Separation and Purification Technology 84 (2012) 104–111

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Separation and Purification Technology

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Review

An overview of research on agricultural non-point source pollution modellingin China

Zhenyao Shen ⇑, Qian Liao, Qian Hong, Yongwei GongState Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, No. 19 Xinjiekouwai St., Beijing 100875, PR China

a r t i c l e i n f o

Article history:Available online 25 January 2011

Keywords:Non-point source pollutionModellingOverviewChina

1383-5866/$ - see front matter � 2011 Elsevier B.V. Adoi:10.1016/j.seppur.2011.01.018

⇑ Corresponding author. Tel.: +86 10 58800398; faxE-mail addresses: zyshen@bnu.edu.cn (Z. Shen), ch

a b s t r a c t

With the development of technology for controlling point source pollution, non-point source (NPS) pol-lution issues have become increasingly prominent worldwide. Because of the wide range, difficult controland complex uncertainties involved in simulation processes, NPS pollution control has become a hotspotin the area of water pollution control. In China, NPS pollution control will be one of the most importantissues in water environmental protection in the next several decades. To control NPS pollution, it isimportant to know how much there is. In this paper, the authors provide an overview of the currentNPS pollution modelling technology in China. We first compared several methods used for estimationof the NPS pollution load in China. We next discussed the advantages and disadvantages of these methodsin detail, both from the method itself and the simulation results. We found that most of these methodsare derived directly from models developed by several developed countries, especially the USA. Althoughthese models may be suitable to the situation of the country they were designed in, they may not be suit-able to the actual situation of China. Other methods have been developed by scholars in China, but theseare all very simple and may not provide a good estimation. Finally, we point out that we can only deter-mine if a NPS model is good or bad according to the actual conditions of the study area and the availabledata for this area. Overall, the results of this study indicated that digesting and absorbing foreign NPSmodels, modifying the related processes and using related key parameters with Chinese characteristicsare the future research direction for NPS pollution modelling in China.

� 2011 Elsevier B.V. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1052. Applications and improvements of exotic NPS pollution modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

2.1. Empirical modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

2.1.1. Statistical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1052.1.2. Export coefficient model (ECM) and improved export coefficient model (IECM). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1062.1.3. Hydrograph separation method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

2.2. Physically based and process-based modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

2.2.1. Introduction of commonly used NPS models in China. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1062.2.2. Applications of widely used NPS models in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

2.3. Other studies of physically based NPS modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

3. Applications and improvements of native NPS pollution modelling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

3.1. Empirical modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

3.1.1. Mean concentration method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1083.1.2. Rainfall deduction method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1083.1.3. Correlation method of water quality and quantity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

3.2. Physically based and process-based modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

3.2.1. IMPULSE model and NPSDSS system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1083.2.2. Other NPS pollution modelling methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

4. Problems associated with NPS pollution modelling in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

ll rights reserved.

: +86 10 58800398.erylliao37@gmail.com (Q. Liao), hongqian0809@gmail.com (Q. Hong), gongyongwei@163.com (Y. Gong).

Z. Shen et al. / Separation and Purification Technology 84 (2012) 104–111 105

4.1. Limitation of exotic models in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1094.2. Limitation of scale of application area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1094.3. Calibration and validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1094.4. Incompleteness in describing the NPS mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1094.5. Evaluation criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

5. Recommendations for future studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

5.1. Model mechanism, model improvement, and model innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1095.2. Model uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1095.3. Model integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

1. Introduction

Unlike pollution from industrial and sewage treatment plants,non-point source (NPS) pollution comes from many diffusesources. NPS pollution is caused by rainfall or snowmelt movingover and through the ground. As the runoff moves, it picks upand carries natural and anthropogenic pollutants, finally deposit-ing them into lakes, rivers, wetlands, coastal waters and groundwater systems [1].

Several studies conducted abroad have shown that NPS is nowan important issue in pollution of water environments, amongwhich agricultural NPS pose the greatest risk [2]. With the develop-ment of agriculture and the increasing amount of chemical fertiliz-ers and pesticides used in China, the proportion of NPS pollutionincreases annually. Investigation of eutrophication in Dianchi Lakesuggests that the total nitrogen (TN) generated by NPS pollutioncomprises 44.5% of the total pollution load, while the total phos-phorus (TP) comprises 26.7% [3]. It is apparent that NPS pollutionhas become a primary threat to watershed environmental health.

