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In Nederland ging in 2017 het Deltaplan Ruimtelijke Adaptatie (DPRA) van start, als onderdeel van het Deltaprogramma. De doelstelling uit het DPRA: “In 2050 is het bebouwde gebied in Nederland, inclusief vitale en kwetsbare functies, zo goed mogelijk waterrobuust en klimaatbestendig ingericht”. Op de radar van het DPRA staan klimaat-gerelateerde kwetsbaarheden en gevolgschade. Waarbij wateroverlast, droogte, hittestress en de gevolgen door overstroming de uitdagingen vormen. Stappen in de aanpak zijn een stresstest, een risicodialoog, het opstellen van een uitvoeringsagenda en het ontwikkelen van instrumenten en tools. Op papier ziet de aanpak van het DPRA er veelbelovend uit, in de zin van een logisch schema voor probleemaanpak en verbetering, oog hebbend voor de toenemende impact van klimaatverandering.

Flusshochwasser werden in lokale und plotzliche Sturzfluten und in Hochwasser an groseren Flussen unterschieden. Fur verschiedene Hochwasserindikatoren und Flusseinzugsgebiete ergeben sich erhebliche Unterschiede, wobei sowohl uberwiegend aus Regen als auch uberwiegend aus Schmelzwasser gespeiste Hochwasserereignisse betrachtet werden. Besondere Aufmerksamkeit finden Hochwasserereignisse an Rhein, Elbe, Weser und Ems sowie die Entwicklung von Sturzfluten infolge von Extremniederschlagen kurzer Dauer, wobei die Beobachtungen und Trends zu Modellierungsergebnissen in Beziehung gesetzt werden. Auch die Notwendigkeit von Anpassungsmasnahmen aufgrund uberwiegend positiver Trends wird diskutiert.

In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers - using the model of their choice - for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Hollander et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of ad, QC 20140811

Ten conceptually different models were applied to predict the discharge from the 6 ha artificial Chicken Creek catchment in Lausatia, North-East Germany, which has been created in an open cast mining area. The study consisted of three steps to make a model intercomparison with the objective of a priori prediction of the water balance and the discharge dynamics. In order to test the ability of each model and modeller to predict water flows in an ungauged catchment, only soil texture, topography, vegetation coverage and climate data were provided to the modellers in the first step. Hydrological data on discharge, soil moisture and groundwater levels were withheld. This enabled us to assess the predictive capabilities of the models under sparse data conditions. The predicted components of the water balance varied in a wide range. None of the model simulations came close to the observed water balance for the entire 3-year study period. Discharge was mainly predicted as subsurface flow with little surface runoff. In reality, surface runoff was a major flow component despite the fairly coarse soil texture. In the second step, additional process knowledge was gained during a joint field visit. The occurence of gully erosion and surface crusting was detected and implemented into the models. Consequently, model predictions changed considerably. The previous simulations dominated by subsurface flow changed to surface flow-dominated simulations. Additional data, provided in the third step, mainly confirmed the parameterisations and assisted in a better definition of initial conditions and subsurface storage. The comparison indicates that, in addition to model philosophy, the personal judgement of the modellers was a major source of the differences in the model results. The model parameterisation and choice of initial conditions depended on the modeller's judgement and were therefore a result of the modellers' experience in terms of model types and case studies., QC 20140908

Ten conceptually different models in predicting discharge from the artificial Chicken Creek catchment in North-East Germany were used for this study. Soil texture and topography data were given to the modellers, but discharge data was withheld. We compare the predictions with the measurements from the 6 ha catchment and discuss the conceptualization and parameterization of the models. The predictions vary in a wide range, e.g. with the predicted actual evapotranspiration ranging from 88 to 579 mm/y and the discharge from 19 to 346 mm/y. The predicted components of the hydrological cycle deviated systematically from the observations, which were not known to the modellers. Discharge was mainly predicted as subsurface discharge with little direct runoff. In reality, surface runoff was a major flow component despite the fairly coarse soil texture. The actual evapotranspiration (AET) and the ratio between actual and potential ET was systematically overestimated by nine of the ten models. None of the model simulations came even close to the observed water balance for the entire 3-year study period. The comparison indicates that the personal judgement of the modellers was a major source of the differences between the model results. The most important parameters to be presumed were the soil parameters and the initial soil-water content while plant parameterization had, in this particular case of sparse vegetation, only a minor influence on the results., QC 20100525

