BUILDING STRONG ® Hydrologic Engineering Center Use of Information on Hydrologic Extremes: US Army Corps of Engineers Perspective Beth Faber, PhD, PE USACE, IWR, Hydrologic Engineering Center Statistical Assessment of Extreme Weather Phenomena under Climate Change Boulder, CO June 13-17, 2011

BUILDING STRONG ® Hydrologic Engineering Center USACE Mission Areas  The US Army Corps of Engineers has a “mission” in several areas of water management  Most deal with streamflow, some with average annual volumes, some with extremes (high or low) o Flood Risk Management o Ecosystem Restoration o Navigation (inland, coastal) o Water Supply o Hydropower o Recreation concerned with extreme HIGH flows concerned with extreme LOW flows concerned with annual flow volumes concerned with sea level

BUILDING STRONG ® Hydrologic Engineering Center USACE “Response” to Climate Change  Our understanding of our hydrologic environments and how we describe them must account for change over time, that is difficult to predict o formerly, used the past variability to describe future variability o and, we’re accustomed to predictable change in that variability…  Corps’ response to climate change is mainly in two areas o Decision-making how we incorporate a new kind of uncertainty o Data (to inform decision-making) how we describe the environments the public exists within (inland hydrology, storms, sea-level)

BUILDING STRONG ® Hydrologic Engineering Center Data, and use of data  I’m going to show what data we use, how we summarize it, and how it enters our decision-making o local-scale daily hydrology, or daily temp/precip  What we need to know from climate scientists: o How can we evaluate climate change impact on extremes? o What are the climate models good at? What aren’t they good at? o What outputs have value, that we can base decisions on? o How should we down-scale spatially and temporally? o What’s the meaning of the model “ensemble”? Does it span the uncertainty? Does it capture the distribution of uncertainty?

BUILDING STRONG ® Hydrologic Engineering Center Natural Variability  We’re used to dealing with natural variability o Our projects are designed to perform for the range of hydrology o Describe hydrology by both annual statistics and by probability distributions of extreme events (frequency curves)  Questions for climate scientists and statisticians: 1. Have the observed climate trends exceeded the range of natural variability? 2. At what point would the plausible climate futures exceed the natural variability (or change it)? 3. How can we estimate the variability of the future?

BUILDING STRONG ® Hydrologic Engineering Center Decision-making  We’re accustomed to making decisions with uncertain info o All of our hydrologic statistics and modeling are uncertain o Decisions span a reasonable range of the uncertainty o But, decisions that span very large uncertainty and array of possible outcomes become less effective  Now focused on adaptive decision-making (hedge and adjust) o Robust actions that are acceptable for a range of outcomes o Staged decisions that are revisited and adjusted over time o Decisions that don’t exclude other future actions or avenues o The life-span of a decision affects the uncertainty included o Uncertain info, want to reduce the impact of being wrong

BUILDING STRONG ® Hydrologic Engineering Center USACE Mission Areas  The US Army Corps of Engineers has a “mission” in several areas of water management  Most deal with streamflow, some with average annual volumes, some with extremes (high or low) o Flood Risk Management o Ecosystem Restoration o Navigation (inland, coastal) o Water Supply o Hydropower o Recreation concerned with extreme HIGH flows concerned with extreme LOW flows concerned with annual flow volumes concerned with sea level

BUILDING STRONG ® Hydrologic Engineering Center Biggest Issue – Extreme Highs  The Corps’ most prominent mission area is flood risk management o Formerly referred to as “flood control” or “flood damage reduction” o The new term reflects a new way of thinking and making decisions about flood risk o More about getting out of the way than changing or blocking the flow, and consciously addressing the risk  For flood risk management, the most important input to characterize hydrology is the probability distribution of annual peak flows (flood-frequency curve)

BUILDING STRONG ® Hydrologic Engineering Center Flood Risk Management  Risk analysis and risk management require estimates of the probability of extreme events with negative consequences o Risk = Probability × Consequence  Need a relationship between flood magnitude and annual exceedance probability o might differ for the present and the planning horizon  This relationship allows us to perform probabilistic analyses involving high streamflows and their consequences (damage, lives lost)

BUILDING STRONG ® Hydrologic Engineering Center Typical Flood Frequency Curve Log Scale Normal Probability Scale Return Period % confidence interval EXTREME

