Optimizing green infrastructure placement under precipitation uncertainty
Optimizing green infrastructure placement under precipitation uncertainty
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DOI：
10.1016/j.omega.2020.102196
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发表时间：
2020-01
影响因子：
6.9
通讯作者：
Masoud Barah;Anahita Khojandi;Xueping Li;J. Hathaway;O. Omitaomu
中科院分区：
文献类型：
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作者：
Masoud Barah;Anahita Khojandi;Xueping Li;J. Hathaway;O. Omitaomu
城市化的加剧、基础设施的退化和气候变化可能会使全国各地的雨水系统不堪重负，使其失效。绿色基础设施（GI）的做法是低成本，低遗憾的战略，可以有助于城市径流管理。然而，问题仍然是如何最好地分配地理信息系统的做法，通过城市流域降水的不确定性和可变的水文响应。我们开发了随机规划模型，以确定最佳位置的GI做法在一组候选人的位置在一个流域，以尽量减少中期降水的不确定性下的总预期径流。具体来说，我们首先开发了一个两阶段的随机规划模型。接下来，我们重新制定这个模型使用扰动参数，以减少所需的计算时间，并将其扩展到多阶段。此外，我们引入的限制，允许将子集水区径流减少的考虑。我们占水文连通性的分水岭使用一个基本的非循环连通图的子流域，并将各种实际的考虑到模型。此外，我们开发了一个系统的方法来缩小现有的日降水预测到小时单位，并有效地估计相应的水文响应。这些进步汇集在一个案例研究的城市分水岭在一个中等规模的城市在美国，我们执行敏感性分析，评估所考虑的约束的重要性，并提供见解。
Increased urbanization, infrastructure degradation, and climate change threaten to overwhelm stormwater systems across the nation, rendering them ineffective. Green Infrastructure (GI) practices are low cost, low regret strategies that can contribute to urban runoff management. However, questions remain as to how to best distribute GI practices through urban watersheds given precipitation uncertainty and the variable hydrological responses to them. We develop stochastic programming models to determine the optimal placement of GI practices across a set of candidate locations in a watershed to minimize the total expected runoff under medium-term precipitation uncertainties. Specifically, we first develop a two-stage stochastic programming model. Next, we reformulate this model using perturbed parameters to reduce the requisite computational time and extend it to multi-stage. In addition, we introduce constraints that allow for incorporating sub-catchment-level runoff reduction considerations. We account for hydrological connectivity in the watershed using an underlying acyclic connectivity graph of sub-catchments and incorporate various practical considerations into the models. In addition, we develop a systemic approach to downscale the existing daily precipitation projections into hourly units and efficiently estimate the corresponding hydrological responses. These advancements are brought together in a case study for an urban watershed in a mid-sized city in the U.S., where we perform sensitivity analyses, evaluate the importance of the considered constraints, and provide insights.