Browsing by Author "Fontane, Darrell G., committee member"

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Open Access
A multi criteria decision support system for watershed management under uncertain conditions
(Colorado State University. Libraries, 2012) Ahmadi, Mahdi, author; Arabi, Mazdak, advisor; Ascough, James C., II, committee member; Fontane, Darrell G., committee member; Hoag, Dana L., committee member
Nonpoint source (NPS) pollution is the primary cause of impaired water bodies in the United States and around the world. Elevated nutrient, sediment, and pesticide loads to waterways may negatively impact human health and aquatic ecosystems, increasing costs of pollutant mitigation and water treatment. Control of nonpoint source pollution is achievable through implementation of conservation practices, also known as Best Management Practices (BMPs). Watershed-scale NPS pollution control plans aim at minimizing the potential for water pollution and environmental degradation at minimum cost. Simulation models of the environment play a central role in successful implementation of watershed management programs by providing the means to assess the relative contribution of different sources to the impairment and water quality impact of conservation practices. While significant shifts in climatic patterns are evident worldwide, many natural processes, including precipitation and temperature, are affected. With projected changes in climatic conditions, significant changes in diffusive transport of nonpoint source pollutants, assimilative capacity of water bodies, and landscape positions of critical areas that should be targeted for implementation of conservation practices are also expected. The amount of investment on NPS pollution control programs makes it all but vital to assure the conservation benefits of practices will be sustained under the shifting climatic paradigms and challenges for adoption of the plans. Coupling of watershed models with regional climate projections can potentially provide answers to a variety of questions on the dynamic linkage between climate and ecologic health of water resources. The overarching goal of this dissertation is to develop a new analysis framework for the development of optimal NPS pollution control strategy at the regional scale under projected future climate conditions. Proposed frameworks were applied to a 24,800 ha watershed in the Eagle Creek Watershed in central Indiana. First, a computational framework was developed for incorporation of disparate information from observed hydrologic responses at multiple locations into the calibration of watershed models. This study highlighted the use of multiobjective approaches for proper calibration of watershed models that are used for pollutant source identification and watershed management. Second, an integrated simulation-optimization approach for targeted implementation of agricultural conservation practices was presented. A multiobjective genetic algorithm (NSGA-II) with mixed discrete-continuous decision variables was used to identify optimal types and locations of conservation practices for nutrient and pesticide control. This study showed that mixed discrete-continuous optimization method identifies better solutions than commonly used binary optimization methods. Third, the conclusion from application of NSGA-II optimization followed by development of a multi criteria decision analysis framework to identify near-optimal NPS pollution control plan using a priori knowledge about the system. The results suggested that the multi criteria decision analysis framework can be an effective and efficient substitute for optimization frameworks. Fourth, the hydrologic and water quality simulations driven by an extensive ensemble of climate projections were analyzed for their respective changes in basin average temperature and precipitation. The results revealed that the water yield and pollutants transport are likely to change substantially under different climatic paradigms. And finally, impact of projected climate change on performance of conservation practice and shifts in their optimal types and locations were analyzed. The results showed that performance of NPS control plans under different climatic projections will alter substantially; however, the optimal types and locations of conservation practices remained relatively unchanged.
