Investigating spatial heterogeneity of the controls of surface water balance in the contiguous United States by considering anthropogenic factors
Investigating spatial heterogeneity of the controls of surface water balance in the contiguous United States by considering anthropogenic factors
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DOI：
10.1016/j.jhydrol.2021.126621
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发表时间：
2021-10
影响因子：
6.4
通讯作者：
Zhiying Li;S. Quiring
中科院分区：
文献类型：
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作者：
Zhiying Li;S. Quiring
了解降水如何被划分为蒸散量和径流量对于评估水的可用性是很重要的。在Budyko框架中，这种划分通过ω参数量化。先前的研究已经模拟了ω的物理表示;然而，ω和它所代表的变量之间的关系的空间异质性还没有被研究。本研究采用地理加权回归模型，以确定控制在126个参考流域的水平衡的因素，最小的人为干扰和765个非参考流域在美国大陆的空间变化。结果表明，降雪量和森林覆盖率是参考流域ω的重要预测因子。相对累积水分盈余，水库蓄水量和河岸区开发土地是非参考流域的重要预测因子。气候是森林覆盖率相对重要性的主要控制因素。森林覆盖率在干旱流域的重要性大于潮湿流域。在东北部和中西部，降雪比森林覆盖率更重要。这项研究表明，大坝建设和城市蔓延有显着的影响，在非参考流域。在21%的非参考流域中，大坝蓄水量是最重要的预测因子，而在13%的非参考流域中，河岸开发土地更为重要。总体而言，有统计学显着的关系，气候，地文，和人类相关的因素和ω参数。本研究所量化的空间变异关系，有助于改善区域流域管理。
Understanding how precipitation is partitioned into evapotranspiration and streamflow is important for assessing water availability. In the Budyko framework, this partitioning is quantified through the ω parameter. Previous studies have modeled the physical representation of ω; however, the spatial heterogeneity of the relationship between ω and the variables that it represents has not been investigated. This study uses a geographically weighted regression model to identify spatial variations in the factors that control the water balance in 126 reference watersheds with minimal human disturbance and 765 non-reference watersheds in the continental United States. Results show that snowfall and forest coverage are important predictors of ω in the reference watersheds. Relative cumulative moisture surplus, dam storage, and developed land in riparian areas are important predictors in non-reference watersheds. Climate is a primary control of the relative importance of forest coverage. The importance of forest coverage is greater in arid watersheds than in humid watersheds. Snowfall is more important than forest coverage in the Northeast and Midwest. This study demonstrates that dam construction and urban sprawl have a significant impact in non-reference watersheds. Dam storage is the most important predictor in 21% of the non-reference watersheds, and riparian developed land is more important in 13% of the non-reference watersheds. Overall, there are statistically significant relationships between climatic, physiographic, and human-related factors and the ω parameter. The spatial variations in the relationship quantified in this study can help to improve regional watershed management.