Monthly Water Demand Research Articles

How tourists ‘escaping the heat’ may drive future increases in municipal water demand in Oregon coastal communities

Little is known about the effect of future weather and climate on municipal water demand in coastal communities with tourist-centric economies. To address this knowledge gap, we used an econometric model of monthly water demand that allowed for non-linear responses to weather variables to estimate temperature-response functions for demand from a sample of communities in the Oregon Mid-Coast. A main result is that local temperature was not a significant driver of variability in monthly water demand but that temperature in the Willamette Valley—the source of most tourists to the Oregon coast—was. We assumed that the increase in demand in response to higher Willamette Valley temperature arose from an increase in tourists escaping the heat in the Willamette Valley for cooler conditions on the coast. Applying the temperature response functions to scenarios of future climate to the year 2070 led to projected increases in water demand independent of other factors. Whether future tourism is either constrained by the local resident population that serves tourism or is constrained by the potential tourist population in the Willamette Valley, the climate-change contribution to projected water demand is generally of comparable magnitude to—if not greater than—the contribution from resident population change alone over the next 50 years. For communities where the population is projected to decline, the climate effect may more than offset the effect of declining population, resulting in a net positive change in demand.

CaRDS - the statewide California Residential water Demand and Supply open dataset

As water scarcity becomes the new norm in the Western United States, states such as California have increased their efforts to improve water resilience. Achieving water security under climate change, population growth, and urbanization requires an integrated multi-sectoral approach, where adaptation strategies combine supply and demand management interventions. Yet, most studies consider supply-side and demand-side management strategies separately. Water conservation efforts are mainly driven by policy requirements and publicly available data to assess the effectiveness of demand- and supply-side management policies is often hard to find and unstructured. Here we present CaRDS - the statewide California Residential water Demand and Supply open dataset. CaRDS encompasses nine years (2013-2021) of monthly water supply and demand time series for 404 water suppliers in California, USA, compiled from different open-access data sources. Access to detailed temporal and spatial water supply operations and demands at the state-level can be useful to researchers and practitioners to realize applications such as evaluating the effectiveness of water conservation policies and discovering regional differences in water resilience measures.

Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia

Accurate prediction of short-term water demand, especially, in the case of extreme weather conditions such as flood, droughts and storms, is crucial information for the policy makers to manage the availability of freshwater. This study develops a hybrid model for the prediction of monthly water demand using the database of monthly urban water consumption in Melbourne, Australia. The dataset consisted of minimum, maximum, and mean temperature (°C), evaporation (mm), rainfall (mm), solar radiation (MJ/m2), maximum relative humidity (%), vapor pressure (hpa), and potential evapotranspiration (mm). The dataset was normalized using natural logarithm and denoized then by employing the discrete wavelet transform. Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. Post-optimization ANN model was trained using eleven different leaning algorithms. Models were trained several times with different configuration (nodes in hidden layers) to achieve better accuracy. The final optimum learning algorithm was selected based on the performance values (regression; mean absolute, relative and maximum error) and Taylor diagram.

A hybrid approach combining the multi-dimensional time series k-means algorithm and long short-term memory networks to predict the monthly water demand according to the uncertainty in the dataset

A hybrid approach combining the multi-dimensional time series k-means algorithm and long short-term memory networks to predict the monthly water demand according to the uncertainty in the dataset