Contrary to point source pollution, NPS pollution is character-ized by random and intermittent occurrence, complex mechanismsand processes, uncertain discharge channels and amounts, variablespatial and temporal pollution loads, and difficulties in monitoring,simulation and control. These characteristics lead to inconveniencein monitoring, controlling and treating NPS pollution.

To address the critical NPS pollution situation, it is important tohave accurate simulations and estimations of NPS. Studies of NPSmodelling have always been a core topic in investigations of NPS[4]. NPS models, which provide a quantitative description for theentire basin system and the complicated pollution generating pro-cess, aid in the analysis of spatial and temporal features of NPS pol-lution, help identify the main source and migration path, forecastpollution load and its impact on the water environment, and pro-vide estimates of the different effects of various land use and man-agement and technical measures on NPS pollution and the waterenvironment, thereby providing a basis for basin environmentalplanning and management [5]. NPS studies in western countrieswere first conducted in the 1960 s, and there have since been manyNPS models developed [6]. However, China has conducted littlework in this field.

Here, the widely used models in recent Chinese agriculture NPSfields are summarized, their advantages and disadvantages are de-scribed, and their present applications and future developmentaltrends in China are discussed.

2. Applications and improvements of exotic NPS pollutionmodelling

Many foreign scholars, especially in North America, have devel-oped mathematical models to assess the NPS pollution load sincethe 1960s. These models can be divided into two categories, empir-ical or statistical models and physically based or process-basedmodels [7]. Empirical models use monitoring data in typical exper-

imental plots to build empirical relationships between hydrologi-cal parameters, such as the export coefficient method [8],hydrograph separation method [9], etc. These models, which areknown as ‘‘black box’’ research methods, seldom take the pollutionprocess and mechanisms into consideration. The advantages ofthese models include a lower demand for input data and a simplercalculation process. However, these models do not describe thecontaminant migration process well and cannot be used in exten-sive or large areas because of their inherent small regionalcharacteristics.

Physically based models combine hydrological models, soil ero-sion models and pollutant migration models to form a relativelycomplete model system. These models describe the occurrence ofNPS pollution quantitatively in a continuous process. These mod-els, which are known as ‘‘white-box’’ research methods, considerthe internal mechanism of the pollution process, are capable of cal-culating long time series and have clearer spatial characteristicsdistributions. However, due to the enormous number of parame-ters, requirements for a large body of input data, and limited avail-able information, it is difficult to calibrate and validate thesemodels, which restricts their use for large-scale regions.

China has a relatively short history in NPS pollution research.The majority of work that has been conducted to date has involvedthe application of sophisticated models developed by westerncountries in some typical areas, and to amend these systemsaccording to various needs of local conditions in China.

2.1. Empirical modelling

2.1.1. Statistical methodsStatistical methods were developed based on simultaneous

monitoring data for water quality and quantity in runoff testingground. The basic idea behind this method is to put aside the actualpollutant migration process in the surface area and calculate theoutput of the pollutants based on quality analysis of the receivingwaters [10]. These methods have been applied in some basins ofChina with satisfactory results. Li and Cai [11] established a linearcorrelation between the unit load of TP, TN and sediment on thebasis of theory analysis and measured single event data in the mid-dle and the southern portion of Shaanxi Province. The correlationcoefficients obtained in their study were larger than 0.9. Li et al.[12] established a fuzzy prediction model that combined precipita-tion, sediment discharge and pollution load to forecast the pointsource pollution and the NPS pollution load in the Xiangxi RiverBasin. Their results showed that these methods are better thansome traditional models and are applicable to the Xiangxi RiverBasin.

Statistical methods based on large quantities of measured dataare appropriate for basins with sufficient base information and canestimate the regional pollution load accurately to a certain extent.However, these methods can only be used in some specific areasand their utilization is limited. With the basin underlying surface

106 Z. Shen et al. / Separation and Purification Technology 84 (2012) 104–111

changing, the reliability of simulation results will be reduced.Moreover, this type of modelling is data-intensive and expensive.

2.1.2. Export coefficient model (ECM) and improved export coefficientmodel (IECM)

The export coefficient model (ECM), which is based on the ideathat the nutrient load exported from a watershed is the sum of thelosses from individual sources, such as land-use, livestock, rurallife, etc., has been in general use because of its simplicity and rel-ative robustness. Moreover, the time step of this method is large(monthly, seasonal or annual), and it allows the use of spatiallyand temporally based lumped data rather than real-time data, aswell as the use of agricultural census data rather than field leveldata.

