Monthly streamflow forecasting is vital for managing water resources. Recently, numerous studies have explored and evidenced the potential of artificial intelligence (AI) models in hydrological forecasting. In this study, feasibility convolutional neural network (CNN), a deep learning method, monthly CNN can automatically extract critical features from inputs with its convolution–pooling mechan...

Skillful seasonal streamflow forecasts obtained from climate and land surface conditions could significantly improve water and energy management. Since climate forecasts are updated on a monthly basis, we evaluate the potential in developing operational monthly streamflow forecasts on a continuous basis throughout the year. Further, basins in the rainfall–runoff regime critically depend on the ...

[1] Using existing data sets of spaceborne soil moisture retrievals, streamflow and precipitation for 26 basins in the United States Southern Great Plains, a 5-year analysis is performed to quantify the value of soil moisture retrievals derived from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.7 GHz) radiometer for forecasting storm event-scale runoff ratios....

Especially for drought periods, the higher the accuracy of reservoir inflow forecasting, the 9 more reliable the water supply from a dam. The article focuses on probabilistic forecasting of 10 seasonal inflow to reservoirs and determines estimates from the probabilistic seasonal inflow 11 according to drought forecast results. The probabilistic seasonal inflow was forecasted by a copula12 based...