Shaheen, A., & Baig, M. A. (2011). Drought severity assessment in arid area of Thal Doab using remote sensing and GIS. Int. J. Water Resour. Arid Environ,1(2), 92.
Drought can have many reciprocal effects and is in many ways universal. Typically associated with abnormally low rainfall and dry, arid conditions, drought can substantially effect a region's agricultural production. For example in the Thal Doab region in Pakistan, the area has soil that can support agriculture, however low rainfall totals make it difficult to support cultivation (Shaheen and Baig 93). For example, gram is a key crop in the region that is difficult to grow due to its water usage (Shaheen and Baig 93). During periods of drought, the production of gram was reduced by 200 kg/acres due to low rainfall (Shaheen and Baig 93). Additionally, drought can reduce the levels of surface and groundwater. Within the study period, Shaheen and Baig observed water availability per capita decrease from 5600 meters cubed to 1200 meters cubed (Shaheen and Baig 92).
The Thal Doab region, PakistaIn order to assess the full impact of drought, Shaheen and Baig analyzed the meteorological and agricultural during 1989, 1995, 2001, and 2007 on a “temporal basis for 19 years with five year intervals” (93). Several different components were calculated in order to ascertain the full severity of the drought in Thal Doab. Some components such as the Normalized Difference Vegetation Index (NDVI), which was used to show the vegetation condition, were calculated from 30 meter Landsat images between 1989 and 2001 and 20 meter Spot image for 2007, and then analyzed via several mathematical equations (Shaheen and Baig 93). Other components, such as the Crop Yield Anomalies, NDVI anomalies, Rainfall anomalies, and Correlation Coefficient analysis were also calculated using mathematical equations. These anomalies showed the general trends in each respective area while the Correlation Coefficient Analysis was used to show the dependency between crop levels, vegetation and rainfall (Shaheen and Baig 94). A Standardized Precipitation Index (SPI) was also calculated from 11 “rainfall stations” using monthly data during the six growing season of October through March (Shaheen and Baig 94). The potential evapotranspiration was calculated from a CROPWAT model to estimate water loss via evaporation (Shaheen and Baig 94). Each category was given a multiplier which represented its relative importance in comparison to the other factors (Shaheen and Baig 94). The aggregate score from “linear combination factor model” was computed and classifications developed based upon the result. These classifications were: very severe, severe, moderate, slight, or no drought (Shaheen and Baig 94). After this was computed, meteorological and agricultural drought maps were overlaid to show general trends in drought throughout the years.
|"Agricultural Drought Severities"|
|"Meteorological Drought Severities"|
The data revealed several key factors concerning the drought. The NDVI anomalies suggest a negative correlation between the NDVI and rainfall, indicating drought (Shaheen and Baig 95). The severity of the drought was shown with SPI values. A wide, increasingly negative range was seen between 1989 and 2001, indicating a lack of rainfall and increasing drought severity. This is supported through the meteorological maps, which suggest that the drought was most severe in 2001. Similarly, the agriculture maps demonstrate that the drought was most severe in southeast and southwestern regions (Shaheen and Baig 99). Taken holistically, the data suggest that 1989 and 2001 were considered drought years of the four years of study.