Paper ID #12959

Stimulating Active Learning in Hydrology Using Research-Driven, Web-basedLearning Modules

Dr. Emad Habib, University of Louisiana at Lafayette

Dr. Emad Habib is a Professor of Civil Engineering at the University of Louisiana at Lafayette. Hisresearch interests are in Hydrology, Water Resources, Rainfall Remote Sensing, Water Management,Coastal Hydrology, and Advances in Hydrology Education Research

Madeleine Bodin, University of Louisiana, Lafayette

MADELEINE BODIN is a Ph.D. student in the Systems Engineering program at the University ofLouisiana at Lafayette. She earned her B.S. degree from the University of Louisiana at Lafayette (CivilEngineering, 2012). Her interests are engineering education, water resources engineering, coastal restora-tion, wetlands protection, and numerical modeling.

Prof. David Tarboton, Utah State University

David Tarboton is a professor of Civil and Environmental Engineering, Utah Water Research Laboratory,Utah State University. He received his Sc.D. and M.S. in Civil Engineering (Water Resources and Hy-drology) from the Massachusetts Institute of Technology and his B.Sc Eng in Civil Engineering from theUniversity of Natal in South Africa. His research and teaching are in the area of surface water hydrol-ogy. His research focuses on advancing the capability for hydrologic prediction by developing modelsthat take advantage of new information and process understanding enabled by new technology. He hasdeveloped a number of models and software packages including the TauDEM hydrologic terrain analysisand channel network extraction package that has been implemented in parallel, and a snowmelt model.He is lead on the National Science Foundation HydroShare project to expand the data sharing capabilityof Hydrologic Information Systems to additional data types and models and to include social interactionand collaboration functionality. He teaches Hydrology and Geographic Information Systems in WaterResources.

Madeline Frances Merck, Utah State UniversityMr. David J Farnham, Department of Earth and Environmental Engineering, Columbia University

c©American Society for Engineering Education, 2015

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Stimulating Active Learning in Hydrology Using Research-Driven, Web-based Learning Modules

Introduction Hydrologists today deal with intricate problems rooted within natural ecosystems with a multitude of interrelated processes. These processes are physical, chemical and biological and cross a wide spectrum of scales. Research advances within the multi-disciplinary field include observational settings,1,2 instrumentation and modeling methods, and hydrologic theory and practices.3,4,5 Together, the complex problems and research advances in the field call for similar improvements in the traditionally textbook-based hydrologic education which typically uses idealized examples focused on specific hydrologic unit processes and/or engineering applications rather than the contextual relations within the processes and the spatial and temporal dynamics which connect climate and ecosystems. A paradigm shift is critically sought in undergraduate hydrology and water resource education by adopting context-rich, student centered and active learning strategies. An appreciation of the natural variability of hydrologic processes cannot be gained in curricula where field components such as observational and experimental data are deficient. These types of data are also critical when using simulation models to create environments that support this type of learning. Additional sources of observations in conjunction with models and field data are key to students’ understanding of the challenges associated with using models to represent such complex systems. Recent advances in scientific visualization and web-based technologies6,7 provide new opportunities for the development of more active learning techniques that utilize ongoing research. The current study describes the development of a set of data and simulation driven learning experiences. Recent developments in hydrologic modeling, observations, and data publishing and web-based technology provide the basis for the new learning modules. Three regional scale ecosystems, Coastal Louisiana, Florida Everglades and the Great Salt Lake Basin, were used as the foundation for the learning experiences. Each ecosystem provides an abundance of concepts and scenarios that can be used in many water resource and hydrology curricula. Learning Modules- Coastal Louisiana The Coastal Louisiana ecosystem provides an unmatched abundance of learning opportunities based upon the unique hydrologic transition from inland to coastal/wetland. The learning modules based on Coastal Louisiana (Figure 1) begin with an introduction to the system. Familiarity with the river systems which dominate the hydrologic basins, the unique geography of the area, and the impact of manmade alterations to the system is established. The introduction to the system is followed by an introduction to the mass-balance, compartment-based computer model which is used to simulate the eco-hydrology of the coastal zone13,14. An exploration of the hydrologic basin as represented by the model is conducted using geo-spatial layers created for the modules and an input file required by the computer model. Following the introductions to the basin and the model, a model simulation is conducted. This includes selection of parameters which includes the selection of climate change scenario. An analysis of input data and boundary conditions are performed, including precipitation and evapotranspiration (ET) datasets, upstream

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riverine inflows and water level in the Gulf of Mexico. The simulation is then run and the output is available for further analysis both on compartment scale and domain scale.

