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Spatiotemporal Dynamics of Precipitation in Southwest Arid-Agriculture Zones of Pakistan

Author

Listed:
  • Muhammad Waseem

    (Centre of Excellence in Water Resources Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Ijaz Ahmad

    (Centre of Excellence in Water Resources Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Ahmad Mujtaba

    (Centre of Excellence in Water Resources Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Muhammad Tayyab

    (College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang 443002, China)

  • Chen Si

    (Hubei Key Laboratory of Regional Development and Environmental Response, Wuhan 430062, China
    School of Resources and Environment, Hubei University, Wuhan 430062, China)

  • Haishen Lü

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

  • Xiaohua Dong

    (College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Collaborative Innovation Center for Water Security, Wuhan 430070, China)

Abstract

Investigation of spatiotemporal precipitation trends from a climate change perspective is essential, especially in those regions with rainfed agriculture in order to propose sustainable adaptation schemes. Some restrictive assumptions may hinder the efficacy of trend detection methods, so it could be supported with variability analysis to have a clear picture of the spatiotemporal precipitation dynamics rather than focusing on a single approach. Hence, in the current study, a spatiotemporal dynamic analysis of precipitation was carried out using trend detection methods (the innovative trend analysis method and Mann–Kendall test) and statistical indices (the consecutive disparity index, entropy-based variability index and absolute inter-variability index) in the southwest arid region of Pakistan. The results indicated that based on the monthly, annual and seasonal time series, no systematic precipitation pattern was observed across the whole study region. However, on average, an increasing trend was observed in the east plateau while decreasing in the west plateau. The variability analysis also signposted the higher variability in the case of the western plateau and coastal area compared to the east plateau. Based on the seasonal analysis, it was concluded that, on average, precipitation in the winter and spring season goes on decreasing with higher variability while a mixture of increasing and decreasing trends resulted for summer and autumn. Conclusively the study found that precipitation in the study area is more erratic and its behaviour abruptly changed over a short distance. Moreover, discrepancies and inconstancies were found in the selected trend detection approaches and variability indices. The results also indicated that climate change is going to seriously affect the region as a decreasing trend prevails in most of the cases and stations.

Suggested Citation

  • Muhammad Waseem & Ijaz Ahmad & Ahmad Mujtaba & Muhammad Tayyab & Chen Si & Haishen Lü & Xiaohua Dong, 2020. "Spatiotemporal Dynamics of Precipitation in Southwest Arid-Agriculture Zones of Pakistan," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2305-:d:332963
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    References listed on IDEAS

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    1. Yixue Zeng & Zhixiang Zhou & Zhaogui Yan & Mingjun Teng & Chunbo Huang, 2019. "Climate Change and Its Attribution in Three Gorges Reservoir Area, China," Sustainability, MDPI, vol. 11(24), pages 1-20, December.
    2. Mojtaba Shadmani & Safar Marofi & Majid Roknian, 2012. "Trend Analysis in Reference Evapotranspiration Using Mann-Kendall and Spearman’s Rho Tests in Arid Regions of Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(1), pages 211-224, January.
    3. Fatemeh Barzegari Banadkooki & Mohammad Ehteram & Ali Najah Ahmed & Chow Ming Fai & Haitham Abdulmohsin Afan & Wani M. Ridwam & Ahmed Sefelnasr & Ahmed El-Shafie, 2019. "Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    4. Shilong Piao & Philippe Ciais & Yao Huang & Zehao Shen & Shushi Peng & Junsheng Li & Liping Zhou & Hongyan Liu & Yuecun Ma & Yihui Ding & Pierre Friedlingstein & Chunzhen Liu & Kun Tan & Yongqiang Yu , 2010. "The impacts of climate change on water resources and agriculture in China," Nature, Nature, vol. 467(7311), pages 43-51, September.
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    1. Leszek Sobkowiak & Adam Perz & Dariusz Wrzesiński & Muhammad Abrar Faiz, 2020. "Estimation of the River Flow Synchronicity in the Upper Indus River Basin Using Copula Functions," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    2. Vipin Kumar Oad & Xiaohua Dong & Muhammad Arfan & Vicky Kumar & Muhammad Salman Mohsin & Syed Saad & Haishen Lü & Muhammad Imran Azam & Muhammad Tayyab, 2020. "Identification of Shift in Sowing and Harvesting Dates of Rice Crop ( L. Oryza sativa ) through Remote Sensing Techniques: A Case Study of Larkana District," Sustainability, MDPI, vol. 12(9), pages 1-15, April.
    3. Muhammad Shehzad Ashraf & Muhammad Shahid & Muhammad Waseem & Muhammad Azam & Khalil Ur Rahman, 2023. "Assessment of Variability in Hydrological Droughts Using the Improved Innovative Trend Analysis Method," Sustainability, MDPI, vol. 15(11), pages 1-20, June.

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