Evapotranspiration and groundwater inputs control the timing of diel cycling of stream drying during low-flow periods

IF 2.6 Q2 WATER RESOURCES
Sara R. Warix, Sarah E. Godsey, Gerald Flerchinger, Scott Havens, Kathleen A. Lohse, H. Carrie Bottenberg, Xiaosheng Chu, Rebecca L. Hale, Mark Seyfried
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引用次数: 0

Abstract

Geologic, geomorphic, and climatic factors have been hypothesized to influence where streams dry, but hydrologists struggle to explain the temporal drivers of drying. Few hydrologists have isolated the role that vegetation plays in controlling the timing and location of stream drying in headwater streams. We present a distributed, fine-scale water balance through the seasonal recession and onset of stream drying by combining spatiotemporal observations and modeling of flow presence/absence, evapotranspiration, and groundwater inputs. Surface flow presence/absence was collected at fine spatial (~80 m) and temporal (15-min) scales at 25 locations in a headwater stream in southwestern Idaho, USA. Evapotranspiration losses were modeled at the same locations using the Simultaneous Heat and Water (SHAW) model. Groundwater inputs were estimated at four of the locations using a mixing model approach. In addition, we compared high-frequency, fine-resolution riparian normalized vegetation difference index (NDVI) with stream flow status. We found that the stream wetted and dried on a daily basis before seasonally drying, and daily drying occurred when evapotranspiration outputs exceeded groundwater inputs, typically during the hours of peak evapotranspiration. Riparian NDVI decreased when the stream dried, with a ~2-week lag between stream drying and response. Stream diel drying cycles reflect the groundwater and evapotranspiration balance, and riparian NDVI may improve stream drying predictions for groundwater-supported headwater streams.
蒸散量和地下水输入量控制着枯水期干流循环的时间
地质、地貌和气候因素已经被假设影响河流干涸的位置,但水文学家很难解释干燥的时间驱动因素。很少有水文学家孤立的作用,植被在控制时间和位置的溪流干涸的源头。通过结合时空观测和水流存在/缺失、蒸散发和地下水输入的建模,我们通过季节性衰退和溪流干燥的开始呈现了一个分布的、精细尺度的水平衡。在美国爱达荷州西南部的一条水源流的25个地点,在精细的空间尺度(~80 m)和时间尺度(15 min)上收集了地表水流的存在/不存在。在同一地点,蒸散发损失采用热水同步模式(SHAW)模拟。使用混合模型方法估计了其中四个地点的地下水输入。此外,我们还比较了高频、精细分辨率河岸归一化植被差异指数(NDVI)与河流流量状况。研究发现,在季节性干枯之前,河流每天都处于湿润和干燥状态,当蒸散发量超过地下水输入量时,通常在蒸散发高峰期发生日干枯。河岸NDVI随着干枯而降低,干枯与响应之间存在约2周的滞后。河流干枯循环反映地下水和蒸散平衡,河岸NDVI可以改善地下水支持的源头河流干枯预测。
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来源期刊
Frontiers in Water
Frontiers in Water WATER RESOURCES-
CiteScore
4.00
自引率
6.90%
发文量
224
审稿时长
13 weeks
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