基于SWAT模型的图们江流域蓝绿水资源供需平衡分析

PDF HTML阅读 XML下载 导出引用 引用提醒 基于SWAT模型的图们江流域蓝绿水资源供需平衡分析 DOI: 10.5846/stxb202112173586 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划项目(2019YFC0409104035679); 国家自然科学基金项目(42061042) Analysis of blue and green water scarcity based on SWAT model in the Tumen River Basin Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:随着经济快速发展和工农业各类用水量大幅增加,对河流的开发程度随之加强,流域水资源可持续利用受到不利影响。从蓝绿水视角出发,基于SWAT模型量化了图们江流域2015-2020年不同时间尺度下的蓝绿水资源并探讨了研究区水资源供需平衡情况。结果表明:研究时段内流域蓝绿水资源均具有明显的季节特征,年均蓝绿水资源量为117.16 亿m3,其中蓝水资源量30.14 亿m3,绿水资源量87.02 亿m3,绿水占水资源量的74.27%。以年为尺度,蓝水资源供需评估指数＞1,可利用蓝水资源量基本不能满足其需求量,降水少的年份蓝水资源供需矛盾突出,而绿水资源可以达到供需平衡,其供需评估指数集中在0.1-0.2之间。在月尺度分析中研究区蓝水供需评估指数出现极端值的频率较高,表明该地区蓝水供需不平衡,尤其是可利用蓝水资源一般在每年的7-8月才较为丰富,而蓝水需求用水集中分布于作物生长期,导致蓝水供应与蓝水需求在时间分配上不均衡,且在实际水资源分配中忽略了生态用水;月尺度绿水供需评估指数集中在0.1-0.4之间,有明显的季节变化特征,整体上绿水资源供需平衡安全性较高。 Abstract:With the rapid economic development, the amount of water used for industry and agriculture has significantly increased, most rivers were blind developed and affected the sustainable utilization of water resources. This study quantified water resources from the perspective of blue and green water. The Soil and Water Assessment Tool (SWAT) model was used to simulate the natural runoff in the Tumen River Basin from 2015 to 2020, quantitatively analyzed the change trend of blue water and green water resources at different time scales, and discussed the balance of water supply and demand in the study area. The results showed that the blue and green water resources of the Tumen River Basin presented significantly seasonal characteristics from 2015 to 2020. The average annual amount of blue and green water resources was 11.716 billion m3. The blue and green water resources of the study river basin from 2015 to 2020 were 3.014 billion m3/a and 8.702 billion m3/a, respectively. The study area had different degrees of water scarcities in different time scales. At the annual scale, the supply and demand index of blue water resource was greater than 1, which indicated the supply of blue water basically could not satisfy the demand. The imbalance degree of supply and demand of blue water resource was evident in less precipitation years. In contrast, the balance of supply and demand of green water resource was relatively stable, the index concentrated between 0.1 and 0.2. At the monthly scale, the blue water supply and demand balance index put up high frequency of extreme value, illustrated there was an imbalance appearance between the blue water supply and demand in this region. Especially the blue water availability in annual July to August had the maximum values. However, the blue water withdrawal intensively distributed in the crop growth period, there had a time mismatch between the supply and demand of blue water in the river basin. Moreover, the ecological water was neglected in actual water resources allocation. The monthly green water supply and demand index concentrated between 0.1 and 0.4, with prominently seasonal variation characteristics. Overall, the balance between the supply and demand of green water resource was relatively safe. 参考文献 相似文献 引证文献

AWWA Water Science Author Spotlight

Having recently published an article in AWWA Water Science, Hongfang Lu answered questions from the publication's editor-in-chief, Kenneth L. Mercer, about the research. A Hybrid Model for Monthly Water Demand Prediction: A Case Study of Austin, Texas Hongfang Lu, John Matthews, and Shuai Han I am now an associate professor at Southeast University in China. My current research focuses on trenchless technology and buried pipeline health monitoring. My research has been going on for nearly a decade, and my projects are all about pipelines. Since I have a background in oil and gas engineering, my initial research was on oil and gas pipelines. During my PhD program, I also did some research on water pipelines. During my PhD program, I was involved in a Water Research Foundation project on water pipeline condition assessment. During that time, I did some research on water. In addition, trenchless technology is more widely used in the water field. During his summer vacation in 2018, Hongfang went to New York City with one of his teachers and her family. So far, three teachers have greatly influenced my career. In chronological order, the first one is Professor Xiaonan Wu from Southwest Petroleum University (Chengdu, China), who was my tutor at the master's degree stage. She told me to have the courage to challenge complex problems in research. The second is my PhD research advisor, Dr. Tom Iseley, a professor at Purdue University (West Lafayette, Ind.) and a leader in the trenchless industry. He told me to keep an open mind in research and be mindful of the intersections between disciplines. The third is my current team leader, Professor Zhao-Dong Xu from Southeast University. He often reminds us to combine theory with practice and do research that is meaningful to people. This research is related to water demand prediction using machine learning methods. The motivation is to establish a hydraulic calculation model of the water supply network through the prediction of water demand. Many buried pipelines worldwide are aging or about to enter the aging stage. Health monitoring and trenchless repair of pipelines are essential; therefore, my research direction is related to these two points. Personally, I am very interested in the structure of pipes. Although pipeline structure is relatively simple compared with other forms, it also involves many scientific problems. The outbreak of COVID-19 in 2019 has triggered a lot of thinking among scholars. But even before the onset of its public health problems, using limited samples to predict future conditions and then using those results to take public measures was an important challenge in research. In June 2021, Hongfang participated in the biennial badminton competition at Southeast University, Nanjing, China. I was born in Nanyang, Henan Province, China, a place with abundant oil resources at that time. So I have been relatively familiar with oil since I was a child. When I was in college, my major was oil and gas storage and transportation engineering. In studying, I was very interested in pipelines, the vehicle for oil and gas resource transportation. To learn more about Hongfang's research, visit the article, available online at https://doi.org/10.1002/aws2.1175.

