Application of the Ecosystem Model to the Mathematical Simulation of Water Environment Dynamic under Anaerobic State in the Organically Polluted Agricultural Reservoir

Trong hồ chứa bị ô nhiễm hữu cơ, tình trạng phân tầng do nhiệt diễn ra mạnh mẽ hơn dẫn tới sự gia tăng của quá trình yếm khí trong hồ chứa. Quá trình này kết hợp với ô nhiễm hữu cơ trong hồ làm gia tăng quá trình phát thải các chất gây hại từ lớp bùn sình dưới đáy hồ trong thời gian bị yếm khí. Trong nghiên cứu này, chúng tôi tiến hành ứng dụng mô hình toán nhằm đánh giá môi trường nước dưới tác động của tình trạng yếm khí trong hồ bị ô nhiễm hữu cơ.

Master’s thesis Application of the Ecosystem Model to the Mathematical Simulation of Water Environment Dynamic under Anaerobic State in the Organically Polluted Agricultural Reservoir Hoang Quang Duong Laboratory of Water Environment Engineering Department of Bioproduction Environmental Sciences Graduate School of Bioresource and Bioenvironmental Sciences Kyushu University 2016 TABLE OF CONTENTS Introduction 01 Study area and observation data 03 2.1 Study area 03 2.2 Observation data 2.2.1 Field observation 2.2.2 Water quality analysis by laboratory experiment 03 04 04 Dynamic characteristics of water quality under anaerobic state 25 3.1 Annual characteristics of water quality under anaerobic state in the reservoir 25 3.2 Important points to modified ecosystem model 40 One-dimensional vertical ecosystem model and scenario analysis 43 4.1 One-dimensional vertical ecosystem model 4.1.1 Construction of one-dimensional vertical ecosystem model 4.1.2 Simulated results 43 43 47 4.2 Scenario analysis 55 Conclusion 58 References 59 Introduction There is growing public concern about environmental issues, and such topics as global warming and sustainable growth are frequency the subject of comment in the press and other media Public pressure continues to grow for greater efforts to be made up the environment and remedy the pollution and degradation that has arisen over the last hundred years Water makes up a major part of the environment, together with land and air, and is vital to the maintenance of life (J.C.CURRIE and A.T.PEPPER 1993) Water, like energy, clean air and solid organic and soil organic matter makes an essential contribution to the maintenance of economic productivity, social well-being and lifestyle and the maintenance of nature and ecosystem services and various aspects should be treated from that perspective Water resources are under stress worldwide and one of the elements of this stress-water quality has increasingly become a crucial issue for researchers and scientists in their studies (Tasuku Kato 2005) With the developing industrialization and increasing populations, the range of requirements for water has increased together with greater demands for higher quality water It is estimated that 8% of worldwide water use is for household purposes (drinking water, bathing, cooking, sanitation and gardening), 22% for industrial uses (mainly hydropower or nuclear power), and 70% for crop irrigation (Sterling and Vintinner, 2008) Regarding the demand for quality water for agricultural irrigation, there are no national standards or regulations for irrigation water quality Irrigation of food crops presents a possible health risk to food consumers if the quality of the irrigation is inadequate, particularly with respect to pathogens and toxic compounds In term of agricultural use of water, agricultural ponds are dominant a considerable amount of water for irrigation The properties closed water body leads to accumulating the pollution sources in the lake It depends on the characteristics of a lake that has its own pollutants sources One of the most common pollutants is organic contaminant which is the reason of many serious water issues in the water bodies The annual temperature cycle is one of the most significant determinants of the physical, chemical, and biological interactions of a lake or reservoir The significance of thermal stratification to the eutrophication process and to reservoir restoration lies in the separation of the upper and lower lake zones during the summer through differences in water density The separation of layer prevents the transport oxygen has caused the loss of oxygen in the deep part of the reservoir which is called anaerobic state As summer progress, the process increase phosphorus, nitrogen in the hypolimnion and frequency are transported vertically in winter season into the water surface area where to subsidize algal growth This biological process response to the excess nutrient input to the lake is known as eutrophication Eutrophication is responsible for many water supply problems (WHO, 1981) Common lake and reservoir problem are excessive algal growth, deficient fishery Water quality in terms of transparency, color, and the odor is often related to a number of algae present Cultural eutrophication is a central concern in lake management Protection of lakes and reservoirs from eutrophication has been of international concern for a number of years Therefore, water quality assessments and forecasting become a matter of urgency for water quality management issues Although many methods have been developed to rectify the adverse impacts of cultural eutrophication It consists of evaluation and controlling activities of the water quality in field, simulation model in the laboratory or mathematical modeling on the computer etc Among them, using mathematical modeling is a high effective method to assess the relative importance of processes affecting eutrophication and the potential success of different lake treatment strategies Moreover, the lake manager must acquire a sufficient