ABSTRACT:

Urban water demand modeling with regression identifies explanatory factors of water use in cities. A generalized demand modeling approach was developed for over 400 urban water supply agencies in California. Using standardized data from self-reported sources for agencies across the state, a batch-processing approach was used to create standardized urban water demand models. The models were developed to test the validity of a simplified and generalized demand modeling approach using monthly available data. Semilog, multivariate regression models were developed for each urban water supply agency. Consumption from residential (single- and multi-family), commercial, industrial, and institutional water use were considered as outcome variables. Explanatory variables include indicator variables for months in a calendar year, periods of water conservation requirements during a 2011-16 severe drought, population, and water rates. The models were of reasonable fit, with adjusted R-squared values ranging from 0.6-0.99. Visual inspection revealed that the monthly models captured trends with reasonable accuracy. The time frame for models was 2013-18, a period with standardized available data through statewide reporting. The modeling approach has been subsequently further extended to incorporate additional climate variables (precipitation and evapotranspiration) for sector-specific models. The models are intended to understand explanatory factors of demand through a generalized modeling approach and not intended to be used for water supply operations without further refinement and testing. The approach can be adapted to many types of cities.

ABSTRACT:

Urban water demand modeling with regression identifies explanatory factors of water use in cities. A generalized demand modeling approach was developed for over 400 urban water supply agencies in California. Using standardized data from self-reported sources for agencies across the state, a batch-processing approach was used to create standardized urban water demand models. The models were developed to test the validity of a simplified and generalized demand modeling approach using monthly available data. Semilog, multivariate regression models were developed for each urban water supply agency. Consumption from residential (single- and multi-family), commercial, industrial, and institutional water use were considered as outcome variables. Explanatory variables include indicator variables for months in a calendar year, periods of water conservation requirements during a 2011-16 severe drought, population, and water rates. The models were of reasonable fit, with adjusted R-squared values ranging from 0.6-0.99. Visual inspection revealed that the monthly models captured trends with reasonable accuracy. The time frame for models was 2013-18, a period with standardized available data through statewide reporting. The modeling approach has been subsequently further extended to incorporate additional climate variables (precipitation and evapotranspiration) for sector-specific models. The models are intended to understand explanatory factors of demand through a generalized modeling approach and not intended to be used for water supply operations without further refinement and testing. The approach can be adapted to many types of cities.

ABSTRACT:

Modeling Integrated Water Resources in Los Angeles

"The history of the growth and development of Los Angeles... reveals its conscious use of water as a tool to build the 'great metropolis of the Pacific'"
-- Vincent Ostrom, 1962

Welcome to the repository for Artes, an integrated model of urban water resources in metropolitan Los Angeles (LA). It evaluates the potential for enhanced local water supplies in LA.

Los Angeles (LA) relies on large infrastructure systems that import water over hundreds of miles. Communities in LA face a future of increased water scarcity and reduced imports. Hundreds of water agencies serve nearly 10 million people within the county. Laws, institutions, and hydrogeology all influence the capacity of these agencies to adapt to future changes. To analyze the potential for future local water reliance and resilience, we used systems analysis of urban water management in metropolitan LA County to assess opportunities for increasing local water reliance. We developed a detailed network flow model to investigate management tradeoffs across engineered, social, and environmental systems.

The model and its underlying data have been used to produce 11 peer-reviewed studies. Model outputs and methods have also informed numerous regional studies and plans, including:
- the LA County Sustainability Plan,
- UCLA's Los Angeles Environmental Report Card,
- the Santa Monica Groundwater Sustainability Plan's evaluation of integrated basin management options,
- California's Fourth Climate Change Assessment (Los Angeles Regional section).

The model is a product of the California Center for Sustainable Communities at UCLA.

Cast and Crew:
Erik Porse, Stephanie Pincetl, Katie Mika, Mark Gold, Madelyn Glickfeld, Eric Fournier, Kartiki Naik, Terri Hogue, Kimberly Manago, Diane Pataki, Liza Litvak

What's In This Repository?
The repository contains source code, data, and a descriptive manual of the model.

Acknowledgements:
This work was supported by the Water Sustainability, & Climate Program at the National Science Foundation (NSF Award # 1204235), the Los Angeles Bureau of Sanitation, and the John Randolph Haynes and Dora Haynes Foundation.

Citing the Model:
Porse, E. (2022). Artes: Modeling Water Resources Management in Los Angeles, HydroShare, http://www.hydroshare.org/resource/c2a8bb7e07b3409995c90a86120b2a9f

Research Studies:
Porse, Erik C., Kathryn B. Mika, Alvar Escriva-Bou, Eric Fournier, Kelly T. Sanders, Edward Spang, Jennifer Stokes-Draut, Felicia Federico, Mark Gold, and Stephanie Pincetl. “Systems Analysis of Energy Use for Urban Water Management by Utilities and Households in Los Angeles”. Environmental Research Communications. 2020: 2.1

Porse, Erik and Stephanie Pincetl. (2018). “Effects of Stormwater Capture and Use on Urban Streamflows.” Water Resources Management. 33.2 (2019): 713-723.

Porse, Erik. (2019). "Merging Network Governance and Systems Analysis for Urban Water Management." Civil Engineering and Environmental Systems. 2019: 1-19.

Pincetl, Stephanie, Thomas W. Gillespie, Diane E. Pataki, Erik Porse, Shenyue Jia, Erika Kidera, Nick Nobles, Janet Rodriguez, and Dong-ah Choi. (2019) "Evaluating the effects of turf-replacement programs in Los Angeles." Landscape and Urban Planning. 185: 210-221.

