Kravchenko et al. (2025) Irrigation of slope lands by subsurface irrigation method using a simulator of horizontal wells

Identification

• Journal: Siberian Journal of Life Sciences and Agriculture
• Year: 2025
• Date: 2025-12-30
• Authors: Lyudmila Kravchenko, Alexander S. Lebedev, Anna E. Khadzhidi, Tatiana Yu. Khashirova
• DOI: 10.12731/2658-6649-2025-17-6-2-1586

Research Groups

• Department of Hydraulics and Agricultural Water Supply, Kuban State Agrarian University named after I.T. Tribulin, Krasnodar, Russian Federation
• Department of Design and Technical Service of Transport and Technological Systems, Don State Technical University, Rostov-on-Don, Russian Federation
• Department of Computer Technologies and Information Security, Kabardino-Balkarian State University, Nalchik, Russian Federation

Short Summary

This study investigated the effectiveness of subsurface irrigation on laboratory models of sloping sand-soil using a novel horizontal well simulator. The research successfully mapped, for the first time, the downward curved trajectory of irrigation water movement from the simulator to the lower boundary of the slope.

Objective

• To investigate the effectiveness of subsurface irrigation on sloping slope models using a horizontal well simulator.

Study Configuration

• Spatial Scale: Laboratory-scale model of a sloping sand-soil terrain, 30 meters long, with a 0.1 meter high sand-soil layer. The model was divided into three sections to test different tilt angles, and U-shaped horizontal well simulators were placed at depths of 0.02 meters, 0.04 meters, and 0.06 meters.
• Temporal Scale: Individual experiments involved measuring fluid passage and soil moisture content over periods of minutes, with time synchronized to an NTP server. The overall duration of the study is not specified.

Methodology and Data

• Models used: Mathematical modeling was employed for the analysis of wetting processes. Statistical methods were used for processing experimental data. A 3D model was developed in Python, and error metrics (Root-Mean-Square Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²)) were calculated using Python.
• Data sources: Data was collected from controlled laboratory experiments using an author-designed setup. Measurements included slope angle, soil moisture level (using a tare moisture meter with a calibrated probe sensor), water volume (up to 1 liter), fluid penetration distance, and water penetration depth in observation wells. A video endoscope was used for visual monitoring of liquid movement.

Main Results

• For the first time, a graph illustrating the trajectory of irrigation water movement during subsurface irrigation using a horizontal well simulator was obtained.
• The main flow of irrigation water was observed to follow a downward curved trajectory, originating directly from the horizontal well simulator, passing at an angle through the slope area, and ending at its lower boundary.
• Graphs were plotted showing the dependence of water penetration distances on water volumes for slope inclination angles ranging from 10 to 30 degrees, and for simulator placement depths of 0.02 meters, 0.04 meters, and 0.06 meters.
• Quantitative analysis of the model yielded the following metrics: Root-Mean-Square Error (MSE) = 0.1284, Mean Absolute Error (MAE) = 0.3062, and Coefficient of Determination (R²) = 0.9265, indicating a good fit of the model to the experimental data.

Contributions

• Development and implementation of a novel laboratory setup for simulating subsurface irrigation on sloping sand-soil using U-shaped horizontal well simulators.
• First-time experimental mapping and graphical representation of the trajectory of irrigation water movement from a horizontal well simulator on a sloping surface, demonstrating a characteristic downward curve.
• Quantitative evaluation of water penetration distances and depths under varying irrigation volumes, slope angles, and simulator placement depths, providing valuable data for optimizing subsurface irrigation systems on slopes.
• Application of mathematical and statistical modeling, including Python-based analysis, to characterize and validate the observed irrigation processes.

Funding

Not specified in the provided text.

Citation

@article{Kravchenko2025Irrigation,
  author = {Kravchenko, Lyudmila and Lebedev, Alexander S. and Khadzhidi, Anna E. and Khashirova, Tatiana Yu.},
  title = {Irrigation of slope lands by subsurface irrigation method using a simulator of horizontal wells},
  journal = {Siberian Journal of Life Sciences and Agriculture},
  year = {2025},
  doi = {10.12731/2658-6649-2025-17-6-2-1586},
  url = {https://doi.org/10.12731/2658-6649-2025-17-6-2-1586}
}