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Ensemble-Based Uncertainty Quantification Can Improve Large-Scale Precipitation Data for Hydrologic Prediction
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DOI：10.1002/hyp.70635.png)
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Abstract
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Substantial uncertainties have hindered uptake of large-scale precipitation data from satellites, reanalysis, and ‘merged’ datasets in hydrologic applications. Whilst this problem could be addressed by quantifying uncertainty and propagating it through hydrologic models, there is little consensus on what form of uncertainty information is needed. In this commentary, we define precipitation error and uncertainty across scales. We also describe the hydrologic conditions in which this uncertainty matters. We argue that progress requires ensemble representations of uncertainty, which can be readily integrated into existing hydrologic modelling frameworks.
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