学位授与大学：京都大学 ; 取得学位: 博士(工学) ; 学位授与年月日: 2007-09-25 ; 学位の種類: 新制・課程博士 ; 学位記番号: 工博第2849号 ; 請求記号: 新制/工/1419 ; 整理番号: 25534 / Understanding river water quantity and quality variation is one of the fundamental requirements for the integrated watershed management. Monitoring is usually preferred to examine and understand the river water quantity and quality, especially focusing on pre-specified objectives. Although monitoring is invaluable in many instances, it is of less use to forecast the foreseeable changes, especially, for the long-term prediction that is usually required by the decision-makers. Therefore, for the decision-making, modeling is widely practiced. Due to the limited understanding of hydrological processes inside a watershed, models often fail to estimate properly, which in worst case could often mislead the targeted plans. Among several aspects, spatial variability such as land cover, topography, soil, geology is believed to affect the overall performance of the model. Such thought lead to the concept of distributed models that were supposed to represent spatial variability through modeling specific variations inside the watershed by using several representative units or grids. In that meaning, distributed models required to identify and assign the values of its parameters to represent the physical processes defined by the governing equations for each grid. Due to the unavailability of required spatial information at appropriate grid sizes, even physically based and conceptually sound distributed models fail to estimate properly thereby offsetting the credibility of distributed models. Therefore, in this study, we set a major objective to develop a new distributed water quantity and water quality model to address some of the stated issues. Major emphasis was given to conceptually sound but simple structure of the model. In addition to that, model aimed to utilize the potential of recent advances in spatial information, such as remote sensing and GIS, to generate and process the spatial data, and to determine the values of its essential parameters. The approach was expected to provide an example that the complexity of the model should be preferred only if the defined processes could be ascertained within some reasonable limit. At the initial stage, several spatial data were collected from different sources and they were processed into raster format, which was one of the essential requirements for the distributed model. Analysis of spatial database indicated that the watershed was characterized by forested parts in the hills, and densely populated urban areas in plains. Rainfall occurred quite frequently but they were of short duration. Besides constructing spatial database, several water quantity and quality surveys were also conducted at different spatial and temporal conditions from 2000 to 2006. The data were mainly used to understand variation patterns of water quantity and quality at both spatial and temporal conditions. Later on, some of the data were also used for the verification of model in study area. 28 water quality indices (WQIs) were observed for each observation, which were mainly utilized to understand the overall variation pattern of river water quality. Initial analysis of flow rate condition of the river showed that the rainfall-runoff responses were quite rapid after the rainfall but such effect appear for very short duration (< 2 days). Then, analysis of variance (ANOVA) and two multivariate analysis techniques (MVA), namely, principle component analysis (PCA) and cluster analysis (CA) were used to explore effectively the river water quality datasets. Analysis showed that the observed covariation among majority of WQIs could be due to the inter-linkages among rainfall pattern, atmospheric deposition of acidic ions, soil and geology of dominant forest areas, topography, and climatic conditions. The identified pattern indicated that there could be close relationship between the biogeochemical processes in the forest areas with both river water quantity and quality variation. A new distributed water quantity and quality model was developed especially focusing on the biophysical characteristics of the watershed. Basic structure of the model was similar to the concept of lumped tank model, which was often credited for its simple and sound conceptual structure. Two storey tanks were conceptualized for each grid, but model also took into consideration of drainage channels in urban areas and natural river channels as rapidly conveying structures. Besides, the model considered all major aspects affecting the estimation of water quantity, such as interception of the rainfall, evapotranspiration loss, surface runoff, sub-surface runoff, and ground water runoff. Compared with the original tank model, major emphasis was given to assign the values major parameters, such as coefficients and storage heights of the outlets, by relating them with the hilly topography of the study area and the variation in land cover, soil, and geology. The model was further