Multivariate monthly water demand prediction using ensemble and gradient boosting machine learning techniques

Water management planning requires reliable and accurate water demand forecasting. Water demand prediction is affected by variables, such as climate, socio-economic, and demographic data. This paper investigates urban monthly average water demand prediction, using classical, ensemble, and gradient boosting-based machine learning models, using the available monthly water demand, climatic, economic, and demographic data. Three train-test data split schemes on water demand timeseries were considered to determine the effect of data size on water demand prediction. Sensitivity analysis was employed to reduce input feature dimensionality while maintaining model accuracy. A univariate timeseries (water demand only) produced R2 scores up to 0.91, which increased to 0.94 with the addition of calendar and climatic features. Increasing the training data size from 70% to 90% improved the RMSE and MAE scores by ensemble and gradient boosting methods, with the random forest and the AdaBoost models showing improvements of up to 69%. The sensitivity analysis revealed a successful input reduction scheme from a potential 17 input attributes to seven inputs. Gradient boosting models showed robust and faster execution time, especially with the increase in training data, which is attractive for medium-term urban water demand forecasting.

Rainwater harvesting as an alternative of freshwater supply in Balikpapan city – a case study of Institut Teknologi Kalimantan

Currently, 73% of the water demand in Balikpapan was only supplied from Manggar Reservoir, and it will continue decreasing parallelly with the increasing population. Regarding the population issues and the government’s planning for the new capital city of Indonesia, Balikpapan will be a buffer city facing serious risk in water supply issues in the future. Therefore, this study seeks to analyze an alternative of water supply. Following the concepts in Water Sensitive Urban Design (WSUD), The rainwater harvesting (RWH) method was chosen for preventive analysis, and Institut Teknologi Kalimantan (ITK) as one of the reputable’s universities in Balikpapan will be taken as a study location. The results obtained that the monthly water demand in ITK was ranged from 3228.34 m3 to 16632.97 m3. Using RWH, water supply analysis was obtained from 3790.62 m3 to 10697.31 m3 in various rainfall durations (0.5 to 2 hours) and 20 years projections (2022 to 2042). Following the obtained water supply. This study also reveals that the savings of total water usage can be reached from 24% to 100%, and around IDR 14,082,002 to IDR 37,035,390 is converted to currency. The highest saving reached due to water supply meets the water demand.

Understanding the intra-annual variability of streamflow by incorporating terrestrial water storage from GRACE into the Budyko framework in the Qinba Mountains