BUILDING STRONG ® Hydrologic Engineering Center Flood Frequency Analysis  When possible, we infer probability from a record of annual maximum observations (Bulletin 17B) o using the past variability as a representation of the future o use LogPearson type III distribution (least wrong for 100yr event)  Treat annual peak flows as a random, representative sample of realizations from the flood population of interest  Generally, we assume the sample is IID o annual peak flows are random and independent o peak flows are identically distributed – homogeneous data set o sample is adequately representative of the population  These assumptions are becoming more difficult…

BUILDING STRONG ® Hydrologic Engineering Center Generation of the Flood Frequency Curve 1903 – years Log Scale Normal Probability Scale Return Period LogPearson III

BUILDING STRONG ® Hydrologic Engineering Center Uncertainty in the Flood Frequency Curve 1965 – years Log Scale Normal Probability Scale Return Period

BUILDING STRONG ® Hydrologic Engineering Center Corps Activities that require Flood-Frequency  Corps of Engineers responsibilities involve both water management and development of infrastructure  Make decisions based on probabilistic analysis, and where possible, explicit incorporation of uncertainty o … uncertainty in probabilistic estimates of flow, and uncertainty in other modeling (hydrologic, hydraulic and economic)  We use the probabilistic description of flooding in: o Safety of projects o Scale of projects (and updated “performance”) o Operation of projects

BUILDING STRONG ® Hydrologic Engineering Center Safety of Projects  In the past, required that projects can withstand the occurrence of the Probable Maximum Flood (PMF) without failure (e.g., sizing of spillway) o note: capacity exceedance ≠ failure  Now, we evaluate overall risk (exceedance OR failure), and target a tolerable risk to remain below o Risk = probability of occurrence × consequence o Evaluate all possible failure modes and their likelihoods, as well as the likelihoods of loading thresholds (from the flood- frequency curve) levees, dams, locks, ports, hydropower

BUILDING STRONG ® Hydrologic Engineering Center 16 Likelihood Consequences Tolerable Risk Limit Dynamics of Likelihood vs. Consequences: 1.original estimate of risk 2.risk management implemented probability: strengthen, raise capacity consequence: remove structures 3.due to aging and wear&tear due to climate change? 4.due to maintenance, repairs, and improved operations 5.resulting from development pressure 6.on-going risk management activities a)Land use management b)Flood proofing c)Warnings and preparedness plans

BUILDING STRONG ® Hydrologic Engineering Center Scale of Projects  This is the “ Planning ” function  Federal dollars are spent to provide an equal or greater benefit to the Nation o formerly, the explicit benefit of a project was a reduction in annualized flood damage (levees, dams, “non-structural” actions) o now, also incorporate reduction in loss of life  Projects are scaled (sized) such that either net benefits (benefit – cost) or benefit/cost ratio are maximized o no defined target, but now must be below tolerable risk limit  Annualized benefit is the reduction in mean annual damage (or, EAD), based on the probability distribution of peak annual flow

BUILDING STRONG ® Hydrologic Engineering Center  EAD = Expected Annual Damage  The probability distribution of annual flood damage for a river reach is computed by combining summary curves developed from hydrologic frequency, hydraulic and economic analyses: flow-frequency curveto obtain a: flow-stage curve damage-frequency curve stage-damage curve  The mean of the damage-frequency curve is the expected value of annual damage, or EAD. Computing EAD with summary curves

BUILDING STRONG ® Hydrologic Engineering Center Peak Flow (cfs) Exceedance Probability 1 0 Stage (ft) Flow (cfs) Stage (ft) Damage ($) p Exceedance Probability 1 0 Damage ($) p Computing EAD with summary curves AREA = mean = Expected Annual Damage F(D) F(Q)

BUILDING STRONG ® Hydrologic Engineering Center Peak Flow (cfs) Exceedance Probability 1 0 Stage (ft) Flow (cfs) Stage (ft) Damage ($) p Exceedance Probability 1 0 Damage ($) p Use Monte Carlo simulation to sample many realizations of each curve to compute many realizations of EAD Uncertainty in EAD AREA = mean = Expected Annual Damage F(D) F(Q)