In recent years, there has been a growing number of ECM basedstudies of water quality relative to different NPS land uses in China.Luo et al. [13] selected 15 catchments of the Hujiashan Watershedto examine the effect of land use on NPS pollution and found thatland use was a controlling factor that determined the amount ofnitrogen export, but that the effects varied with time. Other studieshave used simulated rainfall and field-scale monitoring methods todetermine the export coefficients of different land use types as well[14,15].

The improved export coefficient modelling method (IECM) is amethod that was developed to modify the ECM by taking the im-pact of the temporal–spatial heterogeneity of precipitation and ter-rain on NPS pollution into account. Chinese scholars haveimproved the ECM according to the actual situation in China overthe past decade. Li et al. [16] considered the spatial distributionsof precipitation and runoff to improve ECM and then integratedECM into the geographical information system (GIS) to composea half-distribution model of export coefficients. They estimatedthe nutrient export loads based on the improved ECM in the Xitia-oxi Watershed located in the upper portion of the Taihu Lake area,and the predicted result was approximately in accordance with theobserved data.

Ding et al. [17] improved the ECM by introducing precipitationimpact and terrain impact factors, which were defined to charac-terize the non-uniformity of precipitation and terrain, respectively.Specifically, they tested the proposed model in the Upper Reach ofthe Yangtze River and found that the relative error between simu-lated dissolved nitrogen and the observed value was effectively re-duced when compared to that obtained from the original model(22% vs. 44% in 1990 and 18% vs. 38% in 2003). Their findings dem-onstrated that the improved model could provide more accurateresults of large-scale agricultural watersheds.

Overall, the ECM and IECM methodologies are easy to use andcan be scaled up to large watersheds. However, their export coef-ficients are inherently highly variable and reflect particular siteconditions for each study. Therefore, the choice of an export coef-ficient from the literature is inevitably subject to considerableuncertainty, especially as agricultural land management practicesin China differ greatly from those in the US or UK.

2.1.3. Hydrograph separation methodThis common method relies on the runoff effect, in which sep-

aration of the runoff hydrograph into base flow and storm flowshould permit separation of the point source (base flow) and NPSloads (storm flow). In North America, this method is usually re-stricted to small basins in which there is no surface storage andwhere there are no other data with which to predict the NPS pol-lution load.

Among the studies of the hydrograph separation method thathave been conducted in China, Cai et al. [9] took use of this methodto divide the pollution load into the point source and NPS pollutionof the Weihe River Watershed in China. They found that the point

source pollution was slightly higher during dry years, while theNPS pollution load was much larger during wet years. However,this methodology is less used in China because rivers in NorthAmerica are perennial with a sustained annual base flow and rela-tively low levels of pollution, while the flow of rivers in China isseasonal, and can even be reduced to no flow in dry seasons dueto over-pumping of groundwater. This situation is more visible inthe northern region of China, and even there has ‘‘base flow’’, thebase flow is comprised mainly of wastewater. In storm event, thiswastewater is diluted and flows downstream. Thus, the pointsource pollution component also appears in the runoff effect,which is no longer a true base flow. Under this circumstance theNPS pollution estimates are exaggerated.

Overall, this technique is well known to hydrologists and widelyused in the hydrological field; however, the storm flow/base flowseparation does not discriminate between NPS (storm flow) andpoint source (base flow) under actual conditions in China.

2.2. Physically based and process-based modelling

2.2.1. Introduction of commonly used NPS models in ChinaThe applications of distributed, physically based, and wa-

tershed-scale models introduced from developed countries to esti-mate the NPS pollution in various river basins, reservoirs, and lakeshave recently received a great deal of attention in China. The mod-els shown in Table 1 are public domain models widely used in Chi-na, as well as other models such as the WEPP (Water ErosionPrediction Project) [18], SWRRB (Simulator for Water Resourcesin Rural Basins) [19] and BASINS (Better Assessment Science Inte-grating Point and Non-point Sources) [20], which are also used byChinese scholars [21–23], but not discussed in depth here.

2.2.2. Applications of widely used NPS models in ChinaAmong the models shown in Table 1, the SWAT model, AGNPS

model and HSPF model are used extensively in China.

