Multi-decadal prediction of estuarine sedimentation with high-fidelity hydromorphodynamic models presents high computation costs, especially when accounting for stochasticity and uncertainty. A StochAstic model for Multi-decadaL Estuarine Sedimentation (SeAMLESS) is formulated here to support a specific decision-need related to resilience planning and coastal management: estimating future sedimentation and dredging within a sedimentation basin for different scenarios of sea level rise and rules for dredging. SeAMLESS combines a reduced-dimension process model and a response-surface surrogate model to yield an ordinary differential equation that can be integrated over stochastic time series of storm events. Applications show that SeAMLESS can predict probabilities and amounts of future basin sedimentation and dredging with minimal loss of accuracy, compared to a high-fidelity model, while delivering $\mathcal{O}({10}^4-{10}^5)$ reduction in computational costs.

使用高保真水动力模型对河口沉积进行多年代预测，显示出很高的计算成本，尤其是考虑到随机性和不确定性时。此处建立了多年代河口沉积的随机模型（SeAMLESS），以支持与韧性规划和海岸管理相关的特定决策需求：针对海平面上升的不同场景和疏rules规则，估算未来在一个沉积盆地内的沉积和疏ed 。SeAMLESS结合了降维过程模型和响应面替代模型，以生成可在风暴事件的随机时间序列上积分的常微分方程。应用表明，SeAMLESS可以以最小的精度损失来预测未来盆地沉积和疏ging的可能性和数量，$\mathcal{Ø}（{10}^4-{10}^5）$ 降低计算成本。