Living Within Our Means: Adapting Colorado River Basin Depletions to Available Water

Over any time period, the inflows to minus the depletions from any controlled area must equal the change of storage. When users\u27 aspirations to deplete water exceed inflows, water managers must either a) draw down reservoir storage to meet some or all of users’ demands, or b) cut back customary deliveries to adapt to inflows and stabilize reservoir storage. We use a new open-source, Python reservoir model for the Colorado River Basin to simulate new demonstrative adaptive reservoir operations across many short-duration, severe flow sequences observed or reconstructed between 1416 and 2020. Results show: 1) the existing rules to operate Lake Powell and Lake Mead will draw down both reservoirs to their critical storages of 6.0 million acre-feet (3,525 and 1,025 feet) in 3 to 5 years. 2) Triggering the new rule at a Lake Mead elevation of 1,060 feet to adapt basin wide depletions to inflow can sustain both reservoirs above their critical levels for long periods of time. The new rule asks or requires Lower and Upper Basin users to conserve from 0.5 maf per year less to 1.0 maf per year more water than the largest mandatory cutback of 1.375 maf per year. To adopt these adaptive operations, the parties will need to creatively combine five water conservation principles to convert lose-lose conflicts into more positive processes

A Data Model to Manage Data for Water Resources Systems Modeling

Current practices to identify, organize, analyze, and serve data to water resources systems models are typically model and dataset-specific. Data are stored in different formats, described with different vocabularies, and require manual, model-specific, and time-intensive manipulations to find, organize, compare, and then serve to models. This paper presents the Water Management Data Model (WaMDaM) implemented in a relational database. WaMDaM uses contextual metadata, controlled vocabularies, and supporting software tools to organize and store water management data from multiple sources and models and allow users to more easily interact with its database. Five use cases use thirteen datasets and models focused in the Bear River Watershed, United States to show how a user can identify, compare, and choose from multiple types of data, networks, and scenario elements then serve data to models. The database design is flexible and scalable to accommodate new datasets, models, and associated components, attributes, scenarios, and metadata

An open-source Python implementation of California's hydroeconomic optimization model

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

Advancing Water Resources Systems Modeling Cyberinfrastructure to Enable Systematic Data Analysis, Modeling, and Comparisons

Water resources systems models aid in managing water resources holistically considering water, economic, energy, and environmental needs, among others. Developing such models require data that represent a water system’s physical and operational characteristics such as inflows, demands, reservoir storage, and release rules. However, such data is stored and described in different formats, metadata, and terminology. Therefore, Existing tools to store, query, and visualize modeling data are model, location, and dataset-specific, and developing such tools is time-consuming and requires programming experience. This dissertation presents an architecture and three software tools to enable researchers to more readily and consistently prepare and reuse data to develop, compare, and synthesize results from multiple models in a study area: (1) a generalized database design for consistent organization and storage of water resources datasets independent of study area or model, (2) software to extract data out of and populate data for any study area into the Water Evaluation and Planning system, and (3) software tools to visualize online, compare, and publish water management networks and their data for many models and study areas. The software tools are demonstrated using dozens of example and diverse local, regional, and national datasets from three watersheds for four models; the Bear and Weber Rivers in the USA and the Monterrey River in Mexico