Multivariate evaluation of four high-resolution hydrological models at&#160; global scale
Authors: Samaniego Luis , Rakovec Oldrich , Martinez de la Torre Alberto , Sutanudjaja Edwin , Shrestha Pallav K. , Blyth Eleanor , Wanders Niko , Kelbling Matthias , Thober Stephan 
Published: March 2022

&lt;p&gt;Global hydrological models (GHMs) are a fundamental component of the Earth&amp;#160;System Modeling &amp;#160;initiative that aims to realize a Digital Twin in the&amp;#160;next five to ten years [1]. Recent model evaluations of the state-of-the-art&amp;#160;global hydrological models [2,3], however, indicate that existing models have&amp;#160;several deficiencies that lead to poor model efficiencies of key&amp;#160;terrestrial environmental variables such as runoff, evapotranspiration, and&amp;#160;soil moisture, among others.&amp;#160;&lt;/p&gt;&lt;p&gt;In this study, we evaluate four hydrological models: JULES, HTESSEL,&amp;#160;mHM, and PCR-GLOBWB. These models are part of the modelling chain of the&amp;#160;Copernicus Climate Change Service project ULYSSES [4], which aims to&amp;#160;deliver global operational hydrological forecasts at a spatial&amp;#160;resolution of 0.1$^\circ$. The operational service started in July 2020&amp;#160;and the data will be provided every month through the Copernicus Data&amp;#160;Store.&lt;/p&gt;&lt;p&gt;The initial conditions of the GHMs for the hindcast skill assessment are&amp;#160;obtained with the ERA5-land reanalysis [5]. This global dataset provides&amp;#160;meteorological forcings (e.g., precipitation and temperature) since 1950&amp;#160;with daily time steps. For this reason, historical simulations of&amp;#160;streamflow, obtained with these GHMs from 1981 until 2020 will be&amp;#160;cross-evaluated against observed streamflow provided by 2850 GRDC&amp;#160;gauging stations. Simulations of evapotranspiration and terrestrial&amp;#160;water storage anomalies were evaluated against GRACE and FLUXNET&amp;#160;datasets, respectively.&lt;/p&gt;&lt;p&gt;During the model calibration phase, models were evaluated in a&amp;#160;stratified sample of size 120 basins (i.e., considering hydroclimatic&amp;#160;regions and locations around the world). The results of the evaluation&amp;#160;indicate that the median value of the Nash-Sutcliffe efficiency obtained&amp;#160;with daily streamflow for these models varies from 0.20 to 0.50. The&amp;#160;mean Kling-Gupta efficiency (KGE) metric ranges from 0.45 to 0.63. The&amp;#160;maximum KGE value corresponds to the mHM model, while the other models&amp;#160;are clustered around 0.45.&lt;/p&gt;&lt;p&gt;This result alone is quite promising considering the results presented&amp;#160;in Beck et al. [2]. One reason for these good results is the relation&amp;#160;between the standardization of the input data sets and the common&amp;#160;routing model (mRM [6]) with a very detailed river network [7]. The&amp;#160;considerable difference in performance between mHM and the other GHMs&amp;#160;can be attributed to the parameterization of the models and model&amp;#160;structure. mHM is the only GHM that employs the MPR technique [8] and&amp;#160;includes fast and slow interflow components. Evaluation metrics obtained&amp;#160;with the ILAMB [8] tool indicate that all models have exhibited&amp;#160;satisfactory efficiencies (&gt; 0.5 variable score) for monthly&amp;#160;climatologies of latent heat, evapotranspiration and runoff. mHM, JULES,&amp;#160;and PCR-GLOBWB, perform relatively well, representing the terrestrial&amp;#160;water storage anomaly, although any of these models have explicit a&amp;#160;detailed representation of the groundwater aquifers.&lt;/p&gt;&lt;p&gt;In this presentation, specific results of the model cross-validation,&amp;#160;per geographic region will be presented. Finally, recommendations for&amp;#160;further GHM model improvement will be discussed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;:&lt;br&gt;[1] Bauer, P. et al. https://doi.org/10.1038/s43588-021-00023-0 &amp;#160;2021&lt;br&gt;[2] Beck, H.E., et al. &amp;#160;https://doi.org/10.1002/2015WR018247, 2016.&lt;br&gt;[3] Harrigan, S et al. https://doi.org/doi:10.5194/essd-12-2043-2020 2020.&amp;#160;&lt;br&gt;[4] https://www.ufz.de/ulysses, ECMWF/COPERNICUS/2019/C3S\_432\_Lot3\_UFZ&lt;br&gt;[5] https://www.ecmwf.int/en/era5-land&lt;br&gt;[6] Thober et al. https://doi.org/10.5194/gmd-12-2501-2019, 2019&lt;br&gt;[7] http://hydro.iis.u-tokyo.ac.jp/~yamadai/cama-flood/index.html&lt;br&gt;[8] Samaniego et al. https://doi.org/10.1029/2008WR007327, 2010&lt;br&gt;[9] https://www.ilamb.org.&amp;#160;&lt;/p&gt;