Abstract
Hydrological drought forecasts outperform meteorological ones, which is anticipated coming from catchment memory. Yet, the importance of catchment memory in explaining hydrological drought forecast performance has not been studied. Here, we use the Baseflow Index (BFI) and the groundwater Recession Coefficient (gRC), which through the streamflow, give information on the catchment memory. Performance of streamflow drought forecasts was evaluated using the Brier Score (BS) for rivers across Europe. We found that BS is negatively correlated with BFI, meaning that rivers with high BFI (large memory) yield better drought prediction (low BS). A significant positive correlation between gRC and BS demonstrates that catchments slowly releasing groundwater to streams (low gRC), i.e. large memory, generates higher drought forecast performance. The higher performance of hydrological drought forecasts in catchments with relatively large memory (high BFI and low gRC) implies that Drought Early Warning Systems have more potential to be implemented there and will appear to be more useful.
Highlights
Hydrological drought forecasts outperform meteorological ones, which is anticipated coming from catchment memory
The Baseflow Index (BFI) is commonly used to indicate the portion of the flow, i.e. baseflow, that comes from groundwater storage or other delayed sources, which is derived from recessions in streamflow[26,28,29] (“Methods”)
The rivers located in mountain ranges or snow-dominated regions generate high BFI (Supplementary Fig. 1) that may come from water stored and released from snow and ice and the presence of lakes and wetlands[20,31]
Summary
Hydrological drought forecasts outperform meteorological ones, which is anticipated coming from catchment memory. The higher performance of hydrological drought forecasts in catchments with relatively large memory (high BFI and low gRC) implies that Drought Early Warning Systems have more potential to be implemented there and will appear to be more useful. Drought forecasts, do not require detailed day-to-day evolution, as demanded in conventional weather forecasts Instead, drought predictions, such as SPI, need estimates of monthly total precipitation, that provide information on how likely the coming months will be wetter or drier than the median[13]. Sutanto et al.[7] show that hydrological drought forecasts with 1-month accumulation period identified using the Standardized Runoff Index (SRI-1)[17] and Standardized Groundwater Index (SGI-1)[18] and LT = 1 have perfect forecasts up to 71.5% and 73.2% of the pan-European area, respectively, which is higher than meteorological drought scores (53.7% for SPI). Our results show that the highest forecast performance is found in rivers with higher BFI and lower gRC values, associated with higher memory, which explain the importance of considering catchment memory in improving the performance of hydrological drought forecasts
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