satellites (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)), and from model simulations (Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The data cover the period since 2001 for Kanpur (South Asia) and Beijing (East Asia), two locations taken as being broadly representative of the respective regions. Since 2010 a dipole in aerosol optical depth (AOD) is maintained, but the trend is reversed-the decrease in AOD over Beijing (East Asia) is rapid since 2010, being 17% less in current decade compared to first decade of twenty-first century, while the AOD over South Asia increased by 12% during the same period. Furthermore, we find that the aerosol composition is also changing over time. The single scattering albedo (SSA), a measure of aerosol's absorption capacity and related to aerosol composition, is slightly higher over Beijing than Kanpur, and has increased from 0.91 in 2002 to 0.93 in 2017 over Beijing and from 0.89 to 0.92 during the same period over Kanpur, confirming that aerosols in this region have on an average become more scattering in nature. These changes have led to a notable decrease in aerosol-induced atmospheric heating rate (HR) over both regions between the two decades, decreasing considerably more over East Asia (- 31%) than over South Asia (- 9%). The annual mean HR is lower now, it is still large (>= 0.6 K per day), which has significant climate implications. The seasonal trends in AOD, SSA and HR are more pronounced than their respective annual trends over both regions. The seasonal trends are caused mainly by the increase/decrease in anthropogenic aerosol emissions (sulfate, black carbon and organic carbon) while the natural aerosols (dust and sea salt) did not change significantly over South and East Asia during the last two decades. The MERRA-2 model is able to simulate the observed trends in AODs well but not the magnitude, while it also did not simulate the SSA values or trends well. These robust findings based on observations of key aerosol parameters and previously unrecognized diverging trends over South and East Asia need to be accounted for in current state-of-the-art climate models to ensure accurate quantification of the complex and evolving impact of aerosols on the regional climate over Asia.
Documenting the early tectonic and magmatic evolution of the lzu-Bonin-Mariana (IBM) arc system in the Western Pacific is critical for understanding the process and cause of subduction initiation along the current convergent margin between the Pacific and Philippine Sea plates. Forearc igneous sections provide firm evidence for seafloor spreading at the time of subduction initiation (52 Ma) and production of "forearc basalt". Ocean floor drilling (International Ocean Discovery Program Expedition 351) recovered basement-forming, low-Ti tholeiitic basalt crust formed shortly after subduction initiation but distal from the convergent margin (nominally reararc) of the future IBM arc (Amami Sankaku Basin: ASB). Radiometric dating of this basement gives an age range (49.3-46.8 Ma with a weighted average of 48.7 Ma) that overlaps that of basalt in the present-day IBM forearc, but up to 3.3 m.y. younger than the onset of forearc basalt activity. Similarity in age range and geochemical character between the reararc and forearc basalts implies that the ocean crust newly formed by seafloor spreading during subduction initiation extends from fore-to reararc of the present-day IBM arc. Given the age difference between the oldest forearc basalt and the ASB crust, asymmetric spreading caused by ridge migration might have taken place. This scenario for the formation of the ASB implies that the Mesozoic remnant arc terrane of the Daito Ridges comprised the overriding plate at subduction initiation. The juxtaposition of a relatively buoyant remnant arc terrane adjacent to an oceanic plate was more favourable for subduction initiation than would have been the case if both downgoing and overriding plates had been oceanic. (C) 2017 The Authors. Published by Elsevier B.V.
AIC-based diffraction stacking for local earthquake locations at the Sumatran Fault (Indonesia)
(2018)
We present a new workflow for the localization of seismic events which is based on a diffraction stacking approach. In order to address the effects from complex source radiation patterns, we suggest to compute diffraction stacking from a characteristic function (CF) instead of stacking the original waveform data. A new CF, which is called in the following mAIC (modified from Akaike Information Criterion) is proposed. We demonstrate that both P- and S-wave onsets can be detected accurately. To avoid cross-talk between P and S waves due to inaccurate velocity models, we separate the P and S waves from the mAIC function by making use of polarization attributes. Then, the final image function is represented by the largest eigenvalue as a result of the covariance analysis between P-and S-image functions. Results from synthetic experiments show that the proposed diffraction stacking provides reliable results. The workflow of the diffraction stacking method was finally applied to local earthquake data from Sumatra, Indonesia. Recordings from a temporary network of 42 stations deployed for nine months around the Tarutung pull-apart basin were analysed. The seismic event locations resulting from the diffraction stacking method align along a segment of the Sumatran Fault. A more complex distribution of seismicity is imaged within and around the Tarutung basin. Two lineaments striking N-S were found in the centre of the Tarutung basin which support independent results from structural geology.
As Albania is accelerating its preparations towards the European Union candidate status, numerous areas of public policy and practices undergo intensive development processes. Regional development policy is a very new area of public policy in Albania, and needs research and development. This study focuses on the process of sustainable development in Albania, by analyzing and comparing the regional development of regions of Tirana, Shkodra and Kukes. The methodology used consists of a literature/desk review; analytical and comparative approach; qualitative interviews; quantitative data collection; analysis. The research is organized in five chapters. First chapter provides an overview of the study framework. The second outlines the theory and scientific framework for sustainable and regional development in relation with geography. The third chapter presents the picture of the regional development in Albania, analyzing the disparities and regional development in the light of EU requirements and NUTS division. Chapter 4 continues by analyzing and comparing the regional development of the regions: Tirana – driver for change, Shkodra – the North in Development and Kukes – the “shrinking” region. Chapter 5 presents the conclusions and recommendations. This research comes to the conclusions that if growth in Albania is to be increased and sustained, a regional development policy needs to be established.
The sensitivity of fluvial systems to tectonic and climatic boundary conditions allows us to use the geomorphic and stratigraphic records as quantitative archives of past climatic and tectonic conditions. Thus, fluvial terraces that form on alluvial fans and floodplains as well as the rate of sediment export to oceanic and continental basins are commonly used to reconstruct paleoenvironments. However, we currently lack a systematic and quantitative understanding of the transient evolution of fluvial systems and their associated sediment storage and release in response to changes in base level, water input, and sediment input. Such knowledge is necessary to quantify past environmental change from terrace records or sedimentary deposits and to disentangle the multiple possible causes for terrace formation and sediment deposition. Here, we use a set of seven physical experiments to explore terrace formation and sediment export from a single, braided channel that is perturbed by changes in upstream water discharge or sediment supply, or through downstream base-level fall. Each perturbation differently affects (1) the geometry of terraces and channels, (2) the timing of terrace cutting, and (3) the transient response of sediment export from the basin. In general, an increase in water discharge leads to near-instantaneous channel incision across the entire fluvial system and consequent local terrace cutting, thus preserving the initial channel slope on terrace surfaces, and it also produces a transient increase in sediment export from the system. In contrast, a decreased upstream sediment-supply rate may result in longer lag times before terrace cutting, leading to terrace slopes that differ from the initial channel slope, and also lagged responses in sediment export. Finally, downstream base-level fall triggers the upstream propagation of a diffuse knickzone, forming terraces with upstream-decreasing ages. The slope of terraces triggered by base-level fall mimics that of the newly adjusted active channel, whereas slopes of terraces triggered by a decrease in upstream sediment discharge or an increase in upstream water discharge are steeper compared to the new equilibrium channel. By combining fillterrace records with constraints on sediment export, we can distinguish among environmental perturbations that would otherwise remain unresolved when using just one of these records.
The sensitivity of fluvial systems to tectonic and climatic boundary conditions allows us to use the geomorphic and stratigraphic records as quantitative archives of past climatic and tectonic conditions. Thus, fluvial terraces that form on alluvial fans and floodplains as well as the rate of sediment export to oceanic and continental basins are commonly used to reconstruct paleoenvironments. However, we currently lack a systematic and quantitative understanding of the transient evolution of fluvial systems and their associated sediment storage and release in response to changes in base level, water input, and sediment input. Such knowledge is necessary to quantify past environmental change from terrace records or sedimentary deposits and to disentangle the multiple possible causes for terrace formation and sediment deposition. Here, we use a set of seven physical experiments to explore terrace formation and sediment export from a single, braided channel that is perturbed by changes in upstream water discharge or sediment supply, or through downstream base-level fall. Each perturbation differently affects (1) the geometry of terraces and channels, (2) the timing of terrace cutting, and (3) the transient response of sediment export from the basin. In general, an increase in water discharge leads to near-instantaneous channel incision across the entire fluvial system and consequent local terrace cutting, thus preserving the initial channel slope on terrace surfaces, and it also produces a transient increase in sediment export from the system. In contrast, a decreased upstream sediment-supply rate may result in longer lag times before terrace cutting, leading to terrace slopes that differ from the initial channel slope, and also lagged responses in sediment export. Finally, downstream base-level fall triggers the upstream propagation of a diffuse knickzone, forming terraces with upstream-decreasing ages. The slope of terraces triggered by base-level fall mimics that of the newly adjusted active channel, whereas slopes of terraces triggered by a decrease in upstream sediment discharge or an increase in upstream water discharge are steeper compared to the new equilibrium channel. By combining fillterrace records with constraints on sediment export, we can distinguish among environmental perturbations that would otherwise remain unresolved when using just one of these records.
