Statistical Downscaling of High Resolution Precipitation in India using Convolutional Long Short Term Memory Networks

Abstract Different empirical-statistical downscaling methods are widely applied in weather and climate studies for assessment of effect of global-scale climate conditions on local-scale climate variables. Statistical downscaling of the General Circulation Model (GCM) simulations are widely used for accessing changes in future climate at different spatiotemporal scales. The process of downscaling is affected by uncertainty associated with the GCM selection and downscaling model. This study proposes a novel Statistical Downscaling (SD) model established on Convolutional Long Short Term Memory (ConvLSTM) Network. The methodology is applied to obtain future projection of rainfall at 0.25° spatial resolution over entire 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. This particular modeling framework shares a ConvLSTM model across multiple neighboring regions in order to capture the similarity in rainfall patterns of and customizes it to individual grid points. This novel downscaling approach provides a single end-to-end supervised model for predicting the future precipitation series for entire India. The model captures the regional variability in rainfall superior to a region wise trained model. The proposed methodology performs superior as compared to the presently available state of the art LSTM and Kernel Regression (KR) based methodologies previously applied for Indian sub continental region. The projected future rainfall for different scenarios of climate change, obtained with the help of the proposed downscaling model reveals an overall increase in the rainfall mean over India. The changes in future rainfall extremes over India are spatially nonuniform with a probable increase at the western-ghats and northeastern India. The rainfall extreme at the same time is observed to decrease in northern and western India as well as along the southeastern coastline. These results highlight the importance of conducting in depth hydrologic study for different river basins of the country for future water availability assessment and making water resource policies.