Abstract
Abstract. This study focuses on the Iberian Peninsula and investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period spanning 11 years (2000–2010). To simulate the hydrological variables of surface runoff, subsurface runoff, and evapotranspiration, we used four land surface models (LSMs) – JULES (Joint UK Land Environment Simulator), ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), SURFEX (Surface Externalisée), and HTESSEL (Hydrology – Tiled European Centre for Medium-Range Weather Forecasts – ECMWF – Scheme for Surface Exchanges over Land) – and one global hydrological model, WaterGAP3 (Water – a Global Assessment and Prognosis). Simulations were carried out for five precipitation products – CMORPH (the Climate Prediction Center Morphing technique of the National Oceanic and Atmospheric Administration, or NOAA), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), 3B42V(7), ECMWF reanalysis, and a machine-learning-based blended product. As a reference, we used a ground-based observation-driven precipitation dataset, named SAFRAN, available at 5 km, 1 h resolution. We present relative performances of hydrologic variables for the different multi-model and multi-forcing scenarios. Overall, results reveal the complexity of the interaction between precipitation characteristics and different modeling schemes and show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure. Surface runoff is strongly sensitive to precipitation uncertainty, and the degree of sensitivity depends significantly on the runoff generation scheme of each model examined. Evapotranspiration fluxes are comparatively less sensitive for this study region. Finally, our results suggest that there is no single model–forcing combination that can outperform all others consistently for all variables examined and thus reinforce the fact that there are significant benefits to exploring different model structures as part of the overall modeling approaches used for water resource applications.
Highlights
Improved estimation of global precipitation is important to the analysis of continental water resources and dynamics
Examination of SAFRAN-based annual average values of surface runoff shows that WaterGAP3 estimates considerably higher surface runoff than the rest of the models, in the northern and northwestern part of the study area (Fig. 3)
Subsurface runoff (Fig. 4) and evapotranspiration (Fig. 5) from WaterGAP3 were lower in that part of the study area
Summary
Improved estimation of global precipitation is important to the analysis of continental water resources and dynamics. Over the past few decades, several studies have described the use of different precipitation algorithms to develop precipitation products (http://ipwg.isac.cnr.it/algorithms.html, last access: 31 March 2019 and http://reanalyses.org, last access: 31 March 2019) at high spatial and temporal resolution on a quasi-global scale and for different hydrological applications, such as flood early warning and control and drought monitoring Ehsan Bhuiyan et al.: Multi-parameter water resource reanalysis uncertainty characterization et al, 2012, amongst others). Precipitation estimates suffer, from various sources of error that impact hydrologic investigations (Mei et al, 2015, 2016; Seyyedi et al, 2014, 2015; Bhuiyan et al, 2017; Nikolopoulos et al, 2013)
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