Reconstruction of droughts in India using multiple  land-surface models (1951–2015)

Mishra, Vimal; Shah, Reepal; Azhar, Syed; Shah, Harsh L.; Modi, Parthkumar; Kumar, Rohini

doi:10.5194/hess-22-2269-2018

Cited by 80 publications

(77 citation statements)

References 54 publications

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“…Soil moisture is a plant-centric measure of the atmospheric water demand 34 . We did not consider deeper soil moisture in our analysis to avoid the influence of high soil moisture persistence 35 on monsoon-season dry and hot extremes. This exercise reinforced our results with estimated changes in PET, wherein surface soil moisture is projected to decrease and the combination with increased warming results in the increased frequency of concurrent hot and dry extremes over the 21st century ( Supplementary Fig.…”

Section: Future Monsoon Extremesmentioning

confidence: 99%

Future exacerbation of hot and dry summer monsoon extremes in India

et al. 2020

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Summer monsoon (June-September) precipitation is crucial for agricultural activities in India. Extremes during the monsoon season can have deleterious effects on water availability and agriculture in the region. Here, we show that hot and dry extremes during the summer monsoon season significantly impact food production in India and find that they tend to occur during El Niño years during the observed record of 1951-2018. We then use an ensemble of climate simulations for the historic (1971-2000) and future (2006-2100) that capture this coupling between El Niño and the Indian monsoon to show that the frequency of concurrent hot and dry extremes increases by a factor of 1.5 under continued greenhouse warming during the 21st century. Despite projections of summer monsoon intensification on the order of~10%, the rise in surface air temperatures as well as increase in rainfall variability contributes to more severe hot and dry monsoon extremes over India, thereby posing a substantial challenge to future food security in India.

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Section: Future Monsoon Extremesmentioning

confidence: 99%

Future exacerbation of hot and dry summer monsoon extremes in India

et al. 2020

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“…GRUN offers a unique view of large-scale features of runoff variability in regions with limited or no observational coverage. The new dataset can be exploited (i) to study the onset and development of large-scale extreme events such as droughts, (ii) to investigate links between runoff and modes of climate variability, (iii) to conduct large-scale water resource assess-ments, (iv) to detect changes in water availability and dynamics, (v) to reconstruct droughts in the last millennium in combination with tree rings (Nicault et al, 2008;Cook et al, 2010aCook et al, , b, 2015Meko et al, 2012), (vi) to benchmark regional streamflow archives and hydrological reconstructions (Wang et al, 2009;Wu et al, 2011;Caillouet et al, 2017;Mishra et al, 2018;Moravec et al, 2019;Smith et al, 2019), and (vii) to address other scientific challenges in water cycle research (Wagener et al, 2010;Montanari et al, 2013;Greve et al, 2014;Trenberth and Asrar, 2014;Hegerl et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

GRUN: an observation-based global gridded runoff dataset from 1902 to 2014

Ghiggi

Humphrey

Seneviratne

et al. 2019

Earth Syst. Sci. Data

207

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Abstract. Freshwater resources are of high societal relevance, and understanding their past variability is vital to water management in the context of ongoing climate change. This study introduces a global gridded monthly reconstruction of runoff covering the period from 1902 to 2014. In situ streamflow observations are used to train a machine learning algorithm that predicts monthly runoff rates based on antecedent precipitation and temperature from an atmospheric reanalysis. The accuracy of this reconstruction is assessed with cross-validation and compared with an independent set of discharge observations for large river basins. The presented dataset agrees on average better with the streamflow observations than an ensemble of 13 state-of-the art global hydrological model runoff simulations. We estimate a global long-term mean runoff of 38 452 km3 yr−1 in agreement with previous assessments. The temporal coverage of the reconstruction offers an unprecedented view on large-scale features of runoff variability in regions with limited data coverage, making it an ideal candidate for large-scale hydro-climatic process studies, water resource assessments, and evaluating and refining existing hydrological models. The paper closes with example applications fostering the understanding of global freshwater dynamics, interannual variability, drought propagation and the response of runoff to atmospheric teleconnections. The GRUN dataset is available at https://doi.org/10.6084/m9.figshare.9228176 (Ghiggi et al., 2019).

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“…We aggregated daily soil moisture for each grid to monthly to avoid the influence of precipitation and temperature variability within a month. We estimated monthly soil moisture percentiles at 60-cm depth, which is a typical root zone depth for most crops, following Mishra et al (2018). We estimated soil moisture percentiles (SMP) using the empirical Weibull plotting position method (Andreadis & Lettenmaier, 2006).…”

Section: Methodsmentioning

confidence: 99%

Drought and Famine in India, 1870–2016

Mishra

Tiwari

Aadhar

et al. 2019

Geophysical Research Letters

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143

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Millions of people died due to famines in India in the nineteenth and twentieth centuries; however, the relationship of historical famines with drought is complicated and not well understood. Using station‐based observations and simulations, we reconstruct soil moisture (agricultural) drought in India for the period 1870–2016. We show that over this century and a half period, India experienced seven major drought periods (1876–1882, 1895–1900, 1908–1924, 1937–1945, 1982–1990, 1997–2004, and 2011–2015) based on severity‐area‐duration analysis of reconstructed soil moisture. Out of six major famines (1873–74, 1876, 1877, 1896–97, 1899, and 1943) that occurred during 1870–2016, five are linked to soil moisture drought, and one (1943) was not. The three most deadly droughts (1877, 1896, and 1899) were linked with the positive phase of El Niño–Southern Oscillation. Five major droughts were not linked with famine, and three of those five nonfamine droughts occurred after Indian independence in 1947.

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Reconstruction of droughts in India using multiple land-surface models (1951–2015)

Cited by 80 publications

References 54 publications

Future exacerbation of hot and dry summer monsoon extremes in India

Future exacerbation of hot and dry summer monsoon extremes in India

GRUN: an observation-based global gridded runoff dataset from 1902 to 2014

Drought and Famine in India, 1870–2016

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