Regional water demand forecasting based on shared socio-economic pathways in the Zhanghe River Basin

Due to the limited amount of water resources and the disorder of water use, water demand regulation has gradually become the focus of water resources management. With population growth and economic development, the demand for water resources is constantly increasing (da Encarnação Paiva et al., 2020; Wang & He, 2022). At the same time, human activities aggravate climate change, which not only affects the water resources availability but also further aggravates the uncertainty of socio-economic development, thus forming a negative cycle. In order to maintain the sustainability of natural and social water systems, it is necessary to make scientific forecasts of water demand, so as to allocate water resources reasonably and maintain normal production and life under the background of limited water.

China is the largest developing country in the world with a large population and rapid development of industry and agriculture. According to the Ministry of Water Resources, although China ranks sixth in the world in total water resources, its per capita water resources are less than 28% of the world average. Water problems in transboundary basins are more severe and complex. Transboundary basins often involve multiple water management agencies and multiple water users with different interest objectives, and conflicts are likely to occur between upstream and downstream due to water resource competition. In the context of limited available water resources, the traditional water resources management mode from the perspective of supply has made it difficult to solve the problem of water conflicts in transboundary basins, and special attention should be paid to water demand management. Scientific assessment of water demand is the basis of water demand management. As a prospective work, the multi-scenario forecast of socio-economic water demand is of great significance to the rational allocation of water resources and the prevention of water conflicts under the uncertain socio-economic trends in the future.

Under the background of water resources shortage, more and more studies have shifted the focus of water resources management from supply to demand. Nivesh et al. (2022) emphasized the importance of rational allocation of water resources among different water-use sectors. Shahangian et al. (2020) pointed out the necessity of water demand management in the context of increasing water supply cost. There are scholars exploring water demand management policies and best practices. For example, Mohseni et al. (2022) demonstrated the importance of price policies in water demand management. Wang & He (2022) believed that water demand management requires reasonable determination of water resource utilization thresholds, comprehensive regulation of social and economic systems and the development of efficient water-saving technologies, so as to reduce the interference of human water consumption on the water cycle process. In the context of climate change and intensified human activities, effective water demand management must consider different scenarios of socio-economic development, and grasp multiple possibilities of future water demand conditions through water demand forecasting (Distefano & Kelly, 2017). Zubaidi et al. (2018) emphasized the important role of reliable water demand prediction for the construction of urban water conservancy infrastructure. Cabral et al. (2019) used Portugal as an example to forecast water demand and emphasized that the key variables affecting water demand and uncertainties in future trends need to be taken into account when constructing water demand scenarios.

Population size and economic level are key elements of socio-economic scenario setting and have a significant impact on socio-economic water demand (Gondo et al., 2020), which has been demonstrated in existing studies. For example, Nivesh et al. (2022) believe that future population growth, industrial development and increased acreage will lead to a sharp increase in water demand in the Dasan River basin in India. da Encarnação Paiva et al. (2020) took the Paraiba South Basin in Brazil as an example and found that urban expansion and population increase would significantly increase household water demand. Many studies have taken population and economy as important parameters in the scenario prediction of resource and environmental problems. Zhang et al. (2021) obtained China's future carbon emission data based on the combination of population and economic forecast data and regression model. Yan & Xu (2022) predicted the socio-economic water demand of Jiangsu Province under the scenario of industrial structure change and water consumption change of 10,000 yuan GDP, which provided a reference for the sustainable utilization of local water resources.

In order to more comprehensively reflect the multiple possibilities of socio-economic development in the context of climate change, the Intergovernmental Panel on Climate Change (IPCC) proposed the SSPs framework (O'Neill et al., 2014). The framework considers five different paths for future socio-economic development, each of which represents a development mode (van Vuuren et al., 2017). At present, the SSP method is widely used in the estimation of basic factors such as population, GDP and urbanization level. For example, Dellink et al. (2017) used the SSPs to obtain the economic situation of 184 countries. Chen et al. (2020) predicted the population and education level of Chinese provinces during 2010–2100. Bai et al. (2021) narrowed the study scale to counties and obtained population data for 101 banners and counties in Inner Mongolia. Zhu et al. (2020) conducted a basin-scale exploration and derived the population and economic changes in the Yangtze River basin during 2010–2100 based on the SSPs.

The SSPs method has also found some applications in water demand forecasting. Roson & Damania (2017) predicted potential water demand and water availability in 15 regions around the world under different SSPs and analyzed the economic impact of future water shortage. Flörke et al. (2018) used SSPs to project the water demand of the world's 482 largest cities by 2050 and analyzed water competition between cities and agriculture driven by climate change and urban growth. There are also some representative predictive studies related to water resources at the watershed scale. Alizadeh et al. (2022) combined SSPs with representative concentration pathways (RCPs) to predict future human water system vulnerability in the Rechna Doab watershed, Pakistan. Yin et al. (2017) used SSPs to predict water demand in the Yellow River basin in the 21st century.

