Scientific and technical changes to flood forecasting models are implemented to improve forecasts. However, responses to such changes are complex, particularly in global models, and evaluation of improvements remains focussed on generalised skill assessments and not on the most relevant outcomes for those taking decisions. Recently, the Global Flood Awareness System (GloFAS) flood forecasting model has been upgraded from version 2.1 to 3.1 with a significant change to its hydrological model structure. In the updated version 3.1, a single fully configured hydrological model (LISFLOOD) has been adopted, including ground water and river routing processes, instead of two coupled models, a land surface and a simplified hydrological model, of the previous version 2.1. This study aims to evaluate changes in the simulated behaviour of floods and the forecast skill of the two GloFAS versions based on different decision criteria for early action. We evaluate GloFAS reforecasts for the Brahmaputra and the Ganges Rivers in Bangladesh for the period 1999–2018. For the Brahmaputra River, the old GloFAS 2.1 version performs better than the 3.1 version, especially in predicting low- (90th percentile) and medium-level (95th percentile) floods. For the Ganges, GloFAS 3.1 shows improved probability of detection of low- to medium-level floods compared to version 2.1, especially for lead times longer than 10 days. Both versions show limited skill for more extreme floods (99th percentile) but results are less robust for these less frequent floods given the lower number of events. Using lead-time dependent thresholds improves the false alarm ratio while reducing the probability of detection. The changes in model structures influence the model performance in a complex and varied way and forecast skill needs further investigation across regions and decision-making criteria. Understanding the skill changes between different model versions is important for decision-makers; however, focused case studies such as this should also be used by model developers to guide future changes to the system to ensure that they lead to improvements in decision-making ability.

中文翻译：

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洪水预报系统开发的决策主导评估方法：在孟加拉国全球洪水意识系统中的应用

对洪水预报模型进行科学和技术变革以改进预报。然而，对此类变化的反应是复杂的，特别是在全球模型中，并且对改进的评估仍然侧重于一般技能评估，而不是与决策者最相关的结果。近期，全球洪水感知系统（GloFAS）洪水预报模型由2.1版升级至3.1版，水文模型结构发生重大变化。在更新的3.1版本中，采用了单一的完全配置的水文模型（LISFLOOD），包括地下水和河流演进过程，而不是先前版本2.1的两个耦合模型（陆地表面和简化的水文模型）。本研究旨在评估洪水模拟行为的变化以及基于不同早期行动决策标准的两个 GloFAS 版本的预报技巧。我们评估了 GloFAS 对 1999 年至 2018 年期间孟加拉国雅鲁藏布江和恒河的重新预测。对于布拉马普特拉河，旧的 GloFAS 2.1 版本比 3.1 版本表现更好，特别是在预测低水位（第 90 个百分位）和中水位（第 95 个百分位）洪水方面。对于恒河，与版本 2.1 相比，GloFAS 3.1 提高了检测中低洪水的概率，特别是在交货时间超过 10 天的情况下。两个版本都显示出应对更极端洪水（第 99 个百分位数）的能力有限，但鉴于事件数量较少，对于这些不太频繁的洪水，结果不太稳健。使用与提前期相关的阈值可以提高误报率，同时降低检测概率。模型结构的变化以复杂多样的方式影响模型性能，预测技巧需要进一步跨区域和决策标准的研究。了解不同模型版本之间的技能变化对于决策者来说非常重要；然而，模型开发人员也应该使用诸如此类的重点案例研究来指导未来对系统的更改，以确保它们能够提高决策能力。