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.

中文翻译：

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预测近期气候变化下的洪水频率曲线

洪水频率曲线对水利基础设施设计至关重要，通常是根据固定气候假设开发的。然而，气候变化预计会违反这一假设。在这里，我们提出了一种新的、以气候为依据的方法，用于估计非平稳未来气候条件下的洪水频率曲线。该方法开发了一个异步、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。我们以月降雨量和温度作为预测因子，估计来自 ASLLR 的潜在 log-Pearson Type-3 分布的前两个边际矩 (MM) - 均值和方差。建议的方法 ASLLR-MM 适用于 40 US 覆盖美国本土 18 个水资源地区的地质调查局。对估计方差应用基于干旱指数的校正，之后对历史（1951-2005）和预测（2006-2035，RCP4.5 和 RCP8.5）月降水量和预测的 ASLLR-MM 方法进行评估来自由 39 个集合成员组成的 8 个全球环流模型 (GCM) 的温度。由 ASLLR-MM 和 GCM 成员估计的洪水频率分位数与使用潮湿盆地观测气候和洪水信息的历史时期估计的洪水频率分位数相比很好，而干旱盆地模型估计的不确定性更高。