As a relatively new forecasting tool, most of the current application scales of SSPs are global, national or provincial scales, with few applications at watershed or county level. From the perspective of water demand forecasting studies, most of them focus on administrative regions and seldom pay attention to the natural unit of river basin, let alone the special object of transboundary small river basin. In addition, water demand prediction is rarely combined with the evolution of population and economy to realize the coupling of natural and social dual water cycles. Based on the Sixth Census data and statistical data, this paper focuses on the Zhanghe River Basin and narrows the research scope to the county level. In this paper, SSPs are used to calculate the population and economy under five scenarios from 2011 to 2030, in which the results from 2011 to 2020 are used to compare with the historical data to test the prediction accuracy. Next, the water demand under different population and economy conditions is predicted by combining with water quotas.

Demographic and economic changes are primary considerations in social development scenarios (Yin et al., 2017). Based on the historical data of population and economic development in the Zhanghe River Basin, this paper constructs a localized parameter system and predicts the population and economic conditions of the five counties in the basin under different SSPs, which serves as the basis for the prediction of water demand for living and production. The population and economic parameters of each county are set according to the proportional relationship between the county and the whole city in the historical period.

In Formula 3, Y (t) denotes GDP in the year t, L denotes labor input, K denotes capital input, α is the elasticity coefficient of capital output, TFP(0) denotes total factor productivity in the base period, and λ denotes the level of scientific and technological progress.

In order to predict the future economy, it is necessary to make assumptions about labor participation rate, capital output elasticity coefficient and total factor productivity under different SSPs. Based on the characteristics of different scenarios and the scenario assumptions of existing studies, the above parameters are calibrated, as shown in Table 2 (Jiang et al., 2018; Pan et al., 2019; Bai et al., 2021). In addition, historical data were obtained from the sixth National Population Census, China Population and Employment Statistical Yearbook and the statistical yearbook of each city.

In terms of domestic water use, in order to implement the strictest water resources management system and promote the construction of a water-saving society, the government departments of Shanxi, Hebei and Henan provinces involved in the Zhanghe River Basin have formulated official documents of water quota, limiting the per capita daily domestic water consumption. The documents are ‘Water Quota of Shanxi Province’, ‘Water Quota of Hebei Province’, ‘Local Standard of Henan Province – Agricultural and Rural domestic water Quota’ and ‘Local Standard of Henan Province – Industrial and urban domestic water Quota’. These documents provide water quota constraints for cities with different population sizes. According to the population prediction results, the corresponding water quota can be determined for each city, as shown in Table 3.

In terms of economic water demand, the governments in the basin have issued relevant plans for the construction of water-saving society, restricting the water consumption quota of 10,000 yuan of GDP. According to the ‘14th Five-Year Plan for Ecological and Environmental Protection in Hebei Province’, ‘14th Five-Year Plan for the Construction of Water-saving Society in Henan Province’, and the ‘14th Five-Year Plan for the Construction of Water-saving Society in Shanxi Province’, the water quota for 10,000 yuan GDP in She County and Ci County will be reduced by 15% in five years, by 16% in Anyang County and Linzhou County and by 12% in Pingshun County. This study obtains the water consumption quota in 2020 from the water resources bulletin of each city and then calculates the water consumption quota in 2025 and 2030 according to the requirements of the above government documents, as shown in Table 4.

In order to test the accuracy of population and economic forecasts, the forecast results under the SSP2, which most closely matches the current development trend, are compared with the actual statistics for 2011–2020. The prediction errors are shown in Table 5 and are generally acceptable. In addition, the population and GDP trends predicted in this paper under the five SSPs in the Zhanghe River Basin are similar to those predicted by previous studies in China (Jiang et al., 2018), which also indicates that the predicted results of this study are reasonable.

From the perspective of different scenarios, except Pingshun County, the scenarios with the largest and smallest population size in other counties are SSP3 and SSP4, respectively. The population of SSP1 is very close to that of SSP5. The population under the SSP3 is less educated and the birth rate is higher, resulting in the largest population size under this path. Under the path of SSP4, regional development is not balanced. As a region with low fertility rate, China still maintains a low fertility rate (Wang et al., 2020), with a small number of births, but the death rate is at a medium level, resulting in a smaller population than other paths. The birth and death assumptions under the SSP1 and SSP5 paths were consistent. Due to the small study scale, population migration between counties was not considered, leading to similar population sizes under the two paths. The population of She County and Ci County under SSP1 and SSP5 is slightly higher than that under SSP2, while the population of Anyang County and Linzhou County under SSP1 and SSP5 is slightly lower than that under SSP2. In addition to the SSP4, the population size of the other four paths in Pingshun County is similar, which is also related to the small population base of the county.