Climate change fundamentally transforms glaciated high-alpine regions, with well-known cryospheric and hydrological implications, such as accelerating glacier retreat, transiently increased runoff, longer snow-free periods and more frequent and intense summer rainstorms. These changes affect the availability and transport of sediments in high alpine areas by altering the interaction and intensity of different erosion processes and catchment properties.
Gaining insight into the future alterations in suspended sediment transport by high alpine streams is crucial, given its wide-ranging implications, e.g. for flood damage potential, flood hazard in downstream river reaches, hydropower production, riverine ecology and water quality. However, the current understanding of how climate change will impact suspended sediment dynamics in these high alpine regions is limited. For one, this is due to the scarcity of measurement time series that are long enough to e.g. infer trends. On the other hand, it is difficult – if not impossible – to develop process-based models, due to the complexity and multitude of processes involved in high alpine sediment dynamics. Therefore, knowledge has so far been confined to conceptual models (which do not facilitate deriving concrete timings or magnitudes for individual catchments) or qualitative estimates (‘higher export in warmer years’) that may not be able to capture decreases in sediment export. Recently, machine-learning approaches have gained in popularity for modeling sediment dynamics, since their black box nature tailors them to the problem at hand, i.e. relatively well-understood input and output data, linked by very complex processes.
Therefore, the overarching aim of this thesis is to estimate sediment export from the high alpine Ötztal valley in Tyrol, Austria, over decadal timescales in the past and future – i.e. timescales relevant to anthropogenic climate change. This is achieved by informing, extending, evaluating and applying a quantile regression forest (QRF) approach, i.e. a nonparametric, multivariate machine-learning technique based on random forest.
The first study included in this thesis aimed to understand present sediment dynamics, i.e. in the period with available measurements (up to 15 years). To inform the modeling setup for the two subsequent studies, this study identified the most important predictors, areas within the catchments and time periods. To that end, water and sediment yields from three nested gauges in the upper Ötztal, Vent, Sölden and Tumpen (98 to almost 800 km² catchment area, 930 to 3772 m a.s.l.) were analyzed for their distribution in space, their seasonality and spatial differences therein, and the relative importance of short-term events. The findings suggest that the areas situated above 2500 m a.s.l., containing glacier tongues and recently deglaciated areas, play a pivotal role in sediment generation across all sub-catchments. In contrast, precipitation events were relatively unimportant (on average, 21 % of annual sediment yield was associated to precipitation events). Thus, the second and third study focused on the Vent catchment and its sub-catchment above gauge Vernagt (11.4 and 98 km², 1891 to 3772 m a.s.l.), due to their higher share of areas above 2500 m. Additionally, they included discharge, precipitation and air temperature (as well as their antecedent conditions) as predictors.
The second study aimed to estimate sediment export since the 1960s/70s at gauges Vent and Vernagt. This was facilitated by the availability of long records of the predictors, discharge, precipitation and air temperature, and shorter records (four and 15 years) of turbidity-derived sediment concentrations at the two gauges. The third study aimed to estimate future sediment export until 2100, by applying the QRF models developed in the second study to pre-existing precipitation and temperature projections (EURO-CORDEX) and discharge projections (physically-based hydroclimatological and snow model AMUNDSEN) for the three representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.
The combined results of the second and third study show overall increasing sediment export in the past and decreasing export in the future. This suggests that peak sediment is underway or has already passed – unless precipitation changes unfold differently than represented in the projections or changes in the catchment erodibility prevail and override these trends. Despite the overall future decrease, very high sediment export is possible in response to precipitation events. This two-fold development has important implications for managing sediment, flood hazard and riverine ecology.
This thesis shows that QRF can be a very useful tool to model sediment export in high-alpine areas. Several validations in the second study showed good performance of QRF and its superiority to traditional sediment rating curves – especially in periods that contained high sediment export events, which points to its ability to deal with threshold effects. A technical limitation of QRF is the inability to extrapolate beyond the range of values represented in the training data. We assessed the number and severity of such out-of-observation-range (OOOR) days in both studies, which showed that there were few OOOR days in the second study and that uncertainties associated with OOOR days were small before 2070 in the third study. As the pre-processed data and model code have been made publically available, future studies can easily test further approaches or apply QRF to further catchments.
In the following article, the focus is on the transformative potentials created by so-called persistence avant-gardes and prevention innovators. The text extends Bluhdorn's guiding concept of narratives of hope (Bluhdorn 2017; Bluhdorn and Butzlaff 2019) by considering those groups that are marginalized within debates on socio-ecological transformation. With a closer look at the narratives of prevention and blockade that these actors engage, the ambiguous nature of postgrowth avant-gardes is carved out. Their discursive, argumentative, and effective inhibition of transitory policies is interpreted as a pro-active potential, rather than a mere obstacle to socio-ecological transformation. Adding a geographical perspective, the paper pleads for a more precise theoretical penetration of the ambivalent figure of avantgardes when analyzing processes of local and regional postgrowth.
Flood polders are part of the flood risk management strategy for many lowland rivers. They are used for the controlled storage of flood water so as to lower peak discharges of large floods. Consequently, the flood hazard in adjacent and downstream river reaches is decreased in the case of flood polder utilisation. Flood polders are usually dry storage reservoirs that are typically characterised by agricultural activities or other land use of low economic and ecological vulnerability. The objective of this thesis is to analyse hydraulic, environmental and economic impacts of the utilisation of flood polders in order to draw conclusions for their management. For this purpose, hydrodynamic and water quality modelling as well as an economic vulnerability assessment are employed in two study areas on the Middle Elbe River in Germany. One study area is an existing flood polder system on the tributary Havel, which was put into operation during the Elbe flood in summer 2002. The second study area is a planned flood polder, which is currently in the early planning stages. Furthermore, numerical models of different spatial dimensionality, ranging from zero- to two-dimensional, are applied in order to evaluate their suitability for hydrodynamic and water quality simulations of flood polders in regard to performance and modelling effort. The thesis concludes with overall recommendations on the management of flood polders, including operational schemes and land use. In view of future changes in flood frequency and further increasing values of private and public assets in flood-prone areas, flood polders may be effective and flexible technical flood protection measures that contribute to a successful flood risk management for large lowland rivers.
We conducted a PUB (predictions in ungauged basins) experiment looking at hydrology and crop dynamics in the semi-arid rural Mod catchment in India. The experiment was motivated by the aims (a) to develop a coupled eco-hydrological model capable of analysing land-use strategies concerning crop water need, erosion protection, crop yield and resistivity against droughts and floods, and (b) to assess the feasibility of a strategy for collecting the necessary data in a data-scarce region. Our experiment combines parsimonious data assessment and eco-hydrological model coupling at the lower mesoscale. Linking bottom-up sampling of functionally representative soil classes and top-down regionalization based on spectral properties of the same resulted in a comprehensive distributed data basis for the model. A clear focus on the dominating processes and the catena as the organizing landscape element in the given environmental setting enabled this. We employed the WASA (Water Availability in Semi-Arid environments) model for uncalibrated process-based water balance modelling and integrated a crop simulation subroutine based on the SWAP (Soil Water Atmosphere Plant) model to account for crop dynamics, feedbacks and yield estimation. While we found the data assessment strategy and the hydrological model application largely feasible, in terms of its accounting for scale, processes and model concepts, the simulation of feedbacks with crops was problematic. Contributing to the PUB issue, more general conclusions are drawn concerning spatially-distributed structural information and uncalibrated modelling.
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Editor Z.W. Kundzewicz; Associate editor F. Hattermann
The Ca Mau peninsula (CMP) is a key economic region in southern Vietnam. In recent decades, the high demand for water has increased the exploitation of groundwater, thus lowering the groundwater level and leading to risks of degradation, depletion, and land subsidence, as well as salinity intrusion in the groundwater of the whole Mekong Delta region. By using a finite element groundwater model with boundary expansion to the sea, we updated the latest data on hydrogeological profiles, groundwater levels, and exploitation. The basic model setup covers seven aquifers and seven aquitards. It is determined that the inflow along the coastline to the mainland is 39% of the total inflow. The exploitation of the study area in 2019 was 567,364 m(3)/day. The most exploited aquifers are the upper-middle Pleistocene (qp(2-3)) and the middle Pliocene (n(2)(2)), accounting for 63.7% and 24.6%, respectively; the least exploited aquifers are the upper Pleistocene and the upper Miocene, accounting for 0.35% and 0.02%, respectively. In the deeper aquifers, qp(2-3) and n(2)(2), the change in storage is negative due to the high exploitation rate, leading to a decline in the reserves of these aquifers. These groundwater model results are the calculations of groundwater reserves from the coast to the mainland in the entire system of aquifers in the CMP. This makes groundwater decision managers, stakeholders, and others more efficient in sustainable water resources planning in the CMP and Mekong Delta (MKD).
The temporal dynamics of hydrological model performance gives insights into errors that cannot be obtained from global performance measures assigning a single number to the fit of a simulated time series to an observed reference series. These errors can include errors in data, model parameters, or model structure. Dealing with a set of performance measures evaluated at a high temporal resolution implies analyzing and interpreting a high dimensional data set. This paper presents a method for such a hydrological model performance assessment with a high temporal resolution and illustrates its application for two very different rainfall-runoff modeling case studies. The first is the Wilde Weisseritz case study, a headwater catchment in the eastern Ore Mountains, simulated with the conceptual model WaSiM-ETH. The second is the Malalcahuello case study, a headwater catchment in the Chilean Andes, simulated with the physicsbased model Catflow. The proposed time-resolved performance assessment starts with the computation of a large set of classically used performance measures for a moving window. The key of the developed approach is a data-reduction method based on self-organizing maps (SOMs) and cluster analysis to classify the high-dimensional performance matrix. Synthetic peak errors are used to interpret the resulting error classes. The final outcome of the proposed method is a time series of the occurrence of dominant error types. For the two case studies analyzed here, 6 such error types have been identified. They show clear temporal patterns, which can lead to the identification of model structural errors.
