Quantifying the Impact of the Billion Tree Afforestation Project (BTAP) on the Water Yield and Sediment Load in the Tarbela Reservoir of Pakistan Using the SWAT Model

Muhammad Shafeeque, Abid Sarwar (), Abdul Basit, Abdelmoneim Zakaria Mohamed, Muhammad Waseem Rasheed, Muhammad Usman Khan, Noman Ali Buttar, Naeem Saddique, Mohammad Irfan Asim and Rehan Mehmood Sabir
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Muhammad Shafeeque: Department of Irrigation and Drainage, University of Agriculture, Faisalabad 38000, Pakistan
Abid Sarwar: Department of Irrigation and Drainage, University of Agriculture, Faisalabad 38000, Pakistan
Abdul Basit: Department of Irrigation and Drainage, University of Agriculture, Faisalabad 38000, Pakistan
Abdelmoneim Zakaria Mohamed: Division of Agriculture and Natural Resources—Kearney Agricultural Research and Extension Center, University of California, Parlier, CA 93648, USA
Muhammad Waseem Rasheed: Key Laboratory of Mountain Surface Processes and Ecological Regulation, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 61004, China
Muhammad Usman Khan: Department of Energy Systems Engineering, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
Noman Ali Buttar: Department of Agricultural Engineering, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan
Naeem Saddique: Department of Irrigation and Drainage, University of Agriculture, Faisalabad 38000, Pakistan
Mohammad Irfan Asim: Department of Irrigation and Drainage, University of Agriculture, Faisalabad 38000, Pakistan
Rehan Mehmood Sabir: Department of Irrigation and Drainage, University of Agriculture, Faisalabad 38000, Pakistan

Land, 2022, vol. 11, issue 10, 1-20

Abstract: The live storage of Pakistan’s major reservoirs, such as the Tarbela reservoir, has decreased in recent decades due to the sedimentation load from the Upper Indus Basin, located in High Mountain Asia. The government of Khyber Pakhtunkhwa took the initiative in 2014 and introduced the Billion Tree Afforestation Project (BTAP). They planted one billion trees by August 2017, mostly in hilly areas. In 2018, the Government of Pakistan also launched a project of 10 billion trees in five years. We assessed the effect of different land-use and land-cover (LULC) scenarios on the water yield and sediment load in the Tarbela reservoir of Pakistan. The soil and water assessment tool (SWAT) model was used to predict the impacts of the LULC changes on the water yield and sediment load under three distinct scenarios: before plantation (2013), after planting one billion trees (2017), and after planting ten billion trees (2025). The model calibration and validation were performed from 1984 to 2000 and 2001 to 2010, respectively, using the SUFI2 algorithm in SWAT-CUP at the Bisham Qila gauging station. The statistical evaluation parameters showed a strong relationship between observed and simulated streamflows: calibration (R 2 = 0.85, P BIAS = 11.2%, NSE = 0.84) and validation (R 2 = 0.88, P BIAS = 10.5%, NSE = 0.86). The validation results for the sediment load were satisfactory, indicating reliable model performance and validity accuracy (R 2 = 0.88, P BIAS = −19.92%, NSE = 0.86). Under the LULC change scenarios, the water yield’s absolute mean annual values decreased from 54 mm to 45 mm for the first and second scenarios, while the third scenario had an estimated 35 mm mean annual water yield in the Tarbela reservoir. The sediment load results for the second scenario (2017) showed a 12% reduction in the sediment flow in the Tarbela reservoir after 1 billion trees were planted. In the third scenario (2025), following the planting of 10 billion trees, among which 3 billion were in the Tarbela basin, the sediment load was predicted to decrease by 22%. The overall results will help to inform the water managers and policymakers ahead of time for the best management and planning for the sustainable use of the water reservoirs and watershed management.

Keywords: billion tree project; Tarbela reservoir; SWAT model; sediment load; water yield (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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