Importance of Soil Moisture Sensor Data in Flood Early Warning Systems

Abstract

Soil moisture data is crucial for improving the accuracy of flood early warning systems. The Bartin Basin includes three main tributaries feeding the Bartin River. Soil moisture data stations have been established, distributed homogeneously across these areas. The soil moisture information obtained from these stations indicates how much water the soil contains during rainy weather. Each data station is equipped with three moisture sensors. These sensors are placed at depths of 20 ?cm , 40 ?cm, and 80 cm within the soil. This setup aims to monitor the moisture content in the upper, middle, and lower soil layers in real time. The sensors at three different depths also enable the calculation of the infiltration rate of rainwater into the soil. Within the scope of this study, soil moisture data stations were installed at 32 pre-identified locations. During the installation of each station, soil samples were collected from the respective areas. These samples were analyzed in a laboratory environment, serving both to calibrate the moisture sensors and to determine the physical properties of the soil in those regions. The data obtained from the soil moisture stations were used as input for a developed hydrological model, with the aim of enhancing the model's accuracy. The hydrological model will be operated on a central server established at the Kutlubey Campus of Bartin University. To transmit the IoT data collected from the soil moisture stations to the central server, 4 G modems were employed. With the goal of ensuring a more sustainable operation, the soil moisture stations were designed to meet their energy needs through solar power. The energy requirement of each station is approximately 29 Wh, and the total energy storage capacity has been established at 960 Wh. © 2025 Elsevier B.V., All rights reserved.