Analysis and modelling of nutrient transport and transformation processes on the catchment scale
(2007)
Analysis and modelling of nutrient transport and transformation processes on the catchment scale
(2007)
River reaches protected by dikes exhibit high damage potential due to strong value accumulation in the hinterland areas. While providing an efficient protection against low magnitude flood events, dikes may fail under the load of extreme water levels and long flood durations. Hazard and risk assessments for river reaches protected by dikes have not adequately considered the fluvial inundation processes up to now. Particularly, the processes of dike failures and their influence on the hinterland inundation and flood wave propagation lack comprehensive consideration. This study focuses on the development and application of a new modelling system which allows a comprehensive flood hazard assessment along diked river reaches under consideration of dike failures. The proposed Inundation Hazard Assessment Model (IHAM) represents a hybrid probabilistic-deterministic model. It comprises three models interactively coupled at runtime. These are: (1) 1D unsteady hydrodynamic model of river channel and floodplain flow between dikes, (2) probabilistic dike breach model which determines possible dike breach locations, breach widths and breach outflow discharges, and (3) 2D raster-based diffusion wave storage cell model of the hinterland areas behind the dikes. Due to the unsteady nature of the 1D and 2D coupled models, the dependence between hydraulic load at various locations along the reach is explicitly considered. The probabilistic dike breach model describes dike failures due to three failure mechanisms: overtopping, piping and slope instability caused by the seepage flow through the dike core (micro-instability). The 2D storage cell model driven by the breach outflow boundary conditions computes an extended spectrum of flood intensity indicators such as water depth, flow velocity, impulse, inundation duration and rate of water rise. IHAM is embedded in a Monte Carlo simulation in order to account for the natural variability of the flood generation processes reflected in the form of input hydrographs and for the randomness of dike failures given by breach locations, times and widths. The model was developed and tested on a ca. 91 km heavily diked river reach on the German part of the Elbe River between gauges Torgau and Vockerode. The reach is characterised by low slope and fairly flat extended hinterland areas. The scenario calculations for the developed synthetic input hydrographs for the main river and tributary were carried out for floods with return periods of T = 100, 200, 500, 1000 a. Based on the modelling results, probabilistic dike hazard maps could be generated that indicate the failure probability of each discretised dike section for every scenario magnitude. In the disaggregated display mode, the dike hazard maps indicate the failure probabilities for each considered breach mechanism. Besides the binary inundation patterns that indicate the probability of raster cells being inundated, IHAM generates probabilistic flood hazard maps. These maps display spatial patterns of the considered flood intensity indicators and their associated return periods. Finally, scenarios of polder deployment for the extreme floods with T = 200, 500, 1000 were simulated with IHAM. The developed IHAM simulation system represents a new scientific tool for studying fluvial inundation dynamics under extreme conditions incorporating effects of technical flood protection measures. With its major outputs in form of novel probabilistic inundation and dike hazard maps, the IHAM system has a high practical value for decision support in flood management.
The management of water resources in a river basin experiencing the expansion of agricultural activities requires a proper understanding of impacts on its hydrologic cycle. This study focused on the analysis of impacts of infield rainwater harvesting (IRWH) and future agricultural expansion as land and water uses change (LWUC) on the hydrologic cycle in the Wami River basin (Tanzania) using the Soil and Water Assessment Tool (SWAT). In the SWAT model, IRWH was implemented by fragmenting rainwater harvesting hydrological response units (HRUs) from cropland HRUs and assigning them as potholes for rainwater impoundment. LWUC was implemented by customizing land cover types and their corresponding model parameters in all original HRUs, and introducing projected water uses in the model. The study thus demonstrated the successful modelling of IRWH and land use change in the SWAT model using HRU fragmentation and customization approaches, respectively. The results indicated that IRWH applications in croplands led to a large increase in evapotranspiration (ET) and the soil water content, and a decrease in percolation, especially in the dry years. However, the average annual streamflow showed negligible changes when IRWH was implemented, even in 50% of current low-coverage croplands in the river basin. Thus, IRWH applications in the river basin are recommended. The results also indicated that LWUC caused huge changes in ET, the soil water content, percolation and the streamflow from the river basin. The average annual streamflow was predicted to decrease by 26% due to LWUC. However, land use change alone without projected water uses was predicted to cause a minor decrease of about 1% in the average annual streamflow. Therefore, further studies on the eco-hydrology of the river basin under various water use scenarios are recommended prior to the expansion of agricultural areas.
Meteoroids largely disintegrate during their entry into the atmosphere, contributing significantly to the input of cosmic material to Earth. Yet, their atmospheric entry is not well understood. Experimental studies on meteoroid material degradation in high-enthalpy facilities are scarce and when the material is recovered after testing, it rarely provides sufficient quantitative data for the validation of simulation tools. In this work, we investigate the thermochemical degradation mechanism of a meteorite in a high-enthalpy ground facility able to reproduce atmospheric entry conditions. A testing methodology involving measurement techniques previously used for the characterization of thermal protection systems for spacecraft is adapted for the investigation of ablation of alkali basalt (employed here as meteorite analog) and ordinary chondrite samples. Both materials are exposed to a cold-wall stagnation point heat flux of 1.2 MW m(-2). Numerous local pockets that formed on the surface of the samples by the emergence of gas bubbles reveal the frothing phenomenon characteristic of material degradation. Time-resolved optical emission spectroscopy data of ablated species allow us to identify the main radiating atoms and ions of potassium, calcium, magnesium, and iron. Surface temperature measurements provide maximum values of 2280 K for the basalt and 2360 K for the chondrite samples. We also develop a material response model by solving the heat conduction equation and accounting for evaporation and oxidation reaction processes in a 1D Cartesian domain. The simulation results are in good agreement with the data collected during the experiments, highlighting the importance of iron oxidation to the material degradation.
Pattern-process analysis is one of the main threads in landscape ecological research. It aims at understanding the complex relationships between ecological processes and landscape patterns, identifying the underlying mechanisms and deriving valid predictions for scenarios of landscape change and its consequences. Today, various studies cope with these tasks through so called "landscape modelling" approaches. They integrate different aspects of heterogeneous and dynamic landscapes and model different driving forces, often using both statistical and process-oriented techniques. We identify two main approaches to deal with the analysis of pattern-process interactions: the first starts with pattern detection, pattern description and pattern analysis, the second with process description, simulation and pattern generation. Focussing on the interplay between these two approaches, landscape analysis and landscape modelling will improve our understanding of pattern-process interactions. The comparison of simulated and observed pattern is a prerequisite for both approaches. Therefore, we identify a set of quantitative, robust, and reproducible methods for the analysis of spatiotemporal patterns that is a starting point for a standard toolbox for ecologists as major future challenge and suggest necessary further methodological developments. (c) 2006 Elsevier B.V. All rights reserved.
The most severe flood events in Turkey were determined for the period 1960-2014 by considering the number of fatalities, the number of affected people, and the total economic losses as indicators. The potential triggering mechanisms (i.e., atmospheric circulations and precipitation amounts) and aggravating pathways (i.e., topographic features, catchment size, land use types, and soil properties) of these 25 events were analyzed. On this basis, a new approach was developed to identify the main influencing factor per event and to provide additional information for determining the dominant flood occurrence pathways for severe floods. The events were then classified through hierarchical cluster analysis. As a result, six different clusters were found and characterized. Cluster 1 comprised flood events that were mainly influenced by drainage characteristics (e.g., catchment size and shape); Cluster 2 comprised events aggravated predominantly by urbanization; steep topography was identified to be the dominant factor for Cluster 3; extreme rainfall was determined as the main triggering factor for Cluster 4; saturated soil conditions were found to be the dominant factor for Cluster 5; and orographic effects of mountain ranges characterized Cluster 6. This study determined pathway patterns of the severe floods in Turkey with regard to their main causal or aggravating mechanisms. Accordingly, geomorphological properties are of major importance in large catchments in eastern and northeastern Anatolia. In addition, in small catchments, the share of urbanized area seems to be an important factor for the extent of flood impacts. This paper presents an outcome that could be used for future urban planning and flood risk prevention studies to understand the flood mechanisms in different regions of Turkey.
The most severe flood events in Turkey were determined for the period 1960–2014 by considering the number of fatalities, the number of affected people, and the total economic losses as indicators. The potential triggering mechanisms (i.e., atmospheric circulations and precipitation amounts) and aggravating pathways (i.e., topographic features, catchment size, land use types, and soil properties) of these 25 events were analyzed. On this basis, a new approach was developed to identify the main influencing factor per event and to provide additional information for determining the dominant flood occurrence pathways for severe floods. The events were then classified through hierarchical cluster analysis. As a result, six different clusters were found and characterized. Cluster 1 comprised flood events that were mainly influenced by drainage characteristics (e.g., catchment size and shape); Cluster 2 comprised events aggravated predominantly by urbanization; steep topography was identified to be the dominant factor for Cluster 3; extreme rainfall was determined as the main triggering factor for Cluster 4; saturated soil conditions were found to be the dominant factor for Cluster 5; and orographic effects of mountain ranges characterized Cluster 6. This study determined pathway patterns of the severe floods in Turkey with regard to their main causal or aggravating mechanisms. Accordingly, geomorphological properties are of major importance in large catchments in eastern and northeastern Anatolia. In addition, in small catchments, the share of urbanized area seems to be an important factor for the extent of flood impacts. This paper presents an outcome that could be used for future urban planning and flood risk prevention studies to understand the flood mechanisms in different regions of Turkey.
