SciELO - Scientific Electronic Library Online

 
vol.42 número1Explotación de peces asociada a la pesquería artesanal de langosta de Juan Fernández (Jasus frontalis)Peces linterna (Myctophidae) de la costa oriental de Brasil, Atlántico suroeste índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Revista

Articulo

Indicadores

Links relacionados

Compartir


Latin american journal of aquatic research

versión On-line ISSN 0718-560X

Lat. Am. J. Aquat. Res. vol.42 no.1 Valparaíso mar. 2014

http://dx.doi.org/103856/vol42-issue1-fulltext-19 

Research Article

 

Integrated modeling of water quantity and quality in the Araguari River basin, Brazil

Modelación integrada de cantidad y calidad del agua en la cuenca del río Araguari, Brasil

 

Marcio Ricardo Salla1, Javier Paredes-Arquiola2, Abel Solera2, Joaquín Andreu Álvarez2, Carlos Eugênio Pereira1, José Eduardo Alamy Filho1 & André Luiz De Oliveira1

1 Faculdade de Engenharia Civil, Universidade Federal de Uberlândia, Brasil. Corresponding author: Marcio Ricardo Salla (mrsalla@feciv.ufu.br)
2 Instituto de Ingeniería del Agua y Medio Ambiente, Universidad Politécnica de Valencia España.


ABSTRACT. The Araguari River basin has a huge water resource potential. However, population and industrial growth have generated numerous private and collective conflicts of interest in the multiple uses of water, resulting in the need for integrated management of water quantity and quality at the basin scale. This study used the AQUATOOL Decision Support System. The water balance performed by the SIMGES module for the period of October 2006 to September 2011 provided a good representation of the reality of this basin. The parameters studied were dissolved oxygen, biochemical oxygen demand, organic nitrogen, ammonia, nitrate and total phosphorus. The coefficients of biochemical reactions, sedimentation rates and sediment dissolved oxygen release for this period were calibrated and validated in the quality modeling using the GESCAL module. A sensitivity analysis indicated that the coefficients of carbonaceous matter decomposition, nitrification, water temperature, and sediment oxygen demand interfered more significantly in the variables of state. To prevent eutrophication in the Nova Ponte reservoir and in the other cascade reservoirs, the local River Basin Committee should adopt restrictive actions against the use of agricultural fertilizers. On the other hand, in the sub basin of the Uberabinha River, new alternatives for public water supply to the city of Uberlândia and improvements in the treatment efficiency of the main wastewater treatment plant (WWTP) should be proposed, since the biochemical oxygen demand, ammonia and total phosphorus failed to meet the requirements of COPAM (2008) in the driest months.

Keywords: water, modelling, AQUATOOL, Araguari River, basin, Brazil.


RESUMEN. La cuenca del río Araguari tiene un enorme potencial de recursos hídricos. Sin embargo, la población y crecimiento industrial han generado numerosos conflictos de interés, privados y colectivos, en los usos múltiples del agua, dando lugar a la necesidad de una gestión integrada de la cantidad y calidad del agua a nivel de la cuenca. En este estudio se utilizó el Sistema de Soporte de Decisión AQUATOOL. El balance hídrico realizado por el módulo SIMGES, para el período de octubre 2006 a septiembre 2011 proporcionó una buena representación de la realidad de esta cuenca. Los parámetros estudiados fueron el oxígeno disuelto, demanda bioquímica de oxígeno, nitrógeno orgánico, amonio, nitrato y fósforo total. Los coeficientes de las reacciones bioquímicas, tasas de sedimentación y demanda de oxígeno disuelto del sedimento para este período fueron calibrados y validados en la modelación de calidad del agua, mediante el módulo GESCAL. El análisis de sensibilidad indica que los coeficientes de degradación de la materia orgánica, nitrificación, temperatura del agua y demanda de oxígeno del sedimento interfirieron más significativamente en las variables de estado. Para evitar la eutrofización en el embalse de Ponte Nova y en el resto de los embalses en cascada, el Comité Local de la Cuenca del Río debería adoptar medidas restrictivas contra el uso de fertilizantes agrícolas. Por otra parte, en la subcuenca del Río Uberabinha, nuevas alternativas para el suministro público de agua a la ciudad de Uberlandia y mejoras en la eficiencia del tratamiento de la principal Estación Depuradora de Aguas Residuales (EDAR) deben ser considerados, ya que la demanda bioquímica de oxígeno, amonio y fósforo total no han cumplido con los requisitos de la COPAM (2008) en los meses con más sequías.

Palabras clave: agua, modelación, AQUATOOL, cuenca río Araguari, Brasil.


 

INTRODUCTION

In developing countries, such as Brazil, which lack financial resources for basic sanitation and proper wastewater treatment, the problem of dissolved oxygen consumption in waterways after wastewater has been discharged into them is still significant, justifying the use of the assimilative capacity of waterways to complement the treatment process. Sustainable development and rational water use require the existence of a proper relationship between water quantity and quality. In this context, joint mathematical modeling allows for the diagnosis and prediction of impacts resulting from multiple water uses and the discharge of pollutant loads.

Numerous researchers have designed a variety of models and Decision Support Systems (DSS) that are useful for water resource planning and management at the basin scale. It is well known that the main focus of computational tools is quantitative water resource management and planning, considering the increasing demands and need to implement optimal rules for the operation of water resources. In this context, with different mathematical complexities, the main quantity models that stand out are: HEC-HMS (Klipsch & Hurst, 2007; Fan et al, 2009) and the MIKE SHE (McMichael et al., 2006) models, designed to simulate the precipitation-runoff processes of watershed systems which integrate all the important processes of the hydrologic cycle at catchment scale. HEC-ResSim and WRAP (Wurbs, 2005) models are used to model reservoir operations at one or more reservoirs and the interactions with rivers. MODFLOW (Rodriguez et al, 2008; Xu et al, 2012) and IRAS (Salewicz & Nakayama, 2004; Matrosov et al., 2011) models are used to simulate flow of groundwater through aquifers interactive river-aquifer simulation. However, environmental concerns regarding water quality at the basin scale, driven by the continuous discharge of domestic and industrial wastewater, have led to the design of increasingly complete water quality models (De Paula, 2011). These models have been in use since the development of Streeter & Phelps's classical model (Streeter & Phelps, 1925), which is a benchmark in the history of sanitary and environmental engineering. Several other models have been designed with increasing complexity and number of modeled variables. Those models can be used to simulate different water quality problems. For example, while the Qual2E model (Palmieri & De Carvalho, 2006; Chapra, 2008) and its updated version Qual2K model (Von Sperling, 2007; Chapra et al., 2008; De Paula, 2011) are used to model water quality in river and stream, WASP model (Lai et al., 2012; Zhang & Rao, 2012; Yenilmez & Aksoy, 2013) has been used to examine eutrophication in lakes or streams and heavy metal pollution in rivers. AQUATOX model (Mamaqani et al., 2011; McKnight et al., 2012) is a valuable tool in ecological risk assessment for aquatic ecosystems.