Streamflow from forested mountain watersheds is critical to aquatic ecosystems and social development in watersheds. However, understanding the intra-annual variability of streamflow is limited by the lack of observation of terrestrial water storage (TWS) in large-scale watersheds. This study developed a monthly Budyko framework incorporating TWS from the Gravity Recovery and Climate Experiment (GRACE). The extended Budyko framework was applied using four classic Budyko equations in the Qinba Mountains. The results showed that the extended Budyko framework could competently represent the relationship between monthly water supply and demand, with better performance than the original Budyko framework. Based on the extended Budyko framework, this study further quantified the contributors of streamflow variability using the variance decomposition method. The dominant contributor to intra-annual streamflow variability was precipitation (50%), followed by TWS (11%) and their covariance (-21%) in this region. Specifically, precipitation played a dominant role on streamflow variability in summer and autumn, while evapotranspiration and TWS significantly impacted streamflow in spring and winter, respectively. Furthermore, the hydrologic effects of rainfall intensity and vegetation were investigated to explain streamflow variability. As the rainfall intensity decreases, more precipitation is partitioned into evapotranspiration and TWS, while the increase of rainfall intensity leads to the partitioning of precipitation into streamflow. Similarly, monthly vegetation promotes the partitioning of precipitation into TWS, while inhibiting the partitioning of precipitation into streamflow. The opposite effect of vegetation on streamflow and TWS is weakened due to the neglect of TWS at an annual timescale, which may lead to an overestimation of the effect of annual vegetation on streamflow. The results have implications for improving the performance of the Budyko framework to reveal the relationship between monthly water supply and demand and understanding streamflow variability at an intra-annual timescale.

Short-term forecasting of monthly water consumption in hyper-arid climate using recurrent neural networks

Freshwater supply is a major challenge in regions with limited water resources and extremely arid climatic conditions. The objective of this study is to model the monthly water demand in Kuwait using the nonlinear autoregressive with exogenous input (NARX) neural network approach. The country lacks conventional surface water resources and is characterized by extremely arid climate. In addition, it has one of the fastest growing populations. In this study, linear detrending is performed on the water consumption time series for the period from January 1993 to December 2018 to eliminate the influence of population growth before application to the NARX model. Monthly temperature data are selected as exogenous input to the NARX model, because they are strongly associated with the water consumption data. Correlation analyses are performed to determine the input and feedback delays of the NARX model. The results demonstrate that the recurrent NARX model is efficient and robust for forecasting the short-term water demand, with a Nash-Sutcliffe (NS) coefficient of 0.837 in the validation period. Seasonal model assessment shows that the model performs best during the critical summer season. The NARX-based recurrent model is established as a powerful and promising tool for predicting urban water demand. Thus, it can efficiently aid the development of resilient water supply plans.

Rainwater harvesting (RWH): a supplement to domestic water supply in Mvog-Betsi, Yaoundé-Cameroon

Abstract This research is aimed at assessing the possibility of using a rainwater harvesting (RWH) system to supplement domestic water supply in the Mvog-Betsi neighbourhood of Yaoundé. The research made use of a 63-year long data series on the amount of rainfall available in the study area, an analysis of rainwater quality and an estimate of the population's monthly water demand in relation to the monthly harvestable rainwater supply. Rainwater supply for the months of September, October and November is 53.05 m3 which is considerably greater than the households' water demand of 25.56 m3 during the long dry season. This led to the design of a 27 m3 ferrocement tank as minimum storage requirement. Furthermore, a rainwater quality analysis showed that all tested parameters conform to water quality standards except for microbiological quality. The rainwater needs to be disinfected before consumption as potable water. Finally, cost estimates for installing RWH systems for low ($419), medium ($549) and high standard ($668) habitations were calculated. RWH can effectively serve as a water supply supplement in the Mvog-Betsi neighbourhood.

Water Quality Restoration with Ecological Water Demand Allocation Regulation

Ecological water replenishment is the main means of ecological restoration in urbanized rivers. The control targets of utilizing water resources, allocating water and restoring the water quality have become the research hotspot. In this study, the Chengnan River Basin in Jiangbei New District of Nanjing was taken as the object, and the ecological water demand threshold in the Chengnan River Basin was evaluated by using the calculation result of the ecological water demand obtained by the improved Tennant method. According to the ecological water replenishment configuration plan, a water quality and quantity coupling model was constructed to simulate and analyze the water quality improvement effects at the control section of the Chengnan River Basin under different guarantee conditions. The results show that the monthly water demand allocation scheme based on the improved Tennant method can be used as the basis for water replenishment of river ecological restoration, and it can effectively improve the water quality of the Chengnan River. The use of 40,000 t / d water replenishment for 11 days can make the Longwangmiao section meet the water quality target control requirements.

