Abstract
Given the complexity of coastal drought events and their unique impacts on coastal ecosystems, it is vital to develop drought indices that can reflect both freshwater availability and impacts on ecological resources to better assess drought conditions over coastal regions. Existing drought indices developed for upland areas may not be suitable to capture the timing and severity of drought in the coastal regions. To explore coastal drought issue more comprehensively, this paper constructed the Coastal Composite Drought Indices (CCDIs) based on the entropy-weighted Euclidean distance method by freely combining six variables: precipitation (P), evaporation (E), runoff (R), salinity (S), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), in the Jinjiang River Basin (JJRB) and the Jiulong River Basin (JLRB). The CCDIs were later compared to the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI) to select the Optimized Drought Indices (ODIs). The results demonstrated that the Precipitation Evaporation Runoff Composite Index (PER) is more sensitive to capturing short-term coastal drought events compared to other CCDIs. PER performed better than SPI and PDSI in detecting the onset of drought development and reflecting drought persistence. The salinity exhibited mixed responses to long-term drought events in different river basins depending on the size of each coastal system and its characteristics, thus raising the issue of regional applicability. Through a series of cross-comparisons and validations, the Runoff NDVI LST Composite Index (RNL) and the Runoff Salinity NDVI Composite Index (RSN) were chosen to monitor long-term droughts in the JJRB and JLRB, respectively. Overall, all three indices have the ability to capture the dynamics and trends of the variables and provide better information on the occurrence, persistence, and end of droughts in coastal areas.
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