This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles. in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in mult

An ensemble of 10 hydrological models was applied to the same set of land use change scenarios. There was general agreement about the direction of changes in the mean annual discharge and 90% discharge percentile predicted by the ensemble members, although a considerable range in the magnitude of predictions for the scenarios and catchments under consideration was obvious. Differences in the magnitude of the increase were attributed to the different mean annual actual evapotranspiration rates for each land use type. The ensemble of model runs was further analyzed with deterministic and probabilistic ensemble methods. The deterministic ensemble method based on a trimmed mean resulted in a single somewhat more reliable scenario prediction. The probabilistic reliability ensemble averaging (REA) method allowed a quantification of the model structure uncertainty in the scenario predictions. It was concluded that the use of a model ensemble has greatly increased our confidence in the reliability of the model predictions.

This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in mult

This paper analyses the effect of spatial resolution and distribution of model input data on the results of regional-scale land use scenarios using three different hydrological catchment models. A 25 m resolution data set of a mesoscale catchment and three land use scenarios are used. Data are systematically aggregated to resolutions up to 2 km. Land use scenarios are spatially redistributed, both randomly and topography based. Using these data, water fluxes are calculated on a daily time step for a 16 year time period without further calibration. Simulation results are used to identify grid size, distribution and model dependent scenario effects. In the case of data aggregation, all applied models react sensitively to grid size. WASIM and TOPLATS simulate constant water balances for grid sizes from 50 m to 300-500 m, SWAT is more sensitive to input data aggregation, simulating constant water balances between 50 m and 200 m grid size. The calculation of scenario effects is less robust to data aggregation. The maximum acceptable grid size reduces to 200-300 m for TOPLATS and WASIM. In case of spatial distribution, SWAT and TOPLATS are slightly sensitive to a redistribution of land use (below 1.5% for water balance terms), whereas WASIM shows almost no reaction. Because the aggregation effects were stronger than the redistribution effects, it is concluded that spatial discretisation is more important than spatial distribution. As the aggregation effect was mainly associated with a change in land use fraction, it is concluded that accuracy of data sets is much more important than a high spatial resolution.

This paper introduces the project on 'Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM)' that aims at investigating the envelope of predictions on changes in hydrological fluxes due to land use change. As part of a series

Large-scale hydrological modelling has become increasingly wide-spread during the last decade. An annual workshop series on large-scale hydrological modelling has provided, since 1997, a forum to the German-speaking community for discussing recent developments and achievements in this research area. In this paper we present the findings from the 2007 workshop which focused on advances and visions in large-scale hydrological modelling. We identify the state of the art, difficulties and research perspectives with respect to the themes "sensitivity of model results", "integrated modelling" and "coupling of processes in hydrosphere, atmosphere and biosphere". Some achievements in large-scale hydrological modelling during the last ten years are presented together with a selection of remaining challenges for the future.

Ensemble modelling, whereby predictions from several models are pooled in an attempt to improve prediction accuracy, has often been used in the climate and atmospheric sciences, but until recently, has received little attention in hydrology. One of the key aims of the ensemble approach is to reduce uncertainty in the modelled predictions. This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to produce ensemble predictions of the effects of several projected land use changes. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. Overall, the simpler models tend to perform better in both calibration and validation, but while all models tend to show improved performance during the lessextreme validation period, this improvement is greatest for some of the more complex models. Despite the disparity in model performance, three ensemble predictions made up of various combinations of the 10 model predictions outperform all of the individual models. In calibration, the ensemble based on a multi-variable regression of all models provides the best predictions, but its prediction accuracy declines to a greater extent than all of the models in terms of both its bias and Nash-Sutcliffe efficency when used in the validation period. In the validation period, the best predictions are provided by an ensemble consisting of the daily median model predictions. The predictions of this median ensemble also improve more between calibration and validation than any of the other models, thus indicating its robustness. The calibrated models are applied to three land use change scenarios. In the scenarios, the projected patterns of land use are based on assumed average field sizes of 0.5 ha, 1.5 ha and 5.0 ha, respectively. These contrast w