Abstract
Drought is an extreme climate phenomenon that mainly results from abnormally low rainfall leading to water scarcity in an ecological system. Pakistan is already facing a serious threat of water shortages. The situation could further be intensified due to the prevailing drought conditions. Therefore, there is a need of consistent drought monitoring to observe drought severity; its duration and spread, to ensure effective planning to help mitigate its possible adverse effects. This study has utilized both satellite and in-situ data for consistent and accurate drought monitoring over Pakistan. Three major drought-intensive periods such as 1968–1975, mid-1980s and 1999–2003 were reflected in the standardized precipitation index (SPI) time series. The deviations of MODIS-NDVI values from their long-term mean were used as a tool to identify wetness and dryness conditions. The tropical rainfall measuring mission (TRMM) precipitation (PPTN) data were used to derive monthly and annual accumulated rainfall. The deviations from the long-term mean (2001–2012) were calculated and mapped to identify drought-prone regions on country scale. It is hard to detect drought signal just from NDVI anomaly values on regional scale. Therefore, DSI index with high resolution and incorporated with MODIS-derived NDVI and ET/PET data was chosen as a tool for regional drought monitoring. The performance of MODIS-derived DSI was evaluated with satellite and in-situ precipitation data. The spatial correlation maps between DSI, TRMM-PPTN and SPI-3 were generated to evaluate the performance of DSI. Significant positive correlation values were present in winter (DJF); spring (MAM) and autumn (SON) seasons, in central parts of the country, portraying strong evidence that DSI is a good indicator for drought conditions especially for agricultural land in plain areas during these seasons. Most of this region is characterized by agricultural land cover and plays an important role in agricultural productivity of the country. Therefore, good performance of DSI would help scientists and policy makers to implement planning and risk reduction strategies in the region .
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