This Article Appeared in a Journal Published by Elsevier. the Attached

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright Author's personal copy

Environmental Modelling & Software 26 (2011) 1502e1514

Contents lists available at ScienceDirect

Environmental Modelling & Software

journal homepage: www.elsevier.com/locate/envsoft

An integrated model for water management in a rapidly urbanizing catchment

Hua-Peng Qin a,b,*, Qiong Su a, Soon-Thiam Khu b a Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, 518055 Shenzhen, China b Faculty of Engineering and Physical Sciences, University of Surrey, Civil Engineering (C5), Guildford, Surrey GU2 7XH, UK article info abstract

Article history: Existing water and environmental management models usually separately simulate socio-economic, Received 23 July 2010 water infrastructure and natural receiving water systems and thus cannot effectively capture the inter- Received in revised form actions among economic and population growth, water resource supply and depletion as well as envi- 4 July 2011 ronmental changes, especially in analyzing long-term scenarios of urbanization. In this paper, a system Accepted 6 July 2011 dynamics and water environmental model (SyDWEM) was developed to improve the understanding of Available online 18 August 2011 the integrated socio-economic and water management system in a rapidly urbanizing catchment. The integrative character of SyDWEM is featured by putting the socio-economic component as an internal Keywords: Socio-economic sub module of the whole system. It also contains water consumption and pollution load module, water Water management supply module, wastewater treatment module as well as receiving water module. The Shenzhen River Environmental model catchment was used as a case study to demonstrate usage of the functionality and purpose of the System dynamics integrated model. The results indicate that SyDWEM has the capacity to predict the socio-economic and Urbanization environment changes at a catchment scale under proposed socio-economic policies and water infrastructure planning. Therefore, it can help support the integration of decision making in socio-economic development and environment management. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction urbanizing area often face serious water environmental problems such as water resource shortage, water quality deterioration and Urbanization is usually accompanied by population expansion, environmental pollution. From urban development planning point of economic growth and increase in built-up areas (Alig et al., 2004; view, the reason may be that socio-economic planning and water Deng et al., 2008). It is driven by economic and development poli- infrastructure planning are performed by different sectors without cies that encourage a change from agrarian to urbanized economic adequate understanding of the others’ needs. Herein, socio-economic activities (Knox, 1994). Urbanization is not only associated with planning is deﬁned as a middle- or long-term strategy for social material civilization and spiritual civilization, but also a reﬂection of change and economic development in a region or country, which the integrated development of social politics, economy, culture, aims to analyze development conditions, formulate development science and technology. Compared to rural areas, urbanized areas goals and conceptualize alternative strategies for solutions (Poppe, have rich and colorful life, better industrial base, complete structure, 2004). On one hand, socio-economic planners are not expected to and more job opportunities (Zhao and Liu, 2010). However, urbani- fully apprehend the capacity limitations of existing/future water zation in some developing countries is happening so rapidly that the infrastructure in their decision making process. They usually assume infrastructure development cannot provide effective or adequate that infrastructure development can keep the pace of socio-economic support for population and economic growth (Biswas and Tortajada, growth. On the other hand, water infrastructure planners do not fully 2009). For example, water supply systems may not be able to provide account for the extent of rapid socio-economic development in enough water to meet growing residential and industrial demands; decision making. Infrastructure planners usually estimate certain sewer and wastewater treatment system cannot effectively collect socio-economic growth potentials in their planning. But they might and dispose all the wastewater. As a result, the catchments in rapidly over- or under-estimate the growth and cannot make timely adjustment in infrastructure development. For example, the increase in wastewater treatment capacity fell behind the population and * Corresponding author. Key Laboratory for Urban Habitat Environmental Science economic growth from 1990 to 2010 in Guangzhou, China (Dong and and Technology, School of Environment and Energy, Peking University Shenzhen Mei, 2010). Megacities in developing countries like Dhaka, Jakarta or Graduate School, 518055 Shenzhen, China. Tel.: þ44 (0) 1483686625; fax: þ44 (0) 1483682135. Karachi have been unable to cope with their rapid economic growth E-mail addresses: [email protected] (H.-P. Qin), [email protected] (S.-T. Khu). in terms of providing satisfactory drinking water and wastewater

H.-P. Qin et al. / Environmental Modelling & Software 26 (2011) 1502e1514 1503 management services in recent decades (Biswas and Tortajada, 2009). aforementioned models could be used for reservoir release policies, Therefore there are often mismatches between water demand and urban drainage system planning, optimization of process design and supply capacity, and between wastewater generation and treatment operation of existing WWTPs, water resources supply planning, and capacity, during rapid urbanization. To minimize these mismatches, wastewater reuse strategies. However, the framework does not integrated sustainable catchment management is required between include socio-economic module, and cannot support decision different planners and policy makers across different sectors and making on water management for socio-economic planning. spatial scales (Macleod et al., 2007). Furthermore, in order to provide Besides well known factors such as infrastructure aging and better support for socio-economic and infrastructure growth in inadequate replacements, land use changes caused by urban renewal a sustainable manner, planners from different sectors need to consult also exert considerable influence on the quantity and quality of and discuss with each other, and a decision support system, usually in urban water systems, and have only been recently considered by the form of an integrated modeling system, is needed to facilitate the decision-makers (Fu et al., 2008). Thus, it necessitates a modeling communication. approach for social-economic planning which simulates the inter- Integrated modeling has been applied to both urban and rural connections among land use change (i.e. urban growth patterns, settings. In rural areas, a number of integrated models have been different demographic scenarios; new developments), wastewater developed to support land use management (Schaldach et al., infrastructure (drainage and treatment) and receiving water bodies. 2011), water resource management (de Lange et al., 2010; Letcher Fu et al. (2008) established an integrated model (SSDIM) which et al., 2007; Molina et al., 2010; Teegavarapu, 2010; Victoria et al., includes a Land Use Change model (LUC), an Urban Drainage model 2005), water quality management (Cools et al., 2011; Lynam et al., (UD), a Waste Water Treatment Plant model (WWTP) and a River 2010; Santhi et al., 2006) and water infrastructure planning Quality simulation model (RQ). The LUC model is applied to generate (Adenso-Díaz et al., 2005). To evaluate the impacts of agriculture land use allocation, population densities, demographic growth and policies on the rural water system, Merritt et al. (2004) developed the impact of associated planning policies, and these information are a DSS which links socio-economic models with a crop model, fed into subsequent models. Thus, SSDIM provides a platform for a hydrologic model and a soil model. Van Delden et al. (2007) planners to assess the impact of their decisions on the performance developed an evaluation system (‘MEDACTION’ policy support of the water infrastructural system and quality of water bodies. system) to support planning and policy making in the fields of However, as there is no economic development component present water management, land degradation, desertification and sustain- in SSDIM, and it lacks the ability to evaluate the impacts of water able farming. demand/supply and socio-economic planning policies on the Many models of integrated water system in urban area have quantity and quality of urban water systems. been developed to support infrastructure decision making. They The ‘Water Evaluation And Planning’ system (WEAP) is another can consider integrated water infrastructure and natural receiving user-friendly software tool that takes an integrated approach to water system, which usually include two or more infrastructures water resources planning (Sieber et al., 2007). It has the ability to and receiving water components, e.g. water supply/allocation, calculate water demand, supply, runoff, infiltration, crop require- sewer, wastewater treatment, or reclaimed wastewater reuse ments, flows, and storage, and pollution generation, treatment, systems (Achleitner et al., 2007; Bixio and Wintgens, 2006; de discharge and instream water quality under varying hydrologic and Azevedo et al., 2000; Mitchell et al., 2001; Sieber et al., 2007). For policy scenarios. It can be used to evaluate a full range of water example, CITY DRAIN (Achleitner et al., 2007) is a block-wise development and management options. Elbe-DSS (Matthies et al., modeling of urban drainage system, which enables modeling of 2006) is a decision support system for integrated river basin catchment, sewer system, storage devices and receiving water management of the German part of the Elbe river basin. The user system. It is a pragmatic and simplified model with aforementioned can evaluate effectiveness of management actions like reforesta- systems and could be used for real time control and predictive tion, improvement of treatment plant technology or the application control of urban drainage system. Another software, SIMBA 5.0 of buffer strips under the influence of external constraints on (IFAK, 2005), has similar functions but encompasses more climate, demographic and agro-economic changes to meet water complicated sub-models such as: KOSIM for the sewer system, management objectives such as water quality standards and Activated Sludge Model No.1 (ASM1) for the treatment plant, and discharge control. Both WEAP and Elbe-DSS can evaluate the effect EPA storm water management model (SWMM5) for the river. of socio-economic and engineering system change on water envi- SIMBA has been mainly applied in engineering practice such as ronment. However, the socio-economic components (e.g. pop- optimization of process design and operation of existing waste- ulation level and economic development patterns) are regarded as water treatment plants (WWTPs), strategies for sewer system external scenarios/constraints of the integrated water system, and operation etc.. However, neither CITY DRAIN nor SIMBA 5.0 thus the inter-relationships between different socio-economic includes water supply module and socio-economic module, and components, and between engineering and socio-economic thus both of them do not explicitly consider the inter-relationship measures, cannot be fully considered by the models. among socio-economic, water supply and drainage system. With increasing requirements of multi-scale and multi- The Aquacycle model (Mitchell et al., 2001) accounts for water objective assessment, and decision making that considers pathways by simulating two sub-systems of the urban water cycle economic and social systems, as well as the ecosystem, integration (rainfall-runoff and water supply-wastewater processes), and the of management activities, and also of the modeling undertaken to interactions between them. Wastewater reuse system is viewed as support management, has become a high priority (Argent, 2004). interlinked in a holistic structure. The model does not include One manifestation is the development of integrated hydro- receiving water module and socio-economic module, and thus it economic models, with specific focus on evaluating economic lacks the ability to consider the interactions among socio-economic effects of water resources or water quality management, and the development, urban water cycle and receiving water. capital cost of integrated water infrastructure planning (Assaf and AQUAREC-WTRNet (Joksimovic et al., 2006) is a DSS for water Saadeh, 2008). They offer the potential to efficiently and consis- treatment for reuse and distribution network, which has a broader tently integrate hydrologic, economic, and institutional policies to aim of developing the design principles for water reuse systems. The support basin scale cost-benefit environmental assessments (Ward, integrated framework consists of water reuse optimization in 2009). However, the economic modules in the models focus on treatment and distribution and in the selection of end-users. The maximizing profit or minimizing capital and operating costs of the Author's personal copy

