Drought hotspot analysis and risk assessment using probabilistic drought monitoring and severity–duration–frequency analysis

Abstract

Drought hotspot identification requires continuous drought monitoring and spatial risk assessment. The present study analysed drought events in the agriculture‐dominated mid‐Mahanadi River Basin in Odisha, India, using crop water stress as a drought indicator. This drought index incorporated different factors that affect crop water deficit such as the cropping pattern, soil characteristics, and surface soil moisture. The drought monitoring framework utilized a relevance vector machine model‐based classification that provided the uncertainty associated with drought categorization. Using the proposed framework, drought hotspots are identified in the study region and compared with indices based on precipitation and soil moisture. Further, a bivariate copula is employed to model the agricultural drought characteristics and develop the drought severity–duration–frequency (S–D–F) relationships. The drought hotspot maps and S–D–F curves are developed for different locations in the region. These provided useful information on the site‐specific drought patterns and the characteristics of the devastating droughts of 2002 and 2012, characterized by an average drought duration of 7 months at several locations. The site‐specific risk of short‐ and long‐term agricultural droughts are then investigated using the conditional copula. The results suggest that the conditional return periods and the S–D–F curves are valuable tools to assess the spatial variability of drought risk in the region.

Assessment of impacts of potential climate change on meteorological drought characteristics at regional scales

“…Copula has been successfully used to develop S‐D‐F curves (Shiau and Modarres, 2009; Janga Reddy and Ganguli, 2012; Amirataee et al ., 2018; Samantaray et al ., 2019). A copula function describes joint probability distribution function ( H 1, 2, …, d ( x 1 , x 2 , … x d )) of a d ‐dimensional random vector ( X (1) , X (2) , …, X ( d ) ) as given by Equation ).…”

“…In a recent study, Samantaray et al . (2019) developed the agricultural drought S‐D‐F curves for locations in the mid‐Mahanadi river basin in Odisha, India, using bivariate copula. The S‐D‐F relationships were found to vary spatially reflecting the differences in vulnerability of the locations to droughts.…”

Analysis of persistence in the flood timing and the role of catchment wetness on flood generation in a large river basin in India

“…The combined soil moisture estimates (Chung et al 2018) are generated by blending passive and active microwave soil moisture retrieval algorithm. The surface soil moisture were obtained from the Essential Climate Variable-Soil Moisture (ECV-SM) data under the European Space Agency (ESA)-Climate Change Initiative (Liu et al 2012;Dorigo et al 2017;Gruber et al 2017;Samantaray et al 2019). The ECV-SM global soil moisture combined dataset (Chung et al 2018) provides volumetric soil moisture (m 3 /m 3 ) at daily time step and at 0.25˚ grid resolution from 1978 to 2018.…”

“…Although a very few studies (Samantaray et al 2019) is motivated by the fact that floods over MRB is becoming more frequent in the recent past (Mahapatra 2015;Jena et al 2014). To understand the effect of catchment wetness on flood properties (i.e., severity and the timing of the event), following earlier studies (Ettrick et al 1987; Ivancic and Shaw 2015) we select lagged d-day soil moisture data as an indicator for the catchment wetness over the sub-catchments of MRB.…”

An advanced data collection procedure in bivariate drought frequency analysis

“…The occurrence of a drought affects planning and managing available water resources (Dracup, Lee, & Paulson, 1980). In this context, drought duration and drought severity, as two main dependent variables for real-time drought management, have been more examined by researchers (Abdi et al, 2017;Byakatonda, Parida, Moalafhi, & Kenabatho, 2018;Hao & Singh, 2013;Kang & Jiang, 2019;Kim, Valdes, & Yoo, 2006;Lee, Modarres, & Quarda, 2013;Samantaray, Singh, Ramadas, & Panda, 2019;Shiau, Feng, & Nadarajah, 2007;Shiau & Modarres, 2009). Note that the drought duration and severity are tightly interdependent.…”