Assessment of drought with a real-time web-based application for drought management in humid tropical Kerala, India

Gopinath, Girish; Surendran, U.; Abhilash, S.; Nagakumar, K.Ch.V.; Anusha, Ch.

doi:10.1007/s10661-020-08665-9

Cited by 5 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the Indian government, severe droughts lower the gross domestic productivity by 2%–5% (Reference therein). India witnessed some of the worst droughts in the last 100 years, including 1917–1918, 1965–1966, 1986–1987, 2002, 2009, and 2012 (Gopinath et al, 2020). According to the National Institute of Disaster Management (Gupta et al, 2014), 33% of India's cropland received less than 750 mm of annual rainfall, considered as a ‘drought hotspot’.…”

Section: Introductionmentioning

confidence: 99%

Spatio‐temporal drought susceptibility assessment of Ken River Basin, Central India, and its evaluation through river's morphometry

et al. 2022

View full text Add to dashboard Cite

Regular monitoring of drought events, watershed characterization, management, and development efforts is crucial for future disaster predictions and mitigation strategies. The drought susceptibility investigation has been carried out in the Ken River Basin of Bundelkhand region, one of the most drought-prone areas in India. Proxies used in the present study are long-term climatological data (rainfall, standardized precipitation index, and aridity index), satellite data (slope, drainage density [DD], distance to river, and normalized difference vegetation index), lithology, lineament density, and groundwater depth. By the analytical hierarchy process, weightage of each factor is assigned according to its importance. Study shows that nearly 48% of the area of the basin experiences moderate to severe drought conditions. In the last five decades, there have been 22 years of extreme drought, with the most extended period being 1972-1974 and the driest year being 2006-2007. Sensitivity analysis reveals that lithology, slope, and DD are the most significant parameters in the susceptibility analysis. Model validation through an artificial neural network demonstrates the model's high accuracy (0.9) and sensitivity with minor errors. An integrated study of drought susceptibility and morphometry is useful for identifying the drought risk hotspots in the basin. The investigation will be helpful in river basin management and disaster management strategies.

show abstract

Section: Introductionmentioning

confidence: 99%