Climate change poses significant challenges for decision-making processes across a range of sectors. From the water resources planning and management perspective, the interest is often in evaluating the performance of a water supply system in a future state considering the potential changes in rainfall and streamflow characteristics. With observed climate change signals, scenario-based projections of rainfall and streamflow simulations are crucial for evaluating the potential impacts of climate change on water resource systems. Given the complexity of the existing approaches, their applications for generating scenario-based projections of streamflow and rainfall are limited. We developed a non-parametric bootstrapping approach, NPScnGen, for future scenario generation of any hydroclimatic variable. The developed approach is flexible and can be used with any physical hydrological or data-driven stochastic model that provides simulations of hydroclimatic variables of interest for the historical climate condition. In NPScnGen, samples of any set of time-series characteristics, such as mean and standard deviation, are generated from a multivariate Gaussian process for the considered scenario, and then bootstrapping is performed to select the closest sample from the historical simulation of that hydroclimatic variable. We have also proposed a modified wavelet-based model, Wavelet-HMM, and used that model to synthetically generate historical climate time-series as a baseline. We present the application of the developed framework consisting of historical climate simulation and future climate projection approaches on rainfall and streamflow datasets for the Tampa Bay region in Florida.

中文翻译：

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使用两阶段随机模拟框架生成水文气候场景


气候变化给多个部门的决策过程带来了重大挑战。从水资源规划和管理的角度来看，人们通常关注的是考虑降雨和水流特征的潜在变化来评估供水系统在未来状态下的性能。根据观测到的气候变化信号，基于情景的降雨和水流模拟预测对于评估气候变化对水资源系统的潜在影响至关重要。鉴于现有方法的复杂性，它们在生成基于场景的水流和降雨预测方面的应用受到限制。我们开发了一种非参数引导方法 NPScnGen，用于生成任何水文气候变量的未来场景。所开发的方法非常灵活，可与任何物理水文或数据驱动的随机模型一起使用，该模型提供历史气候条件感兴趣的水文气候变量的模拟。在 NPScnGen 中，任何一组时间序列特征（例如平均值和标准差）的样本都是根据所考虑场景的多元高斯过程生成的，然后执行自举以从该水文气候变量的历史模拟中选择最接近的样本。我们还提出了一种改进的基于小波的模型，即小波隐马尔可夫模型（Wavelet-HMM），并使用该模型综合生成历史气候时间序列作为基线。我们介绍了已开发框架的应用，该框架包括历史气候模拟和未来气候预测方法，用于佛罗里达州坦帕湾地区的降雨和水流数据集。