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The first table in the popup window ('Point information') provides metadata information of the station (Figure 2). These are the station ID and also an internal ID (Point ID), the station name (if available), country, basin and river names, and also coordinates (lat/lon) and upstream area in two flavours, the provided ones and the LISFLOOD river network equivalent. The provided coordinates and upstream area are from the users as those represent the real river gauge locationlocations. These are available only for the fixed points (sometimes provided upstream area is missing). For the basin-representative points, however, only the LISFLOOD coordinates and upstream area are available, as these points were defined solely on the simulated LISFLOOD river network. The fixed reporting points have a Point ID in the metadata table starting with 'SI', while the basin-representative points starting with 'SR'.
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The left half of the plot, left of the horizontal dotted line, which indicates the forecast start date, shows the past (see Figure 3a). The black dots (connected by black line) indicate the so-called water balance, the proxi observations, which are produced as a LISFLOOD simulation forced with either gridded meteorological observations in EFAS, or ERA5 meteorological reanalysis fields in GloFAS. These black dots show the simulated reality of the river discharge conditions, as close as the simulations can go at the actual conditions over the forecast periods (average river discharge over months in seasonal and calendar weeks in sub-seasonal).
These black dots are added to the hydrographs retrospectively, after each week (in sub-seasonal) or month (in seasonal) passes and the weekly or monthly mean proxi-observed river discharge values become available. The users are encouraged to go back and check previous forecasts to see how well the earlier forecasts predicted the anomalies.
The right half of the plot covers the forecast horizon, in the displayed example in Figure 2 and 3 this means 7 lead times of 7 calendar month months from August to February the (next year) (see Figure 2a). The forecast distribution is indicated by box-and-whiskers, displaying the minimum and maximum values in the ensemble forecasts of all the 51 members and the lower and upper quartiles (which are the 25th and 75th percentiles as well) and the median (which is the 50th percentilespercentile).
The coloured background is the model climatology (see Figure 3b). This climatology is generated using reforecasts over a 20-year period. Further information on the climatologies and their generation is given here: Placeholder CEMS-flood sub-seasonal and seasonal forecast signal generation methodology for the sub-seasonal and Placeholder CEMS-flood seasonal forecast generation methodology for the seasonal. In the past half of the hydrograph, the climatology is always lead time 1, so first week (always as days 1-7) or first month (whichever month of the year it is), as that is the closest equivalent to the proxi-observation-based climatology. While in the forecast half, the climatologies are lead time dependent and for each forecast lead time the equivalent climatology is plotted with that specific lead time. From the climatology, the 5 anomaly categories are coloured, below the 10th the 'Extreme low' with red, above the 90th percentile the 'Extreme high' with blue, the 10th to 25th percentiles 25th percentiles zone as 'Low' with orange, the 75th to 90th percentiles as 'High' with cyan and finally the remaining 25th to 75th percentile as 'Near normal' with grey. This 'Near normal' category is the extended one (see it being was mentioned also with the river network summary map above), by merging the original 25-40, 40-60 and 60-75 percentile categories, including the narrower 'Near normal', the 'Bit low' and 'Bit high' categories.
As Figure 3c describeshighlights it, the seasonal hydrograph (it would not work for the sub-seasonal due to the much shorter weekly lead times) indicates a property of the model climatologies. The seasonal hydrograph is designed to have exactly 13 month as covered period monthly periods, which guarantees , that the last month of the forecast (February in Figure 2 and 3) will feature as a month-7 forecast climatology and also as a month-1 forecast climatology in the past period as the oldest period plotted. This way, the hydrograph gives a visual impression of the drift in the river discharge forecasts. Drift in this context means, the month-7 reforecast-based climatology percentiles could occasionally be even very different to the month-1 reforecast-based percentiles and by this show a noticeable shift or drift in the forecast behaviour (i.e. . This means, values going lower or higher ) from shorter to longer ranges (see Figure 3c for visual indication of this). In fact, for this comparison, the left-most and right-most parts of the hydrograph need contrasting. In the example in Figure 2-3, the shaded climatological categories highlight that 'Extreme low' and 'Low' categories shift only very little, with the median being very stable. However, the higher percentiles (75th and 90th percentiles) are noticeably larger higher in the month-7 climatology, indicating a noticeable drift for larger values in the reforecasts. While in the the month-1 average river discharge climatological distribution, the 90th percentile is about 20 m3/s, so about 10% of the time the monthly mean can exceed this value, in the longer range month-7 reforecasts the same 90th percentile, the 10% of the time to exceed this value, increases to 25 m3/s. So, in sort, the forecast is more likely to show larger values in the longer ranges than in the short range.
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The cells in the table are not coloured, with one exception, which is the dominant forecast anomaly category (of the 7 anomaly categorieswhole ensemble). That cell's number is bold and the cell is coloured by the same colour that the river pixel has on the river network summary map. As described in in Placeholder CEMS-flood sub-seasonal and seasonal forecast anomaly and uncertainty computation methodology, the dominant category is determined by the rank-mean (the mean of the ensemble members' ranks in the climatological percentile distribution). The coloured cell's number is not always the highest, like in many forecasts in the example in Figure 2' probability table. For example, although the forecast for August show shows a gradual progression from near normal (grey colour), which is the original 'Bit low' of the 7 categories) and high uncertainty (lightest grey colour of the three); , to 'Extreme low' category with low uncertainty (darkest red colour). Moreover, the number of the coloured cells is also on the increase generally, as we go towards the shorter lead times. Until the June forecast, the colours are the lightest of the three versions, highlighting high uncertainty (light orange in the June forecast), while in the July forecast the uncertainty drops to medium level (medium dark orange) and finally in the August forecast we arrive to the low uncertainty 'Extreme low' situation. At However, at the same time, for all of these forecasts the more likely of the 7 categories are constantly the 'Extreme low' category, with the probability values gradually increasing from 2X to 100% in the August forecast (all of the 51 ensemble members being in the 'Extreme low' category). The reason why the earlier forecasts shift towards the normal conditions in the mean sense, is the larger uncertainty with most or all of the categories having some ensemble members.
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Figure 4 shows an example snapshot in the same area as Figure 1a above. One can compare, how the variable signal on the river network averages into the basin signal. The averaging is done from the river pixel information. A Rank-mean and a Rank-std value will be calculated for each basin and with those the exact same method is used (as for the river pixels on the river network summary map) to define the dominant forecast anomaly category and the uncertainty category and thus the colour of the basin (see the inset figure with the colour legend in Figure 4). The colours used are the same as for the river network summary map, except there is a slight shift in transparency, in order to allow visibility for all river pixels, even for those with the same colour as the basin colour.
The basin Rank-mean and Rank-std values are determined using all the large enough river pixels in the basin. Currently, only pixels above 50km2 are used in EFAS and pixels above 250 km2 are used in GloFAS in the averaging. The basin Rank-mean/Rank-std values are calculated as an arithmetic average of the Rank-mean and Rank-std values of the individual river pixels, weighted by the square of the upstream area, as described below:
Figure 4. Example snapshot of the sub-seasonal and seasonal river network summary maps with the animation panel and river pixel colours explained.
The forecasts can be advanced (or even animated if needed) with the lead time controller, both on the river network and the basin summary layers (see Figure 1a and Figure 4 bottom left corner) and the users can check the individual signal for each lead time, which currently is 5 or 6 weeks for the sub-seasonal (depending on which day of the week the run date is) and always 7 months for the seasonal.
Figure 4. Example snapshot of the sub-seasonal and seasonal river network summary maps with the animation panel and river pixel colours explained.