2016
DOI: 10.1016/j.ecolind.2016.06.041
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Application of indicators for identifying climate change vulnerable areas in semi-arid regions of India

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Cited by 50 publications
(27 citation statements)
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“…We designate wells as “dry” in a given season and year if they did not record any data in that time frame, and estimate the % dry wells in a region as ratio of the number of dry wells to the total number of monitoring wells in that region. For climate analysis, we aggregate the % dry wells at the regional scale of NI and SI between 1996 and 2016 (Figure S6), and detrend the data using the least squares approach to reduce the effect of systematic changes (e.g., changing technology or groundwater irrigated area) in a region (Hosseini‐Moghari et al, ; Kumar et al, ). Overall, we find a significant negative correlation between the 36‐month standardized precipitation index ( r = −0.44, p value < 0.1; Figure a) for each year, and the residuals of the % dry well time series.…”
Section: Resultsmentioning
confidence: 99%
“…We designate wells as “dry” in a given season and year if they did not record any data in that time frame, and estimate the % dry wells in a region as ratio of the number of dry wells to the total number of monitoring wells in that region. For climate analysis, we aggregate the % dry wells at the regional scale of NI and SI between 1996 and 2016 (Figure S6), and detrend the data using the least squares approach to reduce the effect of systematic changes (e.g., changing technology or groundwater irrigated area) in a region (Hosseini‐Moghari et al, ; Kumar et al, ). Overall, we find a significant negative correlation between the 36‐month standardized precipitation index ( r = −0.44, p value < 0.1; Figure a) for each year, and the residuals of the % dry well time series.…”
Section: Resultsmentioning
confidence: 99%
“…The study was undertaken in two rural districts of Karnataka -Kolar and Gulbarga, and one urban site -Bangalore city (a growing destination for migrants) (Figure 1). Kolar and Gulbarga were chosen because they see significant commuting and migration, are among the least developed districts in Karnataka state (Government of Karnataka, 2014), and face several environmental risks such as severe water scarcity, recurrent droughts, and increasing natural resource degradation (BCCI-K, 2012;Kumar, Raizada, Biswas, Srinivas, & Mondal, 2016;Singh et al, 2017). Climate change is projected to affect both districts significantly: the average temperature is projected to increase by 1.96°C in Kolar and 2.19°C in Gulbarga.…”
Section: Description Of Study Sitesmentioning
confidence: 99%
“…A diferencia de Latinoamérica, en otros países se aplicaron metodologías de evaluación del índice de vulnerabilidad de los sistemas agrícolas al cambio climático, uno de ellos fue el Estado de Karnataka en Canadá para lo cual recopilaron datos de pérdidas de producción de cultivos, exposición, sensibilidad y capacidad de adaptación donde asignaron ponderaciones a cada indicador logrando priorizar los distritos más vulnerables, con el fin de aplicar medidas de mitigación a cultivos de cereales, legumbres y oleaginosas. También estimaron que, alrededor del 70% de la superficie cultivada se enfrenta a un nivel de vulnerabilidad de "extremo a alto" proponiendo la implementación de sitios específicos como mejores instalaciones de salud y educación, expansión de oportunidades de empleo en otros sectores o reducir la dependencia excesiva de la agricultura (Kumar et al, 2016). Mientras que en la región rural de Le Marche (Italia) la actividad agrícola arable cumple un rol importante en el desarrollo económico de la zona.…”
Section: Efectos En La Agricultura De Otras Zonasunclassified