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Article

Early Warning and Joint Regulation of Water Quantity and Quality in the Daqing River Basin

by
Liang Chen
1,2,*,
Mingxiang Yang
1,2,*,
Yang Liu
3 and
Linjiang Nan
4
1
China Institute of Water Resources and Hydropower Research, Beijing 100038, China
2
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing 100038, China
3
School of Civil Engineering, Tianjin University, Tianjin 300350, China
4
College of Water Resource & Hydropower, Sichuan University, Chengdu 610065, China
*
Authors to whom correspondence should be addressed.
Water 2022, 14(19), 3068; https://doi.org/10.3390/w14193068
Submission received: 31 August 2022 / Revised: 21 September 2022 / Accepted: 23 September 2022 / Published: 29 September 2022
(This article belongs to the Special Issue Water Environmental System Analysis)
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Abstract

:
In the recent decades, the issue of water-resource security of the Daqing River Basin, which is one of the five major rivers in the Haihe River Basin, has become increasingly serious affected by climate change and human activities. In this paper, a dynamic simulation and early warning model of water quantity and quality in this basin based on the SWAT model was constructed to promote the implementation of water environment quality and safety bottom line in the Beijing-Tianjin-Hebei region. The results of the study are as follows: (1) When encountering a once-in-a-century rainstorm, the flood pressure of Zijingguan in the flood season is the highest, with the highest water level reaching 521.23 m, and the overall maximum runoff follows the order of Zijingguan > Fuping > Zhangfang. (2) When the NH3-N emissions are reduced by 37.64~85.10% in each month (based on the level in 2017), the water quality at the outlet of the basin can reach the standard, and the upper limit of NH3-N emissions is 504.5 t/m. (3) The regulation and control scheme seeking to “ensure the base flow with standard water quality” and “optimize NH3-N annual emission” is proposed in this paper. The NH3-N concentration at the outlet of all watersheds can reach the standard when the basic runoff of each sub-basin reaches 0.01 to 10.32 m3/s. In addition, concentrating the emission in July, August, and September and reducing the emission intensity of NH3-N in proportion can significantly reduce the monthly average NH3-N concentration (<1.99 mg/L) at the outlet section of the basin.

1. Introduction

The Daqing River is one of the five major rivers in the Haihe River Basin and an important water system in the Beijing-Tianjin-Hebei region [1]. The Xiong’an New Area is also located in the hinterland of the Daqing River Basin. The national strategy for “Integrated Development of Beijing-Tianjin-Hebei”, which was launched in 2014, further promoted the position of the Daqing River Basin in China’s major strategy. In recent decades, as a sensitive area affected by climate change and human activities [2], the climate and environmental conditions of the Daqing River Basin have undergone tremendous changes, and the issue of water resources security in the basin has become increasingly serious.
The river basin, as a type of freshwater resource, is generally put in a state of jeopardy in terms of quantity and quality due to the development in industry, agriculture, and urbanization. The quality of surface water in river basins plays a key role in water-resource security. So, measuring water quality, which is of high importance in water-resource management, is normally carried out in disparate ways. In this way, an accurate estimation of WQI (Water Quality Index) is one of the most challenging issues in the water-quality studies of surface-water resources [3,4]. Due to the intrinsic limitations of conventional models, Data-Driven Models (DDMs) have been frequently employed to assess the WQI for natural streams [5]. The DDMs-based WQI formulation is used to assess river in in the reliability-based probabilistic framework to consider the effect of any uncertainty and randomness in the input parameters. In addition, multiple-kernel support vector regression (MKSVR) algorithm was also proposed to estimate the hard-to-measure parameters from those that can be measured easily [6,7,8,9]. For solving the optimization problem of the MKSVR, the particle swarm optimization (PSO) algorithm was used.
With the increasing complexity of global water-resources problem [10,11], The single study of water resources’ allocation has gradually changed to a field of sustainable utilization of water resources which comprehensively considers water-quality constraints, water environmental pressures, etc. [12,13]. The principles and methods of water-resource-optimization management have been normally proposed by linear decision-making, multi-objective planning, and multi-level management [14,15]. On this basis, the joint application of multi-objective and multistage water resources’ optimal management has further been put forward [16,17], so as to realize the optimal regulation and allocation of water resources. With the development of complexity analysis and simulation optimization technologies, model simulation has become one of the important means of regulation and control of water resources [18].
At the watershed scale, numerous simulations and predictions research studies regarding the watershed hydrological process have been performed based on hydrological models and global climate change [19,20,21]. The rationality and effectiveness of water resources’ allocation has been analyzed to improve the available water resources [22,23]. The SWAT hydrological model with downscaling deviation analysis has been applied to explore the response of runoff change to different climate scenarios [24,25,26,27,28]. Furthermore, the coupled programming model [29,30,31] has often been used to study the water-resource allocation and system benefits under different water inflow conditions. At the regional scale, the management model of surface water, groundwater, and sewage was established, with the goal of maximizing the benefits, so as to provide the basic data for the regulation of water quantity and quality [32]. By using the stochastic fuzzy neural network model (SFNN model), the indicators of social economy, water quantity, and water quality in regional water resources systems were treated as fuzzy parameters, and the linkage relationship between water quantity and water quality was fully considered, so as to study the sustainable allocation of water resources [33,34]. The dynamic nonlinear programming model was applied to the integrated management of water quality and quantity [35], and the efficiency, fairness, and sustainability of the urban water supply were explored.
Other scholars have adopted the copula joint distribution model for the evaluation of water quality and quantity and proposed measures to strengthen regional pollutant control and treatment [36,37]. A joint probability distribution approach is used to describe relationship between water quality and flow discharge by multiple variables [38]. The copula theory has been widely applied in the field of hydrology and water environment in recent decades, as it can relate different univariate marginal distributions without changing the dependence structure and the initial data information in the transformation process [39,40]. Three copula types are commonly used: Archimedean copulas, which are relatively more convenient for symmetric relationships; Elliptic copulas, which are suitable for symmetric relationships; and Vine copulas, which can combine different copula types in low dimensions [41,42]. These copula functions can be applied to detect the joint change-point of the precipitation–discharge relationship and perform flood-risk analysis [43,44,45,46].
Unlike the previous studies on the joint distribution of water quantity and quality, the main novelty of this study is that it explored a new joint regulation scheme of water quantity and quality by developing a dynamic simulation and early warning model in the Daqing River Basin, based on the SWAT model. The water-level elevation and the rainstorm in different recurrence intervals were simulated. The relationship between the pollutant discharge amount and concentration in multiple pollutant discharge and meteorological scenes were further analyzed. These two major steps are involved in the detection of early warning values of water quantity and pollutant discharge in this basin, and they illustrate a suitable joint regulation scheme, using the proposed method. Therefore, it is of great significance to carry out joint early warning and regulation research by incorporating both water quantity and quality in the Daqing River Basin, which can serve as a useful and reliable tool for an impartial diagnosis and management of water resources. It will further promote the implementation of the three red lines of “water resources utilization upper line, ecological space protection red line and water environment quality and safety bottom line” in the Beijing-Tianjin-Hebei region [47].

