Hydrological model

GloFAS v5 renalysis and forecasts are generated using OS LISFLOOD, a physically based spatially distributed rainfall-runoff-routing hydrological model. OS LISFLOOD hydrological modelling suite, including a set of tools for model set-up, parameter calibration, and results post-processing is described in Grimaldi et al.¹ [in preparation].  OS LISFLOOD model documentation includes detailed technical information about modelled physical processes, while the OS LISFLOOD user guide provides step-by-step instructions on how to set-up a simulation.

Specifically, GloFAS v5 benefits of the improvements of OS LISFLOOD v5.0.0, an upgraded version compared to OS LISFLOOD v4.1.1, which was used to produce GloFAS v4.

The main novelties of OS LISFLOOD v5.0.0 modelling approach compared to LISFLOODv4.1.3 are:

• Diffusive routing for the modelling of channel flow. Up to OS LISFLOOD v5.0.0, modelling of river routing was based on the kinematic wave approximation. The kinematic wave approximation is a computationally efficient numerical solution which is well suited for medium to high slope rivers (river bed slope > 0.001). However, it does not include diffusion effects, and this can lead to inaccuracies in the modelling of mild to low slope rivers (river slope < 0.001), with overestimation of flood peak magnitude and underestimation of flood peak arrival time. To improve modelling accuracy in mild to low slope rivers, LISFLOOD v5.0.0 implements the Muskingum-Cunge-Todini (MCT) wave routing method (Todini et al. 2007²), according to the solution presented in Reggiani et al. (2016)³. MCT wave routing method accounts for both the convection and diffusion of the flood wave, it is simple and accurate enough to be suited for large-scale hydrologic modelling applications.
• An enhanced routine for the modelling of reservoirs. The study Benchmarking reservoir operation schemes for large-scale hydrological models by Casado-Rodríguez et al. (2026)⁴ demonstrated that a slightly adapted version of Hanasaki et al. (2022)⁵ method for reservoir modelling allows an adequate trade-off between model accuracy and data requirements. While in the previous OS LISFLOOD routine (based on Burek et al. 2013⁵) reservoir outflow was computed solely as a function of reservoir storage, in the updated approach, reservoir outflow depends on both reservoir storage and inflow. 
• A revised strategy for the initialization of soil moisture state variables. The novel analytical solution for the computation of soil moisture initial states ensures robust interactions between hydrological states and fluxes, in all climates and pedological contexts. This improvement eliminates the spurious decreasing soil moisture trends observed in the deep soil layer of some areas in GloFASv4 release. 
• A corrected computation of snow melting at very high altitudes. Excessive, non-realistically indefinite increase of snow cover at very high latitude (as observed in some pixels of GloFAsv4) is avoided by promoting snow ablation. 
• An adapted management of water abstraction from groundwater. OS LISFLOOD currently does not impose restrictions to groundwater abstraction, neither models horizontal groundwater fluxes. These model limitations can induce spurious return flow values in arid areas where human abstraction mainly (or totally) relies on groundwater resources. To avoid this inconvenience, water abstraction from groundwater is limited to the water consumptive use. 

More details on each improvement are available from the above mentioned OS LISFLOOD model documentation.

OS LISFLOOD simulations can be accomplished at the desired spatial resolution (from m to km) and temporal resolution (from hourly to daily).

GloFAS v5 OS LISFLOOD simulations have 0.05 degrees (or 3 arcmin, ~ 5km at the Equator) spatial resolution, which is the same resolution as GloFAS v4.

Consistently with all the previous GloFAS versions, the computations for GloFAS v5 are completed with daily time steps for the general hydrological processes. River routing is modelled using internal 3-hourly computing steps (GloFAs v4 used 4-hourly steps), with outputs being successively aggregated to daily steps. All the output variables are available with daily resolution.

