[1] Runoff generation in Alpine regions is typically affected by snow processes. Snow accumulation, storage, redistribution, and ablation control the availability of water. In this study, several robust parameterizations describing snow processes in Alpine environments were implemented in a fully distributed, physically based hydrological model. Snow cover development is simulated using different methods from a simple temperature index approach, followed by an energy balance scheme, to additionally accounting for gravitational and wind-driven lateral snow redistribution. Test site for the study is the Berchtesgaden National Park (Bavarian Alps, Germany) which is characterized by extreme topography and climate conditions. The performance of the model system in reproducing snow cover dynamics and resulting discharge generation is analyzed and validated via measurements of snow water equivalent and snow depth, satellite-based remote sensing data, and runoff gauge data. Model efficiency (the Nash-Sutcliffe coefficient) for simulated runoff increases from 0.57 to 0.68 in a high Alpine headwater catchment and from 0.62 to 0.64 in total with increasing snow model complexity. In particular, the results show that the introduction of the energy balance scheme reproduces daily fluctuations in the snowmelt rates that trace down to the channel stream. These daily cycles measured in snowmelt and resulting runoff rates could not be reproduced by using the temperature index approach. In addition, accounting for lateral snow transport changes the seasonal distribution of modeled snowmelt amounts, which leads to a higher accuracy in modeling runoff characteristics.

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Abstract. The water balance in high Alpine regions is often characterized by significant variation of meteorological variables in space and time, a complex hydrogeological situation and steep gradients. The system is even more complex when the rock composition is dominated by soluble limestone, because unknown underground flow conditions and flow directions lead to unknown storage quantities. Reliable distributed modeling cannot be implemented by traditional approaches due to unknown storage processes at local and catchment scale. We present an artificial neural network extension of a distributed hydrological model (WaSiM-ETH) that allows to account for subsurface water transfer in a karstic environment. The extension was developed for the Alpine catchment of the river "Berchtesgadener Ache" (Berchtesgaden Alps, Germany), which is characterized by extreme topography and calcareous rocks. The model assumes porous conditions and does not account for karstic environments, resulting in systematic mismatch of modeled and measured runoff in discharge curves at the outlet points of neighboring high alpine subbasins. Various precipitation interpolation methods did not allow to explain systematic mismatches, and unknown subsurface hydrological processes were concluded as the underlying reason. We introduce a new method that allows to describe the unknown subsurface boundary fluxes, and account for them in the hydrological model. This is achieved by an artificial neural network approach (ANN), where four input variables are taken to calculate the unknown subsurface storage conditions. This was first developed for the high Alpine subbasin Königsseer Ache to improve the monthly water balance. We explicitly derive the algebraic transfer function of an artificial neural net to calculate the missing boundary fluxes. The result of the ANN is then implemented in the groundwater module of the hydrological model as boundary flux, and considered during the consecutive model process. We tested several ANN setups in different time increments to investigate ANN performance and to examine resulting runoff dynamics of the hydrological model. The ANN with 5-day time increment showed best results in reproducing the observed water storage data (r 2 = 0.6). The influx of the 20-day ANN showed best results in the hydrological model correction. The boundary influx in the subbasin improved the hydrological model, as performance increased from NSE = 0.48 to NSE = 0.57 for subbasin Königsseetal, from NSE = 0.22 to NSE = 0.49 for subbasin Berchtesgadener Ache, and from NSE = 0.56 to NSE = 0.66 for the whole catchment within the test period. This combined approach allows distributed quantification of water balance components including subsurface water transfer.

The water balance in high Alpine regions is often characterized by significant variation of meteorological variables in space and time, a complex hydrogeological situation and steep gradients. The system is even more complex when the rock composition is dominated by soluble limestone, because unknown underground flow conditions and flow directions lead to unknown storage quantities. Reliable distributed modeling cannot be implemented by traditional approaches due to unknown storage processes at local and catchment scale. We present an artificial neural network extension of a distributed hydrological model (WaSiM-ETH) that allows to account for subsurface water transfer in a karstic environment. The extension was developed for the Alpine catchment of the river "Berchtesgadener Ache" (Berchtesgaden Alps, Germany), which is characterized by extreme topography and calcareous rocks. The model assumes porous conditions and does not account for karstic environments, resulting in systematic mismatch of modeled and measured runoff in discharge curves at the outlet points of neighboring high alpine sub-catchments. Various precipitation interpolation methods did not allow to explain systematic mismatches, and unknown subsurface hydrological processes were concluded as the underlying reason. We introduce a new method that allows to describe the unknown subsurface boundary fluxes, and account for them in the distributed model. This is achieved by an Artificial Neural Network approach (ANN), where three input variables are taken to calculate the unknown subsurface storage conditions. We explicitly derive the algebraic transfer function of an artificial neural net to calculate the missing boundary fluxes. The result of the ANN is then implemented in the groundwater module of the distributed model as boundary flux, and considered during the consecutive model process. The ANN was able to reproduce the observed water storage data sufficiently (<i>r</i>² = 0.48). The boundary influx in the sub-catchment improved the distributed model, as performance increased from NSE = 0.34 to NSE = 0.57. This combined approach allows distributed quantification of water balance components including subsurface water transfer

The Berchtesgaden Alps are situated in the Northern Limestone Alps, characterized by individual mountain plateaus and ridges in close proximity to each other, intersected by valleys, with an altitudinal gradient of 2 100 meters. The limestone has been exposed to dissolution processes since the Cretaceous, leading to a massive karstified aquifer with a wide range of subsurface flow channels. There are hundreds of springs as groundwater recharge locations, feeding the seven rivers of the region that contribute to the Danube watershed. Several studies were conducted to examine the hydro-geological conditions and the resulting groundwater flow. This paper aims to evaluate and summarize research in the basin describing groundwater flow to identify the main drainage direction, travel times, spring dynamics and possible subsurface redistribution in the individual mountain ranges and the whole basin. To this end, we evaluate several tracer experiments, two isotope studies and a spring database. The tracer experiments are generating knowledge about flow directions in the individual mountain ranges, groundwater redistribution, water storage and mean travel times. Five experiments prove increased groundwater flow remaining within a valley and four experiments indicate groundwater redistribution through mountain ranges. The isotope studies indicate potential water storage in the Wimbach valley of an estimated 100 x 10 6 to 470 x 10 6 m 3 and mean transit times of about four years. The analysis of the spring database focuses on locations and discharge classification. Overall, there are 289 springs recorded in the spring database, distributed from 600 to over 2 000 m altitude, with major springs at the northern base of the mountains Hochkalter, Watzmann and at the north shore of lake Königssee. The conclusion summarizes the effect of the karst aquifer on the hydrology of the region. The outlook introduces current research within the area and the distributed water balance modelling. 19R e search eco.m o n t -Vo l u m e 3 , N u m b e r 1 , J u n e 2 0 1 1 ISSN 2 0 7 3 -1 0 6 X p r i n t v e r s i o n ISSN 2 0 7 3 -1 5 5 8 o n l i n e v e r s i o n : h t t p : / / e p u b . o e a w. a c . a t / e c o . m o n t