23 research outputs found
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
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
Classifying connectivity to guide aquatic habitat management in an arctic coastal plain watershed experiencing land use and climate change
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
Two-dimensional hydrodynamic modelling for urban flood risk assessment using unmanned aerial vehicle imagery: A case study of Kirsehir, Turkey
WOS: 000485980500006This study is an assessment of flash flood risk in the downstream part of an intermittent stream which lost its natural bed due to intense human interventions, with the example of Hastane Brook in the province of Kirsehir (Turkey). The effects of flooding events with high, medium, and low probability of occurrence are investigated on a street-by-street basis with a two-dimensional (2D) hydrodynamic model constructed in HEC-RAS 5.0 software. Due to the lack of records of past flood events required for model calibration, it is aimed to utilise high-quality data as much as possible in model development. Therefore, to more accurately simulate the movement of water and thereby to advise adequate measures for reducing the most likely flood effects, the required high-resolution terrain and land use data are produced by processing the aerial images acquired by the unmanned aerial vehicle (UAV) flights over the flood risk zone. The estimation of flood hydrographs is only based on the synthetic unit hydrograph methods due to the absence of representative stream gauging stations inside or near the region. The resultant flood hazard maps are cautionary in terms of demonstrating the effects of possible floods that are unexpected to come from such an intermittent stream basin.Kirsehir Ahi Evran University Scientific Research Projects Coordination UnitAhi Evran University [MMF.A3.17.004]The Kirsehir Ahi Evran University Scientific Research Projects Coordination Unit, Grant/Award Number: MMF.A3.17.00
Two‐dimensional hydrodynamic modelling for urban flood risk assessment using unmanned aerial vehicle imagery: A case study of Kirsehir, Turkey
HydroShare: Advancing Hydrology through Collaborative Data and Model Sharing
Advancing Hydrologic Understanding
- requires integration of information from multiple sources
- is data and computationally intensive
- requires collaboration and working as a team/communit