2.2.2.1. SWAT model. The SWAT model is an advanced, physicallybased, distributed hydrological model developed by USDA-ARS[24], which is now widely used in China to evaluate watershedhydrology, non-point source pollution, and management of thewatershed integrated with remote sensing (RS) and GIS [25].Domestic studies and applications of SWAT currently include fourmain aspects: simulations of sediment and runoff, prediction ofNPS pollution load, modification of model components for Chinesenatural conditions, and research investigating the uncertainty ofparameters and input data.

Among hydrological studies, Zhang et al. [26] applied the SWATmodel in the Bailian River basin of Hubei Province to discuss itsadaptability to flow simulation of medium and small basins. Theyfound that the SWAT model can reflect the daily flow hydrologicalprocess of the basin. Moreover, Li et al. [27] discussed the applica-bility of the SWAT model to semi-arid and semi-humid regions inChina, while Wang et al. [28] provided important hydrology infor-mation supporting a foundation for water resources managementand restoration of the eco-environment in the Haihe River Basinby applying it to simulate the hydrological results.

In the field of water quality studies, Fan et al. [29], Wang et al.[30], and Lai and Yu [31] used the SWAT model to calculate thespatial distribution of NPS pollution in the watershed of DaningRiver, Miyun Reservoir and Taihu Lake, respectively. They all foundthat different land uses contributed different amounts to the totalNPS pollution load, and that farmland generally contributed thehighest NPS loading in China, which demonstrated that agricul-tural NPS pollution is a serious problem.

To conduct an application study of the SWAT model in Chinaand improve the simulation precision of the model in hydrological

Table 1Agricultural NPS models commonly used in China.

Model Major modules of model Advantages Disadvantages

1. AGNPS (agricultural non-point source pollution)and AnnAGNPS(annualized AGNPS)

Hydrology, erosion, sedimenttransportation, chemical transportation.

Can simulate spatial distribution of soil erosion withinthe catchments and the impact of erosion on waterquality; calculation process can be completed directlyby computer.

Distributed model, the simulationis based on the unit grid, requiresa large number of inputparameters.Application is restricted in someareas in which detailedinformation is lacking.

2. ANSWERS (areal non-point source watershedenvironment responsesimulation)

Runoff, infiltration, sedimenttransportation evapotranspiration.

Predicted results of runoff and sediment are identicalwith observed data.

Erosion module is empirical to agreat extent.Cannot simulate many sub-processes.

3. GREAMS (chemicals,runoff and erosion formagriculturalmanagement systems)

Hydrology, soil erosion, chemicaltransportation.

Cannot only forecast single rainfall events, but alsoaverage value of long-term rainfall.

Cannot be used for larger-scaleriver basins.Cannot provide processinformation and lacks somesimulation functions.

4. HSPF (HydrologicalSimulation Program-FORTRAN)

Climate, hydrology, nutrients, sedimenttransportation, water quality.

Can simulate the detailed processes of runoffformation.Good at continuous time steps.Predicted results are satisfactory.

Detailed description of the lack ofspace.Many parameters are not suitablefor areas lacking research-basedinformation.

5. SWAT (soil and waterassessment tool)

Climate, surface and underground runoff,soil type, sediment, nutrients, pesticides,vegetation growth, agriculturemanagement.

Considers the process of conflux and sedimentconfluence.Develops soil and water conservation modulescombined with GIS.Easy to use.

Cannot be used to simulate singleflood events.Need to modify database whenused in different study areas.

Z. Shen et al. / Separation and Purification Technology 84 (2012) 104–111 107

process studies, Zhang et al. [32] made some expansions andimprovements to the modelling system for the actual conditionsin the arid, semiarid Heihe River basin in northwestern Chinaand the humid Hanjiang River Basin in midwestern China. Theyadded the soil grain size transferring module and data pre-process-ing module for the weather generator (WGEN) to the originalSWAT model, and then improved it by replacing the original inter-polation method of the weather data and evapotranspiration esti-mation module. The results suggested that there was a significantimprovement of modelling performance based on a greatly im-proved Nash–Sutcliffe efficiency (NSE) and correlation coefficient.