This brief review reveals the marked existence of river and reservoir water quality models that are not linked with any DSS in the quantitative management and planning of water resources. According to Paredes-Arquiola et al. (2010a), many scientific researches disregard the interactions between qualitative and quantitative aspects in water resource management at the basin scale. Due to this situation, many researchers around the world, e.g., Dai & Labadie (2001), Paredes & Lund (2006), Argent et al. (2009), Zhang et al. (2010), Paredes-Arquiola et al. (2010a, 2010b), Zhang et al. (2011), Sulis (2013) and Welsh et al. (2013), are focusing on relating water quality within a DSS in water management at a basin scale.

According to the State Environmental Foundation, the state of Minas Gerais has the highest water resource potential in Brazil and accounts for the generation of 18.5% of all the electricity produced in the country. Nevertheless, there is a lack of scientific research on the integrated management of water quantity and quality at the basin scale. Many water resource management proposals have been put forward by local river basin committees. However, these proposals are not underpinned by integrated studies of water quantity and quality in lentic and lotic environments, but instead focused only on the implementation of quantitative and qualitative telemetric information systems, on user registration and updating, on the creation of criteria for granting water rights, on charging for the use of water and on payment to the surrounding municipalities, watercourse guidelines, conflict prognosis between demands and capacities, and the creation of environmental protection units.

In this context, based on the AQUATOOL Decision Support System (DSS), this article presents an integrated modeling of water quantity (using the SIMGES module) and quality (using the GESCAL module) of the three main watercourses of the Araguari River basin (Araguari, Quebra-Anzol and Uberabinha rivers). Based on water flow and water quality data monitored by the National Water Agency (ANA), the Minas Gerais Water Management Institute (IGAM) and the Minas Gerais Electric Company (CEMIG), this article presents the results of the water balance and calibration of the water quality model for the period of October 2006 to September 2009, and its validation for the period of October 2009 - September 2011. The calibration and validation of the biochemical reaction coefficients, sedimentation rates and sediment oxygen demand will serve as a basis for future studies on quantitative or qualitative interventions in this basin.

The coefficients that are part of the natural self-purification process of a watercourse, be it lentic or lotic, have distinct influences on the final water quality in the water system. Thus, using the factor model, this study performed a sensitivity analysis of the four main coefficients of biochemical reactions involved in the modeling (the re-aeration coefficient Ka, decomposition coefficient of CBOD Ka, coefficient of decomposition of organic nitrogen KNoa, and coefficient of ammonia nitrification KNai), of the water temperature (Temp) and the sediment oxygen demand (SOD).

MATERIALS AND METHODS

AQUATOOL DSS

There are few computational tools or models that simulate water quality linked to quantity at a basin scale. Andreu et al. (1996) developed a DSS called AQUATOOL, which is an interface for editing, simulating, reviewing and analyzing basin management simulation models, including a lentic and lotic water quality simulation module, that is widely used in Europe, Africa, Asia and Latin America (Paredes-Arquiola et al., 2010a, 2010b; Nakamura, 2010; Sulis & Sechi, 2013).The GESCAL and SIMGES modules are intercon-nected, sharing georeferenced quality and quantity data through a graphical interface (Paredes-Arquiola et al., 2010a). Thus, hypothetically considering a basin with multiple and transient uses, water quality can be simulated for any simulated outfall, recharge and environmental flow scenario.

SIMGES module

In this study, the quantitative water management module SIMGES was used in the water balance model in the Araguari River basin. In this water balance was considered the flow in rivers and reservoirs at the basin scale, based on the spatial and quantitative definition of outfalls (point wise outfall for irrigation, industries and human consumption). Simulations were performed by means of a network flow optimization algorithm, which controls the surface flow within the basin while aiming to minimize the deficits and maximize the liquid levels in reservoirs to meet irrigation, human consumption and hydropower demands.

GESCAL module

In order to simulate water quality linked to quantitative management in lentic and lotic environments previously defined in the SIMGES module, Paredes-Arquiola et al. (2009) developed the water quality module GESCAL. Although GESCAL allows modeling eutrophication, temperature, toxics and conventional contaminants, in our case, due to the lack of data and planning purpose of the study, the contaminants modeled were DO, CBOD, organic nitrogen, ammonia, nitrate and total phosphorus. In the modeling process adopted in this study, the relationship between nitrogen cycle and carbonaceous organic matter and the effect on dissolved oxygen, and total phosphorus as an arbitrary parameter was considered, according to the scheme illustrated in Fig. 1.

 
Figure 1. Relationship among the modeled quality parameters.

Study area: Araguari river basin

The Araguari River basin (Fig. 2) is located in the western region of the state of Minas Gerais, Brazil (18°20'-20°10'S, 46°00'-48°50'W). Headwaters are located in Serra da Canasta National Park, in the municipality of São Roque de Minas, covering 475 km to its mouth in the Parnaíba River (which is a tributary of the Grande River, that belongs to Transnational Paraná River basin). This basin covers an area of approximately 22,000 km2, with altitudes ranging from 465 m to 1,350 m and rainfall exceeding 1600 mm year-1. The weather condition is warm, with the dry season between May and September and a wet season between October and April (Rosa et al., 2004). It has a resident population of approximately 1.2 million, distributed in 18 municipalities, 14 of which discharge their wastewater into the basin (Fig. 2). Only the municipalities of Araxá, Nova Ponte, Patrocínio and Uberlândia (which accounts for approximately 70% of the total population in the basin) have wastewater treatment plants (WWTPs), while the other 10 municipalities discharge their untreated wastewaters directly into the surface water bodies. According to the IGAM, surface and groundwater demands allocated in 2006 for human consumption, irrigation, industry, and livestock watering were 250.6 and 3.6 hm3 year-1, respectively.

 
Figure 2. Location of the Araguari river basin (18°20'-20°10'S, 46°00'-48°50'W).

This basin has six hydroelectric power stations (HP), the four largest ones located on the Araguari River with cascade reservoirs (Fig. 2). The first one, situated on the upper Araguari River, is a reg3ulation reservoir with a storage capacity of 12,792 hm3 (Nova Ponte HP), while the other three reservoirs, located on the lower Araguari River, are trickle reservoirs (from up to downstream, Miranda HP, Capim Branco HP 1, and Capim Branco HP 2). There are also two small hydroelectric power stations (SHP) situated on the Uberabinha River (Martins SHP and Malagone SHP). However, in the 2006-2011 period they had not yet entered into production that, for modeling purpose, make us to consider this region as a simple river segment.

In the 1980s, the joint effect of economic valuation of soybeans and the scientific discovery of suitability of the crop to the soil of the Araguari River, transformed the region through the practice of a modern agriculture, associated with the intensive use of phosphate fertilizers and agrochemicals. Also, the presence of phosphate rocks in the region contributes to the existence of that nutrient from their natural deposits (EPE, 2006; Rosolen et al., 2009; Flauzino et al., 2010; Danelon et al., 2012). Figure 2 shows that the basin may be divided into 18 sub basins, whose main economic activities are agriculture, aquaculture, farming, mining, power generation, manufacturing, agribusiness and tourism.