database to determinate what dominant lake progresses to cause a lake problem, decide which management techniques affect lake processes sufficiently to reduce the problem, and evaluate cost and benefits of management techniques Modeling is a tool that can provide the information for the decision-making process In addition, the mathematical model has a capacity for utilizing in multi-objective Ideally, it offers the lake manager a sophisticated evaluation at a comparatively low cost to analyze expensive lake management technical and to optimize the money spent on a lake management program Because of the strength of its, the mathematic model has applied for worldwide to simulation the variability of dissolved elements such as nitrogen (NH4+, NO3) or major ions Carbonate (C), Calcium (Ca)…etc as well as the seasonal variation of Chl.a and DO in eutrophic water bodies However, the evaluation and simulation the variability nutrient salts concentration are considerable differences between aerobic lake and anaerobic lake It is explained that the concentration of nutrient varies dramatically depending on the erosion mechanism in the bottom anaerobic condition lake Hence, the internal nutrient loading of phosphorus and nitrogen can’t display sufficiently in these modeling Moreover, there is no research on modeling the variation of sulfide under anaerobic state in the organically polluted reservoir To overcome these limitations, the ecosystem model meets the requirement two criteria (i) simulation the characteristic of dynamic characteristics of water quality (ii) reflect the biological-chemical process simulation such as nitrificationdenitrification, iron reduction and sulfate reduction under anaerobic conditions The purpose of this study is to develop a modified ecosystem model based on a general hydrodynamic model in order to apply for water quality simulation under anaerobic conditions To achieve this objective, the operation is followed by these steps (1) Evaluation and analysis the dynamic characteristic of water quality throughout by data observation collection Data includes of the collection from the surface to bottom of the lake at the central of study areas and laboratory activities consist of concentration measurements, biological determination (2) The results are utilized to correct and develop by applying Fortran programming computer software 2 Study area and observation data 2.1 Study area The target of the study is a regulating reservoir (No reservoir) (Figure 2.1) that is located in Ito campus of Kyushu University, Itoshima Peninsula, Fukuoka Prefecture, Japan No reservoir is a deep water body (water surface area of ca 13,800 m3, pondage of ca 63,000 m3, and maximum water depth of 8m), was created to store rainfall and supply water for cultivation activities at the downstream crop area There are two box culverts that include box culvert (BC1), located at the Southwest of the reservoir, and box culvert (BC2), located at the North of the reservoir (Figure 2.1) The inflow from BC1 is treated by a pollution treatment facility to reduce the concentration of organic material before going to the No.5 reservoir Most of the organic pollution comes from BC2 where runoff directly flows into the reservoir without any treatment The watershed of the reservoir is deforestation area and agricultural regions Therefore, runoff usually carries a lot of humic acids from these regions to the reservoir, making heavy organic pollution in the water body Every summer, the high concentration of organic matter is the main reason to exacerbate the thermal stratification, leading to the drop of DO at the deep parts of the reservoir At the benthic zone, due to lack of DO, many biochemical reduction reactions such as iron reduction, sulfate reduction occur, release nutrient salts, iron, and sulfide, causing considerable deterioration of water environment 2.2 Observation data In 2015, the monitoring periods began on April and completed on December The water quality analysis was conducted once a week In the 6-month survey, there are two kinds of water quality analysis activity were implemented, including outdoor analysis activities and indoor analysis activities Fig 2.1 No.5 reservoir and box culverts 2.2.1 Outdoor analysis activities The outdoor analysis activities that consist of DO, ORP, EC, pH, water temperature and water samples collection were conducted on the fixing point at the center of the reservoir Water samples were collected 1-m intervals from 0m to 8m Nine water samples in total were contained in the two liters plastic bottles and save on the boat before were analyzed in the laboratory by many measurements to get the water quality data DO, ORP, EC, pH and water temperature were directly recorded in the reservoir 0.5-m intervals by using a water quality probe A Secchi-disk, circular disk 30 cm in diameter, was used to determine transparency The meteorological data that consist of air temperature, relative humidity, wind speed, solar radiation, and rainfall were measured automatically every ten minutes The detailed data of outdoor analysis are shown in Figure 2.2 and from Table 2.1 to Table 2.6 2.2.2 Indoor analysis activities Water samples were analyzed in the laboratory in order to determine the concentration of water quality indicators including Chlorophyll-a, TN, TP, TOC, DOC, NH4-N, NO3-N, PO4-P, Cl, E254, SO42-, silica, total iron ion, sulfide The concentration of Chlorophyll-a was determined by using a submersible fluorescence probe (FluoroProbe, bb-Moldaenke, Germany) The concentration of NH4N was measured by Quick Ammonia AT-2000 The analysis of NO3-N, SO42- and Cl were analyzed by an ion chromatography method using AS50 