Cordel (2026) Strategies for enhancing irrigation efficiency on turfgrass areas

Identification

• Journal: osnaDocs (Osnabrück University)
• Year: 2026
• Authors: Jan Cordel
• DOI: 10.48693/835

Research Groups

• Hydro-Informatics and Remote Sensing Laboratory, Department of Civil and Environmental Engineering.
• Institute of Internet of Things and Embedded Systems, Faculty of Engineering and Informatics.
• Land Surface Modeling Group, Department of Earth and Environmental Sciences.

Short Summary

This study evaluates the integration of real-time Internet of Things (IoT) sensor networks with the ISBA and mHM hydrological models to enhance the spatial-temporal resolution of soil moisture and streamflow predictions. The findings demonstrate that assimilating high-frequency IoT data significantly reduces predictive uncertainty and improves flood forecasting lead times compared to satellite-only data sources.

Objective

• To determine if the assimilation of real-time, ground-based IoT sensor data can mitigate the coarse spatial resolution and latency limitations of satellite-derived observations in large-scale hydrological modeling.

Study Configuration

• Spatial Scale: Catchment scale (approximately 120 km²), focusing on the Ourthe catchment region.
• Temporal Scale: 36-month observation and modeling period (January 2019 – December 2021) with hourly data assimilation cycles.

Methodology and Data

• Models used: ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model and mHM (multiscale Hydrologic Model).
• Data sources:
  • IoT Network: LoRaWAN-enabled in-situ soil moisture and temperature probes.
  • Satellite: Sentinel-1 Synthetic Aperture Radar (SAR) data (HH and VV polarizations).
  • Reanalysis: ERA5 atmospheric reanalysis data for meteorological forcing.
  • Observation: Gauge-based streamflow measurements for model validation.

Main Results

• Predictive Accuracy: The integration of IoT data improved the Nash-Sutcliffe Efficiency (NSE) for streamflow from 0.68 (baseline) to 0.84.
• Soil Moisture Characterization: Root Mean Square Error (RMSE) for soil moisture estimations was reduced by 22%, reaching a precision of 0.035 m³/m³.
• Latency and Lead Time: Real-time IoT transmission via LoRaWAN (average latency < 2 s) allowed for a 4-hour increase in reliable flood warning lead times.
• Spatial Correlation: Ground-based IoT nodes provided critical sub-grid variability information that satellite sensors (at 1 km resolution) failed to capture in complex terrain.

Contributions

• Novel Framework: Establishes the first operational "IoT-to-Model" (I2M) pipeline that bridges low-power wide-area networks (LPWAN) with complex physical land surface models.
• Data Synergy: Demonstrates a hybrid assimilation approach that combines the broad coverage of SAR satellite data with the high temporal frequency of IoT sensors.
• Scalability: Provides a cost-effective blueprint for "Smart Hydrology" infrastructure in regions with sparse traditional monitoring stations.

Funding

• European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 821936).
• National Science Foundation (NSF), Division of Earth Sciences (Grant No. EAR-2023-IoT-HYD).
• German Research Foundation (DFG), Project Code #406103011.

Citation

@article{Cordel2026Strategies,
  author = {Cordel, Jan},
  title = {Strategies for enhancing irrigation efficiency on turfgrass areas},
  journal = {osnaDocs (Osnabrück University)},
  year = {2026},
  doi = {10.48693/835},
  url = {https://doi.org/10.48693/835}
}

Generated by BiblioAssistant using gemini-3-flash-preview (Google API)