Prediction of the Irrigation Area Carrying Capacity in the Tarim River Basin under Climate Change

Qi Liu, Yi Liu, Jie Niu, Dongwei Gui and Bill X. Hu
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Qi Liu: College of Life Science and Technology, Jinan University, Guangzhou 510632, China
Yi Liu: Safety, Environment and Technology Supervision Research Institute, Petro China Southwest Oil and Gas Field Company, Chengdu 610041, China
Jie Niu: College of Life Science and Technology, Jinan University, Guangzhou 510632, China
Dongwei Gui: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Urumqi 830011, China
Bill X. Hu: School of Water Conservancy and Environment, University of Jinan, Jinan 250024, China

Agriculture, 2022, vol. 12, issue 5, 1-14

Abstract: The Tarim River Basin (TRB) is one of the world’s largest cotton-producing areas, and its agricultural water use accounts for up to 95% of the total water consumption in the basin. Quantifying the future changes in the irrigation area carrying capacity under global warming is therefore essential in TRB. In this study, we analyzed the variation in the irrigation area in TRB over the last few decades, utilized the nonlinear autoregressive with an exogenous input neural network to simulate the future changes in the available water resources, and predicted the future irrigation area carrying capacity based on the water balance equation. The results showed that the present (1970–2020) irrigation area in TRB exhibited an increasing trend from 491 km 2 in 1970s to 1382 km 2 in 2020, as most of the natural vegetation was transformed into cropland. In the future (2022–2050), the available water resource will show an upward tendency while the irrigation area carrying capacity mainly ranges from 12 × 10 2 – 21 × 10 2 km 2 and 17 × 10 2 – 30 × 10 2 km 2 under scenarios SSP (shared socioeconomic pathway) 245 and SSP585, respectively. The simulated results will provide useful information for the allocation of water resources and the regional sustainable development of TRB.

Keywords: arid region; snowmelt-driven runoff; climate change; irrigation area; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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