Comprehensive assessment of urban water resources carrying capacity based on basin unit: a case study of Qingdao, China

Water resources are the indispensable basic resources to maintain the function of natural ecosystem and to sustain the sustainable development of local society (Walter et al. 2012). However, due to human activities, climate changes and other factors, the contradiction between supply and demand of regional water resources is sharpening, which seriously hinders the development of social economy (Cai et al. 2011a, 2011b; Safavi et al. 2016). According to statistics analysis, nearly two-thirds of China's cities are facing the problem of water shortages and groundwater overextraction (Shang et al. 2016). Urban water resources carrying capacity (UWRCC), defined as the maximum supporting capacity of virtuous cycle of the ecosystem and social economic development of a city based on the available water resources, attracts extensive attention from the academia and relevant government departments (Song et al. 2011; Dou et al. 2015; Shi et al. 2015; Ait-Aoudia & Berezowska-Azzag 2016). Recently, there has been a lot of research on UWRCC, which mainly focused on the improvement of indicator system and model construction (Ait-Aoudia & Berezowska-Azzag 2016; Wang et al. 2017; Lu et al. 2017; Dai et al. 2019; Magri & Berezowska-Azzag 2019). However, little attention has been paid to the research units on UWRCC. Most studies on UWRCC take administrative unit as the research unit. Water resources is characterized by natural attributes, social attributes, overall circularity with the basin as the carrier, and externality of regional rights. Therefore the evaluation of UWRCC based on basin unit is consistent with the basic principles of sustainable development and scientific. It is urgent to evaluate UWRCC based on basin unit.

To date, there are a variety of methods to evaluate UWRCC, such as the fuzzy comprehensive evaluation method, ecological footprint method, analytic hierarchy process (Wang et al. 2013; Jia & Yu 2014; Wang et al. 2017; Chi et al. 2019). The above methods are static methods based on the optimal allocation of water resources. The impact of inputs and their changes on water supply and demand are not considered in these methods, and the dynamic feedback relationship between economic activities, population development and water resources system are also ignored (Fan 2008). The system dynamics method (SD) can simulate the behavior of complex systems dynamically using computer simulation technology. It can solve the problem that static research methods cannot reflect the change of UWRCC with time (Sun 2005; Wang et al. 2017), and the analytic hierarchy process (AHP) provides a feasible approach to accurately determine the weight of each index of UWRCC (Yang et al. 2019). Therefore, we used the AD–AHP method to integrate evaluation UWRCC of Qingdao, China.

UWRCC is an important index to measure water resources security and an important basis to explore urban water use countermeasures. This study uses a case study of Qingdao City to establish system dynamics model of UWRCC in each basin by the SD–AHP method. The aims are: (1) to calculate the UWRCC of each basin, and quantitatively evaluate the status as well as the dynamic development trend of water resources; (2) to reveal the temporal and spatial characteristics of water resources in Qingdao City, and provide effective suggestions for the rational allocation and sustainable development of water resources.

Qingdao (119°30′−121°00′E, 35°35′−37°09′N) is one of the cities with severe water shortage in north China and coastal areas. According to the meteorological data of Qingdao in the last hundred years, the average annual temperature is 12.3 °C, with 220 frostless days. Annual average precipitation is 688.2 mm, and the total amount of water resources is 22.1 × 10⁸ m³, 12 and 15% of the national average respectively. The average water resource per capita occupancy amount is 312.8 m³, far lower than the internationally recognized average of 500 m³ per capita.

There are 224 rivers in Qingdao, all of which are rainfall-sources. In this study, remote sensing images covering the study area were used as data sources (http://www.gscloud.cn/). Combined with the available data (water resources quantity, socioeconomic data, etc.), water system extraction and basin division were carried out in Qingdao by using ArcGIS hydrologic analysis module. The study area was divided into nine basins, including Dagu river basin (DGRB), Baima river basin (BMRB), Baisha river basin (BSRB), Beijiaolai river basin (BJLRB), Feng river basin (FRB), Moshui river basin (MSRB), Nanjiaolai river basin (NJLRB), Yang river basin (YRB) and Zhoutuan river basin (ZTRB) (Figure 1).