2. Materials and Methods

2.1. Study Area

The Daqing River Basin is located in the middle of the Haihe River Basin on the North China Plain (113°40′ E–117°00′ E, 38° N–40° N) and has a total basin area of 43,060 km2, with the annual precipitation of 727 mm and annual runoff of 60.2 m3/s (Figure 1). Rainfall in the basin is unevenly distributed throughout the year, mostly occurring in the form of rainstorms, mainly falling from July to August, a period that accounts for more than 70% of the annual rainfall. During the flood season, due to the influence of cold and warm air flow and subtropical high, heavy rain forms easily, especially in the Fuping–Zijingguan area. The annual average temperature in the basin is 12.4 °C, and the average annual evaporation is 1309 mm. Soil types are divided into 16 categories and 36 subclasses—mainly cinnamon soil. Cultivated land accounts for 52.4%, followed by woodland, grassland, and construction land. The water pollution in the Daqing River Basin is complex and diverse, and the pollution mainly originates from domestic sewage discharge, pesticide and fertilizer application, and factory sewage discharge, with the standard rate of the river being lower than 30%.

2.2. Data Materials

The data covered in this study mainly include DEM, land use, soil type, meteorology, hydrology, and pollution (Table 1).

2.3. Method

In this study, the dynamic simulation model of water quantity and quality in the Daqing River Basin was constructed by using ArcSWAT2012, and its parameters were calibrated and verified. Next, the relationship curve of runoff and water level in the Daqing River Basin was established, and the variation law of runoff and water-level elevation in different recurrence intervals was analyzed by simulating the rainstorm in recurrence intervals, so as to realize the early warning of water quantity in the basin. The relationship between the pollutant discharge (point source and nonpoint source) amount and concentration in a river in multiple-pollutant discharge and meteorological scenes was analyzed, and the early warning value of pollutant discharge was proposed, so as to realize the early warning of water quality. On this basis, a joint regulation scheme of water quantity and quality by which to ensure the river-water quality was put forward. The schematic diagram of the model is shown in Figure 2.
For this research, in the model, the Daqing River Basin was divided into 35 sub-basins and 247 hydrological cells. The division of sub-basins is shown in Figure 3. Floods in the Daqing River Basin are mainly caused by rainstorms occurring in the flood season, with the rainstorm centers often appearing in Fuping and Zijingguan. Therefore, three representative hydrologic stations, namely Zhangfang (Sub-basin 4), Zijingguan (Sub-basin 6), and Fuping (Sub-basin 25), were selected in this paper for water-quantity early warning, and Taitou (sub-basin 14) was selected as the representative station of basin outlet for water-quality early warning and joint regulation.