A hybrid model for monthly water demand prediction: A case study of Austin, Texas

AbstractWater demand prediction is an essential prerequisite for urban water resource allocation and pipeline network management. This article uses a hybrid model to improve the accuracy of predicting water demand. The model combines a simulated annealing (SA) algorithm, genetic cross factor (GCF), fruit fly optimization algorithm (FFOA), and support vector machine (SVM) to predict monthly water demand. The monthly water consumption data from 2012 to 2017 in Austin, Texas, are used to develop and test the model. The prediction results are compared with other intelligent algorithms, such as the hybrid model combination particle swarm optimizer with SVM and back‐propagation neural network. The results show that the mean absolute percentage error of FFOA‐GCFSA‐SVM is the smallest, and it is reduced by 16% and 51.17%, respectively, compared with the other two models. Moreover, the influences of kernel function and input variables on prediction results are analyzed, and the effects of sliding window length, data classification, and forecasting period on prediction results are discussed. We concluded that (1) for the city of Austin, the model with a Gaussian kernel function and historical data period of 6 months has higher prediction accuracy; (2) the monthly water demand in Austin is mainly influenced by the local population; and (3) data classification by season can reduce the interference of temperature on prediction.

A Novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks

Valid and dependable water demand prediction is a major element of the effective and sustainable expansion of municipal water infrastructures. This study provides a novel approach to quantifying water demand through the assessment of climatic factors, using a combination of a pretreatment signal technique, a hybrid particle swarm optimisation algorithm and an artificial neural network (PSO-ANN). The Singular Spectrum Analysis (SSA) technique was adopted to decompose and reconstruct water consumption in relation to six weather variables, to create a seasonal and stochastic time series. The results revealed that SSA is a powerful technique, capable of decomposing the original time series into many independent components including trend, oscillatory behaviours and noise. In addition, the PSO-ANN algorithm was shown to be a reliable prediction model, outperforming the hybrid Backtracking Search Algorithm BSA-ANN in terms of fitness function (RMSE). The findings of this study also support the view that water demand is driven by climatological variables.

Assessing the significance of tourism and climate on residential water demand: Panel-data analysis and non-linear modelling of monthly water consumptions

The concentration in time and space of tourists and of specific water-demanding touristic activities can add considerable pressure on available water supplies in coastal regions.The impact of tourism has not been adequately addressed in the water demand literature, especially at sub-annual scale: the present study includes the role of tourism on the monthly water demand in a set of Mediterranean coastal municipalities in a panel data framework.The influence of both climatic and touristic drivers on the water demand is investigated through a correlation analysis, thus deconstructing the seasonal variability of the consumption, and the development of both linear and non-linear models. The results demonstrate the improvement allowed by non-linear over linear modelling and the value of the information embedded in both climatic (in particular temperature daily maxima and minima and number of rainy days) and touristic determinants as drivers for the water demand at sub-annual scale.

Forecasting urban household water demand with statistical and machine learning methods using large space-time data: A Comparative study

Forecasts of water use are crucial to efficiently manage water utilities to meet growing demand in urban areas. Improved household-level forecasts may be useful to water managers in order to accurately identify, and potentially target for management and conservation, low-efficiency homes and relative high-demand customers. Advanced machine learning (ML) techniques are available for feature-based predictions, but many of these methods ignore multiscale spatiotemporal associations that may improve prediction accuracy. We use a large dataset collected by Tampa Bay Water, a regional water wholesaler in southwest Florida, to evaluate an array of spatiotemporal statistical models and ML algorithms using out-of-sample prediction accuracy and uncertainty quantification to find the best tools for forecasting household-level monthly water demand. Time series models appear to provide the best short-term forecasts, indicating that the temporal dynamics of water use are more important for prediction than any exogenous features.