Figure 1: Coastal Louisiana learning module interface

Inter-annual and intra-annual analyses of the water budget components are conducted using a 20 year record. The inter-annual analysis begins with the creation of an “average year” using the long record and includes an examination of riverine inflows to determine the dominant system and seasonality pattern. Examination of exchange flow with the Gulf of Mexico is also conducted and a seasonality pattern is also determined. These examinations also include an assessment of salinity intrusion on a seasonal basis. This is followed by the establishment of the water budget for the entire model domain. An analysis of the seasonality of the water budget components (precipitation, ET, river inflows, exchange with the Gulf and change in storage) includes a determination of seasonality patterns and the possible causes of these patterns. The intra-annual analysis of the water budget for the entire domain focuses on the year-to-year variations in the components. Years with extremes, such as drought years or wetter than average years, are determined and compared to the 20 year record average. The water budget analysis continues on a compartment scale, which allows for significantly more detailed analyses. The previous inter- and intra-annual analyses are conducted on both a compartment which contains only a water body and a compartment which contains predominantly marsh. The dominant water budget components are determined for each compartment type, drought/wet versus average and marsh versus open water behavior comparisons are also conducted. Water control structures play an important role in the hydrology of the region and as such, are incorporated into the simulation model and are subject to analysis as well. These analyses lead to an evaluation of salinity intrusion problems and assessment of restoration strategies that are proposed for alleviating ecosystem stresses.

Learning Modules- Florida Everglades The Florida Everglades ecosystem has undergone large scale hydrologic modification and is currently in the midst of equally large scale restoration efforts. As a highly managed system, it provides a wealth of detailed models and a comprehensive, 40 year historical set of physical, chemical and biological data, which can be used to study the impact of various water resource

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management strategies. The Florida Everglades learning modules begin with an introduction to the ecosystem including its geographical location (Figure 2), ecological assets, hydrologic characteristics and restoration. Exploration of the Everglades ecosystem is conducted using GE layers as well as several animations with special attention to Everglades Agricultural Areas (EAA), Water Control Areas (WCA) and the Everglades National Park (ENP), which are important components of the water management restoration exercise later in the learning modules. The introduction is followed by an analysis of the climate of the Everglades. Historical rainfall and ET data are accessed through one of several rainfall and ET stations embedded in the Google map user interface (Figure 2) and analyzed to determine and compare seasonal variability and to determine drought years and wetter than average years. Spatial variability and time scale dependence for precipitation and ET are then examined and compared. Following a brief introduction to the El Niño/ La Niña phenomena, an analysis of the effects of El Niño/ La Niña is conducted using the available rainfall data.

Figure 2: Florida Everglades learning module interface

A climate-hydrology module is also developed about climate variability and teleconnections of ocean and atmospheric conditions to regional precipitation, temperature and hydrology (Figure 3). These concepts are important for understanding how the local water balance evolves over long periods of time, and also how extreme hydrologic events, e.g., floods and droughts, may result from climate patterns that are organized at global scales. Specifically, this module investigates the relationship between climate indices and regional hydrological conditions.

Figure 3: Example of climatology analysis performed as part of the climate teleconnections

modules.

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In addition to the climate analysis, an important exercise conducted in these modules is centered upon restoration. Data for a selected WCA’s are downloaded and analyzed for a specific period and timescale. Based upon these analyses, evaluations of suitability for a range of specific objectives are conducted. These assessments often have competing objectives, as the water resources needs of a community often conflict with the ecological needs of an ecosystem. This exercise is followed by the use of a computer model simulation analysis known as iModel15 (Figure 4). An optimization run is performed, followed by an analysis of the results with the desired objectives. This is followed by several more customized simulation runs, each focusing on a specific restoration objective. Each time, the simulation results are compared and contrasted with the desired objectives and the effectiveness evaluated. The customized runs are also compared with the optimization run and the impact of each simulation evaluated in its impact on other objectives.