3. Results and Discussion

3.1. Model Calibration and Verification

In this study, the runoff parameters were checked by the measured runoff data of Zhangfang, Zijingguan, and Fuping in the Daqing River Basin, and the water-quality parameters were checked by the pollutant amount of the Taitou Station. The calibration period of Zhangfang Station was set as 2006–2010, and the verification period was set as 2011–2013. The calibration period of Zijingguan and Fuping Stations was set as 2006–2012, and the verification period was set as 2013–2016. The calibration period of pollutants at Taitou Station was set as 2013–2014, and the verification period was set as 2015–2016. In this study, the determination coefficient (R2) [48,49] and Nash–Sutcliffe efficiency coefficient (NSE) [50] were selected to evaluate the simulation effect of the model. The calibration and validation results of the model are shown in Table 2.
It can be seen from Table 2 that the monthly simulation results of stations in Zhangfang, Zijingguan, and Fuping are all “satisfactory” (NSE > 0.5), while those of NH3-N at Taitou Station are “very good” (0.6 < R2 ≤ 0.7) [51,52]. By comparing and analyzing the simulated and measured values of monthly runoff at three stations, respectively, in Zhangfang, Zijingguan and Fuping (Figure 4), and the simulated and measured values of water quality at Taitou Station (Figure 5), it can be seen that the simulated and observed values have the same trend, the model fitting effect is good, and the model can be used for the further research.

3.2. Water Quantity and Water Quality Early Warning

3.2.1. Water Quantity Early Warning

Due to the fact that Zijingguan and Zhangfang are driven by Yuxian meteorological data, while Fuping County is driven by Lingqiu meteorological data, the data of the Yuxian and Lingqiu meteorological stations were used in this paper as the meteorological data required for scene simulation. By analyzing the measured rainfall data of the Daqing River Basin from 1951 to 2019, the daily rainfall in the recurrence intervals of the Yuxian and Lingqiu meteorological stations was calculated and determined (Table 3), which was taken as the input item of rainfall scene, and the daily average runoffs of Zijingguan, Zhangfang, and Fuping were simulated.
In view of the characteristics of short rainstorm duration and concentrated flood in the Daqing River Basin, the daily average runoff is unable to meet the function of water quantity early warning. In this study, the fitting analysis was further performed regarding the relationship between the daily average runoff and maximum flow and the relationship between water level and flow at three stations, and the results are shown in Figure 6 and Figure 7.
Table 4 shows the response relationship of rainfall, runoff, and water level at Zijingguan, Zhangfang, and Fuping under different rainfall recurrence intervals. It can be seen that, under the same rainfall recurrence interval, there are some differences in the warning values of maximum runoff and maximum water level at different stations. When encountering a once-in-a-century rainstorm, the water level at Zijingguan, Zhangfang, and Fuping reached 521.23 m, 106.12 m, and 253.85 m, respectively, and the flood pressure of Zijingguan during flood season was the highest.

3.2.2. Water-Quality Early Warning

According to the water-quality-monitoring data of Taitou Station from 2015 to 2019, as well as the Environmental Quality Standards for Surface Water (GB3838-2002), the monthly average water quality of Taitou Station for many years was Inferior Class V, and the water quality of all townships in the Daqing River is Class V. The overall water quality is quite poor [53]. NH3-N, the main pollutant according to monitoring data in long time series, is selected as the control pollutant for early warning of water quality in the Daqing River Basin. According to the Langfang Key Rivers Water Quality Standards Plan (Revised Edition) issued in 2019, the local authorities aim to keep the water quality of the Daqing River Basin stable and for it to remain as Class V water, i.e., the NH3-N concentration is not to exceed 2 mg/L.
Through the analysis of historical runoff data, 2012, 2017, and 2000 were selected as the representative years of high-, normal-, and low-flow years, respectively. Based on the current year (2017), the point-source discharge was increased or decreased (+50~−50%), and a scene was set every 10%. A total of 33 combined scenes of rainfall and point-source discharges were set (Table 5), so as to simulate the relationship between the emission intensity of pollution sources and the NH3-N concentration in rivers in different level years (Figure 8).
The scenario simulation results show that, when the point-source discharge increases by 50% (based on the emissions in 2017), 75% (January–June and October–December), and 75% (January–June and October–December), then the monthly average water quality in both high- and normal-flow years is Inferior Class V. In low-flow years, due to the lower amount of rainfall, some rivers stopped flowing, thereby blocking the movement of pollutants, and 58.3% of the monthly average water quality reached Inferior Class V. When the point-source discharge did not increase, then 58.3% (January, February, March, May, June, November, and December), 58.3% (January, February, March, April, June, November, and December), and 58.3% (January, February, March, April, May, November, and December) of the monthly average water quality reached Inferior Class V in the high-, normal-, and low-flow years, respectively. When the point-source discharge was reduced by 50%, then 41.7% (January, February, March, June, and December), 50% (January, February, March, April, June, and December), and 58.3% (January, February, March, April, May, November, and December) of the monthly average water quality reached Inferior Class V in high-, normal-, and low-flow years, respectively.
Due to the lower amount of natural runoff in the Daqing River Basin, the NH3-N emission is high. For example, when the current discharge is reduced to 50% in a normal-flow year, the monthly average NH3-N concentration still reaches Inferior Class V. However, if the emission is reduced to 85.1%, then the monthly average NH3-N concentration can reach the standard. Therefore, with the aim of reaching the standard of NH3-N concentration at the outlet of the basin (2 mg/L), the early warning values of NH3-N emission in each month and sub-basin in different level years were determined (Table A1 in Appendix A) according to the previous fitting relationship between the emission intensity of pollution sources and NH3-N concentration. The simulation results show that the upper emission limit of July, August, and September in high-, normal-, and low-flow years, respectively, accounted for 68.69%, 68.74%, and 75.05% of the annual emission, while the emission upper limits of July, August, and September in the same level years were significantly higher than those in other months. In addition, for different level years, the upper limit of NH3-N emission was greatly affected by river cutoff, and the total emission amounts in low-flow years were 13.56% and 6.08% higher than those in high- and normal-flow years, respectively.

