Since the implementation of EFAS version 5.4 (XXX 2025) and GloFAS v4.3 (YYY 2025), the same design of products are used for the sub-seasonal range both in EFAS and GloFAS. This means, the previous EFAS sub-seasonal products were replaced by the new ones, while for GloFAS the sub-seasonal products are completely new. 

The new sub-seasonal products have calendar weekly periods (i.e. always Monday-Sunday) as lead time. The forecast signal is derived from the relationship between the calendar weekly mean river discharge and the climatological distribution of the possible weekly mean values. The fixed calendar weeks, as forecast lead times, will allow the users to compare forecasts from different runs, as the verification period is fixed (as the calendar weeks). The generation of the sub-seasonal products rely on three major components, listed below:

Component-1. Real time forecasts

This part is the hydrological forecasts produced in real time. This will give the actual predicted conditions for the sub-seasonal products that will be compared to climatologies to derive the forecast anomalies. In the following we describe the characteristics of these forecast simulations. Where appropriate, the difference between EFAS and GloFAS is specified. If there is no EFAS/GloFAS mentioned, then the method is identical between the two forecast systems:

Component-2. Reforecasts

The sub-seasonal products rely on range-dependent climatologies, that change with the forecast lead time. The climatologies are produced from a large set of hydrological reforecasts. In the following we describe the characteristics of these reforecast simulations. Where appropriate, the difference between EFAS and GloFAS is specified. If there is no EFAS/GloFAS mentioned, then the method is identical between the two systems:

Component-3. Climatologies

The sub-seasonal products rely on range-dependent climatologies, that change with the forecast lead time, and which are produced from the hydrological reforecasts. The climatologies will give the reference point for the different anomaly categories applied in the sub-seasonal range. These reference points are some of the specific quantiles from the climate distribution, such as the 10th and 90th percentile values. In the following we describe the main characteristics of the climatologies. Where appropriate, the difference between EFAS and GloFAS is specified. If there is no EFAS/GloFAS mentioned, then the method is identical between the two systems:

Generation of the forecast anomaly and uncertainty signal

In a sub-seasonal forecast, especially at the the longer ranges, the day-to-day variability of the river flow, with prediction of the actual expected flood severities, can not be predicted due to the very high uncertainties. What is possible, is to rather give an indication of the river discharge anomalies and confidence in those predicted anomalies. As the forecast range increases, the uncertainty will also generally increase and with it the sharpness of the forecast will gradually decrease and more and more often the forecast just going to show the climatologically expected conditions.

The determination of the sub-seasonal forecast signal is reflective of this and was designed to deliver a simple to understand categorical information on the anomalies and uncertainties present in the forecast, relative to the underlying climatology.

From the 660 reforecast values in the climate sample, 99 climate percentiles are determined (y-axis), which represent equally likely (1% chance) segments of the river discharge value range that occurred in the 20-year climatological sample. Figure 1 shows an example climate distribution, with only the deciles (every 10%), the two quartiles (25%, 50% and 75%), of which the middle (50%) is also called median, and few of the extreme percentiles are plotted near the minimum and maximum of the climatological range indicated by black crosses. Each of these percentiles have an equivalent river discharge value on the x-axis. From one percentile to the next, the river discharge value range is divided into 100 equally likely bins, some of which is indicated in Figure 1, such as bin1 of values below the 1st percentile, bin2 of values between the 1st and 2nd percentiles or bin 100 of river discharge values above the 99th percentiles, etc. 


Figure 1. Schematic of the forecast anomaly categories, defined by the climatological distribution.

Based on the percentiles and the related 100 bins, there are seven anomaly categories defined. These are indicated in Figure 1 by shading. The two most extreme categories are the bottom and top 10% of the climatological distribution (<10% as red and 90%< as blue). Then the moderately low and high river discharge categories from 10-25% (orange) and 75-90% (middle-dark blue). The smallest negative and positive anomalies are defined by 25-40% and 60-75% and displayed by yellow and light blue colours in Figure 1. Finally, the normal condition category is defined from 40-60%, so the middle 1/5th of the distribution, coloured grey in Figure 1.

The forecast has 51 ensemble members, which all are sorted into one of the 100 bins along the climate distribution. This bin number will be the extremity of each ensemble member, with values from 1 to 100.  

This means, we rank the verifying WB, or the forecast values (every member) in the 99-value percentile model climatology. So, for example +3 would mean, the forecast is between the 52nd and 53rd percentiles, while -21 would mean the forecast is between the 29th and 30th percentiles. The lowest value is -50 (forecast is below the 1st percentile), while the largest is +50 (forecast is larger than the 99th percentile).