The most severe flood events in Turkey were determined for the period 1960–2014 by considering the number of fatalities, the number of affected people, and the total economic losses as indicators. The potential triggering mechanisms (i.e., atmospheric circulations and precipitation amounts) and aggravating pathways (i.e., topographic features, catchment size, land use types, and soil properties) of these 25 events were analyzed. On this basis, a new approach was developed to identify the main influencing factor per event and to provide additional information for determining the dominant flood occurrence pathways for severe floods. The events were then classified through hierarchical cluster analysis. As a result, six different clusters were found and characterized. Cluster 1 comprised flood events that were mainly influenced by drainage characteristics (e.g., catchment size and shape); Cluster 2 comprised events aggravated predominantly by urbanization; steep topography was identified to be the dominant factor for Cluster 3; extreme rainfall was determined as the main triggering factor for Cluster 4; saturated soil conditions were found to be the dominant factor for Cluster 5; and orographic effects of mountain ranges characterized Cluster 6. This study determined pathway patterns of the severe floods in Turkey with regard to their main causal or aggravating mechanisms. Accordingly, geomorphological properties are of major importance in large catchments in eastern and northeastern Anatolia. In addition, in small catchments, the share of urbanized area seems to be an important factor for the extent of flood impacts. This paper presents an outcome that could be used for future urban planning and flood risk prevention studies to understand the flood mechanisms in different regions of Turkey.
On a planetary scale human populations need to adapt to both socio-economic and environmental problems amidst rapid global change. This holds true for coupled human-environment (socio-ecological) systems in rural and urban settings alike. Two examples are drylands and urban coasts. Such socio-ecological systems have a global distribution. Therefore, advancing the knowledge base for identifying socio-ecological adaptation needs with local vulnerability assessments alone is infeasible: The systems cover vast areas, while funding, time, and human resources for local assessments are limited. They are lacking in low an middle-income countries (LICs and MICs) in particular.
But places in a specific socio-ecological system are not only unique and complex – they also exhibit similarities. A global patchwork of local rural drylands vulnerability assessments of human populations to socio-ecological and environmental problems has already been reduced to a limited number of problem structures, which typically cause vulnerability. However, the question arises whether this is also possible in urban socio-ecological systems. The question also arises whether these typologies provide added value in research beyond global change. Finally, the methodology employed for drylands needs refining and standardizing to increase its uptake in the scientific community. In this dissertation, I set out to fill these three gaps in research.
The geographical focus in my dissertation is on LICs and MICs, which generally have lower capacities to adapt, and greater adaptation needs, regarding rapid global change. Using a spatially explicit indicator-based methodology, I combine geospatial and clustering methods to identify typical configurations of key factors in case studies causing vulnerability to human populations in two specific socio-ecological systems. Then I use statistical and analytical methods to interpret and appraise both the typical configurations and the global typologies they constitute.
First, I improve the indicator-based methodology and then reanalyze typical global problem structures of socio-ecological drylands vulnerability with seven indicator datasets. The reanalysis confirms the key tenets and produces a more realistic and nuanced typology of eight spatially explicit problem structures, or vulnerability profiles: Two new profiles with typically high natural resource endowment emerge, in which overpopulation has led to medium or high soil erosion. Second, I determine whether the new drylands typology and its socio-ecological vulnerability concept advance a thematically linked scientific debate in human security studies: what drives violent conflict in drylands? The typology is a much better predictor for conflict distribution and incidence in drylands than regression models typically used in peace research. Third, I analyze global problem structures typically causing vulnerability in an urban socio-ecological system - the rapidly urbanizing coastal fringe (RUCF) – with eleven indicator datasets. The RUCF also shows a robust typology, and its seven profiles show huge asymmetries in vulnerability and adaptive capacity. The fastest population increase, lowest income, most ineffective governments, most prevalent poverty, and lowest adaptive capacity are all typically stacked in two profiles in LICs. This shows that beyond local case studies tropical cyclones and/or coastal flooding are neither stalling rapid population growth, nor urban expansion, in the RUCF. I propose entry points for scaling up successful vulnerability reduction strategies in coastal cities within the same vulnerability profile.
This dissertation shows that patchworks of local vulnerability assessments can be generalized to structure global socio-ecological vulnerabilities in both rural and urban socio-ecological systems according to typical problems. In terms of climate-related extreme events in the RUCF, conflicting problem structures and means to deal with them are threatening to widen the development gap between LICs and high-income countries unless successful vulnerability reduction measures are comprehensively scaled up. The explanatory power for human security in drylands warrants further applications of the methodology beyond global environmental change research in the future. Thus, analyzing spatially explicit global typologies of socio-ecological vulnerability is a useful complement to local assessments: The typologies provide entry points for where to consider which generic measures to reduce typical problem structures – including the countless places without local assessments. This can save limited time and financial resources for adaptation under rapid global change.
The design of an array configuration is an important task in array seismology during experiment planning. Often the array response function (ARF), which depends on the relative position of array stations and frequency content of the incoming signals, is used as the array design criterion. In practice, additional constraints and parameters have to be taken into account, for example, land ownership, site-specific noise levels or characteristics of the seismic sources under investigation. In this study, a flexible array design framework is introduced that implements a customizable scenario modelling and optimization scheme by making use of synthetic seismograms. Using synthetic seismograms to evaluate array performance makes it possible to consider additional constraints. We suggest to use synthetic array beamforming as an array design criterion instead of the ARF. The objective function of the optimization scheme is defined according to the monitoring goals, and may consist of a number of subfunctions. The array design framework is exemplified by designing a seven-station small-scale array to monitor earthquake swarm activity in Northwest Bohemia/Vogtland in central Europe. Two subfunctions are introduced to verify the accuracy of horizontal slowness estimation; one to suppress aliasing effects due to possible secondary lobes of synthetic array beamforming calculated in horizontal slowness space and the other to reduce the event’s mislocation caused by miscalculation of the horizontal slowness vector. Subsequently, a weighting technique is applied to combine the subfunctions into one single scalar objective function to use in the optimization process.
Rain fall-runoff response in temperate humid headwater catchments is mainly controlled by hydrolo gical processes at the hillslope scale. Applied tracer experiments with fluore scent dye and salt tracers are well known tools in groundwater studies at the large scale and vadose zone studies at the plot scale, where they provide a means to characterise subsurface flow. We extend this approach to the hillslope scale to investigate saturated and unsaturated flow path s concertedly at a forested hill slope in the Austrian Alps. Dye staining experiments at the plot scale revealed that crack s and soil pipe s function as preferential flow path s in the fine-textured soils of the study area, and these preferenti al flow structures were active in fast subsurface transport of tracers at the hillslope scale. Breakthrough curves obtained under steady flow conditions could be fitted well to a one-dimensional convection-dispersion model. Under natural rain fall a positive correlation of tracer concentrations to the transient flows was observed. The results of this study demon strate qualitative and quantitative effects of preferential flow feature s on subsurface stormflow in a temperate humid headwater catchment. It turn s out that , at the hill slope scale, the interaction s of structures and processes are intrinsically complex, which implies that attempts to model such a hillslope satisfactorily require detailed investigation s of effective structures and parameters at the scale of interest.
Approximation of Groundwater - Surface Water - Interactions in a Mesoscale Lowland River Catchment
(2004)
Increasing arctic coastal erosion rates imply a greater release of sediments and organic matter into the coastal zone. With 213 sediment samples taken around Herschel Island-Qikiqtaruk, Canadian Beaufort Sea, we aimed to gain new insights on sediment dynamics and geochemical properties of a shallow arctic nearshore zone. Spatial characteristics of nearshore sediment texture (moderately to poorly sorted silt) are dictated by hydrodynamic processes, but ice-related processes also play a role. We determined organic matter (OM) distribution and inferred the origin and quality of organic carbon by C/N ratios and stable carbon isotopes delta C-13. The carbon content was higher offshore and in sheltered areas (mean: 1.0 wt.%., S.D.: 0.9) and the C/N ratios also showed a similar spatial pattern (mean: 11.1, S.D.: 3.1), while the delta C-13 (mean: -26.4 parts per thousand VPDB, S.D.: 0.4) distribution was more complex. We compared the geochemical parameters of our study with terrestrial and marine samples from other studies using a bootstrap approach. Sediments of the current study contained 6.5 times and 1.8 times less total organic carbon than undisturbed and disturbed terrestrial sediments, respectively. Therefore, degradation of OM and separation of carbon pools take place on land and continue in the nearshore zone, where OM is leached, mineralized, or transported beyond the study area.