3.3. Joint Regulation of Water Quantity and Quality

3.3.1. Ensuring the Base Flow with Standard Water Quality

The flow with standard water quality is the lowest flow of the river at which the water quality of the river section must be maintained to meet the local standards [54]. Under the current emission scenario, the monthly average NH3-N concentration in the Daqing River Basin from July to October did not exceed the standard. In these four months, the rainfall was abundant, the river runoff was high, and the river channel’s pollutant-holding capacity was relatively large. In other months, the runoff was small, and the river channel’s pollutant-holding capacity was poor. Therefore, under the premise of not reducing the discharge, both ensuring a certain river flow and improving the river’s pollutant-holding capacity can effectively reduce the river pollution degree [55].
In this paper, the same temperature, relative humidity, wind speed, and sunshine intensity were given, and different daily rainfall scenarios were set to simulate the minimum flow of 35 sub-basins (Table 6). The simulation results show that Sub-basin 5 has the lowest demand for water quality, while Sub-basin 13 has the highest. The NH3-N concentration at the outlet of all watersheds can reach the standard when the basic runoff of each sub-basin reaches 0.01 to 10.32 m3/s.

3.3.2. Optimize NH3-N Annual Emission

The Daqing River Basin has abundant rainfall, high runoff, a relatively large water environment capacity, and a strong river’s pollutant-holding capacity in summer and autumn, while the opposite is true in spring and winter. The reasonable adjustment of the annual discharge of pollutants and optimization measures of discharge intensity based on water environmental capacity can be applied to effectively utilize the river’s pollutant-holding capacity in summer and autumn, as well as to ensure that the water quality at the outlet of the basin reaches the standard [55].
Therefore, in this study, on the premise of not increasing the amount of pollutant discharge (based on the emissions in 2017), the warning value of NH3-N emissions in each month in different level years under the water quality standards was reduced in proportion. In addition, the concentration of NH3-N after optimized emissions and the maximum and minimum emissions of NH3-N under the water-quality standards were simulated and calculated according to the optimized emission-intensity and meteorological-data scenarios.
Through simulation (Table 7), taking the high-flow years as an example, the average runoff of the river is the highest in July and August, i.e., 42.57 m3/s and 42.69 m3/s, respectively, and the optimized NH3-N discharge amount is 3004.87 t and 3118.49 t, respectively. At this time, the discharge intensity of NH3-N is still able to meet the Class Ⅱ water standard, which can fully meet the water-quality requirements of the management department. In the normal-flow years, it is found that the water qualification rate of NH3-N in each sub-basin reached 57.14% in July, August, and September, and the runoff accounts for 57.29% of the total annual runoff. By optimizing the discharge intensity of NH3-N in the year, the average monthly NH3-N concentration at the outlet section of the basin was significantly reduced (<1.99 mg/L), and it met the Class V water standard. The simulation results of low-flow years show a similar condition that concentrating the discharge amount in July, August, and September can effectively utilize the river’s pollutant-holding capacity, reduce the concentration of pollutants in the river, ensure the river’s water quality meets the standard, and promote the ecological improvement surrounding the river, thus proving it to be a strong regulation measure [56,57,58].