System dynamics (SD) is a modeling method which can perform better simulation for a multivariable system, reflecting the internal mechanism of complex systems (Che et al. 2006; Xu & Sun 2008). The SD model in essence is a system of differential equations with time delay. It is expected in dealing with nonlinear, high-order, multi-variable, multi-feedback and complex time-varying phenomena, and can quantitatively analyze the internal relationship between the structure and function of various complex systems.

In this study, AHP is used to determine the weights of different indexes on UWRCC. The key steps of AHP are as follows (Fan et al. 2000; Wen et al. 2000; Ouma & Tateishi 2014):

• (1)

  Select relevant factors and determine their hierarchy. According to the dominant relationship, the factors are divided into object hierarchy, rule hierarchy, and index hierarchy.

• (2)

  Compare these factors and determine their comparative significance. The pairwise comparison judgment matrices are constructed according to the 1–9 scale for the degree of importance, and the corresponding grade is determined (Table 1).

• (3)
  Check the consistency of the matrices. The formulas are as follows:

  

  (1)

  

  (2)

Combining the distribution map of river basins in Qingdao (Figure 1) with the Qingdao administrative map, the towns in each river basin were divided. If the town crosses two river basin, it was divided into the river basin where the town center is located. Then the relevant socio-economic data of each basin were obtained by integrating and analyzing the statistical data of each town in the Statistical Yearbook (2010–2018) of 10 districts. And there were also some data available from the official website of each district of Qingdao.

The hydrological data of each basin mainly were derived from Qingdao Water Resources Bulletin (2010–2018). Some important indexes and sources are listed in Table 3.

The water resource system is a complex system, which is closely related to population, economy and society (Wang et al. 2005; Feng et al. 2006). By referring to the existing literatures and considering the actual water resources situation in each basin of Qingdao as well as the availability of data, the evaluation index system was divided into four subsystems, i.e. water resources subsystem (B1), social subsystem (B2), economic subsystem (B3) and ecological environment subsystem (B4) (Yang et al. 2010) (Table 4). The water resource subsystem (B1) included total available water resource (C1), the difference between supply and demand (C2), and sewage return rate (C3). Social subsystem (B2) included total population (C4), urbanization rate (C5), irrigated area (C6), urban domestic water quota (C7) and rural domestic water quota (C8). Economic subsystem (B3) included industrial production (C9) and water consumption per 10,000 RMB of industrial production (C10). Ecological environment subsystem (B4) included green area (C11), road land (C12) and treatment rate of sewage (C13).

The SD model was used to simulate the dynamic change of water resource system of each basin in Qingdao over 2011–2030. The SD model boundary of each basin was the boundary of each basin. The time boundary was from 2011 to 2030, and the simulation step length was one year with 2011 as the base year. Vensim-PLE software was used to establish the flow diagram of the UWRCC system of each basin (Figure 2).

The system flow diagram only showed the logical relationship between the system structure and variables, not the quantitative relationship between different variables. In order to show the quantitative relationship among variables, it was necessary to analyze the logical relationship among the variables of each subsystem. Function and constant were used to construct state variable equation, rate equation and auxiliary equation. The main function (in Vensim language) is summarized in Table A.1 in the appendix.

In this study, total population, industrial production, irrigated area, road land, and green area were selected for the error test of the model. The observed values of the above five indexes can be directly obtained, rather than calculated, and the weighted values of total population and industrial production on UWRCC were high (Table 4). The weighted values of irrigated area, road land, and green area on UWRCC were not high, but these three indexes are decisive factors in the model (Wu et al. 2013). The observed values and simulated values of these five indexes (a total of 630 samples) from 2011 to 2017 were used to test the model accuracy.

