Jia et al. (2025) A Novel Algorithm for Optimizing Structure of In-Situ Supplementary Irrigation Device for Afforestation in Dryland

Identification

• Journal: Water Resources Management
• Year: 2025
• Date: 2025-12-29
• Authors: Bokun Jia, Sen Zhai, Lin Zhang, Lijie Liu, Yong Yang, Xufei Liu, Shoujun Wu, Xin Hui
• DOI: 10.1007/s11269-025-04454-6

Research Groups

• College of Water Resources and Architecture Engineering, Northwest A&F University, Yangling, Shaanxi, China
• Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi, China
• China Railway 23Th Construction Bureau. Ltd, Chengdu City, Sichuan Province, China

Short Summary

This study developed and validated a novel algorithm to optimize the structural parameters (rated area, capacity, and flow) of in-situ supplementary irrigation devices (SIDs) for afforestation in drylands, demonstrating a significant increase in sapling survival rate (83.6%) compared to traditional methods (46.9%) in a Lhasa case study.

Objective

• To develop a novel algorithm that directly determines suitable structural parameters (rated area, rated capacity, and rated flow) of in-situ supplementary irrigation devices (SIDs) based on annual mean precipitation data for afforestation in drylands.
• To validate the algorithm's reliability through a field experiment and provide recommended SID parameters for different arid regions.

Study Configuration

• Spatial Scale: 96 sites across arid and semiarid regions of northwestern and northern China (Xinjiang, Inner Mongolia, Gansu, Qinghai, Ningxia, Shaanxi), categorized into extremely arid (< 100 mm annual precipitation), arid (100–200 mm), and semiarid (200–400 mm). A validation experiment was conducted in Lhasa, Tibet (91°06′ E, 29°36′ N, altitude 3747.00 m).
• Temporal Scale: Daily water balance calculations over one year for each site, using historical daily precipitation data. Sapling growth and survival rates were measured after one year in the validation experiment (May 2023 onwards).

Methodology and Data

• Models used:
  • Daily water balance model (considering rainfall harvesting, overflow, water delivery, soil water content, sapling water demand).
  • Multiple linear regression model (to predict optimal structural parameters based on latitude, altitude, annual mean temperature, and annual mean precipitation).
• Data sources:
  • Daily precipitation data from 96 sites obtained from the China Meteorological Data Service Center (http://data.cma.cn).
  • Standard Precipitation Index (SPI) for identifying wet, dry, and normal years.
  • Annual water demand for saplings recalculated based on precipitation distribution, referencing GLDAS, GLEAM, Terra Climate, Harvard Dataverse, and National Tibetan Plateau Data Center (CR) for evapotranspiration data.
  • Geographical factors (latitude, altitude) and annual meteorological factors (annual mean temperature, annual mean precipitation).
  • Soil physical parameters (texture, field capacity, wilting point) measured locally for the Lhasa experiment.
  • Sapling growth parameters (ground diameter, crown width, plant height) measured before and after the experiment.
  • i-Tree Species (https://species.itreetools.org/) for pioneer sapling species recommendations.

Main Results

• A novel algorithm was developed that couples water storage tank volume and ceramic emitter flow rate, dynamically accounting for operating pressure variations, to optimize SID structural parameters.
• The optimal rated area for the rainfall harvesting board was consistently found to be 1.2 m² across most regions.
• Multiple linear regression equations were established to determine the optimal rated flow of the microporous ceramic emitter and the rated capacity of the rainwater storage tank, with R² values ranging from 0.782 to 0.857, indicating high accuracy.
• A validation experiment in Lhasa, Tibet, showed that SIDs with algorithm-optimized parameters resulted in a sapling survival rate of 83.6%, which was 36.7% higher than traditional planting methods (46.9%).
• Optimized SIDs maintained significantly higher and more stable soil moisture content in the sapling root zone compared to bare soil pits.
• Sapling growth (ground diameter, crown width, plant height) under optimized SIDs was substantially higher (e.g., 76.00% higher plant height growth) than with bare soil pits.
• Recommended structural parameters were provided in a convenient table for extremely arid (Q: 25-45 mL·h⁻¹, A: 1.2 m², S: 25-30 L), arid (Q: 45-85 mL·h⁻¹, A: 1.2 m², S: 25-35 L), and semiarid (Q: 65-105 mL·h⁻¹, A: 1.2 m², S: 30-40 L) regions, along with suitable pioneer sapling species.

Contributions

• Developed a novel optimization algorithm for in-situ SIDs that dynamically couples water storage and emitter flow, accounting for variable operating pressure, which previous methods often overlooked.
• Streamlined the optimization process by using readily available geographical and annual climatic data (latitude, altitude, mean annual precipitation, mean annual temperature) as inputs for multiple linear regression, reducing data acquisition constraints.
• Provided a practical and convenient table of recommended optimal structural parameters for SIDs and pioneer sapling species across different dryland aridity levels, facilitating wider application in afforestation projects.
• Experimentally validated the effectiveness of the optimized SIDs in significantly improving sapling survival rates and growth in a challenging dryland environment (Lhasa, Tibet).

Funding

• Open Project of Shaanxi Agricultural Laboratory in Arid Areas in 2024 (2024ZY-JCYJ-02-02)
• China Railway 23Th Construction Bureau. Ltd. National Reserve Forest Project Command Support Project (LQS-KYHT-2024-002)
• National Natural Science Foundation of China (52279044)

Citation

@article{Jia2025Novel,
  author = {Jia, Bokun and Zhai, Sen and Zhang, Lin and Liu, Lijie and Yang, Yong and Liu, Xufei and Wu, Shoujun and Hui, Xin},
  title = {A Novel Algorithm for Optimizing Structure of In-Situ Supplementary Irrigation Device for Afforestation in Dryland},
  journal = {Water Resources Management},
  year = {2025},
  doi = {10.1007/s11269-025-04454-6},
  url = {https://doi.org/10.1007/s11269-025-04454-6}
}