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Water resources from Central Asia’s mountain regions have a high relevance for the water supply of the water scarce lowlands. A good understanding of the water cycle in these mountain regions is therefore needed to develop water management strategies. Hydrological modeling helps to improve our knowledge of the regional water cycle, and it can be used to gain a better understanding of past changes or estimate future hydrologic changes in view of projected changes in climate. However, due to the scarcity of hydrometeorological data, hydrological modeling for mountain regions in Central Asia involves large uncertainties.
Addressing this problem, the first aim of this thesis was to develop hydrological modeling approaches that can increase the credibility of hydrological models in data sparse mountain regions. This was achieved by using additional data from remote sensing and atmospheric modeling. It was investigated whether spatial patterns from downscaled reanalysis data can be used for the interpolation of station-based precipitation data. This approach was compared to other precipitation estimates using a hydrologic evaluation based on hydrological modeling and a comparison of simulated and observed discharge, which demonstrated a generally good performance of this method. The study further investigated the value of satellite-derived snow cover data for model calibration. Trade-offs of good model performance in terms of discharge and snow cover were explicitly evaluated using a multiobjective optimization algorithm, and the results were contrasted with single-objective calibration and Monte Carlo simulations. The study clearly shows that the additional use of snow cover data improved the internal consistency of the hydrological model. In this context, it was further investigated for the first time how many snow cover scenes were required for hydrological model calibration.
The second aim of this thesis was the application of the hydrological model in order to investigate the causes of observed streamflow increases in two headwater catchments of the Tarim River over the recent decades. This simulation-based approach for trend attribution was complemented by a data-based approach. The hydrological model was calibrated to discharge and glacier mass balance data and considered changes in glacier geometry over time. The results show that in the catchment with a lower glacierization, increasing precipitation and temperature both contributed to the streamflow increases, while in the catchment with a stronger glacierization, increasing temperatures were identified as the dominant driver.
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.