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Floodplains provide a variety of hydrological and ecological functions and are therefore of great importance. The flooding frequency, as well as the height and duration of inundations are particularly relevant for ecosystem states and are dependent on the exchange between surface water and groundwater. In this study, we developed a fully distributed model approach to simulate distributed groundwater levels in a floodplain in Hesse, Germany (14.8 km2). To overcome the problem of large computation times, we simplified the surface water equation. Thus, the water surface of flooding is at the same level everywhere and the dynamic effect of the flooding is ignored. In this way, it was possible to run the model 5000 times and investigate its parameter uncertainty using Latin hypercube sampling. Behavioral model runs were selected based on a threshold criterion of a mean root‐mean‐square error that was smaller than 0.26 m. All the simulated groundwater wells show an individual RMSE between 0.17 and 0.41 m for the calibration period. Regarding the parameterization, the model shows rather large variance in parameters that are capable of generating good simulations: a range of saturated conductivity of 2793 m/d, porosity of 0.4 m3/m3, residual wetness of soil of 0.2 m3/m3/soil and range of soil thickness of 2.9 m.

Floodplains are highly complex and dynamic systems in terms of their hydrology.Thus, they comprise a wide habitat heterogeneity and therefore harbour highly specialized species. For future projections of habitat and species diversity, processbased models simulating ecohydrological conditions and resulting habitat and species distributions are needed. We present a new modelling framework that includes a physically based, surface water-groundwater model coupled with a habitat model.Using the model framework, we simulate the occurrence of 23 flood meadow plant species in a Rhine River floodplain. To benchmark the data, results are compared with a conventional approach with simple spatial hydrological information. Our results show that models with predictors obtained from the surface water-groundwater model are significantly more accurate for rare and endangered species, as well as for typical flood meadow species. A total of 15 hydrological predictors were defined, which look relevant and promising for a good prediction, but at the same time reflect very different hydrological conditions. The standard deviation of the groundwater level, wet soil conditions, and inundation belong to the most relevant predictors for an accurate prediction. Therefore, we recommend including more specific hydrological information in habitat models of species in complex floodplain ecosystems. Such spatial explicit habitat models can also open up further possibilities, such as the projection of global change impact studies or nature conservation planning.KEYWORDS ensembles of small models, flood meadow, habitat modelling, hydrological model, rare species, Rhine River, riparian ecosystems, surface water-groundwater model *These authors contributed equally to the work.

Floodplains are highly complex and dynamic systems in terms of their hydrology. Thus, they harbor highly specialized floodplain plant species depending on different inundation characteristics. Climate change will most likely alter those characteristics. This study investigates the potential impact of climate change on the inundation characteristics of a floodplain of the Rhine River in Hesse, Germany. We report on the cascading uncertainty introduced through climate projections, climate model structure, and parameter uncertainty. The established modeling framework integrates projections of two general circulation models (GCMs), three emission scenarios, a rainfall-runoff model, and a coupled surface water-groundwater model. Our results indicate large spatial and quantitative uncertainties in the simulated inundation characteristics, which are mainly attributed to the GCMs. Overall, a shift in the inundation pattern, possible in both directions, and an increase in inundation extent are simulated. This can cause significant changes in the habitats of species adapted to these highly-endangered ecosystems.