Integrated Assessment of Drought Vulnerability Using Indicators for Dhasan Basin in Bundelkhand Region, Madhya Pradesh, India

Kar, Saswat Kumar; Thomas, T.; Singh, R. P.; Patel, Lokesh

doi:10.18520/cs/v115/i2/338-346

Cited by 23 publications

(8 citation statements)

References 12 publications

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“…This sensitive index is formed by combining the different satellite-based drought indicators with the help of GIS overlay method. According to Sensitivity Index, the study area is divided into ve sub classes, namely 'very high' (81-100), 'high'(61-80), 'medium' (41-60), 'low' (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) and 'very poor' (0-20) covering an area of 54.66, 257.85, 590.52, 869.96 and 171.01 sq km accounting 2.83, 13.33, 30.53, 44.47 and 8.84%, respectively, of the total area (Fig. 9).…”

Section: Resultsmentioning

confidence: 99%

Geospatial assessment of agricultural drought vulnerability using integrated three-dimensional model in the upper Dwarakeshwar river basin in West Bengal, India

Senapati

Das

2022

Environ Sci Pollut Res

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It is essential to measure the degree of agricultural drought vulnerability in an underdeveloped rain-fed agro-based economy at the local, regional, and national level. Agricultural drought has become a major concern in respect to the global food crisis for investigation and development of a sustainable agricultural system that sustains the food security of a country. In this research, delineation of the agricultural drought vulnerability (ADV) status has been carried out by multidimensional mixed-method index approach using remote sensing and geographic information system. An integrated three-dimensional model has been adopted for this study. The three indices of this model are -Exposure Index (EI), Sensitivity Index (SI) and Adaptive Capacity Index (ACI).Exposure Index has been calculated using NDDI, LULC, ADI, DF, ADD and PI. Sensitivity Index has been calculated using satellitebased remote sensing factor VHI, NDWI, EVI, NDVI, VCI, NDWI, LST, and TCI. The ACI has been formed by combining the Environmental Adaptive Capacity (EAC), Social Adaptive Capacity (SAC) and Economical Adaptive Capacity (EcAC) Index. Each index has been computed by assigning the weights based on their relative importance by using the Analytic Hierarchy process (AHP) approach. Final results were classi ed into ve vulnerability zones, e.g., very low, low, moderate, high, and very high covering an area 362.32 km 2 , 186.68 km 2 , 568.69 km 2 , 547.05 km 2 and 266.89 km 2 respectively. Finally, results have been validated with long term Aman paddy yield data (2004 to 2014) through the Yield Anomaly Index (YAI). Data Source Exposure index LANDSAT 8 (spatial resolution 30 m) (LC08_L1TP_139044_20181220_20181227_01_T1) USGS earth explorer (https://earthexplorer.usgs.gov/) Rainfall data (Daily basis) from 2003 to 2013 Climate forecast System Reanalysis (https://globalweather.tamu.edu/) Sensitivity index LANDSAT/LC08/C01/T1_8DAY_NDVI (30 meters spatial resolution) in 2016 Google earth engine (https://code.earthengine.google.com/) LANDSAT/LC08/C01/T1_8DAY_NDWI (30 meters spatial resolution) in 2016 Google earth engine (https://code.earthengine.google.com/) LANDSAT/LC08/C01/T1_8DAY_EVI(30 meters spatial resolution) in 2016 Google earth engine (https://code.earthengine.google.com/) LST (MODIS/006/MOD11A1) 1000 meters spatial resolution in 2016 Google earth engine (https://code.earthengine.google.com/) Adaptive capacity index Ground water data Central Ground Water Board (CGWB), India. (http://cgwb.gov.in/) SRTM DEM(n23_e086_1arc_v3,n23_e087_1arc_v3)spatial resolution 30 meters USGS earth explorer (https://earthexplorer.usgs.gov/) Rainfall data Indian Meteorological Department (IMD),Pune (http://dsp.imdpune.gov.in/) Soil depth, texture, and Drainage (Scale -1: 50,000) National Bureau of Soil Survey and Land Use planning (NBSS&LUP) Aquifer systems of India (Scale -1: 50,000) Central Ground water Board, Ministry of water Resources, Government of India. Population, no of agricultural labour and farmer, Literacy relates Census of India, 2011 Health data, O...

