This is a short course intended for those who are interested in large-scale water optimization modeling with CALVIN model. Some basic optimization knowledge, especially linear programming, is assumed. This crash course introduces basics of CALVIN model with Pyomo modeling environment. CALVIN is a large-scale hydroeconomic optimization model for California, aiming to optimize water allocation to users. CALVIN is the same model as CALVIN but modeled in a different environment employing different solvers. It is an integrated model with surface and groundwater representation, covering California's most water and water-related economic activities.
See CALVIN website for documentation and more information
Online CALVIN schematic and data visualization tool
Recordings from the Workshop
https://ucmerced.box.com/s/1bhs3f4myazj5g0j8wd00yb3nwjh5xwr
Agenda from the Workshop
Recordings from the Workshop
https://ucmerced.box.com/s/ttta7lfh1pspuh7xl6yurn8ogmnjmkr5
Recordings from the Workshops and documentation
https://ucmerced.box.com/s/y7mciauhpod3e6lblawumxy22hngrnkm
This repository has CALVIN's model structure, including decision variables, objective function and constraints. There are also example runs and some brief documentation.
https://github.com/ucd-cws/calvin
HOBBES database hosting CALVIN network data. Database includes California's water infrastructure data (capacities, network properties etc) and hydrology data (inflow, groundwater etc.)
https://github.com/ucd-cws/calvin-network-data
CALVIN network tool is used to export water network matrix of California from HOBBES database in a format that CALVIN python version can read.
https://github.com/ucd-cws/calvin-network-tools
Change in California's overall surface water storage over the 82-year period:
Change in California's overall groundwater storage over the 82-year period:
Water deliveries to agricultural and urban users (USV: Upper Sacramento Valley; LSVD: Lower Sacramento Valley and Delta; SJSB: San Joaquin and South Bay; TB: Tulare Basin; SC: Southern California
