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Human-induced climate change is impacting the global water cycle by, e.g., causing changes in precipitation patterns, evapotranspiration dynamics, cryosphere shrinkage, and complex streamflow trends. These changes, coupled with the increased frequency and severity of extreme hydrometeorological events like floods, droughts, and heatwaves, contribute to hydroclimatic disasters, posing significant implications for local and global infrastructure, human health, and overall productivity.
In the tropical Andes, climate change is evident through warming trends, glacier retreats, and shifts in precipitation patterns, leading to altered risks of floods and droughts, e.g., in the upper Amazon River basin. Projections for the region indicate rising temperatures, potential glacier disappearance or substantial shrinkage, and altered streamflow patterns, highlighting challenges in water availability due to these expected changes and growing human water demand. The evolving trends in hydroclimatic conditions in the tropical Andes present significant challenges to socioeconomic and environmental systems, emphasizing the need for a comprehensive understanding to guide effective adaptation policies and strategies in response to the impacts of climate change in the region.
The main objective of this thesis is to investigate current hydrological dynamics in the tropical Andes of Peru and Ecuador and their responses to climate change. Given the scarcity of hydrometeorological data in the region, this objective was accomplished through a comprehensive data preparation and analysis in combination with hydrological modeling using the Soil and Water Assessment Tool (SWAT) eco-hydrological model. In this context, the initial steps involved assessing, identifying, and/or generating more reliable climate input data to address data limitations.
The thesis introduces RAIN4PE, a high-resolution precipitation dataset for Peru and Ecuador, developed by merging satellite, reanalysis, and ground-based data with surface elevation through the random forest method. Further adjustments of precipitation estimates were made for catchments influenced by fog/cloud water input on the eastern side of the Andes using streamflow data and applying the method of reverse hydrology. RAIN4PE surpasses other global and local precipitation datasets, showcasing superior reliability and accuracy in representing precipitation patterns and simulating hydrological processes across the tropical Andes. This establishes it as the optimal precipitation product for hydrometeorological applications in the region.
Due to the significant biases and limitations of global climate models (GCMs) in representing key atmospheric variables over the tropical Andes, this study developed regionally adapted GCM simulations specifically tailored for Peru and Ecuador. These simulations are known as the BASD-CMIP6-PE dataset, and they were derived using reliable, high-resolution datasets like RAIN4PE as reference data. The BASD-CMIP6-PE dataset shows notable improvements over raw GCM simulations, reflecting enhanced representations of observed climate properties and accurate simulation of streamflow, including high and low flow indices. This renders it suitable for assessing regional climate change impacts on agriculture, water resources, and hydrological extremes.
In addition to generating more accurate climatic input data, a reliable hydrological model is essential for simulating watershed hydrological processes. To tackle this challenge, the thesis presents an innovative multiobjective calibration framework integrating remote sensing vegetation data, baseflow index, discharge goodness-of-fit metrics, and flow duration curve signatures. In contrast to traditional calibration strategies relying solely on discharge goodness-of-fit metrics, this approach enhances the simulation of vegetation, streamflow, and the partitioning of flow into surface runoff and baseflow in a typical Andean catchment. The refined hydrological model calibration strategy was applied to conduct reliable simulations and understand current and future hydrological trajectories in the tropical Andes.
By establishing a region-suitable and thoroughly tested hydrological model with high-resolution and reliable precipitation input data from RAIN4PE, this study provides new insights into the spatiotemporal distribution of water balance components in Peru and transboundary catchments. Key findings underscore the estimation of Peru's total renewable freshwater resource (total river runoff of 62,399 m3/s), with the Peruvian Amazon basin contributing 97.7%. Within this basin, the Amazon-Andes transition region emerges as a pivotal hotspot for water yield (precipitation minus evapotranspiration), characterized by abundant rainfall and lower atmospheric water demand/evapotranspiration. This finding underlines its paramount role in influencing the hydrological variability of the entire Amazon basin.
Subsurface hydrological pathways, particularly baseflow from aquifers, strongly influence water yield in lowland and Andean catchments, sustaining streamflow, especially during the extended dry season. Water yield demonstrates an elevation- and latitude-dependent increase in the Pacific Basin (catchments draining into the Pacific Ocean), while it follows an unimodal curve in the Peruvian Amazon Basin, peaking in the Amazon-Andes transition region. This observation indicates an intricate relationship between water yield and elevation.
