Towards Improved Global River Discharge Prediction in Ungauged Basins Using Machine Learning and Satellite Observations

The recent increase in frequency and severity of natural disasters is a clear indication of an immediate need to address the cascading impacts of climate change. However, climate change cannot be measured directly. In a weather cycle, river discharge is the end result of any hydrologic process, and thus directly measures the effect of two major parameters used to measure impacts of climate change; Temperature and Precipitation. Unlike current methods that are able to infer climate change patterns over a long period of time, river discharge is an effective proxy for measuring the effects of climate change within a short period of time. Unfortunately, current statistical and physics-based models neither take full advantage of hydro-meteorological information encoded in over 100 years of historical hydrologic data nor are they applicable on a global scale. In this work, we train Long Short Term Memory (LSTM) Recurrent Neural Network models on satellite observations and daily discharge from gauged basins. Our models outperform the latest state-of-the-art process-based hydrology models with Kling-Gupta and Nash-Sutcliffe Efficiency scores of 85\% and 81\% respectively in ungauged basins with limited to no-existing data. This will allow accurate predictions in the majority of the global river basins that do not have in-situ measurements.