The results of error test of SD are shown in Table 5. The mean of R² was 0.8495. Except for R² of industrial production in BSRB (0.2902), irrigated area in DGRB (0.0451), and green area in DGRB (0.3682), the R² of other indexes were greater than 0.6. The mean of NSE was 0.70019. Except for NSE of industrial production in BSRB (−0.0405), total population in DGRB (−0.02), irrigated area in DGRB (−0.5895), green area in DGRB (0.2642), and irrigated area in ZTRB (−0.3647), the NSE of other indexes were greater than 0.44. MARE of each index in the model were no more than 10%. Therefore the simulation results were in good agreement with the actual values, indicating that the model reflected the reality relationship between water resources and social-economic systems in each river basin.

Industrial water demand is mainly restricted by industrial output value, product structure, industrial water price, and water-saving rate (Lei & Li 2015). The industrial water demand of each basin over 2017–2030 is predicted in the order of DGRB > MSRB > NJLRB > YRB > FRB > BJLRB > BSRB > BMRB > ZTRB (Figure 3(a)). The industrial water demand of DGRB was the largest in 2017, at 0.25257 × 10⁸ m³. The industrial water demand of ZTRB is predicted to be 0.02017 × 10⁸ m³ by 2024, which is the smallest among all basins. The industrial water demand of MSRB, NJLRB, YRB, FRB, BJLRB and ZTRB both show an upward annual trend, while the industrial water demand of DGRB, BSRB and BMRB show a downward annual trend.

Agricultural water demand is mainly affected by irrigated area and irrigation quota (Wu et al. 2018). As shown in Figure 3(b), the agricultural water demand of each basin is predicted in the order of BJLRB > DGRB > NJLRB > YRB > MSRB > BMRB > ZTRB > FRB > BSRB. The agricultural water demand of BJLRB is predicted to rise to 0.905 × 10⁸ m³ by 2030, which is the largest one among all basins. This is mainly because BJLRB is widely surrounded by an agricultural zone area and the agricultural water demand is extremely large (Li et al. 2017). The agricultural water demand of ZTRB is predicted to be 0.3268 × 10⁸ m³ by 2030, which is the smallest among all basins. This is mainly related to the small agricultural planting area in ZTRB. The agricultural water demand of each basin over 2017–2030 has little change trend and basically presents a stable state. The reason is that the irrigated area of each basin increases year by year with the improvement of economy, but the irrigation quota is gradually reduced considering the influence of tillage technology, irrigation technology and variety improvement (Yang et al. 2016; Wu et al. 2018).

Total domestic water demand is the sum of the domestic water demand of urban residents and rural residents. Domestic water demand is mainly determined by the number of residents and domestic water quota. As the domestic water quota of urban and rural residents is different, the domestic water demand of each need to be calculated separately (Yang et al. 2019). As shown in Figure 3(c), the domestic water demand of each basin is predicted in the order of DGRB > MSRB > BJLRB > NJLRB > YRB > BMRB > FRB > BSRB > ZTRB. The total domestic water demand in MSRB and BJLRB over 2017–2030 will increase significantly. The county-level Jimo City, where MSRB is located, was adjusted to Jimo District in 2017. Therefore, the urban population of MSRB increased greatly, resulting in a significant increase in domestic water demand of this basin. The main reason for the substantial increase of domestic water demand in BJLRB is that the government has taken a series of measures such as a surface water retention project, water plant expansion and rural tap water construction (Sun et al. 2014). The total domestic water demand in other basins over 2017–2030 will increase slowly. The reason is that the domestic water demand will increase correspondingly with the development of urbanization and the continuous improvement of people's living quality in the future. However, with the continuous enhancement of people's awareness of water conservation and the effective reuse of water resources, the domestic water demand will not increase too much (Song et al. 2018).

With the development of social economy, urban ecological water demand becomes more and more important and cannot be ignored. In this study, urban ecological water demand is the sum of road sprinkler water demand and green area water demand, which is mainly affected by road area, green area and water quota (Altunkaynak et al. 2005; Wei et al. 2015). As shown in Figure 3(d), the urban ecological water demand of each basin is predicted in the order of DGRB > FRB > NJLRB > BSRB > MSRB > BJLRB > BMRB > YRB > ZTRB. The urban ecological water demand of each basin over 2017–2030 shows an upward annual trend. This is because of the increasing green area and road area and the ecological environmental water demand of each basin.