In Amazon lowlands rivers, particularly in the Ucayali River, floodplains play a significant role in shaping streamflow seasonality by attenuating and delaying peak flows for up to two months during periods of high discharge. This observation underscores the critical importance of incorporating floodplain dynamics into hydrological simulations and river management strategies for accurate modeling and effective water resource management.
Hydrological responses vary across different land use types in high Andean catchments. Pasture areas exhibit the highest water yield, while agricultural areas and mountain forests show lower yields, emphasizing the importance of puna (high-altitude) ecosystems, such as pastures, páramos, and bofedales, in regulating natural storage.
Projected future hydrological trajectories were analyzed by driving the hydrological model with regionalized GCM simulations provided by the BASD-CMIP6-PE dataset. The analysis considered sustainable (low warming, SSP1-2.6) and fossil fuel-based development (high-end warming, SSP5-8.5) scenarios for the mid (2035-2065) and end (2065-2095) of the century. The projected changes in water yield and streamflow across the tropical Andes exhibit distinct regional and seasonal variations, particularly amplified under a high-end warming scenario towards the end of the century. Projections suggest year-round increases in water yield and streamflow in the Andean regions and decreases in the Amazon lowlands, with exceptions such as the northern Amazon expecting increases during wet seasons. Despite these regional differences, the upper Amazon River's streamflow is projected to remain relatively stable throughout the 21st century. Additionally, projections anticipate a decrease in low flows in the Amazon lowlands and an increased risk of high flows (floods) in the Andean and northern Amazon catchments.
This thesis significantly contributes to enhancing climatic data generation, overcoming regional limitations that previously impeded hydrometeorological research, and creating new opportunities. It plays a crucial role in advancing hydrological model calibration, improving the representation of internal hydrological processes, and achieving accurate results for the right reasons. Novel insights into current hydrological dynamics in the tropical Andes are fundamental for improving water resource management. The anticipated intensified changes in water flows and hydrological extreme patterns under a high-end warming scenario highlight the urgency of implementing emissions mitigation and adaptation measures to address the heightened impacts on water resources.
In fact, the new datasets (RAIN4PE and BASD-CMIP6-PE) have already been utilized by researchers and experts in regional and local-scale projects and catchments in Peru and Ecuador. For instance, they have been applied in river catchments such as Mantaro, Piura, and San Pedro to analyze local historical and future developments in climate and water resources.
Flooding is an imminent natural hazard threatening most river deltas, e.g. the Mekong Delta. An appropriate flood management is thus required for a sustainable development of the often densely populated regions. Recently, the traditional event-based hazard control shifted towards a risk management approach in many regions, driven by intensive research leading to new legal regulation on flood management. However, a large-scale flood risk assessment does not exist for the Mekong Delta. Particularly, flood risk to paddy rice cultivation, the most important economic activity in the delta, has not been performed yet. Therefore, the present study was developed to provide the very first insight into delta-scale flood damages and risks to rice cultivation. The flood hazard was quantified by probabilistic flood hazard maps of the whole delta using a bivariate extreme value statistics, synthetic flood hydrographs, and a large-scale hydraulic model. The flood risk to paddy rice was then quantified considering cropping calendars, rice phenology, and harvest times based on a time series of enhanced vegetation index (EVI) derived from MODIS satellite data, and a published rice flood damage function. The proposed concept provided flood risk maps to paddy rice for the Mekong Delta in terms of expected annual damage. The presented concept can be used as a blueprint for regions facing similar problems due to its generic approach. Furthermore, the changes in flood risk to paddy rice caused by changes in land use currently under discussion in the Mekong Delta were estimated. Two land-use scenarios either intensifying or reducing rice cropping were considered, and the changes in risk were presented in spatially explicit flood risk maps. The basic risk maps could serve as guidance for the authorities to develop spatially explicit flood management and mitigation plans for the delta. The land-use change risk maps could further be used for adaptive risk management plans and as a basis for a cost-benefit of the discussed land-use change scenarios. Additionally, the damage and risks maps may support the recently initiated agricultural insurance programme in Vietnam.
River floods are among the most devastating natural hazards worldwide. As their generation is highly dependent on climatic conditions, their magnitude and frequency are projected to be affected by future climate change. Therefore, it is crucial to study the ways in which a changing climate will, and already has, influenced flood generation, and thereby flood hazard. Additionally, it is important to understand how other human influences - specifically altered land cover - affect flood hazard at the catchment scale.