Cities can be severely affected by climate change. Hence, many of them have started to develop climate adaptation strategies or implement measures to help prepare for the challenges it will present. This study aims to provide an overview of climate adaptation in 104 German cities. While existing studies on adaptation tracking rely heavily on self-reported data or the mere existence of adaptation plans, we applied the broader concept of adaptation readiness, considering five factors and a total of twelve different indicators, when making our assessments. We clustered the cities depending on the contribution of these factors to the overall adaptation readiness index and grouped them according to their total score and cluster affiliations. This resulted in us identifying four groups of cities. First, a pioneering group comprises twelve (mainly big) cities with more than 500,000 inhabitants, which showed high scores for all five factors of adaptation readiness. Second, a set of 36 active cities, which follow different strategies on how to deal with climate adaptation. Third, a group of 28 cities showed considerably less activity toward climate adaptation, while a fourth set of 28 mostly small cities (with between 50,000 and 99,999 inhabitants) scored the lowest. We consider this final group to be pursuing a 'wait-and-see' approach. Since the city size correlates with the adaptation readiness index, we recommend policymakers introduce funding schemes that focus on supporting small cities, to help them prepare for the impact of a changing climate.
Food system innovations will be instrumental to achieving multiple Sustainable Development Goals (SDGs). However, major innovation breakthroughs can trigger profound and disruptive changes, leading to simultaneous and interlinked reconfigurations of multiple parts of the global food system. The emergence of new technologies or social solutions, therefore, have very different impact profiles, with favourable consequences for some SDGs and unintended adverse side-effects for others. Stand-alone innovations seldom achieve positive outcomes over multiple sustainability dimensions. Instead, they should be embedded as part of systemic changes that facilitate the implementation of the SDGs. Emerging trade-offs need to be intentionally addressed to achieve true sustainability, particularly those involving social aspects like inequality in its many forms, social justice, and strong institutions, which remain challenging. Trade-offs with undesirable consequences are manageable through the development of well planned transition pathways, careful monitoring of key indicators, and through the implementation of transparent science targets at the local level.
Asian climate patterns, characterised by highly seasonal monsoons and continentality, are thought to originate in the Eocene epoch (56 to 34 million years ago - Ma) in response to global climate, Tibetan Plateau uplift and the disappearance of the giant Proto-Paratethys sea formerly extending over Eurasia. The influence of this sea on Asian climate has hitherto not been constrained by proxy records despite being recognised as a major driver by climate models. We report here strongly seasonal records preserved in annual lamina of Eocene oysters from the Proto-Paratethys with sedimentological and numerical data showing that monsoons were not dampened by the sea and that aridification was modulated by westerly moisture sourced from the sea. Hot and arid summers despite the presence of the sea suggest a strong anticyclonic zone at Central Asian latitudes and an orographic effect from the emerging Tibetan Plateau. Westerly moisture precipitating during cold and wetter winters appear to have decreased in two steps. First in response to the late Eocene (34-37 Ma) sea retreat; second by the orogeny of the Tian Shan and Pamir ranges shielding the westerlies after 25 Ma. Paleogene sea retreat and Neogene westerly shielding thus provide two successive mechanisms forcing coeval Asian desertification and biotic crises.
Largescale patterns of global land use change are very frequently accompanied by natural habitat loss. To assess the consequences of habitat loss for the remaining natural and semi-natural biotopes, inclusion of cumulative effects at the landscape level is required. The interdisciplinary concept of vulnerability constitutes an appropriate assessment framework at the landscape level, though with few examples of its application for ecological assessments. A comprehensive biotope vulnerability analysis allows identification of areas most affected by landscape change and at the same time with the lowest chances of regeneration.
To this end, a series of ecological indicators were reviewed and developed. They measured spatial attributes of individual biotopes as well as some ecological and conservation characteristics of the respective resident species community. The final vulnerability index combined seven largely independent indicators, which covered exposure, sensitivity and adaptive capacity of biotopes to landscape changes. Results for biotope vulnerability were provided at the regional level. This seems to be an appropriate extent with relevance for spatial planning and designing the distribution of nature reserves.
Using the vulnerability scores calculated for the German federal state of Brandenburg, hot spots and clusters within and across the distinguished types of biotopes were analysed. Biotope types with high dependence on water availability, as well as biotopes of the open landscape containing woody plants (e.g., orchard meadows) are particularly vulnerable to landscape changes. In contrast, the majority of forest biotopes appear to be less vulnerable. Despite the appeal of such generalised statements for some biotope types, the distribution of values suggests that conservation measures for the majority of biotopes should be designed specifically for individual sites. Taken together, size, shape and spatial context of individual biotopes often had a dominant influence on the vulnerability score.
The implementation of biotope vulnerability analysis at the regional level indicated that large biotope datasets can be evaluated with high level of detail using geoinformatics. Drawing on previous work in landscape spatial analysis, the reproducible approach relies on transparent calculations of quantitative and qualitative indicators. At the same time, it provides a synoptic overview and information on the individual biotopes. It is expected to be most useful for nature conservation in combination with an understanding of population, species, and community attributes known for specific sites. The biotope vulnerability analysis facilitates a foresighted assessment of different land uses, aiding in identifying options to slow habitat loss to sustainable levels. It can also be incorporated into planning of restoration measures, guiding efforts to remedy ecological damage. Restoration of any specific site could yield synergies with the conservation objectives of other sites, through enhancing the habitat network or buffering against future landscape change.
Biotope vulnerability analysis could be developed in line with other important ecological concepts, such as resilience and adaptability, further extending the broad thematic scope of the vulnerability concept. Vulnerability can increasingly serve as a common framework for the interdisciplinary research necessary to solve major societal challenges.
This paper introduces a novel measure to assess similarity between event hydrographs. It is based on cross recurrence plots (CRP) and recurrence quantification analysis (RQA), which have recently gained attention in a range of disciplines when dealing with complex systems. The method attempts to quantify the event runoff dynamics and is based on the time delay embedded phase space representation of discharge hydrographs. A phase space trajectory is reconstructed from the event hydrograph, and pairs of hydrographs are compared to each other based on the distance of their phase space trajectories. Time delay embedding allows considering the multidimensional relationships between different points in time within the event. Hence, the temporal succession of discharge values is taken into account, such as the impact of the initial conditions on the runoff event. We provide an introduction to cross recurrence plots and discuss their parameterization. An application example based on flood time series demonstrates how the method can be used to measure the similarity or dissimilarity of events, and how it can be used to detect events with rare runoff dynamics. It is argued that this methods provides a more comprehensive approach to quantify hydrograph similarity compared to conventional hydrological signatures.
Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools such as soil moisture, snow or vegetation. The intrinsic integration over a radial hectare-scale footprint is a clear advantage for averaging out small-scale heterogeneity, but on the other hand the data may become hard to interpret in complex terrain with patchy land use.
This study presents a directional shielding approach to prevent neutrons from certain angles from being counted while counting neutrons entering the detector from other angles and explores its potential to gain a sharper horizontal view on the surrounding soil moisture distribution.
Using the Monte Carlo code URANOS (Ultra Rapid Neutron-Only Simulation), we modelled the effect of additional polyethylene shields on the horizontal field of view and assessed its impact on the epithermal count rate, propagated uncertainties and aggregation time.
The results demonstrate that directional CRNS measurements are strongly dominated by isotropic neutron transport, which dilutes the signal of the targeted direction especially from the far field. For typical count rates of customary CRNS stations, directional shielding of half-spaces could not lead to acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates should be feasible.
Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools such as soil moisture, snow or vegetation. The intrinsic integration over a radial hectare-scale footprint is a clear advantage for averaging out small-scale heterogeneity, but on the other hand the data may become hard to interpret in complex terrain with patchy land use.
This study presents a directional shielding approach to prevent neutrons from certain angles from being counted while counting neutrons entering the detector from other angles and explores its potential to gain a sharper horizontal view on the surrounding soil moisture distribution.
Using the Monte Carlo code URANOS (Ultra Rapid Neutron-Only Simulation), we modelled the effect of additional polyethylene shields on the horizontal field of view and assessed its impact on the epithermal count rate, propagated uncertainties and aggregation time.
The results demonstrate that directional CRNS measurements are strongly dominated by isotropic neutron transport, which dilutes the signal of the targeted direction especially from the far field. For typical count rates of customary CRNS stations, directional shielding of half-spaces could not lead to acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates should be feasible.
An ensemble of 10 hydrological models was applied to the same set of land use change scenarios. There was general agreement about the direction of changes in the mean annual discharge and 90% discharge percentile predicted by the ensemble members, although a considerable range in the magnitude of predictions for the scenarios and catchments under consideration was obvious. Differences in the magnitude of the increase were attributed to the different mean annual actual evapotranspiration rates for each land use type. The ensemble of model runs was further analyzed with deterministic and probabilistic ensemble methods. The deterministic ensemble method based on a trimmed mean resulted in a single somewhat more reliable scenario prediction. The probabilistic reliability ensemble averaging (REA) method allowed a quantification of the model structure uncertainty in the scenario predictions. It was concluded that the use of a model ensemble has greatly increased our confidence in the reliability of the model predictions.