Open Access
Confronting input, parameter, structural, and measurement uncertainty in multi-site multiple-response watershed modeling using Bayesian inferences
(Colorado State University. Libraries, 2012) Yen, Haw, author; Arabi, Mazdak, advisor; Fontane, Darrell G., committee member; Hoag, Dana L., committee member; Loftis, Jim C., committee member
Simulation modeling is arguably one of the most powerful scientific tools available to address questions, assess alternatives, and support decision making for environmental management. Watershed models are used to describe and understand hydrologic and water quality responses of land and water systems under prevailing and projected conditions. Since the promulgation of the Clean Water Act of 1972 in the United States, models are increasingly used to evaluate potential impacts of mitigation strategies and support policy instruments for pollution control such as the Total Maximum Daily Load (TMDL) program. Generation, fate, and transport of water and contaminants within watershed systems comprise a highly complex network of interactions. It is difficult, if not impossible, to capture all important processes within a modeling framework. Although critical natural processes and management actions can be resolved at varying spatial and temporal scales, simulation models will always remain an approximation of the real system. As a result, the use of models with limited knowledge of the system and model structure is fraught with uncertainty. Wresting environmental decisions from model applications must consider factors that could conspire against credible model outcomes. The main goal of this study is to develop a novel Bayesian-based computational framework for characterization and incorporation of uncertainties from forcing inputs, model parameters, model structures, and measured responses in the parameter estimation process for multisite multiple-response watershed modeling. Specifically, the following objectives are defined: (i) to evaluate the effectiveness and efficiency of different computational strategies in sampling the model parameter space; (ii) to examine the role of measured responses at various locations in the stream network as well as intra-watershed processes in enhancing the model performance credibility; (iii) to facilitate combining predictions from competing model structures; and (iv) to develop a statistically rigorous procedure for incorporation of errors from input, parameter, structural and measurement sources in the parameter estimation process. The proposed framework was applied for simulating streamflow and total nitrogen at multiple locations within a 248 square kilometer watershed in the Midwestern United States using the Soil and Water Assessment Tool (SWAT). Results underlined the importance of simultaneous treatment of all sources of uncertainty for parameter estimation. In particular, it became evident that incorporation of input uncertainties was critical for determination of model structure for runoff generation and also representation of intra-watershed processes such as denitrification rate and dominant pathways for transport of nitrate within the system. The computational framework developed in this study can be implemented to establish credibility for modeling watershed processes. More importantly, the framework can reveal how collection of data from different responses at different locations within a watershed system of interest would enhance the predictive capability of watershed models by reducing input, parametric, structural, and measurement uncertainties.
Open Access
Disaggregation of precipitation records
(Colorado State University. Libraries, 1991) Cadavid, Luis Guillermo, author; Salas, Jose D., advisor; Boes, Duane C., committee member; Yevjevich, Vujica M., 1913-, committee member; Fontane, Darrell G., committee member
This investigation is related to temporal disaggregation of precipitation records. The objective is to formulate algorithms to disaggregate precipitation defined at a given time scale into precipitation of smaller time scales, assuming that a certain mechanism or stochastic process originates the precipitation process. The disaggregation algorithm should preserve the additivity property and the sample statistical properties at several aggregation levels. Disaggregation algorithms were developed for two models which belong to the class of continuous time point processes: Poisson White Noise (PWN) and Neyman-Scott White Noise (NSWN). Precipitation arrivals are controlled by a counting process and storm activity is represented by instantaneous amounts of precipitation (White Noise terms). Algorithms were tested using simulated samples and data collected at four precipitation stations in Colorado. The PWN model is the easiest and formulation of the disaggregation model was successful. The algorithm is based on the distribution of the number of arrivals (N) conditional on the total precipitation in the time interval (Y) , the distribution of the White Noise terms conditional on N and Y, and the distribution of the arrival times conditional on N. Its application to disaggregate precipitation is limited due to its lacl; of serial correlation. However, PWN disaggregation model performs well on PWN simulated samples. The NSWN is more complex. Required distributions are the same as for the PWN model. Formulation of a disaggregation algorithm was based on theoretical and empirical results. A procedure for model parameters estimation based on weighted least squares was implemented. This procedure reduces the number of estimation failures as compared to method of moments. NSWN disaggregation model performed well on simulated and recorded samples given that parameters used are similar to those controlling the process at the disaggregation scale. The main shortcoming is the incompatibility of parameter estimates at different aggregation levels. This renders the disaggregation model of limited application. Examination of variation of parameter estimates with the aggregation scale suggests the existence of a region where estimated values appear to be compatible. Finally, it is shown that the use of information at a nearby precipitation station with similar precipitation regime may improve parameter values to use in disaggregation.
