![]() ![]() ![]() We first compare historical streamflow predictions from all models under spatial and temporal validation, and also assess model skill in estimating watershed-scale evapotranspiration. The deep learning models include a regional Long Short-Term Memory network (LSTM), a mass-conserving LSTM (MC-LSTM) that preserves the water balance, and a novel variant of the MC-LSTM that also respects the relationship between PET and water loss (MC-LSTM-PET). We conduct this assessment using three process-based rainfall-runoff models and three deep learning models, trained and tested across 212 watersheds in the Great Lakes basin. Consequently, we assess the reliability of streamflow projections under warming by comparing projections with both temperature-based and energy budget-based PET estimates, assuming that reliable streamflow projections should exhibit less water loss when forced with smaller (energy budget-based) projections of future PET. Previous research has shown that temperature-based methods to estimate PET lead to overestimates of water loss in rainfall-runoff models under warming, as compared to energy budget-based PET methods. We investigate this question here, focusing specifically on modeled responses to increases in temperature and potential evapotranspiration (PET). However, it remains unclear whether deep learning models can produce physically plausible projections of streamflow under significant amounts of climate change. Deep learning rainfall-runoff models have recently emerged as state-of-the-science tools for hydrologic prediction that outperform conventional, process-based models in a range of applications. ![]()
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