Groundwater is the biggest single source of high-quality freshwater worldwide, which is also continuously threatened by the changing climate. In this paper, we investigate the response of the regional groundwater system to climate change under three global warming levels (1.5, 2, and 3 ∘C) in a central German basin (Nägelstedt). This investigation is conducted by deploying an integrated modeling workflow that consists of a mesoscale hydrologic model (mHM) and a fully distributed groundwater model, OpenGeoSys (OGS). mHM is forced with climate simulations of five general circulation models under three representative concentration pathways. The diffuse recharges estimated by mHM are used as boundary forcings to the OGS groundwater model to compute changes in groundwater levels and travel time distributions. Simulation results indicate that groundwater recharges and levels are expected to increase slightly under future climate scenarios. Meanwhile, the mean travel time is expected to decrease compared to the historical average. However, the ensemble simulations do not all agree on the sign of relative change. Changes in mean travel time exhibit a larger variability than those in groundwater levels. The ensemble simulations do not show a systematic relationship between the projected change (in both groundwater levels and travel times) and the warming level, but they indicate an increased variability in projected changes with adjusting the enhanced warming level from 1.5 to 3 ∘C. Correspondingly, it is highly recommended to restrain the trend of global warming.
Groundwater is the biggest single source of high-quality freshwater worldwide, which is also continuously threatened by the changing climate. In this paper, we investigate the response of the regional groundwater system to climate change under three global warming levels (1.5, 2, and 3 ∘C) in a central German basin (Nägelstedt). This investigation is conducted by deploying an integrated modeling workflow that consists of a mesoscale hydrologic model (mHM) and a fully distributed groundwater model, OpenGeoSys (OGS). mHM is forced with climate simulations of five general circulation models under three representative concentration pathways. The diffuse recharges estimated by mHM are used as boundary forcings to the OGS groundwater model to compute changes in groundwater levels and travel time distributions. Simulation results indicate that groundwater recharges and levels are expected to increase slightly under future climate scenarios. Meanwhile, the mean travel time is expected to decrease compared to the historical average. However, the ensemble simulations do not all agree on the sign of relative change. Changes in mean travel time exhibit a larger variability than those in groundwater levels. The ensemble simulations do not show a systematic relationship between the projected change (in both groundwater levels and travel times) and the warming level, but they indicate an increased variability in projected changes with adjusting the enhanced warming level from 1.5 to 3 ∘C. Correspondingly, it is highly recommended to restrain the trend of global warming.
Apple replant disease (ARD) is a specific apple-related form of soil fertility loss due to unidentified causes and is also known as soil fatigue. The effect typically appears in monoculture production sites and leads to production decreases of up to 50%, even though the cultivation practice remains the same. However, an indication of replant disease is challenged by the lack of specification of the particular microbial group responsible for ARD. The objective of this study was to establish an algorithm for estimating growth suppression in orchards irrespective of the unknowns in the complex causal relationship by assessing plant-soil interaction in the orchard several years after planting. Based on a comparison between no-replant and replant soils, the Alternaria group (Ag) was identified as a soil-fungal population responding to replant with abundance. The trunk cross-sectional area (CSA) was found to be a practical and robust parameter representing below-ground and above-ground tree performance. Suppression of tree vigour was therefore calculated by dividing the two inversely related parameters, Q = ln(Ag)/CSA, as a function of soil-fungal proportions and plant responses at the single-tree level. On this basis, five clusters of tree vigour suppression (Q) were defined: (1) no tree vigour suppression/vital (0%), (2) escalating (- 38%), (3) strong (- 53%), (4) very strong (- 62%), and (5) critical (- 74%). By calculating Q at the level of the single tree, trees were clustered according to tree vigour suppression. The weighted frequency of clusters in the field allowed replant impact to be quantified at field level. Applied to a case study on sandy brown, dry diluvial soils in Brandenburg, Germany, the calculated tree vigour suppression was 46% compared to the potential tree vigour on no-replant soil in the same field. It is highly likely that the calculated growth suppression corresponds to ARD-impact This result is relevant for identifying functional changes in soil and for monitoring the economic effects of soil fatigue in apple orchards, particularly where long-period crop rotation or plot exchange are improbable.
Apple replant disease (ARD) is a specific apple-related form of soil fertility loss due to unidentified causes and is also known as soil fatigue. The effect typically appears in monoculture production sites and leads to production decreases of up to 50%, even though the cultivation practice remains the same. However, an indication of replant disease is challenged by the lack of specification of the particular microbial group responsible for ARD. The objective of this study was to establish an algorithm for estimating growth suppression in orchards irrespective of the unknowns in the complex causal relationship by assessing plant-soil interaction in the orchard several years after planting. Based on a comparison between no-replant and replant soils, the Alternaria group (Ag) was identified as a soil-fungal population responding to replant with abundance. The trunk cross-sectional area (CSA) was found to be a practical and robust parameter representing below-ground and above-ground tree performance. Suppression of tree vigour was therefore calculated by dividing the two inversely related parameters, Q = ln(Ag)/CSA, as a function of soil-fungal proportions and plant responses at the single-tree level. On this basis, five clusters of tree vigour suppression (Q) were defined: (1) no tree vigour suppression/vital (0%), (2) escalating (- 38%), (3) strong (- 53%), (4) very strong (- 62%), and (5) critical (- 74%). By calculating Q at the level of the single tree, trees were clustered according to tree vigour suppression. The weighted frequency of clusters in the field allowed replant impact to be quantified at field level. Applied to a case study on sandy brown, dry diluvial soils in Brandenburg, Germany, the calculated tree vigour suppression was 46% compared to the potential tree vigour on no-replant soil in the same field. It is highly likely that the calculated growth suppression corresponds to ARD-impact This result is relevant for identifying functional changes in soil and for monitoring the economic effects of soil fatigue in apple orchards, particularly where long-period crop rotation or plot exchange are improbable.
Assignments, curriculum framework and background information as the base of developing lessons
(2012)
1. What are the general strengths of the assignments? 2. Structure of the assignment 3. Resources of the assignment 4. Fostering self-expression 5. How could you improve the assignment? 6. Lack of specific examples 7. Not relating the issue to the students 8. Language Problems 9. Infeasibility to adaptation 10. In what ways was the additional information useful ? How could this be improved? 11. Was the framework useful for you and in what way? 12. In what ways did the assignments reflect the steps identified in the framework?
German football stadiums are well known for their atmosphere. It is often described as 'electrifying,' or 'cracking.' This article focuses on this atmosphere. Using a phenomenological approach, it explores how this emotionality can be understood and how geography matters while attending a match. Atmosphere in this context is conceptualized based on work by as a mood-charged space, neither object- nor subject-centered, but rather a medium of perception which cannot not exist. Based on qualitative research done in the home stadium of Hertha BSC in the German Bundesliga, this article shows that the bodily sensations experienced by spectators during a visit to the stadium are synchronized with events on the pitch and with the more or less imposing scenery. The analysis ofin situdiaries reveals that spectators experience a comprehensive sense of collectivity. The study presents evidence that the occurrence of these bodily sensations is strongly connected with different aspects of spatiality. This includes sensations of constriction and expansion within the body, an awareness of one's location within the stadium, the influence of the immediate surroundings and cognitive here/there and inside/outside distinctions.
Atmospheric dynamics of extreme discharge events from 1979 to 2016 in the southern Central Andes
(2020)
During the South-American Monsoon season, deep convective systems occur at the eastern flank of the Central Andes leading to heavy rainfall and flooding. We investigate the large- and meso-scale atmospheric dynamics associated with extreme discharge events (> 99.9th percentile) observed in two major river catchments meridionally stretching from humid to semi-arid conditions in the southern Central Andes. Based on daily gauge time series and ERA-Interim reanalysis, we made the following three key observations: (1) for the period 1940-2016 daily discharge exhibits more pronounced variability in the southern, semi-arid than in the northern, humid catchments. This is due to a smaller ratio of discharge magnitudes between intermediate (0.2 year return period) and rare events (20 year return period) in the semi-arid compared to the humid areas; (2) The climatological composites of the 40 largest discharge events showed characteristic atmospheric features of cold surges based on 5-day time-lagged sequences of geopotential height at different levels in the troposphere; (3) A subjective classification revealed that 80% of the 40 largest discharge events are mainly associated with the north-northeastward migration of frontal systems and 2/3 of these are cold fronts, i.e. cold surges. This work highlights the importance of cold surges and their related atmospheric processes for the generation of heavy rainfall events and floods in the southern Central Andes.
The detection of auto-fluorescence in phytogenic, hydrated amorphous silica depositions (phytoliths) has been found to be a promising approach to verify if phytoliths were burnt or not, especially in archaeological contexts. However, it is unknown so far at what temperature and how auto-fluorescence is induced in phytoliths. We used fluorescence microscopy, scanning electron microscope-energy dispersive X-ray spectroscopy (SEM-EDX), and Fourier transform infrared spectroscopy to analyze auto-fluorescence in modern phytoliths extracted from plant samples or in intact leaves of winter wheat. Leaves and extracted phytoliths were heated at different temperatures up to 600 degrees C. The aims of our experiments were i) to find out what temperature is needed to induce auto-fluorescence in phytoliths, ii) to detect temperature-dependent changes in the molecular structure of phytoliths related to auto-fluorescence, and iii) to derive a mechanistic understanding of auto-fluorescence in phytoliths. We found organic compounds associated with phytoliths to cause auto-fluorescence in phytoliths treated at temperatures below approx. 400 degrees C. In phytoliths treated at higher temperatures, i.e., 450 and 600 degrees C, phytolith auto-fluorescence was mainly caused by molecular changes of phytolith silica. Based on our results we propose that auto-fluorescence in phytoliths is caused by clusterization-triggered emissions, which are caused by overlapping electron clouds forming non-conventional chromophores. In phytoliths heated at temperatures above about 400 degrees C dihydroxylation and the formation of siloxanes result in oxygen clusters that serve as non-conventional chromophores in fluorescence events. Furthermore, SEM-EDX analyses revealed that extractable phytoliths were dominated by lumen phytoliths (62%) compared to cell wall phytoliths (38%). Our findings might be not only relevant in archaeological phytolith-based examinations, but also for studies on the temperature-dependent release of silicon from phytoliths and the potential of long-term carbon sequestration in phytoliths.
