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How to Optimize Ecosystem Services Based on a Bayesian Model: A Case Study of Jinghe River Basin

Author

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  • Chengyan Tang

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China)

  • Jing Li

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China)

  • Zixiang Zhou

    (College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Li Zeng

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China)

  • Cheng Zhang

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China)

  • Hui Ran

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China)

Abstract

Based on a Bayesian Network Model (BBN), we established an ecological service network system of the Jinghe River Basin in 2015. Our method consisted of using the distributed eco-hydrological model (Soil and Water Assessment Tool (SWAT) model) to simulate water yield, the Carnegie-Ames-Stanford Approach (CASA) model to estimate Net Primary Productivity (NPP), the Universal Soil Loss Equation (USLE) model to calculate soil erosion and the Crop Productivity (CP) model to simulate agricultural productivity to quantify the four ecosystem services. Based on the network established, the key variable subset and the visual optimal state subset, which we visualized, were analyzed and used to provide spatial optimization suggestions for the four kinds of ecosystem services studied. Our results indicate that water yield, concentrated in the middle and lower reaches of the mountain and river areas, is increasing in the Jinghe River Basin. NPP is continuously increasing and is distributed in the middle and lower reaches of the mountain areas on both sides of the river. Agricultural productivity also shows an upward trend, with areas of high productivity concentrated in the southern downstream mountain areas. On the contrary, the amount of soil erosion is declining, and the high erosion value is also declining, mainly in the upper reaches of the basin (in the Loess Hilly Area). Additionally, we found that a synergistic relationship exists between water yield, NPP and agricultural productivity, which can increase vegetation cover, leading to enhanced agricultural productivity. However, water yield can be reduced as required in order to balance the tradeoff between water yield and soil erosion. Clear regional differences exist in ecosystem services in the river basin. In the future, the two wings of the middle and lower reaches of the river basin will be the main areas of optimization, and it is likely that an optimal ecosystem services pattern can be reached.

Suggested Citation

  • Chengyan Tang & Jing Li & Zixiang Zhou & Li Zeng & Cheng Zhang & Hui Ran, 2019. "How to Optimize Ecosystem Services Based on a Bayesian Model: A Case Study of Jinghe River Basin," Sustainability, MDPI, vol. 11(15), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4149-:d:253653
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    Cited by:

    1. Ting Zheng & Zixiang Zhou & Yufeng Zou & Bakhtiyor Pulatov & Asim Biswas, 2021. "Analysis of Spatial and Temporal Characteristics and Spatial Flow Process of Soil Conservation Service in Jinghe Basin of China," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    2. Forio, Marie Anne Eurie & Villa-Cox, Gonzalo & Van Echelpoel, Wout & Ryckebusch, Helena & Lock, Koen & Spanoghe, Pieter & Deknock, Arne & De Troyer, Niels & Nolivos-Alvarez, Indira & Dominguez-Granda,, 2020. "Bayesian Belief Network models as trade-off tools of ecosystem services in the Guayas River Basin in Ecuador," Ecosystem Services, Elsevier, vol. 44(C).
    3. Pham, Hung Vuong & Sperotto, Anna & Furlan, Elisa & Torresan, Silvia & Marcomini, Antonio & Critto, Andrea, 2021. "Integrating Bayesian Networks into ecosystem services assessment to support water management at the river basin scale," Ecosystem Services, Elsevier, vol. 50(C).

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