Linking water quality and simulation models

Transcription

1 River Basin Management III 339 Linking water quality and simulation models J. P. Arquiola 1, Á. R. García 2 & J. A. Álvarez 3 1 Department of Hydraulic and Environmental Engineering, Polytechnic University of Valencia, Spain 2 INITEC INFRAESTRUCTURAS S.A., Spain 3 Department of Hydraulic and Environmental Engineering, Polytechnic University of Valencia, Spain Abstract Júcar river water resources system is located in the East of Spain, a typical Mediterranean basin with very spatial temporal irregular pluviometry and a high use of the water resources. The area of La Ribera, the last part of the river, is where the main part of the use is located. Due to these demands and their returns this area presents several important water quality problems such as low concentration of dissolved oxygen and high concentrations of nitrogen, phosphorous, etc. This paper explains the link between a water quality model and a simulation model in order to improve the water quality of this part of the river maintaining the guarantees of the demands of the system. The first step was building a water quality model of the low part of the river with the module QUAL2E. A model of 8 constituents conductivity, suspended solids, CBOD, dissolved oxygen, ammonium, nitrate, nitrites and phosphorous was developed, calibrated and tested. By the other side, a simulation model for the entire Júcar basin was available. This model was developed using the SIMGES module. SIMGES is part of the Decision Support System AQUATOOL. With the goal of improving the water quality on the river, an application has been developed to connect both models. Several wastewater treatment and management alternatives can be combined and tested with the application. The simulation model estimates the flows and returns in the river that can then be used as input for the water quality model. The results of the water quality model represent the efficiency of each alternative. This approach allows us to achieve the best alternative that assures the water quality physical-chemical conditions for aquatic life and the lower impact over the guarantees of the demands. Keywords: water quality, water management, simulation models, QUAL2E, SIMGES, AQUATOOL, Júcar River.

2 34 River Basin Management III 1 Introduction One of the main factors of the water quality is the river flow. Generally, water quality models have been calibrated and simulated with low flows, analyzing exclusively the water body in a pessimistic situation. Combining water quality and simulation models allows the estimation of water quality under different situations facing several alternatives of management, allocation, treatment, etc. Historically, water quantity and water quality concerns have been separated, considering both aspects in a common strategy is commonly advocated (Somlyody et al. [11], Arnold and Orlob [4], Strzepek and Garcia [12] and Chapra [8]). This paper presents a link between water quality and simulation models to analyze different possibilities to improve the water quality in a river. The case of the study is the low part of the Júcar River currently with a high degree of pollution due to urban, industrial and agricultural activities. A river water quality model has been developed with QUAL2E [] tool. By the other side, there was available a simulation model of the Júcar River Basin, developed with SIMGES program. SIMGES is part of the Decision Support System AQUATOOL (Andreu et al. [1]). 2 Jucar River Júcar river basin is located in the east of Spain, see figure 1. It has an area near 22,km 2, and a length of the main course of approximately 5 km. Annual average precipitation is 51 mm/year. Human water consume is 15 hm 3 /year supplying a population of 8, people. The irrigable surface is about 158,5 ha consuming 1 hm 3 /year. It is a much regulated basin, with a reservoir capacity of 2,9 hm 3. Figure 1: Situation of Júcar River Basin.

3 River Basin Management III 341 The river has a good water quality except in the lowest part of the river. This area is affected with the wastewater loads from several urban areas, especially near village, and the returns from an extensive agricultural area. These two effects produce low concentrations of DO (under 1 mg/l), and high concentrations of CDBO (up to 12 mg/l) and Suspended solids (up to 32 mg/l). Moreover there is an increment of concentrations of nitrates (35 mg/l) and phosphates (up to 1 mg/l) mainly due to the agricultural activity. An explanatory scheme of the low part of the river can bee seen in figure 2. Escalona-Antella irrigation ditch Sumarcarcer Sellent River Alcántera de Xuquer Villanueva de Castellón Albaida River Farming polygon Tous Dam Carcagente Dam Acequia Real del Júcar Antella Alberique SECTION 1 SECTION 2 SECTION 3 Barcheta Ravine Industrial Polygon Industrial Polygon Verde River Magro River Algemesí SECTION 4 SECTION 5 Sueca and Cuatro Pueblos irrigation ditch LLaurí y Corbera WWTP Riola, Fortaleny, Benicull y Polinya Xuquer Poultry abattoir Cullera irrigation ditch SECTION SECTION 7 Marquesa dam SECTION 8 Figure 2: Scheme of the low part of the Júcar River. 3 The quality model The module QUAL2E allows modelling of up to 15 constituents in a river water body. A one-dimensional hypothesis is considered for modelling the river considering advection and dispersion processes. Several point and diffused loads

