Water balance estimation in high Alpine terrain by combining distributed modeling and a neural network approach (Berchtesgaden Alps, Germany)

Abstract

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.

Dynamics of water fluxes and storages in an Alpine karst catchment under current and potential future climate conditions

“…The melt factor and the radiation coefficient are empirical coefficients and can be estimated by model calibration. The distributed potential clear-sky direct solar radiation is dependent on surface topography and calculated with 100 m × 100 m grid resolution for the investigated area using the approach developed by Kumar et al (1997) and a digital elevation model for the study area.…”

Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment

“…Finally, it is probable that simulated SCA inaccuracies could also be explained independently from topographic characteristics and result from local wind systems (Lehning, Löwe, Ryser, & Raderschall, 2008) or spatial extrapolation of precipitation (Kraller et al, 2012;Magnusson et al, 2011) not matching the precipitation dynamics at the micro-scale. Smaller inaccuracies of MODIS SCA could also be explained by snow/cloud confusion (Hall & Riggs, 2007) as cloud coverage was generally detected in vicinity to the glacier located northeast in the catchment.…”

Performance of complex snow cover descriptions in a distributed hydrological model system: A case study for the high Alpine terrain of the Berchtesgaden Alps

“…T, air temperature; H, relative humidity; WS, wind speed; WD, wind direction; SD, snow depth; SWE, snow water equivalent; SS, sunshine duration; GR, global radiation; DR, direct radiation; RR, reflected radiation; P, precipitation; AP, atmospheric pressure at sea level; TS, surface temperature; LWD, Bavarian Avalanche Warning Service; NPV, Administration Berchtesgaden National Park; ZAMG, Central Institute for Meteorology and Geodynamics; DWD, German Weather Service. Kraller et al [2012]. To investigate the influence of the different snow process parameterizations on discharge modeling, four model runs with different combinations of snow cover modeling methods (Table 6) are performed.…”

“…The large altitudinal gradient (2110 m) between these two tourist attractions and the resulting different climatic conditions at a horizontal distance of about 3500 m illustrate the large spatial heterogeneity of the catchment. The mean annual precipitation ranges from 1500 mm in the valleys up to 2600 mm at elevated and peak regions [ Konnert , ; Kraller et al ., ]. Despite the dense station network in the catchment, the latter figure is still subject to high uncertainties because of the usual measurement errors [ Sevruk , ] and a limited number of meteorological stations at high elevations.…”

“…Additional information on model setup and performance is listed in Kraller et al . []. To investigate the influence of the different snow process parameterizations on discharge modeling, four model runs with different combinations of snow cover modeling methods (Table ) are performed.…”