Towards a robust parameterization of subsurface stormflow in hydrological models at the catchment scale

In addition to overland flow, subsurface stormflow (SSF) can play a major role for runoff generation during certain events. Even though SSF is a well-recognised process, there is a lack of systematic studies on SSF, partly because it is very difficult to quantify. In hydrological modelling, this can lead to an unclear distinction of SSF from other processes. If parameters that describe SSF processes or thresholds are implemented in hydrological models, they are often used as fitting parameters and can contribute to overall model uncertainty. So far, there has been no systematic benchmarking of SSF routines in hydrological models.

This presentation discusses how we plan to address this research gap. In order to address the inconsistency of SSF representation in hydrological models, different existing lumped hydrological models will be set up for four study sites located in the Alps, Ore Mountains, Black Forest and Sauerland. In a first step, different models will be calibrated using only basic data like discharge observations, climate data and readily available geodata. Differences in SSF simulations will be detected and quantified and the models will be benchmarked regarding the simulation of SSF dynamics and associated uncertainties. In a next step, we will include new experimental data on SSF derived at the four study sites in the calibration of the lumped hydrological models by a multi-objective calibration and evaluation framework. In order to consider SSF observations collected at scales different than the scale of model application, new SSF metrics will be developed. Testing different combinations of these metrics for model calibration it will be possible to state which SSF proxies can lead to the most productive improvement of SSF simulations.

Identifying current weaknesses in SSF representation of current models, and providing directions for improving them by including the most beneficial SSF metrics, this project will show potential for the improvement of SSF simulations through SSF data collection. In a final application of the most reliable SSF simulations to all study sites, we will show the impact of extreme wet or extreme dry conditions on SSF occurrence and SSF volumes.