Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)

• Published in: Sustainability Vol. 14; no. 12; p. 6974
• Main Authors: Jayanthi, Sri Lakshmi Sesha Vani, Keesara, Venkata Reddy, Sridhar, Venkataramana
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022
• Subjects: Arid zones; Artificial lakes; Availability; Climate change; Correlation coefficient; Correlation coefficients; Crop growth; Datasets; Fluctuations; Global climate models; Hydrologic models; Hydrology; Irrigation; Irrigation requirements; Learning algorithms; Machine learning; Precipitation; Predictions; Regions; Seasons; Semi arid areas; Semiarid lands; Soil water; Stream flow; Support vector machines; Surface runoff; Surface water; Water availability; Water level fluctuations; Water levels; Water management; Water resources; Water resources management; Water shortages; Watersheds; India
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su14126974

Lakes are major surface water resource in semi-arid regions, providing water for agriculture and domestic use. Prediction of future water availability in lakes of semi-arid regions is important as they are highly sensitive to climate variability. This study is to examine the water level fluctuations in Pakhal Lake, Telangana, India using a combination of a process-based hydrological model and machine learning technique under climate change scenarios. Pakhal is an artificial lake built to meet the irrigation requirements of the region. Predictions of lake level can help with effective planning and management of water resources. In this study, an integrated approach is adopted to predict future water level fluctuations in Pakhal Lake in response to potential climate change. This study makes use of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset which contains 21 Global Climate Models (GCMs) at a resolution of 0.25 × 0.25° is used for the study. The Reliability Ensemble Averaging (REA) method is applied to the 21 models to create an ensemble model. The hydrological model outputs from Soil and Water Assessment Tool (SWAT) are used to develop the machine-learning based Support Vector Regression (ν-SVR) model for predicting future water levels in Pakhal Lake. The scores of the three metrics, correlation coefficient (R2), RMSE and MEA are 0.79, 0.018 m, and 0.13 m, respectively for the training period. The values for the validation periods are 0.72, 0.6, and 0.25 m, indicating that the model captures the observed lake water level trends satisfactorily. The SWAT simulation results showed a decrease in surface runoff in the Representative Concentration Pathways (RCP) 4.5 scenario and an increase in the RCP 8.5 scenario. Further, the results from ν-SVR model for the future time period indicate a decrease in future lake levels during crop growth seasons. This study aids in planning of necessary water management options for Pakhal Lake under climate change scenarios. With limited observed datasets, this study can be easily extended to the other lake systems.