4. Conclusions

Water quantity and quality are two key regulation factors affecting the performance of integrated watershed management. Conventional water-resource assessment and simulation of rivers in the previous literature often dealt with water quantity and quality separately. The nonmonotonic relationship between the water quality and flow discharge of a river, especially in watersheds with significant human activity impacts and high spatiotemporal variations in flows, such as the Daqing River Basin, is often ignored in contrast to near-natural conditions. More specifically, the previous literature on the analysis of water quantity and quality based on copula functions often dealt with the uncertainty analysis of multivariable correlations, risk analysis for water quality, or forecasting of water quality.
The present results indicate that the proposed method can take into account the near-natural law between water quantity and quality to make a more reliable simulation for integrated water resources’ management and regulation.
When encountering a once-in-a-century rainstorm, the flood pressure of Zijingguan in the flood season is the highest, with the highest water level reaching 521.23 m, and the overall maximum runoff follows the order of Zijingguan > Fuping > Zhangfang. In addition, when the hydrological station determines the water-level-warning value, the corresponding rainfall can be obtained based on the relationship between rainstorm and water level [59]. Furthermore, when the weather station monitors a certain amount of rainfall, it can also send corresponding water-level-warning information to the hydrological station.
According to the relationship between emission ratio and NH3-N concentration at the outlet of the basin, when the monthly NH3-N emissions in different level years were reduced by 37.64~85.10% (based on the emissions in 2017), the water quality at the outlet of the basin could reach the standard, and the upper limit of NH3-N emissions was 504.5 t/m. The upper emission limits in July, August, and September in high-, normal-, and low-flow years accounted for 68.69%, 68.74%, and 75.05% of the whole year, respectively, and the upper emission limits in July, August, and September in the same level year were significantly higher than those in other months.
In this paper, the regulation-and-control method of basic flow to ensure that the water quality is up to standard was proposed. On the premise of not reducing emissions, we could ensure a certain river flow and improve the river’s pollutant-holding capacity. The simulation results showed that the NH3-N concentration at the outlet of all watersheds could reach the standard when the basic runoff of each sub-basin reaches 0.01 to 10.32 m3/s.
The optimization measures of NH3-N annual emissions based on water environmental capacity were proposed. By optimizing the annual emission intensity of NH3-N, the monthly average concentration of NH3-N in the outlet section of the basin was significantly reduced (<1.99 mg/L), and it reached the water standard of Class V and above. In addition, the pollutant-holding capacity of rivers in summer and autumn could be effectively utilized, and that the water quality at the outlet of the river basin reaches the standard could be ensured by concentrating the discharge in July, August, and September.

