A quantitative analysis to objectively appraise drought indicators and model drought impacts
A quantitative analysis to objectively appraise drought indicators and model drought impacts
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DOI：
10.5194/hess-20-2589-2016
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发表时间：
2015-09
影响因子：
6.3
通讯作者：
S. Bachmair;C. Svensson;J. Hannaford;L. Barker;K. Stahl
中科院分区：
文献类型：
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作者：
S. Bachmair;C. Svensson;J. Hannaford;L. Barker;K. Stahl
摘要。干旱监测预警是提高抗旱能力的重要措施。虽然有许多业务系统使用不同的干旱指标，但对于哪个指标最能代表任何特定部门的干旱影响发生情况，没有达成共识。此外，阈值被广泛应用于这些指标，但迄今为止，很少有经验证据表明哪些指标阈值会触发对社会、经济和生态系统的影响。评价常用干旱指标的主要障碍是缺乏关于干旱影响的信息。因此，我们的目标是利用来自欧洲干旱影响报告清单（EDII）的基于文本的数据来确定对区域、部门和季节特定影响发生有意义的指标，并根据经验确定指标阈值。此外，我们还测试了基于最佳表现指标的影响发生的可预测性。为了实现这些目标，我们应用了相关分析和集合回归树方法，使用德国和英国（EDII中数据最丰富的国家）作为测试平台。我们选择了两个气象指标（标准化降水指数SPI和标准化降水蒸发指数SPEI）和两个水文指标（河流流量和地下水位百分位数）作为候选指标。分析显示，与德国相比，英国与影响发生最相关的SPI和SPEI积累期更长，但每个国家内部、影响类别之间以及在某种程度上，季节之间存在差异。回归树分裂值的中位数（我们将其视为影响发生阈值的估计值）在英国的SPI和SPEI约为−1；德国北部/东北部与南部/中部地区之间存在明显差异。对于影响数据覆盖较好的地区，采用集合回归树方法进行预测得到了合理的结果。这些预测还提供了对EDII的见解，特别是强调了干旱事件，其中缺少影响报告可能反映的是缺乏记录，而不是真正没有影响。总的来说，所提出的定量框架被证明是评价干旱指标和模拟影响发生的有用工具。综上所述，本研究通过影响数据的收集和分析，为干旱监测和预警提供了信息。它强调了具有影响数据的定量分析在为干旱指标提供“基础真相”方面可以发挥的重要作用，以及更传统的利益相关者主导的方法。
Abstract. Drought monitoring and early warning is an important measure to enhance resilience towards drought. While there are numerous operational systems using different drought indicators, there is no consensus on which indicator best represents drought impact occurrence for any given sector. Furthermore, thresholds are widely applied in these indicators but, to date, little empirical evidence exists as to which indicator thresholds trigger impacts on society, the economy, and ecosystems. The main obstacle for evaluating commonly used drought indicators is a lack of information on drought impacts. Our aim was therefore to exploit text-based data from the European Drought Impact report Inventory (EDII) to identify indicators that are meaningful for region-, sector-, and season-specific impact occurrence, and to empirically determine indicator thresholds. In addition, we tested the predictability of impact occurrence based on the best-performing indicators. To achieve these aims we applied a correlation analysis and an ensemble regression tree approach, using Germany and the UK (the most data-rich countries in the EDII) as test beds. As candidate indicators we chose two meteorological indicators (Standardized Precipitation Index, SPI, and Standardized Precipitation Evaporation Index, SPEI) and two hydrological indicators (streamflow and groundwater level percentiles). The analysis revealed that accumulation periods of SPI and SPEI best linked to impact occurrence are longer for the UK compared with Germany, but there is variability within each country, among impact categories and, to some degree, seasons. The median of regression tree splitting values, which we regard as estimates of thresholds of impact occurrence, was around −1 for SPI and SPEI in the UK; distinct differences between northern/northeastern vs. southern/central regions were found for Germany. Predictions with the ensemble regression tree approach yielded reasonable results for regions with good impact data coverage. The predictions also provided insights into the EDII, in particular highlighting drought events where missing impact reports may reflect a lack of recording rather than true absence of impacts. Overall, the presented quantitative framework proved to be a useful tool for evaluating drought indicators, and to model impact occurrence. In summary, this study demonstrates the information gain for drought monitoring and early warning through impact data collection and analysis. It highlights the important role that quantitative analysis with impact data can have in providing "ground truth" for drought indicators, alongside more traditional stakeholder-led approaches.