For parameter uncertainty research, Yu and Shen [33] and Shenet al. [34] used the Monte Carlo (MC) method and the first-ordererror analysis (FOEA) method to analyze the effect of parameteruncertainty on SWAT model outputs in the Daning RiverWatershed of the Three Gorges Reservoir area, respectively. Someother scholars also conducted detailed studies of the parameteruncertainty of the SWAT model. Yao et al. [35] and Qin et al. [36]found that five spatial data parameters, the scale and size of thedigital elevation Model (DEM), the number of sub watersheds,the scale of soil data and land use and cover change (LUCC) data,and the spatial distribution of rainfall stations, affected the accu-racy of model outputs, especially for the sediment and nutrients.However, there are no recognized standards for the selection ofthese parameters, and studies that use alternative rainfall data re-quire greater attention to cope with the lower number of weatherstations in China.

Overall, the above applications and studies have shown that theSWAT model can provide good simulated and predicted results formodelling runoff and sediment assessment. This model can alsosimulate the migration process of pollutants in the farmland andriver network. However, due to the limitation of parameters usedin various areas and the imperfect description of the groundwatermechanism, the determination of model parameters and simula-tion of groundwater flow and solute transport are major issues thatrequire further research and improvement [37].

2.2.2.2. AGNPS and AnnAGNPS model. Studies of the AGNPS model inChina are currently focused on its applicability, model parameter

sensitivity, and effects on the management practices [38,39]. Caoet al. [38] employed the AGNPS model in the Jiulong River Wa-tershed to estimate runoff and nutrients losses, verify the modelapplicability in the Southeast Asian tropics, and identify the effectsof rainfall intensity on the accuracy of simulated runoff results.Zhang et al. [39] calibrated the AGNPS model using all rainfallevents that occurred in the same area studied by Cao et al. [38]from May to August of 2002. They then evaluated the model basedon comparison of the measured values and predicted values andobtained high NSE values. After validating the model, the authorsused the AGNPS model to estimate the nutrient loss from thecatchments. The results showed that total nitrogen loss in thesecatchments was slightly higher than that from some areas in China,and that the AGNPS model would be an effective tool to predictnutrient loading as well. They then employed the AGNPS modelto simulate the performance of two existing management practicesas well as three scenarios using current management practices asbackground losses of NPS pollutants. The simulations showed thatthe existing management practices (contour cultivation and multi-pond system) were very effective at reducing nutrient losses,although they performed differently for the reduction of variousnutrients; therefore, AGNPS can be used to help develop a wa-tershed management program.

Because AGNPS is an event driven model, there are manyshortcomings for its application. Therefore, the USDA developedan improved version of this model, AnnAGNPS. The biggestimprovement of the modified model is that the simulated resultsof runoff, sediment, nutrients, and pesticides are based on a dailystep, and the model can evaluate the long-term effects of NPS pol-lution in larger scale watersheds [40]. Evaluation of the applica-bility of AnnAGNPS in China is currently a popular topic. Forexample, Li and Liu [41] conducted computer modelling of thestudy area of Lianshui Basin, Jiangxi Province using the AnnA-GNPS model. The average simulation error and deterministic coef-ficient of their study were 11.8% and 0.94 for annual surface flowand 19.71% and 0.77 for annual sediment yield, respectively.These simulation results indicated that the model has an accept-able performance for the prediction of surface flow and sedimentloading.

108 Z. Shen et al. / Separation and Purification Technology 84 (2012) 104–111

Overall, the AGNPS series are feasible both technically and eco-nomically. Therefore, they are worth promoting and will havewidespread applicability in the future.

2.2.2.3. HSPF model. The HSPF model is a hydrological model thatapplies mathematical methods to hydrologic calculations and fore-casting to simulate the hydrologic route and pollutant transporta-tion on the land surface, sub-surface and in groundwater. The HSPFhas been widely applied in NPS pollution simulation studies in dif-ferent scale watersheds, providing a strong basis for establishmentand implementation of watershed management measures [42].

Some scholars have used the HSPF model to simulate hydrolog-ical elements and water quality in China in the last decade. Forexample, Xue and Wang [42] selected the Dage River Watershed,located at the upper portion of the Miyun Reservoir, as the studyarea to simulate runoff using the distributed HSPF model. Meiet al. [43] employed the HSPF model to simulate the water quantityand water quality of the Xili Reservoir Watershed in Shenzhen.They calibrated and validated this model by comparing varioussimulated and monitored scenarios, including the stream flowand concentration of pollutants in river outlets. Finally, they calcu-lated the total loadings of SS, TN and TP in each sub-basin and ob-tained some satisfactory results. The biggest constraint of the useof the HSPF model to simulate NPS pollution is the shortage of ob-served data, which affects the accuracy of simulated results inChina.

