River Floodplain 1D/2D Hydraulic Modelling Combined with Recent LiDAR DTM Technology

Kolaković, Slobodan; Kolaković, Srđan; Fabian, Julius; Jeftenić, Goran; Trajković, Slaviša

doi:10.17559/tv-20200429024034

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Corps of Engineers [9,10]. For example, Kolakovic et al [11] used terrestrial LiDARderived stream channel models in HEC-RAS to produce hydraulic flood modeling for the Danube River in Southeast Europe. A recent study used HEC-RAS to identify flood-prone areas for different rainfall scenarios in the Prosa basin in Brazil [12].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

Bolick

Post

Naser

et al. 2023

Water

View full text Add to dashboard Cite

Flooding in urban streams can occur suddenly and cause major environmental and infrastructure destruction. Due to the high amounts of impervious surfaces in urban watersheds, runoff from precipitation events can cause a rapid increase in stream water levels, leading to flooding. With increasing urbanization, it is critical to understand how urban stream channels will respond to precipitation events to prevent catastrophic flooding. This study uses the Prophet time series machine learning algorithm to forecast hourly changes in water level in an urban stream, Hunnicutt Creek, Clemson, South Carolina (SC), USA. Machine learning was highly accurate in predicting changes in water level for five locations along the stream with R2 values greater than 0.9. Yet, it can be challenging to understand how these water level prediction values will translate to water volume in the stream channel. Therefore, this study collected terrestrial Light Detection and Ranging (LiDAR) data for Hunnicutt Creek to model these areas in 3D to illustrate how the predicted changes in water levels correspond to changes in water levels in the stream channel. The predicted water levels were also used to calculate upstream flood volumes to provide further context for how small changes in the water level correspond to changes in the stream channel. Overall, the methodology determined that the areas of Hunnicutt Creek with more urban impacts experience larger rises in stream levels and greater volumes of upstream water during storm events. Together, this innovative methodology combining machine learning, terrestrial LiDAR, 3D modeling, and volume calculations provides new techniques to understand flood-prone areas in urban stream environments.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

Bolick

Post

Naser

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…A computationally and data less demanding variant is the combination of 1D and 2D models. Models with 2D schematisation are used for local inundation mapping, and 1D models for describing large river reaches (Kolakovic et al, 2021; Samarasinghe et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

The site dynamics of Corrigiola litoralis (Strapwort) on the Elbe River in Czechia: A combined hydrological and hydrodynamic approach

Havlíček,

Heřmanovský,

Bureš

et al. 2023

Ecohydrology

View full text Add to dashboard Cite

This study aims to identify suitable sites for Corrigiola litoralis (Strapwort) using hydrological and hydrodynamic models and geographical information techniques. It was conducted on the lower Elbe River in Czechia for 1981–2020 at six research sites. Hydrological models BILAN, GR4J and TUW were used together with HEC‐RAS hydrodynamic model. In addition to hydrological model simulations, we also used observed flows. We addressed identifying sites suitable for Corrigiola litoralis by introducing conditions reflecting the ecological requirements of this species. We compared the results with the findings recorded in the literature. The accuracy of this approach was 0.750 when the source of hydrological data was the observations, and the accuracy was, on average, 0.776 when using model simulations. The determined size of a suitable area had a significant variance. The closest to the size determined from the observed data were results based on the simulation of the TUW model. The GR4J model and BILAN model overestimated the size of suitable areas. The presented approach proved to be reasonable for modelling spatio‐temporal dynamics of sites suitable for Corrigiola litoralis.

show abstract

River Floodplain 1D/2D Hydraulic Modelling Combined with Recent LiDAR DTM Technology

Cited by 2 publications

References 0 publications

Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

The site dynamics of Corrigiola litoralis (Strapwort) on the Elbe River in Czechia: A combined hydrological and hydrodynamic approach

Contact Info

Product

Resources

About