Faced with persistent flooding and water quality challenges, water managers are now seeking to build digital twins of surface water systems that combine sensor data with online models to better understand and control system dynamics. Towards this goal, this study presents pipedream—an end-to-end simulation engine for real-time modeling and state estimation in natural/urban drainage networks. The engine combines (i) a new hydraulic solver based on the one-dimensional Saint-Venant equations and (ii) a Kalman filtering scheme that efficiently updates hydraulic states based on observed data. Using sensor data from a real-world watershed, we find that the simulation engine is effective at both interpolating hydraulic states and forecasting future states based on current measurements. By providing a complete, real-time view of system hydraulics, our software will enable rapid detection of flooding, improved characterization of maintenance and remediation needs, and robust real-time control for both small-scale (stormwater) and large-scale (river/reservoir) networks.

面对持续的洪水和水质挑战，水资源管理者现在正在寻求建立数字双胞胎将传感器数据与在线模型相结合以更好地理解和控制系统动态的地表水系统。为实现这一目标，本研究提出了 pipedream——一种用于在自然/城市排水网络中进行实时建模和状态估计的端到端仿真引擎。该引擎结合了 (i) 基于一维圣维南方程的新液压求解器和 (ii) 基于观测数据有效更新液压状态的卡尔曼滤波方案。使用来自真实世界分水岭的传感器数据，我们发现模拟引擎在插入水力状态和基于当前测量预测未来状态方面都很有效。通过提供系统液压的完整实时视图，我们的软件将能够快速检测洪水，改进维护和修复需求的表征，