Earth observations offer potential pathways for accurately closing the water and energy balance of watersheds, a fundamental challenge in hydrology. However, previous attempts based on purely satellite‐based estimates have focused on closing the water and energy balances separately. They are hindered by the lack of estimates of key components, such as runoff. Here, we posit a novel approach based on Budyko’s water and energy balance constraints. The approach is applied to quantify the degree of long‐term closure at the watershed scale, as well as its associated uncertainties, using an ensemble of global satellite data sets. We find large spatial variability across aridity, elevation, and other environmental gradients. Specifically, we find a positive correlation between elevation and closure uncertainty, as derived from the Budyko approach. In mountainous watersheds the uncertainty in closure is 3.9 ± 0.7 (dimensionless). Our results show that uncertainties in terrestrial evaporation contribute twice as much as precipitation uncertainties to errors in the closure of water and energy balance. Moreover, our results highlight the need for improving satellite‐based precipitation and evaporation data in humid temperate forests, where the closure error in the Budyko space is as high as 1.1 ± 0.3, compared to only 0.2 ± 0.03 in tropical forests. Comparing the results with land surface model‐based data sets driven by in situ precipitation, we find that Earth observation‐based data sets perform better in regions where precipitation gauges are sparse. These findings have implications for improving the understanding of global hydrology and regional water management and can guide the development of satellite remote sensing‐based data sets and Earth system models.

中文翻译：

---

基于地球观测的基于Budyko的全球流域长期水和能源平衡封闭

地球观测提供了精确关闭流域水和能量平衡的潜在途径，这是水文学的一项基本挑战。但是，以前基于纯卫星估计的尝试都集中在分别关闭水和能源平衡上。由于缺乏对径流等关键要素的估算，它们受到了阻碍。在这里，我们提出了一种基于Budyko的水和能量平衡约束的新颖方法。该方法用于通过使用全球卫星数据集的集合来量化分水岭规模上的长期封闭程度及其相关的不确定性。我们发现在干旱，海拔和其他环境梯度上存在较大的空间变异性。具体而言，我们发现从Budyko方法得出的高程与关闭不确定性之间存在正相关关系。在山区流域，封闭的不确定性为3.9±0.7（无因次）。我们的结果表明，陆地蒸发的不确定性对降水的不确定性造成的水和能量平衡误差的影响是降水不确定性的两倍。此外，我们的研究结果突出表明需要改善湿润温带森林中基于卫星的降水和蒸发数据，因为在Budyko空间中，封闭误差高达1.1±0.3，而热带森林中的封闭误差仅为0.2±0.03。将结果与由原地降水驱动的基于陆地表面模型的数据集进行比较，我们发现基于地球观测的数据集在降水量稀疏的地区表现更好。