A Remotely Sensed Global Terrestrial Drought Severity Index

Abstract

Regional drought and flooding from extreme climatic events are increasing in frequency and severity, with significant adverse ecosocial impacts. Detecting and monitoring drought at regional to global scales remains challenging, despite the availability of various drought indices and widespread availability of potentially synergistic global satellite observational records. The authors have developed a method to generate a near-real-time remotely sensed drought severity index (DSI) to monitor and detect drought globally at 1-km spatial resolution and regular 8-day, monthly, and annual frequencies. The new DSI integrates and exploits information from current operational satellite-based terrestrial evapo-transpiration (ET) and vegetation greenness index [normalized difference vegetation index (NDVI)] products, which are sensitive to vegetation water stress. Specifically, this approach determines the annual DSI departure from its normal (2000–11) using the remotely sensed ratio of ET to potential ET (PET) and NDVI. The DSI results were derived globally and captured documented major regional droughts over the last decade, including severe events in Europe (2003), the Amazon (2005 and 2010), and Russia (2010). The DSI corresponded favorably (correlation coefficient r = 0.43) with the precipitation-based Palmer drought severity index (PDSI), while both indices captured similar wetting and drying patterns. The DSI was also correlated with satellite-based vegetation net primary production (NPP) records, indicating that the combined use of these products may be useful for assessing water supply and ecosystem interactions, including drought impacts on crop yields and forest productivity. The remotely sensed global terrestrial DSI enhances capabilities for nearreal-time drought monitoring to assist decision makers in regional drought assessment and mitigation efforts, and without many of the constraints of more traditional drought monitoring methods.

Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants

“…Overall, E/E PT appears superior to E/R n in representing water stress in the frame of TEC GPP model. In addition, E/E PET -based drought severity index (DSI) has been successfully applied to study the variation of global drought (Mu et al, 2013).…”

Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary

“…This analysis method is very popular (Hayes et al, 2012) in Hungary too (DMCSEE, 2010-14;Blanka et al, 2014). Mu et al (2013) used a drought index called Drought Severity Index (DSI), which can be generated from the ratio of evapotranspiration and potential evaporation (ET/PET), resp. Normalized Difference Vegetation Index (NDVI), for MODIS sensor data.…”

Early Warning Techniques for Local Climate Resilience: Smallholder Rice in Lao PDR

“…In the present analysis, we utilize a new measure developed by Mu et al (2013) called the Drought Severity Index (DSI). Mu and colleagues produce global DSI measures from satellite data covering the globe averaged over eight day periods from 2000 through 2011 at a resolution of 0.05 × 0.05 degrees (~5.5 × 5.5 km).…”