Abstract. A better understanding of the reasons why hydrological model performance is unsatisfying represents a crucial part of meaningful model evaluation. However, current evaluation efforts are mostly based on aggregated efficiency measures such as Kling–Gupta efficiency (KGE) or Nash–Sutcliffe efficiency (NSE). These aggregated measures provide a relative gradation of model performance. Especially in the case of a weak model performance it is important to identify the different errors which may have caused such unsatisfactory predictions. These errors may originate from the model parameters, the model structure, and/or the input data. In order to provide more insight, we define three types of errors which may be related to their source: constant error (e.g. caused by consistent input data error such as precipitation), dynamic error (e.g. structural model errors such as a deficient storage routine) and timing error (e.g. caused by input data errors or deficient model routines/parameters). Based on these types of errors, we propose the novel diagnostic efficiency (DE) measure, which accounts for these three error types. The disaggregation of DE into its three metric terms can be visualized in a plain radial space using diagnostic polar plots. A major advantage of this visualization technique is that error contributions can be clearly differentiated. In order to provide a proof of concept, we first generated time series artificially with the three different error types (i.e. simulations are surrogated by manipulating observations). By computing DE and the related diagnostic polar plots for the reproduced errors, we could then supply evidence for the concept. Finally, we tested the applicability of our approach for a modelling example. For a particular catchment, we compared streamflow simulations realized with different parameter sets to the observed streamflow. For this modelling example, the diagnostic polar plot suggests that dynamic errors explain the overall error to a large extent. The proposed evaluation approach provides a diagnostic tool for model developers and model users and the diagnostic polar plot facilitates interpretation of the proposed performance measure as well as a relative gradation of model performance similar to the well-established efficiency measures in hydrology.

Abstract. Better understanding of the reasons why hydrological model performance is good or poor represents a crucial part for meaningful model evaluation. However, current evaluation efforts are mostly based on aggregated efficiency measures such as Kling-Gupta Efficiency (KGE) or Nash-Sutcliffe Efficiency (NSE). These aggregated measures only distinguish between good and poor model performance. Especially in the case of a poor model performance it is important to identify the different errors which may have caused such unsatisfying predictions. These errors may origin from the model parameters, the model structure, and/or the input data. In order to provide more insight, we define three types of errors which may be related to their origin: constant error (e.g. caused by consistent input data error such as precipitation), dynamic error (e.g. structural model errors such as a deficient storage routine) and timing error (e.g. caused by input data errors or deficient model routines/parameters). Based on these types of errors, we propose the novel Diagnostic Efficiency (DE) measure, which accounts for the three error types. The disaggregation of DE into its three metric terms can be visualized in a plain radial space using diagnostic polar plots. A major advantage of this visualization technique is that error contributions can be clearly differentiated. In order to provide a proof of concept, we first generated errors systematically by mimicking the three error types (i.e. simulations are surrogated by manipulating observations). By computing DE and the related diagnostic polar plots for the mimicked errors, we could then supply evidence for the concept. Finally, we tested the applicability of our approach for a modelling example. For a particular catchment, we compared streamflow simulations realized with different parameter sets to the observed streamflow. For this modelling example, the diagnostic polar plot suggests, that dynamic errors explain the model performance to a large extent. The proposed evaluation approach provides a diagnostic tool for model developers and model users and the diagnostic polar plot facilitates interpretation of the proposed performance measure.

Understanding the transport processes and travel times of pollutants in the subsurface is crucial for an effective management of drinking water resources. Transport processes and soil hydrologic processes are inherently linked to each other. In order to account for this link, we couple the process-based hydrologic model RoGeR with StorAge Selection (SAS) functions. We assign to each hydrological process a specific SAS function (e.g. power law distribution function). To represent different transport mechanisms, we combined a specific set of SAS functions into four transport model structures: complete-mixing, piston flow, advection-dispersion and advection-dispersion with time-variant parameters. In this study, we conduct modelling experiments at the Rietholzbach lysimeter, Switzerland. All modelling experiments are benchmarked with HYDRUS-1D. We compare our simulations to the measured hydrologic variables (percolation and evapotranspiration fluxes and soil water dynamics) and the measured water stable isotope signal (18O) in the lysimeter seepage for a period of ten years (1997-2007). An additional virtual bromide tracer experiment was used to benchmark the models. Additionally, we carried out a sensitivity analysis and provide Sobol indices for soil hydrologic model parameters and SAS parameters. Our results show that the advection-dispersion transport model produces the best results. And thus, advective-dispersive transport processes play a dominant role at Rietholzbach lysimeter. Our modelling approach provides the capability to test hypotheses of different transport mechanisms and to improve process understanding and predictions of transport processes. Overall, the combined model allows a very effective simulation of combined flux and transport processes at various temporal and spatial scales.

In Environmental Impact Assessment (EIA), the either positive or negative impacts that specific N project actions might generate on a number M of environmental components are typically summarized in the form of Interaction Matrix (IM). This is a NxM tabular array containing numbers whose sign and magnitude represent the type and severity of impacts. Current approaches to interpret the IM mainly remain on a qualitative level which are limiting its practical usage. In this work, we build on previous works and adopt network theory as a methodology to represent the IM in a graphical form, and to obtain quantitative information that can be inserted in the EIA. The IM corresponds to a bipartite network linking actions and environmental components. The associated network is useful to perform quantitative statistical analyses, which summarise the complexity of cross and mutual interconnections between actions and environmental components under simple and understandable metrics. We show some results from EIAs related to water projects and other case studies, whose different complexity helps to appreciate the general applicability of the method.