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

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.

Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

“…If the KGE metric is used, emphasizing certain aspects of a simulation is straightforward by attaching weights to the individual KGE components to reduce or increase the impact of certain errors on the overall KGE score, treating the calibration as a multi-objective problem (e.g. Gupta et al, 1998) with varying weights assigned to the three objectives. An example of the necessity of such an approach can be found in Fig.…”

Assessment of precipitation error propagation in multi-model global water resource reanalysis

“…So far there are several studies that have analyzed uncertainty in precipitation forcing and its impact on hydrologic simulations by usually evaluating hydrologic simulations based on multiple forcing applied to a single model (Falck et al, 2015;Bitew et al, 2012;Behrangi et al, 2011;Mei et al, 2016;Bhuiyan et al, 2018;Gelati et al, 2018 among others). On the other hand, there are also past studies that have evaluated the model structural uncertainty and its impact on hydrologic simulations, usually by analyzing the simulation outputs from multiple models and a single forcing dataset (Breuer et al, 2009;Haddeland et al, 2011;Gudmundsson et al, 2012;Smith et al, 2013;Huang et al, 2017;Beck et al, 2017b). However, fewer studies have been dedicated to the analysis of the integrated impact of both forcing and model uncertainty on hydrologic simulations, and from the existing ones, most of them were focused on a single hydrologic variable such as streamflow (see, for example, Qi et al 2016), evapotranspiration (Vinukollu et al, 2011), or a given hydrologic index such as the drought index (Prudhomme et al, 2014;Samaniego et al, 2017).…”

Remote Sensing of River Discharge: A Review and a Framing for the Discipline

“…Models frequently require calibration to function well, and remote sensing was seen as an early source of independent calibration/validation data for such models ( [27,28]; Gupta et al, 1998;Sivapalan et al, 2003). In fact, Gelati et al ( [29]; 2018) refer to RS data as "ideal benchmarks for spatially distributed evaluations of land surface models." As a consequence, we believe that no review of RSQ is complete without inclusion of the literature coupling hydrologic modelling and remote sensing to produce discharge.…”