Projecting Flood Frequency Curves Under Near‐Term Climate Change
Projecting Flood Frequency Curves Under Near‐Term Climate Change
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DOI：
10.1029/2021wr031246
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发表时间：
2021-09
影响因子：
5.4
通讯作者：
C. Awasthi;S. Archfield;K. Ryberg;A. Sankarasubramanian;J. Kiang
中科院分区：
文献类型：
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作者：
C. Awasthi;S. Archfield;K. Ryberg;A. Sankarasubramanian;J. Kiang
洪水频率曲线对于水基础设施设计至关重要，通常是根据固定气候假设制定的。然而，气候变化预计将违反这一假设。在这里，我们提出了一种新的气候信息方法，用于估计非平稳未来气候条件下的洪水频率曲线。该方法开发了一种异步半参数局部似然回归（ASLLR）模型，该模型使用广义线性模型将年度最大洪水时刻与气候变量联系起来。我们以每月降雨量和温度作为预测因子，估计 ASLLR 的基础对数皮尔逊 3 型分布的前两个边际矩 (MM)（均值和方差）。所提出的方法 ASLLR-MM 适用于覆盖美国本土 18 个水资源区域的 40 个美国地质调查局水流计。对估计方差进行基于干旱指数的修正，然后使用由 39 个集合成员组成的 8 个全球环流模型 (GCM) 的历史（1951-2005）和预测（2006-2035，在 RCP4.5 和 RCP8.5 下）月降水量和温度对 ASLLR-MM 方法进行评估。 ASLLR-MM 和 GCM 成员估计的洪水频率分位数与利用潮湿盆地历史时期观测到的气候和洪水信息估计的洪水频率分位数进行了很好的比较，而干旱盆地模型估计的不确定性较高。考虑额外的大气和地表条件以及包括一个地区其他盆地的多级模型结构可以进一步提高干旱盆地的模型性能。
Flood‐frequency curves, critical for water infrastructure design, are typically developed based on a stationary climate assumption. However, climate changes are expected to violate this assumption. Here, we propose a new, climate‐informed methodology for estimating flood‐frequency curves under non‐stationary future climate conditions. The methodology develops an asynchronous, semiparametric local‐likelihood regression (ASLLR) model that relates moments of annual maximum flood to climate variables using the generalized linear model. We estimate the first two marginal moments (MM) – the mean and variance – of the underlying log‐Pearson Type‐3 distribution from the ASLLR with the monthly rainfall and temperature as predictors. The proposed methodology, ASLLR‐MM, is applied to 40 U.S. Geological Survey streamgages covering 18 water resources regions across the conterminous United States. A correction based on the aridity index was applied on the estimated variance, after which the ASLLR‐MM approach was evaluated with both historical (1951–2005) and projected (2006–2035, under RCP4.5 and RCP8.5) monthly precipitation and temperature from eight Global Circulation Models (GCMs) consisting of 39 ensemble members. The estimated flood‐frequency quantiles resulting from the ASLLR‐MM and GCM members compare well with the flood‐frequency quantiles estimated using the historical period of observed climate and flood information for humid basins, whereas the uncertainty in model estimates is higher in arid basins. Considering additional atmospheric and land‐surface conditions and a multi‐level model structure that includes other basins in a region could further improve the model performance in arid basins.