1504 H.-P. Qin et al. / Environmental Modelling & Software 26 (2011) 1502e1514 water system, rather than evaluate the effect of socio-economic water infrastructure development to improve the capacity of water development on the water system. In addition, the hydro- supply and pollution treatment. Therefore, the water system in an economic models neglect the modules of population develop- urbanizing catchment is an integrated system and comprises of ment, urban drainage system and WWTPs. Therefore, the models three sub-systems: (1) socio-economic system; (2) water infra- also fail to provide effective decision making support for both structure system; and (3) receiving water system. The socio- socio-economic planners and water infrastructure planners. economic system can be further divided into the population/GRP Some macroeconomic models, such as inputeoutput models, module and the water demand/pollution generation module; the have the ability to represent how water policy or planning affects the water infrastructure system usually includes water supply, sewer entire economic system (Harou et al., 2009). Based on inputeoutput and WWTPs modules; and the receiving water system comprises of relationships, Ni et al. (2001) were able to study the dynamic rela- hydrodynamics and water quality simulation modules for river, lake tionships between the rapid economic development, water pollution or estuary, etc. The population/GRP module is an important and and the subsequent waste-load allocation in different economic integrated part of the SyDWEM framework since the dynamic and sectors. However, the models cannot describe water infrastructure fast changing population/GRP growth influences water demand/ system and their relationships with socio-economic system. pollutant generation. Although most of the wastewater is supposed Existing environment and socio-economics models often neglect to be collected by sewer system and conveyed to WWTPs, other the inter-relationship among socio-economic, water infrastructure untreated wastewater may be discharged into the receiving water and natural systems, thus cannot effectively capture the linkages and due to insufficient wastewater infrastructure connectivity or interactions among economic and population growth, water capacity. There are interactions among different systems and resource supply and depletion, as well as changes in the hydro- modules (Fig. 1): water supply module supports the economic and ecosystem, especially when analyzing long-term scenarios on pop- population growth; the wastewater from domestic and industrial ulation growth and urban expansion. Moreover, as these models are activities collected by sewer system, treated by WWTPs and then often developed for water infrastructure planners and focus on the discharged into the receiving water; some of the wastewater is water related services and problems, they cannot provide effective reclaimed and becomes a source of water supply; and the receiving support for multi-sector management or serve as communication water is one of the conventional sources of water supply, and its tools between socio-economic planners and water infrastructure quality is affected by the pollutant loads from the catchment. planners effectively. In rapidly urbanizing catchment, due to the dynamic changes of economic, population and built-up area, new integrated models should consider dynamic socio-economic growth 2.2. Individual models within SyDWM and its impact on water infrastructure and receiving water system. Thus, there is a need for a new integrated model with the following 2.2.1. Population-GRP module capabilities: 1) to simulate the interaction between socio-economic, Economic performance can be evaluated by the gross domestic 1 water infrastructure and receiving waters; 2) to enable evaluation of product (GDP) at national scale or GRP at regional scale using the different measures for water management; and 3) to enable better Cobb-Douglas production function for growth forecasting, which communication between different decision makers. has been successfully used in rapidly urbanization areas (Chow and The aim of this paper is to develop a conceptual integrated Li, 2002; Holz, 2005). The equations are: model that encompasses economic, social and nature resources ^ ¼ þ ^ þ ^ considerations. More explicitly, the objectives are to: (1) develop an Yt c bLLt bK Kt (1) integrated model (known as SyDWEM) incorporating dynamic ^ socio-economic, water infrastructure and receiving water systems Lt ¼ðLt Lt1Þ=Lt1 (2) appropriate for a rapidly urbanizing catchment; (2) demonstrate the efficiency and efficacy of the SyDWEM model using a case study Yt in Shenzhen, China; (3) predict gross regional production (GRP) and Lt ¼ (3) GLt population growth for the next 10 years, and evaluate the impact of socio-economic development on water resource and water quality ¼ ð þ ^Þ in the study catchment. Yt Yt1 1 Y (4) This paper is structured as follows: Section 1 discussed the By substituting Eqs. (4) into (3) and then Eqs. (3) into (2), we arrive characteristics of an rapidly urbanizing catchment including at problems associated with current modeling practices and the motivation for the development of an integrated system; In Section ^ GLt1 ^ GLt1 Lt ¼ Yt þ 1 (5) 2, the generalized water system of the SyDWEM framework, GLt GLt together with details of each individual model, are illustrated to ^ show how they are coupled using System Dynamics (SD). Section 3 By substituting Eqs. (5) into (1) and moving Yt to the left-hand side, presents the SyDWEM for Shenzhen River catchment and the we arrive at calibration and validation results of the model. Section 4 discusses þ ð = Þþ ^ the GRP/population growth in the catchment in 2010e2020, and ^ ¼ c bL GLt1 GLt 1 bK Kt Yt = (6) evaluates its impact on water resource balance and water quality. 1 bL GLt1 GLt Finally, the main conclusions are summarized in Section 5. where, the hats denote growth rates; t is time (year); Yt, Lt and Kt are GRP, labor force and capital stock (billion Yuan), respectively; bL, 2. SyDWEM framework bK and c are coefficients of labor output elasticity, and capital output elasticity and technological progress (or growth in total 2.1. Generalized water system in SyDWEM

An urbanizing catchment is characterized by rapid economic and 1 GRP is identical to GDP in the study area because GDP is widely used in China to population growth, with corresponding rapid changes in water measure the economic activity at country, province, city and town levels (http:// demand and pollutant generation. This commonly results in massive www.stats.gov.cn/). Author's personal copy

