Case Study for Guided Project in Stochastic Hydrology

Attached are two guided project activities for hydrology and climate data of Eagle Creek Watershed, Indiana, USA. The zip files have flow and precipitation datasets at daily, monthly, and annual time scales

Stochastic Hydrology

Syllabus for graduate class in Stochastic Hydrolog

THE USE OF A MULTI-OBJECTIVE GENETIC ALGORITHM FOR CALIBRATION OF WATER QUALITY NUMERICAL MODEL OF EAGLE CREEK RESERVOIR, IN

poster abstractWater quality models used for water resource management require large amounts of input parameters, whose values may or may not be readily available. The calibration of these models involves the adjustment of several input parameters. The credibility of calibrated models is judged based on their agreement with actual data. However, calibration of water quality numerical models can be an exceptionally computationally challenging process. In this research, the Environmental Fluid Dynamic Code’s (EFDC) HEM3D water quality model was developed for the Eagle Creek Reservoir in order to model three algal groups (cyanobacteria, diatoms, and greens) as well as reservoir nutrient dynamics. A multi-objective genetic algorithm was then used for calibration by adjusting predetermined input parameters within a certain range and based on the model’s agreement with observed data in the reservoir. The genetic algorithm was parallelized to work across a network of machines and on multiple threads. This presentation will demonstrate the advantages of using such a parallelized genetic algorithm for efficiently calibrating computationally expensive numerical models

Center for Earth and Environmental Science: A Program of Excellence in Water Resources Research

poster abstractResearch and training into the impacts of environmental insults on water systems and the links between water resources and human health are critical needs nationally and internationally. IUPUI is in an excellent position to take on a leadership role in scholarship and teaching about water quality and health. CEES has built its program and reputation around excellence in water resources and ecosystem restoration research. Key to our success has been the development of a research network founded on strong corporate, governmental and community partnerships and collaborations. This framework is strengthened by the mutual benefit realized by all partners and helps to support IUPUI’s core value of community engagement as an urban research university. In order to maximize the efficient use of resources, CEES is pursuing four strategic objectives in a manner that will further the universities goals of pursuing excellence in 1) research, scholarship and creative activity, 2) teaching and learning, and 3) civic engagement while also enhancing the resource base of the university. The Center places the highest priority on four strategic initiatives: 1. The Center will engage in cutting-edge research and training for mixed agricultural and urban watersheds 2. Evaluate and assess watershed Best Management Practices targeting atrazine, nutrients and emerging contaminants and pathogens 3. Establish a K-12 technology based science education program in water, air and energy 4. Work with state agencies to identify watershed issues associated with Major Moves and other economic development initiatives, the standards to be applied and training needs To this end, the Signature Center program in CEES has focused on building new collaborations with water resources and human health risks. Signature Center funding has provided for new faculty member Dr. Meghna Babbar-Sebens to join the Earth Sciences faculty as an Assistant Professor. Her research is focused on the modeling of water-borne contaminants, and decision support systems for management of water quality and associated ecological and human health risks. Dr. Babbar-Sebens research focuses on a) analysis of uncertainty when models are used to conduct spatially referenced systems-scale environmental assessments, b) incorporation of uncertainty analysis within decision support systems used for risk assessment and management, and c) optimization of water resources planning and management strategies for emergency response and water-borne disease prevention

Remote Sensing Data Assimilation In Water Quality Numerical Model Of Eagle Creek Reservoir Using Ensemble Kalman Filter Method

Numerical models are used as effective tools for simulating complex processes in aquatic systems, such as hydrodynamic and water quality processes. The accuracy of the model is reliant on the multiple model parameters and variables which need to be calibrated and regularly updated to reproduce changing conditions accurately. Different sources of observations such as remote sensing data or in-situ monitoring technologies can improve the model accuracy by providing benefits of individual monitoring technology within the model updating process. Remote sensing technology can provide the spatially dense surface temperature of water body, while in-situ technology is able to prepare more frequent time interval data along the depth. Hence, a framework is required to find the relationships between the remote sensing and in-situ measurements, especially if they are used together. Moreover, a data assimilation approach is needed to incorporate spatially continuous remote sensing temperature observations and spatially discrete in-situ observations to change initial conditions of the numerical model. Although several studies have used remote sensing and in-situ observations to assimilate water temperature, it is unclear of whether updating temperature based on remote sensing observations would improve the model’s prediction of temperature with respect to in-situ observation. This study explores a direct observer data assimilation method to overcome the challenge of using data from heterogeneous sources for improving the model performance. The main goal of this study is to adjust the water column temperature using surface temperature and present an ensemble Kalman filter data assimilation framework that combines three-dimensional finite difference numerical model with multiple sources of observations to simulate water column temperature in Eagle Creek Reservoir (ECR) in central Indiana

