Assessment of Hydroclimatic Lag and Memory Across Major European Basins Using GRACE and MERRA
Year: 2026
Type: article
Abstract: Large basins show substantial variation in the timing and persistence of hydrological responses to climatic forcing. Quantifying hydroclimatic lags and basin memory is important for understanding hydrological response dynamics, assessing drought persistence, and informing future research on drought prediction and water-resource management. This study presents a harmonised comparison of hydroclimatic response delays and hydrological memory across ten major European basins. Basin-scale Water Budgets (WBs) were derived from precipitation, evapotranspiration, and runoff components, while hydroclimatic forcing was represented by the Combined Climatologic Deviation Index (CCDI), based on precipitation anomalies and Gravity Recovery and Climate Experiment (GRACE) total water storage anomalies (TWSAs). Cross-correlation (CCF), autocorrelation (ACF), partial autocorrelation (PACF), and autocorrelation decay time (e-folding) were used to quantify response timing and persistence. The results show basin-specific differences in lag relationships and autocorrelation-based persistence. Estimated memory durations depend on the diagnostic used, highlighting the need to distinguish e-folding time, zero-crossing lag, and significance-decay lag. Because WB and CCDI share precipitation-related information, the observed cross-correlations are interpreted as statistical associations rather than direct evidence of causality. The findings provide a comparative assessment of hydroclimatic lag and persistence characteristics for the selected basins and study period; their predictive value requires separate validation.
Source: Applied Sciences
DOI: 10.3390/app162111058
Authors: Monika Birylo, Wioleta Błaszczak-Bąk
Institution: University of Warmia and Mazury in Olsztyn
Language: English
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7225620877