Quantity modeling

The initial procedure in the quantity modeling was to outline the topology of the model using AQUATOOL, which basically corresponds to the situational diagram of the Araguari River basin, including the unscaled elements of the model, as illustrated in Figure 3. To improve visualization, the elements that represent the smaller tributaries and the diffuse distribution along the Quebra-Anzol, Araguari and Uberabinha rivers were removed from Figure 3.

 
Figure 3. Model topology applied to the Araguari River basin.

In the quantity and quality modeling processes, the three main watercourses of this basin (Araguari, Quebra-Anzol and Uberabinha rivers) were divided into 20 segments, each of which was identified by a numbered node upstream and another numbered node downstream (Fig. 3).

Data input

Based on the water flow data monitored by the National Water Agency and the Minas Gerais Electric Company (Fig. 2), a text file was arranged containing the model's quantity input data for the calibration and validation periods. According to Figure 3, all the tributaries and point wise discharges of domestic wastewater with and without the wastewater treatment plant (WWTP) are identified as inputs.

Quebra-Anzol and Araguari rivers

The quantity data of the upper Araguari River and upper Quebra-Anzol River were used directly as input data in the simulation. However, the diffuse and point wise inputs from the other tributaries were obtained from the specific outfall in m3 s-1 km-2 (Eq.1), taking into account the existing quantity data of the upper Araguari and Quebra-Anzol rivers and of the four cascading hydroelectric plants (data on turbine flow, downstream flow and volume variations in the reservoir, which enabled the flow upstream from each hydropower plant to be estimated).

(1)

where:

Qi = inflow i
Qupstream
= flow at any point upstream
Qdownstream =flow at any point downstream from the inflow Qi;
An = total area between two monitoring stations,
Ai = area contribution of the inflow i, obtained by means of a GIS tool that enables the simultaneous acquisition of the area from the perimetral outline.

Uberabinha River

Existing data for the upper Uberabinha River were used directly as input data in the simulation of the model. The absence of water flow data from the mouth of this sub basin and from the two small hydroelectric plants precluded the use of the specific discharge method to estimate the diffuse and point wise flow rates. Thereby a specific rainfall-runoff model is needed for the water balance in this sub basin.

The curve number method (CN) for urban sub basins was used in our study (SCS, 1986). This is a distributed model widely accepted worldwide due to the reduced number of parameters and their relationship with the physical characteristics of the basin (Tucci, 2008; Rezende, 2012).

The HBV model developed by Bergström (1998) was used for the rural sub basins. This is a semi-distributed model that is part of a range of models which use the most important surface runoff processes by means of a simple structure and with a reduced number of parameters. The model functions on a daily or monthly time scale and uses precipitation, ground-level air temperature and average monthly evapo-transpiration as input data (Hundecha & Bárdossy, 2004; Das et al., 2006). Detailed descriptions of the equations used in the HBV model are given by Bergström (1998) and Paredes-Arquiola et al. (2011).

The parameters of the HBV model were calibrated using the evolutionary algorithm for calibration, SCE-UA (Shuffled Complex Evolution method, University of Arizona) (Duan et al., 1992). To this end, the results of the time series of surface flow obtained from the HBV model were compared with the existing time series of surface flow in the upper Uberabinha River. Self-calibration was performed adapting the original code of the SCE-UA algorithm from Duan et al. (1992) and reprogrammed in a Visual Basic platform. Each assessment of the objective function implies the execution of the HBV model. This algorithm has been used successfully to solve nonlinear problems in various applications of hydrological models at the basin scale (Paredes-Arquiola et al., 2011).

In our study, the model was applied to the sub basin corresponding to the single water flow monitoring station existing in the upper Uberabinha River (Fig. 2), whose area of contribution is 801.6 km2. Due to the similarity of climate, geology, land use and occupation throughout the Uberabinha River sub basin, the initially calibrated parameters for this sub basin were used as input data to estimate the surface flow into the other rural sub basins. As it can be seen in Fig. 2, the Bom Jardim River sub basin (394.6 km2) and the Das Pedras River sub basin (389.4 km2) are the main rural sub basins.

WWTP

The WWTP's inflows were calculated using the drinking water flow distribution equation multiplied by the coefficient of return, which, according to the Brazilian standards ABNT: NBR 9649 (1986) and ABNT: NBR 14486 (2000), is set to 0.80 for these situations in which there are no observed data available.

Point wise demand with and without consumption

The data on granted and georeferenced surface water demands for human consumption, irrigation, industry, and livestock watering were obtained from the IGAM, based on 2006 data. Data relating to variable requirements for hydroelectric purposes were obtained from CEMIG.

Water balance

The water balance was determined using the SIMGES module after completion of the topographic map, along with inputs of quantity data required for each element of the model, which include the point wise surface consumption demands, point wise requirements for hydroelectric purposes without consumption, point wise entries of tributaries, point wise effluents with and without WWTP, and the diffuse inputs from the main rivers (Quebra-Anzol, Araguari and Uberabinha). Various input data on storage reservoirs and hydroelectric plants are also essential in modeling, such as the dead volume of each reservoir (hm3), volume set aside in each reservoir at the beginning of the simulation (hm3), maximum storage capacity in each reservoir (hm3), base depth (m), minimum turbine depth (m), energy coefficient (GW hm3 m-1), maximum turbine requirement (m3 s-1), evapo-transpiration for each month, and bathymetric data of the reservoirs.

Quality modeling

In AQUATOOL, quality modeling with the GESCAL module is performed after quantity modeling. Another text file was created containing data on the water quality of tributaries and point wise discharges of WWTP treated and untreated domestic wastewater. The text file was introduced into the GESCAL module to start the simulations.

The data on water quality of the tributaries and the WWTPs were obtained from IGAM and CEMIG.

With respect to the 10 municipalities that discharge their untreated wastewaters directly into the water courses (approximately 30% of the total population of this basin), the water quality was estimated based on the characteristics of raw wastewater. The per capita gross load of BOD of 84 g day-1 was adopted based on the recommendation of the Brazilian standard ABNT: NBR 12209 (2011), in the absence of available measured data. Likewise, the per capita gross pollutant loads of organic nitrogen, ammonia, nitrate, and inorganic and organic phosphorus were estimated, to be 8.0, 7.0, 0.8, 1.0 and 1.8 g day-1, respectively. These estimates are based on the numerous experimental results reported by several authors, such as Tchobanoglous et al. (2003) and Von Sperling (2007). The number of inhabitants per municipality was obtained from census of the Brazilian Institute of Geography and Statistics (IBGE, 2013).

