Statistical downscaling of high-resolution precipitation in India using convolutional long short-term memory networks

• Published in: Journal of water and climate change Vol. 15; no. 3; pp. 1120 - 1141
• Main Authors: Misra, Saptarshi, Sarkar, Sudeshna, Mitra, Pabitra, Shastri, Hiteshri
• Format: Journal Article
• Language: English
• Published: 01.03.2024
• ISSN: 2040-2244; 2408-9354
• DOI: 10.2166/wcc.2024.497

Statistical downscaling of the General Circulation Model (GCM) simulations are widely used for accessing climate changes in the future at different spatiotemporal scales. This study proposes a novel Statistical Downscaling (SD) model established on the Convolutional Long Short-Term Memory (ConvLSTM) Network. The methodology is applied to obtain future projection of rainfall at 0.25° spatial resolution over the Indian sub-continental region. The traditional multisite downscaling models typically perform downscaling on a single homogeneous rainfall zone, predicting rainfall at only one grid point in a single model run. The proposed model captures spatiotemporal dependencies in multisite local rainfall and predicts rainfall for the entire zone in a single model run. The study proposes a Shared ConvLSTM model providing a single end-to-end supervised model for predicting the future precipitation for entire India. The model captures the regional variability in rainfall better than a region-wise trained model. The projected future rainfall for different scenarios of climate change reveals an overall increase in the rainfall mean and spatially non-uniform changes in future rainfall extremes over India. The results highlight the importance of conducting in-depth hydrologic studies for different river basins of the country for future water availability assessment and making water resource policies.