Paper doi abstract bibtex

Reliable forecasts of water demand that account for factors that drive demand are imperative to understanding future urban water needs. The effects of meteorological dynamics and sociocultural settings are expressed weakly in many published municipal water demand models, limiting their utility for high-accuracy urban water demand modeling. To fill this gap, this paper presents an empirical daily urban water demand model based on a 365-day trailing average per capita demand that incorporates functions and factors for meteorological, seasonal, policy, and cultural driving forces. A nonlinear iterative regression model of daily water demand was calibrated and validated with historical data (2005–2015) for El Paso, Texas, a major urban area in the American southwest which had a consistent water conservation policy during the study period. The model includes daily temperature and precipitation response functions (which modify demand by as much as ±20%±20%\textlessmath display="inline" overflow="scroll"\textgreater\textlessmrow\textgreater\textlessmo form="prefix"\textgreater±\textless/mo\textgreater\textlessmn\textgreater20\textless/mn\textgreater\textlessmo\textgreater%\textless/mo\textgreater\textless/mrow\textgreater\textless/math\textgreater relative to the annual average), as well as factors that capture effects of month of the year, day of the week, and special holidays (which modify demand within ±15%±15%\textlessmath display="inline" overflow="scroll"\textgreater\textlessmrow\textgreater\textlessmo form="prefix"\textgreater±\textless/mo\textgreater\textlessmn\textgreater15\textless/mn\textgreater\textlessmo\textgreater%\textless/mo\textgreater\textless/mrow\textgreater\textless/math\textgreater relative to the annual average). For the validation period (2011–2015), the model performed well, with a coefficient of determination (R2R2\textlessmath display="inline" overflow="scroll"\textgreater\textlessmrow\textgreater\textlessmsup\textgreater\textlessmi\textgreaterR\textless/mi\textgreater\textlessmn\textgreater2\textless/mn\textgreater\textless/msup\textgreater\textless/mrow\textgreater\textless/math\textgreater) of 0.95, a Nash–Sutcliff efficiency of 0.94, a mean absolute-value relative error of 4.38%, a relative standard error of estimate of 5.82%, a relative RMS error of 5.71%, and a mean absolute-value peak-day error of 2.78%. The use of these site-specific demand variables and response curves facilitates parsimonious urban water demand forecast modeling for regional water security.

@article{capt_urban_2021, title = {Urban {Water} {Demand}: {Statistical} {Optimization} {Approach} to {Modeling} {Daily} {Demand}}, volume = {147}, copyright = {This work is made available under the terms of the Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/.}, issn = {1943-5452}, shorttitle = {Urban {Water} {Demand}}, url = {https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29WR.1943-5452.0001315}, doi = {10.1061/(ASCE)WR.1943-5452.0001315}, abstract = {Reliable forecasts of water demand that account for factors that drive demand are imperative to understanding future urban water needs. The effects of meteorological dynamics and sociocultural settings are expressed weakly in many published municipal water demand models, limiting their utility for high-accuracy urban water demand modeling. To fill this gap, this paper presents an empirical daily urban water demand model based on a 365-day trailing average per capita demand that incorporates functions and factors for meteorological, seasonal, policy, and cultural driving forces. A nonlinear iterative regression model of daily water demand was calibrated and validated with historical data (2005–2015) for El Paso, Texas, a major urban area in the American southwest which had a consistent water conservation policy during the study period. The model includes daily temperature and precipitation response functions (which modify demand by as much as ±20\%±20\%{\textless}math display="inline" overflow="scroll"{\textgreater}{\textless}mrow{\textgreater}{\textless}mo form="prefix"{\textgreater}±{\textless}/mo{\textgreater}{\textless}mn{\textgreater}20{\textless}/mn{\textgreater}{\textless}mo{\textgreater}\%{\textless}/mo{\textgreater}{\textless}/mrow{\textgreater}{\textless}/math{\textgreater} relative to the annual average), as well as factors that capture effects of month of the year, day of the week, and special holidays (which modify demand within ±15\%±15\%{\textless}math display="inline" overflow="scroll"{\textgreater}{\textless}mrow{\textgreater}{\textless}mo form="prefix"{\textgreater}±{\textless}/mo{\textgreater}{\textless}mn{\textgreater}15{\textless}/mn{\textgreater}{\textless}mo{\textgreater}\%{\textless}/mo{\textgreater}{\textless}/mrow{\textgreater}{\textless}/math{\textgreater} relative to the annual average). For the validation period (2011–2015), the model performed well, with a coefficient of determination (R2R2{\textless}math display="inline" overflow="scroll"{\textgreater}{\textless}mrow{\textgreater}{\textless}msup{\textgreater}{\textless}mi{\textgreater}R{\textless}/mi{\textgreater}{\textless}mn{\textgreater}2{\textless}/mn{\textgreater}{\textless}/msup{\textgreater}{\textless}/mrow{\textgreater}{\textless}/math{\textgreater}) of 0.95, a Nash–Sutcliff efficiency of 0.94, a mean absolute-value relative error of 4.38\%, a relative standard error of estimate of 5.82\%, a relative RMS error of 5.71\%, and a mean absolute-value peak-day error of 2.78\%. The use of these site-specific demand variables and response curves facilitates parsimonious urban water demand forecast modeling for regional water security.}, language = {EN}, number = {2}, urldate = {2021-01-28}, journal = {Journal of Water Resources Planning and Management}, author = {Capt, Tallen and Mirchi, Ali and Kumar, Saurav and Walker, W. Shane}, month = feb, year = {2021}, note = {Publisher: American Society of Civil Engineers}, keywords = {Annual peak-day demand, Forecasting parameters, Modeling, Nonlinear regression, Precipitation response curve, Temperature response curve, Urban water demand}, pages = {04020105}, }

Downloads: 0