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Estimation of soil moisture using modified antecedent precipitation index with application in landslide predictions

机译:利用改进的先发制沉淀指数在滑坡预测中估算土壤水分

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摘要

Soil moisture plays a key role in land-atmosphere interaction systems. Although it can be estimated through in situ measurements, satellite remote sensing, and hydrological modelling, using indicators to index soil moisture conditions is another useful way. In this study, one of these indicators, the antecedent precipitation index (API), is explored. Modifications were proposed to the conventional version of API by introducing two parameters to make it more in line with the physical process. First, the recession coefficient is allowed to vary with the change of air temperature, which could take into account the variation of the evapotranspiration process. Second, the API value is restricted by the maximum value of API, accounting for the maximum water holding capacity of the soil. The modified API was then calibrated and validated by comparing with the in situ measured soil moisture. The better correlation between these two datasets demonstrates that the modified API could better indicate soil moisture conditions, compared with the conventional API. The capability of the modified API to index soil moisture conditions was further explored by applying it to landslide predictions in the Emilia-Romagna region, northern Italy. Here, the recent 3-day rainfall vs the antecedent soil wetness thresholds (RS thresholds) were constructed, in which the soil wetness is indexed by the modified API. The validation of RS thresholds was carried out with the use of the contingency matrix and receiver operating characteristic (ROC) curves. By comparing the prediction performance between RS thresholds and rainfall thresholds, it is found that RS threshold could provide better prediction capabilities in terms of higher hit rate and lower false alarm rate. The positive results indicate that the modified API could provide superior performance of indexing soil moisture conditions, demonstrating the effectiveness of the proposed modifications.
机译:土壤水分在陆地互动系统中起着关键作用。虽然可以通过原位测量,卫星遥感和水文建模估计,但使用指标的土壤水分条件是另一种有用的方式。在本研究中,探讨了这些指标之一,先行降水指数(API)。通过引入两个参数来提出传统版本的API的修改,使其更加符合物理过程。首先,允许经济衰退系数随空气温度的变化而变化,这可能考虑蒸散过程的变异。其次,API值受API的最大值的限制,占土壤的最大水持续容量。然后通过与原位测量的土壤水分进行比较来校准修饰的API并验证。与传统API相比,这两个数据集之间的更好相关性表明修饰的API可以更好地指示土壤湿度条件。通过将其应用于意大利北部的艾米利亚 - 罗马纳地区的滑坡预测,进一步探索了修饰API对指标土壤湿度条件的能力。这里,最近的3天降雨量与先进的土壤湿度阈值(RS阈值)构建,其中土壤湿度由修饰的API指定。利用应急矩阵和接收器操作特征(ROC)曲线进行RS阈值的验证。通过比较RS阈值和降雨阈值之间的预测性能,发现RS阈值可以在更高的命中率和更低的误报率方面提供更好的预测能力。阳性结果表明,改性API可以提供索引土壤湿度条件的卓越性能,证明了所提出的修改的有效性。

著录项

  • 来源
    《Landslides》 |2019年第12期|共13页
  • 作者单位

    Hohai Univ Coll Water Conservancy &

    Hydropower Engn Nanjing Peoples R China;

    Nanjing Normal Univ Minist Educ Key Lab VGE Nanjing Peoples R China;

    Univ Bristol Dept Civil Engn Bristol Avon England;

    Hohai Univ Coll Water Conservancy &

    Hydropower Engn Nanjing Peoples R China;

    Hohai Univ Coll Water Conservancy &

    Hydropower Engn Nanjing Peoples R China;

    Univ Bristol Dept Civil Engn Bristol Avon England;

    Anhui Univ Sci &

    Technol Coll Earth &

    Environm Huainan Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 崩塌;
  • 关键词

    Antecedent precipitation index; Soil moisture; Landslide prediction;

    机译:前一种降水指数;土壤水分;滑坡预测;

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