Water table depth assimilation in integrated terrestrial system models at the larger catchment scale

Abstract

As a vital supply of water resources for human society, groundwater plays a significant part in the water cycle, and is closely linked to precipitation, surface water and soil moisture (SM). Groundwater modelling often suffers from a variety of uncertainties, including uncertain forcing data, parameters and initial conditions. To reduce the uncertainties of model predictions, data assimilation (DA) can be used to correct model predictions with observations to improve the estimation of unknown states and parameters. To investigate the effects of assimilation of groundwater data into the integrated model Terrestrial System Modelling Platform (TSMP) on groundwater table depth (WTD) simulations, groundwater assimilation experiments were conducted for the Rur catchment in Germany. 128 ensemble members were generated by perturbing atmospheric forcing variables and saturated hydraulic conductivity, and then the measured daily groundwater data from 2018 were assimilated into the model TSMP by the Localized Ensemble Kalman Filter (LEnKF). The measured data were screened rigorously before assimilation. The spatial autocorrelation analysis of the measured groundwater data and the open loop (OL) simulations showed consistency in the spatial variability of groundwater levels between measurements and simulations. Based on the results of a spatial autocorrelation analysis, three different local radii (10 km, 5 km and 2.5 km) were selected for the assimilation experiments. Comparing the results of the OL and DA experiments, the simulated WTD bias (simulated - measured) and root mean square error (RMSE) were reduced for all DA runs compared to OL. The 10km localization radius gives the smallest RMSE at assimilation locations, with 81% RMSE reduction compared to the OL. Validation with WTD data from independent verification sites shows that localized assimilation improves groundwater simulations only when the distance to assimilated sites is smaller than 2.5km. Independent WTD validation showed a reduction in RMSE of 30% and the best results were from the DA run with 10 km radius. Also soil moisture measurements from the Cosmic Ray Neutron Sensor (CRNS) were used for validation. The simulated SM reproduced the observed temporal fluctuations, with a high correlation between measured and simulated SM (from 0.70 to 0.89, except for the Wuestebach site). However, there was no RMSE reduction of SM for the DA runs compared to the OL.