integrated with water quality component, which was based on two fundamental assumptions of build-up and wash-off of the WQIs in the environment. Build-up was based on the land cover type and population, while wash off was based on the estimated runoff volume. Remote sensing and GIS techniques were used to assist in the modeling process. At first, remote sensing was mainly focused in the classification of land cover by utilizing seasonal Landsat ETM+ images. In addition to urban and vegetated urban categories, four major forest categories (shaded, deciduous, mixed, and evergreen) were identified. Then leaf area index (Lai) was determined for each vegetation category. Lai was mainly used to determine the rainfall interception by the canopy in the forest areas. In this study, forest areas showed the capacity to intercept as high as 1.2 mm of rainfall, which could be quite important during smaller rainfall events. Remote sensing was further used to determine the transpiration coefficient of the vegetations, which was a major requirement for the estimation of evapotranspiration (Et) loss by the FAO Penman- Monteith method used in the model simulation. Et was estimated even reached more than 4 mm/d in summer months, but it was relatively lower (< 2 mm/d) in the winter months. These facts suggested that consideration of both interception and Et loss in a forested watershed could have significant influence on the estimation of flow rates by the model. At the final stage, model was applied in the study area. Mainly three approaches were considered to assess the estimation by the model. First was conventional approach in which comparison between the observed and estimated data were done considering different spatial and temporal contexts. Assigned values of the parameters gave satisfactory prediction for both water quantity and quality for the selected grid size of 50 m in which the relative error was usually less than 1. The second approach evaluated the model by considering different scale of the grids ranging from 100m to 500m. It was observed that grid resizing usually affected the basin attributed such as slope, outlet height, drainage characteristics following nearly proportionate pattern than other categorical variables such as land cover or geology. Usually same parameter values gave very different prediction level for both magnitude and shape of the hydrographs (or pollutographs), in which increasing grid size was accompanied by the increasing peak event estimation or overall error. The effects were further assessed by changing the values of key parameters for each grid size targeting the minimum differences between the observed and estimated values. Interestingly, the parameters also showed some identifiable (increasing or decreasing) trend with the change in grid size. Particularly, due to the direct effect of predicted runoff on the reference WQIs, its showed more complex variation pattern at different grid sizes. Overall assessment of the distributed model indicated that the model was quite sensitive to the selection of key parameters for different grid sizes. It indicated that the values of calibrated parameters might not give stable result if the scale of input data were changed. It could further indicate that the choice of grid size should be assessed before the actual application of the model considering the spatial variability of the watershed. In the third approach, model was utilized to estimate at different scenarios, namely, rainfall variation and land cover changes. The differences in the estimated results could indicate that the model could be available for the watershed management at different runoff and land cover scenarios in future. / Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第13378号 / 工博第2849号 / 新制||工||1419(附属図書館) / 25534 / UT51-2007-Q779 / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 田中 宏明, 教授 藤井 滋穂, 教授 清水 芳久 / 学位規則第4条第1項該当

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Darbo objektas: Virvyčios upės vandens kokybė. Darbo tikslas: įvertinti pasklidosios taršos, iš ekologinės ir įprastinės žemės ūkio gamybos poveikį Virvyčios upės vandens kokybei. Darbo uždaviniai: atlikti Virvyčios upės vandens kokybės analizę ties ekologinės gamybos ūkiu ir greta esančiu įprastinės gamybos ūkiu pagal svarbiausius taršos rodiklius; nustatyti sezoninę upės vandens kokybės rodiklių kaitą; palyginti ekologinės ir įprastinės žemės ūkių gamybos poveikį Virvyčios upės vandens kokybei. . Tyrimo metodai: loginis – analitinis, matematinis – statistinis. Vandens ištekliai nuolat atsinaujina, tačiau dėl taršos blogėjanti vandens kokybė riboja vandens naudojimą, kelia grėsmę mūsų sveikatai ir vandens ekosistemų funkcionavimui. Paviršinio vandens kokybė labiausiai priklauso nuo į vandens telkinius patenkančių teršalų savybių ir jų kiekių. Pagrindiniai upių cheminiai vandens teršalai yra organinės medžiagos, azoto ir fosforo junginiai, patenkantys iš pasklidosios ir sutelktosios taršos šaltinių. Darbe analizuojama Virvyčios upės vandens kokybė. Remiantis