The ways in which flood generation is influenced by climatic and land cover conditions differ substantially in different regions. The spatial variability of these effects needs to be taken into account by using consistent datasets across large scales as well as applying methods that can reflect this heterogeneity. Therefore, in the first study of this cumulative thesis a complex network approach is used to find 10 clusters of similar flood behavior among 4390 catchments in the conterminous United States. By using a consistent set of 31 hydro-climatological and land cover variables, and training a separate Random Forest model for each of the clusters, the regional controls on flood magnitude trends between 1960-2010 are detected. It is shown that changes in rainfall are the most important drivers of these trends, while they are regionally controlled by land cover conditions.
While climate change is most commonly associated with flood magnitude trends, it has been shown to also influence flood timing. This can lead to trends in the size of the area across which floods occur simultaneously, the flood synchrony scale. The second study is an analysis of data from 3872 European streamflow gauges and shows that flood synchrony scales have increased in Western Europe and decreased in Eastern Europe. These changes are attributed to changes in flood generation, especially a decreasing relevance of snowmelt. Additionally, the analysis shows that both the absolute values and the trends of flood magnitudes and flood synchrony scales are positively correlated. If these trends persist in the future and are not accounted for, the combined increases of flood magnitudes and flood synchrony scales can exceed the capacities of disaster relief organizations and insurers.
Hazard cascades are an additional way through which climate change can influence different aspects of flood hazard. The 2019/2020 wildfires in Australia, which were preceded by an unprecedented drought and extinguished by extreme rainfall that led to local flooding, present an opportunity to study the effects of multiple preceding hazards on flood hazard. All these hazards are individually affected by climate change, additionally complicating the interactions within the cascade. By estimating and analyzing the burn severity, rainfall magnitude, soil erosion and stream turbidity in differently affected tributaries of the Manning River catchment, the third study shows that even low magnitude floods can pose a substantial hazard within a cascade.
This thesis shows that humanity is affecting flood hazard in multiple ways with spatially and temporarily varying consequences, many of which were previously neglected (e.g. flood synchrony scale, hazard cascades). To allow for informed decision making in risk management and climate change adaptation, it will be crucial to study these aspects across the globe and to project their trajectories into the future. The presented methods can depict the complex interactions of different flood drivers and their spatial variability, providing a basis for the assessment of future flood hazard changes. The role of land cover should be considered more in future flood risk modelling and management studies, while holistic, transferable frameworks for hazard cascade assessment will need to be designed.
Hydrological models are important tools for the simulation and quantification of the water cycle.
They therefore aid in the understanding of hydrological processes, prediction of river discharge, assessment of the impacts of land use and climate changes, or the management of water resources.
However, uncertainties associated with hydrological modelling are still large.
While significant research has been done on the quantification and reduction of uncertainties, there are still fields which have gained little attention so far, such as model structural uncertainties that are related to the process implementations in the models.
This holds especially true for complex process-based models in contrast to simpler conceptual models.
Consequently, the aim of this thesis is to improve the understanding of structural uncertainties with focus on process-based hydrological modelling, including methods for their quantification.
To identify common deficits of frequently used hydrological models and develop further strategies on how to reduce them, a survey among modellers was conducted.
It was found that there is a certain degree of subjectivity in the perception of modellers, for instance with respect to the distinction of hydrological models into conceptual groups.
It was further found that there are ambiguities on how to apply a certain hydrological model, for instance how many parameters should be calibrated, together with a large diversity of opinion regarding the deficits of models.
Nevertheless, evapotranspiration processes are often represented in a more physically based manner, while processes of groundwater and soil water movement are often simplified, which many survey participants saw as a drawback.
A large flexibility, for instance with respect to different alternative process implementations or a small number of parameters that needs to be calibrated, was generally seen as strength of a model.
Flexible and efficient software, which is straightforward to apply, has been increasingly acknowledged by the hydrological community.
This work further elaborated on this topic in a twofold way.
First, a software package for semi-automated landscape discretisation has been developed, which serves as a tool for model initialisation.
This was complemented by a sensitivity analysis of important and commonly used discretisation parameters, of which the size of hydrological sub-catchments as well as the size and number of hydrologically uniform computational units appeared to be more influential than information considered for the characterisation of hillslope profiles.
Second, a process-based hydrological model has been implemented into a flexible simulation environment with several alternative process representations and a number of numerical solvers.
