Water shortage and water pollution are two prominent issues in North China. Understanding hydrological cycle and water quality changes in response to pollution control measures is fundamental for a better water management there. Using coupled MIKE SHE/MIKE 11 modeling, various hydrological components in Yanghe Basin in a semiarid area of North China were quantified for three typical hydrological years and concentrations of COD and TP in a national monitoring section of Yanghe were evaluated with/without pollution control measures. The modeling results show that the underground water storage of Yanghe Basin becomes depleted due to evapotranspiration compensation and groundwater utilization regardless of hydrological condition, indicating an unsustainable in-situ water resource utilization. Water quality goals set for Yanghe (COD ≤ 20 mg/L and TP ≤ 0.2 mg/L) can hardly be achieved if pollution control measures are not taken, especially for a dry hydrological year. Depending on hydrological conditions, non-point source control technology-related projects in a 109-km2 village and a 7-km river-channel wetland in mainstream of Yanghe will have a positive effect or negligible effect on water quality improvement. To meet water quality goals, implementation of three wetlands is an effective and economic way.

  • A coupled MIKE SHE/MIKE 11 model is constructed for Yanghe Basin in North China.

  • Yanghe Basin undergoes unsustainable water resource utilization, with evapotranspiration surpassing rainfall.

  • Demonstration water projects on a small scale have either a positive or negligible effect on water quality of Yanghe.

  • Implementation of three river-channel wetlands is an effective and economic way to achieve water quality goals.

Graphical Abstract

Graphical Abstract
Graphical Abstract

North China, including Capital City Beijing, Municipality Tianjin, Hebei Province, Shanxi Province and Inner Mongolia Autonomous Region, plays a very important role in China in terms of politics, economy and culture. It is located within the semiarid and semi humid temperate climate zones, with droughts occurring more frequently than floods, and faces a serious water shortage. Under this background, the development and utilization of water resources in North China is intensive. The largest water system of North China, the Hai River system, is exploited to an extent beyond 90%, and sharp decline in both stream flow and groundwater level has been observed after decades of overexploiting (Xia 2004). The facts indicate that the impact of human disturbance on the water cycle is considerable. Qualification of current hydrological water balance components can help to better understand the water cycle and water resource status, lighting the way for a better water resource management and water shortage solution.

In addition to water shortage, water pollution is also severe in North China. The 2016 data showed that only 31% of water function zones within Hai River Basin complied with the corresponding water quality standards and about half the length of the Hai River was categorized as highly polluted. To improve the water environment in these basins, as well as realize energy saving/emission reduction, China has initiated the ‘National Science and Technology Major Program of Water Pollution Control and Treatment’ (shortened to the ‘National Water Program’ in the study) over 2008–2020. To date, various research projects have been conducted and pilot demonstration projects have been implemented in major basins like the Hai River Basin. By the end of 2020, assessment and evaluation of these water quality improvement projects on the watershed scale are necessary, which will lay the fundamental basis for a nation-wide water pollution control and treatment.

As an important tributary of the Yongding River flowing through Beijing in the Hai River Basin, Yanghe is the mother river of Zhangjiakou City in Hebei Province. The semiarid Yanghe Basin is the core zone for social and economic development of Zhangjiakou City. As a result, the hydrological cycle and water environment status in Yanghe Basin do not only influence local development, but also are a concern for water conservation in the Beijing–Tianjin–Hebei region and water quality improvement in Hai River Basin. Based on the MIKE SHE model (Danish Hydraulic Institute 2009), this study aimed to: (1) investigate hydrological water balance components under different precipitation conditions in Yanghe Basin, and (2) evaluate water quality improvement of Yanghe for different implementation scenarios of water quality control measures.

Study area

The total length of the Yanghe is 278 km, with its mainstream 106 km, running through the most developed areas of Zhangjiakou City (Figure 1). In Huailai County, Yanghe meets another stream, Sangganhe, and afterwards the converged flow passes by a national monitoring section Bahaoqiao before entering Guanting Reservoir, the largest backup drinking water source for Beijing. Yanghe Basin is between 39°59′42″–41°15′3″N latitude and 113°29′32″–155°43′40″E longitude (Figure 1(a)), covering an area of 1.62 × 104 km2. The main land-use type and soil type in the basin are agricultural land and Cambisols, respectively, and the elevation variation is about 2,000 m. Yanghe Basin has a semiarid climate which is characterized by high temperature variability, long insolation duration, small amounts of rain and intensive evaporation.

