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Reproducible environmental modelling often relies on spatial datasets as inputs, typically manually subset for specific areas. Yet, models can benefit from a data distribution approach facilitated by online repositories, and automating processes to foster reproducibility. This study introduces a method leveraging diverse state-scale spatial datasets to create cohesive packages for GIS-based environmental modelling. These datasets were generated and shared via GeoServer and THREDDS Data Server Connected to HydroShare, contrasting with conventional distribution methods. Using the Regional Hydro-Ecologic Simulation System (RHESSys) across three U.S. catchment-scale watersheds, we demonstrate minimal errors in spatial inputs and model streamflow outputs compared to traditional approaches. This spatial data-sharing method facilitates consistent model creation, fostering reproducibility. Its broader impact allows scientists to tailor the method to various use cases, such as exploring different scales beyond state-scale or applying it to other online repositories using existing data distribution systems, eliminating the need to develop their own.more » « lessFree, publicly-accessible full text available January 1, 2026
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This resource includes Jupyter Notebooks that combine (merge) model results with observations. There are four folders: - NWM_SnowAssessment: This folder includes codes required for combining model results with observations. It also has an output folder that contains outputs of running five Jupyter Notebooks within the code folder. The order to run the Jupyter Notebooks is as follows. First run Combine_obs_mod_[*].ipynb where [*] is P (precipitation), SWE (snow water equivalent), TAir (air temperature), and FSNO (snow covered area fraction). This combines the model outputs and observations for each variable. Then, run Combine_obs_mod_P_SWE_TAir_FSNO.ipynb. - NWM_Reanalysis: This folder contains the National Water Model version 2 retrospective simulations that were retrieved and pre-processed at SNOTEL sites using https://doi.org/10.4211/hs.3d4976bf6eb84dfbbe11446ab0e31a0a and https://doi.org/10.4211/hs.1b66a752b0cc467eb0f46bda5fdc4b34. - SNOTEL: This folder contains preprocessed SNOTEL observations that were created using https://doi.org/10.4211/hs.d1fe0668734e4892b066f198c4015b06. - GEE: This folder contains MODIS observations that we downloaded using https://doi.org/10.4211/hs.d287f010b2dd48edb0573415a56d47f8. Note that the existing CSV file is the merged file of the downloaded CSV files.more » « less
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The creation of high-quality curricular materials requires knowledge of curriculum design and a considerable time commitment. Instructors often have limited time to dedicate to the creation of curricular materials. Additionally, the knowledge and skills needed to develop high-quality materials are often not taught to instructors. Furthermore, similar learning material is often prepared by multiple instructors working at separate institutions, leading to unnecessary duplication of effort and inefficiency that can impact quality. To address these problems, we established the HydroLearn platform and associated professional learning experiences for hydrology and water resources instructors. HydroLearn is an online platform for developing and sharing high-quality curricular materials, or learning modules, focused on hydrology and water resources. The HydroLearn team has worked with three cohorts of instructors from around the world who were dedicated to creating high-quality curricular materials to support both their students and the broader community. In order to overcome some of the aforementioned barriers, we tested and revised several different models of professional learning with these cohorts. These models ranged from (a) instructors working individually with periodic guidance from the HydroLearn team, to (b) small groups of instructors collaborating on topics of shared interests guided through an intensive HydroLearn training workshop. We found the following factors to contribute to the success of instructors in creating modules: (1) instructor pairs co-creating modules enhanced the usability and transferability of modules between universities and courses, (2) dedicating an intensive block of time (∼63 h over 9 days) to both learning about and implementing curriculum design principles, (3) implementing structures for continuous feedback throughout that time, (4) designing modules for use in one’s own course, and (5) instituting a peer-review process to refine modules. A comprehensive set of learning modules were produced covering a wide range of topics that target undergraduate and early graduate students, such as: floodplain analysis, hydrologic droughts, remote sensing applications in hydrology, urbanization and stormwater runoff, evapotranspiration, snow and climate, groundwater flow, saltwater intrusion in coastal regions, and stream solute tracers. We share specifics regarding how we structured the professional learning models, as well as lessons learned and challenges faced.more » « less
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null (Ed.)Abstract Engineering students need to spend time engaging in mathematical modeling tasks to reinforce their learning of mathematics through its application to authentic problems and real world design situations. Technological tools and resources can support this kind of learning engagement. We produced an online module that develops students’ mathematical modeling skills while developing knowledge of the fundamentals of rainfall-runoff processes and engineering design. This study examined how 251 students at two United States universities perceived mathematical modeling as implemented through the online module over a 5-year period. We found, subject to the limitation that these are perceptions from not all students, that: (a) the module allowed students to be a part of the modeling process; (b) using technology, such as modeling software and online databases, in the module helped students to understand what they were doing in mathematical modeling; (c) using the technology in the module helped students to develop their skill set; and (d) difficulties with the technology and/or the modeling decisions they had to make in the module activities were in some cases barriers that interfered with students’ ability to learn. We advocate for instructors to create modules that: (a) are situated within a real-world context, requiring students to model mathematically to solve an authentic problem; (b) take advantage of digital tools used by engineers to support students’ development of the mathematical and engineering skills needed in the workforce; and (c) use student feedback to guide module revisions.more » « less
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null (Ed.)Engineering graduates need a deep understanding of key concepts in addition to technical skills to be successful in the workforce. However, traditional methods of instruction (e.g., lecture) do not foster deep conceptual understanding and make it challenging for students to learn the technical skills, (e.g., professional modeling software), that they need to know. This study builds on prior work to assess engineering students’ conceptual and procedural knowledge. The results provide an insight into how the use of authentic online learning modules influence engineering students’ conceptual knowledge and procedural skills. We designed online active learning modules to support and deepen undergraduate students’ understanding of key concepts in hydrology and water resources engineering (e.g., watershed delineation, rainfall-runoff processes, design storms), as well as their technical skills (e.g., obtaining and interpreting relevant information for a watershed, proficiency using HEC-HMS and HEC-RAS modeling tools). These modules integrated instructional content, real data, and modeling resources to support students’ solving of complex, authentic problems. The purpose of our study was to examine changes in students’ self-reported understanding of concepts and skills after completing these modules. The participants in this study were 32 undergraduate students at a southern U.S. university in a civil engineering senior design course who were assigned four of these active learning modules over the course of one semester to be completed outside of class time. Participants completed the Student Assessment of Learning Gains (SALG) survey immediately before starting the first module (time 1) and after completing the last module (time 2). The SALG is a modifiable survey meant to be specific to the learning tasks that are the focus of instruction. We created versions of the SALG for each module, which asked students to self-report their understanding of concepts and ability to implement skills that are the focus of each module. We calculated learning gains by examining differences in students’ self-reported understanding of concepts and skills from time 1 to time 2. Responses were analyzed using eight paired samples t-tests (two for each module used, concepts and skills). The analyses suggested that students reported gains in both conceptual knowledge and procedural skills. The data also indicated that the students’ self-reported gain in skills was greater than their gain in concepts. This study provides support for enhancing student learning in undergraduate hydrology and water resources engineering courses by connecting conceptual knowledge and procedural skills to complex, real-world problems.more » « less