It turned out that, even though computation times were still long, enhanced computational capabilities nowadays in combination with innovative methods for statistical analysis allow for the exploration of structural uncertainties of even complex process-based models, which up to now was often neglected by the modelling community.
In a further study it could be shown that process-based models may even be employed as tools for seasonal operational forecasting.
In contrast to statistical models, which are faster to initialise and to apply, process-based models produce more information in addition to the target variable, even at finer spatial and temporal scales, and provide more insights into process behaviour and catchment functioning.
However, the process-based model was much more dependent on reliable rainfall forecasts.
It seems unlikely that there exists a single best formulation for hydrological processes, even for a specific catchment.
This supports the use of flexible model environments with alternative process representations instead of a single model structure.
However, correlation and compensation effects between process formulations, their parametrisation, and other aspects such as numerical solver and model resolution, may lead to surprising results and potentially misleading conclusions.
In future studies, such effects should be more explicitly addressed and quantified.
Moreover, model functioning appeared to be highly dependent on the meteorological conditions and rainfall input generally was the most important source of uncertainty.
It is still unclear, how this could be addressed, especially in the light of the aforementioned correlations.
The use of innovative data products, e.g.\ remote sensing data in combination with station measurements, and efficient processing methods for the improvement of rainfall input and explicit consideration of associated uncertainties is advisable to bring more insights and make hydrological simulations and predictions more reliable.
Semi-arid areas are, due to their climatic setting, characterized by small water resources. An increasing water demand as a consequence of population growth and economic development as well as a decreasing water availability in the course of possible climate change may aggravate water scarcity in future, which often exists already for present-day conditions in these areas. Understanding the mechanisms and feedbacks of complex natural and human systems, together with the quantitative assessment of future changes in volume, timing and quality of water resources are a prerequisite for the development of sustainable measures of water management to enhance the adaptive capacity of these regions. For this task, dynamic integrated models, containing a hydrological model as one component, are indispensable tools. The main objective of this study is to develop a hydrological model for the quantification of water availability in view of environmental change over a large geographic domain of semi-arid environments. The study area is the Federal State of Ceará (150 000 km2) in the semi-arid north-east of Brazil. Mean annual precipitation in this area is 850 mm, falling in a rainy season with duration of about five months. Being mainly characterized by crystalline bedrock and shallow soils, surface water provides the largest part of the water supply. The area has recurrently been affected by droughts which caused serious economic losses and social impacts like migration from the rural regions. The hydrological model Wasa (Model of Water Availability in Semi-Arid Environments) developed in this study is a deterministic, spatially distributed model being composed of conceptual, process-based approaches. Water availability (river discharge, storage volumes in reservoirs, soil moisture) is determined with daily resolution. Sub-basins, grid cells or administrative units (municipalities) can be chosen as spatial target units. The administrative units enable the coupling of Wasa in the framework of an integrated model which contains modules that do not work on the basis of natural spatial units. The target units mentioned above are disaggregated in Wasa into smaller modelling units within a new multi-scale, hierarchical approach. The landscape units defined in this scheme capture in particular the effect of structured variability of terrain, soil and vegetation characteristics along toposequences on soil moisture and runoff generation. Lateral hydrological processes at the hillslope scale, as reinfiltration of surface runoff, being of particular importance in semi-arid environments, can thus be represented also within the large-scale model in a simplified form. Depending on the resolution of available data, small-scale variability is not represented explicitly with geographic reference in Wasa, but by the distribution of sub-scale units and by statistical transition frequencies for lateral fluxes between these units. Further model components of Wasa which respect specific features of semi-arid hydrology are: (1) A two-layer model for evapotranspiration comprises energy transfer at the soil surface (including soil evaporation), which is of importance in view of the mainly sparse vegetation cover. Additionally, vegetation parameters are differentiated in space and time in dependence on the occurrence of the rainy season. (2) The infiltration module represents in particular infiltration-excess