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    半干旱区流域尺度植被依赖地下水程度评价:以鄂尔多斯高原海流兔河流域为例

    Groundwater dependent ecosystems assessment in catchment scale of semi−arid regions: A case study in the Hailiutu catchment of the Ordos Plateau

    • 摘要:
      研究目的 综合利用地表、地下信息,探索建立适用于半干旱区流域尺度植被依赖地下水程度评价的新方法,以期为干旱区地下水合理开发利用与生态保护修复提供技术支撑。
      研究方法 以黄河流域中游鄂尔多斯高原海流兔河流域为研究区,选取地下水位埋深、土壤类型、干旱期蒸散发、植被覆盖度等涵盖水、土、气、生4个圈层的评价指标,以野外数据采集和遥感解译为主要手段,获取以上指标的空间分布,最后在GIS平台中采用综合评价的方法确定依赖地下水生态系统的分布及依赖程度分级。
      研究结果 评价结果表明,依赖地下水程度非常高和高的植被分布面积占流域总面积的10.2%。尽管依赖地下水植被生态系统在整个流域占比不高,但由于地下水提供了比降水更加稳定的供水水源,该地区的生物多样性与生物量远高于其他地区,具有极其重要的生态价值。
      结论 通过场地尺度地下水依赖程度的对比,该方法评价的结果是可靠的,在其他的半干旱区也具有一定的适用性;指标灵敏度分析表明,土壤类型与地下水位埋深是最敏感的指标,因此在野外调查时应加强这两类指标的数据采集。

       

      Abstract:
      This paper is the result of hydrogeological survey engineering.
      Objective A new evaluation method for catchment scale dependent groundwater ecosystem in semi-arid region was established. The method will include both surface and sub−surface information and provides a technical support to groundwater resources development and ecosystem protection in semi−arid areas.
      Methods Taking the Hailiutu catchment in the Ordos Plateau as the study area. Four indicators including depth to water table, soil types, vegetation coverage and evapotranspiration were selected and the spatial distributions of these indicators were determined based on field survey and remote sensing interpretation. Finally, a systematic assessment was performed in a GIS platform to identify the distribution of GDE and the degree of dependency.
      Results The results indicate that the areas very high and high dependent on groundwater accounts for 10.2%. Although the areas are small, biodiversity and bio-mass in such place are much higher with high ecological value than the that of others due to the contribution of groundwater to vegetation growth.
      Conclusions Based on the comparison between this study and site studies, the results have a good agreement and are reliable, indicating that the proposed method is applicable to other similar regions. The sensitivity analysis shows that the most sensitive parameters are soil types and depth to water table that should be paid more attention during data collection in field works.

       

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