Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

The understanding of land surface hydrology is critical for planning human activities involving freshwater 10 resources. We assess how atmospheric forcing data uncertainties affect land surface model (LSM) simulations by means of an extensive evaluation exercise using a number of state-of-the-art remote sensing and station-based datasets. For this purpose, we use the CO2-responsive ISBA-A-gs LSM coupled the CNRM version of the Total Runoff Integrated Pathways (CTRIP) river routing model. We perform multi-forcing simulations over the Euro-Mediterranean area (25°–75.5°N, 11.5°W–62.5°E, at 0.5° resolution) from 1979 to 2012. The model is forced using four atmospheric datasets. Three of them 15 are based on the ERA-Interim reanalysis (ERA-I). The fourth dataset is independent from ERA-Interim: PGF, developed at Princeton University. The hydrological impacts of atmospheric forcing uncertainties are assessed by comparing simulated surface soil moisture (SSM), leaf area index (LAI) and river discharge against observation-based datasets: SSM from the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects (ESACCI); LAI of the Global Inventory Modeling and Mapping Studies (GIMMS); and Global Runoff Data Centre (GRDC) river 20 discharge. The atmospheric forcing data are also compared to reference datasets. Precipitation is the most uncertain forcing variable across datasets, while the most consistent are air temperature, and SW and LW radiation. At the monthly time scale, SSM and LAI simulations are relatively insensitive to forcing uncertainties. Some discrepancies with ESA-CCI appear to be forcing-independent and may be due to different assumptions underlying the LSM and the remote sensing retrieval algorithm. All simulations overestimate average summer and early autumn LAI. Forcing uncertainty impacts on simulated 25 river discharge are larger on mean values and standard deviations than on correlations with GRDC data. Anomaly correlation coefficients are not inferior to those computed from raw monthly discharge time series, indicating that the model reproduces inter-annual variability fairly well. However, simulated river discharge time series generally feature larger variability compared to measurements. They also tend to overestimate winter-spring high flows and underestimate summer-autumn low flows. Considering that several differences emerge between simulations and reference data, which may not be completely 30 explained by forcing uncertainty, we suggest several research directions. These range from further investigating the Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-341 Manuscript under review for journal Hydrol. Earth Syst. Sci. Discussion started: 7 August 2017 c © Author(s) 2017. CC BY 4.0 License.