Broderick Ciaran, Murphy Conor, Wilby Robert L, Matthews Tom, Prudhomme Christel, Adamson Mark
Irish Climate Analysis and Research UnitS (ICARUS), Department of Geography Maynooth University Maynooth Ireland.
Department of Geography Loughborough University Loughborough UK.
Water Resour Res. 2019 Feb;55(2):1079-1104. doi: 10.1029/2018WR023623. Epub 2019 Feb 7.
This study develops a coherent framework to detect those catchment types associated with a high risk of maladaptation to future flood risk. Using the "scenario-neutral" approach to impact assessment the sensitivity of Irish catchments to fluvial flooding is examined in the context of national climate change allowances. A predefined sensitivity domain is used to quantify flood responses to +2 °C mean annual temperature with incremental changes in the seasonality and mean of the annual precipitation cycle. The magnitude of the 20-year flood is simulated at each increment using two rainfall-runoff models (GR4J, NAM), then concatenated as response surfaces for 35 sample catchments. A typology of catchment sensitivity is developed using clustering and discriminant analysis of physical attributes. The same attributes are used to classify 215 ungauged/data-sparse catchments. To address possible redundancies, the exposure of different catchment types to projected climate is established using an objectively selected subset of the Coupled Model Intercomparison Project Phase 5 ensemble. Hydrological model uncertainty is shown to significantly influence sensitivity and have a greater effect than ensemble bias. A national flood risk allowance of 20%, considering all 215 catchments is shown to afford protection against ~48% to 98% of the uncertainty in the Coupled Model Intercomparison Project Phase 5 subset (Representative Concentration Pathway 8.5; 2070-2099), irrespective of hydrological model and catchment type. However, results indicate that assuming a standard national or regional allowance could lead to local over/under adaptation. Herein, catchments with relatively less storage are sensitive to seasonal amplification in the annual cycle of precipitation and warrant special attention.
本研究构建了一个连贯的框架,以检测那些与未来洪水风险适应不良高风险相关的集水区类型。采用“情景中性”方法进行影响评估,在国家气候变化津贴的背景下,研究了爱尔兰集水区对河流洪水的敏感性。使用预定义的敏感性域来量化洪水对年平均气温升高2°C以及年降水周期季节性和平均值的增量变化的响应。使用两个降雨径流模型(GR4J、NAM)在每个增量处模拟20年一遇洪水的规模,然后将其拼接为35个样本集水区的响应面。利用聚类和物理属性判别分析建立了集水区敏感性类型。使用相同的属性对215个无实测数据/数据稀少的集水区进行分类。为了解决可能的冗余问题,利用耦合模式比较计划第5阶段集合中客观选择的子集,确定了不同集水区类型对预估气候的暴露情况。结果表明,水文模型不确定性对敏感性有显著影响,且其影响大于集合偏差。考虑所有215个集水区,20%的国家洪水风险津贴被证明能够抵御耦合模式比较计划第5阶段子集中约48%至98%的不确定性(代表性浓度路径8.5;2070 - 2099),无论水文模型和集水区类型如何。然而,结果表明,假设采用标准的国家或地区津贴可能会导致局部适应过度/不足。在此,蓄水量相对较少的集水区对年降水周期中的季节性放大较为敏感,值得特别关注。