科罗拉多河上游流域遥感年际植被变化的水文影响

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES
Qianqiu Longyang, Ruijie Zeng
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引用次数: 0

摘要

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方面发挥着至关重要的作用。地表模型(LSM)通常通过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时变植被特征,如对气候波动、长期趋势和年际变化的响应。目前仍不清楚植被与气候变异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,并需要大量时间进行分析。本研究为广泛使用的 LSM(即 Noah)开发了一种深度学习替代物,作为快速诊断工具。经过校准的替代物可量化来自多个遥感 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。以科罗拉多河上游流域(UCRB)为测试案例,我们发现与静态植被配置相比,时变植被对气候波动的缓冲作用更大,从而降低了非生物蒸发成分(如土壤蒸发)的变化。此外,与静态植被方案相比,来自多源遥感产品的时变植被始终预测出较小的生物蒸发成分(如蒸腾作用),从而提高了 UCRB 的产水量(约 14%)。我们还强调了动态植被参数化与静态参数化(如土壤)在校准过程中的相互作用。在评估气候变化对植被和全流域水资源的影响时,可能需要对参数进行重新校准,并对某些模型假设进行重新评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hydrological Impact of Remotely Sensed Interannual Vegetation Variability in the Upper Colorado River Basin
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.
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来源期刊
Water Resources Research
Water Resources Research 环境科学-湖沼学
CiteScore
8.80
自引率
13.00%
发文量
599
审稿时长
3.5 months
期刊介绍: Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.
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