The simulated water quality parameters are: dissolved oxygen, biochemical oxygen demand (BOD5), organic nitrogen, ammonia, nitrate and total phosphorus. Due to the absence of eutrophication in the reservoirs for the time series under study, the modeling of water quality assumed thoroughly mixed reservoirs, for which the simulations were performed adopting only the upper region of the epilimnion. Although we thought that the behavior of the water quality in the reservoirs are enough defined with the model, overall, based on the available data, new information regarding temperature profiles and dynamics of nutrients could improve the model of the reservoir. Generally, the model is related to phosphorous and the internal sediment source of phosphorous. In this case, the developed CSTR model could be incremented to two layer model and could include the effect of the sediment, improving the knowledge of the system and the robustness of the model.

Fig. 4 shows the line diagram of the integrated modeling of water quantity and quality in the Araguari River basin. This plot shows the longitudinal distance between all the elements of the model, the longitudinal distance of the 20 river segments, and the location of the water quality monitoring stations used in the calibration model and its validation process. To calibrate the model in each segment of the river, existing water quality data was used in the node downstream from the segment (Figs. 3, 4). The GESCAL module allows the re-aeration coefficient in each segment of the river to be obtained by the Covar method (Von Sperling, 2007; Paredes-Arquiola et al., 2009) or through the direct introduction of its value in the calibration process. The Covar method (empirical equations that depend on the mean flow velocity and the net depth) showed a good fit between observed and simulated dissolved oxygen data only in the headwater segments of the rivers involved. Table 1 identifies the 20 segments, the longitudinal length of each segment, and the hydraulic relationships used in the headwater segments.

 
Figure 4. Single line diagram of the model.

Table 1. Identification of the 20 segments, longitudinal length (L) of each segment, and hydraulic relationships used in the headwater segments. Q: average flow (m3 s-1); u: average velocity (m s-1); h: average depth (m); b: width of the transverse section (m); α1, β1, α2, β2, α3 and β3 are coefficients of the potential relationships of u = f(Q), h = f(Q) and b = f(Q), adjusted by optimizing the Nash-Sutcliffe efficiency coefficient (Nash & Sutcliffe, 1970).
 

Calibration, validation and sensitivity analysis

In this study, the coefficients of biochemical reactions, sedimentation rates and sediment oxygen release in the 20 segments identified in Figures 3 and 4 were calibrated through a process of trial and error. The coefficients of reactions and sedimentation rates include: re-aeration, decomposition of carbonaceous organic matter, sedimentation rate of carbonaceous organic matter, hydrolysis of organic nitrogen, sedimentation rate of organic nitrogen, ammonia nitrification and denitrification, phytoplankton growth, phytoplankton death/respiration, phytoplankton sedimentation rate, organic phosphorus decay rate and organic phosphorus sedimentation rate.

A sensitivity analysis was performed of all the segments defined in Figures 3 and 4 in view of the changes in the input values of the four main previously calibrated coefficients of reactions (re-aeration coefficient Ka, coefficient of carbonaceous organic matter decomposition Kd, decomposition coefficient of organic nitrogen KNoa, and coefficient of ammonia nitrification of KNai), sediment oxygen demand SOD and water temperature Temp.

Unlike what was done in the calibration process, in which each segment was calibrated separately, using the data observed in the node downstream from the segment as the base for calibration, the sensitivity analysis joined two or more sequential segments in some cases in which the simulated and calibrated values of the node downstream from the last sequential segment were used as the standard in the analyses. The analyses of sequential segments were organized as follows: Araguari segments (1), (2), (3-48-6), (7-8) and (9-10-11) correspond, respectively, to the nodes 2, 3, 7, 9 and 12; Quebra-Anzol segments (1) and (2-3-4) correspond, respectively, to the nodes 18 and 3; finally, Uberabinha segments (1), (2-3) and (4) correspond, respectively, to the nodes 19, 21 and 13.

The factor method used in the sensitivity analysis enabled the assessment of changes in the concentrations of quality parameters based on the simultaneous variation of Ka, Kd, KNoa, KNai1 and SOD by ± dual-level analysis. According to Loucks et al. (2008) and Nakamura (2010), in dual-level analysis, 2n different simulations are performed, where n is the number of coefficients. However, for each river segment, 1 x 2 x 2n simulations were made, in which the number 1 corresponds to the number of + and -pairs, the first number 2 corresponds to the two simulations +10% and -10%, and n corresponds to the number of coefficients (n is equal to 4 in the segments that have no sediment oxygen demand SOD). With respect to temperature, a relative method was used to assess changes in the concentrations of quality parameters on the isolated variation by +10% and -10% from water temperature.

RESULTS

Quantity modeling

Figure 5a illustrates the variation of simulated flow during the period of calibration and validation of the main sections in the basin. The flow at the mouth of Uberabinha River varies from 54.28 to 310.81 hm3 month-1. In the upper Araguari River (node 2) and upper Quebra-Anzol River (node 15) vary, respectively, from 80.06 to 603.56 hm3 month-1 and from 83.89 to 761.98 hm3 month-1, while at the mouth of the Araguari River basin (node 13) the flow varied from 799.23 to 2654.52 hm3 month-1.

 
Figure 5. a) Variation of the simulated flow over the period of calibration and validation at the main points in the basin. Longitudinal profile of the simulated flows in the driest and rainiest months, with values of average, maximum and minimum flow rates observed and 28-78% percentiles observed in b) Araguari River, c) Quebra-Anzol River, and d) Uberabinha River.

Figures 5b, 5c and 5d, respectively, illustrate the longitudinal profiles of the simulated flows of the Araguari, Quebra-Anzol and Uberabinha rivers in the driest and rainiest months, along with the maximum flow observed, minimum flow observed, average flow observed and 28-78% percentile observed. Downstream to the Nova Ponte reservoir, the box-plot graph (Fig. 5) shows that the extreme model scenarios-driest and rainiest months-are between 28-78% percentiles. In the upper Quebra-Anzol and Uberabinha Rivers, it is observed that the extreme model scenario in the driest month is between minimum observed and 28% percentile observed and the extreme model scenario in rainiest month is between maximum observed and 78% percentile observed.

Quality modeling

Figures 6 and 7 show longitudinal profile of simulated quality parameters in the driest and rainiest months, and average values, maximum and minimum flow rates observed and 28-78% percentiles observed in the period of calibration and validation in the Araguari, Quebra-Anzol and Uberabinha rivers. In the three major rivers, the longitudinal profile of simulate 10% from their calibrated value, which is called a quality parameters always remained within the minimum and maximum values observed in all the nodes studied.

 
Figure 6. Longitudinal profile of simulated quality parameters in the driest and rainiest months, and values of average, maximum and minimum flow rates observed and 28-78% percentile observed in the period of calibration and validation: a) Araguari River, b) Quebra-Anzol River.

 
Figure 7. Longitudinal profile of the simulated quality parameters for the rainiest and driest months, and values of average, maximum and minimum flow rates observed and 28-78% percentiles observed in the period of calibration and validation in Uberabinha River.