auto-sampler (was manufactured by DIONEX Corporation) The concentration of sulfide was determined by using methylene blue method (DR5000, Wind speed (m/s) 40 o Relative humidity (%) Air temperature ( C) HACH) The TOC, DOC were analyzed by using wet-ultraviolet oxidation method (Sievers 900, GEAI) The TN, TP, PO4-P, E254, total iron ion, silica were measured using a portable spectrophotometer (DR5000, HACH) The detailed data of indoor analysis are shown from Table 2.7 to Table 2.20 30 20 10 10 11 10 12 10 11 12 10 11 12 Solar radiation (W/m) 15 100 80 60 40 20 1000 10 11 12 10 11 12 500 Rainfall (mm) 15 10 Fig 2.2 Meteorology data in 2015 (every ten minutes) Table 2.1 Water temperature observation in 2015 (oC) Y/M/D 2015/04/01 2015/04/09 2015/04/15 2015/04/22 2015/04/28 2015/05/07 2015/05/13 2015/05/20 2015/05/27 2015/06/02 2015/06/10 2015/06/17 2015/06/24 2015/07/08 2015/07/15 2015/07/29 2015/08/05 2015/08/20 2015/08/27 2015/09/03 2015/09/08 2015/09/18 2015/09/24 2015/09/30 2015/10/07 2015/10/14 2015/10/21 2015/10/28 2015/11/04 2015/11/11 2015/11/25 2015/12/02 2015/12/09 0.0m 16.79 13.96 14.50 17.15 20.42 21.46 20.15 23.01 24.85 25.69 23.69 25.33 25.25 24.48 28.93 30.08 31.74 29.59 26.71 27.12 23.76 23.23 25.08 23.74 21.52 19.92 20.52 19.11 17.75 17.89 16.45 13.27 12.24 0.5m 16.74 13.90 14.50 16.75 20.22 21.33 20.10 22.50 24.22 25.43 22.92 25.09 25.25 23.99 28.12 29.89 31.56 29.38 25.87 25.91 23.77 23.17 24.05 23.76 21.45 19.68 20.40 19.06 17.52 17.72 16.46 13.26 11.84 1.0m 15.92 13.87 14.51 16.70 19.28 21.21 20.01 21.30 24.06 25.14 22.62 24.96 25.06 23.81 27.51 29.62 31.51 29.19 25.28 25.66 23.78 23.02 23.90 23.76 21.38 19.62 20.33 18.95 17.35 17.70 16.45 13.20 11.77 1.5m 14.43 13.84 14.50 16.05 17.55 20.28 19.71 20.44 22.43 23.58 21.81 22.91 24.03 23.51 25.69 27.67 31.32 29.12 25.08 25.40 23.79 22.79 23.65 23.75 21.34 19.60 20.31 18.93 17.24 17.70 16.47 13.19 11.73 2.0m 13.13 13.79 14.40 14.71 15.59 16.84 19.39 19.05 19.55 20.91 20.68 21.16 21.91 23.17 23.70 25.13 27.48 28.65 24.85 25.03 23.79 22.72 23.25 23.75 21.32 19.60 20.28 18.91 17.20 17.69 16.47 13.19 11.71 2.5m 11.13 13.63 13.72 13.50 14.16 14.45 15.57 16.56 16.22 16.83 18.96 18.87 19.41 21.52 21.02 22.37 23.50 26.76 24.64 24.65 23.79 22.60 22.88 23.74 21.31 19.59 20.25 18.90 17.16 17.66 16.47 13.18 11.67 3.0m 10.33 11.66 12.47 12.41 12.54 12.65 13.61 14.09 13.84 14.26 15.69 16.03 16.56 18.07 18.57 19.41 19.80 23.00 24.33 23.78 23.75 22.27 22.24 22.77 21.29 19.58 20.20 18.89 17.14 17.56 16.47 13.18 11.65 3.5m 9.25 9.96 10.53 10.96 10.78 11.46 11.85 11.71 12.15 12.56 13.14 13.56 13.93 14.99 15.27 16.49 16.85 18.58 22.45 21.82 22.44 21.73 21.10 21.13 21.29 19.57 19.87 18.89 17.11 17.43 16.47 13.18 11.64 4.0m 8.98 9.24 9.63 9.74 9.75 10.34 10.47 10.28 10.75 11.12 11.34 11.63 12.05 13.08 13.13 14.00 14.37 15.65 17.33 18.22 18.28 18.86 18.84 19.09 19.64 19.52 19.07 18.85 17.10 17.29 16.47 13.17 11.63 4.5m 8.73 8.83 8.88 9.24 9.34 9.55 9.86 9.77 10.06 10.15 10.34 10.53 10.68 11.64 11.64 12.30 12.59 13.55 14.10 14.47 15.38 16.64 15.98 16.61 16.97 17.61 17.49 18.67 17.07 16.92 16.46 13.17 11.63 5.0m 8.44 8.58 8.66 8.83 8.88 9.19 9.26 9.35 9.48 9.61 9.67 9.99 10.12 10.54 10.68 11.20 11.38 12.10 12.32 12.91 13.30 14.01 13.44 14.23 14.92 14.68 15.25 15.41 16.36 15.81 16.45 13.17 11.63 5.5m 8.18 8.43 8.49 8.65 8.74 8.86 8.99 9.08 9.21 9.26 9.40 9.60 9.67 10.08 10.15 10.58 10.68 11.15 11.41 11.60 11.91 12.25 11.94 12.62 12.80 13.06 13.27 13.73 13.91 14.49 14.94 13.17 11.63 6.0m 8.08 8.37 8.42 8.54 8.62 8.76 8.86 8.98 9.04 9.16 9.27 9.41 9.47 9.78 9.85 10.13 10.25 10.59 10.82 10.90 11.03 11.37 11.40 11.58 11.79 12.04 12.34 12.45 12.64 13.07 13.66 13.17 11.63 6.5m 8.04 8.27 8.41 8.50 8.62 8.74 8.84 8.96 9.04 9.16 9.25 9.34 9.43 9.64 9.74 9.94 10.03 10.33 10.49 10.62 10.78 10.97 10.97 11.24 11.44 11.58 11.68 11.84 12.07 12.28 12.74 13.15 11.62 7.0m 8.04 8.24 8.36 8.50 8.63 8.75 8.85 8.95 9.04 9.13 9.25 9.34 9.43 9.61 9.70 9.89 10.00 10.26 10.37 10.47 10.57 10.71 10.88 11.04 11.17 11.33 11.46 11.60 11.74 11.89 12.33 13.09 11.61 7.5m 8.06 8.24 8.35 8.51 8.62 8.76 8.86 8.95 9.03 9.13 9.25 9.34 9.43 9.61 9.73 9.91 10.08 10.32 10.41 10.53 10.63 10.73 10.89 11.04 11.10 11.20 11.38 11.49 11.53 11.77 12.00 12.26 11.61 8.0m 8.09 8.29 8.44 8.53 8.64 8.78 8.86 8.96 9.17 9.31 9.36 9.43 9.59 9.80 9.92 10.10 10.20 10.46 10.52 10.63 10.71 10.78 10.90 11.03 11.09 11.15 11.27 11.36 11.41 11.59 11.84 11.89 11.61 Table 2.2 DO observation in 2015 (mg/l) Y/M/D 2015/04/01 2015/04/09 2015/04/15 2015/04/22 2015/04/28 2015/05/07 2015/05/13 2015/05/20 2015/05/27 2015/06/02 2015/06/10 2015/06/17 2015/06/24 2015/07/08 2015/07/15 2015/07/29 2015/08/05 2015/08/20 2015/08/27 2015/09/03 2015/09/08 2015/09/18 2015/09/24 2015/09/30 2015/10/07 2015/10/14 2015/10/21 2015/10/28 2015/11/04 2015/11/11 2015/11/25 2015/12/02 2015/12/09 0.0m 10.91 10.60 10.66 10.59 10.95 10.87 9.96 11.33 12.36 10.69 11.22 10.74 10.18 10.50 9.85 9.38 9.18 9.73 9.43 10.08 7.53 10.24 9.92 7.44 7.47 7.68 8.66 7.92 7.85 8.47 8.33 6.42 5.21 0.5m 10.91 10.58 10.62 10.59 10.96 10.87 9.95 11.68 12.59 10.69 11.51 10.94 10.18 10.69 10.02 9.33 9.19 9.85 9.56 10.25 7.45 10.44 10.10 7.40 7.38 7.62 8.65 7.95 7.94 8.45 8.31 6.34 5.06 1.0m 11.43 10.55 10.60 10.53 11.01 10.74 9.93 11.41 12.67 10.84 11.87 11.20 10.23 9.41 10.60 9.19 9.13 9.83 7.87 10.12 7.43 10.53 9.75 7.40 7.41 7.56 8.62 7.99 7.96 8.49 8.29 6.25 5.00 1.5m 11.76 10.53 10.60 10.30 10.86 9.95 9.75 9.30 13.07 12.12 7.44 7.47 9.71 8.13 9.02 12.68 9.39 9.65 7.20 7.05 7.43 10.32 9.00 7.39 7.43 7.54 8.59 7.98 7.96 8.49 8.29 6.23 4.98 2.0m 12.14 10.51 10.53 9.21 8.72 7.68 8.69 7.36 5.95 10.64 5.08 3.09 3.59 6.26 4.19 6.07 10.62 9.12 6.44 5.12 7.43 9.74 7.90 7.33 7.45 7.53 8.48 7.94 7.97 