Lietuvos Respublikos aplinkos apsaugos agentūros duomenimis, Virvyčios upė priklauso vandens telkinių rizikos grupei dėl vagos ištiesinimo ir vandens kokybės, nes yra veikiama pasklidosios žemės ūkio taršos. Žemės ūkio taršą, patenkančią į paviršinius vandenis, yra sunku išmatuoti ir kontroliuoti, nes ji patenka iš šaltinių, plačiai pasklidusių tam tikroje teritorijoje. Darbe siekta nustatyti pasklidosios taršos poveikį... [toliau žr. visą tekstą] / Research object: Virvyčia river water quality Research aim: assessment of diffuse pollution from organic and conventional agricultural production, the impact on the water quality of the river Virvyčia. Objectives: Perform Virvyčia river water quality analysis on organic farm and an adjacent conventional farms by main pollution indicators; Identify seasonal river water quality parameters change; Compare organic and conventional production on the farm Virvyčia river water quality. Water resources are constantly renewing, but due to the pollution, water quality is declining and limiting the water usage, threatening our health and lives in the aquatic ecosystems. Surface water quality is the most dependent on the pollutants discharged into water bodies, their characteristics and quantities. The main chemical pollutants in river water are organic matter, nitrogen and phosphorus compounds, finding their way from diffuse or point sources of pollution. This article analyzes the water quality of Virvyčia River, which belongs to the Venta River basin. According to the Lithuanian Environmental Protection Agency, Virvyčia river water may be at risk due to channel straightening and water quality, as it is exposed to diffuse agricultural pollution. Agricultural pollution discharged into the surface water is difficult to measure and control, because it comes from sources widely scattered in a certain area. The research was aimed to determine the effects of diffuse pollution influence to... [to full text]

Thesis (M.A.)--Miami University, Dept. of Geography, 2005. / Title from first page of PDF document. Document formatted into pages; contains [1], iv, 49, [6] p. : ill., maps. Includes bibliographical references (p. 44-49).

La préservation de la richesse aquatique est devenue un souci majeur d'ordre mondial suite au risque de pénurie en eau. Au Liban, les rejets anthropiques incontrôlés et incontrôlables au bord des rivières menacent la qualité de ses eaux de surface. Pour cela, quatre cours d'eau libanais ont été choisis comme cadre d'étude : la rivière Damour, la rivière Kadisha-Abou Ali, la rivière Ibrahim et la rivière Oronte.Les différents paramètres physico-chimiques et microbiologiques étudiés ont permis dans un premier temps la construction d'une base de données de chacune de ces rivières. Une seule analyse spatio-temporelle des paramètres séparément n'aide pas à définir l'état trophique des rivières. Partant de l'idée des corrélations que peuvent exister entre certains paramètres, l'Analyse en Composante Principale (ACP) retenant la totalité de l'information sera utilisée en dépit des méthodes classiques. Cet outil statistique a permis de classer les rivières par niveau de pollution. Il a aussi aidé à observer l'impact des apports des polluants sur les différentes stations de rivières étudiées.Pour suivre le devenir des coliformes fécaux dans les eaux, l'ACP des variables microbiologiques a montré la persistance des colonies bactériennes dans les eaux malgré les conditions climatiques diverses et le régime méditerranéen torrentiel des rivières. En effet, les sédiments constituent des réservoirs potentiels de microorganismes pathogènes. Le décrochage bactérien des agrégats sédimentaires et la remise en suspension dans l'eau pose un problème alarmant de santé publique. / Preservation of aquatic wealth has worldwide become a major concern due to the risk of water shortage. In Lebanon, uncontrolled and uncontrollable anthropogenic rejections along rivers threaten the quality of its surface waters. Four Lebanese rivers were selected as study framework: The Damour river, Kadisha-Abu Ali river, Ibrahim river and Orontes river. The physico-chemical and microbiological parameters analyzed has formed a large database of these rivers. The spatio-temporal analysis of separate parameters did not help defining the trophic status of rivers. The assumption that correlations exist between certain parameters, guides us to use the Principal Component Analysis (PCA) in spite of conventional methods. This statistical tool was used to define the pollution levels in rivers. It also leads to observe the impact of pollutants inflows on different sites studied. To follow the fate of fecal coliform in water, the ACP of microbiological variables showed the persistence of bacterial colonies in water despite the various climatic conditions and the Mediterranean flow rate. Indeed, sediments are potential reservoirs of pathogenic microorganisms. The bacteria aggregated to the sediment can be present again in water column causing an alarming public health problem.

