Moving university hydrology education forward with community-based geoinformatics, data and modeling resources

In this opinion paper, we review recent literature related to data and modeling driven instruction in hydrology, and present our findings from surveying the hydrology education community in the United States. This paper presents an argument that that data and modeling driven geoscience cybereducation (DMDGC) approaches are essential for teaching the conceptual and applied aspects of hydrology, as a part of the broader effort to improve science, technology, engineering, and mathematics (STEM) education at the university level. The authors have undertaken a series of surveys and a workshop involving university hydrology educators to determine the state of the practice of DMDGC approaches to hydrology. We identify the most common tools and approaches currently utilized, quantify the extent of the adoption of DMDGC approaches in the university hydrology classroom, and explain the community's views on the challenges and barriers preventing DMDGC approaches from wider use. DMDGC approaches are currently emphasized at the graduate level of the curriculum, and only the most basic modeling and visualization tools are in widespread use. The community identifies the greatest barriers to greater adoption as a lack of access to easily adoptable curriculum materials and a lack of time and training to learn constantly changing tools and methods. The community's current consensus is that DMDGC approaches should emphasize conceptual learning, and should be used to complement rather than replace lecture-based pedagogies. Inadequate online material publication and sharing systems, and a lack of incentives for faculty to develop and publish materials via such systems, is also identified as a challenge. Based on these findings, we suggest that a number of steps should be taken by the community to develop the potential of DMDGC in university hydrology education, including formal development and assessment of curriculum materials, integrating lecture-format and DMDGC approaches, incentivizing the publication by faculty of excellent DMDGC curriculum materials, and implementing the publication and dissemination cyberinfrastructure necessary to support the unique DMDGC digital curriculum materials

Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities

Previous research has suggested that the use of more authentic learning activities can produce more robust and durable knowledge gains. This is consistent with calls within civil engineering education, specifically hydrology, that suggest that curricula should more often include professional perspective and data analysis skills to better develop the ‘T-shaped’ knowledge profile of a professional hydrologist (i.e., professional breadth combined with technical depth). It was expected that the inclusion of a data driven simulation lab exercise that was contextualized within a real-world situation and more consistent with the job duties of a professional in the field, would provide enhanced learning and appreciation of job duties beyond more conventional paper-and-pencil exercises in a lower division undergraduate course. Results indicate that while students learned in both conditions, learning was enhanced for the data-driven simulation group in nearly every content area. This pattern of results suggests that the use of data-driven modeling and visualization activities can have a significant positive impact on instruction. This increase in learning likely facilitates the development of student perspective and conceptual mastery, enabling students to make better choices about their studies, while also better preparing them for work as a professional in the field.This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by The Author(s) and published by Copernicus Publications on behalf of the European Geosciences Union. It can be found at: https://doi.org/10.5194/hess-20-1289-201

Comparison of performance of tile drainage routines in SWAT 2009 and 2012 in an extensively tile-drained watershed in the Midwest

Subsurface tile drainage systems are widely used in agricultural watersheds in the Midwestern US and enable the Midwest area to become highly productive agricultural lands, but can also create environmental problems, for example nitrate-N contamination associated with drainage waters. The Soil and Water Assessment Tool (SWAT) has been used to model watersheds with tile drainage. SWAT2012 revisions 615 and 645 provide new tile drainage routines. However, few studies have used these revisions to study tile drainage impacts at both field and watershed scales. Moreover, SWAT2012 revision 645 improved the soil moisture based curve number calculation method, which has not been fully tested. This study used long-term (1991–2003) field site and river station data from the Little Vermilion River (LVR) watershed to evaluate performance of tile drainage routines in SWAT2009 revision 528 (the old routine) and SWAT2012 revisions 615 and 645 (the new routine). Both the old and new routines provided reasonable but unsatisfactory (NSE  <  0.5) uncalibrated flow and nitrate loss results for a mildly sloped watershed with low runoff. The calibrated monthly tile flow, surface flow, nitrate-N in tile and surface flow, sediment and annual corn and soybean yield results from SWAT with the old and new tile drainage routines were compared with observed values. Generally, the new routine provided acceptable simulated tile flow (NSE  =  0.48–0.65) and nitrate in tile flow (NSE  =  0.48–0.68) for field sites with random pattern tile and constant tile spacing, while the old routine simulated tile flow and nitrate in tile flow results for the field site with constant tile spacing were unacceptable (NSE  =  0.00–0.32 and −0.29–0.06, respectively). The new modified curve number calculation method in revision 645 (NSE  =  0.50–0.81) better simulated surface runoff than revision 615 (NSE  =  −0.11–0.49). The calibration provided reasonable parameter sets for the old and new routines in the LVR watershed, and the validation results showed that the new routine has the potential to accurately simulate hydrologic processes in mildly sloped watersheds

Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities

Previous research has suggested that the use of more authentic learning activities can produce more robust and durable knowledge gains. This is consistent with calls within civil engineering education, specifically hydrology, that suggest that curricula should more often include professional perspective and data analysis skills to better develop the "T-shaped" knowledge profile of a professional hydrologist (i.e., professional breadth combined with technical depth). It was expected that the inclusion of a data-driven simulation lab exercise that was contextualized within a real-world situation and more consistent with the job duties of a professional in the field, would provide enhanced learning and appreciation of job duties beyond more conventional paper-and-pencil exercises in a lower-division undergraduate course. Results indicate that while students learned in both conditions, learning was enhanced for the data-driven simulation group in nearly every content area. This pattern of results suggests that the use of data-driven modeling and visualization activities can have a significant positive impact on instruction. This increase in learning likely facilitates the development of student perspective and conceptual mastery, enabling students to make better choices about their studies, while also better preparing them for work as a professional in the field

Featured series introduction : SWAT applications for emerging hydrologic and water quality challenges

International audienceMany global to local-scale issues such as changing climate, urbanization, intensification of agricultural activities, deforestation, and geopolitical conflicts pose serious challenges to the availability, accessibility, and management of water resources. Researchers, decision makers, and the general public are interested in understanding the impacts of these emerging issues so that water resources can be managed sustainably in the future. Besides availability, decision makers and the general public are also interested in how water quality will be impacted by pesticides, nutrients, pathogens, and emerging contaminants including pharmaceuticals and hormones, which are a serious concern for water security. Simulation models play a critical role in understanding the hydrologic processes of a system, the system's behavior, and how it will change in the future due to natural and anthropogenic factors. A plethora of models exist in the field of hydrology to address hydrologic and water quality issues at various spatial and temporal scales. Among these, the Soil and Water Assessment Tool (SWAT) (Arnold et al., 1998) has been used globally to address issues related to hydrology, water quality, and agricultural management (e.g., Gassman et al., 2014; Abbaspour et al., 2015; Cousino et al., 2015; Basheer et al., 2016).[...

Model Sharing and Collaboration using HydroShare

How do you manage, track, and share hydrologic data and models within your research group? Do you find it difficult to keep track of who has access to which data and who has the most recent version of a dataset or research product? Do you sometimes find it difficult to share data and models and collaborate with colleagues outside your home institution? Would it be easier if you had a simple way to share and collaborate around hydrologic datasets and models? HydroShare is a new, web-based system for sharing hydrologic data and models with specific functionality aimed at making collaboration easier. Within HydroShare, we have developed new functionality for creating datasets, describing them with metadata, and sharing them with collaborators. In HydroShare we cast hydrologic datasets and models as “social objects” that can be published, collaborated around, annotated, discovered, and accessed. In this presentation, we will discuss and demonstrate the collaborative and social features of HydroShare and how it can enable new, collaborative workflows for you, your research group, and your collaborators across institutions. HydroShare’s access control and sharing functionality enable both public and private sharing with individual users and collaborative user groups, giving you flexibility over who can access data and at what point in the research process. HydroShare can make it easier for collaborators to iterate on shared datasets and models, creating multiple versions along the way, and publishing them with a permanent landing page, metadata description, and citable Digital Object Identifier (DOI). Functionality for creating and sharing resources within collaborative groups can also make it easier to overcome barriers such as institutional firewalls that can make collaboration around large datasets difficult. Functionality for commenting on and rating resources supports community collaboration and quality evaluation of resources in HydroShare

Hybrid kriging methods for interpolating sparse river bathymetry point data

ABSTRACT Terrain models that represent riverbed topography are used for analyzing geomorphologic changes, calculating water storage capacity, and making hydrologic simulations. These models are generated by interpolating bathymetry points. River bathymetry is usually surveyed through cross-sections, which may lead to a sparse sampling pattern. Hybrid kriging methods, such as regression kriging (RK) and co-kriging (CK) employ the correlation with auxiliary predictors, as well as inter-variable correlation, to improve the predictions of the target variable. In this study, we use the orthogonal distance of a (x, y) point to the river centerline as a covariate for RK and CK. Given that riverbed elevation variability is abrupt transversely to the flow direction, it is expected that the greater the Euclidean distance of a point to the thalweg, the greater the bed elevation will be. The aim of this study was to evaluate if the use of the proposed covariate improves the spatial prediction of riverbed topography. In order to asses such premise, we perform an external validation. Transversal cross-sections are used to make the spatial predictions, and the point data surveyed between sections are used for testing. We compare the results from CK and RK to the ones obtained from ordinary kriging (OK). The validation indicates that RK yields the lowest RMSE among the interpolators. RK predictions represent the thalweg between cross-sections, whereas the other methods under-predict the river thalweg depth. Therefore, we conclude that RK provides a simple approach for enhancing the quality of the spatial prediction from sparse bathymetry data