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Cited by 16

The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.

Remote Sensing with UAVs for Flood Modeling: A Validation with Actual Flood Records

Clasing, Robert, Muñoz, Enrique, Arumí, José Luis, Parra, Víctor·2023·Water
DOI 10.3390/w15213813
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Abstract

The use of unmanned aerial vehicles (UAVs) is steadily increasing due to their capacity to capture terrain elevation data with remarkable precision and cost-effectiveness. Nonetheless, their application for estimating water surface elevations and submerged terrain, such as channel bathymetry, remains constrained. Consequently, the development of a digital terrain model that relies on UAV data during low-water periods assumes a more extensive dry channel surface area, thus alleviating the information gap regarding submerged terrain. The objective of this brief report is to validate a hydraulic model for flood calculation. To this end, a 1D steady-state hydrological model of the Ñuble River based on a UAV survey in the low-water period of 2016 was constructed in HEC-RAS v.5.0.3 and compared to water surface elevation observations of the flood on 24 June 2023. The model tends to overestimate the flood, but the errors are considered tolerable for flood calculation (on average, a 10.6% depth error was obtained for a 30-year return period flood); therefore, the hydraulic model derived from remote sensing seems to be an effective alternative for the construction of hydraulic models for flood studies.

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Figures

Figure 1 Study area on the Ñuble River. ( a ) Studied river section. The main river channel is mar by the blue line, and the green line denotes the position of a cross-section. ( b ) Location of the st area relative to Chile. The green dot indicates the measured river area and the orange dot shows location of the Río Ñuble en San Fabián N2 stream gauging station (Lat/Lon 36° 35 ′ 09 ″ S, 71° 31 ′ W). ( c ) Cross-section of the DTMRS indicated by a green line in ( a ). Figure 1. Study area on the Ñuble River. ( a ) Studied river section. The main river channel is marked by the blue line, and the green line denotes the position of a cross-section. ( b ) Location of the study area relative to Chile. The green dot indicates the measured river area and the orange dot shows the location of the R í o Ñuble en San Fabi á n N2 stream gauging station (Lat/Lon 36 ◦ 35 ′ 09 ′′ S, 71 ◦ 31 ′ 32 ′′ W). ( c ) Cross-section of the DTMRS indicated by a green line in ( a ).
Figure 1 Study area on the Ñuble River. ( a ) Studied river section. The main river channel is mar by the blue line, and the green line denotes the position of a cross-section. ( b ) Location of the st area relative to Chile. The green dot indicates the measured river area and the orange dot shows location of the Río Ñuble en San Fabián N2 stream gauging station (Lat/Lon 36° 35 ′ 09 ″ S, 71° 31 ′ W). ( c ) Cross-section of the DTMRS indicated by a green line in ( a ). Figure 1. Study area on the Ñuble River. ( a ) Studied river section. The main river channel is marked by the blue line, and the green line denotes the position of a cross-section. ( b ) Location of the study area relative to Chile. The green dot indicates the measured river area and the orange dot shows the location of the R í o Ñuble en San Fabi á n N2 stream gauging station (Lat/Lon 36 ◦ 35 ′ 09 ′′ S, 71 ◦ 31 ′ 32 ′′ W). ( c ) Cross-section of the DTMRS indicated by a green line in ( a ).CC-BY · From the PDF
Figure 2 Results of the modeling of the Ñuble River for the 24 June 2023, flood. ( a ) Longitudinal profile of the WSERS results, along with WSEOBS . ( b ) Distribution of differences between WSEOBS and WSERS . ( c ) Boxplot of the MAPE between DepthRS and DepthOBS . In the boxplots, the lower whisker represents the minimum value in the dataset, or the 1st percentile, while the upper whisker represents the maximum value in the dataset, or the 99th percentile. Figure 2. Results of the modeling of the Ñuble River for the 24 June 2023, flood. ( a ) Longitudinal profile of the WSERS results, along with WSEOBS . ( b ) Distribution of differences between WSEOBS and WSERS . ( c ) Boxplot of the MAPE between DepthRS and DepthOBS . In the boxplots, the lower whisker represents the minimum value in the dataset, or the 1st percentile, while the upper whisker represents the maximum value in the dataset, or the 99th percentile.
Figure 2 Results of the modeling of the Ñuble River for the 24 June 2023, flood. ( a ) Longitudinal profile of the WSERS results, along with WSEOBS . ( b ) Distribution of differences between WSEOBS and WSERS . ( c ) Boxplot of the MAPE between DepthRS and DepthOBS . In the boxplots, the lower whisker represents the minimum value in the dataset, or the 1st percentile, while the upper whisker represents the maximum value in the dataset, or the 99th percentile. Figure 2. Results of the modeling of the Ñuble River for the 24 June 2023, flood. ( a ) Longitudinal profile of the WSERS results, along with WSEOBS . ( b ) Distribution of differences between WSEOBS and WSERS . ( c ) Boxplot of the MAPE between DepthRS and DepthOBS . In the boxplots, the lower whisker represents the minimum value in the dataset, or the 1st percentile, while the upper whisker represents the maximum value in the dataset, or the 99th percentile.CC-BY · From the PDF

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Citations

Cites

Show all 60 references

Research works printed but not linked: 3

  1. Brasington, 2000, Monitoring and modelling morphological change in a braided gravel-bed river using high resolution GPS-based survey, Earth Surf. Process. Landforms, 25, 973, 10.1002/1096-9837(200008)25:9<973::AID-ESP111>3.0.CO;2-Y · doi:10.1002/1096-9837(200008)25:9<973::aid-esp111>3.0.co;2-y
  2. Hicks, D.M. (2012). Gravel-Bed Rivers, John Wiley & Sons, Ltd.
  3. Rajabi, 2021, Morphological classification stability of Zab river channel on Rosgen method, Geogr. Plan., 25, 141

Cited by

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Metadata

Authors
Clasing, R., Muñoz, E., Arumí, J. L., & Parra, V.
Affiliations
  • Department of Civil Engineering, Universidad Católica de la Santísima Concepción, Concepción 4090541, Chile
  • Department of Water Resources, Universidad de Concepción, Chillán 3812120, Chile
  • Department of Envioromental Engineering, Universidad de Concepción, Concepción 4070386, Chile
ORCID
Venue
Water
Publisher
MDPI AG
Published
2023-10-31
Volume
15
Issue
21
Pages
3813
ISSN
2073-4441
DOI
10.3390/w15213813
Cited by
16
References
60
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • CIBAS 2019-01 (DI-FME 03/2021, ANID/FONDAP/15130015)
  • CRHIAM Center (DI-FME 03/2021, ANID/FONDAP/15130015)

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CoChat. (2026). Citation check: Remote Sensing with UAVs for Flood Modeling: A Validation with Actual Flood Records. https://app.cochat.ai/papers/10.3390/w15213813
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Clasing, R., Muñoz, E., Arumí, J. L., & Parra, V. (2023). Remote Sensing with UAVs for Flood Modeling: A Validation with Actual Flood Records. Water. https://doi.org/10.3390/w15213813

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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