Copernicus Emergency Management Service - CEMS > GloFAS v5 calibration methodology and parameters > CEMS_GloFAS_banner.png

Modelling of streamflow losses. Streamflow losses are the loss in the flow volume of a river as water moves downstream; they can be caused by evaporation, transpiration by macrophytes and riparian vegetation, as well as groundwater recharge. They can be exacerbated by intensive groundwater pumping, and they have been observed in arid areas, but not only (e.g. Jasachko et al, 2021, Uchôa et al, 2024)

The 0.05 degrees quasi-global (-180,180,90,-60) implementation of the LISFLOOD OS model was calibrated using in-situ discharge observations and information about reservoirs.

In-situ discharge stations included in GloGASv5 calibration have a minimum drainage area of 500 km²and at least 4-years-long time series of measurements more recent than 01 January 1980. The 5379 selected calibration stations entailed 51.6 % of the quasi-global domain (Figure 1, yellow area). The parameter values of these catchments (with the exception of reservoir parameters, as explained below) were identified using the Distributed Evolutionary Algorithm for Python (DEAP, Fortin et al. 2012¹). The parameter values of the catchments for which in situ discharge data were not available (Figure 1, yellow area) were estimated by parameter regionalization. This combined approach delivered 12 quasi-global parameter maps. These maps allow the tuning of snow melt, water infiltration into the soil, surface water flow, groundwater flow, lakes dynamic.

Reservoir information are in-situ observations of inflow, outflow, and volume, and a set of climate, geometric, use attributes. Information from a total of 211 reservoirs were used to calibrate 2 parameters of the reservoir modelling routine. This approach delivered 2 tables including the parameters for all the reservoirs included in GloFASv5 set-up.

Copernicus Emergency Management Service - CEMS > GloFAS v5 calibration methodology and parameters > GloFASv5map_cal_reg.png

Figure 1 - In yellow the area of the semi-global domain for which discharge observations were available; in grey the area of the semi-global domain for which discharge observations were NOT available (a parameter regionalisation approach was used in these areas) for GloFAS v5 calibration. Major hydrological basins from FAO.

Parameter estimation for catchments with discharge data

The Distributed Evolutionary Algorithm for Python (DEAP, Fortin et al. 2012¹), as implemented by the open-source calibration tool, was used to explore the parameter space and identify the parameter set leading to the highest value of the JDKGE objective function.

JDKGE is a revised version of the widely used the modified Kling Gupta Efficiency (KGE', Gupta et al., 2009, Kling et al, 2012²). Specifically, JDKGE was proposed by Ficchì et al. (2026)³, and it has the purpose to optimize both low and high flows.

Copernicus Emergency Management Service - CEMS > GloFAS v5 calibration methodology and parameters > barplot_numyearsobs_newclasses.png

Copernicus Emergency Management Service - CEMS > GloFAS v5 calibration methodology and parameters > GloFASv5map_interstationcatchments.png

Copernicus Emergency Management Service - CEMS > GloFAS v5 calibration methodology and parameters > barplot_intercatchemnt_area_calreg.png

References

¹ Fortin, F. A., De Rainville, F. M., Gardner, M. A. G., Parizeau, M., & Gagné, C. (2012). DEAP: Evolutionary algorithms made easy. The Journal of Machine Learning Research, 13(1), 2171-2175. https://jmlr.org/papers/volume13/fortin12a/fortin12a.pdf

Jasechko, S., Seybold, H., Perrone, D. et al. Widespread potential loss of streamflow into underlying aquifers across the USA. Nature 591, 391–395 (2021). https://doi.org/10.1038/s41586-021-03311-x

Uchôa, J.G.S.M., Oliveira, P.T.S., Ballarin, A.S. et al. Widespread potential for streamflow leakage across Brazil. Nat Commun 15, 10211 (2024). https://doi.org/10.1038/s41467-024-54370-3