Discerning Hydroclimatic Behavior with a Deep Convolutional Residual Regressive Neural Network

Albert Larson, Ali S Akanda, Abdeltawab Hendawi, Soni Pradhanang, Thomas Boving

Keywords: water, climate, sustainability, supervised representation learning, societal considerations

TL;DR: We analyze and visualize the water cycles of four large US watersheds using representation learning techniques.

Abstract: Water impacts the globe daily in new and familiar ways such as the ongoing western United States drought and the 2022 Pakistan flood. These events sustain uncertainty, risk, and loss forces to the global ecosystem. Better forecasting tools are mandatory to calibrate our response in an effort to mitigate such natural hazards in our watersheds and adapt to the planet’s dynamic environment. Here, we present a Deep Convolutional Residual Regressive Neural Net (DCRRNN - pronounced “discern”) platform for obtaining, visualizing, and analyzing the basin response of watersheds to water cycle fluxes. We examine four very large basins, simulating river response to the hydroclimatic fluxes they face. Experiments modulating the lever of time lag between remotely sensed and ground truth measurements are performed to assess the metrological limits of this forecasting device. The resultant grand mean Nash Sutcliffe and Kling Gupta efficiency values are both of greater value than 90\%. Our results show that DCRRNN can become a powerful resource to simulate and forecast the impacts of hydroclimatic events as they relate to watershed response in a globally changing climate.

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