Vegetation plays a crucial role in atmosphere-land water and energy exchanges, global carbon cycle and basin water conservation. Land Surface Models (LSMs) typically represent vegetation characteristics by monthly climatological indices. However, static vegetation parameterization does not fully capture time-varying vegetation characteristics, such as responses to climatic fluctuations, long-term trends, and interannual variability. It remains unclear how the interaction between vegetation and climate variability propagates into hydrologic fluxes and water resources. Multi-source satellite data sets may introduce uncertainties and require extensive time for analysis. This study developes a deep learning surrogate for a widely used LSM (i.e., Noah) as a rapid diagnosic tool. The calibrated surrogate quantifies the impacts of time-varying vegetation characteristics from multiple remotely sensed GVF products on the magnitude, seasonality, and biotic and abiotic components of hydrologic fluxes. Using the Upper Colorado River Basin (UCRB) as a test case, we found that time-varying vegetation provides more buffering effect against climate fluctuation than the static vegetation configuration, leading to reduced variability in the abiotic evaporation components (e.g., soil evaporation). In addition, time-varying vegetation from multi-source remote sensing products consistently predicts smaller biotic evaporation components (e.g., transpiration), leading to increased water yield in the UCRB (about 14%) compared to the static vegetation scheme. We also highlight the interaction between dynamic vegetation parameterization and static parameterization (e.g., soil) during calibration. Parameter recalibration and a re-evaluation of certain model assumptions may be required for assessing climate change impacts on vegetation and basin-wide water resources.

中文翻译：

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科罗拉多河流域上游植被年际变化遥感水文影响


植被在大气-陆地水和能量交换、全球碳循环和流域水源保护中发挥着至关重要的作用。陆地表面模型 (LSM) 通常通过每月气候指数来表示植被特征。然而，静态植被参数化并不能完全捕捉随时间变化的植被特征，例如对气候波动的响应、长期趋势和年际变化。目前尚不清楚植被和气候变化之间的相互作用如何传播到水文通量和水资源中。多源卫星数据集可能会带来不确定性，并且需要大量时间进行分析。这项研究为广泛使用的 LSM（即 Noah）开发了一种深度学习替代品作为快速诊断工具。校准替代物量化了多个遥感 GVF 产品的时变植被特征对水文通量的大小、季节性以及生物和非生物成分的影响。使用科罗拉多河上游流域（UCRB）作为测试案例，我们发现时变植被比静态植被配置对气候波动提供更多的缓冲作用，从而减少非生物蒸发成分（例如土壤蒸发）的变异性。此外，来自多源遥感产品的时变植被一致预测较小的生物蒸发成分（例如蒸腾作用），从而导致与静态植被方案相比，UCRB 的水产量增加（约 14%）。我们还强调了校准过程中动态植被参数化和静态参数化（例如土壤）之间的相互作用。 为了评估气候变化对植被和全流域水资源的影响，可能需要重新校准参数和重新评估某些模型假设。