This study introduces a novel heat tracing method for estimating lateral groundwater flow velocity induced and sustained by heavy rainfall events in lowland areas, leveraging the distinct temperature difference between rainfall and groundwater. The method is motivated by the observation that the rainfall-induced groundwater temperature signal dissipates along the flow path. To explain the observed temperature anomaly and then estimate the lateral flow velocity, we develop a semi-analytical model for heat transport in the aquifer, accounting for conduction losses to adjacent layers. Our findings reveal that interactions between the aquifer, vadose zone, and bedrock significantly influence the temperature signal, thereby affecting velocity estimation. Inaccuracies in measured aquifer properties, such as thickness, porosity, and thermal conductivity of surrounding layers, increase the uncertainty of velocity estimates. However, variations in aquifer thermal conductivity have a minimal effect on the method's overall accuracy. When estimating multiple parameters, velocity estimates tend to be less reliable, especially if aquifer porosity remains uncertain. This is due to the challenges of simultaneously inverting both velocity and porosity. Overall, this work underscores the potential of using heat as a tracer for assessing lateral groundwater flow following rainfall, offering a practical, low-cost solution applicable in a wide range of settings.

中文翻译：

---

使用降雨诱导的地下水温度响应来估计侧向流速


本研究利用降雨和地下水之间的明显温差，引入了一种新的伴热方法，用于估计低地地区强降雨事件诱发和维持的横向地下水流速。该方法的动机是观察到降雨引起的地下水温度信号沿流道消散。为了解释观察到的温度异常，然后估计侧向流速，我们开发了一个含水层中热传递的半解析模型，考虑了到相邻层的传导损失。我们的研究结果表明，含水层、包气带和基岩之间的相互作用显着影响温度信号，从而影响速度估计。测量的含水层特性（例如周围层的厚度、孔隙率和热导率）的不准确会增加速度估计的不确定性。然而，含水层热导率的变化对方法的整体精度影响很小。当估计多个参数时，速度估计往往不太可靠，尤其是在含水层孔隙度仍然不确定的情况下。这是由于同时反转速度和孔隙率的挑战。总体而言，这项工作强调了使用热量作为示踪剂来评估降雨后地下水横向流动的潜力，提供了一种适用于各种环境的实用、低成本的解决方案。