Influence of potential factors for characterizing the drought propagation from meteorological to hydrological and agricultural droughts in Krishna river basin
Abstract
Droughts are the kind of extreme natural disasters resulting from decreases in precipitation in many parts of world. Dry areas, where precipitation patterns are seasonal (highly variable), are the most susceptible. Unlike most natural disasters, drought is a creeping phenomenon making its onset difficult to identify. Understanding the propagation from meteorological to hydrological/ agricultural drought is very essential for the early warning to the water managers to effectively mitigate the impacts of drought. Despite the profound research done in assessing and characterizing the droughts, understanding the drought propagation and its influencing factors at sub basin scale has been lacking. In this study we have evaluated the drought propagation from meteorological to hydrological/agricultural drought and analyzed the potential factors influencing the propagation for each sub basin in the Krishna River. Meteorological (SPI), Hydrological (SRI), and Agricultural (SSMI) drought indices were computed with the selection of more than 30 years. The temporal accumulation of the meteorological indices with the time step of monthly, seasonal, biannual, and annual were taken to estimate the hydrological propagation with SRI-1 and agricultural drought propagation with SSMI-1 to account the propagation time within each sub basin. The correlational analysis at sub basin scale has reasonably captured the drought propagation of 1 to 3-month for the hydrological drought (r>0.6) and 3 to 6-month for the agricultural drought (r>0.7). Moreover, the potential influencing factors on the drought propagation from the climate, catchment, and hydraulic perspectives were analyzed. The basin experiences, a variation in the propagation times like near time occurrence, long time occurrence and very long-time occurrence from meteorological to hydrological/ agricultural are mainly due to the influences of the land use changes (Δ > 30%) mainly change in forest cover, precipitation, and the number of storage structures
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H32O1105G