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Section: Resultsmentioning

confidence: 99%

Geospatial assessment of agricultural drought vulnerability using integrated three-dimensional model in the upper Dwarakeshwar river basin in West Bengal, India

Senapati

Das

2022

Environ Sci Pollut Res

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“…In agricultural drought, it is expressed as the response or sensitivity degree of crop growth to the loss of water deficit intensity in growth period (Xu et al 2010). At present, the research on agricultural drought mainly focuses on drought risk assessment (Hoque et al 2021;Jin et al 2016), drought vulnerability assessment (Cui et al 2019;Tanago et al 2018), and drought warning (Kar et al 2018;Ewbank et al 2019), but most of them are in view of the whole growth period, and the difference in water sensitivity of crops in different growth periods will lead to different drought losses. Huang et al (2019) used crop model to simulate the effect of drought of different durations at the seedling, jointing, and filling stages on corn yield.…”

Section: Introductionmentioning

confidence: 99%

Quantitative research on drought loss sensitivity of summer maize based on AquaCrop model

Manjing

Liu

2022

Nat Hazards

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In this study, the growth periods of summer maize was divided into seedling, booting and flowering-grain stage. Based on the simulation results of AquaCrop model, the drought loss sensitivity of summer maize in different growth periods was analyzed. The sensitivity curves fitting using the soil moisture content of the effective root zone and the fixed soil layer both indicated that the booting stage was the most sensitive to water stress, which was the critical period for irrigation, followed by the seedling stage. Compared with the curve parameters fitted by the soil water content of the effective root zone, the maximum Biomass Loss Rate fitted by the fixed soil layer water content was higher and the Drought Hazard Index corresponding to the disaster-causing point and the turning point in the seedling stage moved backward. Accordingly, the best irrigation opportunity may be missed and resulting in a large reduction in production if an irrigation scheme is formulated at the seedling stage based on the sensitivity curve of summer maize fitted by the water content of a fixed soil layer. This study also adapted the Jensen model to calculate the normalized moisture sensitivity coefficient and studied the response of final crop yield to water deficit in different growth periods. The results showed that the normalized moisture sensitivity coefficients at the seedling stage, booting stage, and floweringgrain stage were 0.251, 0.524, 0.224 respectively, which verified the rationality and feasibility of using the cumulative loss of biomass to measure the final yield loss.

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“…Similarly, different sets of drought parameters have been taken into consideration in a study conducted by Kar et al (2018) to identify the vulnerable regions through assigning appropriate weights parameters. The Standardized Drought Vulnerability Index (SDVI) is an index developed by Oikonomou et al (2019) which incorporates precipitation patterns, the supply and demand trends, and the socio-economic background to drought vulnerability.…”

Section: Introductionmentioning

confidence: 99%

Observed and projected changes in extreme drought and wet-prone regions over India under CMIP5 RCP8.5 using a new vulnerability index

Jena

Azad

2021

Preprint

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Past versions of vulnerability index have shown ability to detect susceptible region by assessing socio-economic parameters at local scales. However, due to variability of these vulnerability index respect to socio-economic parameters, cann’t be utilized to predict the susceptibility region. The present endeavor aims to develops a new vulnerable index which identify and predict the spatio-temporal imprint of extreme drought and wet events at various scales 1o×1o in India by analyzing monthly observed and Coupled Model Inter-Comparison Phase 5 (CMIP5) rainfall data at spatial scale of time period pertaining to 1901-2100. New vulnerability index is proposed by consolidating the outcomes of Standard Precipitation Index (SPI) at different time scales such as 3- and 12-month and along with weights of individual grids. The weights of individual grid is calculated through the occurrence of extreme drought and wet events in the recent past which is to include a climate change factor in the proposed index. Based on the spatial distribution of high index values, the expected vulnerable regions concerning extreme drought events will be in Northeast, Northeast Central, East Coast, West, Northwest, Northcentral, and some grids in South part of India. Similarly, vulnerable regions concerning extreme wet events are likely to be in the Northeast, West Coast, East Coast, and some grids in the Peninsular region.Further, a conceptual model is presented to quantify the severity of extreme events. The analyses reveal that on the CMIP5 model data, it is obtained that 2024, 2026-27, 2035, 2036-37, 2043-44, 2059-60, 2094 are likely to be the most prominent drought years in all-India monsoon rainfall and their impact will persist for a longer time. Similarly, the most prominent wet events are predicted to be 2076, 2079-80, 2085, 2090, 2092, and 2099.

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Integrated Assessment of Drought Vulnerability Using Indicators for Dhasan Basin in Bundelkhand Region, Madhya Pradesh, India

Cited by 23 publications

References 12 publications

Geospatial assessment of agricultural drought vulnerability using integrated three-dimensional model in the upper Dwarakeshwar river basin in West Bengal, India

Geospatial assessment of agricultural drought vulnerability using integrated three-dimensional model in the upper Dwarakeshwar river basin in West Bengal, India

Quantitative research on drought loss sensitivity of summer maize based on AquaCrop model

Observed and projected changes in extreme drought and wet-prone regions over India under CMIP5 RCP8.5 using a new vulnerability index

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