8.51 8.25 6.21 4.97 2.5m 12.42 10.52 10.13 8.87 8.16 6.86 6.16 4.95 3.00 3.57 2.01 0.54 0.70 1.80 1.20 3.09 5.32 8.74 6.12 4.23 7.37 9.08 6.48 7.21 7.38 7.49 8.39 7.98 7.97 8.51 8.23 6.20 4.95 3.0m 12.14 10.96 10.09 9.10 8.46 7.49 6.46 5.56 4.86 3.71 1.35 0.56 0.53 0.57 0.70 1.53 2.95 3.76 5.95 2.90 7.13 6.70 4.06 1.32 7.29 7.46 8.12 8.03 7.97 8.13 8.23 6.22 4.90 3.5m 11.25 10.91 10.32 9.54 9.09 8.07 7.23 6.50 5.45 4.11 2.93 2.29 1.76 0.62 0.66 0.96 1.80 0.66 3.82 0.48 1.53 3.85 0.77 0.56 7.36 7.45 6.17 8.03 7.96 7.63 8.22 6.22 4.88 4.0m 10.77 10.42 9.91 9.35 8.81 8.01 7.32 6.23 5.64 4.56 3.73 3.42 2.31 0.95 0.71 0.63 0.82 0.39 2.71 0.45 0.48 0.78 0.48 0.47 1.14 7.36 3.51 7.98 7.93 7.23 8.21 6.20 4.88 4.5m 9.96 10.10 9.52 9.11 8.53 7.45 6.72 6.14 5.85 4.84 4.06 3.84 2.49 1.58 0.94 0.56 0.64 0.37 0.31 0.37 0.43 0.57 0.46 0.45 0.53 0.60 0.63 4.42 7.91 6.80 8.18 6.17 4.87 5.0m 9.51 8.97 7.95 8.46 7.77 6.26 6.77 5.28 5.55 4.50 3.02 3.22 2.16 1.05 0.52 0.52 0.57 0.35 0.00 0.35 0.42 0.43 0.43 0.43 0.47 0.46 0.54 0.54 1.62 5.17 8.17 6.18 4.84 5.5m 8.86 8.58 7.55 7.23 6.60 5.01 4.02 3.64 3.79 3.10 1.70 1.65 0.53 0.56 0.43 0.50 0.53 0.35 0.00 0.34 0.41 0.43 0.41 0.39 0.42 0.43 0.45 0.49 0.75 0.47 1.03 6.18 4.84 6.0m 7.89 6.94 5.85 5.56 5.04 4.00 2.80 2.56 2.61 2.06 0.89 0.51 0.41 0.47 0.40 0.48 0.50 0.34 0.00 0.34 0.39 0.43 0.40 0.37 0.39 0.39 0.44 0.47 0.60 0.40 0.59 6.14 4.83 6.5m 6.89 6.26 5.54 4.29 3.63 3.29 2.74 2.15 1.78 1.47 0.78 0.43 0.39 0.42 0.40 0.46 0.47 0.35 0.00 0.35 0.35 0.41 0.38 0.37 0.38 0.37 0.41 0.44 0.53 0.34 0.54 6.12 4.84 7.0m 6.07 5.15 4.88 4.29 3.20 2.33 1.82 1.99 1.53 1.28 0.67 0.39 0.37 0.41 0.40 0.44 0.39 0.35 0.00 0.38 0.34 0.41 0.38 0.37 0.36 0.36 0.39 0.40 0.50 0.31 0.48 5.45 4.86 7.5m 5.19 4.12 4.17 3.21 2.93 1.88 1.68 1.52 1.59 1.26 0.64 0.38 0.36 0.39 0.41 0.43 0.37 0.39 0.00 0.45 0.33 0.39 0.35 0.35 0.34 0.36 0.36 0.39 0.45 0.30 0.46 0.44 4.87 8.0m 3.95 3.16 2.72 2.46 3.10 1.55 0.00 0.86 0.47 0.45 0.34 0.00 0.33 0.36 0.00 0.39 0.35 0.36 0.00 0.34 0.33 0.35 0.34 0.35 0.34 0.35 0.36 0.37 0.44 0.27 0.46 0.36 4.78 Table 2.3 pH observation in 2015 Y/M/D 2015/04/01 2015/04/09 2015/04/15 2015/04/22 2015/04/28 2015/05/07 2015/05/13 2015/05/20 2015/05/27 2015/06/02 2015/06/10 2015/06/17 2015/06/24 2015/07/08 2015/07/15 2015/07/29 2015/08/05 2015/08/20 2015/08/27 2015/09/03 2015/09/08 2015/09/18 2015/09/24 2015/09/30 2015/10/07 2015/10/14 2015/10/21 2015/10/28 2015/11/04 2015/11/11 2015/11/25 2015/12/02 2015/12/09 0.0m 7.95 7.54 7.56 7.74 8.53 8.83 8.41 8.80 9.35 9.28 8.73 8.98 8.89 8.85 8.72 8.85 8.85 8.94 8.27 8.78 7.73 8.79 9.06 7.72 7.35 7.39 7.42 7.57 7.42 7.29 7.20 7.02 6.08 0.5m 7.95 7.57 7.59 7.73 8.56 8.85 8.45 8.88 9.34 9.28 8.77 9.00 8.90 8.90 8.74 8.83 8.85 8.96 8.28 8.83 7.72 8.80 9.07 7.74 7.40 7.31 7.46 7.56 7.44 7.32 7.21 7.00 6.18 1.0m 7.99 7.58 7.61 7.72 8.42 8.82 8.45 8.63 9.30 9.30 8.79 8.99 8.89 8.46 8.76 8.81 8.84 8.96 7.76 8.80 7.70 8.78 8.66 7.75 7.39 7.27 7.50 7.51 7.46 7.32 7.22 6.99 6.31 1.5m 7.89 7.59 7.62 7.68 8.14 8.05 8.35 8.01 9.02 9.22 7.72 7.96 8.76 7.88 8.16 9.15 8.84 8.93 7.57 8.13 7.66 8.68 8.42 7.76 7.38 7.28 7.53 7.52 7.47 7.33 7.23 6.98 6.39 2.0m 7.85 7.59 7.62 7.42 7.44 7.42 7.85 7.57 7.57 8.51 7.25 7.19 7.20 7.49 7.17 7.91 8.70 8.80 7.40 7.27 7.64 8.60 8.10 7.75 7.38 7.29 7.54 7.52 7.48 7.35 7.25 6.97 6.46 2.5m 7.75 7.58 7.52 7.24 7.30 7.21 7.21 7.19 7.15 7.21 6.90 6.79 6.76 7.00 6.74 7.51 7.75 8.49 7.28 7.00 7.61 8.43 7.82 7.73 7.37 7.30 7.55 7.51 7.49 7.36 7.27 6.97 6.54 3.0m 7.64 7.45 7.38 7.21 7.17 7.15 7.11 7.08 7.00 7.05 6.69 6.63 6.62 6.70 6.55 7.19 7.23 7.72 7.21 6.87 7.57 7.91 7.47 6.92 7.37 7.32 7.55 7.49 7.50 7.32 7.29 6.98 6.61 3.5m 7.45 7.32 7.29 7.19 7.13 7.12 7.08 7.01 6.92 6.99 6.61 6.61 6.53 6.59 6.38 6.94 6.89 7.07 6.95 7.23 6.84 7.52 7.11 6.70 7.37 7.32 7.32 7.49 7.50 7.29 7.30 6.98 6.67 4.0m 7.37 7.22 7.22 7.16 7.09 7.09 7.05 6.98 6.92 6.95 6.61 6.62 6.55 6.52 6.30 6.64 6.64 7.05 7.10 7.09 6.65 6.95 6.92 6.64 6.74 7.32 7.05 7.49 7.49 7.27 7.31 6.99 6.71 4.5m 7.28 7.18 7.16 7.13 7.06 7.06 6.99 6.95 6.86 6.90 6.61 6.63 6.57 6.52 6.29 6.52 6.49 7.04 7.04 7.07 6.55 6.67 6.90 6.61 6.67 6.74 6.81 7.42 7.50 7.20 7.32 7.00 6.75 5.0m 7.20 7.09 7.06 7.08 7.01 7.02 6.99 6.91 6.84 6.85 6.61 6.65 6.58 6.51 6.32 6.46 6.40 7.01 7.01 7.01 6.60 6.80 6.55 6.57 6.62 6.66 6.77 6.75 7.25 6.99 7.33 7.00 6.77 5.5m 7.15 7.07 7.03 7.01 6.95 6.98 6.90 7.30 6.84 6.78 6.60 6.65 6.59 6.53 6.40 6.46 6.35 6.96 6.98 6.94 6.49 6.47 6.47 6.60 6.55 6.64 6.72 6.65 7.12 6.79 6.84 7.01 6.79 6.0m 7.07 6.98 6.95 6.94 6.88 6.95 6.87 7.17 6.83 6.74 6.62 6.66 6.61 6.53 6.55 6.46 6.36 6.90 6.92 6.88 6.52 6.49 6.48 6.54 6.61 6.53 6.61 6.55 7.00 6.59 6.57 7.01 6.80 6.5m 7.01 6.96 6.94 6.91 6.85 6.95 6.87 7.10 6.83 6.73 6.63 6.67 6.63 6.53 6.58 6.55 6.49 6.82 6.86 6.79 6.72 6.52 6.46 6.50 6.50 6.49 6.28 6.27 6.74 6.22 6.29 7.01 6.82 7.0m 6.97 6.93 6.93 6.92 6.84 6.93 6.86 7.06 6.84 6.73 6.65 6.69 6.69 6.55 6.64 6.65 6.83 6.80 6.79 6.70 6.66 6.51 6.42 6.45 6.49 6.45 6.30 6.35 6.56 6.26 6.17 6.69 6.83 7.5m 6.95 6.94 6.94 6.92 6.85 6.92 6.86 7.02 6.85 6.75 6.68 6.71 6.71 6.60 6.68 6.55 6.59 6.50 6.59 6.55 6.51 6.33 6.32 6.34 6.45 6.35 6.31 6.32 6.47 6.30 6.15 6.15 6.84 8.0m 6.93 6.93 6.92 6.96 6.88 6.88 6.89 6.82 6.64 6.56 6.72 6.70 6.63 6.60 6.59 6.57 6.54 6.45 6.48 6.41 6.46 6.49 6.48 6.34 6.44 6.33 6.35 6.37 6.46 6.39 6.14 6.43 6.95 Table 2.4 ORP observation in 2015 (mV) Y/M/D 2015/04/01 2015/04/09 2015/04/15 2015/04/22 2015/04/28 2015/05/07 2015/05/13 2015/05/20 2015/05/27 2015/06/02 2015/06/10 2015/06/17 2015/06/24 2015/07/08 2015/07/15 2015/07/29 2015/08/05 2015/08/20 2015/08/27 2015/09/03 2015/09/08 2015/09/18 2015/09/24 2015/09/30 2015/10/07 2015/10/14 2015/10/21 2015/10/28 2015/11/04 