Figure 1

Location of study area: (a) Yanghe Basin, (b) demonstration zone and wetlands.

Figure 1

Location of study area: (a) Yanghe Basin, (b) demonstration zone and wetlands.

Close modal

Several pilot demonstration projects (Figure 1(b)) sponsored by the National Water Program are under construction in Zhangjiakou City, in order to cope with an increase in pollutant discharge to the Yanghe (Figure 2) under increasingly strict water quality requirements. The loads of COD and TP to the Yanghe have increased from 11.44 thousand tons and 0.16 thousand tons in 2014 to 18.14 thousand tons and 0.23 thousand tons in 2019, respectively. In addition, water quality requirements for the national monitoring section Bahaoqiao were to be raised, from the previous Grade IV water quality standards (China's State Environment Protection Agency 2002) to the Grade III water quality standards (that is, COD <20 mg/L and TP < 0.2 mg/L) by 2020. Aiming at a better water quality, these demonstration projects involve a demonstration zone applying various non-point pollution control technologies and a demonstration river-channel wetland in the middle part of Yanghe for both point and non-point pollution control (Institute of Environmental and Sustainable Development in Agriculture 2017). The demonstration zone covers a 109-km2 village in the Xuanhua District, promoting non-point pollution control technologies for domestic sewage and agricultural pollution in the village. In particular, innovative sewage treatment and reclamation have been developed for a zero discharge to Yanghe. Precision agriculture practices have been established for less water demand from Yanghe and less agricultural pollution to Yanghe. Ecological ditch/riverbank design has been carried out for a further reduction in agricultural pollution discharge. The 7-km-long demonstration wetland aims to achieve a nutrients removal rate up to 40%, with a green area of 8,100 m2, adopting both emergent subtype (e.g. Phragmites australis) and submergent subtype (e.g. Hydrilla verticillata). To date, the effectiveness of these projects on water quality improvement of Yanghe remains unknown, not to mention the performance of their further application over the basin when a large-scale project implementation is considered.

Figure 2

Spatial distributions of pollution loads to Yanghe: (a) COD, (b) TP.

Figure 2

Spatial distributions of pollution loads to Yanghe: (a) COD, (b) TP.

Close modal

Overview of MIKE SHE/MIKE 11

MIKE SHE is a physically based, distributed hydrologic model with a modular structure, which enables its water movement module to be coupled with the channel flow simulation component of MIKE 11 (Danish Hydraulic Institute 2009). The related equations describing major hydrological processes such as evapotranspiration, overland flow, unsaturated flow, saturated groundwater flow and channel flow are as follows.

In the Evapotranspiration (ET) module, the Kristensen and Jensen method is used:
(1)
where Ecan is the canopy evaporation, Imax is the interception storage capacity of the vegetation, Ep is the potential evapotranspiration, Δt is the time step length for the simulation, Eat is the actual transpiration, f1(LAI) is a function based on the leaf area index (the ratio of area of leaves to area of the ground), f2(θ) is a function based on the soil moisture content, RDF is a root distribution function, Es is the soil evaporation, and f3(θ) and f4(θ) are different functions based on the soil moisture content.
In the Overland Flow (OL) module, the two-dimensional, kinematic wave Saint-Venant equation is used:
(2)
where hw the flow depth above the ground surface, t is the time, u and v are flow velocities in the x- and y-directions, respectively, qi is the net input into overland flow, Sf is the friction slopes in the x- and y-directions, SΟ is the slopes of the ground surface in the x- and y-directions, and g is the gravity acceleration.
In the Unsaturated Zone Flow (UZ) module, the one-dimensional Richards equation is used:
(3)
where θ is the soil moisture, t is the time, z is vertical coordinate distance, Κ(θ) is the hydraulic conductivity function, ψ is the pressure head, and Sr is the root extraction sink. An iterative coupling procedure between the unsaturated zone and the saturated zone in MIKE SHE enables the computation of both the soil moisture and the water table dynamics in the lower part of the soil profile.
In the Saturated Zone Flow (SZ) module, the governing equation for the groundwater flow process is:
(4)
where kxx, kyy, kzz the hydraulic conductivity along the x, y and z axes of the model, hs is the hydraulic head, Rs represents the source/sink terms, and Ss is the specific storage coefficient.
In the Rivers and Lakes (OC) module, MIKE 11, a modeling system for one-dimensional river hydrodynamic (HD) and advection-dispersion (AD) simulation, was coupled to MIKE SHE via river links. The governing equations used in MIKE 11 are:
(5)
where Q is the discharge, x is the distance in the downstream direction, A is the cross-section flow area, t is the time, q is the lateral inflow, g is the gravity acceleration, h is the water level above the reference datum, n is the Manning resistance coefficient, R is the hydraulic radius, α is the momentum distribution coefficient, C is the concentration, D is the dispersion coefficient, Kd is the decay coefficient and C2 is the source/sink concentration.