Table 2 presents the calibrated values of the main coefficients of biochemical reactions (Ka, Kd, KNoa, KNai and Kphosph), the sedimentation rates (VSd, VSNo and VSphosph) and sediment oxygen demand (SOD) in each river segment. The values in this table are within limits recommended in the literature (Chapra, 2003; Von Sperling, 2007; Paredes-Arquiola et al., 2009). Also in Table 2, the values set at -1 for Ka in some segments indicate that this coefficient was estimated by the Covar method. Note that there was sediment oxygen demand in much of the basin, ranging from nodes 2 (upper course of Araguari River) and 18 (upper course of Quebra-Anzol River) to node 9 (Capim Branco HP 1).

Table 2. Calibrated coefficients: Ka: re-aeration, Kd: decomposition of carbonaceous matter, KNoa: nitrogen mineralization, KNai: ammonia nitrification and Kphosph: decomposition of total phosphorus; Sedimentation rates: VSd: carbonaceous organic matter, VSNo: organic nitrogen and VSphosph: particulate phosphorus; SOD: sediment oxygen demand.
 

According to Figure 8, a comparison was made for the main coefficients found in this paper with values from the literature. ka values upstream to the Nova Ponte reservoir are similar to the found by Paredes-Arquiola et al. (2010a, 2010b), Nakamura (2010) and Salla et al. (2013), which varied between 0.8 and 6.4 day-1, kd values presented two bands, a range between 0.001 to 0.1 day-1 (similar to Paredes-Arquiola et al., 2010a) and another range from 0.1 to 0.6 day-1 (similar to Paredes-Arquiola et al., 2010b; Nakamura, 2010; and Salla et al., 2013). With respect to SOD, the same range of values found in this study was found in Paredes-Arquiola et al. (2010a), which varied between 0.10 and 0.23 day-1. In all references consulted KNoa coefficient ranged from 0.002 to 0.6 day-1. The range of values found in this study to KNai (0.007 to 0.2 day-1) is within the limits found by Paredes-Arquiola et al. (2010a, 2010b) and Salla et al. (2013).

 
Figure 8. Comparison of the main coefficients found in this paper with literature values.

The low value of the constants of biochemical reactions (Table 2) are associated with the high pollutant dilution capacity due to high surface water flows in all the river segments under study and to the low pollutant loads discharged point wise by the 13 aforementioned municipalities (Figs. 3, 4). The models that have been calibrated are intended for basin planning so that the aim is not to obtain the same adjustment to specific models or detail of water masses, as general data have been used. This approach allows to consider a reasonable fit between the time series of the simulated and observed values of water quality parameters studied here, with the best results achieved in the upper course of Araguari River, the upper course of Quebra-Anzol River and in Uberabinha River, according to the results indicated by the most representative nodes of this basin (Fig. 9).

 
Figure 9. Time series of simulated and observed values in: a) Node 18, b) Node 2, c) Node 21, d) Node 8, and e) Node 13.

In this study, a analysis was made of the sensitivity of the variables of state to changes of +10% and -10% in the values of the coefficients of re-aeration Ka, decomposition of carbonaceous organic matter Kd, decomposition of organic nitrogen KNoa and ammonia nitrification KNai, water temperature Temp and for some segments, also the sediment oxygen demand SOD.

Figure 10 illustrates the percentage of variation of the parameters DO, BOD5, organic nitrogen, ammonia and nitrate as a function of the segments. In general, it was found that variations in the coefficients and in sediment oxygen demand display a low sensitivity with respect the previously calibrated results, while water temperature generated the largest one. With regard to the parameter DO, the highest sensitivities occurred as a result of changes in SOD and Temp in the segments of the Nova Ponte reservoir. With respect to SOD, the parameter DO ranged from -2.1 to +10% SOD and +1.9 to -10% SOD in Araguari segment 2 and from -3.8 to +10% SOD and +3.3 to -10% SOD in Quebra-Anzol segments 2, 3 and 4. With respect to Temp, the parameter DO has reached -6.4 to -10% Temp in Araguari segment 2 and +6.7 to +10% Temp in Quebra-Anzol segments 2, 3 and 4. The variation of Ka generated little sensitivity in the calibrated results of DO (≤ 1.2% in all the segments).

 
Figure 10. Sensitivity Analysis - Percentages of variation of the DO, BOD5, organic nitrogen, ammonia and nitrate parameters as a function of the segments of river.

The parameter BOD8 showed sensitivity only in Araguari segment 2 and Quebra-Anzol segments 2 a 4 (Nova Ponte reservoir) and Araguari segments 3 a 6 (between Nova Ponte HP and Miranda HP) due to variations in the coefficient Kd and Temp. With respect to Kd, the parameter BOD5 ranged from -8.2 to +10% Kd and +8.6 to -10% Kd in Araguari segment 2; -4.8 to +10% Kd and +8.0 to -10% Kd in Araguari segments 36; and -2.8 to +10% Kd and +3.6 to -10% Kd in Quebra-Anzol segments 2-4 (Fig. 10). With respect to Temp, the parameter BOD5 has reached -18.0 to -10% Temp in Araguari segment 2 and -4.8 to -10% Temp in Quebra-Anzol segments 2-4 (Fig. 10).

The highest variations in the organic nitrogen occurred due to variations in the coefficient KNoa and water temperature Temp. The higher sensitivities observed where of ± 1.7% in Quebra-Anzol segments 2 to 4 and of ± 2.1% in Uberabinha segments 2 and 3 due to variations in the coefficient KNoa. With respect to Temp, organic nitrogen has reached -8.4 to -10% Temp in Araguari segment 2 and -4.4 to -10% Temp in Uberabinha river segments 2 and 3 (Fig. 10).

Ammonia showed low sensitivity (≤1.1%) due to variations in the coefficients KNoa and KNai. With respect to water temperature Temp, the ammonia has reached -2.1 to +10% Temp and -3.2 to -10% Temp in Quebra-Anzol segment 1.

And nitrate showed the highest sensitivity due to variations in the coefficient KNai and water temperature Temp, showed the highest sensitivity of ± 6.4% in Quebra-Anzol segments 2 to 4 and of ± 3.4% in Uberabinha segments 2 and 3 due to variations in the coefficient KNai. With respect to Temp, the nitrate has reached +19.3 to +10% Temp and +27.8 to -10% Temp in Quebra-Anzol segment 1.

DISCUSSION

In the quantity simulations performed in the SIMGES module, from October 2006 to September 2011, the adjustments were satisfactory for scale work used in this paper, in which we tried to represent the mean behavior of the system. In Figure 8a, the greater amplitude of oscillation of the flow in the Nova Ponte HP (node 3) compared to the Miranda HP (node 7), Capim Branco HP 1 (node 9) and Capim Branco HP 2 (node 12) indicates the regulatory behavior of the Ponte Nova reservoir vis-à-vis the other three cascade reservoirs. The regulatory behavior of the Nova Ponte reservoir (node 3) is also shown in Figure 8b. An analysis of node 3 reveals that there is storage of liquid volume in the rainy season and release during the dry months, which causes a considerable decrease in the difference in flow between the rainy and dry seasons (note the segments upstream and downstream from node 3).