As can be seen from Figure 5, domestic water demand is increasing between the three forecast years, but there is little difference between different scenarios, which is also related to the small population base at the county level. By 2030, domestic water demand can reach 13,765 × 10⁴m³ at most, which poses a great challenge to the local water supply system. In order to narrow the gap between domestic water supply and demand, all regions must promote household water-saving measures and improve the efficiency of water resources utilization. In terms of counties, Pingshun County always has the least water demand due to the smallest population scale, followed by She county. Domestic water use is similar in the other three counties.

As shown in Figure 6, economic water demand has more significant differences between scenarios and between counties than domestic water demand. Due to the lag of economic development, the economic water demand of SSP3 is much lower than other paths, and the growth between different years is not very obvious. The increase in the water demand under the SSP4 is also small, and its water demand is slightly larger than the SSP3. The water demand under the SSP1 and SSP2 paths is similar, with the former slightly higher than the latter, mainly related to the higher output level under the SSP1. The SSP2 path maintains the current development trend, and the economic water demand is also at the medium level. The SSP5 path attaches great importance to economic development, which cannot be separated from a large amount of water resources to support production. Therefore, it becomes the path with the highest economic water demand, and its economic water demand will increase rapidly from 2020 to 2030. In terms of counties, Anyang County's economic water demand is much higher than that of other counties due to its high GDP and large water demand quota set by the government. On the contrary, Pingshun County has a low GDP and a low water demand quota, so its economic water demand accounts for a small proportion of the total economic water demand in the basin.

On the basis of obtaining the domestic water demand and economic water demand of the Zhanghe River basin, the total socio-economic water demand can be obtained by adding the two together. By comparing Figures 6 and 7, it can be found that since economic water demand accounts for a large share in the total water demand, the change trend of total water demand, its scenario differences and county differences are consistent with economic water demand.

For a long time, the large-scale diversion irrigation and the development of high-water consumption industries have made the water consumption in the Zhanghe River basin far exceed the amount of surface available water resources, and the overexploitation of groundwater has existed for many years. Moreover, with global warming and disorderly development and utilization of water resources, the river desertification and the reduction of reservoir water storage have occurred in the basin, implying a trend of further reduction of water supply (Gao & Hu, 2016). Although the water quota formulated by the governments of the Zhanghe River Basin is constantly decreasing under the water-saving constraint such as technological progress and the improvement of laws and regulations, the water demand of the Zhanghe River Basin will remain on the rise by 2030 due to the continuous population increase and economic development.

The water quotas adopted is a kind of water-saving constraint that is expected to be achievable in the future, formulated by the governments of Zhanghe River Basin under the existing development path, which is close to SSP2. The water quotas under other paths may deviate from them but are difficult to quantify, which needs attention when analyzing the water demand of different SSPs for greater rigor. Specifically, SSP3 requires the least water, but its technical level and water-saving consciousness are lower than SSP2, so it is likely to exceed the given water quotas, and then exceed the predicted water demand. Moreover, SSP3 is at the lowest economic level. It is not the ideal path for long-term development. SSP4 has slightly less water demand than SSP2, but it lags behind SSP2 in economic terms. The predicted water demand of SSP1 is slightly higher than that of SSP2, but it has higher resource utilization efficiency and attention to the ecological environment, so that the water quota constraint can be better met, and even the water demand can be lower than the predicted value. SSP5 has the largest water demand and lacks attention to the environment. Its economy is highly dependent on fossil fuels, which require a lot of water in the exploitation process. Overall, SSP1 is a relatively ideal development path for coordinating economic development and controlling water demand.

As a relatively new scenario analysis tool, SSPs have made rich achievements in population and economic forecasting, but few studies have carried out water demand forecasting on this basis. In addition, SSPs are rarely used at the basin scale, especially in transboundary watersheds, which involve multiple water management administrations and are often subject to water conflicts in the event of water scarcity. In this paper, the population and economy of the Zhanghe River Basin are projected up to 2030 under the SSPs framework, and five different socio-economic development scenarios are constructed. Furthermore, this paper combined the prediction results under the SSPs framework with the potential constraints of water quotas to forecast the water demand of the basin. It is a new attempt to apply the SSPs framework to forecasting water demand in transboundary watersheds, which has important reference value for watershed water resource managers to identify potential water resource shortage risks and take prospective water demand management measures to resolve water-use conflicts.

