Alberto Martínez-De la Torre

Hydrological and land-surface simulations depend on precipitation forcing and model structure, yet where and to what extent each controls the spread among simulations remains poorly understood. Here, we spatially attribute this spread to the two sources. We evaluate global simulations from four large-scale models (HTESSEL, JULES, mHM, and PCRGLOBWB), each driven by four precipitation forcing datasets (EM-Earth, ERA5-Land, MSWEP, and W5E5) over 1981–2019, against reference datasets of streamflow, latent heat, runoff, and terrestrial water storage, and assess their water-balance consistency. Relative to ERA5-Land, regional mean precipitation is 26–27% higher over Africa for EM-Earth and W5E5 and 15% lower over Asia for MSWEP. Water-balance closure differs markedly between regions. The largest deficit occurs in Africa, where reference evapotranspiration and runoff together exceed EM-Earth precipitation by 42% (regional median), and the largest surpluses occur in Asia and Europe, where up to 12% of precipitation is not accounted for by evapotranspiration and runoff. Median runoff elasticities to precipitation of 1.77–2.10 across the four models indicate that runoff changes proportionally more than precipitation. We then partition the spread among simulations, our measure of uncertainty, into model and forcing contributions. Model choice dominates across most of North America, Australia and southern Africa, whereas forcing choice dominates over the Sahara, the Arabian Peninsula and the high-latitude belt spanning Scandinavia and Siberia, consistent with the larger disagreement among precipitation products in arid and high-latitude regions. These regimes show where the choice of precipitation forcing or model matters most, and hence where further evaluation is most informative. Plain Language Summary Global hydrological and land-surface models estimate how precipitation (rain and snow) over land is partitioned into evaporation, runoff, and soil and groundwater storage. These estimates depend on both the model and the precipitation dataset that drives it, and available precipitation datasets disagree considerably in some regions, with some giving over a quarter more precipitation than others over Africa. We combined four such models with four precipitation datasets and compared the sixteen resulting simulations of river flow, evaporation, runoff and water storage with measurements at 1445 river gauges and with satellite-based estimates. Runoff responds more strongly than precipitation, so a 10% change in precipitation changes simulated runoff by roughly 18 to 21%. We then mapped where differences between simulations arise mainly from model choice and where they arise mainly from precipitation dataset choice. The model matters most over much of North America, Australia and southern Africa, whereas the precipitation dataset matters most over the Sahara, the Arabian Peninsula, Scandinavia and Siberia. These maps indicate where better models or better precipitation data would most change land-water-cycle estimates.

Hydrological droughts can last months to years and impact large areas, leading to a multitude of ecological and socio‐economic harm. The role of evapotranspiration (ET) in drought is very variable and there is contradicting evidence on the impact of anthropogenic warming on groundwater drought in the UK. We integrated a distributed groundwater model into the JULES land surface model (JULES‐DGW) and simulated a chalk catchment in southern England over the period 1901–2015. The model showed a good match to river flows (Kling–Gupta efficiencies 0.73–0.83) and groundwater levels ( r ² = 0.92). We found a general trend of drying over time with small decreases in average moisture in the unsaturated zone and average groundwater levels, caused by increases in annual ET and decreases in recharge as a fraction of precipitation. The model suggests drier conditions in the unsaturated zone in late summer/early autumn in the late 20th–early 21st century have led to a delay in the recharge season. No increase in capillary rise was simulated throughout the modelling period and ET was found to decrease in most cases of severe drought, thus acting to limit the fall in groundwater levels.

Long-lasting droughts have become more common worldwide in recent decades, such as in Australia (2001-2009), California (2012-2014), Chile (2010-2023), and Europe (2018-2022). The combination of droughts and heatwaves has led to intense flash droughts, worsening soil moisture deficits. This has resulted in global shortages of essential food, serious public health issues, and prolonged forest fires that harm air quality in populated areas. Extended droughts also contribute to food insecurity, reduced energy production, increased health crises, and the destruction of natural landscapes, causing significant economic setbacks in various regions. International agencies, such as the WMO, and water authorities are actively promoting the advancement of seasonal soil moisture monitoring and forecasting systems. In this presentation, we'll give you an update on ULYSSES [2], the global multi-model hydrological seasonal predictions system supported by the Copernicus Climate Change Service. This fully operational system runs directly at the ECMWF's HPC and aims to be the first seamless multi-model hydrological seasonal prediction system with global coverage at a spatial resolution of 0.1 degrees.The ULYSSES modeling chain builds on the successful EDgE proof of concept [3], employing four advanced hydrological models (Jules, HTESSEL, mHM, PCR-GLOBWB). Notably, this production chain features a distinctive aspect: the utilization of a standard set of physiographical datasets (e.g., DEM, soil properties) with consistent spatio-temporal resolutions and similar forecast inputs for all hydrological models, as well as the same multi-scale routing model (mRM). The seasonal forecasts are initialized using the ERA5-land product from ECMWF. The Equitable Thread Score (ETS) skill is employed to assess the ensemble forecasting abilities for drought events, specifically when soil moisture exceeds 80% of the time, across lead times ranging from one to three months.In a recent assessment, the global ensemble Equitable Thread Score (ETS) for the system stands at 63%, 43%, and 34% for lead times ranging from 1 to 3 months. Notably, over Europe, the ensemble ETS is significantly higher, reaching 91%, 71%, and 61% for the corresponding lead times. Contrasting these findings with a prior study that employed the mHM initialized with E-OBS forcing and the NMME ensemble over Europe [4], our analysis suggests potential reasons for the diminished performance of the current system. These factors may include: 1) the meteorological forcings utilized for initializing the hydrological models, and/or 2) the skill level of the NWF model ensemble. In this study, we will present the sensitivity of ETS when one of the models (mHM) is initialized with different available forcings procucts available such as EM-EARTH, MSWEP, WE5E, and E-OBS. Finding of this study is key for the further improvement of the system.References[1] https://doi.org/10.1029/2021EF002394 [2] https://www.ufz.de/ulysses [3] https://doi.org/10.1175/BAMS-D-17-0274.1 [4] https://doi.org/10.1175/JHM-D-12-075.1 [5] https://doi.org/10.1175/JHM-D-19-0095.1