Implementation

Static maps

Accurate representation of rainfall-runoff-routing processes in different climatic and socio-economic contexts requires a set of maps showing the morphological, pedological, vegetation, land cover, land and water use characteristics of the catchments. Nearly 100 implementation maps are required as input to OS LISFLOOD. This set of implementation maps is referred to as “static maps” or “surface fields dataset”, as described in Choulga et al. 2024⁷.

Detailed information on the use, features, and preparation of each map is available from a dedicated section of OS LISFLOOD user guide: OS LISFLOOD User Guide – Static maps.

GloFAS v5 static maps have 0.05 degrees (or 3 arcmin, ~ 5km at the Equator) spatial resolution, the same resolution of GloFASv4 static maps.

The main improvements of GloFAS v5 static maps compared to the previous release are listed below.

• A revised definition of maximum soil depth value, for higher consistency with OS LISFLOOD modelling approach. Specifically, the maximum soil depth was defined as the shallowest level between bedrock and groundwater table depth. In the former implementation, the maximum soil depth value always extended to bedrock. Water table depth was derived from Fan et al. (2013)⁸ . The updated definition generally affected the thickness of the third soil layer. Similarly to previous versions, the thickness of the first soil layer was 5 cm and of the second soil layer extended to root depth.
• Soil hydraulic properties were update to account for the revised definition of maximum soil depth and making use of ISRIC SoilGrids250m v2.0 dataset.
• The land cover fraction of irrigated areas was updated according to the FAO "Global Map of Irrigation Areas" (Siebert et al., 2013⁹). This dataset shows the amount of area equipped for irrigation in percentage of the total area with a resolution of 5 minutes, and it was deemed better suited to OS LISFLOOD applications compared to the formerly used modelled data (Spatial Production Allocation Model).
• Reservoirs dataset and metadata were substantially reviewed and expanded leveraging on openly available datasets:  the Global Reservoir and Dams (GRanD¹⁰), the Global Dam Watch (GDW¹¹, which in fact constitutes the updated version of GRanD), and the Global Lakes and Wetlands Database (GLWD¹²), the latter being a legacy from GloFAS v4 . GloFAS v5 reservoir dataset includes reservoirs with storage capacity ≥100 hm3, catchment area ≥500 km2, and Degree of Regulation ≥0.8. The total number is 1486 (an increase of +799 compared to GloFAS v4), representing over 80% of the global total storage (an increase of over 20% compared to GloFASv4).
• Lakes dataset and metadata were substantially reviewed and expanded leveraging on openly available datasets: mostly HydroLAKES¹³, followed by GLWD, and GDW (reservoirs with Degree Of Regulation < 0.8 were modeled as lakes).  The GloFAS v5 lake dataset includes lakes with volume ≥100 hm3, catchment area ≥500 km2, surface area ≥50 km2. The total number is 1230 (an increase of +767 compared to GloFASv4).
• The temporal coverage of sectoral water demand maps for domestic, livestock, industrial, energy (cooling) use was extended until 31/12/2023 (GloFAS v4 dataset stopped on 31/12/2019). Furthermore, the entire dataset (01/01/1975-31/12/2023) was re-generated to leverage on updated versions of the source data. 
• Maps of groundwater bodies, fraction of groundwater use and non-conventional water use were updated according to the most recent information made available from FAO AQUASTAT.

All GloFASv5 static maps are available for download from the JRC data catalogue.

Meteorological forcings

The meteorological variables used by GloFAS v5 are total precipitation, 2-metre temperature, 2-metre dew temperature, 10-metre U wind component, 10-metre V wind component, surface solar radiation downwards, surface thermal radiation. These variables are provided as input to GloFAS from different numerical weather prediction (NWP) datasets, depending on the purpose of the model simulation: hydrological reanalysis, medium range forecast, seasonal forecast. The list of meteorological datasets used for each purpose is available from GloFAS meteorological forcings - Copernicus Emergency Management Service - CEMS - ECMWF Confluence Wiki.

NWPs are upsampled from their native resolution to 0.05 degrres resolution using bilinear delaunay interpolation scheme, as implemented in the OS LISFLOOD pre-processor pyg2p (it is here noted that GloFASv4 used bilinear interpolation).

