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. Physically consistent descriptions of land surface hydrology arecrucial for planning human activities that involve freshwater resources,especially in light of the expected climate change scenarios. We assess howatmospheric forcing data uncertainties affect land surface model (LSM)simulations by means of an extensive evaluation exercise using a number ofstate-of-the-art remote sensing and station-based datasets. For thispurpose, we use the CO 2 -responsive ISBA-A-gs LSM coupled with the CNRM version ofthe Total Runoff Integrated Pathways (CTRIP) river routing model. We performmulti-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 atmosphericdatasets. Three of them are based on the ERA-Interim reanalysis (ERA-I). Thefourth dataset is independent from ERA-Interim: PGF, developed at PrincetonUniversity. The hydrological impacts of atmospheric forcing uncertaintiesare assessed by comparing simulated surface soil moisture (SSM), leaf areaindex (LAI) and river discharge against observation-based datasets: SSM fromthe European Space Agency's Water Cycle Multi-mission Observation Strategyand Climate Change Initiative projects (ESA-CCI), LAI of the GlobalInventory Modeling and Mapping Studies (GIMMS), and Global Runoff DataCentre (GRDC) river discharge. The atmospheric forcing data are alsocompared to reference datasets. Precipitation is the most uncertain forcingvariable across datasets, while the most consistent are air temperature andSW and LW radiation. At the monthly timescale, SSM and LAI simulations arerelatively insensitive to forcing uncertainties. Some discrepancies withESA-CCI appear to be forcing-independent and may be due to differentassumptions underlying the LSM and the remote sensing retrieval algorithm.All simulations overestimate average summer and early-autumn LAI. Forcinguncertainty impacts on simulated river discharge are larger on mean valuesand standard deviations than on correlations with GRDC data. Anomalycorrelation coefficients are not inferior to those computed from raw monthlydischarge time series, indicating that the model reproduces inter-annualvariability fairly well. However, simulated river discharge time seriesgenerally feature larger variability compared to measurements. They alsotend to overestimate winter–spring high flows and underestimatesummer–autumn low flows. Considering that several differences emerge betweensimulations and reference data, which may not be completely explained byforcing uncertainty, we suggest several research directions. These rangefrom further investigating the discrepancies between LSMs and remote sensingretrievals to developing new model components to represent physical andanthropogenic processes.

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