Advancing Operational Drought Monitoring Through Propagation Analysis and Vegetation-Specific Precipitation Deficits

Droughts are becoming more frequent and severe in the Netherlands, particularly affecting sandy soil regions that depend strongly on local precipitation and groundwater. Current operational monitoring, however, faces two related but distinct limitations. First, drought is typically assessed using individual indicators, without explicitly analysing drought propagation in the hydrological cycle, limiting insight into when precipitation deficits begin to affect the subsurface water state. Second, the main operational indicator for the growing season, i.e. precipitation deficit, assumes a uniform reference grass evapotranspiration (RET), thereby neglecting substantial differences in water demand among vegetation types. Together, these limitations constrain both the interpretation of drought dynamics and the representation of spatially differentiated drought conditions. This study addresses these challenges for the Aa of Weerijs catchment in the Netherlands by analysing drought propagation and refining the operational precipitation deficit indicator.
Drought propagation was analysed for the period 1993–2024 using indices representing different drought types: meteorological (Standardized Precipitation Index, SPI, and Standardized Precipitation Evapotranspiration Index, SPEI), agricultural (Palmer Drought Severity Index, PDSI), and hydrological (Standard groundwater Index, SGI). The results reveal clear differences in timing and persistence across drought types. Agricultural droughts (PDSI) respond rapidly to meteorological anomalies and generally recover quickly, whereas groundwater droughts show delayed onset and prolonged recovery due to relatively slow water replenishment in the subsurface.
In parallel, the study refines the commonly used precipitation deficit (PD), which is currently based on RET for well-watered grass and therefore ignores vegetation heterogeneity. A vegetation-specific precipitation deficit (PD_veg) was developed by replacing the uniform RET with vegetation-specific potential evapotranspiration (PET_veg). PETveg was generated at 80 m spatial resolution by modifying PyWaPOR framework to generate zero moisture stress conditions. The resulting PDveg reveals strong spatial variability in drought development that is masked by the conventional indicator, with markedly different deficit dynamics across forests, crops, natural areas, and tree nurseries. To support operational use, percentile-based thresholds (P70–P95) were derived from 14-day PD_veg gains for each vegetation type. These thresholds distinguish four levels of drought severity, from mild to extreme.
Finally, irrigation-intensive areas were identified using unsupervised clustering of remote-sensing indicators. High AET/PET ratios, together with small differences between precipitation deficits derived from AET and PET, indicated such areas. This approach provides a data-driven way to map high water-use zones without relying on extensive in-situ data.
Together, these results show that drought propagation analysis enhances understanding of temporal drought dynamics, while vegetation-sensitive indicators improve the representation of spatial variability in drought conditions, providing complementary insights for spatially targeted water management.