Pincetl, Stephanie, Erik Porse, Kathryn B. Mika, Elizabeth Litvak, Kim Manago, Kartiki Naik, Terri Hogue, Mark Gold, Tom Gillespie, and Diane Pataki. (2018). “Adapting Urban Water Systems to Manage Scarcity in the 21st Century: The Case of Los Angeles.” Environmental Management. 63.3. pgs 293-308

Porse, E., Mika, K. B., Williams, R., Gold, M., Blomquist, W., & Pincetl, S. (2018). “Groundwater Exchange Pools and Urban Water Supply Sustainability: Modeling Directed and Undirected Networks.” Journal of Water Resources Planning and Management, 144(8)

Porse, Erik, Kathryn B. Mika, Elizaveta Litvak, Kimberly F. Manago, Terri S. Hogue, Mark Gold, Diane E. Pataki, and Stephanie Pincetl. (2018). “The Economic Value of Local Water Supplies in Los Angeles.” Nature Sustainability, May.

Porse, Erik. (2018). “Open Data and Stormwater Infrastructure in Los Angeles: Implications for Green Infrastructure and Sustainability”. Local Environment. 1-13.

Porse, Erik C., Kathryn B. Mika, Elizabeth Litvak, Kim Manago, Kartiki Naik, Madelyn Glickfeld, Terri Hogue, Mark Gold, Diane Pataki, and Stephanie Pincetl. (2017). “Systems Analysis and Optimization of Local Water Supplies in Los Angeles.” Journal of Water Resources Planning and Management. 143(9).

Pincetl, Stephanie, Erik C. Porse, and Deborah Cheng (2016). “Fragmented Flows: Water Supply in Los Angeles County”. Environmental Management. 58(2). Pg. 208-222

Porse, Erik C., Madelyn Glickfeld, Keith Mertan, and Stephanie Pincetl. (2015) “Pumping for the Masses: Evolution of Groundwater Rights in Metropolitan Los Angeles.” Geojournal.

ABSTRACT:

Urban water management systems connect water supply, wastewater, and stormwater infrastructure. In California, over 400 water supply agencies provide water to 36 million people living in cities and suburbs. The wastewater generated by homes, businesses, and industrial facilities is managed by hundreds of wastewater collection, treatment, reuse, and recovery systems. A network model of system-level connectivity to water supply and wastewater infrastructure was developed to simulate how changes in water demand affect downstream flows in wastewater collection, treatment, and reuse systems. The model was created to estimate potential impacts to flow in wastewater management systems from water use efficiency and conservation by linking where water is used to the collection systems and wastewater treatment/reuse facilities where it is managed and recovered. The model was used to support regulatory rulemaking for California’s “Making Water Conservation a Way of Life” associated with Assembly Bill 1668 and Senate Bill 606 (AB 1668-SB 606). Details of the model’s development and use were presented in public workshops in December 2021 and May 2022, which can be found at this link: https://www.waterboards.ca.gov/water_issues/programs/conservation_portal/regs/water_efficiency_legislation.html

ABSTRACT:

In California, over 400 water supply agencies provide water to 36 million people living in cities and suburbs. The wastewater generated by homes, businesses, and industrial facilities is managed by hundreds of wastewater collection, treatment, reuse, and recovery systems. A procedure was developed to extrapolate modeled effects of water conservation from a sample of collection systems (50) to all potentially-affected systems throughout the state (over 300). An Excel-based model of wastewater collection system operations was developed to evaluate physical and chemical changes in pipes from lower flows. The model was run for fifty systems across the state of varying sizes and locations. Key impacts of corrosion, hydrogen sulfide production, sediment deposition, and use of added chemicals were evaluated based on changes in wastewater influent flow rates. To then extrapolate effects statewide, collection systems were grouped by the key characteristics that correlate with modeled impacts. The R code associated with this Hydroshare resource demonstrates how to use modeled results from the subset of collection systems to: 1) Identify key explanatory attributes of impacts using statistically-significant regression models, 2) Use the key explanatory attributes to identify non-geographic clusters of collection systems, and 3) Use the clusters to group systems for extrapolating effects. The modeling was used to support regulatory rulemaking for California’s “Making Water Conservation a Way of Life” associated with Assembly Bill 1668 and Senate Bill 606 (AB 1668-SB 606). Details of the model’s development and use were presented in public workshops in December 2021 and May 2022, which can be found at this link: https://www.waterboards.ca.gov/water_issues/programs/conservation_portal/regs/water_efficiency_legislation.html

ABSTRACT:

In California, water systems, submit annual operational data such as demographics, water production, water demand, and retail rates to the State Water Resources Control Board. The State Water Resources Control Board publishes data in a flat file text format (https://www.waterboards.ca.gov/drinking_water/certlic/drinkingwater/ear.html). From 2013-2019, distinct data was published for small and large systems. Since 2020, data is combined in a single file.

This Hydroshare repository publishes user-friendly versions of the 2020-2022 eAR files, which were created to improve accessibility. Flat files of raw data were formatted to have all questions associated with a water system (PWSID) on one line. This allows for data to be viewed and analyzed in typical worksheet software programs.

This repository contains 1) Python script templates for parsing the 2020, 2021, and 2022 flat data files, and 2) the formatted eAR data files, saved as an Excel worksheet. There are separate Python scripts for parsing 2020 data and 2021/2022

Use of the script and files is permitted with attribution. Users are solely responsible for any issues that arise in using or applying data. If any errors are spotted, please contact the author.