H.-P. Qin et al. / Environmental Modelling & Software 26 (2011) 1502e1514 1505

Socio-economic Population/ GRP Water demand / module pollutant system generation module

Water transferred Wastewater from other catchment Water Water Sewer& infrastructure supply WWTP system module Wastewater module reclamation Untreated Abstraction and reuse Treated wastewater wastewater River system

Fig. 1. Generalized water system for an urbanization catchment. factor productivity, TFP), respectively; GLt is GRP per labor force (or includes: residential water demand, economic water use for the production efficiency). needs of primary, secondary, and tertiary industries. If necessary, the labor force can be sub-divided based on different The total water demand for residential activities, e.g. in-house economic sectors such as primary (e.g. agriculture), secondary (e.g. water use for kitchen, laundry and bath, are estimated according manufacturing) and tertiary (e.g. services) industries. And each type to population size and water consumption per capita. The total of industry can be further sub-divided according to the primary water demand for economic activities is expressed as a function factor in production process. Thus, the GRP per labor force GLt in Eq. of GRP in different industries (primary, three kinds of secondary, (3) can be expressed as follows: and tertiary) and water demand per unit GRP (water use efficiency) in different industries. Thus, water use efficiency of the X IS GL ¼ 1= ti (7) whole GRP is influenced by the industrial structure (GRP t GL i ti proportions of different industries). The equations can be expressed as follows: where, ISti and GLti denote GRP proportion and GRP per labor force of ith industry in year t, respectively. GRP per labor force for different industries has been estimated by exponential growth RWDt ¼ Popt WDPCt (10) model in some countries or areas, e.g. Japan, Korea, Singapore and X Hong Kong, where it is shown to fit well with historical time series ¼ ð Þ EWDt ISti GRP WGti (11) data (Total Economy Database, 2008). i Capital stock Kt can be estimated by Perpetual Inventory Method and described as: where, RWDt and EWDt denote residential and economic water 3 demand (m )in year t, respectively. WDPC t is water consumption Kt ¼ Kt1ð1 dÞþIt (8) per capita in year t, WGti is water demand per unit GRP of ith herein It is net investment at time t, d is depreciation rate. It can be industry in year t. determined by using various methods, such as total social fixed Within the pollution generation module, two main sources are asset investment, gross capital formation or gross fixed capital considered, they are: domestic activities and secondary industry. formation (Young, 2003) and ‘accumulation’ (Chow, 1993). Pollution from residential activities is estimated according to the Fertility, mortality and migration are principal determinants of population size and pollutant load per capita. Pollution from population growth. However, migration population accounts for secondary industry is estimated based on GRP and pollutant load a large percentage of total population in a rapidly urbanizing area per unit GRP. Water pollutant loads from tertiary industry are (e.g. more than 80% in Shenzhen City, China, 2007). In SyDWEM, usually identified as domestic pollution, so they are included in the population change is simulated according to labor demand of estimation of pollutant generation in residential activities. The economic development (Ledent, 1982) and expressed as: equations can be expressed as follows:

Popt ¼ Lt PLt (9) RPLt ¼ Popt PLPCt (12) herein, PLt is ratio of population to number of labors at year t in a catchment. X ¼ ð Þ The population-GRP module can be used to predict the long- EPLt ISti GRP GPti (13) term GRP and population growth, and to evaluate the effects of i adjustment industrial structure and improvement of labor where, RPLt and EPLt denote residential and economic pollutant productivity on annual GRP and population variation at adminis- load (t) in year t, respectively. PLPCt is pollutant load per capita in trative and sub-catchment scale. year t, GPti is pollutant load per unit GRP of ith secondary industry in year t. 2.2.2. Water demand and pollutant generation module This module can be used to analyze the effects of adjusting the This module estimates the water demand for different users and industrial structure and water efﬁciency in order to assess the their associated pollutants generated. The water demand module impact on annual water demand and pollutant generation. Author's personal copy

1506 H.-P. Qin et al. / Environmental Modelling & Software 26 (2011) 1502e1514

2.2.3. Water supply module between the system and the external environments; and c) It can This module can be used to estimate the total and potential be quickly understood by different users and facilitate public water supply in the catchment. Sources of water supply, such as participation due to its transparency and easy information local water resources, reclaimed wastewater reuse and water exchange among sub-systems (Stave, 2003). SD has been applied to transferred from other catchments, may be estimated from the study public management questions related to population, agri- local water resources development rate, reclaimed wastewater culture, resources, economy and population in urban or global reuse ratio and water transfer quota, respectively, and in accor- scales (Forrester, 1969; Meadows et al., 1972). It also has been used dance with the water resource plans for the catchment. as an integrated platform to simulate the relationship between economic growth, population expansion, water resource develop- 2.2.4. Sewer and WWTPs module ment and water pollution in the environmental system (Chen et al., This module can be used to estimate the total effluent load. As 2005; Guo et al., 2001; Simonovic, 2002). However, system shown in Fig. 1,effluent may arise from these sources: dynamics model is a causal mathematical model and thus it has limited use and acceptance for well-defined and detailed problems (i) wastewater collected and treated by WWTPs; (Barlas, 1996). It is also difficult for SD model to accurately describe (ii) excess wastewater may flow into the nearby river without the temporal and spatial variation of water quality in a receiving treatment; water body (Vezjak et al., 1998). (iii) some effluent of WWTPs may be treated further for residential As mentioned in Section 3.1, water system in the urbanizing or industry reuse; catchment is a complex human-natural system. There are interactions and feedbacks among a number of related social, economic, The effluent and pollutant loads discharged into the river from regulatory and lifestyle factors. In this paper, SD is selected as WWTPs can be calculated by the following expressions: a platform to integrate individual water environment models in X Section 2.2. Fig. 2 shows the stock-flow diagram for the first step in EffluentfromaWWTP ¼ ðeffluentincatchment hÞð1aÞ the establishment of SyDWEM. Understanding of stock and flow, (14) which is the basic of SD, is crucial in comprehending and managing water problems in urban area. In system dynamics, there are three X kinds of variables including stocks, rates as well as auxiliary:1) ¼ ð Þ Pollutant loads from a WWTP pollutant generation h Stocks, which denote states of the system, are variables that accu- ð1 qÞð1 aÞ mulate the effects of other variables; 2) Rates are variables that fl (15) control the ows of material into and out of stocks, which arise as results of system activities and produce the related consequences; where, h, q and a are volumetric wastewater treatment rate, 3) Auxiliaries are variables that modify information as it is passed pollutant removal rate and wastewater reuse rate, respectively. from stocks to rates. Taking the water supply module as an example, the potential and total water supply in the whole catch- 2.2.5. River water system module ment can be taken as a Stocks variable. It is directly related to two In this module, a water quality model is used to simulate the rate variables (water resource development rate and water reuse pollutant behavior (transport, transformation and fate) in river rate), which determine the water supply ability in the catchment system. In this model, the SainteVenant equation (Chanson, 2004) dynamically. These rate variables are significantly affected by the and convection-dispersion equation (Jain et al., 2003) are coupled water resource plans of the catchment. Policy makers can adjust as follows: their decisions in order to provide sufficient water to fulfill future economical and residential activities needs. Initial conditions are vA vQ þ ¼ q (16) needed to start the simulation at the first time step, and the states vt vx of the system (stocks variables), are update to represent consequences of the previous simulation step. In the SyDWEM frame- vQ vðuQÞ v vQ vZ gn2QjQj work (Fig. 2), results of GRP-population model, water consumption þ ε þ gA þ ¼ 0 (17) vt vx vx vx vx AR4=3 model, water supply consideration as well as wastewater treatment system model are coupled together using SD model; and then vC vðuCÞ v vC pollutant loads from the socio-economic and water infrastructure þ D þ KC ¼ S (18) vt vx vx x vx c system are fed into a water quality model. It must be noted that the spatial boundaries and sizes of control where, x is longitudinal distance; t, time; u, cross section averaged areas for different SyDWEM modules may be different as they are flow velocity; Q, flow discharge; q, lateral increment of discharge related to the appropriate spatial units, such as administrative per unit length of x; A, cross-sectional area; Z, water level; g, district, WWTP service area and sub-catchment. For example, the gravitational acceleration, effective viscosity coefficient; n, rough- water supply module may be considered for the whole catchment; ness coefficient; R, hydraulic radius; C, cross section averaged whereas the population/GRP module and water demand/pollutant concentration of specific pollutant; Dx, longitudinal dispersion generation module may be developed at administrative district coefficient; K, decay coefficient; Sc, source intensity. scale. Treated wastewater is usually considered as a point source from a WWTP, while untreated wastewater can either be a point 2.3. Model coupling source or non-point source originated directly from each sub- catchment. To address such scaling issues, the spatial data (e.g. SD was developed by Forrester (1961) to simulate the dynamic population, GRP, water consumption and pollutant generation) are behavior of a complex system. The advantages of using an SD model solely related to and uniformly distributed in built-up land of the as an integrated platform can be summarized as follows: a) It can catchment. And thus spatial data can be mapped to different area/ address problem at different scales and support a variety of appli- district/sub-catchment according to the percentage of built-up land. cation goals (Winz et al., 2009); b) It has the capacity to describe the Temporal scale difference of models is another problem that feedback relations among sub-systems, as well as the relations needs to be considered in model coupling. In the model, population Author's personal copy