4 342 River Basin Management III can be considered in the model. The QUAL2E model has been the most widely used stream model (Drolc and Koncan [1]). A complete description of the model can be found in Brown and Barnwell [5]. For the case of study a water quality model of 8 constituents: Conductivity, Suspended Solids, Dissolved oxygen, CBOD, N-org, NO 4, NO 2, NO 3, and P Tot ) has been developed. The model is composed of 8 streams with inflows, uptakes, point and diffuse loads, and weirs. In order to calibrate the model 8 measurement points were available with series of data. Figure 3 shows calibration results for several parameters in one point of the river. Figure 4 reflects the simulated versus measured average profile of the stream DO Observated calibrated Conductivity Observated Calibrated 8 94 mg O2/l 4 2 ene feb mar abr may jun jul ago sep oct nov dic ene feb mar abr may jun jul ago sep oct nov dic,1,14 NH4 observated calibrated 4 35 NO3 Observated calibrated,12 3 mg/l NH4,1,8,,4,2 mg N/l ene feb mar abr may jun jul ago sep oct nov dic Feb--1 may-1 jul-1 nov-1 feb-2 may-2 jul-2 nov-2 feb-3 may-3 jul-3 nov-3 Figure 3: Calibration results for several parameters. 4 Simulation model Model SIMGES optimizes, using the Oult of Kilter algorithm, a conservative network flow to allocate the resource in the basin. One of the main characteristics of the SIMGES module is the amount of elements available for modelling. Moreover, the importance of the surface and ground water links is incorporated with the possibility of modelling aquifers with different levels of complexity from simple models to distributed ones. The SIMGES module is included inside AQUATOOL (Andreu et al. [1]) Decision Support System. This DSS is a group of modules for modelling planning and managing water resources systems. AQUATOOL includes basically an optimization module, a simulation module, an aquifer module, and several utilities. For the Júcar case a previous model was available. Several modifications were done to obtain more accurate water flows in the lower part.

5 River Basin Management III Conductivity Model Measured average 2 ms/cm ,5 7 73,5 71 8,5 Sellent River 1 58,5 5 Albaida 51 48,5 4 43,5 41 P.K. 38,5 33, ,5 2 23,5 21 Sueca Dam 1 Cullera Dam 11 8, DO Model Measured average 8 mg/l O ,5 7 73,5 71 8,5 Sellent River 1 58,5 5 Albaida 51 48,5 4 43,5 41 P.K. 38,5 33, ,5 2 23,5 21 Sueca Dam 1 Cullera Dam 11 8, mg/l O2 4 2 Model Measured Average CBOD 78,5 7 73,5 71 8,5 Sellent River 1 58,5 5 Albaida River 51 48,5 4 43, ,5 33, ,5 2 23,5 21 Sueca Dam 1 Cullera Dam 11 8,5 P.K. Figure 4: Simulated results versus measured average profile of the stream.

6 344 River Basin Management III 5 Simulated scenarios The simulation model has allowed the estimation of flows, returns, and inflows in all the streams of the river for the different scenarios studied. The goals of this study were: To evaluate the current situation facing different situations of drought and its implication for water quality, to estimate the effect of the future Waste Water Treatment Plant, and finally to evaluate the water quality in several medium and large term situations of the water demands of the basin. Each aspect of study has converted into a different scenario. Each scenario has been simulated in the Simulation model for 4 years of inflows in a monthly scale. The river flows estimated by the simulation model have been analyzed and several critical situations of each scenario have been simulated in the water quality module. Results The results obtained from the simulations of different drought periods show the water quality in the river of the current situation in several critical moments. The results show that while in a normal situation the anoxic length is about 2Km in the drought periods it can be incremented to 1Km. Moreover concentrations of CDBO and Suspended Solids can be over 14 mg/l and 29 mg/l respectively. The second aspect of study was the effect of the future WWTP. The Plant will treat around 14 m 3 /year with a secondary treatment and an advanced nitrification treatment. With this WWTP the anoxic area would be eliminated, the model estimates a DO concentration over 3 mg/l in all periods studied. However phosphorous concentrations will remain high due to there being no treatment in the WWTP to eliminate it. Table 1 shows the mean values comparing the current situation and the WWTP situation in the critical point of the stream and figure 5 shows the evolution of DO in the river profile in those situations. Table 1: Values comparing the current situation versus WWTP. Current Situation Low flow Situation (mg/l) DO CDBO With WWTP Without WWTP With WWTP Without WWTP Suspended solids NH 4 NO 2 NO 3 P Tot Finally, several management basin scenarios have been simulated: The former scenario considers only the increment of upstream water demands. The integral model has allowed the estimation of the new flows and the water quality in this

