GloFAS v5 renalysis and forecasts are generated using LISFLOOD OS, a physically based spatially distributedrainfall-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 [submitted]. Detailed technical information about modelled physical processes is available OS LISFLOOD model documentation.

Specifically, GloFAS v5 benefits of the improvements of LISFLOOD OS v5.0.0, an upgraded version compared to LISFLOOD OS v4.1.1, which was used to produce GloFASv4.

The main novelties of LISFLOODv5 modelling approach compared to LISFLOODv4.1.3 are:

• Diffusive routing for the modelling of channel flow. Up to LISFLOODv5, modelling of river routing was based on the kinematic wave approximation: 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 flood wave peak magnitude and arrival time in mild to low slope rivers (river slope < 0.001). To improve the modelling of flood wave lamination in mild to low slope rivers, LISFLOODv5 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 accounts for both the convection and diffusion of the flood wave, 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 modified version of Hanasaki et al. (2022)⁵ allows an adequate trade-off between model accuracy and data requirements. While in the previous OS LISFLOOD reservoir modelling routine, reservoir outflow was computed solely as a function of reservoir storage, in the updated approach, reservoir outflow depends on both reservoir storage and inflow. For more details, please visit this page [LINK TO MODEL DOC PAGE].
• 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. For more details, please visit this page [LINK TO MODEL DOC PAGE].
• 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. For more details, please visit this page [LINK TO MODEL DOC PAGE].
• A corrected management of return flow from water abstraction. OS LISFLOOD is currently not able to model horizontal groundwater fluxes: this limitation can generate spurious return flow values when water for human use is abstracted from groundwater resources. To avoid this inconvenience, water abstraction from groundwater is limited to the water consumptive use. For more details, please visit this page [LINK TO MODEL DOC PAGE].

GloFAS v5 spatial resolution is 0.05 degrees (or 3 arcmin, ~ 5km at the Equator), which is the same resolution as GloFASv4.

Consistently with all the previous GloFAS versions, the computations for GloFAS v5 are completed with daily time steps for all the hydrological processes, while sub-daily time steps used for the modelling of river routing were reduced to 3 hours (GloFASv4 used 4 hours). All the output variables are available with daily resolution.

Implementation

Static maps

Accurate representation of the rainfall-runoff 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. Over 80 implementation maps are required as input to LISFLOOD OS. This set of implementation maps is referred to as “static maps” or “surface fields dataset”, as described in Choulga et al. 2024⁶.

Detailed information on the use, features, and preparation of each map is available from 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 GloFASv5 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)⁷ . 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., 2013⁸), a dataset showing the amount of area equipped for irrigation around the year 2005 in percentage of the total area on a raster with a resolution of 5 minutes. The former version of the land cover fraction of irrigated areas was based on the Spatial Production Allocation Model (SPAM), Global Spatially-Disaggregated Crop Production Statistics Data for 2010 v2.0 and the Coordination of Information on the Environment (CORINE) Land Cover inventory for 2018 (further referred to as CLC2018).
• Reservoirs dataset and metadata were substantially reviewed and expanded, leveraging on openly available dataset such as the Global Dam Watch (GDW) and the Global Lakes and Wetlands Database (GLWD). GloFASv5 reservoir dataset includes reservoirs with storage capacity larger of equal than 100 hm3, catchment area larger or equal than 250 km2, Degree of Regulation larger than 0.8, a "fair", "good" or "verified" data quality. The total number is 1490 (-4!!!!!!!!) (687 in GlloFASv4), making over 80% of the global total storage, and with an increase of over 20% compared to GloFASv4.
• Lakes dataset and metadata were substantially reviewed and expanded, leveraging on openly available dataset such as HydroLAKES, GDW, GLWD. GloFASv5 lake dataset includes lakes with volume larger of equal than 100 hm3, catchment area larger or equal than 250 km2, surface area larger or equal than 50 km2. The total number is 1230 (463 in GlloFASv4).
• Sectoral water demand maps for domestic, livestock, industrial, energy (cooling) use. The temporal coverage of these maps was extended until 31/12/2023. Furthermore, the entire dataset (01/01/1975-31/12/2023) was generated to leverage on updated versions of the source data. For more details, please visit this page [LINK TO USER GUIDE PAGE].
• Maps of groundwater bodies, fraction of groundwater use and non-conventional water use were updated according to information provided in FAO AQUASTAT, as described in [LINK TO USER GUIDE PAGE].

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 the desired resolution of EFAS and GloFAS using bilinear delaunay interpolation scheme implemented in the open-source pre-processor pyg2p (GloFASv4 used bilinear interpolation).

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

GloFASv5 reanalysis dataset benefits of an amended version of ERA5: spurious high-intensity rainfall values at a single grid point that are not supported by surrounding points (Hersbach et al., 2020⁹ ) were removed following the methodology explained in Mastrantonas et al. Met. App [submitted]¹⁰, as implemented in the open source 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., "LISFLOOD Open Source 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   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

⁶   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.

⁷   Fan Y. et al. Global Patterns of Groundwater Table Depth. Science 339 ,940-943.DOI:10.1126/science.1229881, 2013

⁸   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

⁹   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

¹⁰ Mastrantonas et al. Met. App [submitted] <<TITLE??>>