Domestic water demand forecasting in the Yellow River basin under changing environment

Purpose – The purpose of this paper is to develop a statistical-based model to forecast future domestic water demand in the context of climate change, population growth and technological development in Yellow River. Design/methodology/approach – The model is developed through the analysis of the effects of climate variables and population on domestic water use in eight sub-basins of the Yellow River. The model is then used to forecast water demand under different environment change scenarios. Findings – The model projected an increase in domestic water demand in the Yellow River basin in the range of 67.85 10 to 62.20 10 m in year 2020 and between 73.32 10 and 89.27 10 m in year 2030. The general circulation model Beijing Normal University-Earth System Model (BNU-ESM) predicted the highest increase in water demand in both 2020 and 2030, while Centre National de Recherches Meteorologiques Climate Model v.5 (CNRM-CM5) and Model for Interdisciplinary Research on ClimateEarth System (MIROC-ESM) projected the lowest increase in demand in 2020 and 2030, respectively. The fastest growth in water demand is found in the region where water demand is already very high, which may cause serious water shortage and conflicts amongwater users. Originality/value – The simple regression-based domestic water demand model proposed in the study can be used for rapid evaluation of possible changes in domestic water demand due to environmental changes to aid in adaptation andmitigation planning.


Introduction
Climate change, rapid social economic development and population growth have imposed significant challenges to sustainable development all around the world (Nigel and Ben, 2014, Wang et al., 2014a, 2014b;Shahid et al., 2014Shahid et al., , 2016)).As a unique and increasingly scarce commodity, water is among the most significantly affected resources by these changes.Ensuring continuous and adequate water supply to growing population and developing economy is one of the major concerns for policy makers and scientists (Butler and Memon, 2006;Kim et al., 2014).It is much more challenging in arid and semi-arid river basins in Australia, Middle East, Africa and Northern China due to limited availability of water resources (Howe et al., 2005;Butler and Memon, 2006;Wang et al., 2012a).Understanding possible future changes in water demand is essential for the development of effective climate change adaptation policies (Shahid et al., 2017).
Water demand is influenced by various factors such as population growth, technological development, climate change, social-economic (Wang et al., 2016a).The complex interactions of different influential factors have made the forecasting of water demand a difficult task (Vörösmarty et al., 2000;Downing et al., 2003;Alvisi et al., 2003;Babel et al., 2007;Khatri and Vairavamoorthy, 2009;Wang et al., 2015).A number of studies have been carried out to predict water demand (Combalicer et al., 2010;Dursun, 2010;Jakimavi cius and Kriau ciūnienė, 2013;Wang et al., 2016b).The approaches generally used for forecasting water demand can be classified into three broad classes, namely, time series, econometric and end-use forecasting (Zhou et al., 2000;Alvisi et al., 2003;Altukaynak et al., 2005;Bougadis et al., 2005;Khatri and Vairavamoorthy, 2009;Caiado, 2010;Wang et al., 2017).Besides that, artificial neural network, support vector regression, etc. have been used in a few cases (Ghiassi et al., 2008;Adamowski, 2008).Unfortunately, yet only a few of the traditional forecasting models can adequately manage the dynamics of a water supply system because of the limitations in modeling structures.Potential challenges also arise, as these traditional methods require long-term, continuous historical records of water demand and other variables (Simonovic, 2002;Qi and Chang, 2011).
The impact of climate change on water demand is a major issue in recent years.Most of the recent studies on climate change impacts on water demand are based on the historical relationship between water demand and climate variables such as rainfall, air temperature, sunshine duration, relative humidity and wind speed (Protopapas et al., 2000;Downing et al., 2003;Karamouz et al., 2011).It is widely accepted that water demand will increase due to the increased evapotranspiration caused by higher temperature.However, how to take consideration of climate variables and population growth together for forecasting future domestic water demand is still a challenge.
The Yellow River, originated in the Qinghai-Tibetan plateau of Qinghai province, flows across eight other provinces and autonomous regions of China before it ends into the Bohai Sea (Liu and Zhang, 2002;Wang et al., 2012b).The river plays a key role not only in the country's economic development, but also in the historic and cultural identity of the Chinese people.With approximately 9 per cent of China's population and 17 per cent of its agricultural area depending on the Yellow River basin, it is designated as "the cradle of Chinese Civilization" (Liu and Zhang, 2002;Giordano et al., 2004).Population and economy of the River Basin have grown rapidly in the recent years.This has caused a significant increase in water demand for agriculture, industry and households in the Basin.Consequently, the gap between water supply and demand has widened in recent years (Giordano et al., 2004;Barnett et al., 2006;Gonçalves et al., 2007).Some of the main tributaries of the Yellow River, such as Kuye River and Tuwei River, showed a decreasing trend of runoff, even drying up (Wang et al., 2014b).Increase in demand of water for domestic use due to climate change and population growth may aggravate the existing situation of water shortage in the Basin.Forecasting possible changes in residential water demand is very important for planning and management of water resources of the Basin.
The objective of the present paper is to develop a statistical model to forecast domestic water demand in the context of climate change, population growth and technological development.The proposed model is developed through the analysis of the effects of climate and population on domestic water use and then applied in the Yellow River basin to forecast water demand under different environment change scenarios.IJCCSM 10,3 2. Materials and methods 2.1 Data and sources 2.1.1Water consumption and population growth data.Population growth, domestic water use and economic development data of the Yellow River basin for the period 1980-2012 were collected from China Water Resources Bulletins (MWR, 2000(MWR, -2012)).The recent data of domestic water use in eight sub-basins of the Yellow River, namely, Uperstream of Longyangxia (UL), Longyangxia-Lanzhou (LL), Lanzhou-Hekouzhen (LH), Hekouzhen-Longmen (HL), Longmen-Sanmenxia (LS), Sanmenxia-Huayuankou (SH), Downstream of Huayuankou (DH) and Interior Area (IA), were collected from the Yellow River Water Resources Bulletins (YRCC, 1998(YRCC, -2012)).