7 River Basin Management III 345 situation. The results show a generalized decrement of water quality, overall the conductivity in the current situation in the critical point has an average of 2.25 µs/cm and in the hypothetical situation it is incremented to 3.5 µs/cm. Another aspect studied has been the current modernization of the irrigation system of the Ribera Alta. Results estimate the water quality of the river as a result of this action. The decrease of nitrates to a maximum of 2mg/l-NO 3 and phosphates to.2 mg/l-p is remarkable. Although this measurement will improve the water quality the flows will be lower due to the fall of the returns that are a main part of the flow in the river with WWTP DO With WWTP lo w flo w situatio n Without WWTP Without WWTP low flow situation 8 mg/l O ,5 7 73,5 71 8,5 Sellent 1 58,5 5 Albaida 51 48,5 4 43, ,5 33, ,5 2 23,5 21 P.K. Sueca Dam 1 Cullera 11 8,5 Figure 5: DO profile in the different scenarios. 7 Conclusions This paper presents the advantages of considering conjunctive water quality and quantity aspects in the evaluation of basin management alternatives. Interaction between the water quality developed with the QUAL2E program and the simulation model using the SIMGES module has allowed the problem to be dealt with in an integral approach. Its application to the Júcar River has demonstrated the advantages of this approach in different aspects. References [1] Andreu et al., J. Andreu, J. Capilla and E. Sanchis. "AQUATOOL: a computer-assisted Support System for Water Resources Research Management Including Conjunctive Use", chapter: "Decision Support Systems". Edit. Daniel P. Loucks and Joao da Costa, Vol. G2, pp , Springer-Verlag, Berlín Heidelberg.

8 34 River Basin Management III [2] Andreu. 1993a. J. Andreu. "Análisis de sistemas y modelación". Editado en "Concéptos y métodos para la planificación hidrológica", ed. J. Andreu, CIMNE, Barcelona, pp [3] Andreu et al., 199. J. Andreu, J. Capilla y Emilio Sanchis. "AQUATOOL: A generalized decision support-system for waterresources planning and operational management". Publicado en "Journal of hidrology". 177 (199) [4] Arnold, U. and Orlob, G. T. (1989). Decision support for estuarine water quality management. J. Water Resour. Plng. and Mgmt., ASCE, 115(), [5] Brown, L.C., Barnwell, T.O., The Enhanced Stream Water Quality Models QUAL2E and QUAL2E-UNCAS: [] Documentation and User Manual. US Environmental Protection Agency, Report No. EPA//3 87/7. [7] Cerco, C.F., and Cole, T User s guide to the CE-QUAL-ICM threedimensional eutrophication model, release version 1.. Technical Report EL-95-15, US Army Eng. Waterways Experiment Station, Vicksburg, MS. [8] Chapra, S. C Surface Water Quality Modeling. McGraw-Hill. New York. [9] CHJ Plan Hidrológico de la Cuenca del Júcar. Confederación Hidrográfica del Júcar. Ministerio de Medio Ambiente. [1] Drolc, A., Koncan, J.Z., Calibration of QUAL2E model for the Sava River (Slovenia). Water Science and Technology4 (1), [11] Somlyody, L., Henze, M., Koncsos, L., Rauch, W., Reichert, P., Shanahan, P., Vanrolleghem, P., River quality modelling: III. Future of the art. Water Science and Technology, 38 (11): [12] Strzepek and García, K. Strzepek and L. García. "MITSIM 2.1 river basin simulation model, user manual". Center for Advanced Decision Support for Water and Environmental Systems, University of Colorado, Boulder, Colorado. [13] Yang, M.D., Sykes, R.M., Merry, C.J., 2. Estimation of algal biological parameters using water quality modeling and SPOT satellite data. Ecological Modelling 125, 1 13.