Do you want high quality subseasonal streamflow forecasts? Ask MuTHRE! vEGU 2021 Conference Presentation

<p>Sub-seasonal streamflow forecasts (with lead times of 1-30 days) provide valuable information for many consequential water resource management decisions, including reservoir operation to meet environmental flow and irrigation demands, issuance of early flood warnings, and others. A key aim is to produce “seamless” forecasts, with high quality performance across the full range of lead times and time scales. This presentation introduces the <b><i>Multi-Temporal Hydrological Residual Error model (MuTHRE)</i></b> to address the challenge of obtaining “seamless” sub-seasonal forecasts, i.e., daily forecasts with consistent high-quality performance over multiple lead times (1-30 days) and aggregation scales (daily to monthly). The model is designed to overcome common errors in streamflow forecasts: (1) Seasonality (2) dynamic biases due to hydrological non-stationarity (3) extreme errors poorly represented by the common Gaussian distribution.</p> <p>The model is evaluated comprehensively over 11 catchments in the Murray-Darling Basin, Australia, using multiple performance metrics to scrutinize forecast reliability, sharpness and bias, across a range of lead times, months and years, at daily and monthly time scales. </p> <p>The MuTHRE model provides ”high” improvements, in terms of reliability for (1) Short lead times (up to 10 days), due to representing non-Gaussian errors (2) Stratified by month, due to representing seasonality in hydrological errors (3) Dry years, due to representing dynamic biases in hydrological errors.</p> <p>Forecast performance also improved in terms of sharpness, volumetric bias and CRPS skill score; Importantly, improvements are consistent across multiple time scales (daily and monthly).</p> <p><b><i>This study highlights the benefits of modelling multiple temporal characteristics of hydrological errors, and demonstrates the power of the MuTHRE model for producing seamless sub-seasonal streamflow forecasts that can be utilized for a wide range of applications.</i></b></p>