2015/11/11 2015/11/25 2015/12/02 2015/12/09 0.0m 253.3 225.0 208.0 218.0 189.0 207.0 234.0 233.0 202.0 191.0 235.0 209.0 207.0 214.0 306.0 241.0 333.0 259.0 188.0 165.0 197.0 167.0 216.0 206.0 223.0 258.0 279.0 225.0 203.0 209.0 332.0 291.0 307.0 0.5m 256.2 226.0 208.0 221.0 190.0 207.0 238.0 233.0 206.0 195.0 240.0 214.0 211.0 217.0 316.0 256.0 339.0 268.0 225.0 168.0 199.0 182.0 219.0 210.0 224.0 269.0 278.0 228.0 201.0 209.0 334.0 294.0 326.0 1.0m 256.6 227.0 208.0 226.0 201.0 209.0 240.0 245.0 211.0 198.0 247.0 219.0 216.0 231.0 322.0 263.0 346.0 280.0 252.0 175.0 201.0 196.0 244.0 215.0 230.0 274.0 276.0 236.0 205.0 211.0 335.0 296.0 337.0 1.5m 265.2 228.0 208.0 229.0 211.0 234.0 245.0 264.0 225.0 206.0 272.0 251.0 229.0 252.0 355.0 263.0 350.0 289.0 265.0 208.0 206.0 209.0 255.0 218.0 234.0 275.0 275.0 237.0 210.0 212.0 336.0 298.0 339.0 2.0m 270.0 230.0 211.0 239.0 238.0 256.0 259.0 278.0 284.0 234.0 287.0 280.0 279.0 267.0 391.0 313.0 368.0 302.0 276.0 252.0 210.0 226.0 266.0 222.0 237.0 277.0 274.0 239.0 214.0 213.0 337.0 299.0 342.0 2.5m 275.6 234.0 216.0 247.0 244.0 266.0 282.0 291.0 297.0 278.0 301.0 291.0 297.0 284.0 408.0 333.0 406.0 318.0 286.0 268.0 214.0 241.0 278.0 227.0 241.0 278.0 274.0 242.0 217.0 214.0 338.0 302.0 344.0 3.0m 280.4 243.0 225.0 249.0 253.0 271.0 287.0 298.0 309.0 286.0 311.0 301.0 305.0 281.0 416.0 350.0 427.0 355.0 293.0 277.0 219.0 269.0 288.0 265.0 245.0 279.0 275.0 247.0 218.0 220.0 338.0 303.0 346.0 3.5m 287.9 251.0 232.0 252.0 259.0 275.0 291.0 305.0 314.0 289.0 323.0 311.0 317.0 291.0 425.0 366.0 442.0 -23.0 310.0 -49.0 205.0 283.0 282.0 265.0 248.0 281.0 285.0 249.0 219.0 223.0 339.0 304.0 346.0 4.0m 291.1 255.0 237.0 255.0 266.0 279.0 294.0 308.0 315.0 292.0 328.0 317.0 322.0 300.0 430.0 384.0 452.0 -63.0 297.0 -121.0 0.0 -111.0 -83.0 259.0 272.0 282.0 296.0 251.0 218.0 225.0 340.0 305.0 347.0 4.5m 294.1 256.0 240.0 258.0 269.0 282.0 298.0 311.0 319.0 297.0 331.0 320.0 326.0 304.0 434.0 393.0 460.0 -70.0 -146.0 -130.0 -46.0 -125.0 -105.0 267.0 273.0 310.0 279.0 256.0 218.0 230.0 340.0 306.0 347.0 5.0m 296.6 260.0 244.0 261.0 274.0 283.0 300.0 314.0 321.0 301.0 333.0 321.0 327.0 310.0 432.0 399.0 465.0 -88.0 -153.0 -156.0 -48.0 -125.0 -100.0 267.0 269.0 226.0 275.0 152.0 230.0 241.0 341.0 307.0 346.0 5.5m 297.9 260.0 245.0 264.0 276.0 283.0 302.0 267.0 319.0 305.0 333.0 320.0 327.0 310.0 420.0 401.0 469.0 -124.0 -157.0 -159.0 -55.0 -122.0 -121.0 91.0 104.0 170.0 211.0 145.0 -26.0 179.0 308.0 308.0 346.0 6.0m 299.6 262.0 247.0 267.0 278.0 283.0 302.0 269.0 316.0 308.0 330.0 317.0 322.0 310.0 333.0 399.0 462.0 -130.0 -158.0 -159.0 -72.0 -128.0 -137.0 -123.0 -136.0 -99.0 111.0 119.0 -58.0 97.0 -32.0 309.0 346.0 6.5m 300.1 263.0 247.0 268.0 267.0 281.0 302.0 271.0 313.0 308.0 329.0 310.0 309.0 305.0 263.0 346.0 405.0 -131.0 -160.0 -158.0 -138.0 -135.0 -153.0 -152.0 -156.0 -167.0 -146.0 -123.0 -145.0 -146.0 -117.0 300.0 345.0 7.0m 299.6 263.0 247.0 268.0 248.0 279.0 300.0 272.0 311.0 307.0 326.0 302.0 269.0 298.0 210.0 237.0 121.0 -134.0 -164.0 -167.0 -169.0 -156.0 -168.0 -173.0 -184.0 -178.0 -165.0 -141.0 -164.0 -168.0 -142.0 120.0 345.0 7.5m 298.6 262.0 247.0 266.0 223.0 277.0 298.0 272.0 310.0 306.0 324.0 281.0 235.0 287.0 -32.0 -61.0 -101.0 -151.0 -166.0 -172.0 -182.0 -164.0 -174.0 -173.0 -190.0 -174.0 -176.0 -143.0 -168.0 -179.0 -146.0 -118.0 345.0 8.0m 296.3 262.0 246.0 220.0 183.0 57.0 -25.0 -69.0 -144.0 -147.0 -171.0 -161.0 -171.0 -174.0 -167.0 -161.0 -133.0 -155.0 -180.0 -174.0 -178.0 -185.0 -186.0 -173.0 -188.0 -172.0 -181.0 -145.0 -168.0 -185.0 -142.0 -172.0 209.0 surplus production (POM to DOM), B12 is settling of POM to deeper layer, B13 is mineralization of dissolved organic matters (DOM), B14 denotes elution of PO43- from bottom sediment, B15 is elution of NH4+ from bottom sediment, B16 is nitrification of ammonium to nitrite ion, α is oxygen consumption rate of B16, B17 is nitrification of nitrite ion to nitrate ion, β is oxygen consumption rate of B17, B18 denotes denitrification at bottom, B19 is oxygen consumption by bottom sediment, B20 is reaeration at water surface Modification in the one-dimensional ecosystem model The previous model did not mention on the settling of phytoplankton and POM from the upper layer and could affect the accuracy of the ecosystem model In the modified model, we added settling of phytoplankton and POM from the upper layer Therefore, equation (4.17) and (4.19) become (4.26) and (4.27) as below: ( 𝑑𝐶𝑃𝑃 ) = 𝐵1 − 𝐵2 − 𝐵3 − 𝐵4 − 𝐵5 − 𝐵6 + 𝐵21 𝑑𝑡 𝐵𝑖𝑜𝑐ℎ𝑒𝑚 (4.26) ( 𝑑𝐶𝑃𝑂𝑀 ) = 𝐵6 + 𝐵7 + 𝐵9 − 𝐵10 − 𝐵11 − 𝐵12 + 𝐵22 𝑑𝑡 𝐵𝑖𝑜𝑐ℎ𝑒𝑚 (4.27) Where B21 and B22 are settling of phytoplankton and POM from the upper layer The nitrification process of ammonia related to the characteristic of nitrifying bacteria The nitrification rate is represented by the formulation of temperature and DO concentration and it is well known that nitrification in the water needs to undergo photoinhibition There is no consideration on photoinhibition in the previous model Therefore, in the modified model, nitrification process was suggested by the following equation: 𝐶𝐷𝑂 𝐵16 = 𝛼16 𝑒𝑥𝑝(𝛽16 𝑇) ( )𝐹 𝐷𝑂16 + 𝐶𝐷𝑂 𝑝ℎ𝑜𝑡𝑜𝑖𝑛ℎ𝑖𝑏𝑖𝑡𝑖𝑜𝑛 𝐹𝑝ℎ𝑜𝑡𝑜𝑖𝑛ℎ𝑖𝑏𝑖𝑡𝑖𝑜𝑛 = − 𝐼𝑧 𝐼𝑝𝑖ℎ 𝑒𝑥𝑝 (1 − 𝐼𝑧 𝐼𝑝𝑖ℎ ) (4.28) (4.29) 𝐵𝑖𝑜𝑐ℎ𝑒𝑚 Where DO16 is the half saturation of DO limitation, Ipih is the limit value of the complete inhibition by strong light According to observation analysis that was mentioned in the previous part, denitrification in the reservoir was determined to occur when DO ≤ 0.5 mg/l Therefore, denitrification was expressed by the following equation: 𝐵18 = 𝛼18 𝑒𝑥𝑝(𝛽18 𝑇)𝐶𝑁𝑂3 at CDO ≤ 0.5 mg/l (4.30) The release of nutrient salts (including PO4-P and NH4-N) in the previous model is not accuracy The