Wetlands play a key role in hydrological and biogeochemical cycles and provide multiple ecosystem services to society. However, reliable data on the extent of global inundated areas and the magnitude of their contribution to local hydrological dynamics remain surprisingly uncertain. Global hydrological models and land surface models (LSMs) include only the most major inundation sources and mechanisms; therefore, quantifying the uncertainties in available data sources remains a challenge. We address these problems by taking a leading global data product on inundation extents (Global Inundation Extent from Multi-Satellites, GIEMS) and matching against predictions from a global hydrodynamic model (Catchment-based Macro-scale Floodplain – CaMa-Flood) driven by runoff data generated by a land surface model (Joint UK Land and Environment Simulator, JULES). The ability of the model to reproduce patterns and dynamics shown by the observational product is assessed in a number of case studies across the tropics, which show that it performs well in large wetland regions, with a good match between corresponding seasonal cycles. At a finer spatial scale, we found that water inputs (e.g. groundwater inflow to wetland) became underestimated in comparison to water outputs (e.g. infiltration and evaporation from wetland) in some wetlands (e.g. Sudd, Tonlé Sap), and the opposite occurred in others (e.g. Okavango) in our model predictions. We also found evidence for an underestimation of low levels of inundation in our satellite-based inundation data (approx. 10 % of total inundation may not be recorded). Additionally, some wetlands display a clear spatial displacement between observed and simulated inundation as a result of overestimation or underestimation of overbank flooding upstream. This study provides timely information on inherent biases in inundation prediction and observation that can contribute to our current ability to make critical predictions of inundation events at both regional and global levels.

It is a well-known fact that multi-model forecast systems provide greater reliability over single-model systems, as hydrological models have a solid contribution to forecast uncertainty [1]. Yet many prevalent skill scores for verification of forecasting systems are calculated relative to a benchmark skill. Benchmark skills across hydrological models could vary largely because the benchmark simulation is biased. This bias is not accounted for in the benchmark skill. For example, hydrological models with a high auto-correlation in their state variables tend to have increased skill but do not necessarily have the lowest error when compared against observations [2]. Thus, the correct interpretation of skill can only be conducted if the models are compared against the ground truth, i.e. observations.With this outlook, we assess the performance of ULYSSES [3] - the first seamless global multi-model hydrological seasonal prediction system. Using four state-of-the-art hydrological models (Jules, HTESSEL, mHM, and PCR-GLOBWB), the production chain utilizes identical land surface datasets (e.g. DEM, soil properties) and forecast inputs for all HMs, and the same river routing scheme (i.e., the multi-scale Routing Model; mRM). The system initializes based on the ERA5-Land dataset, and the seasonal forecasts are driven by a 51-member ensemble generated by the ECMWF seasonal forecasting system 5.The skill assessment includes the verification of seasonal streamflow forecast at 2400+ GRDC gauges distributed globally during the period from 1993 to 2019, at a monthly time scale. The set of skill scores considered includes metrics concerning monthly observations (Kling-Gupta efficiency skill score, i.e., KGESS, KGE components, Equitable Threat Score for droughts, relative bias), skills with reference to benchmark run (CRPSS) and skills on forecast characteristic (forecast extremity, spread). On average, the system was found to have the skill (monthly KGESS) at most for two months. At the lead of 1 month, mHM exhibits KGESS of 0.56, HTESSEL has KGESS of 0.5, Jules 0.48, and PCR-GLOBWB 0.46. A KGESS value of one corresponds to a perfect forecast. Evaluating the median KGE r component (or Pearson's correlation), mHM (0.68), PCR-GLOBWB (0.59), HTESSEL (0.59) and Jules (0.57). The percentage of gauges with positive KGESS is distributed evenly with PCR-GLOBWB (84.9 %), HTESSEL (84.2 %), Jules (82.4 %) and mHM (80.5 %). Model performances over median skill and gauges with positive skill indicates models to have contrasting performance at high and low skill gauges. Besides, the spatial distribution of KGESS shows marked seasonal changes in the skill of the hydrological models. All of this provides insights on the strengths and weaknesses of the models for further improvement of the system.In the future, the skill assessment would be expanded to additionally compare forecasted fluxes and state variables (e.g., terrestrial water storage anomalies, soil moisture) against other observations such as GRACE, SMOS, etc. All ULYSSES outputs will be made available in the Copernicus Climate Data Store [4] and will be open access. We aim to engage institutions and researchers around the world that are willing to evaluate the forecasts model performance to improve the system in the future.[1] https://doi.org/10.1175/BAMS-D-17-0274.1[2] https://doi.org/10.1175/JHM-D-18-0040.1[3] https://www.ufz.de/ulysses[4] https://cds.climate.copernicus.eu