Author Contributions

Data curation, L.C. and Y.L.; formal analysis, L.C.; funding acquisition, M.Y.; investigation, M.Y., Y.L., and L.N.; methodology, M.Y.; validation, Y.L.; writing—original draft, L.C.; writing—review and editing, L.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science and Technology Major Project for Water Pollution Control and Treatment (No. 2018ZX07111005), the National Science Foundation for Young Scientists of China (No. 51709271), and the Lift Program for Young Scientists of IWHR (SD0145B102021).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Early warning value of upper limit of NH3-N monthly emission in each sub-basin in different level years (t).
Table A1. Early warning value of upper limit of NH3-N monthly emission in each sub-basin in different level years (t).
SUBJan.Feb.Mar.Apr.MayJun.Jul.Aug.Sep.Oct.Nov.Dec.
High-flow years11.201.080.763.341.960.4318.2310.216.873.922.381.02
25.595.043.5515.529.131.9884.8047.4731.9518.2211.064.77
31.421.280.903.932.310.5021.4912.038.104.622.801.21
47.316.584.6320.2811.932.58110.7862.0241.7323.8014.456.23
50.000.000.000.000.000.000.010.010.000.000.000.00
64.283.862.7111.886.991.5164.9236.3524.4613.958.473.65
710.529.486.6729.1817.163.72159.4189.2460.0634.2420.798.96
85.605.043.5515.539.131.9884.8447.4931.9618.2211.074.77
91.441.300.913.992.350.5121.8012.218.214.682.841.23
107.646.884.8421.2012.472.70115.8064.8343.6224.8715.106.51
1119.3417.4212.2653.6631.566.84293.12164.10110.4362.9738.2316.48
1215.0413.559.5341.7324.545.32227.94127.6185.8748.9729.7312.81
1311.069.967.0130.6818.053.91167.6093.8363.1436.0021.869.42
1414.5013.079.1940.2423.675.13219.80123.0582.8147.2228.6712.35
151.811.631.155.012.950.6427.3915.3310.325.883.571.54
1611.9210.747.5633.0819.464.21180.74101.1868.0938.8323.5710.16
1720.3218.3112.8856.3933.177.18308.05172.46116.0666.1740.1817.31
185.474.933.4715.198.931.9382.9646.4431.2517.8210.824.66
198.147.335.1622.5913.292.88123.3969.0846.4926.5116.096.94
209.258.345.8625.6715.103.27140.2478.5152.8330.1318.297.88
212.312.081.466.413.770.8234.9919.5913.187.524.561.97
225.565.013.5315.449.081.9784.3347.2131.7718.1211.004.74
2332.7729.5320.7790.9453.4911.59496.77278.11187.15106.7264.7927.92
242.262.041.436.283.700.8034.3219.2112.937.374.481.93
2539.6735.7525.14110.0864.7514.02601.39336.67226.56129.1978.4433.80
261.341.210.853.732.190.4820.3711.417.684.382.661.15
271.691.521.074.682.750.6025.5714.319.635.493.341.44
286.605.944.1818.3010.762.3399.9755.9737.6621.4813.045.62
293.302.972.099.155.381.1749.9627.9718.8210.736.522.81
3015.2513.749.6642.3124.895.39231.16129.4187.0949.6630.1512.99
310.160.150.100.460.270.062.491.390.940.530.320.14
3236.9033.2523.39102.3960.2213.04559.33313.13210.72120.1572.9531.44
335.154.643.2614.298.401.8278.0643.7029.4116.7710.184.39
345.314.783.3614.728.661.8880.4245.0230.3017.2810.494.52
351.511.360.964.202.470.5322.9212.838.634.922.991.29
Normal-flow years11.181.141.151.443.790.529.4415.2913.094.992.160.85
25.485.285.336.6917.612.4043.9171.1460.8623.2010.063.95
31.391.341.351.704.460.6111.1318.0315.425.882.551.00
47.166.906.968.7423.013.1457.3792.9379.5130.3013.145.16
50.000.000.000.000.000.000.010.010.010.000.000.00
64.194.044.085.1213.491.8433.6254.4646.6017.767.703.03
710.309.9310.0212.5833.114.5282.55133.73114.4243.6118.917.43
85.485.285.336.6917.622.4143.9371.1760.8923.2110.063.95
91.411.361.371.724.530.6211.2918.2915.655.962.591.02
107.487.217.289.1424.053.2859.9797.1483.1131.6813.745.40
