A water resource simulator in Python

• Published in: Environmental modelling & software : with environment data news Vol. 126; p. 104635
• Main Authors: Tomlinson, J.E., Arnott, J.H., Harou, J.J.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.04.2020; Elsevier Science Ltd
• Subjects: Computer simulation; Decision analysis; Decision making; Decision making under deep uncertainty; Hydrology; Libraries; Linear programming; Multi-reservoir operations; Multiple objective analysis; Network optimisation; Open source; Optimization; planning; Python; Regression analysis; Robustness; Simulation; Solvers; Water allocation; Water resource simulation; Water resources; Network optimisation; Multi-reservoir operations; Open source; Water resource simulation; Python; Decision making under deep uncertainty
• ISSN: 1364-8152; 1873-6726; 1873-6726
• DOI: 10.1016/j.envsoft.2020.104635

A new generalised water resource network modelling Python library, Pywr, is presented. Given hydrological inflows, Pywr simulates customisable water allocation and operation rules throughout complex multi-purpose managed water systems at each user-defined time-step. The model uses a low-level interface to existing linear programming solvers for fast priority-based optimisation-driven simulation. The library uses an object based system for users to provide input data and record simulation outputs. A novel multi-scenario simulation method provides an almost 4-fold improvement in model run-times and supports calculating robustness metrics across scenarios. A flexible interface to specify multi-objective optimisation formulations as part of a model’s input file is included. These features enable analysts to apply advanced water planning approaches, such as robust decision making and robust optimisation, to real systems. The library is available under the GPLv3 open source licence, includes several examples and a regression test suite. •We present an open source water resources simulation library written in Python.•Includes multi-scenario simulation for the application of robust decision making.•Enables the use of multi-objective optimisation.•Uses an extensible component based input–output system.