Open Access
GIS-based soil erosion modeling and sediment yield of the N’djili River basin, Democratic Republic of Congo
(Colorado State University. Libraries, 2015) Ndolo Goy, Patrick, author; Julien, Pierre Y., advisor; Fontane, Darrell G., committee member; MacDonald, Lee H., committee member
In the Democratic Republic of Congo, the N’djili River and its tributaries are the most important potable source of water to the capital, Kinshasa, satisfying almost 70% of its demand. Due to increasing watershed degradation from agricultural practices, informal settlements and vegetation clearance, the suspended sediment load in the N’djili River has largely increased in the last three decades. With an area of 2,097 km², the N’djili River basin delivers high suspended sediment concentration, and turbidity levels that cause considerable economic losses, particularly by disrupting the operation in the N’djili and Lukaya water treatment plants, and increasing dramatically the cost of chemical water treatment. The objectives of this study are to: (1) determine the change in the land cover/use of the N’djili River basin for 1995, 2005 and 2013; (2) predict and map the annual average soil losses at the basin scale and determine the effects of land cover/use change on the soil erosion; (3) estimate the sediment yield and the sediment delivery ratio at the water intake of the N’djili water treatment plant; and (4) quantify the effects of ash concentration on water turbidity in order to understand the high turbidity observed at the beginning of the rainy season. The Revised Universal Soil Loss Equation (RUSLE) model was implemented in a Geographic Information System (GIS) to estimate the spatially distributed soil loss rates in the N’djili basin under different land uses. RUSLE model parameters were derived from digital elevation model (DEM), average annual precipitation, soil type map and land cover maps (1995, 2005, 2013) obtained from Landsat images. The land cover/use change analysis shows that bare land/burned grass/agricultural land cover represented almost 22% of the N’djili basin area in 2013 whereas it was covering only 6% of the basin area in 1995. Settlements, which covered about 8% of the basin area in 1995, represented about 18% of the N’djili Basin area in 2013. The expansion of settlements, bare land, burned areas and agricultural lands was realized at the expense of the forest, grass, and shrubs cover. The annual average soil loss rate of the N’djili River Basin is estimated to be 7 tons/acre/year for 1995, 8.7 tons/acre/year for 2005 and 16 tons/acre/year for 2013. In 2013, bare land, burned areas and rainfed crops produced about 60% of the soil loss. The analysis of the relationship between probability of soil erosion and annual average soil loss rates indicated that up to 82, 79, and 73% of the basin area are in the range of tolerable soil erosion (0 – 5 tons/acre /year) in 1995, 2005 and 2013 respectively. Based on the gross erosion and sediment yield observed in 2005 and 2013, the sediment delivery ratio of 4.6% and 4.1% were predicted in 2005 and 2013, suggesting that most of the soil eroded from upland areas of the basin is trapped on flood plains covered by grass, shrubs and trees. Regarding the effects of ash concentration on turbidity, this study found that turbidity increased as a power function of ash concentration.
Open Access
Integrated decision-making for urban raw water supply in developing countries
(Colorado State University. Libraries, 2011) Soentoro, Edy Anto, author; Grigg, Neil S., advisor; Fontane, Darrell G., committee member; Vlachos, Evan C., committee member; Zahran, Sammy, committee member
Rapid urbanization and development are causing severe problems of raw water extraction and related environmental and social impacts in developing countries. This study demonstrated that an integrated approach to decision making could help solve these problems. A case study of raw water management in the region of Jabotabek, Indonesia, which is in and around Jakarta, exhibited social and environmental problems including land-subsidence. The integrated approach was applied in a simulated planning process for raw water development, to include consideration of the economic, environmental and social demands, the hydrological system, and the institutional systems that exist in particular areas. Simulation and optimization techniques (Supply_sim model) were used to determine the planned water allocation for a series of demand clusters for a suite of alternatives and development strategies. A multi-criteria decision analysis (MCDA) based on a decision support system (DSS) was used as an Integrated Decision-Making model to analyze the important and related aspects as one integrated system and to find the best set of decision options. The overall result of the study showed that the integrated approach could improve the decision process to solve the problem. However, its success ultimately depends on the political will of the government to apply the approach. The government needs to improve coordination among the institutions related to raw water supply development and to carry out a transparent decision-making process. Regulations on land-use planning, groundwater abstraction and water pollution control should be applied strictly and aimed to maintain raw water sources. The study also showed that a decision process tool such as the DSS within an integrated framework of decision making could help decision makers to reach consensus and gain stakeholder participation, accountability and commitment to the decision being made. In dealing with complex raw water problems in large cities, the study also showed that planning systems could help decision makers to think systematically to improve the decision results.