H.-P. Qin et al. / Environmental Modelling & Software 26 (2011) 1502e1514 1507

Fig. 2. Simplified SyDWEM of a catchment. and GRP are estimated at annual step, and the water demand, population expansion, the availability of supply mainly depends on pollutant generation, wastewater collection and treatment are a large-scale water-transfer project, with about 88% of the water considered at daily step. The time step of the river model simula- imported from outside the city boundaries. tion is set to seconds or minutes to capture the dynamic variations With rapid socio-economic growth, the catchment is constantly of the river flow and water quality. In the case study, annual data under threat from severe water shortage and quality deterioration and daily data were downscaled to the smallest time units of problems. Moreover, the spatial variation of socio-economic activi- seconds. ties and water infrastructure in each sub-catchment constrain any future planning efforts. 3. SyDWEM of Shenzhen River catchment 3.2. Model description, calibration and validation 3.1. Shenzhen River catchment Based on the current development in the Shenzhen River The Shenzhen River is located in a rapidly urbanizing coastal catchment, five modules within the SyDWEM framework are active, region of Southeast China, and forms the administrative border namely: population-GRP module, water demand and pollutant between mainland China and Hong Kong (Fig. 3). The total northern generation module, water supply module, sewer and WWTPs catchment area of Shenzhen River is 187 km2, and the length of the module as well as river system module. Integration of these modules main river is about 14 km. Shenzhen River is a typical tidal river and is achieved the use of system dynamics simulation, and in this paper it has several tributaries, of which the Liantang River, Shawan River, using the Vensim PLE (Personal Learning Edition) 5.10 (Ventana Buji River, Futian River, Huanggang River, Xinzhou River and systems, Inc., 2010). Although there are some options to support Fengtang River drain from the Shenzhen side. The main river drains system dynamics modeling (e.g. Dynamo, iThink/Stella, PowerSim southwest into the Deep Bay, which joins the Pearl River estuary on and Vensim), Vensim PLE was chosen because: (1) it provides a user its seaward side. and modeler friendly simulation environment; (2) it is much easier The northern catchment includes three administrative areas of to be understood by the users such as the socio-economic planner Shenzhen: Luohu District, Futian District, and Buji Town. During the and engineering planner; and (3) it is free for educational use and past twenty years, this area has undergone rapid urbanization. inexpensive for commercial use. Between 1990 and 2007, its GRP increased from 9 billion Yuan to 103 billion Yuan, the total population increased from 0.81 million to 3.2.1. Input parameters 2.65 million and the ratio of built-up area to developable land The decision variables in the model such as net investment increased from 16.7% to 81.3%. (which is estimated by total social fixed asset investment), industry Water infrastructure development lags far behind that of structure (GRP proportions of different industries), water transfer economic development and has low efficiency. Although there are quota, reclaimed wastewater ratio (ratio of reclaimed wastewater currently three WWTPs (Luofang, Binhe and Caopu) in the catch- to water consumption), volumetric wastewater treatment rate, ment, an estimated 26% of the wastewater is discharged into pollutant removal rate, etc. can be regarded as input parameters Shenzhen River without treatment. Furthermore, Shenzhen is one and determined by historical statistics or future development of the seven cities in China suffering from severe water shortages. policy/planning (Table 1). The local water resources including ground water and rainwater The net investment of each administrative district (district/ collected in reservoirs at the upstream of the Shenzhen River only town) in Shenzhen River catchment is given in Table 1, with the account for around 10% of water consumption. In order to satisfy total social fixed asset investment is taken as the net investment in the increase of water demand due to economic development and catchment. The industry structures in the study area can be Author's personal copy

1508 H.-P. Qin et al. / Environmental Modelling & Software 26 (2011) 1502e1514

Fig. 3. A general view of Shenzhen River catchment.

categorized into primary, secondary and tertiary industries, and the and other farming by-products etc.; technology-intensive industry secondary industries can be further divided into labor intensive, refers to an industry which requires advanced technology (auto- technology-intensive and capital-intensive secondary industries mated or semi-automated) to support production, examples are the according to the dominant production process. Labor intensive electronic industry and advanced equipment manufacturing industry refers to an industry which requires substantial amount of industry; capital-intensive secondary industries refers to an manual workforce to produce the industrial products e.g. paper and industry that invest a significant amount of capital into expensive paper-board, chemical raw materials and products, textile, farm machines instead of labor e.g. biotechnology and pharmaceutical, new material, new energy and fine chemicals industry. The Table 1 industry structures of Luohu District, Futian District and Buji Town Input parameter values of SyDWEM of Shenzhen River catchment (Shenzhen Statistics Bureau, 1982e2009). from 1990 to 2009 are shown in Table 2. Chemical oxygen demand (COD) is taken as a representative Parameters Unit Values water quality indicator in the study since it is one of the main 1990 2009 pollutants in Shenzhen River. For simplification, COD generation Net investment Billion Yuan per capita and COD load per unit GRP of different industries are Luohu District (in 1990 price) 1.8 3.4 assumed to remain constant. Futian District 1.4 8.8 According to the estimation by Liu and Lu (2006), the pollutant Buji Town 1.5 7.0 Retail price indexes % 100 230 load from primary industry (agriculture) is much smaller compared Depreciation rate % 6 6 to other industries in Shenzhen, e.g., the COD load from agriculture Ratio of population to number of labors % 65 76 is estimated to account for only 0.1% of total pollutant load in 2009. Average birth rate & 2.76 2.67 Thus, it was omitted in the study. Water pollutant loads from & Average death rate 1.11 0.33 tertiary industries are commonly identified as domestic pollution Water consumption per capita L/d 178 243 COD generation per capita g/d 70 70 in China, and hence they are considered under residential activities. COD per unit GRP of different industries t/million Yuan Labor-intensive secondary industry 16.12 16.12 3.2.2. Estimated parameters from calibration Technology-intensive secondary industry 1.41 1.41 In the case study of Shenzhen River catchment, GRP per labor Capital-intensive secondary industry 0.24 0.24 Water transfer quota million m3/d 0.26 1.31 force and water consumption per unit GRP for different industries Reclaimed wastewater reuse ratioa %05are described by exponential growth model (Total Economy Ratio of wastewater generation to %9090Database, 2008): water consumption Volumetric wastewater treatment rate % GLðtÞ¼GL0 expðg tÞ (19) Binhe WWTP service area 43 85 Luofang WWTP service area 51 87 Caopu WWTP service area 0 45 WGðtÞ¼WG0 expðb tÞ (20) COD removal rate in different WWTPs Binhe WWTP service area % 60 80 where, GL0 and WG0 are GRP per labor force (Yuan) and water Luofeng WWTP service area 60 80 demand per unit GRP(m3/million Yuan) in 1989; t is number of year Caopu WWTP service area 0 80 since 1989; g and b are exponential rates of labor productivity and a Ratio of reclaimed wastewater to water consumption. water use efficiency, respectively. Author's personal copy

H.-P. Qin et al. / Environmental Modelling & Software 26 (2011) 1502e1514 1509

Table 2 Industry structure of different administrative areas in Shenzhen River catchment.

