Analysing model disparity in diagnosing the climatic and human stresses on runoff variability over India
Journal of Hydrology 581: 124407-124407
Article 2019 English
Authors
JS
Jhilam Sinha
JD
Jew Das
SJ
Srinidhi Jha
Abstract
1 min read
Water availability is crucial for sustaining the development and even existence of human civilization. Identifying major sources of its variability is of paramount importance. The climate elasticity method provides a suitable platform to quantify the relative influences of climatic variables and anthropogenic stresses using Budyko hypothesis to the changes in runoff but judicious selection of Budyko based equations with relevant runoff elasticities is vital. In this paper, comparative study is carried out using climate elasticity approach in 19 catchments across India, to evaluate the disparity among runoff elasticities and percentage contributions of climatic variables (precipitation, maximum and minimum temperature, wind speed, sunshine duration, and relative humidity) and anthropogenic stress in runoff alterations. Among climatic parameters, precipitation has shown the maximum influence in 16 catchments. In 15 catchments, maximum temperature has higher relative contribution than minimum temperature. In addition, anthropogenic influence is higher in 9 catchments for Two-parameter approach (precipitation and potential evapotranspiration) whereas it has higher impact in 10 catchments as per Multi-parameter (including evapotranspiration elasticities) approach. Decomposing evapotranspiration elasticity to five climatic variables has been proven to be unproductive as it has produced more disparity among the percentage contribution values. The standard deviation values in contributions are more in the case of the Multi-parameter model. Thus, it is pragmatic to adopt Multi-parameter model that constitutes runoff elasticities to different climatic variables, when assignment demands the individual influences of these variables on hydrology. Adding more parameters into the framework introduces more error in the assessment of impacts of climatic variabilities.
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