Reference values of evapotranspiration are computed following the Penmann-Monteith method, which is implemented in the OS LISFLOOD pre-processor LISVAP.

GloFAS v5 reanalysis dataset benefits of an amended version of C3S ERA5: spurious high-intensity rainfall values at a single grid point that are not supported by surrounding points (Hersbach et al., 2020¹⁴ ) were removed following the methodology explained in Mastrantonas et al. Met. App [submitted]¹⁵, as implemented in the OS LISFLOOD utility rainbomb.

References

¹   Grimaldi S., Russo C., Mazzetti C., Carton De Wiart C., Gomes G., Casado-Rodríguez J., Mastrantonas N., Moschini F., Schaffhauser T., Decremer D., Jensen L., Morrison O., Gelati E., Lorini V., Salamon P., "Open Source LISFLOOD suite: a platform for hydrological modelling" - submitted ???

²   Todini, E.: A mass conservative and water storage consistent variable parameter Muskingum-Cunge approach, Hydrol. Earth Syst. Sci., 11, 1645–1659, https://doi.org/10.5194/hess-11-1645-2007, 2007.

³   Reggiani P.,  Todini E., Meißner D., On mass and momentum conservation in the variable-parameter Muskingum method, Journal of Hydrology, Volume 543, Part B, Pages 562-576, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2016.10.030, 2016

⁴   Casado-Rodríguez, J., Disperati, J., Grimaldi, S., and Salamon, P.: Benchmarking reservoir operation schemes for large-scale hydrological models, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-904, 2026.

⁵ Burek P, Van Der Knijff J, De Roo A. LISFLOOD - Distributed Water Balance and Flood Simulation Model - Revised User Manual 2013. EUR EUR 26162. Luxembourg (Luxembourg): Publications Office of the European Union. JRC78917. 2013.

⁶   Hanazaki, R., Yamazaki, D., & Yoshimura, K.,  Development of a reservoir flood control scheme for global flood models. Journal of Advances in Modeling Earth Systems, 14, e2021MS002944. https://doi.org/10.1029/2021MS002944, 2022

⁷   Choulga, M., Moschini, F., Mazzetti, C., Grimaldi, S., Disperati, J., Beck, H., Salamon, P., and Prudhomme, C.: Technical note: Surface fields for global environmental modelling, Hydrol. Earth Syst. Sci., 28, 2991–3036, https://doi.org/10.5194/hess-28-2991-2024, 2024.

⁸   Fan Y. et al. Global Patterns of Groundwater Table Depth. Science 339 ,940-943.DOI:10.1126/science.1229881, 2013

⁹   Siebert S., Henrich V., Frenken K., Burke J.. Global Map of Irrigation Areas version 5. Rheinische Friedrich-Wilhelms-University, Bonn, Germany / Food and Agriculture Organization of the United Nations, Rome, Italy, 2013

¹⁰  Lehner, B., Liermann, C. R., Revenga, C., Vörömsmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., & Wisser, D. (2011). High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. In Frontiers in Ecology and the Environment (Vol. 9, Number 9, pp. 494–502). https://doi.org/10.1890/100125

¹¹  Lehner, B., Beames, P., Mulligan, M. et al. The Global Dam Watch database of river barrier and reservoir information for large-scale applications. Sci Data 11, 1069 (2024). https://doi.org/10.1038/s41597-024-03752-9

¹²  Lehner, B., & Döll, P. (2004). Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology, 296(1–4), 1–22. https://doi.org/10.1016/j.jhydrol.2004.03.028

¹³  Messager, M., Lehner, B., Grill, G. et al. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat Commun 7, 13603 (2016). https://doi.org/10.1038/ncomms13603

¹⁴   Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020;146:1999–2049. https://doi.org/10.1002/qj.3803

¹⁵ Mastrantonas et al., Postprocessing suspiciously high localized precipitation in ERA5 for improved hydrological simulations, Met. App. [submitted]