[A-HW19] Hydrology & Water Environment

[AHW19-12] A coupled data assimilation framework for an integrated surface and subsurface hydrological model: development and examples

*Qi Tang^1,2, Hugo Delottier¹, Oliver S. Schilling^2,3, Wolfgang Kurtz⁴, Philip Brunner¹ (1.Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchatel, Switzerland, 2.Hydrogeology, Department of Environmental Sciences, University of Basel, Basel, Switzerland, 3.Eawag, Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland, 4.Agrometeorology, Branch Office Weihenstephan, German Meteorological Service, Freising, Germany)

Keywords:integrated hydrologic modeling, data assimilation

This study describes an ensemble-based data assimilation (DA) system which is developed for an integrated surface and subsurface hydrologic model. The software environment for DA is the Parallel Data Assimilation Framework (PDAF) which provides various assimilation algorithms like the ensemble Kalman filters, nonlinear filters, 3D-var and a combination among them. The integrated hydrologic model used in this study is the HydroGeoSphere (HGS) (Brunner & Simmons, 2012), which is a physically based modeling platform used to simulate surface water and variably saturated groundwater flow as well as solute transport. The coupled DA system utilizes piezometric heads and soil moisture as well as noble gas concentrations (²²²Rn, ³⁷Ar, and ⁴He) observations. The directly updated variables by DA include the model simulated state (e.g. hydraulic heads, water saturation and tracer concentrations) and the model parameters (e.g. hydraulic conductivity). These observations and model variables can be assimilated and updated separately or jointly by DA. We tested the performance of the developed DA system using a synthetic alluvial plain model (Delottier et al., 2022). Results were evaluated for the estimated model variables by comparing them with independent observation data between the assimilation runs and the open loop run where no data assimilation was conducted. Results from the data assimilation demonstrate that the model states are reasonably constrained especially during the pumping period.
References
Brunner, P., and Simmons, C. T. (2012). HydroGeoSphere: a fully integrated, physically based hydrological model. Groundwater 50, 170–176. doi: 10.1111/j.1745-6584.2011.00882.x
Delottier H., Peel M., Musy S., Schilling O.S, Purtschert R. and Brunner P. (2022). Explicit simulation of environmental gas tracers with integrated surface and subsurface hydrological models. Frontiers in Water. 154.