A Review of Flood Loss Models as Basis for Harmonization and Benchmarking

Risk-based approaches have been increasingly accepted and operationalized in flood risk management during recent decades. For instance, commercial flood risk models are used by the insurance industry to assess potential losses, establish the pricing of policies and determine reinsurance needs. Despite considerable progress in the development of loss estimation tools since the 1980s, loss estimates still reflect high uncertainties and disparities that often lead to questioning their quality. This requires an assessment of the validity and robustness of loss models as it affects prioritization and investment decision in flood risk management as well as regulatory requirements and business decisions in the insurance industry. Hence, more effort is needed to quantify uncertainties and undertake validations. Due to a lack of detailed and reliable flood loss data, first order validations are difficult to accomplish, so that model comparisons in terms of benchmarking are essential. It is checked if the models are informed by existing data and knowledge and if the assumptions made in the models are aligned with the existing knowledge. When this alignment is confirmed through validation or benchmarking exercises, the user gains confidence in the models. Before these benchmarking exercises are feasible, however, a cohesive survey of existing knowledge needs to be undertaken. With that aim, this work presents a review of flood loss–or flood vulnerability–relationships collected from the public domain and some professional sources. Our survey analyses 61 sources consisting of publications or software packages, of which 47 are reviewed in detail. This exercise results in probably the most complete review of flood loss models to date containing nearly a thousand vulnerability functions. These functions are highly heterogeneous and only about half of the loss models are found to be accompanied by explicit validation at the time of their proposal. This paper exemplarily presents an approach for a quantitative comparison of disparate models via the reduction to the joint input variables of all models. Harmonization of models for benchmarking and comparison requires profound insight into the model structures, mechanisms and underlying assumptions. Possibilities and challenges are discussed that exist in model harmonization and the application of the inventory in a benchmarking framework.

近数十年来，基于风险的方法在洪水风险管理（flood risk management）中日益获得认可并得以落地实施。例如，保险业已采用商用洪水风险模型（commercial flood risk models）开展潜在损失评估、保单定价制定以及再保险需求确定等工作。尽管自20世纪80年代以来，损失估算工具（loss estimation tools）的开发已取得长足进步，但损失估算结果仍存在较高的不确定性与差异，这往往引发对其质量的质疑。鉴于此类结果会影响洪水风险管理中的优先级排序与投资决策，以及保险业的监管要求与商业决策，因此有必要对损失模型的有效性与鲁棒性进行评估，进而加大对不确定性量化与验证工作的投入。由于缺乏详细且可靠的洪水损失数据，一阶验证（first order validations）难以开展，因此通过基准测试（benchmarking）进行模型对比至关重要。需核查模型是否依据现有数据与知识构建，以及模型中设定的假设是否与现有认知相符。若通过验证或基准测试工作确认了这种匹配性，便能提升用户对模型的信任度。不过，在开展此类基准测试工作之前，需要先对现有知识进行系统性梳理与综述。为此，本研究对从公共领域及部分专业渠道收集的洪水损失——或称洪水脆弱性——关系展开综述。本次调研共分析了61项来源，包括学术出版物或软件包（software packages），其中47项得到了详细梳理。本次工作或许是迄今为止最全面的洪水损失模型综述，涵盖了近千个脆弱性函数（vulnerability functions）。这些函数的异质性极强，且仅有约半数的损失模型在提出时附带了明确的验证工作。本文示例性地提出了一种定量对比异构模型的方法：将所有模型统一简化至共同的输入变量维度。为实现用于基准测试与对比的模型统一化，需深入理解模型的结构、运行机制与底层假设。本文还讨论了模型统一化以及将本综述数据库应用于基准测试框架中所面临的可能性与挑战。