Climate change could aggravate periodic and chronic shortfalls of water, particularly in arid and semi-arid areas of the world (IPCC, 2001). Climate change is likely to accelerate the global hydrological cycle, with increase in temperature, changes in precipitation patterns, and evapotranspiration affecting the water quantity and quality, water availability and demands. The various components of a surface water resources system affected by climate change may include the water availability, irrigation demands, water quality, hydropower generation, ground water recharge, soil moisture etc. It is prudent to examine the anticipated impacts of climate change on these different components individually or combinedly with a view to developing responses to minimize the climate change induced risk in water resources systems. Assessment of climate change impacts on water resources essentially involves downscaling the projections of climatic variables (e.g., temperature, humidity, mean sea level pressure etc.) to hydrologic variables (e.g., precipitation and streamflow), at regional scale. Statistical downscaling methods are generally used in the hydrological impact assessment studies for downscaling climate projections provided by the General Circulation Models (GCMs). GCMs are climate models designed to simulate time series of climate variables globally, accounting for the greenhouse gases in the atmosphere. The statistical techniques used to bridge the spatial and temporal resolution gaps between what GCMs are currently able to provide and what impact assessment studies require are called as statistical downscaling methods. Generally, these methods involve deriving empirical relationships that transform large-scale simulations of climate variables (referred as the predictors) provided by a GCM to regional scale hydrologic variables (referred as the predictands). This general methodology is characterized by various uncertainties such as GCM and scenario uncertainty, uncertainty due to initial conditions of the GCMs, uncertainty due to downscaling methods, uncertainty due to hydrological model used for impact assessment and uncertainty resulting from multiple stake holders in a water resources system. The research reported in this thesis contributes towards (i) development of methodologies for climate change impact assessment of various components of a water resources system, such as water quality, water availability, irrigation and reservoir operation, and (ii) quantification of GCM and scenario uncertainties in hydrologic impacts of climate change. Further, an integrated reservoir operation model is developed to derive optimal operating policies under the projected scenarios of water availability, irrigation water demands, and water quality due to climate change accounting for various sources of uncertainties. Hydropower generation is also one of the objectives in the reservoir operation. The possible climate change impact on river water quality is initially analyzed with respect to hypothetical scenarios of temperature and streamflow, which are affected by changes in precipitation and air temperature respectively. These possible hypothetical scenarios are constructed for the streamflow and river water temperature based on recent changes in the observed data. The water quality response is simulated, both for the present conditions and for conditions resulting from the hypothetical scenarios, using the water quality simulation model, QUAL2K. A Fuzzy Waste Load Allocation Model (FWLAM) is used as a river water quality management model to derive optimal treatment levels for the dischargers in response to the hypothetical scenarios of streamflow and water temperature. The scenarios considered for possible changes in air temperature (+1 oC and +2 oC) and streamflow (-0%, -10%, -20%) resulted in a substantial decrease in the Dissolved Oxygen (DO) levels, increase in Biochemical Oxygen Demand (BOD) and river water temperature for the case study of the Tunga-Bhadra River, India. The river water quality indicators are analyzed for the hypothetical scenarios when the BOD of the effluent discharges is at safe permissible level set by Pollution Control Boards (PCBs). A significant impairment in the water quality is observed for the case study, under the hypothetical scenarios considered. A multi-variable statistical downscaling model based on Canonical Correlation Analysis (CCA) is then developed to downscale future projections of hydro¬meteorological variables to be used in the impact assessment study of river water quality. The CCA downscaling model is used to relate the surface-based observations and atmospheric variables to obtain the simultaneous projection of hydrometeorological variables. Statistical relationships in terms of canonical regression equations are obtained for each of the hydro-meteorological predictands using the reanalysis data and surface observations. The reanalysis data provided by National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used for the purpose. The regression equations are applied to the simulated GCM output to model future projections of hydro-meteorological predictands. An advantage of the CCA methodology in the context of downscaling is that the relationships between climate variables and the surface hydrologic variables are simultaneously expressed, by retaining the explained variance between the two sets. The CCA method is used to model the monthly hydro-meteorological variables in the Tunga-Bhadra river basin for water quality impact assessment study. A modeling framework of risk assessment is developed to integrate the hydro¬meteorological projections downscaled from CCA model with a river water quality management model to quantify the future expected risk of low water quality under climate change. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold DO level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is adopted to evaluate the future fractional removal policies for each of the dischargers by including the predicted future risk levels. The hydro-meteorological projections of streamflow, air temperature, relative humidity and wind speed are modeled using MIROC 3.2 GCM simulations with A1B scenario. The river water temperature is modeled by using an analytical temperature model that includes the downscaled hydro-meteorological variables. The river water temperature is projected to increase under climate change, for the scenario considered. The IFWLAM uses the downscaled projections of streamflow, simulated river water temperature and the predicted lower and upper future risk levels to determine the fraction removal policies for each of the dischargers. The results indicate that the optimal fractional removal levels required for the future scenarios will be higher compared to the present levels, even if the effluent loadings remain unchanged. Climate change is likely to impact the agricultural sector directly with changes in rainfall and evapotranspiration. The regional climate change impacts on irrigation water demands are studied by quantifying the crop water demands for the possible changes of rainfall and evapotranspiration. The future projections of various meteorological variables affecting the irrigation demand are downscaled using CCA downscaling model with MIROC 3.2 GCM output for the A1B scenario. The future evapotranspiration is obtained using the Penman-Monteith evapotranspiration model accounting for the projected changes in temperature, relative humidity, solar radiation and wind speed. The monthly irrigation water demands of paddy, sugarcane, permanent garden and semidry crops quantified at nine downscaling locations covering the entire command area of the Bhadra river basin, used as a case study, are projected to increase for the future scenarios of 2020-2044, 2045-2069 and 2070-2095 under the climate change scenario considered. The GCM and scenario uncertainty is modeled combinedly by deriving a multimodel weighted mean by assigning weights to each GCM and scenario. An entropy objective weighting scheme is proposed which exploits the information contained in various GCMs and scenarios in simulating the current and future climatology. Three GCMs, viz., CGCM2 (Meteorological Research Institute, Japan), MIROC3.2 medium resolution (Center for Climate System Research, Japan), and GISS model E20/Russell (NASA Goddard Institute for Space Studies, USA) with three scenarios A1B, A2 and B1 are used for obtaining the hydro-meteorological projections for the Bhadra river basin. Entropy weights are assigned to each GCM and scenario based on the performance of the GCM and scenario in reproducing the present climatology and deviation of each from the projected ensemble average. The proposed entropy weighting method is applied to projections of the hydro-meteorological variables obtained based on CCA downscaling method from outputs of the three GCMs and the three scenarios. The multimodel weighted mean projections are obtained for the future time slice of 2020-2060. Such weighted mean hydro-meteorological projections may be further used into the impact assessment model to address the climate model uncertainty in the water resources systems. An integrated reservoir operation model is developed considering the objectives of irrigation, hydropower and downstream water quality under uncertainty due to climate change, uncertainty introduced by fuzziness in the goals of stakeholders and uncertainty due to the random nature of streamflow. The climate model uncertainty originating from the mismatch between projections from various GCMs under different scenarios is considered as first level of uncertainty, which is modeled by using the weighted mean hydro-meteorological projections. The second level of uncertainty considered is due to the imprecision and conflicting goals of the reservoir users, which is modeled by using fuzzy set theory. A Water Quantity Control Model (WQCM) is developed with fuzzy goals of the reservoir users to obtain water allocations among the different users of the reservoir corresponding to the projected demands. The water allocation model is updated to account for the projected demands in terms of revised fuzzy membership functions under climate change to develop optimal policies of the reservoir for future scenarios. The third level of uncertainty arises from the inherent variability of the reservoir inflow leading to uncertainty due to randomness, which is modeled by considering the reservoir inflow as a stochastic variable. The optimal monthly operating polices are derived using Stochastic Dynamic Programming (SDP), separately for the current and for the future periods of 2020-2040 and 2040-2060 The performance measures for Bhadra reservoir in terms of reliability and deficit ratios for each reservoir user (irrigation, hydropower and downstream water quality) are estimated with optimal SDP policy derived for current and future periods. The reliability with respect to irrigation, downstream water quality and hydropower show a decrease for 2020-2040 and 2040-2060, while deficit ratio increases for these periods. The results reveal that climate change is likely to affect the reservoir performance significantly and changes in the reservoir operation for the future scenarios is unable to restore the past performance levels. Hence, development of adaptive responses to mitigate the effects of climate change is vital to improve the overall reservoir performance.