In Brazil, water bodies are classified by CONAMA (2008). In addition to this resolution, the state of Minas Gerais has its own Joint Regulatory Resolution (COPAM, 2008), which is similar to CONAMA (2008) with respect to the parameters studied here. According to the COPAM (2008), the Araguari, Quebra-Anzol and Uberabinha rivers are Class 2 rivers, for which the following limits with respect to the quality parameters studied here must be observed: dissolved oxygen ≥5.0 mg O2 L-1; BOD8 ≤5.0 mg O2 L-1; ammonia ≤3.7 mg NH4+ L-1; nitrate ≤10.0 mg NO3- L-1; phosphorus (lentic environment) ≤0.03 mg P L-1; phosphorus (intermediate environment) ≤0.08 mg P L-1; and phosphorus (lotic environment) ≤0.10 mg P L-1.

However, a general analysis of the longitudinal profiles of the quality parameters simulated for the rainiest and driest months (Figs. 6, 7) reveals discrepancies with regard to the parameters BOD8 in the Uberabinha River (Fig. 7) and total phosphorus in the Uberabinha, Araguari and Quebra-Anzol rivers (Figs. 6, 7). In Uberabinha River, downstream from the site where the municipality of Uberlândia discharges its treated wastewater, to the mouth of Uberlandia River (called Uberabinha segments 3 and 4), the BOD8 and total phosphorus show Class 3 behavior. The BOD8 ranged from 8.1 to 6.8 mg O2 L-1 in the rainiest month and from 6.2 to 9.1 mg O2 L-1 in the driest month. The total phosphorus parameter for lotic environments ranged from 0.10 to 0.14 mg P L-1 in the rainiest month and from 0.17 to 0.28 mg P L-1 in the driest month. The higher concentrations of BOD8 and total phosphorus in the driest month are associated with the lower capacity for natural self-purification and dilution of pollutants due to reduced flows. This problem will increase due to the increasing population of this municipality.

In the Araguari and Quebra-Anzol rivers, the simulated profiles of the parameter total phosphorus show non-compliance with the COPAM (2008) in the Araguari 2, Quebra-Anzol 3 and Quebra-Anzol 4 segments. These segments, which correspond to the flooded areas of the Nova Ponte reservoir, behave like lentic environments, in which phosphorus ranged from 0.04 to 0.06 mg P L-1 in the rainiest month and from 0.02 to 0.04 mg P L-1 in the driest month in the Araguari segment 2, and from 0.03 to 0.09 mg P L-1 in the rainiest month in the Quebra-Anzol segments 1, 2 and 3. In this region of the Araguari River basin, the higher concentrations of total phosphorus in the rainiest month are associated with land use in terms of the excessive application of this nutrient in annual and perennial crops. In the period of this study, land use for pasture, and annual and perennial crops represented approximately 83% of the area of contribution to the sub basin of the Quebra-Anzol River, according to the Committee of Araguari river basin.

An overall analysis of the time series of observed values of quality parameters (Figs. 6, 7) reveals a behavior that does not comply with the recommendations of the COPAM (2008) on certain dates within the period studied. In Uberabinha River, the parameter DO showed values of less than 5.0 mg O2 L-1 on only four occasions in the dry months, downstream from Uberlandia's municipal wastewater treatment plant (nodes 20 and 21). Point wise DO values of less than 5.0 mg O2 L-1 were found in Araguari River segments 3, 4 and 5, indicating the influence of the bottom discharge of Nova Ponte reservoir (lower concentrations of dissolved oxygen). BOD5 values far exceeding the maximum of 5.0 mg O2 L-1 were found only in Uberabinha River downstream from the municipality's WWTP, which reached up to 32.0 mg O2 L-1 to observed data in node 20, However, the box-plot graph (Fig. 7) shows that the extreme model scenarios -driest and rainiest month- are between 28 and 78% percentile observed in node 20. Ammonia values exceeded the maximum of 3.7 mg NH4+ L-1 only in Uberabinha River, also downstream from the municipality's WWTP, which reached up to 11.0 mg NH4+ L-1 in a single month without rain. The nitrate parameter was in compliance with the COPAM (2008) in the analyzed time series. However, in most of the nodes, the total phosphorus parameter presented values not in compliance with the maximum of 0.03 mg P L-1, except for nodes 4, 5, 6, 10, 14, and 18.

The calibrations in the upper courses of Araguari and Quebra-Anzol rivers (Figs. 9a, 9b) showed satisfactory fits to the DO, nitrate and total phosphorus parameters. As for organic nitrogen, the reduced number of observed data precluded a good assessment of the fit. The time series of observed data of the BOD5 and ammonia parameters showed practically constant values, which also made it difficult to assess the fit between observed and simulated data, indicating the possibility that the laboratory measurements of these parameters have methodological limitations.

Figure 9c shows the time series of simulations and observed data at node 21, located downstream from Uberlandia's municipal WWTP at the lower course of Uberabinha River. The calibrations achieved satisfactory results for the DO, BOD5, ammonia, nitrate and total phosphorus parameters, despite a few observed data scattered of the ammonia, nitrate and phosphorus parameters. The quality of the observed data for the nitrogen parameter hindered their fit to the simulations, as indicated by a comparison of the oscillatory behavior of the data observed for ammonia and its fixed behavior and with the value of 0.2 mg N L-1 for 60% of the data observed for nitrogen. Calibrations in the middle and lower course of Araguari River (Figs. 9d, 9e) showed different behaviors in relation to the nodes located in the upper course of Araguari River and in Uberabinha River. In general, the time series of observed data are highly scattered in these regions of the basin, which hindered the satisfactory fit of the simulations, except for the of dissolved oxygen and phosphorus parameters.

The model was validated from October 2009 to September 2011, as indicated in Figure 9. The fits between simulated and measured data were satisfactory for the upper courses of Quebra-Anzol and Araguari rivers for most of the parameters (Figs. 9a, 9b), except for ammonia in node 15 for the year 2011 (Fig. 9a). As for node 21 (Fig. 9c), the validation was not satisfactory for organic nitrogen and phosphorus due to the marked dispersion observed in the time series. With regard to the validation of the model for the middle and lower course of Araguari River (Figs. 9d, 9e), the same findings as those recorded during the period of calibration persisted.

ACKNOWLEDGMENTS

The first author gratefully acknowledges CAPES (Brazil's Federal Agency for the Support and Improvement of Higher Education) for process No1304/ 12-7 and CEMIG, IGAM-MG and the Araguari River Basin Agency-ABHA-MG for providing water quantity and quality data.

