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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.