Modeling in MIKE SHE/MIKE 11

A coupled MIKE SHE/MIKE 11 modeling system was constructed for Yanghe Basin. The model area in MIKE SHE and river network defined in MIKE 11 are shown in Figure 3(a) and 3(b), respectively. The model area was horizontally divided into a number of computational cells, each with a size of 1,000 m. All the rivers within MIKE 11 were coupled to MIKE SHE so that water exchange was enabled between rivers and adjacent MIKE SHE grid squares. River-channel cross-section data were obtained from the Annual Hydrological Report China (Chinese Ministry of Water Resources 2013 & 2014) as well as from the Digital Elevation Model (DEM) (Geospatial Data Cloud 2017). Hydraulic parameters such as Manning's M (the inverse of Manning's n in Equation (5)) and leakage coefficient were first set as fixed values and then were calibrated in accepted data ranges (Li 2020). In the MIKE 11 HD module, while time-varying flow boundary conditions were applied to the four upstream open ends of the river network, a time-varying water level condition was applied to the downstream end (Figure 3(b)). In the MIKE 11 AD module, 55 pollutant discharge units on land were categorized and 16 water quality boundaries were defined within the river network for COD and TP simulation. The dispersion coefficient D as needed in Equation (5) was calculated by an empirical equation D=8 V2 (where V is the stream velocity), while the decay coefficient was first specified for initial run and then was calibrated. Meteorological information such as rainfall and evaporation data were obtained from the Annual Hydrological Report China (Chinese Ministry of Water Resources 2013 & 2014). Potential ET was estimated by multiplying pan measurement by a fixed value of 0.72 (Liu 2005). Additional inputs for the ET module, such as LAI and RD, were estimated based on the land use (Figure 3(c) (China Institute of Water Resources and Hydropower Research 2014)). In the OL module, while Manning's M was specified within a wide range of values from 2.5 m1/3s−1 to 100 m1/3s−1 depending on land-use types, the detention storage was set as a fixed value of 0 mm. For the Richards equation used in the UZ module, soil moisture retention characteristics and hydraulic conductivity function were critical inputs, which were estimated by soil digital information (Figure 3(d) (China Soil Map 2017)) and Soil–Plant–Air–Water model (Saxton & Willey 2005). For groundwater modeling, a simplification was made for geological structures, boundary conditions and pumping wells due to limited data available. Three geological layers, comprising of one sand/burnt-on sand layer (∼50 m thick), one loam/sandy loam layer (∼15 m thick) and one sand/burnt-on sand (∼55 m thick), were defined accordingly with different hydraulic properties (e.g. hydraulic conductivity and storage coefficient) in the SZ module. A zero-flux boundary condition was assigned to the lateral boundaries of the model domain. Dozens of dispersedly distributed wells with various time series of pumping rates were specified in different districts/counties for groundwater withdraw representation. Groundwater table information was gathered and inputted to the SZ module as initial conditions after spatial interpolation (Figure 3(e)). Low concentrations of COD and TP were also assigned in the SZ module according to local monitored data (Li 2020).