 

REFERENCES

Andreu, J., J. Capilla & E. Sanchís. 1996. AQUATOOL: a generalized decision support-system for water-resources planning and operational management. J. Hydrol., 177(3-4): 269-291.         [ Links ]

Associação Brasileira de Normas Técnicas (ABNT). 1986. NBR 9649: Projeto de redes coletoras de esgoto sanitário. Rio de Janeiro. [http://www.abntcatalogo.com.br/norma.aspx?ID=4200]. Reviewed: 15 June 2013.         [ Links ]

Associação Brasileira de Normas Técnicas (ABNT). 2011. NBR 12209: Elaboração de projetos hidráulico-sanitários de estações de tratamento de esgotos sanitários. [http://www.abntcatalogo.com.br/norma.aspx?ID=89383]. Reviewed: 15 June 2013.         [ Links ]

Associação Brasileira de Normas Técnicas (ABNT). 2000. NBR 14486: Sistemas enterrados para condução de esgoto sanitário-Projeto de redes coletoras com tubos de PVC. [http://www.abntcatalogo.com.br/norma.aspx?ID=2404]. Reviewed: 15 June 2013.         [ Links ]

Argent, R.M., J.M. Perraud, J.M.Rahman, R.B.Grayson & G.M. Podger. 2009. A new approach to water quality modeling and environmental decision support systems. Environ. Modell. Softw., 24: 809-818.         [ Links ]

Bergström, S. 1995. The HBV model, computer models of watershed hydrology. In: V.P. Singh (ed.). Water resources publications. Littleton, CO, pp. 443-476.         [ Links ]

CBHAraguari-Comitê da Bacia Hidrográfica do Rio Araguari, 2012. [http://www.cbharaguari.org.br]. Reviewed: 10 April 2013.         [ Links ]

Chapra, S.C. 2003. Engineering water quality models and TDMLs. J. Water Res. Pl-ASCE, 129(4): 247-256.         [ Links ]

Chapra, S.C. 2008. Surface water quality modeling. McGraw-Hill, New York, 844 pp.         [ Links ]

Chapra, S.C., G.J. Pelletier & H. Tao. 2008. QUAL2K: a modeling framework for simulating river and stream water quality, Version 2.11: documentation and user's manual. Civil and Environmental Engineering Department, Tufts University, Medford, 109 pp.         [ Links ]

Conselho Estadual de Política Ambiental-Deliberação Normativa Conjunta COPAM/CERH n° 01, de 05 de maio de 2008. Dispõe sobre a classificação dos corpos de água e diretrizes ambientais para o seu enquadramento, bem como estabelece as condições e padrões de lançamento de efluentes, e dá outras providências. [http://www.siam.mg.gov.br/sla/download.pdf?idNorma=8151]. Reviewed: 10 April 2013.         [ Links ]

Conselho Nacional do Meio Ambiente (CONAMA) 2005. Resolução CONAMA N°357, de 17 de março de 2005. Dispõe sobre a classificação dos corpos de água e diretrizes ambientais para o seu enquadramento, bem como estabelece as condições e padrões de lançamento de efluentes, e dá outras providências. [http://www.mma.gov.br/port/conama/res/res05/res35705.pdf]. Reviewed: 10 april 2013.         [ Links ]

Dai, T. & J.W. Labadie. 2001. River basin network model for integrated water quantity/quality management. J. Water Res. Pl-ASCE, 127(5): 295-305.         [ Links ]

Danelon, J.R.B., F.M.L. Netto & S.C. Rodrigues. 2012. Análise do nível de fósforo total, nitrogênio amoniacal e cloretos nas águas do córrego Terra Branca no município de Uberlândia (MG). Rev. Geonorte, 1(4): 412-421.         [ Links ]

Das, T., A. Bárdossy & E. Zehe. 2006. Influence of spatial variability of precipitation on distributed rainfall-runoff modeling. IAHS Publication 303: Promise and Progress, pp. 195-203.         [ Links ]

De Paula, L.M. 2011. Avaliação da qualidade da água e autodepuração do rio Jordão, Araguari (MG). Dissertação de Mestrado, Universidade Federal de Uberlândia, Engenharia Civil, Uberlândia, 177 pp.         [ Links ]

Duan, Q., S. Sorooshian & V. Gupta 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res., 28(4): 1015-1031.         [ Links ]

Empresa de Pesquisas Energéticas (EPE). 2006. AAI dos aproveitamentos hidrelétricos da bacia hidrográfica do rio Paranaíba. Sondotécnica. [http://www.epe.gov.br/Meioambiente/Documents]. Reviewed: 10 April 2013.         [ Links ]

Fan, C., C.H. Ko & W.S. Wang. 2009. An innovative modeling approach using Qual2K and HEC-RAS integration to assess the impact of tidal effect on river water quality simulation. J. Environ. Manage., 90(5): 1824-1832.         [ Links ]

Flauzino, F.S., M.K.A. Silva, L. Nishiyama & R. Rosa. 2010. Geotecnologias aplicadas à gestão dos recursos naturais da bacia hidrográfica do rio Paranaíba no cerrado mineiro. Sociedade & Natureza, 22(1): 75-91.         [ Links ]

Hundecha, Y. & A. Bárdossy. 2004. Modeling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model. J. Hydrol., 292(1-4): 281-295.         [ Links ]

Instituto Brasileiro de Geografia e Estatística (IBGE). 2013. Cidades@. [http://www.ibge.gov.br/cidadesat]. Reviewed: 11 November 2013.         [ Links ]

Klipsch, J.D. & M.B. Hurst. 2007. HEC-ResSim reservoir system simulation user's manual version 3.0, USACE, Davis, CA, 512 pp.         [ Links ]

Lai, Y.C., Y.T. Tu, C.P. Yang, R.Y. Surampalli & C.M. Kao. 2012. Development of a water quality modeling system for river pollution index and suspended solid loading evaluation. J. Hydrol., 478: 89-101.         [ Links ]

Loucks, D.P., E. Van Beek, J.R. Stedinger, J.P.M. Dijkman & M.T. Villars. 2005. Water resources systems planning and management: an introduction to methods, models and applications. UNESCO, Paris, 680 pp.         [ Links ]

Mamaqani, Y.A., N. Khorasani, K. Talebi, S.H. Hashemi, G. Rafiee & F.B. Khosroshahi. 2011. Diazinon fate and toxicity in the Tajan River (Iran) ecosystem. Environ. Eng. Sci., 28(12): 859-868.         [ Links ]

Matrosov, E.S., J.J. Harou & D.P. Loucks. 2011. A computationally efficient open-source water resource system simulator-Application to London and the Thames Basin. Environ. Modell. Softw., 26(12): 1599-1610.         [ Links ]

Mcknight, U.S., J.J. Rasmussen, B. Kronvang, P.L. Bjerg & P.J. Binning. 2012. Integrated assessment of the impact of chemical stressors on surface water ecosystems. Sci. Total Environ., 427-428: 319-331.         [ Links ]

Mcmichael, C.E., A.S. Hope & H.A. Loaiciga. 2006. Distributed hydrological modeling in California semi-arid shrublands: MIKE SHE model calibration and uncertainty estimation. J. Hydrol., 317(3-4): 307-324.         [ Links ]

Nakamura, M.S. 2010. Modelación y análisis de calidad del Río Atibaia en el tramo entre el punto de captación de la ciudad de Atibaia y el punto de vertido de la ciudad de Paulínia (São Paulo- Brasil). Master en Ingeniería Hidráulica y Medio Ambiente, Universidad Politécnica de Valencia, Valencia, 119 pp.         [ Links ]