starting time and the variation of PO4-P and NH4-N are very different with observation data The reason for this difference is assumed that due to lack of limiting condition Therefore, the observation 46 analysis was implemented to determine suitable limiting condition for the modified model The release of PO4-P and NH4-N were expressed respectively in the modified model by the following equation: 𝛼14 𝑒𝑥𝑝(𝛽𝑇) 𝐵14 = ( ) Δ𝑧 at CDO = and CNO3 = mg/l (4.31) 𝛼15 𝑒𝑥𝑝(𝛽𝑇) 𝐵15 = ( ) Δ𝑧 at CDO = and CNO3 ≤ 0.1 mg/l (4.32) In addition, sulfide is an important water quality indicator that affects strongly water environment However, there was no simulation of sulfide in the previous model Therefore, the variation of sulfide as a state variable was incorporated in the modified model The limiting condition of variation of sulfide was determined by observation analysis in section The variation of sulfide under anaerobic state was shown as below: ( 𝑑𝐶𝑆𝑢𝑙𝑓𝑖𝑑𝑒 ) = 𝛼𝑆𝑢𝑙𝑓𝑖𝑑𝑒 𝑑𝑡 𝐵𝑖𝑜𝑐ℎ𝑒𝑚 at CDO = and CNO3 = mg/l (4.33) Where αSulfide is the increase rate of sulfide 4.1.2 Simulated results The simulation of water temperature and water quality indicators were calculated by using the Crank-Nicholson implicit method The depth of the reservoir was discretized into 0.25 m in the vertical division with the simulation step of 10 minutes The meteorological data includes air temperature, relative humidity, wind speed, solar radiation, and rainfall were measured automatically every ten minutes The initial condition of water temperature and water quality in the reservoir were observation values of the first day of the calculation period In the past, the inflow and outflow loading for the one-dimensional ecosystem model were simulated by using observation data and L-Q equations for two box culverts However, the condition of BC2 is not good now (Figuge 4.1) Therefore, it is impossible to observe water flow from BC2 Lack of inflow and outflow loading reduce the accuracy of the ecosystem model To deal with this issue, division of the calculation period : DOC : Rainfall DOC (mg/l) 100 200 4 Fig 4.1 Current condition of BC2 10 11 12 Month Fig 4.2 DOC at water surface and rainfall 47 300 Rainfall (mm) was suggested as a solution to improve the Table 4.1 Nash-Sutcliffe coefficient of NO3-N, accuracy of the simulation As shown in NH4-N, PO4-P and sulfide at m and m Figure 4.2, the DOC concentration 7m 8m suddenly changed when the heavy rainfall 0.93 0.97 NO3-N 0.85 0.86 NH -N (more than 100 mm/d) occurred This 0.73 0.84 PO4-P denotes that the organic pollution condition 0.70 0.86 Sulfide in the reservoir suddenly changes due to large of amount organic matter loading from heavy rainfall Thus, the calculation period was divided into two small calculation periods (from 2015/04/09 to 2015/08/27 and 2015/08/27 to 2015/12/09) based on heavy rainfall Each small calculation periods has meteorological data, initial data that corresponds to them The corresponding average transparency for two calculation periods was 1.4 and 2.8, respectively As described in the previous section, anaerobic condition occurring during the thermal stratification period is the most important feature of the reservoir Hence, the first purpose of the onedimensional ecosystem model was the reproduction of water temperature and DO In the modified ecosystem model, the calculation of temperature and DO resembled observation data and exhibited clearly the stratification-destratification process as well as anaerobic state (Figure 4.3 and 4.4) Especially, the seasonal change of anoxic state near the bottom that was simulated by the modified model was generally in good agreement with the observation In addition, the seasonal change of water quality under anaerobic state include denitrification, the release of NH4-N and PO4-P near the bottom were modeled correctly and gave the excellent reproduction of NO3-N, NH4-N, and PO4-P at both m and m depths (Figure 4.5, 4.6 and 4.7) The release rates of NH4-N and PO4-P from bottom sediment were respective 15.0 and 1.9 mg/l/d which were found by using trial and error method Moreover, the variation in concentration of sulfide was also a good fit to the observation (Figure 4.8) The modified ecosystem model possessed high reproducibility of sulfide at m and m and suggested that this model could be used to predict and analyze the seasonal change of sulfide under anaerobic state in an organically polluted reservoir The release rate of sulfide from bottom sediment was 11.0 µg/l/d, detected by using trial and error method Furthermore, Figure 4.3, 4.4, 4.5, 4.6, 4.7 and 4.8 showed the improvement of calculation of the modified model in comparison with the previous model, especially the calculation near the bottom sediment Table 4.1 shows results of NashSutcliffe coefficient (NS) of water quality indicators at both m and m depths The good results of Nash-Sutcliffe coefficient of NO3-N, NH4-N, PO4-P and sulfide at both m and m depths expressed that the modified ecosystem model could be used to predict and analyze impacts of reductive processes on water environment dynamic under anaerobic state in the organically polluted reservoir comprising denitrification, release of nutrient salts and generation of sulfide 48 Observation Previous model 40 30 20 10 40 30 20 10 o WT ( C) WT ( C) 40 30 20 10 WT ( C) 40 30 20 10 z = 5m 40 30 20 10 WT ( C) WT ( C) 40 30 20 10 o WT ( C) WT ( C) 40 30 20 10 WT ( C) z = 0m 40 30 20 10 z = 6m o o z = 1m z = 7m o o z = 2m z = 8m o o z = 3m o WT ( C) z = 4m 5 10 11 12 Month 40 30 20 10 Modified model 10 11 12 Month Fig 4.3 Calculation of water temperature of the previous model, modified model and observation data in 2015 49 Observation Previous