Probabilistic Water Demand Forecasting Using Projected Climatic Data for Blue Mountains Water Supply System in Australia

Haque, Md. Mahmudul; Rahman, Ataur; Hagare, Dharmappa; Kibria, Golam

doi:10.1007/s11269-014-0587-1

Cited by 31 publications

(20 citation statements)

References 28 publications

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Section: Model Development Resultsmentioning

confidence: 96%

“…For the ARIMAX model with the independent variable of the logarithm of dew point depression, the analysis of the transfer function-noise model derived from Equation (4) with the autoregressive and moving average lag results from Table 3 suggest the identification displayed in Equation (10). After trial and error with autoregressive and moving average parameters, the final model result for the individual meteorological factors using Equation (4) yielded the lowest AIC value ( Table 2).…”

Section: Model Development Resultsmentioning

confidence: 98%

“…The long-term water demand time-series model, in contrast to the short-term demand model, helps to examine water demand projection and to understand the effects of climate change [10][11][12][29][30][31]. The monthly water demand model established the long-term relationships between the climate and water consumption variables.…”

Section: Discussionmentioning

confidence: 99%

“…For the planning, management, and development of water supply systems, the characteristics of water demand, together with uncertainties in natural phenomena, have caught the attention of many water resource researchers [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Kenney et al [7] described that water consumption is mainly composed of three main factors: (1) socioeconomic factors, such as population distribution and sizes of homes and lots; (2) climate factors, such as air temperature, precipitation, and percent of humidity; and (3) policy factors, such as the cost of water utilities, regulations, and conservation programs.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Impact of Meteorological Factors on Water Demand in the Las Vegas Valley Using Time-Series Analysis: 1990–2014

Huntra¹,

Keener²

2017

IJGI

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Many factors impact a city's water consumption, including population distribution, average household income, water prices, water conservation programs, and climate. Of these, however, meteorological effects are considered to be the primary determinants of water consumption. In this study, the effects of climate on residential water consumption in Las Vegas, Nevada, were examined during the period from 1990 to 2014. The investigations found that climatic variables, including maximum temperature, minimum temperature, average temperature, precipitation, diurnal temperature, dew point depression, wind speed, wind direction, and percent of calm wind influenced water use. The multivariate autoregressive integrated moving average (ARIMAX) model found that the historical data of water consumption and dew point depression explain the highest percentage of variance (98.88%) in water use when dew point depression is used as an explanatory variable. Our results indicate that the ARIMAX model with dew point depression input, and average temperature, play a significant role in predicting long-term water consumption rates in Las Vegas. The sensitivity analysis results also show that the changes in average temperature impacted water demand three times more than dew point depression. The accuracy performance, specifically the mean average percentage error (MAPE), of the model's forecasting is found to be about 2-3% from five years out. This study can be adapted and utilized for the long-term forecasting of water demand in other regions. By using one significant climate factor and historical water demand for the forecasting, the ARIMAX model gives a forecast with high accuracy and provides an effective technique for monitoring the effects of climate change on water demand in the area.

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Section: Model Development Resultsmentioning

confidence: 96%

Section: Model Development Resultsmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the Impact of Meteorological Factors on Water Demand in the Las Vegas Valley Using Time-Series Analysis: 1990–2014

Huntra¹,

Keener²

2017

IJGI

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“…In case of emergency, the BMWSS is supplemented with additional water from Warragamba dam. Further information on the BMWSS can be found from Haque et al (2013Haque et al ( , 2014b.…”

Section: Study Areamentioning

confidence: 99%

Estimation of catchment yield and associated uncertainties due to climate change in a mountainous catchment in Australia

et al. 2015

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This paper examines the impacts of climate change on future water yield with associated uncertainties in a mountainous catchment in Australia using a multi‐model approach based on four global climate models (GCMs), 200 realisations (50 realisations from each GCM) of downscaled rainfalls, 2 hydrological models and 6 sets of model parameters. The ensemble projections by the GCMs showed that the mean annual rainfall is likely to reduce in the future decades by 2–5% in comparison with the current climate (1987–2012). The results of ensemble runoff projections indicated that the mean annual runoff would reduce in future decades by 35%. However, considerable uncertainty in the runoff estimates was found as the ensemble results project changes of the 5th (dry scenario) and 95th (wet scenario) percentiles by −73% to +27%, −73% to +12%, −77% to +21% and −80% to +24% in the decades of 2021–2030, 2031–2040, 2061–2070 and 2071–2080, respectively. Results of uncertainty estimation demonstrated that the choice of GCMs dominates overall uncertainty. Realisation uncertainty (arising from repetitive simulations for a given time step during downscaling of the GCM data to catchment scale) of the downscaled rainfall data was also found to be remarkably high. Uncertainty linked to the choice of hydrological models was found to be quite small in comparison with the GCM and realisation uncertainty. The hydrological model parameter uncertainty was found to be lowest among the sources of uncertainties considered in this study. Copyright © 2015 John Wiley & Sons, Ltd.

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Data Analytics Methods in Human Resource Demand Forecasting

Wang,

Zhu,

Wang

2024

Communications in Computer and Information Science

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Probabilistic Water Demand Forecasting Using Projected Climatic Data for Blue Mountains Water Supply System in Australia

Cited by 31 publications

References 28 publications

Evaluating the Impact of Meteorological Factors on Water Demand in the Las Vegas Valley Using Time-Series Analysis: 1990–2014

Evaluating the Impact of Meteorological Factors on Water Demand in the Las Vegas Valley Using Time-Series Analysis: 1990–2014

Estimation of catchment yield and associated uncertainties due to climate change in a mountainous catchment in Australia

Data Analytics Methods in Human Resource Demand Forecasting

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