This study reports on the development and implementation of the HydroLearn online platform that supports active learning in the field of hydrology and water resources engineering. The platform is designed to serve the following two main purposes: to enable instructors to collaboratively develop and share active-learning resources, and to enhance student learning in fundamental and emerging topics in the field (e.g., rainfall-runoff processes, design of flood protection measures, flood forecasting, water-energy-food nexus). Using open-source technology, the HydroLearn platform supports customization of pre-developed learning modules and allows instructors to share components of their learning resources with other interested users. HydroLearn is inspired by the need to address challenges in adoption, scalability, and sustainability identified by research on educational innovations. HydroLearn utilizes research-based active learning methods (e.g., Problem-based Learning; Collaborative and Cooperative Learning) to create authentic online learning modules. The modules engage students in real-world hydrologic problems and provide unique opportunities to expose undergraduate students to modern hydrologic analysis tools that are at the forefront of hydrologic research and engineering practice. The platform includes tools that scaffold instructors’ implementation of sound pedagogical practices. The platform includes wizards and pre-populated templates on how to develop student-centered learning outcomes that ensure constructive alignment with the learning content. The platform also includes guidance for instructors on how to develop assessment rubrics to enhance student achievement through communicating the expected performance levels. The study will also share results on the implementation of a pilot learning module on flood protection. Thirty-six undergraduate students were surveyed before and after the implementation to determine their level of learning engagement. The survey measured their skills engagement, emotional engagement, participation, and performance engagement. The presentation will also report on efforts to engage the community through a fellowship program that aims to develop a network of educators who aspire to adopt active learning approaches and enhance hydrology education.

HydroShare is a domain specific data and model repository operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) to advance hydrologic science by enabling individual researchers to more easily share products resulting from their research. The community platform supports, not just the scientific publication summarizing a study, but also the data, models and workflow scripts used to create the scientific publication and reproduce the results therein. HydroShare accepts data from anybody, and supports Findable, Accessible, Interoperable and Reusable (FAIR) principles. HydroShare is comprised of two sets of functionality: (1) a repository for users to share and publish data and models, collectively referred to as resources, in a variety of formats, and (2) tools (web apps) that can act on content in HydroShare and support web based access to compute capability. Together these serve as a platform for collaboration and computation that integrates data storage, organization, discovery, and analysis through web applications (web apps) and that allows researchers to employ services beyond the desktop to make data storage and manipulation more reliable and scalable, while improving their ability to collaborate and reproduce results. This presentation will describe the capabilities developed for HydroShare to support the full research data management life cycle. Data can be entered into HydroShare as soon as it is collected, and initially shared only with the team directly working on the data. As analysis proceeds, tools, scripts and models that act on the data to produce research results may be stored in HydroShare resources alongside the data. At the time of publication these resources may be permanently published and receive digital object identifiers and cited in research papers. Resources may themselves include citations to the research papers, thereby linking the publications to the supporting data, scripts and models. HydroShare design choices and capabilities for establishing relationships and versioning, based on simplicity, and ease of use, and some of the challenges encountered, will be discussed.

Water managers face the daunting task of managing freshwater resources in the face of industrialization and population growth. As surface water resources become fully allocated, increased groundwater use can fill the void, particularly during periods of drought. Improper groundwater management can result in reduced water quality, land subsidence, increased pumping costs, and in some cases, the complete exhaustion of an aquifer and the loss of groundwater as a buffer during times of drought. Assessing the long-term impact of various groundwater management decisions can be difficult and costly, and therefore many decisions are made without sufficient analysis. Advancements in the acquisition and dissemination of Earth observations, coupled with advances in cloud computing, web apps, online mapping, and visualization provide a unique opportunity to deliver tools and actionable information to groundwater managers to assist them in addressing global and regional challenges and opportunities. We have developed a web-based tool that ingests in situ groundwater level measurements for specific aquifers and generates time series plots, maps, and raster animations showing groundwater depletion over time and short-term projections into the future. This process involves both temporal and spatial interpolation algorithms. In some aquifers, the observation wells are sparse and/or the historical observations have large gaps, leading to greater uncertainty in the interpolation and the resulting groundwater depletion estimates. To address this, we utilize Earth observations (GRACE, SMAP, etc.) and a co-kriging algorithm to enhance the interpolation process. The utility of the Earth observations in improving the estimates is evaluated using a jackknifing process. We present case studies for application of the system in the states of Utah and Texas.

Groundwater development will provide a more stable water source and enhance food security. Sustainable groundwater development requires collecting and analyzing data produced at global and national levels and disseminating that data and knowledge to end users such as States, NGOs, municipalities, businesses, and agropastoralists in a format that is useful for planning and decision-making. In developing countries, analyzing in situ measurements to de can be challenging due to sparsity of data and lack of tools and expertise. To address these problems we have developed a web-based geospatial tool that ingests in situ water level measurements and performs temporal and spatial interpolation to build interactive animated maps, time series plots, and long-term aquifer depletion curves. We use machine learning to find correlations among Earth observation data, such as precipitation or soil moisture, with water level data and perform more accurate interpolation. This approach ensures that scarce in situ data are used as effectively and accurately as possible. This tool helps water managers gain a better understanding of groundwater resources and determine how aquifers are responding to groundwater development, droughts, and climate change.