Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too

نویسندگان

چکیده

Abstract. Subseasonal streamflow forecasts inform a multitude of water management decisions, from early flood warning to reservoir operation. Seamless forecasts, i.e. that are reliable and sharp over range lead times (1–30 d) aggregation timescales (e.g. daily monthly) clear practical interest. However, existing forecast products often non-seamless, developed applied for single timescale time 1 month ahead). If seamless be viable replacement non-seamless it is important they offer (at least) similar predictive performance at the forecast. This study compares two probabilistic post-processing (QPP) models, namely recently Multi-Temporal Hydrological Residual Error (MuTHRE) model more traditional (non-seamless) monthly QPP used in Australian Bureau Meteorology's dynamic forecasting system. Streamflow both models generated 11 catchments, using GR4J hydrological pre-processed rainfall Community Climate Earth System Simulator – Seasonal (ACCESS-S) numerical weather prediction model. Evaluating with key metrics (reliability, sharpness, bias, continuous ranked probability score skill score), we find MuTHRE achieves essentially same as vast majority temporal stratifications (months years). As such, provides capability no loss scale modeller can proverbially “have their cake eat too”. finding demonstrates technologies, such model, not only but also preferred choice future research development adoption forecasting.

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ژورنال

عنوان ژورنال: Hydrology and Earth System Sciences

سال: 2022

ISSN: ['1607-7938', '1027-5606']

DOI: https://doi.org/10.5194/hess-26-5669-2022