A Numerical Framework for Evaluating Flood Inundation Hazard under Different Dam Operation Scenarios—A Case Study in Naugatuck River

Worldwide, many river floodplains contain critical infrastructure that is vulnerable to extreme hydrologic events. These structures are designed based on flood frequency analysis aimed at quantifying the magnitude and recurrence of the extreme events. This research topic focuses on estimating flood vulnerability at ungauged locations based on an integrative framework consisting of a distributed rainfall–runoff model forced with long-term (37 years) reanalysis meteorological data and a hydraulic model driven by high-resolution airborne LiDAR-derived terrain elevation data. The framework is applied to a critical power infrastructure located within Connecticut’s Naugatuck River Basin. The hydrologic model reanalysis is used to derive 50-, 100-, 200-, and 500-year return period flood peaks, which are then used to drive Hydrologic Engineering Center’s River Analysis System (HEC-RAS) hydraulic simulations to estimate the inundation risk at the infrastructure location under different operation strategies of an upstream reservoir. This study illustrates the framework’s potential for creating flood maps at ungauged locations and demonstrates the effects of different water management scenarios on the flood risk of the downstream infrastructure.

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The Convergence of IoT, Machine Learning, and Big Data for Advancing Flood Analytics Knowledge

10.5194/egusphere-egu21-7782 ◽

2021 ◽

Author(s):

Vidya Samadi ◽

Rakshit Pally

Keyword(s):

Machine Learning ◽

Big Data ◽

Language Processing ◽

Situational Awareness ◽

Critical Infrastructure ◽

Probability Distributions ◽

Data Gathering ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Crowd Intelligence

<p>Floods are among the most destructive natural hazard that affect millions of people across the world leading to severe loss of life and damage to property, critical infrastructure, and agriculture. Internet of Things (IoTs), machine learning (ML), and Big Data are exceptionally valuable tools for collecting the catastrophic readiness and countless actionable data. The aim of this presentation is to introduce Flood Analytics Information System (FAIS) as a data gathering and analytics system. &#160;FAIS application is smartly designed to integrate crowd intelligence, ML, and natural language processing of tweets to provide warning with the aim to improve flood situational awareness and risk assessment. FAIS has been Beta tested during major hurricane events in US where successive storms made extensive damage and disruption. The prototype successfully identifies a dynamic set of at-risk locations/communities using the USGS river gauge height readings and geotagged tweets intersected with watershed boundary. The list of prioritized locations can be updated, as the river monitoring system and condition change over time (typically every 15 minutes).&#160; The prototype also performs flood frequency analysis (FFA) using various probability distributions with the associated uncertainty estimation to assist engineers in designing safe structures. This presentation will discuss about the FAIS functionalities and real-time implementation of the prototype across south and southeast USA. This research is funded by the US National Science Foundation (NSF).</p>

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Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality

Hydrology and Earth System Sciences ◽

10.5194/hess-23-2225-2019 ◽

2019 ◽

Vol 23 (5) ◽

pp. 2225-2243 ◽

Cited By ~ 7

Author(s):

Guo Yu ◽

Daniel B. Wright ◽

Zhihua Zhu ◽

Cassia Smith ◽

Kathleen D. Holman

Keyword(s):

Soil Moisture ◽

Frequency Analysis ◽

Probability Distributions ◽

Extreme Rainfall ◽

Hydrologic Model ◽

Flood Frequency ◽

Agricultural Watershed ◽

Flood Frequency Analysis ◽

Antecedent Conditions ◽

Hydrologic Change

Abstract. Floods are the product of complex interactions among processes including precipitation, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into these process interactions and how they “shape” the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use require deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative “process-based” FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm “scenarios” based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied to the 4002 km2 Turkey River watershed in the Midwestern United States, which is undergoing significant climatic and hydrologic change. We show that, using only 15 years of rainfall records, our methodology can produce accurate estimates of “present-day” flood frequency. We found that shifts in the seasonality of soil moisture, snow, and extreme rainfall in the Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. If such mistakes are avoided, however, process-based approaches can provide a useful pathway toward understanding current and future flood frequency in nonstationary conditions and thus be valuable for supplementing existing FFA practices.

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Process-Based Flood Frequency Analysis in an Agricultural Watershed Exhibiting Nonstationary Flood Seasonality

10.5194/hess-2018-513 ◽

2018 ◽

Author(s):

Guo Yu ◽

Daniel B. Wright ◽

Zhihua Zhu ◽

Cassia Smith ◽

Kathleen D. Holman

Keyword(s):

Soil Moisture ◽

Frequency Analysis ◽

Probability Distributions ◽

Extreme Rainfall ◽

Hydrologic Model ◽

Flood Frequency ◽

Agricultural Watershed ◽

Flood Frequency Analysis ◽

Antecedent Conditions ◽

Hydrologic Change

Abstract. Floods are the product of complex interactions of processes including rainfall, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into process interactions and how they shape the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use requires deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative process-based FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm scenarios based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied in Turkey River in the Midwestern United States, a watershed that is undergoing significant climatic and hydrologic change. We show that using only 15 years of rainfall records, our methodology can produce more accurate estimates of present-day flood frequency than is possible using longer discharge or rainfall records. We found that shifts in the seasonality of soil moisture conditions and extreme rainfall in Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. Such mistakes are avoidable, however, and our approach may provide a clearer pathway toward understanding current and future flood frequency in nonstationary conditions compared with more conventional methods.

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