Figure 3

Inputs for MIKE SHE/MIKE 11: (a) model area, (b) river network, (c) land use, (d) soil type, (e) groundwater depth.

Figure 3

Inputs for MIKE SHE/MIKE 11: (a) model area, (b) river network, (c) land use, (d) soil type, (e) groundwater depth.

Close modal
Important parameters which were known for their importance to modeling results (Thompson et al. 2004; Liu 2007) were fixed for initial simulation and then were calibrated by trial and error. The simulation period was from Jan 2013 to Dec 2014. Several time steps were specified in MIKE SHE for efficient simulation, including an initial time step of 6 h, maximum OL time step of 0.5 h, maximum UZ time step of 2 h, and maximum SZ time step of 24 h. The time step in MIKE 11 was set as 3 min. The modeled results were outputted on a monthly basis and were calibrated and validated by comparing model predictions with observations at the national monitoring section Bahaoqiao. These observations included flow rate, COD concentration and TP concentration. For flow rate, the Nash–Sutcliffe efficiency coefficient Ens (Equation (6)) was used to assess the model performance. For COD and TP concentrations, the percent bias PBIAS (Equation (7)) was used:
(6)
(7)
where Qobs,i is the observed flow rate for the ith month, Qsim,i is the modeled flow rate for the ith month, is the averaged flow rate over the N-month period, Cobs,i and Csim,i are the observed and modeled pollutant concentrations for the ith month, respectively. Ens can range from negative infinity to 1, with a value of 1 representing a perfect match and a value higher than 0.5 indicating an acceptable simulation (Moriasi et al. 2007). The optimal value of PBIAS is 0, with low-magnitude values less than 25% regarded as satisfactory (Moriasi et al. 2007).

Various hydrological conditions were considered for water balance computation in the Yanghe Basin. Three typical hydrological years, including a wet year with annual rainfall of 497 mm at the frequency of 10%, a normal year with annual rainfall of 381.8 mm at the frequency of 50%, and a dry year with annual rainfall of 315.8 mm at the frequency of 75%, were defined based on 30-year rainfall data analysis. Using the calibrated MIKE SHE/MIKE 11 model, the water quality of Yanghe in each hydrological year under the pollution load of 2019 (Figure 2) was first identified as the baseline and then compared to those subject to water quality control measures of different implementation scale scenarios. The small-scale implementation scenario involves the application of demonstration projects sponsored by the National Water Program in Xuanhua District, referring to a 109-km2 demonstration zone and a 7-km demonstration wetland (Figure 1(b)). The large-scale implementation scenario involves the promotion of the associated non-point/point pollution control technologies over the whole basin of 1.62 × 104 km2 (Figure 1(a)). That is, the introduction of innovative sewage treatment and reclamation, precision agriculture practices and ecological ditch/riverbank design to various districts/counties of Zhangjiakou City, as well as wetland technology to up to three locations along the mainstream channels of Yanghe and Sangganhe (Figure 1(b)). Based on comparison results, the water quality improvement of Yanghe in the two scenarios can be demonstrated.

Model performance

The monitored data from 2013 were used for calibration, while data from 2014 were used for validation. The calibrated hydrological parameters are listed in Table 1, upon which the water quality parameters were calibrated. The predicted and observed flow rates were compared in Figure 4(a) and the resulted Ens values for calibration and validation were 0.71 and 0.60, respectively. The calibrated decay coefficient of COD was 0.018 h−1, the same as that for TP. The predicted and observed pollutant concentrations were compared in Figure 4(b) and 4(c). The PBIAS values for COD were 3 and 26% for calibration and validation, respectively, and those for TP were 14 and 11%, respectively. The Ens and PBIAS values are considered as acceptable, which supports the further application of the constructed MIKE SHE/MIKE 11 model in the Yanghe Basin. The opposite trends of the modeled flow rate and the modeled pollutant concentrations shown in Figure 4, reflect the dominant role of water movement on pollutant concentration. Basically, a great flow rate brings an enlarged dilution effect, which results in a decreased pollutant concentration.