Detecting whether and how river discharge responds to strong earthquake shaking can be time-consuming and prone to operator bias when checking hydrographs from hundreds of gauging stations. We use Bayesian piecewise regression models to show that up to a fifth of all gauging stations across Chile had their largest change in daily streamflow trend on the day of the M-w 8.8 Maule earthquake in 2010. These stations cluster distinctly in the near field though the number of detected streamflow changes varies with model complexity and length of time window considered. Credible seismic streamflow changes at several stations were the highest detectable in eight months, with an increased variance of discharge surpassing the variance of discharge following rainstorms. We conclude that Bayesian piecewise regression sheds new and unbiased insights on the duration, trend, and variance of streamflow response to strong earthquakes, and on how this response compares to that following rainstorms.
Bayesian geomorphology
(2020)
The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples.
Bayesian geomorphology
(2020)
The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples.
Urbanization as an inexorable global trend stresses the need to identify cities which are eco-efficient. These cities enable socioeconomic development with lower environmental burden, both being multidimensional concepts. Based on this approach, we benchmark 88 European cities using (i) an advanced version of regression residual ranking and (ii) Data Envelopment Analysis (DEA). Our results show that Stockholm, Munich and Oslo perform well irrespective of the benchmarking method. Furthermore, our results indicate that larger European cities are eco-efficient given the socioeconomic benefits they offer compared to smaller cities. In addition, we analyze correlations between a subjective public perception ranking and our objective eco-efficiency rankings for a subset of 45 cities. This exercise revealed three insights: (1) public perception about quality of life in a city is not merely confined to the socioeconomic well-being but rather to its combination with a lower environmental burden; (2) public perception correlates well with both formal ranking outcomes, corroborating the choice of variables; and (3) the advanced regression residual method appears to be more adequate to fit the urbanites' perception ranking (correlation coefficient about 0.6). This can be interpreted as an indication that urbanites' perception reflects the typical eco-efficiency performance and is less influenced by exceptionally performing cities (in the latter case, DEA should have better correlation coefficient). This study highlights that the socioeconomic growth in cities should not be environmentally detrimental as this might lead to significant discontent regarding perceived quality of urban life.
This article gives insight in a running dissertation at the University in Potsdam. Point of discussion is the spatial and temporal distribution of emergencies of German fire brigades that have not sufficiently been scientifically examined. The challenge is seen in Big Data: enormous amounts of data that exist now (or can be collected in the future) and whose variables are linked to one another. These analyses and visualizations can form a basis for strategic, operational and tactical planning, as well as prevention measures. The user-centered (geo-) visualization of fire brigade data accessible to the general public is a scientific contribution to the research topic 'geovisual analytics and geographical profiling'. It may supplement antiquated methods such as the so-called pinmaps as well as the areas of engagement that are freehand constructions in GIS. Considering police work, there are already numerous scientific projects, publications, and software solutions designed to meet the specific requirements of Crime Analysis and Crime Mapping. By adapting and extending these methods and techniques, civil security research can be tailored to the needs of fire departments. In this paper, a selection of appropriate visualization methods will be presented and discussed.
Biogeochemical analyses of lacustrine environments are well-established methods that allow exploring and understanding complex systems in the lake ecosystem. However, most were conducted in temperate lakes controlled by entirely different physical conditions than in tropical climates. The most important difference between the temperate and tropical lakes is lacking seasonal temperature fluctuations in the latter, which leads to a stable temperature gradient in the water column. Thus, the water column in tropical latitudes generally is void of perturbations that can be seen in their temperate counterparts. Permanent stratification in the water column provides optimal conditions for intact sedimentation. The geochemical processes in the water column and the weathering process in the distinct lithology in the catchment leads to the different biogeochemical characteristic in the sediment. Conducting a biogeochemical study in this lake sediment, especially in the Sediment Water Interface (SWI) helps reveal the sedimentation and diagenetic process records influenced by the internal or external loading. Lake Sentani, the study area, is one of the thousands of lakes in Indonesia and located in the Papua province. This tropical lake has a unique feature, as it consists of four interconnected sub-basins with different water depths. More importantly, its catchment is comprised of various different lithologies. Hence, its lithological characteristics are highly diverse, and range from mafic and ultramafic rocks to clastic sediment and carbonates. Each sub-basin receives a distinct sediment input. Equally important, besides the natural loading, Lake Sentani is also influenced by anthropogenic input. Previous studies have elaborated that there is an increase in population growth rate around the lake which has direct consequences on eutrophication. Considering these factors, the government of The Republic of Indonesia put Lake Sentani on the list of national priority lakes for restoration. This thesis aims to develop a fundamental understanding of Lake Sentani's sedimentary geochemistry and geomicrobiology with a special focus on the effects of different lithologies and anthropogenic pressures in the catchment area. We conducted geochemical and geomicrobiology research on Lake Sentani to meet this objective. We investigated geochemical characteristics in the water column, porewater, and sediment core of the four sub-basins. Additional to direct investigations of the lake itself, we also studied the sediments in the tributary rivers, of which some are ephemeral, as well as the river mouths, as connections between riverine and the lacustrine habitat. The thesis is composed of three main publications about Lake Sentani and supported by several publications that focus on other tropical lakes in Indonesia. The first main publication investigates the geochemical characterization of the water column, porewater, and surface sediment (upper 40-50 cm) from the center of the four sub-basins. It reveals that besides catchment lithology, the water column heavily influences the geochemical characteristics in the lake sediments and their porewater. The findings indicate that water column stratification has a strong influence on overall chemistry. The four sub-basins are very different with regard to their water column chemistry. Based on the physicochemical profiles, especially dissolved oxygen, one sub-basin is oxygenated, one intermediate i.e. just reaches oxygen depletion at the sediment-water interface, and two sub-basins are fully meromictic. However, all four sub-basins share the same surface water chemistry. The structure of the water column creates differences on the patterns of anions and cations in the porewater. Likewise, the distinct differences in geochemical composition between the sub-basins show that the lithology in the catchment affects the geochemical characteristic in the sediment. Overall, water column stratification and particularly bottom water oxygenation strongly influence the overall elemental composition of the sediment and porewater composition. The second publication reveals differences in surface sediment composition between habitats, influenced by lithological variations in the catchment area. The macro-element distribution shows that the geochemical characteristics between habitats are different. Furthermore, the geochemical composition also indicates a distinct distribution between the sub-basins. The geochemical composition of the eastern sub-basin suggests that lithogenic elements are more dominant than authigenic elements. This is also supported by sulfide speciation, particle distribution, and smear slide data. The third publication is a geomicrobiological study of the surface sediment. We compare the geochemical composition of the surface sediment and its microbiological composition and compare the different signals. Next Generation Sequencing (NGS) of the 16S rRNA gene was applied to determine the microbial community composition of the surface sediment from a great number of locations. We use a large number of sampling sites in all four sub-basins as well as in the rivers and river mouths to illustrate the links between the river, the river mouth, and the lake. Rigorous assessment of microbial communities across the diverse Lake Sentani habitats allowed us to study some of these links and report novel findings on microbial patterns in such ecosystems. The main result of the Principal Coordinates Analysis (PCoA) based on microbial community composition highlighted some commonalities but also differences between the microbial community analysis and the geochemical data. The microbial community in rivers, river mouths and sub-basins is strongly influenced by anthropogenic input from the catchment area. Generally, Bacteroidetes and Firmicutes could be an indicator for river sediments. The microbial community in the river is directly influenced by anthropogenic pressure and is markedly different from the lake sediment. Meanwhile, the microbial community in the lake sediment reflects the anoxic environment, which is prevalent across the lake in all sediments below a few mm burial depth. The lake sediments harbour abundant sulfate reducers and methanogens. The microbial communities in sediments from river mouths are influenced by both rivers and lake ecosystems. This study provides valuable information to understand the basic processes that control biogeochemical cycling in Lake Sentani. Our findings are critical for lake managers to accurately assess the uncertainties of the changing environmental conditions related to the anthropogenic pressure in the catchment area. Lake Sentani is a unique study site directly influenced by the different geology across the watershed and morphometry of the four studied basins. As a result of these factors, there are distinct geochemical differences between the habitats (river, river mouth, lake) and the four sub-basins. In addition to geochemistry, microbial community composition also shows differences between habitats, although there are no obvious differences between the four sub-basins. However, unlike sediment geochemistry, microbial community composition is impacted by human activities. Therefore, this thesis will provide crucial baseline data for future lake management.