Figure 4: Schematic representation of the optimization model (iModel) used to analyze

restoration targets and constraints of the Everglades ecosystem15 Learning Modules- Great Salt Lake Basin The Great Salt Lake (GSL) Basin (Figure 5) provides a good natural laboratory in a semi-arid region where population growth is rapid, water supply is driven by snowmelt and water resources are critical. The Great Salt Lake is a terminal lake, with a delicate balance between stream flow, evaporation and precipitation. The case-based approach used in these modules is driven by the rich research history in the area. Each case study involves at least one of the following subjects: context, precipitation and water input, runoff generation, terminal lake and human interactions. As each case study is performed, important hydrologic principles are learned and their applications to real world issues often found in similar environments. The first case study developed is centered on the Dry Canyon watershed (Figure 6) in which the exercise is to use data from a publically accessible source to create a design storm to assess the potential risk from a flood and to evaluate alternatives for flood protection. During the course of the case study, the data is used to obtain annual maxima for different durations from historical data. These are then used to create depth-duration-frequency and intensity-duration-frequency

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curves. Then a design storm is constructed and the different flood abatement alternatives are evaluated.

Figure 5: Great Slat Lake Basin, Utah.

Figure 6: Interface for the Dry Canyon watershed near Logan, Utah.

Platform Technology In order to enable wide dissemination, instructional and technical adaptation, open source and free web-based technologies (Table 1) are used in the development of the web modules. Through technical adaptation, it is envisioned that others may contribute to the modules by adding new modules or functionalities.

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Table 1: Summary of web-based technologies used in development of web modules and their functionality Technology Description Use in Web Modules Django A free and open source web

application framework, written in Python, which follows the model-view-controller architectural pattern

Provide MVC architecture, import and convert hydrologic time series data, render HTTP responses and serve REST API.

PHP Slim Framework

PHP is a server-side scripting language designed for web development. Slim is a PHP micro framework that helps quickly write simple yet powerful web applications and APIs

Provide server side scripting for HTTP responses and serve Everglades hydrological data from MySQL database.

MySQL Database SQLite

MySQL is an open source relational database management system (RDBMS) that runs as a server providing multi-user access to a number of databases. SQLite is a software library that implements a self-contained, server-less, zero-configuration, transactional SQL database engine.

Store and serve hydrological, simulation and GIS data in relational database.

Google Earth/Maps JavaScript API

This API enables the creation of sophisticated 3D/2D map applications through simple JavaScript function calls.

Render hydrologic geospatial data on a 3D/2D model of the Earth. Dynamically load hydrologic geospatial data as KML layers and Lat/Long locations on Google maps.

jQuery jQuery UI

A multi-browser JavaScript Library designed to simplify the client-side scripting of HTML. It is considered standard library in web frontend development.

Provide essential functions such as element selector and callback registration. Provides some of the UI style themes and effects such as Layers and other floating dialogs.

Ajax (HTML JavaScript CSS)

Ajax (Asynchronous JavaScript and XML) is a family of technologies (e.g. HTML, cascading style sheets) that enable dynamic updating of a webpage and

Provide customized button panels and tools that enable students to interact and display data in Google Earth.

REST API Representational state transfer (REST) is a style of software architecture for distributed systems such as the World Wide Web.

Serve data as RESTful services.

JSON JavaScript Object Notation, a text-based open standard designed for human-readable data interchange.

The REST service outputs data in JSON format.

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Upcoming Developments & Evaluation Development of further learning modules are under development within the three ecosystems. The learning modules are currently being implemented and evaluated in several undergraduate courses (Hydrology, water resources, and coastal sciences) at multiple institutions. The evaluation model is “improvement focused”11 to ensure early identification and management of issues. Data collection throughout the project are guided by a mixed-method research approach.12 Qualitative data collected will allow improvement based on module success in the classroom. Quantitative data on student learning will allow for evaluation of the effectiveness of the project. Data sources include, but are not limited to, student learning data, analysis of the product usage patterns and technical issues log, stakeholder surveys of students and instructors before and after modules are implemented, and staff interviews regarding project problems and status. Conclusions The purpose of the current study is to use recent developments in hydrologic modeling, data, and resources to develop and deliver visual, case-based, data and simulation driven learning experiences to instructors and students through a web server-based system. The new learning modules are embedded in three regional-scale ecosystems, Coastal Louisiana, Florida Everglades, and Utah Great Salt Lake Basin. The wealth of hydrologic concepts and scenarios provided by these sites can be used in many water resource and hydrology curricula. The modules developed and future modules cover subjects such as: water-budget analysis, hydrologic effects of human and natural changes, climate-hydrology teleconnections, and water-resource management scenarios. Open source web technologies and community-based tools are used to facilitate wide dissemination and adaptation by diverse, independent institutions. Acknowledgment The authors acknowledge the support provided to this study by the National Science Foundation's Transforming Undergraduate Education in Science, Technology, Engineering and Mathematics (TUES) program under Collaborative Award No. 1122898 (Type II). Bibliography

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