Open Access
Integrated flood management model: a socio-technical systems approach to overcome institutional problems in Jakarta
(Colorado State University. Libraries, 2010) Akmalah, Emma, author; Grigg, Neil S., advisor; Fontane, Darrell G., committee member; Salas, Jose D., committee member; Vlachos, Evan C., committee member
Urban flooding is a systemic problem of urban areas in developing countries, which face other difficult problems of urbanization, social inequality, and environmental degradation. The threats may overwhelm the institutional capacity to respond and cities may be unable to cope with the consequences. Although floods are triggered by natural events, the hazards they present are also affected by the social, economic, and political environments where people live. Low-income people suffer most from flood disasters because they tend to live in flood-prone areas, often do not understand the hazards they face, and lack institutional support. This urgent situation of flooding in developing countries led to this study, which uses systems analysis tools to address flood disaster problems from multiple perspectives. Since flooding in Jakarta is a complex socio-technical problem, an integrated approach is used to show how to reduce the risk and mitigate the effects of flooding. The flood management system should be regarded as an integral part of the urban system, which displays very dynamic behavior among its subsystems. The urban system analysis showed the links among attractiveness of the city, migration, poverty, lack of community cohesion, overwhelmed infrastructure and management systems, and the resulting succession of flood disasters. The study applies a model of institutional, socio-economic, technical, financial, and environmental aspects of flooding in developing countries and uses a case study of flooding in Jakarta, Indonesia to test hypotheses about managing flood hazards in an integrated manner. The management model is based on an Integrated Flood Management approach to: identify stakeholders’ roles, responsibilities, and actions to solve the problems; identify gaps between the disaster responses needed and provided; and build collaborative actions among stakeholders to overcome institutional problems. It seeks to identify appropriate flood management strategies that are sensitive to local conditions. An integrated approach emphasizes community participation and a combination of structural and non-structural measures for flood mitigation programs and is directed to both short-term and long-term impacts and consequences. It also presents a framework for institutional analysis to ensure the political commitment for a proper institutional coordination, resources mobilization and enhancement of preparedness. As a key path to a solution to the flood problem in Jakarta, the integrated approach must involve all relevant sectors and communities. This will require a paradigm shift in how flood problems are identified, addressed, and solved. Such an approach must involve a mutual effort at the institutional and community levels by enhancing institutional capacity at the local government level as well as empowerment of the total community. The suggested model can be used in order to help policy makers develop an effective and comprehensive flood management strategy, solve flood problems, and improve local conditions. Considering that many large cities in developing countries face similar problems, the analysis and the case study can provide an example to help other flood-prone cities with similar characteristics and pattern of urban development.
