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Temporal and Spatial Variation (2001–2020) Characteristics of Wind Speed in the Water Erosion Area of the Typical Black Soil Region, Northeast China

Author

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  • Liang Pei

    (Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Chunhui Wang

    (Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Liying Sun

    (Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Lili Wang

    (State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100191, China)

Abstract

Soil erosion is one of the driving factors leading to the land degradation in the black soil region of Northeast China. It is of great significance to analyze the temporal and spatial variation characteristics of wind speed there for the study of wind erosion impacts and geomantic erosion. Based on the daily meteorological data of 51 meteorological stations from 2001 to 2020, the interannual variation, seasonal variation, and spatial characteristics of wind speed were analyzed by cumulative anomaly method, Mann–Kendall test method, and Kriging interpolation method. The natural factors affecting wind speed were discussed by using geographic detectors, and the potential effects of wind speed on soil erosion were further analyzed. The results showed that the maximum annual wind speed in the water erosion area of the typical black soil region fluctuated with a decreasing trend. However, the mean annual wind speed demonstrated a decreasing trend before 2014, and then showed increasing trend. The proportion of the meteorological stations with decreasing mean annual wind speed and maximum annual wind speed during years 2001–2020 was 70% and 60%, respectively. The seasonal variation of the mean monthly wind speed and maximum monthly wind speed showed the same trend as Spring > Autumn > Winter > Summer. The spatial variation of the mean annual wind speed and maximum annual wind speed was consistent. According to the results of the geographic detectors, DEM and temperature are the main factors affecting the spatial heterogeneity of the maximum annual wind speed. The area of ‘severe’ and ‘extremely severe’ of wind impacts account for 23.4%, and specific concerns should be paid to the areas of Nenjiang, Yilan, Tonghe, and Baoqing, located in the north and east sides of the study area. The results of the article could provide reference for the study of wind–water complex erosion in the water erosion area of the typical black soil region for better soil erosion control and ecological protection.

Suggested Citation

  • Liang Pei & Chunhui Wang & Liying Sun & Lili Wang, 2022. "Temporal and Spatial Variation (2001–2020) Characteristics of Wind Speed in the Water Erosion Area of the Typical Black Soil Region, Northeast China," IJERPH, MDPI, vol. 19(17), pages 1-17, August.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10473-:d:895150
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    References listed on IDEAS

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    1. Liu, Heping & Shi, Jing & Erdem, Ergin, 2010. "Prediction of wind speed time series using modified Taylor Kriging method," Energy, Elsevier, vol. 35(12), pages 4870-4879.
    2. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    3. Anatoliy, Kucher & Iryna, Kazakova & Lesya, Kucher & Halina, Kozak & Antonia, Schraml & Hekuran, Koka & Warren, Priest, 2015. "Economics of soil degradation and sustainable use of land in danger of wind erosion," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 1(1), November.
    4. Anatoliy, Kucher & Iryna, Kazakova & Lesya, Kucher & Antonia, Schraml & Hekuran, Koka & Warren, Priest5, 2015. "Sustainable use of land in danger of wind erosion in Ukraine: stakeholder engagement," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 1(2), December.
    5. Cellura, M. & Cirrincione, G. & Marvuglia, A. & Miraoui, A., 2008. "Wind speed spatial estimation for energy planning in Sicily: A neural kriging application," Renewable Energy, Elsevier, vol. 33(6), pages 1251-1266.
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    Cited by:

    1. Fahui Jiang & Xinhua Peng & Qinglin Li & Yongqi Qian & Zhongbin Zhang, 2024. "Potential Reduction of Spatiotemporal Patterns of Water and Wind Erosion with Conservation Tillage in Northeast China," Land, MDPI, vol. 13(8), pages 1-19, August.

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