Hydrological forecasts are critical to inform decision-making in various areas of water policy and management, including flood and drought preparedness, reservoir operation, and agricultural planning. Improving hydrological predictions and forecasts depends on two important factors, namely (i) quality and skill of meteorological forecast inputs, and (ii) different aspects relevant to hydrological modeling. Uncertainties in meteorological forcing can contribute significantly to the overall uncertainties in hydrological model predictions and forecasts. As such, meteorological variables - specifically, precipitation and temperature forecasts from different Numerical Weather prediction Models (NWM) -are being tested and evaluated as potential inputs to hydrological models.
The structures of different meteorological forecasting systems, however, are constantly evolving, with improvements in forecasting techniques, increases in spatial and temporal resolution, improvements in model physics and numerical techniques, and better understanding and modeling of uncertainty. Therefore, routine verification of meteorological forecasts is necessary to understand their improvements and skill. Additionally, recent advances in hydrological models, high-performance computing, and high-resolution environmental datasets provide opportunities for improving hydrological modeling, analyses, and predictions across a wide range of spatial and temporal scales.
To understand and advance science and practices of present hydrological forecasting, this Research Topic invites studies on the following:
- Analysis and verification of meteorological model outputs and forecasts as potential inputs to hydrological modeling;
- Methodological advances in hydrological forecasting, including land-surface modeling, data fusion, machine learning, ensemble techniques, and data assimilation;
- Application of high-resolution environmental datasets (gauge, radar, satellite, remote sensing) for hydrological predictions;
- Downscaling and preprocessing of hydrometeorological model inputs;
- Forecast postprocessing and multimodal approaches;
- Uncertainty quantification and communication; and,
- Forecast verification and visualization.
Hydrological forecasts are critical to inform decision-making in various areas of water policy and management, including flood and drought preparedness, reservoir operation, and agricultural planning. Improving hydrological predictions and forecasts depends on two important factors, namely (i) quality and skill of meteorological forecast inputs, and (ii) different aspects relevant to hydrological modeling. Uncertainties in meteorological forcing can contribute significantly to the overall uncertainties in hydrological model predictions and forecasts. As such, meteorological variables - specifically, precipitation and temperature forecasts from different Numerical Weather prediction Models (NWM) -are being tested and evaluated as potential inputs to hydrological models.
The structures of different meteorological forecasting systems, however, are constantly evolving, with improvements in forecasting techniques, increases in spatial and temporal resolution, improvements in model physics and numerical techniques, and better understanding and modeling of uncertainty. Therefore, routine verification of meteorological forecasts is necessary to understand their improvements and skill. Additionally, recent advances in hydrological models, high-performance computing, and high-resolution environmental datasets provide opportunities for improving hydrological modeling, analyses, and predictions across a wide range of spatial and temporal scales.
To understand and advance science and practices of present hydrological forecasting, this Research Topic invites studies on the following:
- Analysis and verification of meteorological model outputs and forecasts as potential inputs to hydrological modeling;
- Methodological advances in hydrological forecasting, including land-surface modeling, data fusion, machine learning, ensemble techniques, and data assimilation;
- Application of high-resolution environmental datasets (gauge, radar, satellite, remote sensing) for hydrological predictions;
- Downscaling and preprocessing of hydrometeorological model inputs;
- Forecast postprocessing and multimodal approaches;
- Uncertainty quantification and communication; and,
- Forecast verification and visualization.