A Multi-User Interactive Optimization Tool (WRESTORE)

poster abstractThis is NSF funded joint project between Earth Science and Computer Science. It’s one of the objective is to provide best farming practices to the people of Eagle Creek, Indiana, so as to minimize the soil erosion, fertilizer loss and maintain water quality of the region while maximizing profit of farmers. The most important benefit to general public will be increase in quality of drinking water and decrease in flooding of the region. The tool we have built is a distributed system which uses high performance computing techniques to run model simulations in an efficient manner. The tool has various components which run on multiple computers. The user login via a web based interface, the design parameters are specified which are being used to generate different possible designs. The design evaluations are done using powerful cluster of computers (having 768 or 224 CPUs), which uses concept of virtual agents in doing the design evaluation. The user provides their feedback to different designs which are again considered to generate another set of better designs. Various optimization and machine learning techniques are used to model the user’s preferences and provide best possible designs based on given scenario. It is like human computer collaborative search, where human and computer both work together to achieve the goal in a better way. The project is still ongoing, till now we have run simulated user model only, but sooner we will be running the tests for the real human users. This will help the farmers, govt. agencies like USDA and environmentalists in doing environmental planning in an efficient manner. Our collaborators are Empower Results, Eagle Creek Watershed Alliance, Indiana NRCS, Center for Earth and Environmental Sciences, Upper White River Watershed Alliance

Interactive genetic algorithm for user-centered design of distributed conservation practices in a watershed: An examination of user preferences in objective space and user behavior

Interactive Genetic Algorithms (IGA) are advanced human-in-the-loop optimization methods that enable humans to give feedback, based on their subjective and unquantified preferences and knowledge, during the algorithm's search process. While these methods are gaining popularity in multiple fields, there is a critical lack of data and analyses on (a) the nature of interactions of different humans with interfaces of decision support systems (DSS) that employ IGA in water resources planning problems and on (b) the effect of human feedback on the algorithm's ability to search for design alternatives desirable to end-users. In this paper, we present results and analyses of observational experiments in which different human participants (surrogates and stakeholders) interacted with an IGA-based, watershed DSS called WRESTORE to identify plans of conservation practices in a watershed. The main goal of this paper is to evaluate how the IGA adapts its search process in the objective space to a user's feedback, and identify whether any similarities exist in the objective space of plans found by different participants. Some participants focused on the entire watershed, while others focused only on specific local subbasins. Additionally, two different hydrology models were used to identify any potential differences in interactive search outcomes that could arise from differences in the numerical values of benefits displayed to participants. Results indicate that stakeholders, in comparison to their surrogates, were more likely to use multiple features of the DSS interface to collect information before giving feedback, and dissimilarities existed among participants in the objective space of design alternatives

A web-based software tool for participatory optimization of conservation practices in watersheds

WRESTORE (Watershed Restoration Using Spatio-Temporal Optimization of Resources) is a web-based, participatory planning tool that can be used to engage with watershed stakeholder communities, and involve them in using science-based, human-guided, interactive simulation–optimization methods for designing potential conservation practices on their landscape. The underlying optimization algorithms, process simulation models, and interfaces allow users to not only spatially optimize the locations and types of new conservation practices based on quantifiable goals estimated by the dynamic simulation models, but also to include their personal subjective and/or unquantifiable criteria in the location and design of these practices. In this paper, we describe the software, interfaces, and architecture of WRESTORE, provide scenarios for implementing the WRESTORE tool in a watershed community's planning process, and discuss considerations for future developments