Forecast skill and potential added value for drought mitigation actions of sub-seasonal to seasonal AI-enhanced hydrometeorological forecasts: case study of Rijnland, the Netherlands

Droughts have multiple definitions for a range of drought types, and related indicators, in science alone. For persons experiencing and having to cope with droughts, individual, sectoral, and local definitions vary even further. Hydrometeorological sub-seasonal to seasonal forecasts of droughts may help to better plan, communicate, and implement mitigation measures. Forecast skill of such forecasts for droughts may depend on the drought definition or definitions analysed.

This research, therefore, analyses in depth, drought impacts, mitigation measures, decision making processes and the potential added value of using forecasts, for a case study in the Netherlands: Rijnland. This is a low-lying flat area in the West, mostly below sea-level, with a dense irrigation and drainage water system to maintain surface water levels in a narrow target range along with its water quality. Both hydrological droughts in the Rhine river basin, and meteorological droughts locally in Rijnland, affect and may trigger drought mitigation actions in Rijnland. Case study drought definitions for early warning are expressed in terms of time-varying lower thresholds for Rhine discharge, and thresholds of potential precipitation deficit varying for different levels of alert.

Forecast skill and potential added value for case study specific mitigation actions of sub-seasonal to seasonal hydrometeorological reforecasts, both directly available and AI-enhanced, are presented and intercompared with the aim to arrive at well-informed recommendations for their use or non-use in the case study of Rijnland.