District/town GRP proportion of primary, secondary and tertiary GRP proportion of labor, technology and industries capital-intensive secondary industries

1990 2009 1990 2009 Luohu 1.1:46.7:52.2 0.1:17.2:82.7 80:15:5 66.5:28.5:5 Futian 1.5:51.9:46.6 0.0:21.7:78.3 18:60:22 8:77:15 Buji 9.8:69.2:20.9 0.1:66.8:33.1 90: 9: 1 76:19:5

Table 3 Estimated parameter values for labor productivity and water use efﬁciency.

Industry type Exponential growth model of GRP per labor force Exponential growth model of water demand per unit GRP

2 3 2 GL0 (Yuan) g R WG0 (m /million Yuan) b R Primary industry 182 0.0130 0.651 Secondary industry Labor-intensive 687 0.0619 0.669 133 0.00385 0.880 Technology-intensive 1.29 104 0.0966 0.839 36.7 0.0389 0.839 Capital-intensive 2.97 104 0.0966 0.860 129 0.0388 0.870 Tertiary industry 1.56 104 0.0639 0.816 27.9 0.0250 0.839

The parameter g in Eq. (19), b in Eq. (20) and the growth and thus comparisons among different variables meaningful. The parameters c, bL, bK Eq. (1) can be estimated by regression analysis normalized standard errors characterize the average case of model of historical data from 1990 to 2001, and the estimated parameters performance. Therefore, the model performance can be adequately are shown in Tables 3 and 4. evaluated by the method. In river system module, the study reach was divided into 144 segments. The downstream boundary condition was given by the qci qmi M ¼ max (21) typical tidal stage at Tsim Bei Tsui and the monitored pollutant qmi concentration, and the upstream boundary informationwas obtained vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi from the measurements of discharge and pollutant concentration. u u 1 Xn The Preissmann scheme and upwind weighting scheme were adop- t ðq q Þ2 nðn 1Þ ci mi ted for the numerical solution of the SainteVenant equation and i ¼ 1 E ¼ (22) convection-dispersion equation, respectively. The discharges of the 1 Xn main tributaries to Shenzhen River were simulated as lateral inflows. q n ci As a tidal river, water flow and quality in Shenzhen River usually have i ¼ 1 fi e signi cant change in 2 3 h. However, the regular water quality where, q is the ith predicted value; q is the corresponding e ci mi monitoring in the river was carried out at 1 3monthsintervalsby measurement and n is the number of measurements. The absolute local environmental monitoring institution thus the data acquired are maximum relative errors for GRP per labor force and water demand not suitable to verify the unsteady water quality model. Peking per GRP range from 1.1% to12.5%; and the normalized standard University carried out water quality monitoring in the river with errors range from 0.6% to 6.4% (Table 5). e sampling at 2 3 h intervals in March 1994 and May 2004. The water The absolute maximum relative errors for GRP, population and quality model has been calibrated against measured data in March water consumption in different administrative areas range from 1994 (Peking University and Axis Environmental Consultants 1.5% to 9.4%; and the normalized standard errors range from 0.6% to Ltd., 1995). 5.5% (Fig. 4). The simulated wastewater discharge without treatment in each 3.2.3. Model validation sub-catchment was compared with the measured data in April Validation was performed using observed data from 2002 to 2003 (Shenzhen Urban Planning and Land Resources Bureau, 2009. The model performance was evaluated via maximum relative 2003). The results indicated that the calculated discharges match error (M) and normalized standard error (E). The maximum relative the measured data closely (Table 6). errors indicate the maximum possible divergence between observed The model was further validated against a water quality data set and calculated data, which characterize the worst case of model taken at different cross sections along the Shenzhen River in May performance. The normalized standard errors are defined as the square roots of the variances between the observed and calculated data, divided by the mean of observed data. The normalization Table 5 makes the standard errors with different magnitudes dimensionless, Validation results for different industries.

Industry type GRP per labor Water demand per GRP

Table 4 M (%) E (%) M (%) E (%) Estimated parameter values for GRP growth. Primary industry ee 8.9 4.3

2 Secondary industry District/town cbL bK R Labor-intensive 12.5 6.4 4.3 1.3 Luohu 0.0425 0.200 0.880 0.801 Technology-intensive 4.6 2.5 1.1 0.6 Futian 0.0475 0.170 0.615 0.641 Capital-intensive 8.7 4.7 1.3 0.8 Buji 0.0399 0.150 0.619 0.746 Tertiary industry 7.5 4.9 4.4 2.3 Author's personal copy

1510 H.-P. Qin et al. / Environmental Modelling & Software 26 (2011) 1502e1514

Fig. 4. Measured and calculated GRP, Population, and water demand from 2002 to 2009.

2004. As show in Fig. 5, the maximum COD along the river occurs at to be higher than those in Luohu (4%) and Futian (4%) according to the confluence of Buji River and Shenzhen Rive (around 4.5 km local economic development planning. from Sanchahe section), irrespective of flood or ebb tide, from The simulation results indicate that GRP of Shenzhen River which most of the untreated wastewater in the catchment will catchment will increase from 141 billion Yuan in 2011 to 279 billion discharge into Shenzhen River. Due to the integrated impacts of the Yuan in 2020. Compared to the large decrease found in 2000e2009, tide and the wastewater discharge, COD in the upstream of the the annual GRP growth rate will be relatively stable at around 7.9% confluence increases during flood tide, while decreases during ebb for the entire planning horizon (Fig. 7(a)). The main reason is that tide, and the reverse happens downstream. Nash-Sutcliffe coeffi- the capital stock growth rate decreased in the past 10 years but will cient and normalized standard error for the average COD in a tidal be stable after 2010 according to the future economic development period were 0.994 and 5.13%, respectively. Fig. 6 shows the plan (The People’s Government of Shenzhen Municipality, 2008). temporal variation of tidal concentration of COD at station A and B. However, the GRP growth rates of the three administrative districts The predicted COD have a normalized standard error of 6%. are different. The capital stock growth rate in Buji Town will be The validation results indicated that the SyDWEM has the ability higher than those in Luohu District and Futian District, but it will to simulate the relationship between social, economic, resource continuously decrease from 2010 to 2020 (Table 7). Therefore, and environmental issues as well as model the behavior of although Buji Town still has the fastest GRP growth rate, this pollutants in a river. The equations and parameter values of SyD- growth rate will decrease from 2011 to 2020. WEM model can be found on the webpage of http://see.szpku.edu. The population of Shenzhen River catchment is expected to cn/qhp_sydwem.aspx. increase from 2.77 million in 2011 to 3.06 million in 2020 with an average growth rate of 1.1% (Fig. 7(b)), which is lower than the e 4. Impact of future development on water demand and water average growth rate of 6.6% in 2000 2009. This decease is due to quality the much lower GRP growth rate and more improvement of labor productivity in the future. The three administrative districts/towns 4.1. Scenario analysis have again different tendencies of population growth, with the population of Buji Town increasing and the population of Luohu The validated model was subsequently used to simulate the District and Futian District decreasing slightly in the future. The population increase and economic growth for the future 10 years reasons are two folds: On one hand, Buji Town has the fastest GRP (2011e2020) and its impact on water demand and water quality in growth rate among the three administrative districts; On the other Shenzhen River catchment. A future scenario was proposed based hand, the town has the highest percentage of secondary industry on the assumption that the development pattern of socio-economic (67%) in which 76% belong to labor intensive industry, and thus it and water infrastructure system in 2009 will persist for the entire has lower labor productivity (101,000 Yuan per labor in 2020 year). planning horizon from 2011 to 2020. According to the future city Thus Buji Town needs more labor force than other districts to plan, the socio-economic system, industry structure and internal maintain the same GRP growth, resulting in the fastest population owth rate. In comparison to Buji Town, Luohu District and Futian structure of secondary industry in each administrative district are gr District have much lower GRP growth rate, and with more than 78% expected to remain constant since 2009, and no industrial incen- of tertiary industry, and with 77% of secondary industry belonging tives policies, financial assistant, water price adjustment, water to technology-intensive industry, the labor productivity is higher conservation education and promoting water saving devices are (111,000 Yuan and 134,000 Yuan per labor in Luohu District and implemented. For the water infrastructure system, there will not be any increase in water transfer, and the existing wastewater treatment and reclamation capacity remain unchanged. And the average annual net investment growth rate in Buji Town (8.5%) is assumed

Table 6 Simulated and measured wastewater discharge into tributaries (2003).