1118.9318.2518.4323.1360.898.31151.80245.90210.3980.1834.7713.66
1214.7214.1914.3317.9947.356.46118.04191.22163.6162.3527.0410.63
1310.8310.4410.5413.2234.824.7586.80140.60120.3045.8519.887.81
1414.2013.6913.8217.3445.666.23113.83184.39157.7660.1326.0710.25
151.771.711.722.165.690.7814.1822.9819.667.493.251.28
1611.6711.2511.3614.2637.545.1293.60151.62129.7349.4421.448.42
1719.9019.1819.3724.3163.998.73159.53258.43221.1184.2736.5414.36
185.365.175.226.5517.232.3542.9669.5959.5422.699.843.87
197.977.687.769.7425.633.5063.90103.5288.5733.7514.645.75
209.068.738.8211.0729.133.9872.63117.65100.6638.3616.646.54
212.262.182.202.767.270.9918.1229.3625.129.574.151.63
225.455.255.306.6517.522.3943.6770.7560.5323.0710.003.93
2332.0930.9331.2339.20103.2014.08257.26416.75356.56135.8958.9323.16
242.222.142.162.717.130.9717.7728.7924.639.394.071.60
2538.8537.4537.8147.45124.9317.05311.44504.51431.65164.5171.3428.03
261.321.271.281.614.230.5810.5517.0914.625.572.420.95
271.651.591.612.025.310.7213.2421.4518.356.993.031.19
286.466.226.287.8920.772.8351.7783.8771.7627.3511.864.66
293.233.113.143.9410.381.4225.8841.9235.8613.675.932.33
3014.9314.3914.5318.2448.026.55119.71193.92165.9163.2327.4210.77
310.160.150.160.200.520.071.292.091.790.680.300.12
3236.1334.8335.1644.13116.1915.86289.66469.22401.46153.0166.3526.07
335.044.864.916.1616.212.2140.4265.4856.0321.359.263.64
345.195.015.066.3516.712.2841.6567.4757.7222.009.543.75
351.481.431.441.814.760.6511.8719.2316.456.272.721.07
Low-flow years11.701.790.501.521.491.5118.9613.3211.533.990.551.50
27.918.342.357.086.957.0188.1961.9453.6318.542.566.98
32.012.110.601.791.761.7822.3515.7013.594.700.651.77
410.3410.893.079.259.089.16115.2180.9170.0724.233.349.12
50.000.000.000.000.000.000.010.010.010.000.000.00
66.066.381.805.425.325.3767.5247.4241.0614.201.965.34
714.8815.674.4113.3113.0713.19165.79116.43100.8334.864.8113.12
87.928.342.357.086.957.0288.2361.9753.6618.552.566.98
92.042.140.601.821.791.8022.6815.9313.794.770.661.79
1010.8111.383.219.679.499.58120.4384.5873.2425.323.499.53
1127.3628.818.1224.4724.0224.25304.85214.10185.4064.118.8524.12
1221.2822.416.3119.0318.6818.85237.06166.49144.1749.856.8818.76
1315.6416.474.6413.9913.7413.86174.31122.42106.0136.655.0613.79
1420.5221.616.0918.3518.0218.18228.60160.54139.0348.076.6318.09
152.562.690.762.292.242.2728.4920.0117.325.990.832.25
1616.8717.775.0015.0914.8114.95187.97132.01114.3239.535.4614.87
1728.7530.288.5325.7125.2525.48320.38225.00194.8567.379.3025.35
187.748.152.306.926.806.8686.2860.5952.4718.142.506.83
1911.5212.133.4210.3010.1110.21128.3390.1378.0526.993.7210.15
2013.0913.793.8811.7111.4911.60145.85102.4388.7030.674.2311.54
213.273.440.972.922.872.8936.3925.5622.137.651.062.88
227.878.292.347.046.916.9887.7161.6053.3418.442.556.94
2346.3748.8313.7641.4740.7241.09516.65362.84314.21108.6414.9940.88
243.203.370.952.862.812.8435.6925.0721.717.511.042.82
2556.1359.1116.6550.2049.2949.74625.45439.25380.38131.5218.1549.49
261.902.000.561.701.671.6921.1914.8812.894.460.611.68
272.392.510.712.132.102.1226.5918.6816.175.590.772.10
289.339.832.778.348.198.27103.9773.0263.2321.863.028.23
294.664.911.384.174.104.1351.9636.4931.6010.931.514.11
3021.5822.726.4019.3018.9519.12240.41168.84146.2150.556.9819.02
310.230.240.070.210.200.212.591.821.570.540.080.20
3252.2154.9815.4946.6945.8446.27581.70408.53353.78122.3216.8846.02
337.297.672.166.526.406.4681.1857.0149.3717.072.366.42
347.517.912.236.716.596.6583.6458.7450.8717.592.436.62
352.142.250.631.911.881.9023.8416.7414.505.010.691.89

