This document summarizes a presentation on the United States Geological Survey's water use program and efforts to transition to physics-based and data-driven models to provide more detailed estimates of water withdrawals and consumption across various sectors on a monthly and finer spatial scale. Key points included historical national water use reporting, drivers to enhance the program, future plans to automate data input and use machine learning models for sectors like public supply, irrigation, thermoelectric power and mining water use.

1. Water-Use Program
Texas Alliance of Groundwater Districts (TAGD) Business Meeting
Natalie Houston, Rich Niswonger, Jaime Painter, Ayman Alzraiee, Jon Haynes, Melissa Harris, Cheryl Dieter, and Jeremy McDowell
June 6, 2023

2. 2
Historical National Water-Use Reporting
All images are by the U.S. Geological Survey unless otherwise noted

3. 3
Drivers for the Program
• Section 9508 of the SECURE Water Act (Public Law 111-11)
• Maintenance of a comprehensive national water use
inventory to enhance the level of understanding to the
effects of spatial and temporal patterns of water use on
availability and sustainable use of water resources
• Integration of any dataset maintained by any other
Federal or State agency
• Focus on integration of any data related to water use,
water flow, or water quality to generate relevant
information relating to the impact of human activity on
water and ecological resources
• National Academy of Science, 2002 and 2018
• Enhance spatial and temporal data collection
• Increase focus on the relationships between human
activities and water
• Water accounting should include estimates of
consumptive verses non-consumptive water use
• Engage in collaborations to develop new data sources
and platforms

4. 4
Future of National Water-Use Reporting
• Transitioning to Nationally consistent physics-based and data driven models that can:
• Provide estimates and forecasts of withdrawals and consumption at finer spatial and temporal
scales.
• Evaluate the relationships between the amount of water withdrawn and consumed.
• Determine the drivers behind the use.
• Enhance the collection and completeness of water use information
• Many local and state agencies collect data at finer scales both spatially and temporally.
• Automate data input process

5. 5
Project Goals and Model Requirements
• Estimate monthly freshwater withdrawals and consumption (HUC12,
monthly) for all major water-use categories.
• Public Supply
• Thermoelectric
• Irrigation (crop)
• Mining
• Industrial
• Self-supplied Domestic
• Livestock
• Aquaculture
• Irrigation (Golf)
Dieter and others, 2018

6. 6
Public Supply Water-Use Model
Public Supply Service Area
Climate Data
Collector
Census Data
Collector
Automatic Data Collection
Water Use Data
SWUDS Database
Non-SWUDS (aux? ,newly
compiled, new sources)
Database
Data Preparation
Data Cleaning
Data
Exploration
Feature
Engineering
Feature
Selection
Models Training
Models Tuning
Models
Evaluation
Machine Learning Model
Training and Evaluation
Water Use Predictions
Prediction Confidence
Interval
Interpretation of Predictions
Model
Deployment

7. 7
Public Supply Water-Use Model
Annual average per-capita
withdrawals for public supply by
service area

8. 8
Irrigation Water-Use Model
Evapotranspiration (OpenET-
SSEBop)
Irrigation Withdrawals (WUP)
Hydrologic Model (NHM)
We use these estimates
in our hydrologic
modeling to determine
amount of irrigation
needed by crops.
OpenET, a multiple agency
collaboration, provides the
computational resources
and algorithms to provide
evapotranspiration
estimates (irrigation
consumptive use)
Finally, we estimate
groundwater and surface-
water withdrawals by adding
conveyance and irrigation
system efficiencies.
**Constellation of Satellites

9. 9
Irrigated acres
(spatial and temporal)
System efficiencies
Irrigation and soil
water balance (Net
irrigation)
Withdrawals
(validation)
Conveyance losses
Daily surface-
water and
groundwater
withdrawals and
consumption at
the HUC-12 or
finer resolution
Evapotranspiration
Consumed
irrigation
water Groundwater
Water Source
Surface water
Product:
OpenET
LANID
Irrigation Water-Use Model
Xie and others, 2019

10. 10
Thermoelectric Water-Use Model
• Process Driven Physics-Based Model-
requires gross electrical generation and fuel heat but
the latency of the data from EIA is about 6 months
after use
• Data Driven Machine-Learning Model-
used to operationally predict electricity generation
and fuel heat for the Physics Based Model
Model Data
• Plant characteristics:
• Cooling type
• Fuel type (coal, natural gas)
• Boiler type
• Environmental data
• Water temperature (NHM)
• Atmospheric temperature
• Plant operations: *latency*
• Electrical generation
• Fuel Heat
• Reported Water Withdrawals and
consumption

11. 11
Thermoelectric Water-Use Model
Equations describing
physical process (energy
and water conservation)
Process driven physics model
Operational prediction of
withdrawals and consumption
Plant
characteristics,
environmental
data
Data driven machine learning model
Operational prediction of
electricity generation and fuel
heat
Observed
electrical
generation and
fuel heat

12. 12
Mining Precursor Model – Permian Basin Oil &
Gas
Input data processed
to values by county
and year Model trained using
linear regression with
county and year
sampling units
Model tested using
leave-one-out cross-
validation to assess
uncertainty
Output estimates of
total water use using
the per-well
coefficients
Per-well coefficients
output from model

13. 13
Mining Water-Use Model
• Transition to a machine learning approach
• Random forest & gradient boosting
• Prediction and forecasting
• Monthly + HUC12 scale
• Mining commodities
• Fuels
• Oil, gas, uranium,
coal
• Non-fuels
• Metals
• Non-metals
Photograph by Jeremy McDowell, USGS
IN PROGRESS – SUBJECT TO
CHANGE

14. 14
References
• Dieter, C.A., Maupin, M.A., Caldwell, R.R., Harris, M.A., Ivahnenko, T.I.,
Lovelace, J.K., Barber, N.L., and Linsey, K.S., 2018, Estimated use of
water in the United States in 2015: U.S. Geological Survey Circular
1441, 65 p., https://doi.org/10.3133/cir1441.
• Xie, Yanhua, Lark, T.J., Brown, J.F., and Gibbs, H.G., 2019, Mapping
irrigated cropland extent across the conterminous United States at 30 m
resolution using a semi-automatic training approach on Google Earth
Engine: ISPRS Journal of Photogrammetry and Remote Sensing v. 155,
no. 209, p. 136-149.

15. Thank You
Questions?
nhouston@usgs.gov