surface runoff as the dominant runoff component. (3) For the aggregate description of the water balance of reservoirs that cannot be represented explicitly in the model, a storage approach respecting different reservoirs size classes and their interaction via the river network is applied. (4) A model for the quantification of water withdrawal by water use in different sectors is coupled to Wasa. (5) A cascade model for the temporal disaggregation of precipitation time series, adapted to the specific characteristics of tropical convective rainfall, is applied for the generating rainfall time series of higher temporal resolution. All model parameters of Wasa can be derived from physiographic information of the study area. Thus, model calibration is primarily not required. Model applications of Wasa for historical time series generally results in a good model performance when comparing the simulation results of river discharge and reservoir storage volumes with observed data for river basins of various sizes. The mean water balance as well as the high interannual and intra-annual variability is reasonably represented by the model. Limitations of the modelling concept are most markedly seen for sub-basins with a runoff component from deep groundwater bodies of which the dynamics cannot be satisfactorily represented without calibration. Further results of model applications are: (1) Lateral processes of redistribution of runoff and soil moisture at the hillslope scale, in particular reinfiltration of surface runoff, lead to markedly smaller discharge volumes at the basin scale than the simple sum of runoff of the individual sub-areas. Thus, these processes are to be captured also in large-scale models. The different relevance of these processes for different conditions is demonstrated by a larger percentage decrease of discharge volumes in dry as compared to wet years. (2) Precipitation characteristics have a major impact on the hydrological response of semi-arid environments. In particular, underestimated rainfall intensities in the rainfall input due to the rough temporal resolution of the model and due to interpolation effects and, consequently, underestimated runoff volumes have to be compensated in the model. A scaling factor in the infiltration module or the use of disaggregated hourly rainfall data show good results in this respect. The simulation results of Wasa are characterized by large uncertainties. These are, on the one hand, due to uncertainties of the model structure to adequately represent the relevant hydrological processes. On the other hand, they are due to uncertainties of input data and parameters particularly in view of the low data availability. Of major importance is: (1) The uncertainty of rainfall data with regard to their spatial and temporal pattern has, due to the strong non-linear hydrological response, a large impact on the simulation results. (2) The uncertainty of soil parameters is in general of larger importance on model uncertainty than uncertainty of vegetation or topographic parameters. (3) The effect of uncertainty of individual model components or parameters is usually different for years with rainfall volumes being above or below the average, because individual hydrological processes are of different relevance in both cases. Thus, the uncertainty of individual model components or parameters is of different importance for the uncertainty of scenario simulations with increasing or decreasing precipitation trends. (4) The most important factor of uncertainty for scenarios of water availability in the study area is the uncertainty in the results of global climate models on which the regional climate scenarios are based. Both a marked increase or a decrease in precipitation can be assumed for the given data. Results of model simulations for climate scenarios until the year 2050 show that a possible future change in precipitation volumes causes a larger percentage change in runoff volumes by a factor of two to three. In the case of a decreasing precipitation trend, the efficiency of new reservoirs for securing water availability tends to decrease in the study area because of the interaction of the large number of reservoirs in retaining the overall decreasing runoff volumes.
Logging and large earthquakes are disturbances that may significantly affect hydrological and erosional processes and process rates, although in decisively different ways. Despite numerous studies that have documented the impacts of both deforestation and earthquakes on water and sediment fluxes, a number of details regarding the timing and type of de- and reforestation; seismic impacts on subsurface water fluxes; or the overall geomorphic work involved have remained unresolved. The main objective of this thesis is to address these shortcomings and to better understand and compare the hydrological and erosional process responses to such natural and man-made disturbances. To this end, south-central Chile provides an excellent natural laboratory owing to its high seismicity and the ongoing conversion of land into highly productive plantation forests. In this dissertation I combine paired catchment experiments, data analysis techniques, and physics-based modelling to investigate: 1) the effect of plantation forests on water resources, 2) the source and sink behavior of timber harvest areas in terms of overland flow generation and sediment fluxes, 3) geomorphic