Atmospheric Research, Journal Year: 2024, Volume and Issue: 315, P. 107842 - 107842
Published: Dec. 8, 2024
Language: Английский
Atmospheric Research, Journal Year: 2024, Volume and Issue: 315, P. 107842 - 107842
Published: Dec. 8, 2024
Language: Английский
Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132706 - 132706
Published: Jan. 1, 2025
Language: Английский
Citations
3Atmospheric Research, Journal Year: 2024, Volume and Issue: 304, P. 107405 - 107405
Published: April 7, 2024
Language: Английский
Citations
9Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 57, P. 102184 - 102184
Published: Jan. 11, 2025
Language: Английский
Citations
1Remote Sensing, Journal Year: 2025, Volume and Issue: 17(2), P. 281 - 281
Published: Jan. 15, 2025
Analysis of the temporal relationship between meteorological drought and hydrological is crucial in monitoring water resource availability. This study examined linear lagged relationships spread to their joint effects on low-flow variability Oum Er-Rbia (OER) watershed. To this end, random forest (RF) model statistical methods were used characteristics indices at monthly, seasonal, annual scales. The various analyses revealed that mainly a function time scale considered, choice describe each type season considered. surface snow cover synchronized with In contrast, transition from groundwater has lag 1 month statistically significant up t − 5 + 5, i.e., 6 months. correlation rainfall deficit monthly storage index was lowest (0.15) December highest (0.83) March. suggests seasonal response cumulative precipitation deficits. RF analysis highlighted importance regarding severity drought. longer scales have greater impact drought, contribution approximately 10% per index. However, relative contributions factors rarely exceed 5%. Thus, by exploring for first complex interactions among regimes, factors, provides new perspective understanding propagation severe
Language: Английский
Citations
1Agricultural Systems, Journal Year: 2024, Volume and Issue: 220, P. 104056 - 104056
Published: July 14, 2024
Language: Английский
Citations
8The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 949, P. 175114 - 175114
Published: July 29, 2024
Language: Английский
Citations
7Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132196 - 132196
Published: Oct. 1, 2024
Language: Английский
Citations
7The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 948, P. 174834 - 174834
Published: July 19, 2024
Language: Английский
Citations
4The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 950, P. 175399 - 175399
Published: Aug. 8, 2024
Language: Английский
Citations
4Meteorological Applications, Journal Year: 2025, Volume and Issue: 32(1)
Published: Jan. 1, 2025
Abstract Increasing global temperatures have triggered several environmental and ecological challenges. Recurring droughts across the globe are an adverse consequence of warming. In this research, a new drought forecasting index—the Multimodal Forecastable Standardized Precipitation Evapotranspiration Index (MFSPEI)—has been suggested using projections from multiple climate models. The MFSPEI methodology is primarily based on first component Component Analysis (FCA) (SPEI). For application purposes, time series data SPEI 10 climatic models endorsed by Coupled Model Intercomparison Project phase 6 (CMIP‐6) at 50 random locations over region Tibetan Plateau (TP) considered. outcomes show that FCA captures sufficient amount variation while maintaining high forecastability in all selected grid points chosen prominent timescales monitoring indices. To assess predictive performance proposed index (MFSPEI), comparison matrices artificial neural network (ANN) were identified. During training testing phases, forecast efficiency developed indicator (MFSPEI) proved superior to individual SPEI. numerical assessment indicates deviations difficulties interpreting can be addressed more effectively with indicator. Therefore, reinforces predictions for preparedness management context model projections.
Language: Английский
Citations
0