Total water demand is the sum of industrial water demand, domestic water demand, agricultural water demand and urban ecological water demand. The total water demand of all basin shows an upward annual trend. Industrial water demand shows a declining annual trend, but domestic water demand increases significantly, while agricultural water demand and urban ecological water demand slowly rise. The proportion of domestic water demand in total water consumption is increasing in the future.

In this study, the total water supply in each basin mainly includes the total available surface water resources, total available ground water resources and volume of water recycled. As shown in Figure 4, the total water supply of each river basin was small (3.8579 × 10⁸ m³) in 2015. The reason is that the precipitation in Qingdao was relatively low which resulted in extreme drought in 2015. The total water supply of each basin over 2017–2030 is predicted in the order of DGRB > BSRB > FRB > BJLRB > ZTRB > NJLRB > MSRB > YRB > BMRB. The overall trend of regional distribution of water supply in all basins shows a decrease from the southeast coast to the northwest inland region, which is basically consistent with the spatial distribution of precipitation in Qingdao over the years. The supply source of river runoff is mainly atmospheric precipitation in Qingdao and the amount of precipitation determines the change of water resources. The spatial and temporal distribution of precipitation and the difference in precipitation process can lead to the changes of water resources. Therefore, the overall trend of annual runoff regional distribution is basically consistent with precipitation.

The UWRCC index of each basin was low in 2015 (Figure 5). It can be seen from the above analysis that there was little precipitation in Qingdao in 2015, the water supply in each basin was significantly less than the demand, and the contradiction between water supply and demand was obvious. The UWRCC indexes in all basins show a downward annual trend over 2017–2030. It means that the contradiction between water supply and demand will continue to worsen with the current social development trend. So the current development model can no longer meet the needs of sustainable socio-economic and water resources development of each basin in Qingdao.

As shown in Figure 5, the UWRCC indexes over 2017–2030 of FRB and BSRB remain in a normal state; the UWRCC indexes of MSRB and ZTRB are positive to normal to poor; the UWRCC indexes of BMRB, DGRB, YRB and NJLRB are normal to poor; and the UWRCC index of BJLRB is always poor. Overall, the UWRCC indexes of the southeast coast are higher than that of the northwest inland region. Precipitation and the level of economic development are also important factors influencing the development potential of UWRCC. Precipitation affects the water supply in each basin. The level of water resources utilization, sewage treatment capacity and recycling rate of reclaimed water are closely related to economic development (Cai et al. 2016).

In order to improve the UWRCC in Qingdao, the following suggestions are proposed: (1) Industrial structure optimization and upgrading. At present, agricultural water consumption and domestic water consumption account for a high proportion of water resource utilization in all river basins. Popularizing new water-saving irrigation technology and advocating the use of water-saving appliances are feasible ways to reduce urban water consumption; (2) Actively develop new water sources. As a coastal city, Qingdao can give full play to its advantages of being close to the ocean and expand the use of seawater.

In this study, Qingdao was divided into nine river basins according to the situation of water system. The UWRCC indexes of all basin were calculated and analyzed using the SD-AHP method. The results indicated that the total water demand of all basins show an upward annual trend over 2017–2030. Especially, the domestic water consumption show an obvious increasing trend. The total water supply in all basins, except BSRB, are on the rise. The regional distribution trend of water supply in all basins show a decrease from the southeast coast to the northwest inland region, which is basically consistent with the spatial distribution of precipitation in Qingdao over the years, and the UWRCC indexes in all basins will decline over 2017–2030. The contradiction between water supply and demand will continue worsening, which indicates that the current social development model can no longer meet the requirements of sustainable development of water resources in Qingdao. The UWRCC indexes of all basins can be improved through industrial structure optimization and upgrading and active development of new water sources in Qingdao.

This work was supported by the National Key Research and Development Program of China (NO.2018YFC0408000, 2018YFC0408004).

All relevant data are included in the paper or its Supplementary Information.