Climate change effect on water resources in Varanasi district, India

Abstract

Evapotranspiration and water availability are driven by changing climate and land cover parameters. In the present study, climatological records and land cover data were analysed simultaneously to accomplish the spatial distributions of climate change effects on water resources in Varanasi district, north India. Humidity–aridity was assessed by Lang's rain factor and De Martonne's aridity index, based on mean monthly rainfall and air temperature from seven meteorological stations. The climate change effect on water resources was evaluated using a 5 × 5 matrix that includes water availability and the aridity index by considering two time periods: 1941–1970 (1950s) and 1971–2000 (1980s). The methodology is based on seasonal crop evapotranspiration (ETc) (initial, mid‐season, end season and cold season) and annual water availability calculations. The high values (≤ 1,045 mm) of ETc were identified during the mid‐season stage. Water availability indicates decreases in the maximums from 718 to 636 mm during the two analysed periods, with a negative impact at the spatial scale. Lang's rain factor (< 40) indicates an arid climate in the northwest, west, east and central parts of the district and a humid climate (Lang's rain factor > 40) in the south. De Martonne's aridity index indicates rapid aridization from south to north (28.3 in the 1950s and 25.6 in the 1980s). The high and very high climate effects on water resources in Varanasi district were found mainly in the crop lands, while in the urban areas the climate effect is low. The much affected area by climate change and land cover was depicted during the recent period (1980s). This statement was proved also by the Mann and Kendall test, which indicates a negative trend for annual precipitation at all stations (for the period 1941–2000), while the mean annual temperature had a positive trend for four stations. These findings suggest that climate change had a negative effect on water resources during the last 60 years in the study area.

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“…Studying AI and its anomalies (Aridity Index Anomaly -AIA) are like monitoring climate variability and its impact on vegetation. The study of these indices is a good tool that policy makers can use to plan agricultural activities, and the use of water resource [13] and its management.…”

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