River Untreated wastewater discharge (m3/s)

Calculated Measured Liantang River 0.195 0.206 Shawan River 0.686 0.690 Buji River 1.625 1.660 Futian River 0.487 0.477 Huanggang River 0.132 0.130 Xinzhou River 0.776 0.712 Fengtang River 0.335 0.322 Fig. 5. Measured and calculated COD along Shenzhen River in 2004. Author's personal copy

H.-P. Qin et al. / Environmental Modelling & Software 26 (2011) 1502e1514 1511

Fig. 6. Comparison of temporal variations of measured and calculated COD.

Futian District, respectively). Therefore they do not require a pop- population growth as well as the high percentage of heavy ulation growth. pollutant industry and thus the largest COD generation per GRP in The water demand is expected to increase from 1.6 million m3/ Buji Town (0.835 t/106yuan) among the three districts. d in 2011 to 2.1 million m3/d in 2020 with an average growth rate of The potential water supply will increase from 1.59 million m3/ 3.6% (Fig. 7(c)). However, the average growth rate in 2000e2009 d to 1.61 million m3/d due to the increase of reuse water from 2011 was nearly 7.5%.This decrease is due to the lower growth rate of GRP to 2020. And the water shortage index, defined as ratio of water and population as well as the improvement of water use efficiency. demand to potential water supply, will be 0.98 and the water The average water demand growth of Luohu District, Futian District resource is expected to maintain equilibrium in 2011. However, and Buji Town are 1.8%, 2.3% and 7.0%, respectively. Buji Town has water supply cannot meet the fast increase of water demand for the fastest increase of water demand because it has the fastest GRP economic and population growth from 2012. Water shortage will and population growth as well as the high percentage of large- become acute and the water shortage index will increase to 1.33 in water consumption industry and thus the lowest water use effi- 2020. ciency (1395 m3/106yuan) among the three districts. On the other hand, if water demand could be met in the entire The COD load of the untreated water will increase from 280 t/ planning horizon, the wastewater generation in the catchment will d to 417 t/d with an average growth rate of 4.4% (Fig. 7(d)). And the increase from the current 1.36 million m3/d to 1.93 million m3/d. COD load growth rate of Luohu District, Futian District and Buji And wastewater generation of Luohu District, Futian District and Town are 1.6%, 0.79% and 8.8%, respectively. Buji Town has the Buji Town will increase to 0.46 million m3/d, 0.77 million m3/d and fastest increase of COD load because it has the fastest GRP and 0.69 million m3/d in 2020, respectively.

Fig. 7. Simulated annual growth rate of GRP, population, water demand and COD generation. Author's personal copy

1512 H.-P. Qin et al. / Environmental Modelling & Software 26 (2011) 1502e1514

Table 7 catchment, the water demand cannot be satisﬁed by water supply Capital stock of Shenzhen River catchment (2011e2020). system, and water quality in the receiving water is likely to worsen Administrative area Growth rate of capital stock in the next 10 years. The three administrative areas in the catch-

2011 2020 ment will have different socio-economic growth, different capital stock growth rates and different industry structure, which mean Luohu District 2.2% 3.0% fi Futian District 3.7% 3.7% different labor productivity, water use ef ciency and pollutant Buji Town 14% 9.2% generation per GRP. Among the three administrative areas, Buji Town will experience the fastest growth rate in terms of GRP, population, water demand and pollutant load generation. More- over, the wastewater treatment capacity in the town is the most Based on the simulated scenario for 2020, 50% of the wastewater deficient in the three administrative areas. Therefore, some engi- will be collected and treated by WWTPs in Shenzhen river catch- neering measures and socio-economic measures for water envi- ment; 24% of the wastewater will be discharged into Shenzhen ronment improvement should be implemented in Shenzhen river River; and untreated with the rest will be transferred to outside catchment. Greater attention should be given to Buji catchment due catchment. to the greatly disparity of the wastewater and pollution load and its As shown in Table 8 the volumes of wastewater disposed by treatment capacity. Luofang, Binhe and Caopu WWTPs will be 39%, 35% and 26%, respectively, of the treated wastewater in Shenzhen River catch- 4.2. Uncertainty discussion ment. Luofang WWTP has the largest volume, which accounts for 64% and 32% of the treated wastewater in Luohu District and Buji In general, model uncertainty comprises of model structure Town, respectively. uncertainty, parameter uncertainty and input uncertainty As shown in Table 9 70% of untreated wastewater in the catch- (Refsgaard et al., 2007). SyDWEM is a mathematical abstraction of ment will discharge into Buji River, followed by Futian and Xinzhou the real water system in urbanizing catchment. The uncertainty rivers each receiving only about one tenth of the wastewater dis- due to the simplification of the physical processes should be discharged into Buji river, since Buji Town will have the fastest cussed in the application of the model. Generally, there are feed- increase of wastewater in three administrative districts, but 47% of backs from water resource and environmental quality to population the wastewater will discharge without treatment due to insuffi- and economic growth in a catchment. The feedbacks are deter- ciency of wastewater treatment capacity in 2020, and 94% of the mined by the behaviors of governments, industries and individuals untreated wastewater in the town will discharge into Buji River. responding to the water shortage and water pollution in the Furthermore, COD load discharging into Shenzhen River will catchment. The structure of SyDWEM does not include feedbacks increase to 175t/d. COD load from effluent of WWTPs and untreated since they are usually complicated and difficult to identify due to wastewater will account for 25% and 75% of the total load, lack of data. However, from policy makers’ point of view, some respectively. And 81% of the load from untreated wastewater will feedbacks can be regarded as measures or strategies implemented discharge into Buji River. The reasons also relate to the fast increase by stakeholders in the catchment to improve water environment. of COD load and insufficiency of wastewater treatment capacity in Therefore, rather than being represented directly in the model Buji Town. structure, the feedbacks can be considered by scenario analysis in The water quality in Shenzhen River will worsen during the which some possible measures or strategies responding to the planning horizon due to the increase in pollution loads (Fig. 8). The water problems are assumed and their effects on the water system maximum COD along the river will occur at the confluence of Buji are evaluated. River and Shenzhen River where most of the untreated wastewater in The model uncertainty is also induced by parameter uncer- the catchment are discharged into Shenzhen River via Buji River. And tainty. SyDWEM assumes that labor productivity and water use the COD will rise from 86 mg/L in 2011 to 121 mg/L in 2020. Fig. 8 also efficiency change as an exponential function of time, and GRP shows that the COD will decrease largely at the reaches close to Binhe growth is estimated on the basis of a Cobb-Douglas production WWTP. The reason is that 0.33 million m3/d effluent of Binhe WWTP function. The parameters in the functions were estimated by will discharge into Shenzhen River, and COD of the effluent is around nonlinear regression using data from 1990 to 2009. The values of 33 mg/L, which is much better than that in the Shenzhen River. the parameters are uncertain because of lack of long-term historical Furthermore, the COD will gradually decrease at the downstream data to validate them. Furthermore, the parameters will be possibly reaches of Shenzhen River due to the effect of the tidal dilution. affected by rapid change of environmental, socio-economic and The above scenario simulation and analysis indicate that technological condition in the future. To handle the uncertainty, it economic development and population growth interact with one is necessary to re-verify the parameters of SyDWEM once new data other strongly, and both of them have great effect on water are collected and then update the results of the simulation. demand/pollutant generation in Shenzhen river catchment. The prediction uncertainty of SyDWEM is also associated with Because the water infrastructure development cannot provide the uncertainty of the input data, e.g. net investment, industrial effective provision for population and economic growth in the structure, water demand per capita, pollutant load per unit GRP and

Table 8 Simulated treated wastewater and pollutant loads in 2020.

