Urban Water Demand Forecasting: Review of Methods and Models

Donkor, Emmanuel A.; Mazzuchi, Thomas A.; Soyer, Refik; Roberson, J. Alan

doi:10.1061/(asce)wr.1943-5452.0000314

Cited by 334 publications

(248 citation statements)

References 47 publications

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“…For example, having the information of RainDay, WetSpell, and DrySpell weeks in advance will help farmers make decisions for irrigation scheduling to save water costs and improve crop yields. Short-term planning of urban water supply could also benefit from sub-seasonal forecasting information, since those indices describing frequency or duration of precipitation and temperature extremes are known to be directly related to the urban water demand forecasting (e.g., Donkor et al, 2012). As some temperature indices such as CosHighD and CosLowD were used to characterize heat/cold waves, forecasting information of these indices would also be useful for developing strategies for proactive disaster mitigation (e.g., frost damage to crops).…”

Section: Discussionmentioning

confidence: 99%

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

Tian

Wood

Yuan

2017

Hydrol. Earth Syst. Sci.

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Abstract. This paper explored the potential of a global climate model for sub-seasonal forecasting of precipitation and 2 m air temperature. The categorical forecast skill of 10 precipitation and temperature indices was investigated using the 28-year sub-seasonal hindcasts from the Climate Forecast System version 2 (CFSv2) over the contiguous United States (CONUS). The forecast skill for mean precipitation and temperature as well as for the frequency and duration of extremes was highly dependent on the forecasting indices, regions, seasons, and leads. Forecasts for 7-and 14-day temperature indices showed skill even at weeks 3 and 4, and generally were more skillful than precipitation indices. Overall, temperature indices showed higher skill than precipitation indices over the entire CONUS region at sub-seasonal scale. While the forecast skill related to mean precipitations was low in summer over the CONUS, the number of rainy days, number of consecutive rainy days, and number of consecutive dry days showed considerably high skill for the western coastal region. The presence of active Madden-Julian Oscillation (MJO) events improved CFSv2 weekly mean precipitation forecast skill over most parts of the CONUS, but it did not necessarily improve the weekly mean temperature forecasts. The 30-day forecasts of precipitation and temperature indices calculated from the downscaled monthly CFSv2 forecasts were less skillful than those calculated directly from CFSv2 daily forecasts, suggesting the usefulness of CFSv2 for sub-seasonal hydrological forecasting.

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Section: Discussionmentioning

confidence: 99%

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

Tian

Wood

Yuan

2017

Hydrol. Earth Syst. Sci.

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“…Sene, 2010;Donkor et al, 2014;Bodner et al, 2015). This issue is specially harsh in the Mediterranean where the water deficits of dry summers are often unresolved in the wet season, leading to recurrent drought situations (Blinda et al, 2007;Cook et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean

Marcos-Matamoros

Llasat

Quintana-Seguí

et al. 2018

Science of The Total Environment

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In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981-2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter.

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“…In addition, rising investments through mega-projects such as construction of The Yavuz Sultan Selim Bridge, The Third Airport with 150 million passenger capacities and The New City Project have a strong potential for increasing the city population as well as demand for clean water in the near future. Hence, much more attention should be given to probabilistic forecasting methods in order to reflect the role of uncertainty in future supply and demand forecasts of water in Istanbul [6,36]. In addition, real time water demand and supply tracking and management of city water through information technology (IT) are likely to be inevitable in the future and the importance of the algorithm-based IT applications and knowledge-based urban development is highlighted in the previous studies [37,38].…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

“…As commonly used forecasting techniques, traditional methods such as time series, regression and an autoregressive integrated moving average (ARIMA) as well as soft computing techniques such as fuzzy logic, genetic algorithm, and artificial neural networks are being extensively used for a time-series demand forecasting [6][7][8]. Especially for urban water demand modeling, the ARIMA model has performed more accurately than time-series and multiple regression methods when forecasting demand based on climate variables [9].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

et al. 2015

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Abstract:The metropolitan city of Istanbul is becoming overcrowded and the demand for clean water is steeply rising in the city. The use of analytical approaches has become more and more critical for forecasting the water supply and demand balance in the long run. In this research, Istanbul's water supply and demand data is collected for the period during 2006 and 2014. Then, using an autoregressive integrated moving average (ARIMA) model, the time series water supply and demand forecasting model is constructed for the period between 2015 and 2018. Three important sustainability metrics such as water loss to supply ratio, water loss to demand ratio, and water loss to residential demand ratio are also presented. The findings show that residential water demand is responsible for nearly 80% of total water use and the consumption categories including commercial, industrial, agriculture, outdoor, and others have a lower share in total water demand. The results also show that there is a considerable water loss in the water distribution system which requires significant investments on the water supply networks. Furthermore, the forecasting results indicated that pipeline projects will be critical in the near future due to expected increases in the total water demand of Istanbul. The authors suggest that sustainable management of water can be achieved by reducing the residential water use through the use of water efficient OPEN ACCESSSustainability 2015, 7 11051 technologies in households and reduction in water supply loss through investments on distribution infrastructure.

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Urban Water Demand Forecasting: Review of Methods and Models

Cited by 334 publications

References 47 publications

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

Use of bias correction techniques to improve seasonal forecasts for reservoirs — A case-study in northwestern Mediterranean

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

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