Novel Index for Hydrological Drought Monitoring Using Remote Sensing Approach: Standardized Water Surface Index (SWSI)

Abstract

Most of the drought indices designed for hydrological drought monitoring use location-specific data, while there are only a handful of indices designed for hydrological drought monitoring using remote sensing data. This study revealed a novel drought index, Standardized Water Surface Index (SWSI), developed for hydrological drought monitoring. The water surface areas required to calculate the SWSI can be extracted from remote sensing data entirely using both the optical (Landsat 5, 7, and 8) and SAR (Sentinel-1). Furthermore, the developed index was applied to five major reservoirs/tanks; Iranamadu, Mahavilachchiya, Kantale, Senanayaka Samudhraya, and Udawalawa, located in Sri Lanka to monitor respective hydrological drought status for the period from 2000 to 2020. Cloud computing platform such as Google Earth Engine (GEE) provides a good basement to use this index effectively, as it can extract long-term water surface area covering a large geographical area efficiently and accurately. The surface water area extraction from satellite data of those tanks shows an accuracy of more than 95%, and in the event of a severe hydrological drought, the water surface area of the tanks is less than 25% of the total and lasts for more than three to four months. It was also determined that in some years, the surface water area of tanks dropped to as low as 7%. The strong correlation observed between the Standardized Precipitation Index (SPI) and SWSI is indicated by the Pearson correlation coefficient ranging from 0.58 to 0.67, while the correlation between the Vegetation Condition Index (VCI) and SWSI ranges from 0.75 to 0.81. Timely drought monitoring over large geographical areas can be more accurately performed with the SWSI index compared to existing hydrological drought monitoring indices. The SWSI could be more useful for areas that do not have measurable field data.