work and its efficiency as a function of seasonal logging, 4) possible hydrologic responses of the saturated zone to the 2010 Maule earthquake and 5) responses of the vadose zone to this earthquake. Re 1) In order to quantify the hydrologic impact of plantation forests, it is fundamental to first establish their water balances. I show that tree species is not significant in this regard, i.e. Pinus radiata and Eucalyptus globulus do not trigger any decisive different hydrologic response. Instead, water consumption is more sensitive to soil-water supply for the local hydro-climatic conditions. Re 2) Contradictory opinions exist about whether timber harvest areas (THA) generate or capture overland flow and sediment. Although THAs contribute significantly to hydrology and sediment transport because of their spatial extent, little is known about the hydrological and erosional processes occurring on them. I show that THAs may act as both sources and sinks for overland flow, which in turn intensifies surface erosion. Above a rainfall intensity of ~20 mm/h, which corresponds to <10% of all rainfall, THAs may generate runoff whereas below that threshold they remain sinks. The overall contribution of Hortonian runoff is thus secondary considering the local rainfall regime. The bulk of both runoff and sediment is generated by Dunne, saturation excess, overland flow. I also show that logging may increase infiltrability on THAs which may cause an initial decrease in streamflow followed by an increase after the groundwater storage has been refilled. Re 3) I present changes in frequency-magnitude distributions following seasonal logging by applying Quantile Regression Forests at hitherto unprecedented detail. It is clearly the season that controls the hydro-geomorphic work efficiency of clear cutting. Logging, particularly dry seasonal logging, caused a shift of work efficiency towards less flashy and mere but more frequent moderate rainfall-runoff events. The sediment transport is dominated by Dunne overland flow which is consistent with physics-based modelling using WASA-SED. Re 4) It is well accepted that earthquakes may affect hydrological processes in the saturated zone. Assuming such flow conditions, consolidation of saturated saprolitic material is one possible response. Consolidation raises the hydraulic gradients which may explain the observed increase in discharge following earthquakes. By doing so, squeezed water saturates the soil which in turn increases the water accessible for plant transpiration. Post-seismic enhanced transpiration is reflected in the intensification of diurnal cycling. Re 5) Assuming unsaturated conditions, I present the first evidence that the vadose zone may also respond to seismic waves by releasing pore water which in turn feeds groundwater reservoirs. By doing so, water tables along the valley bottoms are elevated thus providing additional water resources to the riparian vegetation. By inverse modelling, the transient increase in transpiration is found to be 30-60%. Based on the data available, both hypotheses, are not testable. Finally, when comparing the hydrological and erosional effects of the Maule earthquake with the impact of planting exotic plantation forests, the overall observed earthquake effects are comparably small, and limited to short time scales.
Water scarcity, adaption on climate change, and risk assessment of droughts and floods are critical topics for science and society these days. Monitoring and modeling of the hydrological cycle are a prerequisite to understand and predict the consequences for weather and agriculture. As soil water storage plays a key role for partitioning of water fluxes between the atmosphere, biosphere, and lithosphere, measurement techniques are required to estimate soil moisture states from small to large scales.
The method of cosmic-ray neutron sensing (CRNS) promises to close the gap between point-scale and remote-sensing observations, as its footprint was reported to be 30 ha. However, the methodology is rather young and requires highly interdisciplinary research to understand and interpret the response of neutrons to soil moisture. In this work, the signal of nine detectors has been systematically compared, and correction approaches have been revised to account for meteorological and geomagnetic variations. Neutron transport simulations have been consulted to precisely characterize the sensitive footprint area, which turned out to be 6--18 ha, highly local, and temporally dynamic. These results have been experimentally confirmed by the significant influence of water bodies and dry roads. Furthermore, mobile measurements on agricultural fields and across different land use types were able to accurately capture the various soil moisture states. It has been further demonstrated that the corresponding spatial and temporal neutron data can be beneficial for mesoscale hydrological modeling. Finally, first tests with a gyrocopter have proven the concept of airborne neutron sensing, where increased footprints are able to overcome local effects.
This dissertation not only bridges the gap between scales of soil moisture measurements. It also establishes a close connection between the two worlds of observers and modelers, and further aims to combine the disciplines of particle physics, geophysics, and soil hydrology to thoroughly explore the potential and limits of the CRNS method.