WWTPs in Shenzhen river catchment Wastewater from different administrative areas Wastewater volume (million m3/d) COD load (t/d) (million m3/d)

Luohu Dis. Futian Dis. Buji Town Luofang WWTP 0.25 e 0.12 0.37 17.7 Binhe WWTP 0.14 0.19 e 0.33 11.0 Caopu WWTP ee0.25 0.25 15.8 Total of treated wastewater 0.39 0.19 0.37 0.95 44.5 Author's personal copy

H.-P. Qin et al. / Environmental Modelling & Software 26 (2011) 1502e1514 1513

Table 9 Simulated untreated wastewater and pollutant loads in 2020.

Tributaries of Shenzhen River Wastewater from different administrative areas Wastewater volume (million m3/d) COD load (t/d) (million m3/d)

Luohu Dis. Futian Dis Buji Town Liantang River 0.011 ee0.011 2.2 Shawan River 0.009 e 0.018 0.027 7.5 Buji River 0.043 0.002 0.305 0.350 105.5 Futian River e 0.038 e 0.038 5.2 Huanggang River e 0.015 e 0.015 2.1 Xinzhou River e 0.041 e 0.041 5.6 Fengtang River e 0.020 e 0.020 2.7 Total of untreated wastewater 0.063 0.116 0.323 0.502 130.7

Bold highlights that the river has the most contribution of pollutant loads to the catchment.

demand and supply as well as water quality variation at a catchment scale. Furthermore, the integrated model can consider the effects of some socio-economic factors (e.g. industrial structure, labor productivity, water use efﬁciency) on the whole water system; it also can consider the effects of engineering factors (such as volumetric wastewater treatment rate, WWTP pollutant removal rate, wastewater reuse ratio) on water supply, pollutant load and water quality in the river. Therefore, SyDWM has the potential to evaluate the effects of not only socio-economic policies (e.g. industrial structure regulation, water conservation) but also water infrastructure planning (e.g. upgrade WWTP) on the water system. SyDWEM can provide a useful tool for the integration of decision making of policy maker and infrastructure planner. Further Fig. 8. Simulated average COD in a tidal period along Shenzhen River catchment. research will concentrate on the application of SyDWM to support scenario development for the improvement of the water environ- reclaimed wastewater reuse ratio. Although the net investment ment in a rapidly urbanizing catchment. growth rate and industrial structure can be estimated according to local economic development planning, the input data will be uncertain due to the complicated economic conditions (e.g. global Acknowledgments or local recession) in the future. Water demand per capita and pollutant load per unit GRP are assumed to remain constant within The authors are grateful for the funding received from the ’ the prediction horizon. However, the input data will vary with the European Community s Seventh Framework Programme under change of residential living standard, water price, technical capa- grant agreement no.PIIF-GA-2008-220448, National Basic Research bility and awareness on water conservation. And reclaimed Program (973 Program) of China (2005CB724202) and Project of wastewater reuse ratio can be estimated according to local water Shenzhen Municipal Water Affairs Bureau. supply planning. But in fact water reuse and reclamation is uncertain due to the public acceptance of the reclaimed wastewater References is difﬁcult to quantify. In SyDWEM, sensitivity analysis or scenario analysis can be considered to evaluate the input uncertainty. Achleitner, S., Möderl, M., Rauch, W., 2007. CITY DRAINÓ-An open source approach for simulation of integrated urban drainage systems. Environ. Modell. Softw. 22, 1184e1195. 5. Conclusions Adenso-Díaz, B., Tuya, J., Goitia, M., 2005. EDSS for the evaluation of alternatives in waste water collecting systems design. Environ. Modell. Softw. 20, 639e649. Alig, R.J., Kline, J.D., Lichtensteinb, M., 2004. Urbanization on the US landscape: A conceptual integrated model (SyDWEM) has been developed looking ahead in the 21st century. landsc. Urban Plann. 69, 219e234. to simulate the dynamic socio-economic, water infrastructure and Argent, R.M., 2004. An overview of model integration for environmental application-components, frameworks and semantics. Environ. Modell. Softw. receiving water system for a rapidly urbanizing catchment. System 19, 219e234. dynamics is selected as a platform to integrate individual modules Assaf, H., Saadeh, M., 2008. Assessing water quality management options in the within SyDWEM. Furthermore, to couple the environmental Upper Litani Basin, Lebanon, using an integrated GIS-based decision support system. Environ. Modell. Softw. 23, 1327e1337. models developed for different types of spatial units (administra- Barlas, Y., 1996. Formel aspects of model validity and validation in system dynamics. tive district, WWTP service area, sub-catchment etc.) at different Syst. Dyn. Rev. 12 (3), 183e210. temporal scale (year, day or second etc.), the spatial data in one Biswas, A.K., Tortajada, C., 2009. Changing global water management landscape. In: model needs to be mapped and re-distributed to other models, and Biswas, A.K., et al. (Eds.), Water Management in 2020 and Beyond, Water Resources Development and management. Springer-Verlag, Berlin Heidelberg, longer time scale data to must be downscaled to provide short time Berlin, pp. 1e34. scale data. Bixio, D., Wintgens, T., 2006. Water reuse system management manual, AQUAREC. In the paper, the Shenzhen River catchment was used as a case European Publishing Services, Brussels, Belgium. Chanson, H., 2004. Environmental Hydraulics of Open Channel Flows. Elsevier study to demonstrate the functionality and usefulness of SyDWEM. Butterworth-Heinemann, Oxford, UK. The results indicate that SyDWM was able to simulate the rela- Chen, C.H., Liu, W.L., Liaw, S.L., Yu, C.H., 2005. Development of a dynamic strategy tionship between social, economic, resource and environmental planning theory and system for sustainable river basin land use management. Sci. Total Environ. 346, 17e37. issues as well as the behavior of pollutants in a river; it can be used Chow, G.C., 1993. Capital formation and economic growth in China. Q. J. Econ. 114, to predict GRP and population growth, balance between water 243e266. Author's personal copy