Open Access
Models for management of water conflicts: a case study of the San-Joaquin Watershed, California
(Colorado State University. Libraries, 2012) Akhbari, Masih, author; Grigg, Neil S., advisor; Vlachos, Evan C., committee member; Fontane, Darrell G., committee member; Laituri, Melinda J., committee member
Competition for use of water is increasing and leads to many conflicts among competing interests with complex goals in water management systems. To deal with the complex competing and conflicting situations, a variety of changes in management policies are required. Technical system models are essential to create performance and other decision information, but models to simulate views of the competing parties are also needed to help resolve or mitigate conflicts. These models can be used as helpful tools to designate effective strategies and water resources management policies that encourage parties to cooperate by accurately simulating the stakeholders' behavior and interactions. In this study a new approach to agent-based modeling (ABM) was introduced to simulate the behavior and interactions of the parties participating in a conflict scenario, which was modeled as a game. Water issues of California's San Joaquin River watershed were used as an example of a long-standing situation. The ABM explained the interactions among the parties and how they could be encouraged to cooperate in the game to work toward a solution. It was confirmed that this model can be used to manage conflicts in complex water resources systems as a powerful tool to establish rules based on the timing of flows, water demands, environmental concerns, and legislative resources. It provides a clear description of human-organizational interactions and a better understanding of complex interactive systems by simplifying the complexity of views and interactions of competing parties. Using this proposed conflict management model, decision-makers will have more reliable support for their decision-making processes.
Open Access
Numerical simulation diagnostics of a flash flood event in Jeddah, Saudi Arabia
(Colorado State University. Libraries, 2014) Samman, Ahmad, author; Cotton, William R., advisor; Schumacher, Russ, committee member; Fontane, Darrell G., committee member
On 26 January 2011, a severe storm hit the city of Jeddah, the second largest city in the Kingdom of Saudi Arabia. The storm resulted in heavy rainfall, which produced a flash flood in a short period of time. This event caused at least eleven fatalities and more than 114 injuries. Unfortunately, the observed rainfall data are limited to the weather station at King Abdul Aziz International airport, which is north of the city, while the most extreme precipitation occurred over the southern part of the city. This observation was useful to compare simulation result even though it does not reflect the severity of the event. The Regional Atmospheric Modeling System (RAMS) developed at Colorado State University was used to study this storm event. RAMS simulations indicted that a quasi-stationary Mesoscale convective system developed over the city of Jeddah and lasted for several hours. It was the source of the huge amount of rainfall. The model computed a total rainfall of more than 110 mm in the southern part of the city, where the flash flood occurred. This precipitation estimation was confirmed by the actual observation of the weather radar. While the annual rainfall in Jeddah during the winter varies from 50 to 100 mm, the amount of the rainfall resulting from this storm event exceeded the climatological total annual rainfall. The simulation of this event showed that warm sea surface temperature, combined with high humidity in the lower atmosphere and a large amount of convective available potential energy (CAPE) provided a favorable environment for convection. It also showed the presence of a cyclonic system over the north and eastern parts of the Mediterranean Sea, and a subtropical anti-cyclone over Northeastern Africa that contributed to cold air advection bringing cold air to the Jeddah area. In addition, an anti-cyclone (blocking) centered over east and southeastern parts of the Arabian Peninsula and the Arabian Sea produced a low level jet over the southern part of the Red Sea, which transported large water vapor amounts over Jeddah. The simulation results showed that the main driver behind the storm was the interaction between these systems over the city of Jeddah (an urban heat island) that produced strong low-level convergence. Several sensitivity experiments were carried out showed that other variables could have contributed to storm severity as well. Those sensitivity experiments included several simulations in which the following variables were changed: physiographic properties were altered by removing the water surfaces, removing the urban heat island environment from the model, and changing the concentration of cloud condensation nuclei. The results of these sensitivity experiments showed that these properties have significant effects on the storm formation and severity.