Nash, J.E. & J.V. Sutcliffe. 1970. River flow forecasting through conceptual models. Part I. A discussion of principles. J. Hidrol., 10: 280-290.         [ Links ]

Palmieri, V. & R.J. Carvalho. 2006. Qual2E model for the Corumbatai River, Ecol. Model., 198: 269-275.         [ Links ]

Paredes, J. & J. Lund. 2006. Refill and drawdown rules for parallel reservoirs: quantity and quality. Water Resour. Manage., 20(1): 359-376.         [ Links ]

Paredes-Arquiola, J., J.A. Álvarez, A. Solera. 2009. Modelo GESCAL para la simulación de la calidad del agua en sistemas de recursos hídricos. Manual de usuario. Versión 1.1. Universidad Politécnica de Valencia, Valencia, 100 pp.         [ Links ]

Paredes-Arquiola, J., J.A. Álvarez & A. Solera. 2010a. A decision support system for water quality issues in the Manzanares River. Sci. Total Environ., 408(12): 2576-2589.         [ Links ]

Paredes-Arquiola, J., J.A. Álvarez, M.M. Monerris & A. Solera. 2010b. Water quantity and quality models applied to the Jucar River Basin, Spain. Water Resour. Manage., 24(11): 2759-2779.         [ Links ]

Paredes-Arquiola, J., A. Solera, J.A. Álvarez, N.L. Elvira. 2011. Manual Técnico v1.0-Herramienta EvalHid para la evaluación de recursos hídricos. Universidad Politécnica de Valencia, Valencia, 31 pp.         [ Links ]

Rezende, G.B.M. 2012. Modelo hidrológico e hidráulico para estimativa e delimitação de áreas inundadas: uma ferramenta de auxílio à elaboração de planos diretores de drenagem urbana. Dissertação de Mestrado. Programa de Pós-Graduação em Engenharia Civil. Universidade Federal de Uberlândia, Uberlândia. 115 pp.         [ Links ]

Rodriguez, L.B., P.A. Cello, C.A. Vionnet & D. Goodrich. 2008. Fully conservative coupling of HEC-RAS with MODFLOW to simulate stream-aquifer interactions in a drainage basin. J. Hydrol., 353(1-2): 129-142.         [ Links ]

Rosa, R., J.L.S. Brito, E.F. Lima, A.C. Siqueira & D. Macedo. 2004. Elaboração de uma base cartográfica e criação de um banco de dados georreferenciados da Bacia do Rio Araguari. In: gestão ambiental da bacia do rio Araguari rumo ao desenvolvimento sustentável. Universidade Federal de Uberlândia, Uberlândia. [http://www.seer.ufu.br/index.php/caminhosdegeografia/issue/view/745]. Reviewed: 11 November 2013.         [ Links ]

Rosolen, V., H. Uwe, N.M.M. Coelho, L.M. Coelho, J.L.S. Brito, L.A. Silva, S.C. Lima. 2009. Qualidade dos sedimentos no rio Uberabinha (Uberlândia, MG) e implicações ambientais. Rev. Bras. Geociências, 39(1): 151-159.         [ Links ]

Salla, M.R., C.E. Pereira, J.E. Alamy Filho, L.M. De Paula & A.M. Pinheiro. 2013. Estudo da autodepuração do Rio Jordão, localizado na bacia hidrográfica do Rio Dourados. Eng Sanit Ambient, 18(2): 105-114.         [ Links ]

Salewicz, K.A & M. Nakayama. 2004. Development of a web-based decision support system (DSS) for managing large international rivers. Sci. Tech., 14(1): 25-37.         [ Links ]

Soil Conservation Service (SCS). 1986. Urban hydrology for small watersheds, Tech, Release, n. 55, U.S. Department of Agriculture, Washington D.C. [http://www.cpesc.org/reference/tr55.pdf]. Reviewed: 15 June 2013.         [ Links ]

Streeter, H.W. & E.B. Phelps. 1925. A Study of the pollution and natural purification of the Ohio River. III. Factors concerned in the phenomena of oxidation and re-aeration, Public Health Bull., 146 pp. [http://dspace.udel.edu:8080/dspace/bitstream/handle/19716/1590/C%26EE148.pdf?sequence=2]. Reviewed: 18 June 2013.         [ Links ]

Sulis, A. & G.M. Sechi. 2013. Comparison of generic simulation models for water resource systems, Environ. Modell Softw., 40: 214-225.         [ Links ]

Tchobanoglous, G., F.L. Burton & H.D. Stensel. 2003. Wastewater engineering: treatment and reuse. Mc Graw-Hill, Boston, 1848 pp.         [ Links ]

Tucci, C.E.M. 2005. Modelos hidrológicos. Universidade Federal do rio Grande do Sul, Editora UFRGS, Porto Alegre, 678 pp.         [ Links ]

Von Sperling, M. 2007. Estudos e modelagem da qualidade da água de rios. Departamento de Engenharia Sanitária e Ambiental, Universidade Federal de Minas Gerais, Ed. UFMG, Belo Horizonte, 588 pp.         [ Links ]

Welsh, W., J. Vaze, D. Dutta, D. Rassam, J. Rahman, I. Jolly, P. Wallbrink, G. Podger, M. Bethune, M. Hardy, J. Teng & J. Lerat. 2013. An integrated modeling framework for regulated river systems. Environ. Modell. Softw., 39: 81-102.         [ Links ]

Wurbs, R.A. 2005. Modeling river/reservoir system management, water allocation and supply reliability. J. Hydrol., 300(1-4): 100-113.         [ Links ]

Xu, X., G. Huanga, H. Zhanc, Z. Que & Q. Huanga. 2012. Integration of SWAP and MODFLOW-2000 for modeling groundwater dynamics in shallow water table areas. J. Hydrol., 412-413: 170-181.         [ Links ]

Yenilmez, F. & A. Aksoy. 2013. Comparison of phosphorus reduction alternatives in control of nutrient concentrations in Lake Uluabat (Bursa, Turkey): Partial versus full sediment dredging. Limnologica, 43: 1-9.         [ Links ]

Zhang, W., Y. Wang, H. Peng, Y. Li, J. Tang & K.B. Wu. 2010. A coupled water quantity-quality model for water allocation analysis. Water Resour. Manage., 24: 485-511.         [ Links ]

Zhang, X. G.H. Huang, X. Nie, Q. Li. 2011. Model-based decision support system for water quality management under hybrid uncertainty. Expert. Syst. Appl., 38(3): 2809-2816.         [ Links ]

Zhang, W. & Y.R. Rao. 2012. Application of a eutrophication model for assessing water quality in Lake Winnipeg. J. Great Lakes Res., 38(3): 158-173.         [ Links ]

 


Received: 9 July 2013; Accepted: 17 January 2014

 

Creative Commons License Todo el contenido de esta revista, excepto dónde está identificado, está bajo una Licencia Creative Commons