Limitation of the Application of Heat Tracer in Investigating Groundwater and River Interactions under Dynamic Flow Conditions

作     者：Mengyan Ge Rui Ma Ziyong Sun 

作者机构：School of Environmental Studies， China University of Geosciences State Key Laboratory of Biogeology and Environmental Geology， China University of Geosciences 

出 版 物：《Journal of Earth Science》 (地球科学学刊(英文版))

年 卷 期：2022年

核心收录：

学科分类：081803[工学-地质工程] 08[工学] 081501[工学-水文学及水资源] 0818[工学-地质资源与地质工程] 0815[工学-水利工程] 

基　　金：Funded by the National Natural Science Foundations of China (NSFC-41722208 and 41002081) the National Key Research and Development Program of China (2017YFC0406105) 

摘      要：The groundwater (GW) in the floodplain riparian area frequently interacts intensely with surface water (SW). Heat as a tracer is one of the hot research fields in investigating GW-SW interactions， and analytical approaches have been proposed for the calculation of exchange flow velocity. However， few studies have considered the effects of very dynamic flow conditions and monitoring instrumentation on the calculation with field measured data. Herein， taking the middle reaches of the Heihe River as the study area， different types of monitoring wells were constructed under the riverbed and near the river， and multiple methods (Darcy’s law， heat tracing， and isotopic mixing methods) were employed to trace the exchanges between the river and groundwater. The results indicate that different methods demonstrate diverse information with obvious unevenly distributed flux along the vertical direction. And the combination of multiple methods has an important role in studying the interaction between GW and SW. Fully screened wells produce intraborehole flow and disturb the heat transport， which is relevant to flow velocity， and further affects the temperature distribution， impacting the temperature-based flow velocity calculation. Dynamic flow conditions aggravate riverbed sediment disturbances， e.g.， scour and deposition， and additionally affect the interaction and monitoring data.