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Predicting the Effects of Land Use Land Cover and Climate Change on Munneru River Basin Using CA-Markov and Soil and Water Assessment Tool

Author

Listed:
  • Kotapati Narayana Loukika

    (Department of Civil Engineering, National Institute of Technology, Warangal 506004, India)

  • Venkata Reddy Keesara

    (Department of Civil Engineering, National Institute of Technology, Warangal 506004, India)

  • Eswar Sai Buri

    (Department of Civil Engineering, National Institute of Technology, Warangal 506004, India)

  • Venkataramana Sridhar

    (Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA)

Abstract

It is important to understand how changing climate and Land Use Land Cover (LULC) will impact future spatio-temporal water availability across the Munneru river basin as it aids in effective water management and adaptation strategies. The Munneru river basin is one of the important sub-basins of the Krishna River in India. In this paper, the combined impact of LULC and Climate Change (CC) on Munneru water resources using the Soil and Water Assessment Tool (SWAT) is presented. The SWAT model is calibrated and validated for the period 1983–2017 in SWAT-CUP using the SUFI2 algorithm. The correlation coefficient between observed and simulated streamflow is calculated to be 0.92. The top five ranked Regional Climate Models (RCMs) are ensembled at each grid using the Reliable Ensemble Averaging (REA) approach. Predicted LULC maps for the years 2030, 2050 and 2080 using the CA-Markov model revealed increases in built-up and kharif crop areas and decreases in barren lands. The average monthly streamflows are simulated for the baseline period (1983–2005) and for three future periods, namely the near future (2021–2039), mid future (2040–2069) and far future (2070–2099) under Representation Concentration Pathway (RCP) 4.5 and 8.5 climate change scenarios. Streamflows increase in three future periods when only CC and the combined effect of CC and LULC are considered under RCP 4.5 and RCP 8.5 scenarios. When compared to the CC impact in the RCP 4.5 scenario, the percentage increase in average monthly mean streamflow (July–November) with the combined impact of CC and LULC is 33.9% (near future), 35.8% (mid future), and 45.3% (far future). Similarly, RCP 8.5 increases streamflow by 33.8% (near future), 36.5% (mid future), and 38.8% (far future) when compared to the combined impact of CC and LULC with only CC. When the combined impact of CC and LULC is considered, water balance components such as surface runoff and evapotranspiration increase while aquifer recharge decreases in both scenarios over the three future periods. The findings of this study can be used to plan and develop integrated water management strategies for the basin with projected LULC under climate change scenarios. This methodology can be applied to other basins in similar physiographic regions.

Suggested Citation

  • Kotapati Narayana Loukika & Venkata Reddy Keesara & Eswar Sai Buri & Venkataramana Sridhar, 2022. "Predicting the Effects of Land Use Land Cover and Climate Change on Munneru River Basin Using CA-Markov and Soil and Water Assessment Tool," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5000-:d:798981
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    References listed on IDEAS

    as
    1. Xiaoyan Gong & Jianmin Bian & Yu Wang & Zhuo Jia & Hanli Wan, 2019. "Evaluating and Predicting the Effects of Land Use Changes on Water Quality Using SWAT and CA–Markov Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4923-4938, November.
    2. Zhang, Dejian & Chen, Xingwei & Yao, Huaxia & Lin, Bingqing, 2015. "Improved calibration scheme of SWAT by separating wet and dry seasons," Ecological Modelling, Elsevier, vol. 301(C), pages 54-61.
    3. Eswar Sai Buri & Venkata Reddy Keesara & Kotapati Narayana Loukika & Venkataramana Sridhar, 2022. "Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach," Sustainability, MDPI, vol. 14(3), pages 1-23, February.
    4. Kotapati Narayana Loukika & Venkata Reddy Keesara & Venkataramana Sridhar, 2021. "Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    5. Angshuman M. Saharia & Arup Kumar Sarma, 2018. "Future climate change impact evaluation on hydrologic processes in the Bharalu and Basistha basins using SWAT model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(3), pages 1463-1488, July.
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    Cited by:

    1. Harik, G. & Alameddine, I. & Zurayk, R. & El-Fadel, M., 2023. "Uncertainty in forecasting land cover land use at a watershed scale: Towards enhanced sustainable land management," Ecological Modelling, Elsevier, vol. 486(C).
    2. Yajuan Wang & Yongheng Rao & Hongbo Zhu, 2022. "Revealing the Impact of Protected Areas on Land Cover Volatility in China," Land, MDPI, vol. 11(8), pages 1-16, August.

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