The Welfare Consequences of the 2015 California Drought Mandate: Evidence from New Results on Monthly Water Demand

In response to the severe California drought, in April 2015, Governor Jerry Brown issued an executive order mandating statewide reductions in water use. The mandate aimed to reduce the amount of water consumed statewide in urban areas by 25% from 2013 levels. The State Water Resources Control Board (SWRCB) proposed regulatory instructions that grouped urban water utilities into nine tiers, with conservation standards ranging from 8% to 36%. In this paper we evaluate welfare losses caused by this mandate. Understanding the proposed regulation’s welfare losses requires estimating water demand. Using fixed effect models and data from 2004 to 2009 on 111 urban water utilities, an annual demand curve is estimated. The estimated demand elasticity is between -0.61 and -0.1 which is heterogeneous across the regions and seasons. In the second step, we use the estimated annual demand function to recover price elasticities for a sample of 53 urban water utilities in California, which provide water for more than 20 million customers. We considered two scenarios to calculate welfare losses in Northern and Southern California: the governor’s mandate, and a hypothetical 25% uniform cut back. The results for Northern California indicate an average welfare loss of $6,132 per acre-foot of shortage for a governor’s mandate and $4,424 for a 25% uniform shortage. In Southern California, the average estimated welfare loss is $2,113 per acre-foot of shortage for a governor’s mandate and $2,171 for a 25% uniform shortage. Results indicate the monthly household-level willingness-to-pay to avoid the governor’s mandate is $36 in Northern California and $25 in Southern California. Our results suggest inefficiencies in the distribution of the percentage cutbacks between Northern California utilities from SWRCB.

Rainwater Availability Assessment for Tourism Development: A Case Study of Turtle Islands Park, Sabah

This study mainly focused on the rainwater availability assessment in Turtle Islands Park, Sabah, Malaysia. Consisting of three small islands, this marine park is home to two endangered turtle species – Green and Hawkbills turtles. This special feature of the islands attracts tourist around the world, thus increasing the demand of freshwater supply for tourism development in the marine park. However, due to the shortage of freshwater from the ground due to salinity intrusion, rainwater is seen to be an alternative in fulfilling the freshwater demand. To evaluate the source of freshwater in these islands, information is obtained from the main users of the water source, which are the Sabah Park officers, the approved tour operator on the islands and the security forces. The rainwater tanks available on the islands are calculated to assess the storing capacity of rainwater. The water demand was estimated by multiplying the number of visitors with the average water usage per visitor. With the existing water tanks, this study found that the Turtle Islands Park has the ability to store 414 m3 of rainwater in Selingaan island, 3.2 m3 in Gulisaan island, and 102.1 m3 in Bakkungan Kechil island. However, the monthly water demand of each island exceeds the existing storage tanks, hence it is proposed that the number of rainwater tanks be increased to harvest as much rainfall as possible for the use of the islands’ inhabitants.

Statistical Trend Analysis of Residential Water Demand in Kisumu City, Kenya

This study sought to analyse trend in the monthly water demand data series in Kisumu city at both seasonal and non-seasonal levels using the parametric method of Ordinary Least Squares (OLS) and non-parametric methods of Mann-Kendall tau and Sen's T test. Sen’s test was applied to validate the Mann Kendall trend test and to estimate the magnitude of the trend and its direction. The significance of the slope of the OLS equation was tested using the F-Test based on the Analysis of Variance (ANOVA). Secondary monthly water consumption data obtained from KIWASCO for the period January 2004 to December 2013 were used. Using logarithmically transformed data, the study established by OLS that residential water demand in Kisumu City had a significant increasing trend (FCalc=(105.13) > F(1;119)(α=0:05)=(5.15)). Kendall's tau test corroborated the OLS results of a significant increasing trend. The Sens T test indicated that most of the months registered significant upward trend with Sen’s slope estimates showing positive rates of change in residential water demand for these months.

Abnormal data refinement and error percentage correction methods for effective short-term hourly water demand forecasting

A short-term hourly water demand forecasting algorithm is needed in order to ensure a stable and safe supply of water. Unlike daily or monthly water demand forecasting, there are a large amount of fluctuation of hourly water demand. Hourly water demand is affected by short time period and abnormal data caused by the sensor, communication, and water treatment plant problems. An effective refinement method that detects and corrects the abnormal data among the historical data is needed to achieve accurate and practical hourly water demand forecasting. In this paper, we suggest an abnormal data refinement out of a confidence interval (ADR-CI) method and an error percentage correction (EPC) method. These methods try to distribute and revise the incoming hourly water demand and past water demand data. The proposed methods are verified by the experiments in a real water supply plant during a year.