Table 1

Calibrated hydrological parameters of MIKE SHE/MIKE 11 in Yanghe Basin

ParameterRangeCalibrated value
Saturated hydraulic conductivity of Cambisols (m s−11 × 10−8 — 1 × 10−3 1.1 × 10−5 
Saturated soil moisture of Cambisols 0.1 — 0.8 0.48 
Horizontal hydraulic conductivity of first SZ layer (m s−11 × 10−8 — 1 × 10−3 9.6 × 10−5 
Vertical hydraulic conductivity of first SZ layer (m s−11 × 10−9 — 1 × 10−4 9.6 × 10−6 
Specific yield of first SZ layer 0.01 — 0.5 0.03 
Storage coefficient (m−11 × 10−6 — 1 × 10−2 2 × 10−6 
Channel flow Manning's M (m1/3 s−110 — 100 30 
Leakage coefficient (s−11 × 10−11 — 1 × 10−6 1 × 10−10 
ParameterRangeCalibrated value
Saturated hydraulic conductivity of Cambisols (m s−11 × 10−8 — 1 × 10−3 1.1 × 10−5 
Saturated soil moisture of Cambisols 0.1 — 0.8 0.48 
Horizontal hydraulic conductivity of first SZ layer (m s−11 × 10−8 — 1 × 10−3 9.6 × 10−5 
Vertical hydraulic conductivity of first SZ layer (m s−11 × 10−9 — 1 × 10−4 9.6 × 10−6 
Specific yield of first SZ layer 0.01 — 0.5 0.03 
Storage coefficient (m−11 × 10−6 — 1 × 10−2 2 × 10−6 
Channel flow Manning's M (m1/3 s−110 — 100 30 
Leakage coefficient (s−11 × 10−11 — 1 × 10−6 1 × 10−10 
Figure 4

Calibration and validation results: (a) flow rate, (b) COD, (c) TP.

Figure 4

Calibration and validation results: (a) flow rate, (b) COD, (c) TP.

Close modal

Water balance computation

Using the calibrated model, hydrological water balance components for each hydrological year were obtained (Table 2). The hydrological components include rainfall as the input to the model area, ET as the water loss leaving the area, and change in UZ and SZ representing storage dynamics of the underground part in the area. The error of water balance computation for each hydrological year is small, accounting for less than 1% of rainfall, which indicates a satisfactory model application. Data in Table 2 show that ET always exceeds rainfall, regardless of the hydrological year, with the deficit compensated by water coming from the underground part. For example, the deficit in the wet year 2013 was 34 mm, which indicates the storage change of 44 mm in UZ and SZ is negative and most of the change (that is, 34 mm) compensates for the deficit. The remain 10 mm storage change accounts for the difference between irrigation (30 mm) and pumping (40 mm), as irrigation is the entering item and pumping is the outgoing item for the UZ–SZ system. These data reveal that underground water storage for the Yanghe Basin experiences an unsustainable utilization. This unsustainability became significant in the dry year 2009, with a reduction of 174 mm in water storage of the underground part. The modeled results are consistent with findings of other research focusing on the North China region. Lou (2020) used a SWAT model to compute the water balance of the Yanghe Basin over 2011–2015 under the conditions of no irrigation or pumping (that is, no human exploitation on the UZ–SZ system). It was found that the average depletion in UZ and SZ would reach 30 mm, which suggests the unsustainability of underground water even without human exploitation. Therefore, it is not surprising to see a larger depletion in the UZ–SZ system when human exploitation is considered in the current modeling study (≥ 44 mm in Table 2). The greater value of ET relative to rainfall in Table 2, may be linked to the practice of irrigation, as irrigation can increase actual ET and accelerate the field water cycle (Pei et al. 2017). Similarly, a research (Qin 2019) qualifying the hydrological cycle of the North China Plain (NCP) reported an average depletion of 63 mm in UZ and SZ and a greater value of total ET relative to total rainfall over 2000–2008, when irrigation and pumping were taken into account. It is noted that the wet years defined for the NCP had annual rainfalls more than 600 mm, notably greater than the rainfall of 497 mm in the wet year for Yanghe Basin. Correspondingly, while water storage of the UZ and SZ in the NCP had the chance to be replenished in wet years, water storage of the UZ and SZ in Yanghe Basin always underwent a decline in any hydrological year (Table 2). Based on multiple-year field data collected in the central piedmont of the NCP, Pei et al. (2017) also observed an average depletion of 22 mm in soil water storage and calculated ET from a double cropping field as 454 mm per year, beyond the annual rainfall of 434 mm. During the year with the lowest rainfall of 228 mm, the largest change in soil water storage (−100 mm) and the biggest difference between rainfall and ET (−97 mm) occurred for the double cropping field. It seems that under low rainfall conditions, the depletion of underground water would be severe. Hao et al. (2019) quantified the groundwater decline trend for a region including Zhangjiakou City and discovered a significant decline in groundwater storage in years of less precipitation (<350 mm). The relatively large change of 174 mm in UZ and SZ for the dry year 2009 shown in Table 2, may be explained by the low rainfall of 316 mm. The consistency between the current study and other studies in the literature suggests that the coupled model is capable of computing the water balance of Yanghe Basin to a reasonable extent. The water balance of a watershed can be expressed by various hydrological components as Equation (8) (Flerchinger & Cooley 2000):
(8)
where P is precipitation, Evap is evaporation, Trans is transpiration, Int is interception, Det is detention storage, Qin is surface inflow, Qout is surface outflow, GWin is groundwater inflow, GWout is groundwater outflow and ΔS is change in storage. It should be noted that due to the approximate modeling of groundwater movement in the study, errors for GWin and GWout are introduced inevitably, which in return introduces errors in ΔS. Therefore, changes in UZ and SZ obtained in this study can only be interpreted as rough estimates rather than accurate values.
Table 2