2.3. Other studies of physically based NPS modelling

Some Chinese scholars have focused on other study fields ofNPS modelling, such as comparing the prediction efficiencies of dif-ferent models in the same study area.

Shen et al. [44] applied the WEPP model and SWAT model tosimulate runoff and sediment yield for the Zhangjiachong Wa-tershed in the Three Gorges Reservoir Area. They found that,although both models were used in the region, WEPP was betterthan SWAT during validation and calibration periods, as well asother criteria of model efficiency. Huang and Hong [45] comparedand assessed the empirical model and AnnAGNPS model to calcu-late diffuse nitrogen and phosphorus emissions in the Jiulong RiverWatershed with intensive agricultural activities. They found thatboth models had similar levels of diffuse total nitrogen emissions.However, the empirical model has advantages in extensive studiesfor preliminary design because it is easily applied to large water-sheds with fewer data requirements, while AnnAGNPS is good atdetailed assessments of emissions.

3. Applications and improvements of native NPS pollutionmodelling

In addition to learning from foreign experience, Chinese schol-ars have developed several methods for studies of NPS. However,most of these are simple statistical methods that may not get agood estimation, and can be adopted only in the situation of datashortage. In recent years, researchers have started to develop phys-ically based models in China, but these have only been applied in afew specific areas.

3.1. Empirical modelling

3.1.1. Mean concentration methodIn view of the shortage of monitoring data in China, Li [46] pro-

posed the mean concentration method to estimate the averageconcentration of NPS pollution load of a watershed. The basic ideaof this method is that the process of annual runoff can be divided

into surface runoff and underground flow (in dry season) pro-cesses. NPS is primarily caused by the surface runoff; therefore,the annual NPS pollution load could be estimated based on theunderground runoff, surface runoff and the average concentrationof pollutants.

The method is a simple and effective methodology to estimateannual NPS pollution load based on limited monitoring data. Inaddition to predicting the average annual NPS pollution load, thismethod can also be used to predict some NPS pollution loads forspecific years (such as particular wet year) or flood events. Appli-cation of this method in the Heihe River Basin showed that it is rea-sonable to obtain the average annual NPS pollution load during anormal river flow year. Furthermore, the method was successfullyapplied in the Tianyu Basin, Fengyu Basin, and Shibianyu Basin ofthe Heihe Watershed, as well as the Hanjiang River and DanjiangRiver in Shaanxi Province.

3.1.2. Rainfall deduction methodCai et al. [47] proposed a rainfall deduction method for estimat-

ing NPS pollution load. In this method, the occurrence of NPS pol-lution is affected by rainfall and rainfall runoff process. If there isno surface runoff, the pollution is only caused by point source pol-lution; however, if surface runoff is generated in rainstorms, bothNPS and PS pollution cause the pollution. Point sources are morestable and can be regarded as constant; thus, the pollution loadcaused by a flood could be generated, increasing the relationshipsbetween rainfall deduction and NPS load, without taking into ac-count the annual point source pollution load.

This method overcomes the disadvantage of inadequate moni-toring data in NPS pollution and makes full use of existing informa-tion in hydrological stations and water quality monitoring stations.The method also prevents the increase of unnecessary monitoringprojects and omits the process of runoff and runoff division, whiletaking into account the atmospheric deposition, human activitiesand other factors. Overall, this method is a simple and effectiveNPS estimation method, and its application in the Weihe River Wa-tershed showed a satisfactory predicted result [47].

3.1.3. Correlation method of water quality and quantityHong and Li [48] proposed a correlation method of water qual-

ity and quantity to simulate NPS pollution load with limited mon-itoring data. In their method, rainfall runoff is the main factorinfluencing the formation of NPS pollution. For this reason, the cor-relation between water quality and quantity is established inaccordance with the monitoring and measured data. The yearlyrunoff is divided into the surface runoff and the underground run-off (runoff during the dry season), and the correlation betweenwater quality and quantity is used for the surface runoff load cal-culation to estimate the yearly load of rainfall-runoff pollution inthe case of limited data.

