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.

Details

Title
Catchment memory explains hydrological drought forecast performance
Author
Jonson, Sutanto Samuel 1 ; Van Lanen Henny A J 2 

 Wageningen University and Research, Hydrology and Quantitative Water Management Group, Environmental Sciences Department, Wageningen, The Netherlands (GRID:grid.4818.5) (ISNI:0000 0001 0791 5666); Wageningen University and Research, Water Systems and Global Change Group, Environmental Sciences Department, Wageningen, The Netherlands (GRID:grid.4818.5) (ISNI:0000 0001 0791 5666); Utrecht University, Institute for Marine and Atmospheric research Utrecht, Utrecht, The Netherlands (GRID:grid.5477.1) (ISNI:0000000120346234) 
 Wageningen University and Research, Hydrology and Quantitative Water Management Group, Environmental Sciences Department, Wageningen, The Netherlands (GRID:grid.4818.5) (ISNI:0000 0001 0791 5666) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2629528798
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.