As the results show, since the evolution of the population pattern is a long historical process, there is little difference in population under different development paths by 2030, and this difference is mainly reflected after 2025. The population size under SSP3 is slightly larger than other paths, and that under SSP4 is the smallest. As a result, the difference in domestic water demand under different paths is also less obvious. Pingshun County has the least domestic water demand due to the least population. The economy of Zhanghe River Basin shows a trend of fluctuating growth in the future. The paths with the largest and smallest economic scale are SSP5 and SSP3, respectively, which are also the paths with the largest and least economic water demand, respectively. Pingshun County has the smallest economic aggregate, so its economic water demand is much lower than other counties, while Anyang County has the largest economic water demand. Since economic water demand accounts for a large proportion of the total water demand in the basin, the scenario heterogeneity and regional heterogeneity of total water demand are similar to that of economic water demand. By 2030, the total water demand will be between 75,978 × 10⁴ and 157,235 × 10⁴m³. Under SSP2, a path most likely to be maintained in the short term, the socio-economic water demand of the basin in 2030 will reach 134,314 × 10⁴m³, which will bring great challenges to the water supply system of the basin.

From the perspective of balancing economic development and water utilization, SSP1 is the most ideal path for long-term development. In order to realize the transformation to SSP1, effective measures should be taken in the Zhanghe River Basin to realize the sustainability of the water resources system and socio-economic development. Specifically, it can be considered from the following aspects:

• (1)

  A critical action is to improve the unified water resources management mechanism at the basin level. Previously, due to the lack of macro-control at the basin level, water competition in the transboundary area led to repeated construction of water conservancy projects, exceeding the local water resources carrying capacity, and weakening project benefits. Subsequently, the Zhanghe Upstream Administration Bureau was established to coordinate the water resource allocation in the basin and resolve water disputes, which has played a positive role in maintaining the water use order in the basin. In the future, it is also necessary to improve water-use efficiency and control water demand under the unified command of the agency, as well as scientifically dispatch water intake. At the same time, it is required to improve the water resources laws and regulations, cultivate and guide the water rights trading market, so as to avoid the damage to the water resources system caused by the disorderly development and utilization.

• (2)

  It is urgent to strengthen water-saving publicity and education and establish a scientific reward and punishment system to maintain water fairness. On the one hand, the governments in the basin should form a sense of community of water resources interests and actively respond to the call for water conservation. At the basin level, a scientific reward and punishment system should be established to effectively deal with water disputes between administrative regions. Water-use supervision should be strengthened, especially for counties with a large population and economic scale. On the other hand, the existence of large-scale irrigation areas and the dependence on high-water consumption industries such as metallurgy and chemical industry have solidified the development mode and increased the difficulty of water conservation. Therefore, it is necessary to adjust the industrial structure to achieve the goal of water saving and sustainable development.

• (3)

  From a micro perspective, the development of all industries in the basin cannot be separated from the availability of water resources in the Zhanghe River Basin. Enterprises should enhance the awareness of social responsibility, strictly implement the wastewater discharge standard and speed up the research and development of water-saving equipment, so as to help the whole society improve the utilization efficiency of water resources. In addition, residents should enhance the awareness of water saving in daily life, use more household water-saving appliances and promote water recycling.

Finally, the demand for water resources comes from the requirements of human life and economic development, so the total socio-economic water demand of the basin is closely related to the size of population and economy. Therefore, it is necessary to pay attention to the tradeoff between socio-economic development and water resource utilization, neither to limit population and economic growth in order to save water resources, nor to blindly pursue economic development while ignoring the rigid constraint of water resources. It is important to save water rationally by improving the utilization efficiency of water resources and improving the allocation of resources while maintaining healthy social and economic development.

This study is a novel exploration of water demand prediction by combining SSPs and water quotas, which is of great significance for water management departments to make long-term water demand management plans and alleviate water conflict in basins. But there is still a limitation. Due to the difficulty in quantifying future water quotas under different paths, the expected water quotas under the current trend is used to forecast water demand, but then specific analysis is made for each path to enhance rigor. In the future, other methods can be considered to obtain more accurate parameters and more exploration can be carried out in the prediction of water demand.

The financial assistance provided by the National Social Science Fund of China (20BGL191); National Natural Science Foundation of China (71704068); MOE (Ministry of Education in China) Project of Humanities and Social Sciences (21YJCZH139; 21YJC790021); the Social Science fund of Jiangsu Province (21GLB007), and Key Research Base of Universities in Jiangsu Province for Philosophy and Social Science ‘Research Center for Green Development and Environmental Governance’ is highly appreciated by researchers of this study. The views and opinions expressed in this article are those of the authors and do not necessarily reflect the views of the funding agencies.

The authors confirm their contribution to the paper as follows. D. S. conceptualized the whole article, developed the methodology, validated the article, rendered support in formal analysis, investigated the data, brought resources, wrote the article, supervised the process and administered the project. M. Y. rendered support in formal analysis, investigated the article, wrote the original draft, wrote the review and edited the article, arranged the software, and visualised the article. X. W. rendered support in formal analysis, investigated the data, wrote the original draft, wrote the review and edited the article, developed the methodology, validated the article, and visualised the article. G. L. conceptualized the whole article, investigated the data, supervised the process and administered the project.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.