Complete protection against flood risks by structural measures is impossible. Therefore flood prediction is important for flood risk management. Good explanatory power of flood models requires a meaningful representation of bio-physical processes. Therefore great interest exists to improve the process representation. Progress in hydrological process understanding is achieved through a learning cycle including critical assessment of an existing model for a given catchment as a first step. The assessment will highlight deficiencies of the model, from which useful additional data requirements are derived, giving a guideline for new measurements. These new measurements may in turn lead to improved process concepts. The improved process concepts are finally summarized in an updated hydrological model. In this thesis I demonstrate such a learning cycle, focusing on the advancement of model evaluation methods and more cost effective measurements. For a successful model evaluation, I propose that three questions should be answered: 1) when is a model reproducing observations in a satisfactory way? 2) If model results deviate, of what nature is the difference? And 3) what are most likely the relevant model components affecting these differences? To answer the first two questions, I developed a new method to assess the temporal dynamics of model performance (or TIGER - TIme series of Grouped Errors). This method is powerful in highlighting recurrent patterns of insufficient model behaviour for long simulation periods. I answered the third question with the analysis of the temporal dynamics of parameter sensitivity (TEDPAS). For calculating TEDPAS, an efficient method for sensitivity analysis is necessary. I used such an efficient method called Fourier Amplitude Sensitivity Test, which has a smart sampling scheme. Combining the two methods TIGER and TEDPAS provided a powerful tool for model assessment. With WaSiM-ETH applied to the Weisseritz catchment as a case study, I found insufficient process descriptions for the snow dynamics and for the recession during dry periods in late summer and fall. Focusing on snow dynamics, reasons for poor model performance can either be a poor representation of snow processes in the model, or poor data on snow cover, or both. To obtain an improved data set on snow cover, time series of snow height and temperatures were collected with a cost efficient method based on temperature measurements on multiple levels at each location. An algorithm was developed to simultaneously estimate snow height and cold content from these measurements. Both, snow height and cold content are relevant quantities for spring flood forecasting. Spatial variability was observed at the local and the catchment scale with an adjusted sampling design. At the local scale, samples were collected on two perpendicular transects of 60 m length and analysed with geostatistical methods. The range determined from fitted theoretical variograms was within the range of the sampling design for 80% of the plots. No patterns were found, that would explain the random variability and spatial correlation at the local scale. At the watershed scale, locations of the extensive field campaign were selected according to a stratified sample design to capture the combined effects of elevation, aspect and land use. The snow height is mainly affected by the plot elevation. The expected influence of aspect and land use was not observed. To better understand the deficiencies of the snow module in WaSiM-ETH, the same approach, a simple degree day model was checked for its capability to reproduce the data. The degree day model was capable to explain the temporal variability for plots with a continuous snow pack over the entire snow season, if parameters were estimated for single plots. However, processes described in the simple model are not sufficient to represent multiple accumulation-melt-cycles, as observed for the lower catchment. Thus, the combined spatio-temporal variability at the watershed scale is not captured by the model. Further tests on improved concepts for the representation of snow dynamics at the Weißeritz are required. From the data I suggest to include at least rain on snow and redistribution by wind as additional processes to better describe spatio-temporal variability. Alternatively an energy balance snow model could be tested. Overall, the proposed learning cycle is a useful framework for targeted model improvement. The advanced model diagnostics is valuable to identify model deficiencies and to guide field measurements. The additional data collected throughout this work helps to get a deepened understanding of the processes in the Weisseritz catchment.
During the last few decades, the rapid separation of the Small Aral Sea from the isolated basin has changed its hydrological and ecological conditions tremendously. In the present study, we developed and validated the hybrid model for the Syr Darya River basin based on a combination of state-of-the-art hydrological and machine learning models. Climate change impact on freshwater inflow into the Small Aral Sea for the projection period 2007–2099 has been quantified based on the developed hybrid model and bias corrected and downscaled meteorological projections simulated by four General Circulation Models (GCM) for each of three Representative Concentration Pathway scenarios (RCP). The developed hybrid model reliably simulates freshwater inflow for the historical period with a Nash–Sutcliffe efficiency of 0.72 and a Kling–Gupta efficiency of 0.77. Results of the climate change impact assessment showed that the freshwater inflow projections produced by different GCMs are misleading by providing contradictory results for the projection period. However, we identified that the relative runoff changes are expected to be more pronounced in the case of more aggressive RCP scenarios. The simulated projections of freshwater inflow provide a basis for further assessment of climate change impacts on hydrological and ecological conditions of the Small Aral Sea in the 21st Century.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
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.
Advances in hydrogravimetry
(2023)
The interest of the hydrological community in the gravimetric method has steadily increased within the last decade. This is reflected by numerous studies from many different groups with a broad range of approaches and foci. Many of those are traditionally rather hydrology-oriented groups who recognized gravimetry as a potential added value for their hydrological investigations. While this resulted in a variety of interesting and useful findings, contributing to extend the respective knowledge and confirming the methodological potential, on the other hand, many interesting and unresolved questions emerged.
This thesis manifests efforts, analyses and solutions carried out in this regard. Addressing and evaluating many of those unresolved questions, the research contributes to advancing hydrogravimetry, the combination of gravimetric and hydrological methods, in showing how gravimeters are a highly useful tool for applied hydrological field research.
