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IWRAM: A hybrid model for irrigation water demand forecasting to quantify the impacts of climate change

Author

Listed:
  • Dang, Chiheng
  • Zhang, Hongbo
  • Yao, Congcong
  • Mu, Dengrui
  • Lyu, Fengguang
  • Zhang, Yu
  • Zhang, Shuqi

Abstract

The phenomenon of climate change exerts a substantial influence on the water consumption patterns of agricultural crops, thereby affecting the overall demand for irrigation water and regional water security. The quantitative assessment of future changes in irrigation water requirements has great importance for long-term water resource planning and the water-energy-food-ecosystem nexus. This work employed a comprehensive approach (IWRAM) by integrating many models and techniques, including a global climate model (CanESM2), a statistical downscaling model (SDSM), a bias correction technique (QTM), a temperature-based crop phenology method (GDD), and deep learning technology (LSTM), in order to assess the potential impact of climate change on irrigation water demand. Therein, the QTM is utilized for the purpose of bias correction for the CanESM2-SDSMed daily precipitation data. Additionally, the LSTM is implemented to forecast ETo using the CanESM2-SDSMed daily temperature data. The findings from the case study conducted in the Jinghuiqu irrigation area (JIA) indicate that the application of bias correction techniques resulted in notable enhancements in the frequency distribution of predicted precipitation data. Consequently, this led to a more coherent relationship between historical observed data and anticipated future data, aligning them more closely with natural patterns. The deep learning technique exhibited a strong degree of concordance, suggesting its exceptional capacity for modeling daily reference evapotranspiration. By using the IWRAM model, relatively accurate predictions were made for future precipitation, temperature, crop evapotranspiration, and effective precipitation. Consequently, an estimation was made regarding the anticipated need for irrigation water in JIA. These results highlighted that JIA total irrigation water demand (or net irrigation water demand) will increase by 121 mm (net irrigation water: 189 mm) and 119 mm (net irrigation water: 187 mm) in the year 2050 under RCP 2.6 and 8.5 scenarios, respectively. Climate change could potentially affect the seasonal distribution of precipitation. Therefore, it is crucial for the authorities in JIA to not only take into account the overall irrigation water requirement but also give priority to the water requirements of each stage of crop growth.

Suggested Citation

  • Dang, Chiheng & Zhang, Hongbo & Yao, Congcong & Mu, Dengrui & Lyu, Fengguang & Zhang, Yu & Zhang, Shuqi, 2024. "IWRAM: A hybrid model for irrigation water demand forecasting to quantify the impacts of climate change," Agricultural Water Management, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:agiwat:v:291:y:2024:i:c:s0378377423005085
    DOI: 10.1016/j.agwat.2023.108643
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