The 1 arcmin (0.01667) pan-European implementation of the LISFLOOD model was calibrated using in-situ discharge observations from stations with a minimum drainage area of 150 km²  and at least 4-years-long time series of measurements more recent than 01 January 1990.

The 1903 selected calibration stations entailed 43.4 % of the pan-European EFAS domain (Figure 1, blue area). The parameter values of these catchments were identified using the Distributed Evolutionary Algorithm for Python (DEAP, Fortin et al. 2012). The parameter values of the catchments for which in situ discharge data were not available (Figure 1, yellow area) were estimated by parameter regionalisation. This combined calibration approach delivered 14 parameter maps with pan-European extent. This page provides an overview of the calibration methodology and parameters.

Figure 1 - In blue the area of the pan-European domain for which discharge observations were available; in yellow the area of the pa-European domain for which discharge observations were NOT available for EFAS v5 calibration. The area in black is not included in the modelling domain. 

Parameter estimation for catchments with discharge data

The Distributed Evolutionary Algorithm for Python (DEAP, Fortin et al. 2012) was used to explore the parameter space and identify the parameter set leading to the highest value of the modified Kling Gupta Efficiency (KGE', Gupta et al., 2009), as implemented by the open-source calibration tool.  

When multiple calibration points were available in one basin, the calibration protocol followed a top-down approach from head-catchments to downstream catchments; each segmentation of the basin area is called inter-catchment. Figure 2 shows the fragmentation of the area with available discharge observations into inter-calibration catchments.

Figure 2 - Subdivision of EFAS pan-European domain into LISFLOOD calibration inter-catchments.

The size of the inter-catchments was mainly driven by data availability. The largest inter-catchment was located in the Neva basin, with a drained area larger than 210.000 km2. Figure 3 shows the distribution of the area of the calibration inter-catchments, the median value was 1.100 km2.

Figure 3– Calibration stations: distribution of the area of the LISFLOOD calibration inter-catchments. Note the logarithmic scale of the x-axis.

The time series considered for calibration covered 31 years, from 01/01/1990 to 31/12/2021. Each calibration station used a different calibration period, depending on length of available discharge observations. The minimum number daily observation data use in calibration was the equivalent of 4 years. A spin-up period of three year was added to each model run to ensure that model's variable were correctly initialised. Nevertheless, some exceptions were implemented: EFAS v5.0 calibration data - Copernicus Emergency Management Service - CEMS - ECMWF Confluence Wiki.

If a period longer than eight years was available, the discharge record was split in two for calibration and verification purposes. The most recent period was used for the calibration because the most recent forcing data and discharge observations are expected to have lower uncertainty and to provide a closer representation of the climatic and hydrological conditions of the forecast period. Figure 4 shows the distribution of the length of the time series used for calibration.

Figure 4 – Calibration stations: number of years used in calibration.

Calibration parameters

14 LISFLOOD parameters were simultaneously calibrated for each catchment, with the purpose to optimise the modelling of snow melt, water infiltration into the soil, surface water flow, groundwater flow, lakes and reservoirs dynamics. Feasible parameter ranges were defined for each parameter used in calibration to obtain more physically realistic calibrated parameters. Table 1 lists the calibration parameters, the acceptable range of values, and the default values. 

Table 1 - LISFLOOD calibration parameters for GloFAS 4.

Parameter regionalization

Catchments for which in situ discharge data was not available entailed 56.6% of the pan-European domain (Figure 1, yellow area). For these catchments, a parameter regionalisation approach was implemented to estimate the parameters that control snow melt, infiltration, runoff, groundwater, and routing (11 parameters in total, see Table 1).

Calibrated parameter values were transferred from calibrated catchments (donors) to uncalibrated catchments. For each uncalibrated catchment, the donor catchment was identified according to a proximity criterion accounting for both climatic similarity and the geographical proximity (Parajka et al 2005, Beck et al. 2016). Parameters controlling the local behaviour of lakes and reservoirs cannot be transferred from donor catchments to target catchments: default lakes and reservoirs parameter values were used in target catchments.

A leave-one-out cross validation approach for a subset of 796 gauged catchments was used to verify the performances of the pragmatic regionalisation approach (Figure 5). Moreover, stations with observation time series shorter than 4 years (excluded from calibration) were used to verify the benefit of using the regionalized parameters over the default parameter values. Finally, it is here noted coastal and endorheic catchments with drainage area smaller than 150 km2 are modelled using the default parameter values (Table 1).

Figure 5 – Cumulative probability distribution of KGE for the regionalisation leave-one-out experiment.