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Water 14 03068 g001 550
Figure 1. Schematic diagram of the location of the Daqing River Basin.
Figure 1. Schematic diagram of the location of the Daqing River Basin.
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Figure 2. The dynamic simulation model of water quantity and quality in the Daqing River Basin.
Figure 2. The dynamic simulation model of water quantity and quality in the Daqing River Basin.
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Figure 3. Schematic diagram of sub-basin division in the Daqing River Basin.
Figure 3. Schematic diagram of sub-basin division in the Daqing River Basin.
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Figure 4. Measured and simulated values of runoff (stations in Zhangfang, Zijingguan, and Fuping).
Figure 4. Measured and simulated values of runoff (stations in Zhangfang, Zijingguan, and Fuping).
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Figure 5. Measured and simulated values of NH3-N (Taitou Station).
Figure 5. Measured and simulated values of NH3-N (Taitou Station).
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Figure 6. Relationship between daily average flow and maximum flow.
Figure 6. Relationship between daily average flow and maximum flow.
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Figure 7. Relationship between water level and flow.
Figure 7. Relationship between water level and flow.
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Figure 8. Relationship between the emission intensity of pollution source and NH3-N concentration in each month (taking the normal-flow year as an example).
Figure 8. Relationship between the emission intensity of pollution source and NH3-N concentration in each month (taking the normal-flow year as an example).
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Table
Table 1. Data sources and treatment.
Table 1. Data sources and treatment.
Data TypesInitial Source DataProcessing Suitable to the Model/Study
ProcessingData Display
Basic data for SWAT DEMASTER GDEM V3 \
Land useRemote sensing monitoring data of land use status in China in 2010Reclassification, land-use grid data, land-use index table\
Soil typeHarmonized World Soil Database (HWSD),
China Soil Data Set (v1.1)		Soil-distribution grid file, soil parameter database, soil-type index table\
MeteorologyDaily scale data of National Meteorological Data Service Center (v3.0)Weather generator parameters (R language programming calculation)Daily scale data of precipitation, temperature, wind speed, relative humidity, and sunshine intensity from 1 January 2011 to 31 December 2017
Data required for researchHydrologyHydrologic yearbook of the Haihe River BasinApplicable format conversion of SWATCUP2019Monthly average flow of each station in the Daqing River Basin from 2006 to 2016
Pollutant dischargeStatistics related to pollutant discharge in the Daqing River Basin Monthly scale data in the Daqing River Basin from 2010 to 2017
Table
Table 2. Calibration and validation results of runoff and water quality in the model.
Table 2. Calibration and validation results of runoff and water quality in the model.
StationsCalibration PeriodVerification Period
R2NSER2NSE
Zhangfang_FLOW0.610.560.720.52
Zijingguan_FLOW0.720.610.880.86
Fuping_FLOW0.760.70.840.72
Taitou_NH3-N0.660.610.70.69
Table
Table 3. Scenes of rainfall recurrence interval.
Table 3. Scenes of rainfall recurrence interval.
Rainfall Recurrence IntervalsMaximum Daily in Yuxian (mm)Maximum Daily Rainfall in Lingqiu (mm)
Once every 5 years46.3856.82
Once every 10 years53.2065.44
Once every 20 years62.6771.78
Once every 50 years81.4377.58
Once every 100 years102.5880.56
Table
Table 4. Runoff and water-level-warning results of different rainfall recurrence intervals at each station.
Table 4. Runoff and water-level-warning results of different rainfall recurrence intervals at each station.
Hydrological StationsRainfall Recurrence IntervalRainfall
(mm)		Simulated Runoff (m³/s)Calculated Maximum Runoff (m³/s)Calculated Maximum Water Level (m)
ZijingguanOnce every 5 years46.3899.8471.52519.04
Once every 10 years53.20226.8571.24519.25
Once every 20 years62.67571.9923.82519.87
Once every 50 years81.431254.01970.95521.23
Once every 100 years102.582094.03899.75522.99
ZhangfangOnce every 5 years46.3893.21224.31104.72
Once every 10 years53.20102234.19104.75
Once every 20 years62.67260.6417.81105.15
Once every 50 years81.43450.4650.77105.55
Once every 100 years102.58762.21064.75106.12
FupingOnce every 5 years56.82579.94886.10252.65
Once every 10 years65.44747.011112.33252.85
Once every 20 years71.78834.281246.03252.95
Once every 50 years77.581133.211784.84253.32
Once every 100 years80.561582.352829.58253.85
Table
Table 5. Combined scenarios of rainfall and point-source discharge.
Table 5. Combined scenarios of rainfall and point-source discharge.
Scenario No.RainfallIntensity of Point-Source DischargeScenario No.RainfallIntensity of Point-Source Discharge
1High-flow years+50%19High-flow years−10%
2Normal-flow years+50%20Normal-flow years−10%
3Low-flow years+50%21Low-flow years−10%
4High-flow years+40%22High-flow years−20%
5Normal-flow years+40%23Normal-flow years−20%
6Low-flow years+40%24Low-flow years−20%
7High-flow years+30%25High-flow years−30%
8Normal-flow years+30%26Normal-flow years−30%
9Low-flow years+30%27Low-flow years−30%
10High-flow years+20%28High-flow years−40%
11Normal-flow years+20%29Normal-flow years−40%
12Low-flow years+20%30Low-flow years−40%
13High-flow years+10%31High-flow years−50%
14Normal-flow years+10%32Normal-flow years−50%
15Low-flow years+10%33Low-flow years−50%
16High-flow years0
17Normal-flow years0
18Low-flow years0
Table
Table 6. Minimum flow of guaranteed water quality in sub-basins.
Table 6. Minimum flow of guaranteed water quality in sub-basins.
Sub-BasinsRunoff (m3/s)Sub-BasinsRunoff (m3/s)
10.16194.56
21.86205.27
31.58214.93
41.60220.80
50.01236.82
60.63240.26
71.48255.68
82.64262.43
90.94271.00
101.75283.45
113.50290.56
126.91302.60
1310.32310.02
144.29326.57
150.16331.51
161.64344.71
178.71350.62
182.66
Table
Table 7. Pollutant discharge amount and NH3-N concentration after optimization.
Table 7. Pollutant discharge amount and NH3-N concentration after optimization.
MonthHigh-Flow YearsNormal-Flow YearsLow-Flow Years
Discharge Amount (t)Simulated Runoff (m3/s)NH3-N (mg/L)Discharge Amount (t)Simulated Runoff (m3/s)NH3-N (mg/L)Discharge Amount (t)Simulated Runoff (m3/s)NH3-N (mg/L)
Jan.0.760.211.7496.131.471.995.230.701.82
Feb.10.090.581.01116.750.591.9424.763.631.94
Mar.53.532.071.58231.810.361.71668.9027.921.43
Apr.787.256.981.30290.944.850.691364.4721.571.70
May565.470.591.81765.964.510.90851.3610.111.12
Jun.1212.943.730.84104.540.350.36532.996.021.79
Jul.3004.8742.570.392216.6724.810.871663.182.090.77
Aug.3118.4942.690.343093.5069.981.272906.433.430.64
Sep.1693.7534.630.712646.5589.791.361840.6134.420.72
Oct.565.2917.391.811008.6688.241.36667.7013.471.43
Nov.112.2415.301.41437.4128.391.51598.9612.371.59
Dec.3.852.931.59119.624.151.953.960.221.61
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MDPI and ACS Style

Chen, L.; Yang, M.; Liu, Y.; Nan, L. Early Warning and Joint Regulation of Water Quantity and Quality in the Daqing River Basin. Water 2022, 14, 3068. https://doi.org/10.3390/w14193068

AMA Style

Chen L, Yang M, Liu Y, Nan L. Early Warning and Joint Regulation of Water Quantity and Quality in the Daqing River Basin. Water. 2022; 14(19):3068. https://doi.org/10.3390/w14193068

Chicago/Turabian Style

Chen, Liang, Mingxiang Yang, Yang Liu, and Linjiang Nan. 2022. "Early Warning and Joint Regulation of Water Quantity and Quality in the Daqing River Basin" Water 14, no. 19: 3068. https://doi.org/10.3390/w14193068

APA Style

Chen, L., Yang, M., Liu, Y., & Nan, L. (2022). Early Warning and Joint Regulation of Water Quantity and Quality in the Daqing River Basin. Water, 14(19), 3068. https://doi.org/10.3390/w14193068

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