Domestic water demand forecast modeling
Domestic water demand includes water needs for all residential purposes such as in-house water use for drinking, preparing food, bathing, washing clothes and dishes and flushing toilets, etc. as well as outdoor water needs for gardening, lawn watering, etc. (Alvisi et al., 2003;Garcia et al., 2004;Blokker et al., 2010;Wang et al., 2017).According to the water resources planning technical specifications of China, domestic water demand and population growth can be linked through the following equations:

Domestic water demand forecasting
where LW / is the domestic water demand (10 4 m 3 ), which can be estimated by multiplying the projected population (10 4 p) with the projected per capita water consumption (l/p.d).The urban water demand, LuW / is estimated by multiplying the urban population, Pu / (10 4 p), with the per capita water consumption by urban population, LQu / (l/p.d); the rural water demand, LrW / , is estimated by multiplying the rural population, Pr / (10 4 p), with the per capita water consumption by rural population, LQr / (l/p.d)(Wang et al., 2011).Equation (1) does not consider climate change for forecasting water demand.Therefore, the equation is modified in this study to incorporate the impact of climate change on water demand, DLW t , as follows: where: where d T is the climatic elasticity of domestic water demand; DLW is the change in domestic water demand (10 4 m 3 ); and DT is the change in temperature (°C).Figure 6 shows that domestic water demand will increase in the range of 67.85 Â 10 8 to 62.20 Â 10 8 m 3 in 2020.The GCM BNU-ESM projected the highest increase, while CNRM-CM5 the lowest.The water demand was projected to increase in the range of 89.27 Â 10 8 to 73.32 Â 10 8 m 3 in 2030.The highest increase was projected by BNU-ESM and the lowest by MIROC-ESM.Among the Sub-Basins, the highest increase in water demand was projected in LS and the lowest in UL.

Conclusion
A statistical model was developed in this paper to forecast domestic water demand due to changes in climate, population and water-saving technologies.Results showed that It can be anticipated that the rapid increase in water demand due to climate change and population growth will aggravate the situation in the future.It will also increase the competition among water users and, therefore, more conflicts.
The method proposed in the study can be used for rapid evaluation of possible changes in domestic water demand in the context of environmental changes.However, more attention is required for improvement of the method.Following points should be considered for future improvement of the model: domestic water demand for different in-house and outdoor activities are different and therefore, impacts of environmental changes on each activity should be evaluated for better projection of water demand; only the average temperature was considered to estimate the impacts of climate change on domestic water demand in this study and other climatic variables such as maximum and minimum temperature and humidity can be considered for the improvement of model accuracy; and furthermore, future study is required to assess uncertainties in water demand projection for better planning and management of water resources.

3.
Domestic water demand under changing environment 3.1 Population growth and domestic water use The historical changes and future projection of population in different sub-basins of the Yellow River basin are shown in Figure 1.The highest population in the Yellow River basin is in LS subbasin, while the lowest population in UL sub-basin.Figure 1 shows that the population in all the sub-basins has increased rapidly over the past years.Population in the Yellow River basin was 8,177 Â 10 4 in 1980 and the urbanization rate was only 17.8 per cent.The highest rate of urbanization was in LH (33.5 per cent).According to the future plan of River Basin management authority, the total population and urbanization in the Basin will reach 12,658 Â 10 4 and 50.4 per cent, respectively, in 2020 and 13,094 Â 10 4 and 58.8 per cent, respectively, in 2030.The growing population has caused a drastic increase in domestic water demand in the Basin.The domestic water consumption in the Yellow River basin has increased from 11.72 Â 10 8 m 3 in 1980 to 22.66 Â 10 8 m 3 in 2000.The per capita water demand has also increased due to urbanization and changes in socioeconomic condition of the people.The changes in per capita domestic water demand in different sub-basins are shown in Figure 2. The figure shows Figure 1.Historical and projected change in population in different sub-basins of the Yellow River Figure 2. Changes in per capita water consumption in the Yellow River basin Figure 4. Projected domestic water demand in year 2020 under RCP4.5 Figure 6.Changes in domestic water demand in year 2020 due to environmental changes Table I summarizes the basic statistics of collected data for different sub-basins of the Yellow River.2.1.2Meteorological data.Daily temperature data of 61 stations for the period 1961-2012 were obtained from National Climate Center of China (NCCC).Climate projections by a suit of seven general circulation models (GCMs), namely, Beijing Climate Center-Climate System Model version 1 (BCC-CSM1-1), BNU-ESM, CNRM-CM5, Goddard Institute for Space Studies Model E version 2 with Russell ocean model (GISS-E2-R), MIROC-ESM, Max Planck Institute-Earth System Model-Low Resolution (MPI-ESM-LR) and Meteorological Research Institute-Coupled Global Climate Model version 3 (MRI-CGCM3), under Representative Concentration Pathways (RCPs) scenario RCP4.5 were also collected from NCCC.