This short communication describes a new open-source implementation of the CALVIN model (CALifornia Value Integrated Network), a large-scale network flow optimization model of California's water supply system. The model is cross-platform, uses common data formats, and connects to several freely available linear programming solvers. Given inputs including hydrology, urban/agricultural demand curves, and variable operating costs, the model minimizes the systemwide cost of water scarcity and operations including surface and groundwater reservoirs, wastewater reuse, desalination, environmental flow requirements, and hydropower. Key outputs include water shortage costs and marginal economic values of water and infrastructure capacity. We benchmark the scalability of different solvers up to roughly 5 million decision variables, using shared-memory parallelization on a high performance computing cluster. Runtimes are reduced by two orders of magnitude relative to the original model when no initial solution is provided, in addition to the benefits such as accessibility and transparency that come with an open-source platform. While this model is specific to California, the data and model structure are separated, so a similar framework could be used in any system where water allocation has been formulated as a network flow problem.

这段简短的交流描述了CALVIN模型（CALifornia价值集成网络）的新开源实现，CALVIN模型是加利福尼亚供水系统的大规模网络流量优化模型。该模型是跨平台的，使用通用的数据格式，并连接到几个免费的线性编程求解器。给定包括水文，城市/农业需求曲线和可变运营成本在内的输入，该模型将系统范围内的水资源短缺和运营成本（包括地表和地下水水库，废水回用，淡化，环境流量需求和水力发电）最小化。主要产出包括缺水成本以及水和基础设施容量的边际经济价值。我们对不同求解器的可扩展性进行基准测试，最多可确定约500万个决策变量，在高性能计算群集上使用共享内存并行化。当没有提供初始解决方案时，运行时间相对于原始模型减少了两个数量级，此外还具有开源平台附带的可访问性和透明性等优点。尽管此模型特定于加利福尼亚州，但数据和模型结构是分开的，因此，在将水分配公式化为网络流量问题的任何系统中，都可以使用类似的框架。