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Hydrological Functioning and Water Availability in a Himalayan Karst Basin under Climate Change
Karst springs are important water sources for both human needs and environmental flows. The responses of karst springs to hydrometeorological factors vary depending on local conditions. In this study, we investigated Martandnag spring in the Liddar catchment in the Kashmir valley of northern India. We used statistical time series (autocorrelation and cross-correlation) and machine-learning (ML) techniques (random forest regression (RFR) and support vector regression (SVR)) to characterize how rainfall, temperature, and snow cover affect the karst spring flow and predict the future responses of the spring stage based on climate scenarios, in the Intergovernmental Panel on Climate Change Assessment Report 6. The statistical time series showed that the memory effect of Martandnag spring varies from 43 to 61 days, indicating moderate karstification and a relatively high storage capacity of the karst aquifer in the Liddar catchment. The delay between recharge and discharge varies from 13 to 44 days, and it is more strongly correlated to snow/ice melt than to rainfall. The ML analysis shows that SVR outperformed RFR in predicting spring flow. Under all climate scenarios, a trained SVR model showed that spring flow increased during the late winter to early spring, and decreased during the summer (except in August) and in autumn. Scenarios with increased greenhouse gas emissions further reduced flow in the summer and autumn. These predictions can be helpful for water-resource planning in similar watersheds in the Western Himalayas.
Hydrological Functioning and Water Availability in a Himalayan Karst Basin under Climate Change
Karst springs are important water sources for both human needs and environmental flows. The responses of karst springs to hydrometeorological factors vary depending on local conditions. In this study, we investigated Martandnag spring in the Liddar catchment in the Kashmir valley of northern India. We used statistical time series (autocorrelation and cross-correlation) and machine-learning (ML) techniques (random forest regression (RFR) and support vector regression (SVR)) to characterize how rainfall, temperature, and snow cover affect the karst spring flow and predict the future responses of the spring stage based on climate scenarios, in the Intergovernmental Panel on Climate Change Assessment Report 6. The statistical time series showed that the memory effect of Martandnag spring varies from 43 to 61 days, indicating moderate karstification and a relatively high storage capacity of the karst aquifer in the Liddar catchment. The delay between recharge and discharge varies from 13 to 44 days, and it is more strongly correlated to snow/ice melt than to rainfall. The ML analysis shows that SVR outperformed RFR in predicting spring flow. Under all climate scenarios, a trained SVR model showed that spring flow increased during the late winter to early spring, and decreased during the summer (except in August) and in autumn. Scenarios with increased greenhouse gas emissions further reduced flow in the summer and autumn. These predictions can be helpful for water-resource planning in similar watersheds in the Western Himalayas.
Hydrological Functioning and Water Availability in a Himalayan Karst Basin under Climate Change
Shishir K. Sarker (author) / Junfeng Zhu (author) / Alan E. Fryar (author) / Ghulam Jeelani (author)
2023
Article (Journal)
Electronic Resource
Unknown
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