Open Access
Optimal reservoir operations for riverine water quality improvement: a reinforcement learning strategy
(Colorado State University. Libraries, 2011) Rieker, Jeffrey Donald, author; Labadie, John W., advisor; Fontane, Darrell G., committee member; Frevert, Donald K., committee member; Anderson, Charles W., committee member
Complex water resources systems often involve a wide variety of competing objectives and purposes, including the improvement of water quality downstream of reservoirs. An increased focus on downstream water quality considerations in the operating strategies for reservoirs has given impetus to the need for tools to assist water resource managers in developing strategies for release of water for downstream water quality improvement, while considering other important project purposes. This study applies an artificial intelligence methodology known as reinforcement learning to the operation of reservoir systems for water quality enhancement through augmentation of instream flow. Reinforcement learning is a methodology that employs the concepts of agent control and evaluative feedback to develop improved reservoir operating strategies through direct interaction with a simulated river and reservoir environment driven by stochastic hydrology. Reinforcement learning methods have advantages over other more traditional stochastic optimization methods through implicit learning of the underlying stochastic structure through interaction with the simulated environment, rather than requiring a priori specification of probabilistic models. Reinforcement learning can also be coupled with various computing efficiency techniques as well as other machine learning methods such as artificial neural networks to mitigate the "curse of dimensionality" that is common to many optimization methodologies for solving sequential decision problems. A generalized mechanism is developed, tested, and evaluated for providing near-real time operational support to suggest releases of water from upstream reservoirs to improve water quality within a river using releases specifically designated for that purpose. The algorithm is designed to address a variable number of water quality constituents, with additional flexibility for adding new water quality requirements and learning updated operating strategies in a non-stationary environment. The generalized reinforcement learning algorithm is applied to the Truckee River in California and Nevada as a case study, where the federal and local governments are purchasing water rights for the purpose of augmenting Truckee River flows to improve water quality. Water associated with those acquired rights can be stored in upstream reservoirs on the Truckee River until needed for prevention of water quality standard violations in the lower reaches of the river. This study shows that in order for the water acquired for flow augmentation to be fully utilized as a part of a longer-term strategy for water quality management, increased flexibility is required as to how those waters are stored and how well the storage is protected from displacement through reservoir spill during times of high runoff. The results show that with those flexibilities, the reinforcement learning mechanism has the ability to produce both short-term and long-term strategies for the use of the water, with the long-term strategies capable of significantly improving water quality during times of drought over current and historic operating practices. The study also evaluates a number of variations and options for the application of reinforcement learning methods, as well as use of artificial neural networks for function generalization and approximation.
Open Access
Optimization of Sangju weir operations to mitigate sedimentation problems
(Colorado State University. Libraries, 2016) Kim, Hwa Young, author; Julien, Pierre Y., advisor; Fontane, Darrell G., committee member; Thornton, Christopher I., committee member; Rathburn, Sara L., committee member
To view the abstract, please see the full text of the document.
Open Access
Stochastic modeling of seasonal streamflow
(Colorado State University. Libraries, 1987) Mendonça, Antonio Sergio Ferreira, author; Salas, Jose D., advisor; Fontane, Darrell G., committee member; Loftis, Jim C., committee member; Gessler, Johannes, committee member
This research examines topics on seasonal (monthly, bimonthly, etc.) hydrologic time-series modeling. A family of periodic models was derived by allowing parameters for a particular Multiplicative Autoregressive Integrated Moving Average model (Multiplicative ARIMA) to vary from season to season. The derived model presents parameters relating data for seasons in the same year and parameters relating data for the same season for consecutive years. PARMA models are particular cases of the proposed model, here called Multiplicative Periodic Autoregressive Moving Average (Multiplicative PARMA). Least-squares estimation based on the Powell algorithm for nonlinear optimization was developed for determining the model parameters. Properties such as seasonal variances and autocorrelations were derived analytically for particular cases of the general model. Analysis of sensitivity of the annual autocorrelograms to the parameters of the model showed that the yearly autoregressive parameters are the most important for the reproduction of high annual autocorrelations. Tests of model were made through data generation. The model was applied to four-and six-season series for river discharge presenting distinct characteristics of variabilty and dependence. Tests for goodness-of-fit and selection criteria of models for seasonal series were also discussed. Results from data generation indicate that the estimation procedure is able to estimate parameters for the Multiplicative PARMA models and can also be used for refinement of estimations made by method-of-moments for other models. Application to discharge data from St. Lawrence, Niger, Elkhorn and Yellowstone rivers showed that the proposed modeling technique is able to preserve long term dependence better than models currently used in practical hydrology. Direct consequence of this improvement is better reproduction of floods and droughts and more accuracy in the design and operation of water resource structures.