Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters
An assessment of uncertainty in flood hydrograph features, e.g., peak discharge and flood volume due to variability in the rainfall-runoff model (HEC-HMS) parameters and rainfall characteristics, e.g., depth and duration, is conducted. Flood hydrographs are generated using a rain pattern generator (...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/7948902 |
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| author | Ahmad Sharafati Mohammad Reza Khazaei Mohamed Salem Nashwan Nadhir Al-Ansari Zaher Mundher Yaseen Shamsuddin Shahid |
| author_facet | Ahmad Sharafati Mohammad Reza Khazaei Mohamed Salem Nashwan Nadhir Al-Ansari Zaher Mundher Yaseen Shamsuddin Shahid |
| author_sort | Ahmad Sharafati |
| collection | DOAJ |
| description | An assessment of uncertainty in flood hydrograph features, e.g., peak discharge and flood volume due to variability in the rainfall-runoff model (HEC-HMS) parameters and rainfall characteristics, e.g., depth and duration, is conducted. Flood hydrographs are generated using a rain pattern generator (RPG) and HEC-HMS models through Monte Carlo simulation considering uncertainty in stochastic variables. The uncertainties in HEC-HMS parameters (e.g., loss, base flow, and unit hydrograph) are estimated using their probability distribution functions. The flood events are obtained by simulating runoff for rainfall events using the generated model parameters. The uncertainties due to rainfall and model parameters on generated flood hydrographs are evaluated using the relative coefficient of variation (RCV). The results reveal a higher RCV index for flood volume (RCV = 153) than peak discharge (RCV = 116) for a 12-hr rainfall duration. The average relative RCV (ARRCV) index computed for hydrological component (e.g., base flow, loss, or unit hydrograph) indicates the highest impact of rainfall depth on flood volume and peak. The results indicate that rainfall depth is the main source of uncertainty of flood peak and volume. |
| format | Article |
| id | doaj-art-4003011fda27444faa56368100b4d2a5 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-4003011fda27444faa56368100b4d2a52025-02-03T06:05:17ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/79489027948902Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological ParametersAhmad Sharafati0Mohammad Reza Khazaei1Mohamed Salem Nashwan2Nadhir Al-Ansari3Zaher Mundher Yaseen4Shamsuddin Shahid5Institute of Research and Development, Duy Tan University, Da Nang 550000, VietnamDepartment of Civil Engineering, Payame Noor University, Tehran, IranFaculty of Engineering, Universiti Teknologi Malaysia (UTM), Skudai, Johor 81310, MalaysiaCivil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea 97187, SwedenSustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, VietnamFaculty of Engineering, Universiti Teknologi Malaysia (UTM), Skudai, Johor 81310, MalaysiaAn assessment of uncertainty in flood hydrograph features, e.g., peak discharge and flood volume due to variability in the rainfall-runoff model (HEC-HMS) parameters and rainfall characteristics, e.g., depth and duration, is conducted. Flood hydrographs are generated using a rain pattern generator (RPG) and HEC-HMS models through Monte Carlo simulation considering uncertainty in stochastic variables. The uncertainties in HEC-HMS parameters (e.g., loss, base flow, and unit hydrograph) are estimated using their probability distribution functions. The flood events are obtained by simulating runoff for rainfall events using the generated model parameters. The uncertainties due to rainfall and model parameters on generated flood hydrographs are evaluated using the relative coefficient of variation (RCV). The results reveal a higher RCV index for flood volume (RCV = 153) than peak discharge (RCV = 116) for a 12-hr rainfall duration. The average relative RCV (ARRCV) index computed for hydrological component (e.g., base flow, loss, or unit hydrograph) indicates the highest impact of rainfall depth on flood volume and peak. The results indicate that rainfall depth is the main source of uncertainty of flood peak and volume.http://dx.doi.org/10.1155/2020/7948902 |
| spellingShingle | Ahmad Sharafati Mohammad Reza Khazaei Mohamed Salem Nashwan Nadhir Al-Ansari Zaher Mundher Yaseen Shamsuddin Shahid Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters Advances in Civil Engineering |
| title | Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters |
| title_full | Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters |
| title_fullStr | Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters |
| title_full_unstemmed | Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters |
| title_short | Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters |
| title_sort | assessing the uncertainty associated with flood features due to variability of rainfall and hydrological parameters |
| url | http://dx.doi.org/10.1155/2020/7948902 |
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