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The river network summary map also contains the reporting points, which are labelled as example in Figure 1b. These are river locations, where detailed information is provided about the evolution of the forecast signal over the forecast horizon. There reporting points are either fixed points, which are also used in the medium-range flood products and the basin-representative points, which are selected locations, on a one point per basin basis. Further details about the basins and the representative points are available here: Placeholder CEMS-flood sub-seasonal and seasonal basins and representative stations.
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Figure 1. Example snapshots of the sub-seasonal and seasonal river network summary maps with the reporting points, animation and river pixel colours explained.
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Next item in the popup window is the hydrograph, which graphically summarises the climatological, antecedent and forecast conditions (see Figure 2 and Figure 3).
Figure 2. Example snapshot of the reporting point pop-up window product (for a seasonal forecast).
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 periods (average river discharge over months in seasonal and weeks in sub-seasonal).
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As Figure 3c describes, 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 which guarantees, 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. 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 in the month-7 climatology, indicating a noticeable drift for larger values. 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, the forecast is more likely to show larger values in the longer ranges than in the short range.
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Figure 3. Different interpretation schemes (a-b-c) for the sub-seasonal and seasonal hydrographs.
Probability evolution table section
Finally, the last part of the reporting point popup window is the probability evolution table. This table shows all the 7 anomaly categories and the related probabilities for all the forecast lead time periods and from all the previous forecast runs that still verify in during the most recent forecast horizon. For the sub-seasonal, this means 5 or 6 calendar weekly forecast lead time periods (depending on which day of the week the run date is, and thus how many calendar weeks the 46-day lead time can cover) and
Figure 2. Example snapshot of the reporting point pop-up window product (for a seasonal forecast).
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Figure 3. Different interpretation helps (a-b-c) and 7 calendar monthly periods. For the seasonal forecasts, there is always 7 rows with the most recent 7 seasonal forecast probabilities (as Figure 2 shows). While for the sub-seasonal and seasonal hydrographsit is more complicated and depending on the actual run date as day of the week there can be 41 to 46 rows, with all these daily (00 UTC) forecast runs verifying.
The bottom right corner of the probability evolution table is empty, as those lead times are not available from the earlier forecast runs.