Waste Load Allocation (WLA) in rivers refers to the determination of required pollutant fractional removal levels at a set of point sources of pollution to ensure that water quality standards are maintained throughout the system. Optimal waste load allocation implies that the selected pollution treatment vector not only maintains the water quality standards, but also results in the best value for the objective function defined for the management problem. Waste load allocation problems are characterized by uncertainties due to the randomness and imprecision. Uncertainty due to randomness arises mainly due to the random nature of the variables influencing the water quality. Uncertainty due to imprecision or fuzziness is associated with setting up the water quality standards and goals of the Pollution Control Agencies (PCA), and the dischargers (e.g., industries and municipal dischargers). Many decision problems in water resources applications are dominated by natural, extreme, rarely occurring, uncertain events. However usually such events will be absent or be rarely present in the historical records. Due to the scarcity of information of these uncertain events, a realistic decision-making becomes difficult. Furthermore, water resources planners often deal with imprecision, mostly due to imperfect knowledge and insufficient or inadequate data. Therefore missing data is very common in most water resources decision problems. Missing data introduces inaccuracy in analysis and evaluation. For instance, the sample mean of the available data can be an inaccurate estimate of the mean of the complete data. Use of sample statistics estimated from inadequate samples in WLA models would lead to incorrect decisions. Therefore there is a necessity to incorporate the uncertainty due to missing data also in WLA models in addition to the uncertainties due to randomness and imprecision. The uncertainty in the input parameters due to missing or inadequate data renders the input parameters (such as mean and variance) as interval grey parameters in water quality decision-making. In a Fuzzy Waste Load Allocation Model (FWLAM), randomness and imprecision both can be addressed simultaneously by using the concept of fuzzy risk of low water quality (Mujumdar and Sasikumar, 2002). In the present work, an attempt is made to also address uncertainty due to partial ignorance due to missing data or inadequate data in the samples of input variables in FWLAM, considering the fuzzy risk approach proposed by Mujumdar and Sasikumar (2002). To address the uncertainty due to missing data or inadequate data, the input parameters (such as mean and variance) are considered as interval grey numbers. The resulting output water quality indicator (such as DO) will also, consequently, be an interval grey number. The fuzzy risk will also be interval grey number when output water quality indicator is an interval grey number. A methodology is developed for the computation of grey fuzzy risk of low water quality, when the input variables are characterized by uncertainty due to partial ignorance resulting from missing or inadequate data in the samples of input variables. To achieve this, an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system to address uncertainties due to randomness, fuzziness and also due to missing data or inadequate data. Monte Carlo Simulation (MCS) incorporating a water quality simulation model is performed two times for each set of randomly generated input variables: once for obtaining the upper bound of DO and once for the lower bound of DO, by using appropriate upper or lower bounds of interval grey input variables. These two bounds of DO are used in the estimation of grey fuzzy risk by substituting the upper and lower values of fuzzy membership functions of low water quality. A backward finite difference scheme (Chapra, 1997) is used to solve the water quality simulation model. The goal of PCA is to minimize the bounds of grey fuzzy risk, whereas the goal of dischargers is to minimize the fractional removal levels. The two sets of goals are conflicting with each other. Fuzzy multiobjective optimization technique is used to formulate the multiobjective model to provide best compromise solutions. Probabilistic Global Search Lausanne (PGSL) method is used to solve the optimization problem. Finally the results of the model are compared with the results of risk minimization model (Ghosh and Mujumdar, 2006), when the methodology is applied to the case study of the Tunga-Bhadra river system in South India. The model is capable of determining a grey fuzzy risk with the corresponding bounds of DO, at each check point, rather than specifying a single value of fuzzy risk as done in a Fuzzy Waste Load Allocation Model (FWLAM). The IFWLAM developed is based on fuzzy multiobjective optimization problem with ‘max-min’ as the