The Impact of Dams on Design Floods in the Conterminous US. Issue 3 (23rd March 2020)

• Record Type: Journal Article
• Authors: Zhao, Gang; Bates, Paul; Neal, Jeffrey
• Is Part Of: Water resources research. Volume 56:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-23
• Subjects: Design flood -- dams -- continental US -- flood hazard -- Ungauged areas -- machine learning; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR025380
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: We propose a method to describe the impact of dams on design floods for ungauged areas and validate the method over the conterminous US (CONUS). A Random Forest (RF) model was chosen to capture the relationship between the change in 100‐year return period flow up‐ and downstream of different dams and dam parameters available in the Global Reservoir and Dam (GRanD) database. The results showed that: (1) the RF model showed a greater accuracy in terms of Nash Sutcliffe efficiency coefficient (0.92 in training and 0.88 in testing) than a benchmark Multiple Linear Regression model (0.68 in training and 0.61 in testing); (2) Dam inflow, upstream catchment area, and long‐term average discharge at reservoir location were the three most important factors for dam outflow; (3) flood attenuation effect indices (FAI) for >1400 dams over the CONUS were derived with the proposed method. To further validate the accuracy of the FAI, a new module considering flood attenuation effects was developed for the LISFLOOD‐FP hydrodynamic model and two dams in the CONUS were selected to compare simulated flooded area against Federal Emergency Management Agency flood hazard maps. The result showed that the overestimation in flood hazard maps caused by not taking dams into account can be significantly corrected using the FAI and the enhanced LISFLOOD‐FP model. We conclude that the proposed methodology is a valid approach to describe the impact of dams on design floods, thereby improving the accuracy of flood hazard maps, especially in ungauged areas. Plain Language Summary: Dams have significantly changed hydrological processes, and this needs to be considered in flood hazard mapping. However, traditional flood hazard maps at national scales cannot accurately describe flooding downstream of dams because of the lack of detailed knowledge of reservoir operating rules. This research proposed a machine learning based approach to describe the impact of dams on downstream design floods using publicly available data. This approach has been successfully demonstrated over the continental US and could be easily extended globally. Furthermore, the proposed method was coupled with an enhanced hydraulic model and applied for flood hazard mapping in two case studies in the continental US. Compared with Federal Emergency Management Agency flood hazard maps, there was an obvious overestimation of flood extent downstream of each dam when reservoir operations are ignored. These overestimations can be significantly corrected using the proposed coupled model framework. Key Points: A method is proposed to analyze the flood attenuation effect of dams in ungauged areas using publicly available data Based on nine factors available in standard dam databases the flood attenuation effect could be predicted with a Nash‐Sutcliffe coefficient of 0.88 Two case studies are used to test the reliability of the proposed method for simulating flooding downstream of dams