Through changes in policy and practice, the inherent intent of the ecosystem services (ES) concept is to safeguard ecosystems for human wellbeing. While impact is intrinsic to the concept, little is known about how and whether ES science leads to impact. Evidence of impact is needed. Given the lack of consensus on what constitutes impact, we differentiate between attributional impacts (transitional impacts on policy, practice, awareness or other drivers) and consequential impacts (real, on-the-ground impacts on biodiversity, ES, ecosystem functions and human wellbeing) impacts. We conduct rigorous statistical analyses on three extensive databases for evidence of attributional impact (the form most prevalently reported): the IPBES catalogue (n = 102), the Lautenbach systematic review (n = 504) and a 5-year in-depth survey of the OPERAs Exemplars (n = 13). To understand the drivers of impacts, we statistically analyse associations between study characteristics and impacts. Our findings show that there exists much confusion with regard to defining ES science impacts, and that evidence of attributional impact is scarce: only 25% of the IPBES assessments self-reported impact (7% with evidence); in our meta-analysis of Lautenbach’s systematic review, 33% of studies provided recommendations indicating intent of impacts. Systematic impact reporting was imposed by design on the OPERAs Exemplars: 100% reported impacts, suggesting the importance of formal impact reporting. The generalised linear models and correlations between study characteristics and attributional impact dimensions highlight four characteristics as minimum baseline for impact: study robustness, integration of policy instruments into study design, stakeholder involvement and type of stakeholders involved. Further in depth examination of the OPERAs Exemplars showed that study characteristics associated with impact on awareness and practice differ from those associated with impact on policy: to achieve impact along specific dimensions, bespoke study designs are recommended. These results inform targeted recommendations for ES science to break its impact glass ceiling.
The 2020s are an essential decade for achieving the 2030 Agenda and its Sustainable Development Goals (SDGs). For this, SDG research needs to provide evidence that can be translated into concrete actions. However, studies use different SDG data, resulting in incomparable findings. Researchers primarily use SDG databases provided by the United Nations (UN), the World Bank Group (WBG), and the Bertelsmann Stiftung & Sustainable Development Solutions Network (BE-SDSN). We compile these databases into one unified SDG database and examine the effects of the data selection on our understanding of SDG interactions. Among the databases, we observed more different than similar SDG interactions. Differences in synergies and trade-offs mainly occur for SDGs that are environmentally oriented. Due to the increased data availability, the unified SDG database offers a more nuanced and reliable view of SDG interactions. Thus, the SDG data selection may lead to diverse findings, fostering actions that might neglect or exacerbate trade-offs.
Detailed organic geochemical and carbon isotopic (delta C-13 and Delta C-14) analyses are performed on permafrost deposits affected by coastal erosion (Herschel Island, Canadian Beaufort Sea) and adjacent marine sediments (Herschel Basin) to understand the fate of organic carbon in Arctic nearshore environments. We use an end-member model based on the carbon isotopic composition of bulk organic matter to identify sources of organic carbon. Monte Carlo simulations are applied to quantify the contribution of coastal permafrost erosion to the sedimentary carbon budget. The models suggest that similar to 40% of all carbon released by local coastal permafrost erosion is efficiently trapped and sequestered in the nearshore zone. This highlights the importance of sedimentary traps in environments such as basins, lagoons, troughs, and canyons for the carbon sequestration in previously poorly investigated, nearshore areas.
Plain Language Summary Increasing air and sea surface temperatures at high latitudes leads to accelerated thaw, destabilization, and erosion of perennially frozen soils (i.e., permafrost), which are often rich in organic carbon. Coastal erosion leads to an increased mobilization of organic carbon into the Arctic Ocean, which there can be converted into greenhouse gases and may therefore contribute to further warming. Carbon decomposition can be limited if organic matter is efficiently deposited on the seafloor, buried in marine sediments, and thus removed from the short-term carbon cycle. Basins, canyons, and troughs near the coastline can serve as sediment traps and potentially accommodate large quantities of organic carbon along the Arctic coast. Here we use biomarkers (source-specific molecules), stable carbon isotopes, and radiocarbon to identify the sources of organic carbon in the nearshore zone of the southern Canadian Beaufort Sea near Herschel Island. We quantify the contribution of coastal permafrost erosion to the sedimentary carbon budget of the area and estimate that more than a third of all carbon released by local permafrost erosion is efficiently trapped in marine sediments. This highlights the importance of regional sediment traps for carbon sequestration.
Calibration of the global hydrological model WGHM with water mass variations from GRACE gravity data
(2010)
Since the start-up of the GRACE (Gravity Recovery And Climate Experiment) mission in 2002 time dependent global maps of the Earth's gravity field are available to study geophysical and climatologically-driven mass redistributions on the Earth's surface. In particular, GRACE observations of total water storage changes (TWSV) provide a comprehensive data set for analysing the water cycle on large scales. Therefore they are invaluable for validation and calibration of large-scale hydrological models as the WaterGAP Global Hydrology Model (WGHM) which simulates the continental water cycle including its most important components, such as soil, snow, canopy, surface- and groundwater. Hitherto, WGHM exhibits significant differences to GRACE, especially for the seasonal amplitude of TWSV. The need for a validation of hydrological models is further highlighted by large differences between several global models, e.g. WGHM, the Global Land Data Assimilation System (GLDAS) and the Land Dynamics model (LaD). For this purpose, GRACE links geodetic and hydrological research aspects. This link demands the development of adequate data integration methods on both sides, forming the main objectives of this work. They include the derivation of accurate GRACE-based water storage changes, the development of strategies to integrate GRACE data into a global hydrological model as well as a calibration method, followed by the re-calibration of WGHM in order to analyse process and model responses. To achieve these aims, GRACE filter tools for the derivation of regionally averaged TWSV were evaluated for specific river basins. Here, a decorrelation filter using GRACE orbits for its design is most efficient among the tested methods. Consistency in data and equal spatial resolution between observed and simulated TWSV were realised by the inclusion of all most important hydrological processes and an equal filtering of both data sets. Appropriate calibration parameters were derived by a WGHM sensitivity analysis against TWSV. Finally, a multi-objective calibration framework was developed to constrain model predictions by both river discharge and GRACE TWSV, realised with a respective evolutionary method, the ε-Non-dominated-Sorting-Genetic-Algorithm-II (ε-NSGAII). Model calibration was done for the 28 largest river basins worldwide and for most of them improved simulation results were achieved with regard to both objectives. From the multi-objective approach more reliable and consistent simulations of TWSV within the continental water cycle were gained and possible model structure errors or mis-modelled processes for specific river basins detected. For tropical regions as such, the seasonal amplitude of water mass variations has increased. The findings lead to an improved understanding of hydrological processes and their representation in the global model. Finally, the robustness of the results is analysed with respect to GRACE and runoff measurement errors. As a main conclusion obtained from the results, not only soil water and snow storage but also groundwater and surface water storage have to be included in the comparison of the modelled and GRACE-derived total water budged data. Regarding model calibration, the regional varying distribution of parameter sensitivity suggests to tune only parameter of important processes within each region. Furthermore, observations of single storage components beside runoff are necessary to improve signal amplitudes and timing of simulated TWSV as well as to evaluate them with higher accuracy. The results of this work highlight the valuable nature of GRACE data when merged into large-scale hydrological modelling and depict methods to improve large-scale hydrological models.
Fast analysis of different species of molecules in soils is investigated by capillary electrophoresis (CE). Several CE techniques for the analysis of inorganic ions and carbohydrates have been tested. With regard to the intents of pedologists and the usually large number of soil analyses a bundle of CE systems is proposed, capable of effecting time-saving soil analyses. Adapted electrolyte systems recently published and new separation systems are described. Examples of the application of these methods to two different soil samples are presented.
Each simulation algorithm, including Truncated Gaussian Simulation, Sequential Indicator Simulation and Indicator Kriging is characterized by different operating modes, which variably influence the facies proportion, distribution and association of digital outcrop models, as shown in clastic sediments. A detailed study of carbonate heterogeneity is then crucial to understanding these differences and providing rules for carbonate modelling. Through a continuous exposure of Bajocian carbonate strata, a study window (320 m long, 190 m wide and 30 m thick) was investigated and metre-scale lithofacies heterogeneity was captured and modelled using closely-spaced sections. Ten lithofacies, deposited in a shallow-water carbonate-dominated ramp, were recognized and their dimensions and associations were documented. Field data, including height sections, were georeferenced and input into the model. Four models were built in the present study. Model A used all sections and Truncated Gaussian Simulation during the stochastic simulation. For the three other models, Model B was generated using Truncated Gaussian Simulation as for Model A, Model C was generated using Sequential Indicator Simulation and Model D was generated using Indicator Kriging. These three additional models were built by removing two out of eight sections from data input. The removal of sections allows direct insights on geological uncertainties at inter-well spacings by comparing modelled and described sections. Other quantitative and qualitative comparisons were carried out between models to understand the advantages/disadvantages of each algorithm. Model A is used as the base case. Indicator Kriging (Model D) simplifies the facies distribution by assigning continuous geological bodies of the most abundant lithofacies to each zone. Sequential Indicator Simulation (Model C) is confident to conserve facies proportion when geological heterogeneity is complex. The use of trend with Truncated Gaussian Simulation is a powerful tool for modelling well-defined spatial facies relationships. However, in shallow-water carbonate, facies can coexist and their association can change through time and space. The present study shows that the scale of modelling (depositional environment or lithofacies) involves specific simulation constraints on shallow-water carbonate modelling methods.