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The results of this chapter, with a more detailed focus on the habitat model, are described in Chapter 4 and are submitted to the journal Ecohydrology under the title:

Gattringer*, J.P., Maier*, N.; Breuer, L.; Otte, A.; Donath, T.W., Kraft, P.;

Harvolk-Schöning, S. (2018). Modeling of rare flood meadow species distribution by a combined habitat-surface water-groundwater model.

*shared first-authorship

Numerous flood meadow species have adapted to the specific hydrological regimes of floodplains. To predict spatially explicit suitable locations for species, species distribution models (SDMs) are a good choice. They are based on statistical correlations between observations and the environmental conditions at the observation point (Elith and Leathwick, 2009; Guisan and Thuiller, 2005; Peterson et al., 2011). In the context of flood meadow species, such environmental variables would involve information on inundation length and depth, as well as recurrence intervals of floods and soil moisture conditions.

Hydrological information implemented in the SDM as environmental variables (also called hydrological predictors) is derived from the developed CMF model. The simulated long-term time series of the water level of both submodels were transformed to static hydrological predictors by downscaling (inverse distance weighting) the water level of the polygons from the CMF model to a high spatial resolution raster (5 x 5 m). From over 80 different a priori conceived hydrological predictors, the 15 most important ones were selected by an iterative

22 process and based on species-specific flooding experiments (Gattringer et al., 2017, 2018).

Hydrological predictors include, for example, information on groundwater level range, standard deviation, or average and longest durations below/above certain groundwater level thresholds. Additionally, one meteorological (longest period of wet days) and three morphological predictors (height above mean sea level, distance to the Rhine or the cut-off meander and distance to any water surface) were added as environmental variables.

In order to test the significance of the high temporal and spatial resolution hydrological predictors obtained from the CMF model (sgm: surface water-groundwater model), two other datasets of hydrological predictors were generated. The first set of data used for comparison was based only on weekly measured groundwater levels at the 16 different sites in the floodplain (gww: groundwater wells). The second set of data was based only on the water level of the adjacent Rhine River (riv: river). Additionally, the model performance without any hydrological information (nhy: non-hydrological), i.e., with only meteorological and morphological predictors, was tested.

The results focus mainly on floodplain meadow species (Arabis nemorensis, Galium boreale, Peucedanum officinale, Sanguisorba officinali, Silaum silaus, Thalictrum flavum, and Veronica maritima) after Burkart (2001) and on rare and engendered species (Arabis nemorensis, Bromus racemosus, Galium boreale, Iris spuria, Peucedanum officinale, Serratula tinctoria, and Veronica maritima), as they are specialized species and species valuable to consider for restoration projects. For both groups, the SDM with hydrological predictors derived from the CMF model is superior to the other setups, i.e., setups considering hydrological predictors from groundwater level or river water level and those not considering any hydrological predictors (Figure 4.3). Testing several combinations and quantities of predictors led to the conclusion that six to ten specific predictors are needed to satisfactorily simulate habitats and occurrences for rare and endangered species. The most frequently used hydrological predictors included parameters indicating inundation length and dry or wet conditions.

These results reflect the complexity of the habitat requirements of flood meadow species being able to cope with both flooding and drought periods (Burkart, 2001). Even though not all the 15 hydrological predictors are essential for simulating all species, it became clear that removing selected predictors for model simplification resulted in a model failure for several species. Thus, it can be concluded that a large set of hydrological predictors is required to be

23 able to simulate whole plant communities with their diverse specific eco-hydrological requirements.

The superior results of the habitat model including hydrological predictors from the CMF model became visible when the time series were analyzed in more detail. The hydrological model is a respectable representation of the natural conditions in a high spatial and temporal resolution, including components such as climatic conditions, soil properties, surface water distribution, surface water-groundwater interaction, and groundwater level. Thus, the model is able to reflect the soil moisture content, altering the water storage capacity of the soil and thus driving the flood extent, flood duration, and inundation height of water in the floodplain. Inundation caused by high water levels of the Rhine can be attenuated if the soil is capable of draining a large volume of water, but at the same time, large floods can occur when the soil is already saturated. The CMF model is capable of representing such conditions. For the two days shown in Figure 1.8, predictors based on the CMF model would result in different values, whereas predictors based on the water level of the Rhine would result in the same value, because the water level of the Rhine is the same on both days.

Overall, creating a habitat model with predictors, derived from perfectly spatially distributed hydrological measurements would be ideal. However, this is nearly impossible due to high costs. A few sporadic or poorly distributed measurements lead to less dependable results, as those involve some failures in the representation. For example, the rapid reaction of groundwater to changes in the water level of the river cannot be represented. Thus, generating hydrological predictors from a hydrological model with an appropriate representation of the natural conditions in a high spatial and temporal resolution including various components (climatic conditions, soil properties, surface water distribution, surface water-groundwater interaction, and groundwater) seems to be a good compromise between cost and the objective to protect rare species.

24 Figure 1.8: Representations of the groundwater and surface water level in the study site at two

different days (07/01/2003 and 16/01/2004). The water level of the Rhine is depicted in the bottom of the figure (MHW: mean high water, MW: mean water, MLW: mean low water). The blue lines indicate the water level of the days depicted in the top of the figure. The water level of the Rhine was 86.15 m on both days.

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