In the first part of the thesis, traditional setups of stationary terrestrial superconducting gravimeters are addressed. They are commonly installed within a dedicated building, the impermeable structure of which shields the underlying soil from natural exchange of water masses (infiltration, evapotranspiration, groundwater recharge). As gravimeters are most sensitive to mass changes directly beneath the meter, this could impede their suitability for local hydrological process investigations, especially for near-surface water storage changes (WSC). By studying temporal local hydrological dynamics at a dedicated site equipped with traditional hydrological measurement devices, both below and next to the building, the impact of these absent natural dynamics on the gravity observations were quantified. A comprehensive analysis with both a data-based and model-based approach led to the development of an alternative method for dealing with this limitation. Based on determinable parameters, this approach can be transferred to a broad range of measurement sites where gravimeters are deployed in similar structures. Furthermore, the extensive considerations on this topic enabled a more profound understanding of this so called umbrella effect.
The second part of the thesis is a pilot study about the field deployment of a superconducting gravimeter. A newly developed field enclosure for this gravimeter was tested in an outdoor installation adjacent to the building used to investigate the umbrella effect. Analyzing and comparing the gravity observations from both indoor and outdoor gravimeters showed performance with respect to noise and stable environmental conditions was equivalent while the sensitivity to near-surface WSC was highly increased for the field deployed instrument. Furthermore it was demonstrated that the latter setup showed gravity changes independent of the depth where mass changes occurred, given their sufficiently wide horizontal extent. As a consequence, the field setup suits monitoring of WSC for both short and longer time periods much better. Based on a coupled data-modeling approach, its gravity time series was successfully used to infer and quantify local water budget components (evapotranspiration, lateral subsurface discharge) on the daily to annual time scale.
The third part of the thesis applies data from a gravimeter field deployment for applied hydrological process investigations. To this end, again at the same site, a sprinkling experiment was conducted in a 15 x 15 m area around the gravimeter. A simple hydro-gravimetric model was developed for calculating the gravity response resulting from water redistribution in the subsurface. It was found that, from a theoretical point of view, different subsurface water distribution processes (macro pore flow, preferential flow, wetting front advancement, bypass flow and perched water table rise) lead to a characteristic shape of their resulting gravity response curve. Although by using this approach it was possible to identify a dominating subsurface water distribution process for this site, some clear limitations stood out. Despite the advantage for field installations that gravimetry is a non-invasive and integral method, the problem of non-uniqueness could only be overcome by additional measurements (soil moisture, electric resistivity tomography) within a joint evaluation. Furthermore, the simple hydrological model was efficient for theoretical considerations but lacked the capability to resolve some heterogeneous spatial structures of water distribution up to a needed scale. Nevertheless, this unique setup for plot to small scale hydrological process research underlines the high potential of gravimetery and the benefit of a field deployment.
The fourth and last part is dedicated to the evaluation of potential uncertainties arising from the processing of gravity observations. The gravimeter senses all mass variations in an integral way, with the gravitational attraction being directly proportional to the magnitude of the change and inversely proportional to the square of the distance of the change. Consequently, all gravity effects (for example, tides, atmosphere, non-tidal ocean loading, polar motion, global hydrology and local hydrology) are included in an aggregated manner. To isolate the signal components of interest for a particular investigation, all non-desired effects have to be removed from the observations. This process is called reduction. The large-scale effects (tides, atmosphere, non-tidal ocean loading and global hydrology) cannot be measured directly and global model data is used to describe and quantify each effect. Within the reduction process, model errors and uncertainties propagate into the residual, the result of the reduction. The focus of this part of the thesis is quantifying the resulting, propagated uncertainty for each individual correction. Different superconducting gravimeter installations were evaluated with respect to their topography, distance to the ocean and the climate regime. Furthermore, different time periods of aggregated gravity observation data were assessed, ranging from 1 hour up to 12 months. It was found that uncertainties were highest for a frequency of 6 months and smallest for hourly frequencies. Distance to the ocean influences the uncertainty of the non-tidal ocean loading component, while geographical latitude affects uncertainties of the global hydrological component. It is important to highlight that the resulting correction-induced uncertainties in the residual have the potential to mask the signal of interest, depending on the signal magnitude and its frequency. These findings can be used to assess the value of gravity data across a range of applications and geographic settings.
In an overarching synthesis all results and findings are discussed with a general focus on their added value for bringing hydrogravimetric field research to a new level. The conceptual and applied methodological benefits for hydrological studies are highlighted. Within an outlook for future setups and study designs, it was once again shown what enormous potential is offered by gravimeters as hydrological field tools.