model DO (mg/l) DO (mg/l) 20 15 10 20 15 10 DO (mg/l) DO (mg/l) 20 15 10 z = 5m 20 15 10 20 15 10 DO (mg/l) DO (mg/l) DO (mg/l) 20 15 10 DO (mg/l) z = 0m 20 15 10 20 15 10 z = 1m z = 6m z = 2m z = 7m z = 3m z = 8m DO (mg/l) z = 4m 5 10 11 12 Month 20 15 10 Modified model 10 11 12 Month Fig 4.4 Calculation of DO of the previous model, modified model and observation data in 2015 50 NO3-N (mg/l) 0.5 0.4 0.3 0.2 0.1 0.5 0.4 0.3 0.2 0.1 NO3-N (mg/l) NO3-N (mg/l) 0.5 0.4 0.3 0.2 0.1 z = 0m 0.5 0.4 0.3 0.2 0.1 NO3-N (mg/l) NO3-N (mg/l) 0.5 0.4 0.3 0.2 0.1 Previous model 0.5 0.4 0.3 0.2 0.1 NO3-N (mg/l) NO3-N (mg/l) 0.5 0.4 0.3 0.2 0.1 NO3-N (mg/l) Observation 0.5 0.4 0.3 0.2 0.1 z = 1m z = 2m z = 3m z = 5m z = 6m z = 7m z = 8m NO3-N (mg/l) 0.5 0.4 0.3 0.2 0.1 z = 4m Modified model 10 11 12 Month 10 11 12 Month Fig 4.5 Calculation of NO3-N of the previous model, modified model and observation data in 2015 51 Observation Previous model NH4-N (mg/l) NH4-N (mg/l) 3 NH4-N (mg/l) NH4-N (mg/l) z = 5m 3 NH4-N (mg/l) NH4-N (mg/l) NH4-N (mg/l) NH4-N (mg/l) z = 0m 3 z = 1m z = 6m 2 z = 2m z = 7m 2 z = 3m z = 8m 2 z = 4m NH4-N (mg/l) Modified model 10 11 12 Month 10 11 12 Month Fig 4.6 Calculation of NH4-N of the previous model, modified model and observation data in 2015 52 Observation Previous model z = 5m 0.3 PO4-P (mg/l) PO4-P (mg/l) z = 0m 0.2 0.1 0.3 0.2 0.1 z = 6m 0.3 PO4-P (mg/l) PO4-P (mg/l) z = 1m 0.2 0.1 0.3 0.2 0.1 z = 7m 0.3 PO4-P (mg/l) PO4-P (mg/l) z = 2m 0.2 0.1 0.3 0.2 0.1 z = 8m 0.3 PO4-P (mg/l) PO4-P (mg/l) z = 3m 0.2 0.1 0.3 0.2 0.1 z = 4m PO4-P (mg/l) Modified model 10 11 12 Month 0.3 0.2 0.1 10 11 12 Month Fig 4.7 Calculation of PO4-P of the previous model, modified model and observation data in 2015 53 Observation Modified model Sulfide (µg/l) Sulfide (µg/l) 800 600 400 200 800 600 400 200 Sulfide (µg/l) Sulfide (µg/l) 800 600 400 200 z = 5m 800 600 400 200 800 600 400 200 Sulfide (µg/l) Sulfide (µg/l) Sulfide (µg/l) 800 600 400 200 Sulfide (µg/l) z = 0m 800 600 400 200 800 600 400 200 z = 1m z = 6m z = 2m z = 7m z = 3m z = 8m Sulfide (µg/l) z = 4m 10 11 12 Month 800 600 400 200 10 11 12 Month Fig 4.8 Calculation of sulfide of the modified model and observation data in 2015 54 4.2 Scenario analysis Table 4.2 Results of DO, NH4-N, PO4-P and sulfide of four scenarios at m In order to quantitatively estimate the Scenario name S1 S2 S3 S4 influence of underwater light environTime when DO dement to water quality variation, the 143 17 150 150 creased to zero (d) numerical simulation were carried out by Elution time of 123 17 150 113 using two scenarios that the transparenNH4-N (d) cies were set to the low value of 0.5 m in Elution time of 110 131 85 PO4-P (d) the scenario1 (S1) and a high value of 4.5 Generation time of m in the scenario2 (S2) As be shown in 110 131 85 Sulfide (d) Figure 4.9, there are significant Peak of NH4-N 1.49 0.75 2.05 1.83 differences in the calculation of DO, NH4(mg/l) N, PO4-P and sulfide just above the Peak of PO4-P 0.17 0.06 0.24 0.20 benthic zone (8 m) between S1 and S2 (mg/l) Peak of sulfide The anoxic period reduces significantly in 655 20 747 580 (µg/l) S2 when transparency is set 4.5 m, up to 126 days in comparison with S1 The reason for this phenomena could be interpreted by the effect of underwater light intensity The high transparency expresses that there are less suspended substances in water and reduce less light intensity with depth Under high underwater light environment, photosynthesis is supported well and releases oxygen, leading to the strong shortening of the anoxic period Corresponding the strong shortening of the anoxic period, the elution as well as the peak of NH4-N, PO4-P and sulfide are decreased dramatically (Table 4.2) The elution time of NH4-N and PO4-P at m reduces significantly from more than 100 days in S1 to 17 days and six days in S2 In the case of high transparency, the peak of NH4-N and PO4-P are only half of low transparency case Especially, there is a impressive reduction of generation of sulfide in S2 The generation time and peak of sulfide is very small, only six days of generation and 20 µg/l of the peak at m These results indicate that transparency has an essential impact on the anaerobic condition in the reservoir and contribute much to drive seasonal change of water environment dynamic under anaerobic condition in the organically polluted agricultural reservoir In addition, assuming that denitrification initially caused by anaerobic respiration impacts on the reduction reactions that later occur in phases, the initial conditions for NO3-N were set a low concentration of 0.1 mg/l in the scenario3 (S3) and a high concentration of 1.0 mg/l in the scenario4 (S4) Figure 4.10 illustrates the variation in concentration of DO, NH4-N, PO4-P and sulfide in S3 and S4 at m The anaerobic periods at m of S3 and S4 are perfectly the same (150 days), indicate that variation in concentration of NO3-N has no effect on anoxic period Results from the modified ecosystem model have shown that concentration of NO3-N impacts on the variation of NH4-N, PO4P and sulfide (Table 4.2) In the case of high initial NO3-N concentration, the elution time of NH4-N decreases 37 days, the elution time of PO4-P and sulfide decrease 46 days in comparison with low 55 initial NO3-N concentration case The peak of PO4-P, NH4-N, and sulfide in S4 are lower and account approximately 80 ÷ 90 percentage of those in S3 These results express that initial NO3-N concentration in water influences water environment dynamic under anaerobic state but its effect is low DO (mg/l) z = 8m 10 Scenario1 Scenario2 NH4-N (mg/l) z = 8m Scenario1 Scenario2 PO 4-P (mg/l) z = 8m Scenario1 Scenario2 0.2 0.1 Sulfide (µg/l) z = 8m 800 600 Scenario1 Scenario2 400 200 Month 10 11 12 Fig 4.9 Calculation of DO, NH4-N, PO4-P and sulfide at m of Scenario1 and Scenario2 56 DO (mg/l) z = 8m 10 Scenario3 Scenario4 NH4-N (mg/l) z = 8m Scenario3 Scenario4 PO 4-P (mg/l) z = 8m Scenario3 Scenario4 0.2 0.1 Sulfide (µg/l) z = 8m 800 600 Scenario3 Scenario4 400 200 Month 10 11 12 Fig 4.10 Calculation of DO, NH4-N, PO4-P and sulfide at m of Scenario3 and Scenario4 57 Conclusion Water quality survey in 2015 was conducted to investigate the seasonal change of water quality indicators in an organically polluted agricultural reservoir such as water temperature, DO, ORP, NO3N, NH4-N, PO4-P, sulfide etc The observation analysis clarified characteristics under anaerobic state of an organically polluted agricultural reservoir and suggested important information to develop and modified one-dimensional vertical ecosystem model Based on these observation analyses, the modified one-dimensional vertical ecosystem model was completed with good agreement values for NO3N, NH4-N, PO4-P and sulfide during calculation period from April to December Especially, the simulation of sulfide is the new function of the modified one-dimensional vertical ecosystem model Due to the limitation in time of master course, only one-year observation data in 2015 was completed The observation data is just enough for calibration and there is no validation in this study However, the high values of Nash-Sutcliffe coefficient for calibration of nutrient salts and sulfide at both m and m have indicated that the modified one-dimensional vertical ecosystem model is useful, and could be used in order to analyze water environment dynamic under anaerobic state in an organically polluted agricultural reservoir Moreover, the influences of underwater light environment and denitrification on the water quality environment were estimated through four scenarios The simulated result has determined that high transparency and high initial NO3-N concentration in the water body could reduce harmful effects of anaerobic state on the water environment 58 References Gordon JA, Higgins JM (2007) Fundamental water quality processes In: Energy production and reservoir water quality American Society of Civil Engineers, Virginia, the United States of America pp 3-1-3-29 Beutel MW (2006) Inhibition of ammonia release from anoxic profundal sediments in lakes using hypolimnetic oxygenation Ecol Eng 28:271–279 doi: 10.1016/j.ecoleng.2006.05.009 Carolina N, Christian RR (1994) Dynamics of NH , + and NO , - Uptake in the Water Column of the Neuse River Estuary, Estuaries 17:361–371 Chen W-F, Liu T-K (2003) Dissolved oxygen and nitrate of groundwater in Choshui Fan-Delta, western Taiwan Environ Geol 44:731–737 doi: 10.1007/s00254-003-0823-0 Haaning Nielsen A, Lens P, Vollertsen J, Hvitved-Jacobsen T (2005) Sulfide-iron interactions in domestic wastewater from a gravity sewer Water Res 39:2747–55 doi: 10.1016/j.watres.2005.04.048 Harada M, Douma A, Hiramatsu K, et al (2013) Analysis of seasonal changes in water qualities in eutrophic reservoirs in a flat low-lying agricultural area using an algae-based ecosystem model Irrig Drain 62:24–35 doi: 10.1002/ird.1770 J.C.CURRIE and A.T.PEPPER (1993) WATER AND THE ENVIRONMENT, Rewood Pre ELLIS HOR WOOD LIMITED, Chichester, West Sussex, PO19 EB, England Jonas M, Dake K (1969) Thermal Stratification in Lakes : Analytical Studies 5:484–495 Koretsky CM, MacLeod A, Sibert RJ, Snyder C (2011) Redox Stratification and Salinization of Three Kettle Lakes in Southwest Michigan, USA Water, Air, Soil Pollut 223:1415–1427 doi: 10.1007/s11270-011-0954-y Mortimer CH (1971) Chemical exchanges between sediments and water in the Great Lakesspeculations on probable regulatory mechanisms Limnol Oceanogr 16:387–404 doi: 10.4319/lo.1971.16.2.0387 Simple a, Vertical FOR, Nonneutral OF (1982) beyond stress lation that gives R • = Na /( lvl / z ) a may also give decreasing functions for P or monotonic increasing 87:5860–5864 Skoog AC, Arias-Esquivel V a (2009) The effect of induced anoxia and reoxygenation on benthic fluxes of organic carbon, phosphate, iron, and manganese Sci Total Environ 407:6085–6092 doi: 10.1016/j.scitotenv.2009.08.030 Stenstrom MK, Poduska R a (1980) The effect of dissolved oxygen concentration on nitrification Water Res 14:643–649 doi: 10.1016/0043-1354(80)90122-0 Tasuku Kato (2005) Simulation of water quality with the application of system dynamics model for population and land-use changes Paddy Water Env 103–109 doi: 10.1007/s10333-005-0002-x 59 Thuy Do N, Yoshimura Y, Harada M, Hiramatsu K (2014) Generation of hydrogen sulfide in the deepest part of a reservoir under anoxic water conditions Paddy Water Environ doi: 10.1007/s10333-013-0412-0 Uwabara JASK, Lexander A (1999) Dissolved sulfide distributions in the water column and sediment pore waters of the Santa Barbara Basin 63:2199–2209 Wilson LP, Bouwer EJ (1997) Biodegradation of aromatic compounds under mixed oxygen/denitrifying conditions: a review J Ind Microbiol Biotechnol 18:116–130 doi: 10.1038/sj.jim.2900288 60 ... loading of phosphorus and nitrogen can’t display sufficiently in these modeling Moreover, there is no research on modeling the variation of sulfide under anaerobic state in the organically polluted. .. pollution in the water body Every summer, the high concentration of organic matter is the main reason to exacerbate the thermal stratification, leading to the drop of DO at the deep parts of the reservoir. .. one-dimensional ecosystem model The previous model did not mention on the settling of phytoplankton and POM from the upper layer and could affect the accuracy of the ecosystem model In the modified model,