1514 H.-P. Qin et al. / Environmental Modelling & Software 26 (2011) 1502e1514

Chow, G.C., Li, K.W., 2002. China’s economic growth 1952e2010. Econ. Dev. Cult. Mitchell, V.G., Mein, R.G., McMahon, T.A., 2001. Modelling the urban water cycle. Change 51, 247e256. Environ. Modell. Softw. 16, 615e629. Cools, J., Broekx, S., Vandenberghe, V., Sels, H., Meynaerts, E., Vercaemst, P., Molina, J.L., Bromley, J., García-Aróstegui, J.L., Sullivan, C., Benavente, J., 2010. Seuntjens, P., Van Hulle, S., Wustenberghs, H., Bauwens, W., Huygens, M., 2011. Integrated water resources management of overexploited hydrogeological Coupling a hydrological water quality model and an economic optimization systems using object-oriented Bayesian networks. Environ. Modell. Softw. 25, model to set up a cost-effective emission reduction scenario for nitrogen. 383e397. Environ. Modell. Softw. 26, 44e51. Ni, J.R., Zhong, D.S., Huang, Y.F., Wang, H., 2001. Total waste-load control and de Azevedo, L.G.T., Gates, T.K., Fontane, D.G., Labadie, J.W., Porto, R.L., 2000. Inte- allocation based on input-output analysis for Shenzhen, South China. J. Environ. gration of water quantity and quality in strategic river basin planning. J. Water Manage. 61, 37e49. Resour. Plan. Manage. 126, 85e97. Peking UniversityAxis environmental Consultants Ltd.Consultants in environmental de Lange, W.J., Wise, R.M., Forsyth, G.G., Nahman, A., 2010. Integrating socio- sciences (Asia) Ltd., 1995. Environmental Impact Assessment Study on Shenz- economic and biophysical data to support water allocations within river hen River Regulation Project Final report. basins: an example from the Inkomati Water Management Area in South Africa. Poppe, M., 2004: Integrated watershed management planning in the lower Mekong Environ. Modell. Softw. 25, 43e50. basin: a Comparative analysis of national and local planning systems in Deng, X.Z., Huang, J.K., Rozelle, S., Uchida, E., 2008. Growth, population and Cambodia, the Lao PDR, Thailand and Viet Nam. Working paper No. 11. MRC / industrialization, and urban land expansion of China. J. Urban Econ. 63, 96e115. GTZ watershed management Project (WSMP). Dong, Y.J., Mei, Y.D., 2010. Influence of urbanization on the surface water quality in Refsgaard, J.C., Van der Sluijs, J.P., Højberg, A.L., Vanrolleghem, P.A., 2007. Uncer- Guangzhou, China. Wuhan Univ. J. Nat. Sci. 15 (1), 78e84. tainty in the environmental modelling processea framework and guidance. Forrester, J.W., 1961. Industrial Dynamics. MIT Press, Cambridge, MA. Environ. Modell. Softw. 22, 1543e1556. Forrester, J.W., 1969. Urban Dynamics. MIT Press, Cambridge, MA. Santhi, C., Srinivasan, R., Arnold, J.G., Williams, J.R., 2006. A modeling approach to Fu G.T., Khu S.T., di Pierro, F., Doglioni, A. and Primativo F., 2008. Development of an evaluate the impacts of water quality management plans implemented in integrated framework for assessing the impact of urban planning on water a watershed in Texas. Environ. Modell. Softw. 21, 1141e1157. quality, in iEMSs 2008: International Congress on Environmental Modelling and Schaldach, R., Alcamo, J., Koch, J., Kölking, C., Lapola, D.M., Schüngel, J., Priess, J.A., Software, Barcelona, Spain. 2011. An integrated approach to modelling land-use change on continental and Guo, H.C., Liu, L., Huang, G.H., Fuller, G.A., Zou, R., Yin, Y.Y., 2001. A system dynamics global scales. Environ. Modell. Softw. 26, 1041e1051. approach for regional environmental planning and management: a study for Shenzhen Statistics Bureau, 1982e2009. Shenzhen Statistical Year Book. China the Lake Erhai Basin. J. Environ. Manage. 61, 93e111. Statistics Press, Beijing (in Chinese). Harou, J.J., Velazquez, M.P., Rosenberg, D.E., Azuara, J.M., Lund, J.R., Howitt, R.E., Shenzhen Urban Planning and Land Resources Bureau, 2003. Water supply system 2009. Hydro-economic models: concepts, design, applications, and future plan and Sewage system plan of Shenzhen (2001e2020) Shenzhen. (in prospects. J. Hydrol. 375, 627e643. Chinese). Holz, C.A., 2005. China’s economic growth 1978e2025: what we know today about Sieber, J., Swartz, C., Huber-Lee, A.H., 2007. Water Evaluation and Planning System China’s economic growth tomorrow. World Dev. 36, 1665e1691. (WEAP): User Guide. Stockholm Environment Institute, Boston. IFAK, 2005. SIMBA 5.0: Simulation Of Wastewater Systems, User’s Guide. Institute of Simonovic, S.P., 2002. World water dynamics: global modeling of water resources. Automation and Communication, e.V., Magdeburg, Germany. J. Environ. Manage. 66, 249e267. Jain, S.K., Singh, V.P., 2003. Water Resources Systems Planning and Management. In: Stave, K.A., 2003. A system dynamics model to facilitate public understanding of Developments in Water Science, vol. 51. Elsevier Science B.V., Amsterdam, The water management options in Las Vegas, Nevada. J. Environ. Manage. 67, Netherlands. 303e313. Joksimovic, D., Kubik, J., Hlavinek, P., Savic, D., Walters, G., 2006. Development of an Teegavarapu, R.S.V., 2010. Modeling climate change uncertainties in water integrated simulation model for treatment and distribution of reclaimed water. resources management models. Environ. Modell. Softw. 25, 1261e1265. Desalination 188, 9e20. The People’s Government of Shenzhen Municipality, 2008. Urban Master Planning Knox, P.L., 1994. Urbanization: An Introduction to Urban Geography Englewoods of Shenzhen (2007e2020) Shenzhen. (in Chinese). Cliff, N.J. Total Economy Database, January 2008. The Conference Board and Groningen Ledent, J., 1982. Long-range regional population forecasting: specification of a minimal Growth and Development Centre. Achieved at. http://www.conference-board. demoeconomic model, with a test for Tucson, Arizona. Pap. Reg. Sci. 49, 37e67. org/economics/. Letcher, R.A., Croke, B.F.W., Jakeman, A.J., 2007. Integrated assessment modelling for Van Delden, H., Luja, P., Engelen, G., 2007. Integration of multi-scale dynamic spatial water resource allocation and management: a generalised conceptual frame- models of socio-economic and physical processes for river basin management. work. Environ. Modell. Softw. 22, 733e742. Environ. Modell. Softw. 22, 223e238. Liu, N., Lu, R.F., 2006. Water Environmental Management in Shenzhen River and Ventana Systems, Inc, 2010. Vensim PLE5.10, Simulation Software. 60 Jacob Gates Deep Bay. China Water Power Press, Beijing (in Chinese). Road, Harvard, MA 01451. http://www.vensim.com/download.html 2010-07-22. Lynam, T., Drewry, J., Higham, W., Mitchell, C., 2010. Adaptive modelling for adap- Vezjak, M., Savsek, T., Stuhler, E.A., 1998. System dynamics of euthrophication tive water quality management in the Great Barrier Reef region, Australia. processes in lakes. Eur. J. Oper. Res. 109, 442e451. Environ. Modell. Softw. 25, 1291e1301. Victoria, F.B., Viegas Filho, J.S., Pereira, L.S., Teixeira, J.L., Lanna, A.E., 2005. Multi- Macleod, C.J.A., Scholefield, D., Haygarth, P.H., 2007. Integration for sustainable scale modeling for water resources planning and management in rural basins. catchment management. Sci. Total Environ. 373 (2e3), 591e602. Agr. Water Manage. 77, 4e20. Matthies, M., Berlekamp, J., Lautenbach, S., Graf, N., Seppelt, R., 2006. System Ward, F.A., 2009. Economics in integrated water management. Environ. Modell. analysis of water quality management for the Elbe river basin. Environ. Modell. Softw. 24, 948e958. Softw. 21, 1309e1318. Winz, I., Brierley, G., Trowsdale, S., 2009. The use of system dynamics simulation in Meadows, D.H., Meadows, D.L., Randers, J., Behrens, W.W., 1972. The Limits to water resources management. Water Resour. Manage. 23, 1301e1323. Growth. Universe books, New York. Young, A., 2003. Gold into base Metals: productivity growth in the People’s Republic Merritt, W.S., Croke, B.F.W., Jakeman, A.J., Letcher, R.A., Perez, P., 2004. A Biophysical of China during the Reform period. J. Political Econ. 111, 1220e1261. toolbox for assessment and management of land and water resources in rural Zhao, B., Liu, J., 2010. Research on pushing effect of urbanization on China Western catchments in Northern Thailand. Ecol. Model. 171, 279e300. investment. Int. J. Econ. Finance 2 (1), 182e185.