There is interest in applying satellite-derived rainfall products for water management in data-sparse areas. However, questions remain around how uncertainties in different products interact with hydrologic models to determine simulation skill. Most related work uses performance statistics that inherently combine rainfall magnitude, timing and persistence, making it unclear which product improvements should be prioritized. We applied six satellite-derived rainfall products in a conceptual hydrologic model (GR4J) across four Australian catchments with dense gauge data for comparison. We found that GR4J's inherent flexibility allowed it to filter errors in rainfall magnitude and variance through parameterization. Therefore, when rainfall observations for bias correction are unavailable, calibration of a flexible model could prove a useful alternative. However, the model was less able to compensate for errors in rainfall occurrence. In fact, the Probability of Detection score explained 59% of the variance in calibration performance (26% for validation), while overall bias explained just 14% (8% for validation). All products underestimated rainfall state persistence, but this had less influence on model skill. We then removed gauges from the observed data set to mimic data sparsity, finding that even a few gauges could reproduce rainfall occurrence and outperform satellite-derived products. Two data-sparse catchments in Vietnam were modeled to check whether the same learnings applied. The gauge data also performed best in Vietnam, and performance of most satellite-derived products was comparable to the Australian case. Efforts to increase the spatial and temporal resolution of satellite observations, which could improve rainfall detection, will enhance satellite-derived precipitation for hydrologic modeling.

中文翻译：

---

水文模型可以处理哪些降雨误差？在稀疏测量流域中使用卫星衍生产品的意义

人们有兴趣将卫星衍生的降雨产品应用于数据稀少地区的水资源管理。然而，关于不同产品中的不确定性如何与水文模型相互作用以确定模拟技能的问题仍然存在。大多数相关工作使用的性能统计数据本质上结合了降雨量、时间和持续性，因此不清楚应该优先考虑哪些产品改进。我们在澳大利亚四个流域的概念水文模型 (GR4J) 中应用了六种卫星衍生的降雨产品，并具有密集的测量数据进行比较。我们发现 GR4J 固有的灵活性使其能够通过参数化过滤降雨量级和方差的误差。因此，当用于偏差校正的降雨观测不可用时，灵活模型的校准可以证明是一种有用的替代方法。然而，该模型不太能够补偿降雨发生的误差。事实上，检测概率分数解释了校准性能差异的 59%（验证为 26%），而总体偏差仅解释了 14%（验证为 8%）。所有产品都低估了降雨状态的持久性，但这对模型技能的影响较小。然后，我们从观测数据集中删除了仪表以模拟数据稀疏性，发现即使是几个仪表也可以重现降雨发生并优于卫星衍生产品。对越南的两个数据稀疏流域进行了建模，以检查是否应用了相同的学习。仪表数据在越南也表现最好，大多数卫星衍生产品的性能与澳大利亚的情况相当。