Do you want Seamless Subseasonal Streamflow Forecasts?    Ask MuTHRE!

<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.  </p><p>This presentation introduces the <em><strong>Multi-Temporal Hydrological Residual Error model (MuTHRE)</strong> </em>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).</p><p>The model is designed to overcome common errors in streamflow forecasts:</p><ul><li>Seasonality</li> <li>Dynamic biases due to hydrological non-stationarity</li> <li>Extreme errors poorly represented by the common Gaussian distribution.</li> </ul><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</p><ul><li>Short lead times (up to 10 days), due to representing non-Gaussian errors</li> <li>Stratified by month, due to representing seasonality in hydrological errors</li> <li>Dry years, due to representing dynamic biases in hydrological errors.</li> </ul><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><em><strong>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.</strong></em></p><p> </p><p> </p><p> </p>