Water balance computation for Yanghe Basin using MIKE SHE/MIKE11

Items (mm a−1)2013 (wet year)1985 (normal year)2009 (dry year)
Rainfall 497 381 316 
Evapotranspiration 531 465 481 
Change in UZ and SZ 44 94 174 
Error 
Items (mm a−1)2013 (wet year)1985 (normal year)2009 (dry year)
Rainfall 497 381 316 
Evapotranspiration 531 465 481 
Change in UZ and SZ 44 94 174 
Error 

The above analysis shows that the water resource management in Yanghe Basin faces serious challenges and powerful measures should be taken to solve the water scarcity problem. On the one hand, water-saving agriculture and industry need to be promoted to reduce water demand, which will, in return, lower the water storage consumption from the UZ and SZ. Considering the large amount of water loss by evapotranspiration in the water cycle, specific strategies for ET suppression, such as crop ET-based irrigation management and agricultural restructuring for less ET, can be considered. On the other hand, additional water resources including inter-basin water diversion, rainwater harvesting and wastewater reuse can be scheduled in the Yanghe Basin for water scarcity alleviation. Furthermore, as water shortage issues will become worse when water pollution occurs, water environment protection of both surface water and groundwater in Yanghe Basin cannot be ignored. Therefore, water quality projects initiated by the National Water Program for Yanghe should be brought into effect.

