Comprehensive evaluation of the WaterGAP3 model across climatic, physiographic, and anthropogenic gradients

Over the last decades macro-scale hydrological models have become common tools to provide a comprehensive view on the state of global renewable freshwater resources. Today, macro-scale hydrological models are applied to answer a wide range of research questions, e.g. to quantify the degree of human interference on the natural hydrological regime or to assess impacts of global and climate change on various aspects of the hydrological cycle including renewable (ground-)water resources, flood risk, droughts, and water stress and scarcity. Their development has tremendously benefited from the recent progress in computational resources as well as from the growing availability of earth observation data and derived data products that can be utilized to force and constrain macro-scale simulations. As any macro to global scale modelling approach, macro-scale hydrologic simulation is subject to substantial uncertainties which (i) originate from spatially distributed input data in terms of both atmospheric forcing and land surface parameters and, (ii) in particular, the (often) simplified physical process representation. Given these large uncertainties, it is indispensable to assess the actual applicability and predictive capability of any macro-scale model under various climatic and physiographic conditions. However, many evaluation studies have limited the comparative evaluation of model performance to a small set of large scale river basins, or have focused on continental-scale fluxes. In contrast, many applications rely on and draw conclusions from simulated states and fluxes at distinctively smaller spatial scales. This thesis extensively evaluates the general applicability of the global hydrological model WaterGAP3 to simulate monthly streamflow dynamics as well as low-flow and high-flow characteristics in the period 1958–2010 on the basis of more than 2400 observed streamflow records. The corresponding river basins are distributed over a wide range of climatic and physiographic conditions with basin sizes ranging from 3000 to several million square kilometers. The aim of model evaluation here is two-fold: First, model performances obtained in the analysis should serve as a benchmark any further model improvements can be tested against. Second, an approach should be developed and tested that facilitates the evaluation of the WaterGAP3 model in a diagnostic manner, i.e. it should point towards possible options of model improvement if model performance is unsatisfactory. To this end, complementary measures of model performance are linked to a set of nine catchments descriptors which quantify the climatic and physiographic settings in each study basin as well as the degree of human interference on the natural hydrologic regime. The WaterGAP3 model is found to perform comparatively well in reproducing observed monthly streamflow dynamics as well as low-flow and high-flow characteristics; however the analysis revealed distinct spatial patterns of model performance. Among the nine catchment descriptors considered, catchment aridity and average slope show the strongest impact on model performance. The model is found to overestimate total streamflow volume with increasing aridity which is characteristic to many macro-scale hydrological models and is attributable to the insufficient representation of processes related to runoff generation and concentration in water-limited environments. In steep-sloped catchments, monthly flow variability and timing are less accurately captured accompanied by a deterioration of low-flow and high-flow performance. This observation points toward a need for improving (i) the partitioning of total runoff into slow and fast component, and (ii) the calculation of flow velocity in the river segment. The approach to diagnostic model evaluation developed in the scope of the thesis—linking complementary measures of model performance to catchment characteristics—is exemplarily tested for the WaterGAP3 model and is proven to be an effective tool to explain spatial patterns in model performance and to identify deficiencies in model structure. The general approach is applicable to any hydrological model, but is especially relevant for macro-scale models and large sample studies as it can partly compensate for the lack of any catchment specific knowledge and targeted field measurements that typically inform model improvement in catchment scale modelling.