Satellite-based precipitation products (SPPs) with short latencies provide a new opportunity for flood forecasting in basins with no precipitation stations. However, the larger uncertainties associated with these near-real-time SPPs can influence the accuracy of flood forecasts. Here, we propose a real-time updating method, referred to as “Constrained Runoff Correction using Multiple SPPs” (CRC-M). The method is based on the hypothesis that the range of runoff volumes computed using different near-real-time SPPs provides an indication of the approximate range in which the true runoff volume lies. Accordingly, a constrained runoff correction is performed using the discharge observed at the basin outlet within this range. Evaluation using real data indicates that the new method performs well with Nash–Sutcliffe (NS) values of 0.85 and 0.87 during calibration and validation, respectively. The necessity of imposing constraints using multiple SPPs is demonstrated by comparing CRC-M with 2 controls, referred to as “Unconstrained Runoff Correction using Single SPP” (URC-S) and “Constrained Runoff Correction using Single SPP with perturbations” (CRC-S). Experiments indicate that the key factors that result in good performance are (a) relatively reliable SPPs and (b) wider constraint ranges. Overall, the CRC-M method can result in accurate and stable flood forecasts in basins with no precipitation stations, without the shortening of leading time and the need for increased model complexity (i.e., the numbers of model parameters).

中文翻译：

---

在没有降水站的流域中改进洪水预报：使用多个卫星降水产品进行约束径流校正

具有短延迟的卫星降水产品 (SPP) 为没有降水站的流域的洪水预报提供了新的机会。然而，与这些近实时 SPP 相关的更大的不确定性会影响洪水预报的准确性。在这里，我们提出了一种实时更新方法，称为“使用多个 SPP 的约束径流校正””（CRC-M）。该方法基于以下假设：使用不同的近实时 SPP 计算的径流体积范围提供了真实径流体积所在的大致范围的指示。因此，使用在此范围内在盆地出口处观察到的流量来执行约束径流校正。使用真实数据进行的评估表明，新方法在校准和验证期间的 Nash-Sutcliffe (NS) 值分别为 0.85 和 0.87 时表现良好。通过将 CRC-M 与 2 个控制进行比较，证明了使用多个 SPP 施加约束的必要性，称为“使用单个 SPP 的无约束径流校正”（URC-S）和“使用具有扰动的单个 SPP 的约束径流校正””（CRC-S）。实验表明，导致良好性能的关键因素是（a）相对可靠的 SPP 和（b）更广泛的约束范围。总体而言，CRC-M方法可以在没有降水站的流域中实现准确稳定的洪水预报，而不会缩短提前时间，也不需要增加模型复杂度（即模型参数的数量）。