Abstract

Abstract. Physically consistent descriptions of land surface hydrology are crucial for planning human activities that involve freshwater resources, especially in light of the expected climate change scenarios. 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 with 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 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 (ESA-CCI), LAI of the Global Inventory Modeling and Mapping Studies (GIMMS), and Global Runoff Data Centre (GRDC) river 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 timescale, 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 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 explained by forcing uncertainty, we suggest several research directions. These range from further investigating the discrepancies between LSMs and remote sensing retrievals to developing new model components to represent physical and anthropogenic processes.

Highlights

  • Freshwater resources are at the core of primary human needs

  • ERA-Interim reanalysis (ERA-I), P-ERA and WATCH Forcing Data ERA-Interim (WFDEI) are characterised by the same precipitation occurrences, while the intensities of individual precipitation events may differ according to the applied bias-correction (Sect. 3.1)

  • To assess the hydrological impacts of atmospheric forcing uncertainties, we compared land surface model simulations forced by several meteorological datasets against stationbased and remote sensing estimates

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Summary

Introduction

Freshwater resources are at the core of primary human needs. In particular, the production and supply of food and energy are closely interconnected with water availability and quality (Bazilian et al, 2011; Ringler et al, 2013; Lawford et al, 2013; Damerau et al, 2016). Large-scale hydrology can be simulated using several approaches, ranging from lumped water balance models to distributed global hydrological models (GHMs) and land surface models (LSMs). LSMs and GHMs are used to study a wide range of water-related problems: hydrological and agricultural droughts (Dirmeyer et al, 2006; Szczypta et al, 2012, 2014), floods (Decharme et al, 2012; Pappenberger et al, 2012; Hirpa et al, 2016), agricultural production and irrigation (Rost et al, 2009; Jägermeyr et al, 2016), and surface freshwater temperature and its impact on energy production (van Beek et al, 2012; van Vliet et al, 2012; Yearsley, 2012)

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