Multiple-objective calibration of a conceptual hydrological model using satellite data of snow cover, soil moisture and limited streamflow observations

Abstract. One way to improve hydrological predictions in data-sparse regions is to assimilate satellite data of water cycle components into the calibration of hydrological models. This study evaluates the value of combining satellite snow cover (MODIS) and soil moisture (ASCAT) data with limited streamflow observations to improve hydrological model calibration at the regional scale. The study compares model performance of eleven calibration variants that differ in (a) whether they use satellite data only, (b) the type and temporal distribution of streamflow observations used, and (c) whether satellite and streamflow data are combined. The streamflow sampling strategies cover two scenarios: regularly spaced observations distributed over multiple years (one value per season or per month), and event-based strategies that mimic a single short-term gauging campaign in the wettest, driest, or average year (a peak-flow event with recession plus six bimonthly background samples). The analysis is performed for 213 catchments in Austria, grouped into 119 alpine and 94 lowland catchments. The results show that calibration to satellite data only provides reliable runoff simulations primarily in lower-elevation, drier, and more agricultural lowland catchments. In alpine catchments, adding any limited streamflow data substantially improves model efficiency. The combination of monthly streamflow observations and satellite data (Vmo+sat) results in the best overall runoff performance in lowland catchments, with a median validation runoff efficiency of 0.67. In alpine catchments, event-based streamflow-only strategies achieve median validation runoff efficiencies of 0.69–0.71, close to the regular monthly variant (Vmo, median 0.75) and substantially better than satellite-only calibration (Vsat, 0.26). In lowland catchments, Vmo+sat (0.67) outperforms all event-based variants. Adding satellite data to any streamflow-based variant reduces median snow-cover errors in alpine catchments by approximately a factor of five and consistently improves the simulated soil moisture, although it can reduce runoff efficiency in alpine catchments compared to streamflow-only calibration. These results support the practical value of short, targeted gauging campaigns combined with satellite remote sensing for hydrological modeling in data-sparse regions.