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In Switzerland, 80% of drinking water originates from groundwater. In the current context of climate change, the pressure on water resources and agricultural demand is increasing which highlights the need for better irrigation management. Quantifying soil water deficit with almost real-time constraints leads to improved irrigation decisions while limiting unnecessary water use. This case-study paper presents the design, implementation and evaluation of an automated system for estimating soil water deficit using environmental sensor data, meteorological data, and hydrological modelling. The system integrates in-situ soil moisture measurements with meteorological data from MeteoSwiss. These measurements and data are stored in a time-series database and processed to generate input files for physics based models jointly simulating surface water and groundwater. We describe in detail the operation workflow of our system in this specific use-case, including model simulations, and the required data transfers. The results show that the system operates reliably and provides consistent outputs suitable for short-term soil water deficit estimation.

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An Automated IoT-Based Infrastructure for Real-Time Soil Water Deficit Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Michèle Fischer,
  • Hugo Delottier,
  • Qi Tang,
  • Oliver S. Schilling,
  • Valerio Schiavoni,
  • Philip Brunner

摘要

In Switzerland, 80% of drinking water originates from groundwater. In the current context of climate change, the pressure on water resources and agricultural demand is increasing which highlights the need for better irrigation management. Quantifying soil water deficit with almost real-time constraints leads to improved irrigation decisions while limiting unnecessary water use. This case-study paper presents the design, implementation and evaluation of an automated system for estimating soil water deficit using environmental sensor data, meteorological data, and hydrological modelling. The system integrates in-situ soil moisture measurements with meteorological data from MeteoSwiss. These measurements and data are stored in a time-series database and processed to generate input files for physics based models jointly simulating surface water and groundwater. We describe in detail the operation workflow of our system in this specific use-case, including model simulations, and the required data transfers. The results show that the system operates reliably and provides consistent outputs suitable for short-term soil water deficit estimation.