Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe

Abstract

High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R>0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.