Flood Risk Analysis – the USACE Approach

Slides:



Advertisements
Similar presentations
STATISTICS Joint and Conditional Distributions
Advertisements

Hydrology Rainfall Analysis (1)
STATISTICS Sampling and Sampling Distributions
STATISTICS HYPOTHESES TEST (III) Nonparametric Goodness-of-fit (GOF) tests Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering.
STATISTICS HYPOTHESES TEST (I)
Professor Ke-Sheng Cheng Dept. of Bioenvironmental Systems Engineering
Applied Hydrology Design Storm Hyetographs
Applied Hydrology RSLAB-NTU Lab for Remote Sensing Hydrology and Spatial Modeling 1 The Hydrological Cycle Professor Ke-Sheng Cheng Dept. of Bioenvironmental.
Hydrologic Modeling with HEC-HMS
RSLAB-NTU Lab for Remote Sensing Hydrology and Spatial Modeling 1 An Introduction to R Pseudo Random Number Generation (PRNG) Prof. Ke-Sheng Cheng Dept.
Detection of Hydrological Changes – Nonparametric Approaches
Dept of Bioenvironmental Systems Engineering National Taiwan University Lab for Remote Sensing Hydrology and Spatial Modeling STATISTICS Hypotheses Test.
Hyetograph Models Professor Ke-Sheng Cheng
Flood Routing & Detention Basin Design
STATISTICS Univariate Distributions
STATISTICS Joint and Conditional Distributions
R_SimuSTAT_1 Prof. Ke-Sheng Cheng Dept. of Bioenvironmental Systems Eng. National Taiwan University.
R_SimuSTAT_2 Prof. Ke-Sheng Cheng Dept. of Bioenvironmental Systems Eng. National Taiwan University.
STATISTICS Random Variables and Distribution Functions
0 - 0.
DIVIDING INTEGERS 1. IF THE SIGNS ARE THE SAME THE ANSWER IS POSITIVE 2. IF THE SIGNS ARE DIFFERENT THE ANSWER IS NEGATIVE.
Addition Facts
Year 6 mental test 10 second questions Numbers and number system Numbers and the number system, fractions, decimals, proportion & probability.
Introduction to modelling extremes
S-Curves & the Zero Bug Bounce:
Chapter 4 Inference About Process Quality
Hydrologic Statistics Reading: Chapter 11, Sections 12-1 and 12-2 of Applied Hydrology 04/04/2006.
Applied Hydrology Regional Frequency Analysis - Example Prof. Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University.
Addition 1’s to 20.
Test B, 100 Subtraction Facts
Week 1.
Hydrologic Statistics
Water Resources Planning and Management
Water Resources Planning and Management Daene C. McKinney River Basin Modeling.
Start Audio Lecture! FOR462: Watershed Science & Management 1 Streamflow Analysis Module 8.7.
Hydraulic Screening and Analysis Needed for USACE Review
Analyses of Rainfall Hydrology and Water Resources RG744
Hydrologic Statistics
STATISTICS HYPOTHESES TEST (I) Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University.
El Vado Dam Hydrologic Evaluation Joseph Wright, P.E. Bureau of Reclamation Technical Services Center Flood Hydrology and Meteorology Group.
Beargrass Creek Case Study Description of the Study Area Hydrology & Hydraulics Economic Analysis Project Planning Assessment of the Risk Based Analysis.
Hydrologic Design and Design Storms Readings: Applied Hydrology Sections /18/2005.
FREQUENCY ANALYSIS.
Engineering Economic Analysis Canadian Edition
Methodology for Risk-Based Flood Damage Assessment David R. Maidment CE 394K.2, Spring 1999.
Basic Hydrology & Hydraulics: DES 601
Floodplain Management D Nagesh Kumar, IISc Water Resources Planning and Management: M8L5 Water Resources Systems Modeling.
Bellwork What factors influence flooding?
Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering National Taiwan University 1/45 GEOSTATISTICS INTRODUCTION.
Applied Hydrology RSLAB-NTU Lab for Remote Sensing Hydrology and Spatial Modeling 1 Streamflow Measurement and Calculation Professor Ke-Sheng Cheng Dept.
CE 3354 ENGINEERING HYDROLOGY Lecture 6: Probability Estimation Modeling.
Hydrological Forecasting. Introduction: How to use knowledge to predict from existing data, what will happen in future?. This is a fundamental problem.
MRC-MDBC STRATEGIC LIAISON PROGRAM BASIN DEVELOPMENT PLANNING TRAINING MODULE 3 SCENARIO-BASED PLANNING for the MEKONG BASIN Napakuang, Lao PDR 8-11 December.
March Urban Flood Risk Management. March Objectives Understand the Nature of Flooding & Flood Damage Alleviation Understand the Nature of.
Analyses of Rainfall Hydrology and Water Resources RG744 Institute of Space Technology October 09, 2015.
STATISTICS HYPOTHESES TEST (I)
Basic Hydrology: Flood Frequency
Hazards Planning and Risk Management Flood Frequency Analysis
Precipitation Analysis
Stochastic Hydrology Random Field Simulation
Water Resources Engineering Hydrological Analysis and Simulation Model HEC-HMS Prof. Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering.
Stochastic Hydrology Hydrological Frequency Analysis (I) Fundamentals of HFA Prof. Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering.
Flood Routing & Detention Basin Design
Applied Hydrology Hydrological Analysis and Simulation Model HEC-HMS
Flood Frequency Analysis
Stochastic Storm Rainfall Simulation
Floods and Flood Routing
Stochastic Simulation and Frequency Analysis of the Concurrent Occurrences of Multi-site Extreme Rainfalls Prof. Ke-Sheng Cheng Department of Bioenvironmental.
Stochastic Hydrology Simple scaling in temporal variation of rainfalls
STATISTICS HYPOTHESES TEST (I)
Presentation transcript:

Flood Risk Analysis – the USACE Approach Professor Ke-Sheng Cheng Dept. of Bioenvironmental Systems Engineering National Taiwan University

The Levee Certification Program Many flood damage reduction projects involve the construction of levees. The USACE’s (US Army Corps of Engineers) historical approach to coping with hydrological and hydraulic uncertainties of large floods was based on a best estimate of the levee height required to withstand a given flood, which was then augmented by a standard increment of levee height called “freeboard”.

The best estimate has traditionally been based on the expected height of a design flood (for example, the100-year flood). A standard freeboard of 3 feet was then added above the expected height. With the Corps adoption of risk analysis techniques in early 1990s, the freeboard standard for levee certification was abandoned in favor of the new risk analysis standard.

The new USACE approach was reviewed by the Committee on Risk-Based Analysis for Flood Damage Reduction of the National Research Council which is organized by the National Academy of Sciences and the National Academy of Engineers.

The committee recommends that the federal levee certification program focus not upon some level of assurance of passing the 100-year flood, but rather upon “annual exceedance probability” – the probability that an area protected by a levee system will be flooded by any potential flood. How sure can we be that if we protect ourselves from a flow of 100-year return period, we will actually be protecting ourselves from all flows that occur less frequently, on average, than once in 100 years?

USACE Risk Analysis Techniques The Corps’s objective in flood damage reduction studies is to determine the expected annual damage (EAD) along a section of river caused by possible floods, and to compare changes in those damages as a function of project alternatives. For a flood of annual probability p (p=1/T, T: return period), a corresponding value of flood damage D(p) can be estimated.

The EAD is the average value of such damages taken over floods of all different annual exceedance probabilities and over a long period of years. The current Corps method divides the calculation of EAD into three steps: Determining flood frequencies, which describe the probability of floods equal or greater than some discharge Q occurring within a given period of time (usually 1-year).

Determining stage-discharge relations, which describe how high the flow of water in a reach of river (the stage) might be for a given discharge. Determining damage-stage relations, which describe the amount of damage that might occur, given a certain height of flow.

Basis of the Corps’s EAD Computation Annual exceedance probabilities of p = 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, 0.004, and 0.002 are used for EAD calculation.

There are uncertainties in the probability of exceedance distribution of annual peak flows the discharge-stage function the stage-damage function Stochastic simulation techniques are used to generate a single realization of each of the three relationships.

Uncertainty in annual peak flow

Peak flow records are commonly used to estimate the chance of a flood of a given or greater magnitude. But such estimates are uncertain due to Limited number of observations of past peak flows The changing and varying character of the drainage basin that influences the peak flow resulting from a specific rainfall.

Monte Carlo simulation is used to generate new realizations of each of the three curves using various exceedance probabilities. The Monte Carlo simulation is repeated for a few thousand cycles of generating realizations and computing EAD.

Assessment of Engineering Performance The target stage is defined as the water surface elevation in a reach at which significant economic damage occurs. The 1% chance of flooding damage is found from the flood-frequency curve. A fraction of this damage (usually 10%) is taken and used to determine the corresponding stage from the damage-stage curve, which then becomes the target stage for the reach.

Engineering performance can be measured by conditional probabilities dependent on the occurrence of a flood of a given severity (e.g., the 100-year flood) or dependent on the annual probabilities integrated over all the floods that could occur within a given year.

Corps’s method for measuring engineering performance Annual exceedance probability – the probability that the target stage will be exceeded in any year considering all potential floods. Conditional nonexceedance probability – the probability that the target stage will not be exceeded given a specific flood severity.

In each cycle of the above Monte Carlo procedure, a new realization of the flood-frequency curve and the stage-discharge curve is generated. The flood-frequency is defined at discrete intervals of annual flood probability (p = 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, 0.004, and 0.002).

Distribution of the target stage nonexceedance probability Each cycle of the Monte Carlo procedure yields an estimate of the annual exceedance probability for the target stage. N cycles of the Monte Carlo procedure are conducted to yield a distribution of the annual exceedance probability for the target stage. The chance that the target stage will be exceeded at least one in n years is computed as , where pe is the expected annual exceedance probability.

Conditional nonexceedance probabilities at the target stage For each value of p*, there corresponds an H*, determined in a similar manner. After N Monte Carlo cycles are completed, a set of N values of H* exists, of which a subset, n, have stages not exceeding the target stage. The conditional nonexceedance probability of the target stage is given by n/N.