The results of a practical example [48] of predicting the annualload of TN and TP in the Hanjiang River and Heihe River basinswere close to those obtained using the average concentrationmethod, indicating its feasibility for use in regions that lackedmonitoring data. Overall, the correlation of water quality andquantity is determined using a simple method of calculating rain-fall runoff pollution load; however, it is not possible to conduct er-ror analysis due to the lack of long sequences of rainfall runoffmonitoring data.

3.2. Physically based and process-based modelling

3.2.1. IMPULSE model and NPSDSS systemThe IMPULSE (integrated model of non-point sources pollution

processes) model is a typical distributed and semi-physically basedNPS model independently developed by scholars at Tsinghua Uni-

Z. Shen et al. / Separation and Purification Technology 84 (2012) 104–111 109

versity [49]. The main physical and chemical structure of IMPULSEis similar to the widely used AGNPS model, including three sub-models of hydrology, soil erosion, sediments and pollutant trans-portation. However, it is more cost-effective than AGNPS becauseit can simulate a large number of space combinations of best man-agement practices (BMPs) efficiently and provide uncertainty anal-ysis function for model parameters.

According to the source compositions and characteristics of NPSpollution in Dianchi Lake Basin, researchers constructed a practicalsystem-NPSDSS system (non-point sources pollution decision sup-port system) based on the IMPULSE model and combined with theNPS survey model, GIS toolbox and screening model of pollutioncontrol practices. They then applied this system to assess theNPS pollution situation in the Dianchi Lake Basin and obtained sat-isfactory results [3].

3.2.2. Other NPS pollution modelling methodsChinese researchers have also developed some other feasible

methods according to Chinese natural conditions for regions whichlimited monitoring data are available.

To identify the optimum management for a small-mid scale riv-er basin, Wang et al. [49] built a synthetic water quality model ser-ies that was combined with the modified QUAL-II FU water qualitymodel and NPS pollution model to calculate the NPS pollution fromagricultural land. Zhang [50] established a mathematical model forcalculation of runoff discharge, soil loss and nutrient loss fromDonghu Lake and determined several model parameters for differ-ent ground surfaces at each runoff area by site test. Zhang and Zhu-ang [51] developed a model system that included a watershedhydrological model, pollutant-yielding model and pollutant-con-verging model for prediction of agricultural NPS pollution basedon the formation-characteristics of NPS pollution. They were con-cerned about the spatial difference in parameters and adoptedthe idea of a distributed model structure. Eventually, the predictedresults revealed good suitability and flexibility of this model sys-tem when applied in the West Liaoning Watershed.

4. Problems associated with NPS pollution modelling in China

4.1. Limitation of exotic models in China

As described above, studies of NPS pollution in China largely de-pend on experiences and methods from other countries. Becausethe application conditions and situations (e.g. different land usepractices) vary from those of China, the direct use of these exoticmodels may cause large errors.

4.2. Limitation of scale of application area

Most currently available models have apparent regionality. Thisis because they rely heavily on parameters (both empirical andphysical), and can only be applied in small-mid regions due tochanges in regional hydrogeology and weather conditions. To date,studies of NPS pollution simulation have mostly been conducted inmid-small scale watersheds and have only described water andcontaminant migration vertically due to the variability of parame-ters; therefore, these models may behave poorly when applied tolarge scale areas.

4.3. Calibration and validation

(1) Due to the various parameter sensitivities to weather, soiltypes and land use in different regions, significant changesin the predicted results may occur. Those changes could bebetter reflected in actual situations rather than the inherent

calculation process. The accuracy of calibration is affecteddue to the lack of field studies of NPS in China.

(2) Due to the incompleteness of monitoring networks in China,the validation of models can only be conducted using mon-itoring data from limited hydrometrical stations.

(3) Due to the large amount of parameters contained by mostcurrently available NPS models, conventional methods forcalibration and validation are inefficient.

4.4. Incompleteness in describing the NPS mechanism

The hydrological process and mechanism involved in pollutantmigration have not been fully developed in currently availablemodels, which has prevented the simulation of NPS pollution frombeing completely accurate. Recent fertilizer loss studies have fo-cused on the process of surface runoff creation and soil erosion,while little work has been conducted to investigate the processof nutrients entering the surface runoff from the underdrainagesystem.

