Authors: Caily Schwartz*, The University of Alabama in Huntsville, Walter Lee Ellenburg, The University of Alabama in Huntsville / NASA SERVIR, Vikalp Mishra, The University of Alabama in Huntsville / NASA SERVIR, Timothy Mayer, The University of Alabama in Huntsville / NASA SERVIR, Robert Griffin, The University of Alabama in Huntsville, Mir Matin, International Centre for Integrated Mountain Development, Faisal Qamer, International Centre for Integrated Mountain Development, Tsegaye Tadesse, National Drought Mitigation Center, University of Nebraska - Lincoln
Topics: Earth Science, Agricultural Geography, Remote Sensing
Keywords: drought, earth observations, agriculture, remote sensing, south asia
Session Type: Virtual Poster
Start / End Time: 3:05 PM / 4:20 PM
Room: Virtual 51
Presentation File: No File Uploaded
Pakistan relies heavily on the agricultural sector and has experienced intense agricultural droughts in recent years. This study defines agricultural droughts as when prolonged dry conditions impact crop growth and sustenance. Identifying and monitoring drought conditions mitigates the impacts that a drought may cause. The onset, persistence and intensity of drought is best determined using multiple variables that account for different aspects of the hydrological cycle. A composite drought index (CDI), using 10 geophysical variables, is developed to improve the spatial and temporal understanding of historical agricultural droughts. Remotely sensed satellite data are used to develop the CDI, allowing for coverage of the entire country. To create the CDI, weights for each dataset are created using the linear component based on month and location. An in depth analysis of the input variables provides information regarding which indicators contribute the most when determining drought in each district. This information will be used to develop a method that removes the least important variables, by month and district, creating a customized composite drought index. Crop production data for wheat, maize, rice, cotton and barley is used to evaluate both CDIs, the CDI using 10 variables and the customized CDI. This will determine whether the customized CDI, with less variables, still recognizes drought in a similar way as the CDI with 10 variables. The framework developed in this study has the ability to enhance existing drought monitoring and forecasting systems in the country.