Developing drought impact functions for drought risk management
Developing drought impact functions for drought risk management
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DOI：
10.5194/nhess-17-1947-2017
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发表时间：
2017-11-16
影响因子：
4.6
通讯作者：
Stahl, Kerstin
中科院分区：
文献类型：
--
作者：
Bachmair, Sophie;Svensson, Cecilia;Stahl, Kerstin
干旱管理框架依赖于监测和预测方法，但仅量化危害可能是不够的；如果要更好地管理干旱，还必须预测降水不足可能产生的负面后果。然而，一些水文气象指标所表达的干旱强度与干旱影响发生之间的联系最近才开始得到解决。挑战之一是缺乏有关干旱的生态和社会经济后果的信息。本研究测试了开发基于干旱指标（标准化降水和标准化降水蒸发指数）的实证“干旱影响函数”作为预测因子和基于文本的干旱影响报告作为干旱损害的替代变量的潜力。虽然已经有研究利用干旱影响的文本证据，但缺乏对影响量化方法的效果和模拟干旱影响的不同函数关系的系统评估。以英格兰东南部为案例研究，我们测试了三种不同的数据驱动模型用于预测从基于文本的报告量化的干旱影响的潜力：逻辑回归、零改变负二项式回归（“障碍模型”）和集成回归树方法（“随机森林”）。逻辑回归模型只能应用于二元影响/无影响时间序列，而其他两个模型还可以预测每个时间点影响发生的完整计数。虽然对二进制数据进行建模可以实现最低的预测不确定性，但对完整计数进行建模的优点是还可以提供影响严重程度的衡量标准，并且发现计数是可以合理预测的。建模方法。对于二元数据，基于留一交叉验证的逻辑回归和随机森林模型表现相似。对于计数数据，随机森林优于障碍模型。总干旱影响和影响类别的两个子集（供水和淡水生态系统影响）存在模型间差异。此外，还研究了定义影响计数的不同方法，发现对预测技能的影响很小。对于所有模型，我们发现除了水文气象指标之外，将前一个月的影响信息作为预测因子也会产生积极的影响。我们的结论是，尽管存在一些局限性，基于文本的干旱影响报告可以为干旱风险管理提供有用的信息，而且我们的研究展示了基于文本数据开发干旱影响函数的不同方法。
Drought management frameworks are dependent on methods for monitoring and prediction, but quantifying the hazard alone is arguably not sufficient; the negative consequences that may arise from a lack of precipitation must also be predicted if droughts are to be better managed. However, the link between drought intensity, expressed by some hydrometeorological indicator, and the occurrence of drought impacts has only recently begun to be addressed. One challenge is the paucity of information on ecological and socioeconomic consequences of drought. This study tests the potential for developing empirical "drought impact functions" based on drought indicators (Standardized Precipitation and Standardized Precipitation Evaporation Index) as predictors and text-based reports on drought impacts as a surrogate variable for drought damage. While there have been studies exploiting textual evidence of drought impacts, a systematic assessment of the effect of impact quantification method and different functional relationships for modeling drought impacts is missing. Using Southeast England as a case study we tested the potential of three different data-driven models for predicting drought impacts quantified from text-based reports: logistic regression, zero-altered negative binomial regression ("hurdle model"), and an ensemble regression tree approach ("random forest"). The logistic regression model can only be applied to a binary impact/no impact time series, whereas the other two models can additionally predict the full counts of impact occurrence at each time point. While modeling binary data results in the lowest prediction uncertainty, modeling the full counts has the advantage of also providing a measure of impact severity, and the counts were found to be reasonably predictable. modeling methodologies. For binary data the logistic regression and the random forest model performed similarly well based on leave-one-out cross validation. For count data the random forest outperformed the hurdle model. The between-model differences occurred for total drought impacts and for two subsets of impact categories (water supply and freshwater ecosystem impacts). In addition, different ways of defining the impact counts were investigated and were found to have little influence on the prediction skill. For all models we found a positive effect of including impact information of the preceding month as a predictor in addition to the hydrometeorological indicators. We conclude that, although having some limitations, text-based reports on drought impacts can provide useful information for drought risk management, and our study showcases different methodological approaches to developing drought impact functions based on text-based data.