Research on environmental risk modeling relies on numerous indicators to quantify the magnitude and frequency of extreme climate events, their ecological, economic, and social impacts, and the coping mechanisms that can reduce or mitigate their adverse effects. Index-based approaches significantly simplify the process of quantifying, comparing, and monitoring risks associated with other natural hazards, as a large set of indicators can be condensed into a few key performance indicators. Data fusion techniques are often used in conjunction with expert opinions to develop key performance indicators. This paper discusses alternative methods to combine data from multiple indicators, with an emphasis on their use-case scenarios, underlying assumptions, data requirements, advantages, and limitations. The paper demonstrates the application of these data fusion methods through examples from current risk and resilience models and simplified datasets. Simulations are conducted to identify their strengths and weaknesses under various scenarios. Finally, a real-life example illustrates how these data fusion techniques can be applied to inform policy recommendations in the context of drought resilience and sustainability.

翻译：环境风险建模研究依赖众多指标来量化极端气候事件的规模与频率、其生态、经济及社会影响，以及能够减轻或缓解其不利影响的应对机制。基于指数的方法显著简化了与其他自然灾害相关的风险量化、比较和监测过程，因为大量指标可被浓缩为少数关键绩效指标。数据融合技术常与专家意见结合以构建关键绩效指标。本文探讨了整合多指标数据的替代方法，重点关注其应用场景、基本假设、数据需求、优势与局限性。通过当前风险与韧性模型及简化数据集的案例，本文展示了这些数据融合方法的应用。通过模拟分析，识别了不同情境下各方法的优缺点。最后，通过一个实际案例说明如何应用这些数据融合技术为干旱韧性与可持续性领域的政策建议提供依据。