Virtually all climate monitoring and forecasting efforts concentrate on hazards rather than on impacts, while the latter are a priority for planning emergency activities and for the evaluation of mitigation strategies. Effective disaster risk management strategies need to consider the prevailing “human terrain” to predict who is at risk and how communities will be affected. There has been little effort to align the spatiotemporal granularity of socioeconomic assessments with the granularity of weather or climate monitoring. The lack of a high-resolution socioeconomic baseline leaves methodical approaches like machine learning virtually untapped for pattern recognition of extreme climate impacts on livelihood conditions. While the request for “better” socioeconomic data is not new, we highlight the need to collect and analyze environmental and socioeconomic data together and discuss novel strategies for coordinated data collection via mobile technologies from a drought risk management perspective. A better temporal, spatial, and contextual understanding of socioeconomic impacts of extreme climate conditions will help to establish complex causal pathways and quantitative proof about climate-attributable livelihood impacts. Such considerations are particularly important in the context of the latest big data-driven initiatives, such as the World Bank’s Famine Action Mechanism (FAM).

中文翻译：

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如果我们需要了解影响，为什么还要预测气候危害？让人类重新回到干旱方程

几乎所有气候监测和预测工作都集中在危害而不是影响上，而后者是规划应急活动和评估缓解战略的优先事项。有效的灾害风险管理战略需要考虑普遍的“人类地形”，以预测谁处于危险之中以及社区将如何受到影响。几乎没有努力将社会经济评估的时空粒度与天气或气候监测的粒度保持一致。缺乏高分辨率的社会经济基线使得机器学习等有条不紊的方法几乎无法用于模式识别极端气候对生计条件的影响。虽然对“更好”的社会经济数据的要求并不新鲜，我们强调需要一起收集和分析环境和社会经济数据，并从干旱风险管理的角度讨论通过移动技术协调数据收集的新策略。更好地了解极端气候条件的社会经济影响的时间、空间和背景，将有助于建立复杂的因果路径和关于气候归因生计影响的定量证据。在世界银行的饥荒行动机制 (FAM) 等最新的大数据驱动计划的背景下，这些考虑尤其重要。以及对极端气候条件的社会经济影响的背景理解将有助于建立复杂的因果路径和关于气候归因于生计影响的定量证据。在世界银行的饥荒行动机制 (FAM) 等最新的大数据驱动计划的背景下，这些考虑尤其重要。以及对极端气候条件的社会经济影响的背景理解将有助于建立复杂的因果路径和关于气候归因于生计影响的定量证据。在世界银行的饥荒行动机制 (FAM) 等最新的大数据驱动计划的背景下，这些考虑尤其重要。