operator, which usually may not result in a unique solution and there exists a possibility of obtaining multiple solutions (Karmakar and Mujumdar, 2006b). Karmakar and Mujumdar (2006b) developed a two-phase Grey Fuzzy Waste Load Allocation Model (two-phase GFWLAM), to determine the widest range of interval-valued optimal decision variables, resulting in the same value of interval-valued optimal goal fulfillment level as obtained from GFWLAM (Karmakar and Mujumdar 2006a). Following Karmakar and Mujumdar (2006b), two optimization models are developed in this study to capture all the decision alternatives or multiple solutions: one to maximize and the other to minimize the summation of membership functions of the dischargers by keeping the maximum goal fulfillment level same as that obtained in IFWLAM to obtain a lower limit and an upper limit of fractional removal levels respectively. The aim of the two optimization models is to obtain a range of fractional removal levels for the dischargers such that the resultant grey fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision-making. The models are applied to the case study of Tunga-Bhadra river system. A range of upper and lower limits of fractional removal levels is obtained for each discharger; within this range, the discharger can select the fractional removal level so that the resulting grey fuzzy risk will also be within specified bounds. In IFWLAM, the membership functions are subjective, and lower and upper bounds are arbitrarily fixed. Karmakar and Mujumdar (2006a) developed a Grey Fuzzy Waste Load Allocation Model (GFWLAM), in which uncertainty in the values of membership parameters is quantified by treating them as interval grey numbers. Imprecise membership functions are assigned for the goals of PCA and dischargers. Following Karmakar and Mujumdar (2006a), a Grey Optimization Model with Grey Fuzzy Risk is developed in the present study to address the uncertainty in the memebership functions of IFWLAM. The goals of PCA and dischargers are considered as grey fuzzy goals with imprecise membership functions. Imprecise membership functions are assigned to the fuzzy set of low water quality and fuzzy set of low risk. The grey fuzzy risk approach is included to account for the uncertainty due to missing data or inadequate data in the samples of input variables as done in IFWLAM. Randomness and imprecision associated with various water quality influencing variables and parameters of the river system are considered through a Monte-Carlo simulation when input parameters (such as mean and variance) are interval grey numbers. The model application is demonstrated with the case study of Tunga-Bhadra river system in South India. Finally the results of the model are compared with the results of GFWLAM (Karmakar and Mujumdar, 2006a). For the case study of Tunga Bhadra River system, it is observed that the fractional removal levels are higher for Grey Optimization Model with Grey Fuzzy Risk compared to GFWLAM (Karmakar and Mujumdar, 2006a) and therefore the resulting risk values at each check point are reduced to a significant extent. The models give a set of flexible policies (range of fractional removal levels). Corresponding optimal values of goal fulfillment level and the grey fuzzy risk are all in terms of interval grey numbers. The IFWLAM and Grey Fuzzy Optimization Model with Grey Fuzzy Risk, developed in the study do not limit their application to any particular pollutant or water quality indicator in the river system. Given appropriate transfer functions for spatial distribution of the pollutants in water body, the models can be used for water quality management of any general river system.

From the Proceedings of the 1974 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 19-20, 1974, Flagstaff, Arizona / In the lower reaches of the Colorado River, damages from the increase in salinity to U.S. water users are now estimated to be about 53 million dollars per year and will increase to about 124 million dollars per year by the year 2000 if no salinity control measures are taken. Physical, legal, economic, and institutional aspects of the salinity problem and proposed actions to mesh salinity control with a total water management plan for the basin are discussed. A scheme is presented for planning under the Colorado River water quality improvement program. Recent legislative action is also discussed which provides control plans to improve the water quality delivered to Mexico as well as upper basin water users. These efforts now under study will assure the continued, full utility of Colorado River water to U.S. users and Mexico. However, more extensive development of the basin's natural resources puts new emphasis on total resources management through improved water and land use planning to conserve a most precious western resource - water.