Abstract
Drought monitoring is essential to detect the presence of drought, and the comprehensive change of drought conditions on a regional or global scale. This study used satellite precipitation data from the Tropical Rainfall Measuring Mission (TRMM), but refined the data for drought monitoring in Java, Indonesia. Firstly, drought analysis was conducted to establish the standardized precipitation index (SPI) of TRMM data for different durations. Time varying SPI spatial downscaling was conducted by selecting the environmental variables, normalized difference vegetation index (NDVI), and land surface temperature (LST) that were highly correlated with precipitation because meteorological drought was associated with vegetation and land drought. This study used time-dependent spatial regression to build the relation among original SPI, auxiliary variables, i.e., NDVI and LST. Results indicated that spatial downscaling was better than nonspatial downscaling (overall RMSEs: 0.25 and 0.46 in spatial and nonspatial downscaling). Spatial downscaling was more suitable for heterogeneous SPI, particularly in the transition time (R: 0.863 and 0.137 in June 2019 for spatial and nonspatial models). The fine resolution (1 km) SPI can be composed of the environmental data. The fine-resolution SPI captured a similar trend of the original SPI. Furthermore, the detailed SPI maps can be used to understand the spatio-temporal pattern of drought severity.
Highlights
The time varying spatial downscaling proposed in this study provides a reliable representation of the fine resolution standardized precipitation index (SPI) maps
This study aims to refine the SPI maps using a time varying spatial downscaling approach
The SPI each time scale, e.g., SPI 3, SPI 6, or SPI 9 in Java, Indonesia is based on five-year Tropical Rainfall Measuring Mission (TRMM) satellite imagery (0.25◦ spatial resolution)
Summary
Satellite observations have been used to assess the effects of drought on the ecosystem, including vegetation growth and health, as well as monitor soil moisture drought, temperature variability, and precipitation [6,7]. Vegetation related drought indicators have been developed, such as normalized difference vegetation index (NDVI) [8], anomaly vegetation index [9], and vegetation health index [10]. The land surface temperature (LST)-derived and soil moisture-derived indices were applied to monitor the impact and duration of drought [6,11]. Spatial variations of rainfalls in a region that have been affected by satellite-based environmental factors, such as NDVI and LST [8,12,13]
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