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

Wetlands play a key role in hydrological and biogeochemical cycles and provide multiple ecosystem services to society. However, reliable data on the extent of global inundated areas and the magnitude of their contribution to local hydrological dynamics remain surprisingly uncertain. Global hydrological models and Land Surface Models (LSMs) include only the most major inundation sources and mechanisms, therefore quantifying the uncertainties in available data sources remains a challenge. We address these problems by taking a leading global data product on inundation extents (GIEMS) and matching against predictions from a sophisticated global hydrodynamic model (CaMa-Flood) that uses runoff data generated from the JULES land surface model. The ability of the model to reproduce patterns and dynamics showed by the observational product is assessed in a number of case studies across the tropics (including the Sudd, Pantanal, Congo and Amazon), which show that it performs well in large wetland regions, with a good match between corresponding seasonal cycles. However, at finer spatial scale, water inputs (e.g. groundwater inflow to wetland) may become underestimated in comparison to water outputs (e.g. infiltration and evaporation from wetland); or the opposite may occur, depending on the wetland concerned. Additionally, some wetlands display a clear spatial displacement between observed and simulated inundation as a result of over- or under-estimation of overbank flooding upstream. This study provides timely data that can contribute to our current ability to make critical predictions of inundation events at both regional and global levels.

Wetlands play a key role in hydrological and biogeochemical cycles and provide multiple ecosystem services to society. However, reliable data on the extent of global inundated areas and the magnitude of their contribution to local hydrological dynamics remain surprisingly uncertain. Global hydrological models and Land Surface Models (LSMs) include only the most major inundation sources and mechanisms, therefore quantifying the uncertainties in available data sources remains a challenge. We address these problems by taking a leading global data product on inundation extents (GIEMS) and matching against predictions from a sophisticated global hydrodynamic model (CaMa-Flood) that uses runoff data generated from the JULES land surface model. The ability of the model to reproduce patterns and dynamics showed by the observational product is assessed in a number of case studies across the tropics (including the Sudd, Pantanal, Congo and Amazon), which show that it performs well in large wetland regions, with a good match between corresponding seasonal cycles. However, at finer spatial scale, water inputs (e.g. groundwater inflow to wetland) may become underestimated in comparison to water outputs (e.g. infiltration and evaporation from wetland); or the opposite may occur, depending on the wetland concerned. Additionally, some wetlands display a clear spatial displacement between observed and simulated inundation as a result of over- or under-estimation of overbank flooding upstream. This study provides timely data that can contribute to our current ability to make critical predictions of inundation events at both regional and global levels.

Wetlands play a key role in hydrological and biogeochemical cycles and provide multiple ecosystem services to society. However, reliable data on the extent of global inundated areas and the magnitude of their contribution to local hydrological dynamics remain surprisingly uncertain. Global hydrological models and Land Surface Models (LSMs) include only the most major inundation sources and mechanisms, therefore quantifying the uncertainties in available data sources remains a challenge. We address these problems by taking a leading global data product on inundation extents (GIEMS) and matching against predictions from a sophisticated global hydrodynamic model (CaMa-Flood) that uses runoff data generated from the JULES land surface model. The ability of the model to reproduce patterns and dynamics showed by the observational product is assessed in a number of case studies across the tropics (including the Sudd, Pantanal, Congo and Amazon), which show that it performs well in large wetland regions, with a good match between corresponding seasonal cycles. However, at finer spatial scale, water inputs (e.g. groundwater inflow to wetland) may become underestimated in comparison to water outputs (e.g. infiltration and evaporation from wetland); or the opposite may occur, depending on the wetland concerned. Additionally, some wetlands display a clear spatial displacement between observed and simulated inundation as a result of over- or under-estimation of overbank flooding upstream. This study provides timely data that can contribute to our current ability to make critical predictions of inundation events at both regional and global levels.