4.5. Evaluation criteria

There is no comprehensive guidance available to facilitate mod-el evaluation in terms of the accuracy of simulated data comparedto measured flow and constituent values. Moriasi et al. [52] recom-mended that three quantitative statistics, NSE, percent bias(PBIAS), and ratio of the root mean square error to the standardof measured data (RSR) be used in simulation evaluation. However,NPS models are often used in data-deficient study areas. Therefore,data is an important factor influencing the performance of models.Even an advanced model, when applied without enough inputdata, will produce unsatisfactory results. Moreover, different mod-els are suitable for different situations due to their inherent char-acteristics. For example, a large-scale and widely distributedmodel may not be suitable for simulation in a small basin. There-fore, we can only determine if a NPS model is good or bad basedon the actual conditions of the study area and the available datafor such areas.

5. Recommendations for future studies

Considering the current study situations and the problems thatoccur, future work on development of NPS models should focus onthe following.

5.1. Model mechanism, model improvement, and model innovation

Considering the structural defects in NPS models, future studiesshould focus on in the following: (1) strengthening the NPS modelmechanism study; (2) improving existing model structures to de-scribe the NPS pollution process more correctly with China’s actualcondition and avoiding introducing too many parameters; (3) con-structing flexible models for different situations with limited dataand shortage of long series monitoring information; (4) developingdistributed hydrological models for multi-scale watersheds to de-scribe the heterogeneity, nonlinear aspects and complexity of spa-tial multiple-scale hydrological parameters.

5.2. Model uncertainty

Due to the limited human cognition, simplifications andassumptions are taken when developing models abstracted fromthe natural systems. Errors also occur with input data and modelparameters, which lead to an inevitable uncertainty of predictedresults [53]. Developing an understanding of NPS model uncer-

110 Z. Shen et al. / Separation and Purification Technology 84 (2012) 104–111

tainty and quantitative evaluation of the uncertainty have becomea front edge topic in NPS model study [54].

Recent major difficulties in NPS uncertainty analysis technologyremain in quantifying various uncertain factors and reducing theuncertainty in model simulation. Future studies will focus on fur-ther improvement of parameter calibration, optimization of dataacquisition solutions, and improvement of uncertainty analysistechnology.

5.3. Model integration

Future studies of NPS model integration should emphasize theintegration of models with different factors, 3S technology (GPS,RS, GIS) and large basin management models.

(1) Integrating models with different elementsNPS pollution is a complicated and synthetic process that

includes multiple processes of hydrology, weather, environ-ments and ecological methodology. However, commonlyused models only simulate the processes of hydrology, ero-sion, and pollutant transportation, and calculation studiesonly focus on the temporal–spatial distribution of NPS pollu-tion load, while there are few systems designed to simulatethe effects and process after NPS pollutants get into the eco-system. Therefore, the integration of different processes andelements is one of the main trends in gradual systematiza-tion of the NPS models.

(2) Integrating models with 3S technologyWith the development and application of 3S technology,

combing 3S technology with NPS models is a popular NPSresearch area. Possible aspects of future studies couldinclude employing the advantages of 3S technology in orga-nizing, managing, and processing the spatial and attributedata to enable precise evaluation of the land use of largebasins, as well as the soil texture, irrigation and drainagemethods, and spatial variability in agriculture measures.The integration could also increase the ability for data acqui-sition, thereby enabling increased accuracy of basic data byobtaining numerical information through remote sensing.Finally, integration could be used to visualize the simulationresults, increase the model calculation speed and simulatethe accuracy.

(3) Integrating the model with large watershed managementmodels

As a physical geographic unit and a management unit,the concept of the watershed has gradually been attachedwith great importance by executive and managementdepartments. Relevant large basin management models con-cerning economics and the environment, as well as harmo-nious development with humans and nature have becomea popular topic for different countries. Because NPS pollutionis one of the main processes that influence basin water qual-ity, the NPS model will be a critical part of these large man-agement models. The emergence of BASINS modeldeveloped by the USEPA marks the appearance of this kindof model, among which the HSPF model is an important part.It is likely that with the concept of basin integrating man-agement being widely accepted and adopted, large basinmanagement models including NPS models will become animportant research topic in the watershed model develop-ment field.

Acknowledgements

The study was supported by the National Science Foundationfor Distinguished Young Scholars (No. 51025933), the Nonprofit

Environment Protection Specific Project (No. 200709024) and theWater Specific Project of China (No. 2008ZX07209-007).

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