Water quality improvement evaluation

The baseline results, marked as grey columns, are shown in Figure 5. It can be seen that, for each hydrological year, COD and TP concentrations at Bahaoqiao Section are beyond the Grade III water quality standards, with the worst water quality shown in the dry year. While the averaged-monthly COD concentrations of the wet and normal years are comparable (23.6 mg/L and 21.6 mg/L, respectively) and close to the standard limit of 20 mg/L (Figure 5(a)), the corresponding value of the dry year reaches 32.3 mg/L. The situation for TP is similar and the associated averaged-monthly TP concentrations of different hydrological years are 0.31 mg/L, 0.28 mg/L and 0.42 mg/L. This suggests that water quality control measures for Yanghe are quite necessary, especially for a dry year with low rainfall and streamflow. Compared to COD concentration (Figure 5(a)), TP concentration is more difficult compliant with the water quality standard (Figure 5(b)), with compliance rates of 8.3% in the wet year and 16.7% in the normal year. When considering water quality control measures at a small-scale scenario, the compliance rates of COD and TP increase correspondingly for either wet or normal year, both reaching to 50% in the wet year and 67% in the normal year (Figure 5). However, in the dry year, the compliance rates of COD and TP still remain zero regardless of reductions in the averaged-monthly concentrations (Figure 5). The results suggest that the demonstration projects involving non-point source control measures, initiated in an area of 109-km2 and a 7-km constructed wetland, will have a positive effect or negligible effect on water quality improvement of Yanghe, depending on hydrological conditions. When considering water quality control measures at the basin-scale scenario, concentrations of COD and TP at Bahaoqiao Section in each hydrological year are far below the corresponding standard limits of 20 mg/L and 0.2 mg/L, which suggests that the water quality of Yanghe is largely improved. Nonetheless, the basin-wide promotion of the non-point/point source control technologies will require great investment. To date, the funds for the 109-km2 demonstration zone and 7-km demonstration wetland are about CNY 72 million, two-thirds of which come from local government finance in addition to the National Water Program. Water quality improvement projects can hardly be implemented without local inputs, and cost benefit analysis must be made before a large-scale promotion.

Figure 5

Water quality of national monitoring section under different water management strategies in hydrological years: (a) COD, (b) TP.

Figure 5

Water quality of national monitoring section under different water management strategies in hydrological years: (a) COD, (b) TP.

Close modal

The relatively large gap between water quality standard and demonstration projects at large-scale scenario (Figure 5), suggests that it is unnecessary to promote basin-wide water quality control measures for water quality goals. Further investigation shows that the wetland technology for both point and non-point pollution control is the most effective measure to improve water quality of Yanghe, as other technologies only target at non-point pollution. Figure 6 shows how the wetland number influences the water status of Bahaoqiao Section. It can be seen that the application of one wetland in Yanghe (demonstration wetland in Figure 1(b)) can effectively reduce the baseline, and when the number grows, the water quality gets improved gradually. The application of two planned wetlands, that is, one of Xuanhua and one of Zhuolu (Figure 1(b)), can achieve the water quality goal for TP but not for COD. The application of all three wetlands can finally meet water quality goals. Also, the water quality at Bahaoqiao Section can be ensured when implementing two wetlands with basin-wide non-point source control technologies (Figure 6). Based on the cost of the demonstration zone/wetland, it is estimated that the implementation of three wetlands will need CNY 73 million, whereas the implementation of two wetlands and basin-wide non-point source control technologies will cost more than CNY 7 billion. Regarding the goal of water quality improvement, the wetland technology is more effective and economic, compared to other non-point source control technologies.

Figure 6

Water quality of national monitoring section with different wetland configurations: (a) COD, (b) TP.

Figure 6

Water quality of national monitoring section with different wetland configurations: (a) COD, (b) TP.

Close modal

Based on coupled MIKE SHE/MIKE 11 modeling, water balance components were obtained for Yanghe Basin under typical hydrological conditions, and water quality status of Yanghe with/without pollution control measures was investigated. Modeling results show that actual evapotranspiration is greater than rainfall and a decline in underground water storage persists in each hydrological year, for which both human interference (i.e. irrigation and pumping) and low rainfall are considered the contributing factors (Pei et al. 2017). There is an urgent need for a sustainable water resources development in the basin. Results also show that without any water pollution control measures, water quality goals (COD < 20 mg/L and TP < 0.2 mg/L) set for Yanghe can hardly be achieved, especially in a dry hydrological year with less rainfall and streamflow. A small-scale implementation scenario of water quality control measures is helpful to increase compliance rates of COD and TP to 50% in the wet year and to 67% in the normal year. A basin-wide implementation scenario of water quality control measures guarantees water quality goals, with great investment needed. A river-channel wetland technology is proven to be effective and economic for water quality control. Future research into groundwater monitoring and modeling for over-exploitation groundwater control and management in the Yanghe Basin is needed.

This research was collaboratively funded by the National Science and Technology Major Project of Water Pollution Control and Treatment, grant number 2017ZX07101003-008, National Natural Science Foundation of China, grant number 51909081 and 52079075.

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

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