Through the Water Availability and Use Science Program, USGS will provide national information on withdrawal, conveyance, consumptive use, and return flow by water-use category at spatial and temporal resolutions important for risk-informed water management decisions. Water-use data provide a foundation for water managers to analyze trends over time, plan more strategically, identify, and ultimately quantify vulnerability thresholds for adaptive water management. These data are vital to water-availability studies and assessments which seek to evaluate the balance between supply and demand and the relative influence of individual components in affecting that balance and achieving water security.
The U.S. Geological Survey is developing a combination of physics-based and data-driven models that rely on artificial intelligence/machine learning to estimate water withdrawal and consumptive use, replacing the previously used method of compiling and reviewing available data and making estimates to complete a national picture every 5-years. These models will allow for a consistent and more frequent reporting of water use information with an enhanced understanding of how water is used and what influences the variation in time and space. The models will be useful in identifying data gaps and data quality issues which in turn will inform future data collection and ultimately improve model predictions.
General model design requirements include:
Models for each use category will rely on different design strategies and development timelines following a set of prioritization factors, including the proportion of water use relative to the nation’s total water use, availability of data, availability of established modeling theories, and understanding of drivers of water use for each category. Model enhancements will be made to incorporate new data products and technological advancements in water use estimation research to maintain the highest levels of accuracy necessary to best align with water resource decision maker’s needs.
Development of models for the three categories that represent over 90% of total water use nationally--public supply, self-supplied thermoelectric power, and self-supplied crop irrigation--are complete. Water use estimates for 2000 through 2020 are now available for self-supplied thermoelectric power generation, self-supplied irrigation, and public supply.
Withdrawals for public supply water use, and withdrawals and consumptive use for irrigation water use are estimated for each month of the period 2000-2020 for all watersheds at the 12-digit hydrologic unit code level (HUC-12) in the conterminous United States. The withdrawal and consumptive use estimates for thermoelectric power water use are available for each month of the period from 2008-2020, by power plant.
Estimates for 2020 for six five additional categories of use (self-supplied industrial, domestic, mining, livestock, and aquaculture, and golf irrigation) will be available in 2025 as the development of those models will be started in 2023. Models will be expanded to include golf irrigation and Alaska, Hawaii, Puerto Rico, and the U.S. Virgin Islands during a future phase of model development.
Public Water Supply
Statistical models, including machine learning, are being developed for estimating withdrawals and consumptive use for public supply water use. These models are data intensive and rely heavily on State, Federal and local information. To model public-supply water use effectively and accurately an understanding of the geographic extent of each system’s customer base (service area) and demographics are required. Information is needed that links the sources of water to the systems, the volume of water delivered to domestic and other uses (commercial, industrial, irrigation, and thermoelectric power), the water losses that happen during transport, and a multitude of climatic, economic, and social variables that influence public supply water use.
Data release includes estimates of water withdrawals (output), scripts to produce input features, input datasets, and model code for the period 2000-2020.
Thermoelectric Power (TE)
A hybrid physics-based and machine learning modeling approach is being used to characterize plant operations and water use requirements for near-real-time estimates and future forecasts. These models add capability to the physics-based models previously developed by the USGS in collaboration with the U.S. Energy Information Administration (EIA).
For each plant, the physics-based models calculate monthly and annual estimates of withdrawal and consumptive use based on linked heat-and-water-budget understanding that are constrained by power plant fuel consumption, electricity generation, cooling-system technology and environmental variables (air and water temperatures, wind speed, and elevation). Although processes and plant characteristics that affect water use are well developed and represented by the physics-based model, data requirements including gross electricity generation and fuel heat which are provided by the EPA and EIA are released quarterly. To provide a TE model that can estimate near-real-time and forecasted withdrawals, machine learning approaches are being used to predict gross electrical generation and fuel heat.
Data releases include estimates of water withdrawals and consumptive use (output), input datasets, power plant characteristics and model code for a period of 2008-2020.
Irrigation
Models for estimating and identifying irrigated lands, crop water requirements, irrigation system types and efficiencies, and source-water information are all needed to construct high spatial and temporal-scale estimates of irrigation water use. Satellite-based surface energy balance models and a hydrologic soil-water balance model are coupled to calculate crop water consumption and applied irrigation. Crops get their water as a mixture of natural precipitation and applied irrigation and depending on the amount and timing of precipitation relative to the growing season, irrigation water requirements can be highly variable. The amount of water withdrawn relies on an understanding of irrigation system type and water conveyance efficiencies. Estimates for irrigated golf courses will be added to the model design after 2022.
Data releases include estimates of consumptive use and withdrawal (output), actual evaporation, effective precipitation, irrigated lands, irrigation efficiencies and model code for the period 2000-2020.
Mining
Model development will begin in 2023 and will include minerals mining as well as oil and gas extraction activity. The information gained from model development for continuous oil and gas water use will be incorporated into the new modeling efforts, and methods developed for previous USGS county-scale mining estimates will be evaluated for inclusion.
Industrial
Model development will begin in 2023.
Livestock and Aquaculture
Model development will begin in 2023 and will include an evaluation of methods developed for previous USGS county-scale estimates (Lovelace 2009a, Lovelace 2009b).
Self-Supplied Domestic
Model development will begin in 2023 and will include an evaluation of methods developed for the public supply model and previous USGS approaches for quantifying domestic water use.
USGS is assessing water withdrawals for bottling in relation to water availability – water quality, groundwater levels, and other factors. The research aims to develop tools that can be used to estimate potential changes in the future as withdrawals, climate, and socioeconomic conditions change.
Project components include:
Water managers across the United States require more complete, timely, and accurate water-availability information to support policy and decision-making, specifically, data associated with water withdrawals and consumptive use. Recognizing the limitations of current water-use data, the SECURE Water Act authorized a program that supports activities related to data collection and methods research and development at the State level. The USGS Water-Use Data and Research program (WUDR) provides financial assistance through cooperative agreements with State water resource agencies to improve the availability, quality, compatibility, and delivery of water-use data that is collected or estimated by States.
Cooperative matching funds (CMF) are matched with State, local and tribal funds to work with partners to solve complex water resource issues in their area of interest and that serve the Federal interest. Many of the collaborative projects being performed across the country are funded at a 2:1 or 3:1 ratio by the participating cooperative entities. This is a testament to the value local, State and tribal cooperators place on the scientific contributions from the USGS. Cooperative projects funded by water-use research CMF typically address water use and the impacts that use has on hydrology and water allocations.