Worksheet titled Data for ECM: Data set used to estimate the error correction model to understand how turbidity and other variables affect drinking water treatment costs. Worksheet titled Data for PDL: Data set used to estimate the polynomial distributed lag model to understand how phosphorus load entering reservoir impacts turbidity at the drinking water treatment plant. This dataset is associated with the following publication: Heberling , M., C. Nietch , H. Thurston , M. Elovitz , K. Birkenhauer, S. Panguluri, B. Ramakrishnan, E. Heiser, and T. Neyer. Comparing drinking water treatment costs to source water protection costs using time series analysis.. WATER RESOURCES RESEARCH. American Geophysical Union, Washington, DC, USA, 51(11): 8741-8756, (2015).

Data set used to estimate the autoregressive distributed lag (ADL) model to understand how water quality measures (e.g., raw water TOC and algal toxin) and other variables affect drinking water treatment costs at the Bob McEwen Water Treatment Plant in southwestern OH. This dataset is associated with the following publication: Heberling, M., J.I. Price, C. Nietch, M. Elovitz, N. Smucker, D.A. Schupp, A. Safwat, and T. Neyer. Linking Water Quality to Drinking Water Treatment Costs Using Time Series Analysis: Examining the Effect of a Treatment Plant Upgrade in Ohio. WATER RESOURCES RESEARCH. American Geophysical Union, Washington, DC, USA, 58(5): e2021WR031257, (2022).

Data set used to estimate the autoregressive distributed lag (ADL) model to understand how water quality measures (e.g., raw water TOC and algal toxin) and other variables affect drinking water treatment costs at the Bob McEwen Water Treatment Plant in southwestern OH. This dataset is associated with the following publication: Heberling, M., J.I. Price, C. Nietch, M. Elovitz, N. Smucker, D.A. Schupp, A. Safwat, and T. Neyer. Linking Water Quality to Drinking Water Treatment Costs Using Time Series Analysis: Examining the Effect of a Treatment Plant Upgrade in Ohio. WATER RESOURCES RESEARCH. American Geophysical Union, Washington, DC, USA, 58(5): e2021WR031257, (2022).

All the data used to generate figures and tables, the background data such as unit emergy value library are in the dataset. This dataset is associated with the following publication: Arden, S., C. Ma, and M. Brown. Holistic Analysis of Urban Water Systems in the Greater Cincinnati Region: (2) Resource Use Profiles by Emergy Accounting Approach - journal needs to be Water Research X. Water Research X. Elsevier B.V., Amsterdam, NETHERLANDS, 2: 100012, (2019).

Simulation results on water age analysis. Comparison of the results between EPAENT 2.2 and 2.0. This dataset is associated with the following publication: Burkhart, B., and R. Janke. Understanding Water Age in Distribution Systems with EPANET. Journal AWWA. American Water Works Association, Denver, CO, USA, 115(2): 24-34, (2023).

Simulation results on water age analysis. Comparison of the results between EPAENT 2.2 and 2.0. This dataset is associated with the following publication: Burkhart, B., and R. Janke. Understanding Water Age in Distribution Systems with EPANET. Journal AWWA. American Water Works Association, Denver, CO, USA, 115(2): 24-34, (2023).

The following datasets are used for the Water Rights Demand Analysis project and are formatted to be used in the calculations. The State Water Resources Control Board Division of Water Rights (Division) has developed a methodology to standardize and improve the accuracy of water diversion and use data that is used to determine water availability and inform water management and regulatory decisions. The Water Rights Demand Data Analysis Methodology (Methodology https://www.waterboards.ca.gov/drought/drought_tools_methods/demandanalysis.html ) is a series of data pre-processing steps, R Scripts, and data processing modules that identify and help address data quality issues related to both the self-reported water diversion and use data from water right holders or their agents and the Division of Water Rights electronic water rights data.

This ZIP file contains the four EPANET network models used for one of the two water distribution system (WDS) network models (N1) analyzed in the paper titled: “The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies”. The EPANET network models provided here are for the network model named “N1” in this paper. This dataset is associated with the following publication: Janke , R., and M. Davis. The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies. Drinking Water Engineering and Science Discussions. Copernicus Gesellschaft mbH, Gottingen, GERMANY, 1-25, (2018).