BUILDING STRONG ® Hydrologic Engineering Center Project Performance  Another measure for project scale is the performance of the project, and how it might change over time o Project performance is a combination of the likelihood of capacity exceedance and the likelihood of failure  A flood protection project is designed having a certain probability of capacity exceedance  a change in the flood-frequency curve causes a change in the probability of capacity exceedance  This change might cause a difference with regard to the National Flood Insurance Program

BUILDING STRONG ® Hydrologic Engineering Center 100-year Levee Certification 90% 90% conditional non-exceedance probability for the 1%-chance event… estimated stage- frequency curve

BUILDING STRONG ® Hydrologic Engineering Center Operation of Projects  This is the Corps’ Water Management function  Reservoirs have Guide Curves (GC) that specify the upper bound of storage  Below GC is Conservation space for storing water for later use o navigation, hydropower, water supply, flow maintenance, flow temperature control, (and recreation – don’t use!)  Above GC is Flood space for storing flood flows o Store flood volume until can release safely downstream o Either maintain a flood pool, or draft enough space to store forecasted snowmelt flood

BUILDING STRONG ® Hydrologic Engineering Center Flood Pool Conservation Pool flood benefits are proportional to flood volume supply benefits are proportional to supply volume Guide Curve Reservoir Storage Pools An increase in the flood-frequency curve would let existing flood pool offer less protection (larger chance exceedance), or require a larger flood pool More variability in flow, or a decrease in average annual volume would decrease the yield of the Conservation pool A decrease the low-flow frequency curve would require more yield for streamflow maintenance

BUILDING STRONG ® Hydrologic Engineering Center Seasonal Reservoir Pools  Get more benefit to each use when have more volume serving it – so multi-use space is efficient  When there’s a limited flood season, or only snowmelt runoff flooding, use seasonal pools  A seasonal Guide Curve calls for a flood pool during part of the year, and allows it to fill when there is little or no probability of flooding o a change in the flood season would require changing GC  When only snowmelt runoff flooding (Pacific Northwest), draft a flood pool volume based on runoff forecasts

BUILDING STRONG ® Hydrologic Engineering Center Seasonal Flood Pool – Rain Flooding rain flood season flood pool conservation pool supply volume flood volume Oct Jan Apr Jul Oct Guide Curve snowmelt runoff Reservoir Volume What if earlier snowmelt runoff before rain-flood season is over? Convenient timing: snowmelt starts when rainy season ends, catch volume.

BUILDING STRONG ® Hydrologic Engineering Center Seasonal Flood Pool – Rain Flooding rain flood season flood pool conservation pool supply volume flood volume Oct Jan Apr Jul Oct Guide Curve snowmelt runoff Reservoir Volume What if earlier snowmelt runoff before rain-flood season is over? Convenient timing: snowmelt starts when rainy season ends, catch volume.

BUILDING STRONG ® Hydrologic Engineering Center Timing of Flow Volume in California

BUILDING STRONG ® Hydrologic Engineering Center Seasonal Flood Pool – Snowmelt Flooding snowmelt flood season flood pool conservation pool Reservoir Volume supply volume flood volume Oct Jan Apr Jul Oct Variable Guide Curves Guide Curve With snowmelt forecast, draft enough to catch flood peak. But some forecasts are based on historical record … snowmelt runoff

BUILDING STRONG ® Hydrologic Engineering Center Frequency Analysis, non-stationarity  Standard (B17B) frequency analysis assumes data is IID (independent and identically distributed) o use the past to estimate likelihood for the future  Some change can be dealt with, if the change is recognizable and predictable o example: urbanization o adjust earlier flood peak data to current basin condition o KEY: the trend must be observable to be removed, and predictable to have an estimate of a future condition  Climate change/trend is more difficult to recognize, more difficult to predict

BUILDING STRONG ® Hydrologic Engineering Center Adjustment for Urbanization

BUILDING STRONG ® Hydrologic Engineering Center Adjustment for Urbanization Return Period

BUILDING STRONG ® Hydrologic Engineering Center Decisions under uncertainty  How can we address this increased uncertainty about future hydrology?  There are two opposite approaches: 1.Find a way to make better estimates of the future 2.Work with the uncertainty. Make decisions (project scaling, operation plans) more robust (able to perform well in a wider array of conditions) and able to adapt as change is recognized (realized) o NOTE: adaptation is quite feasible for project operations, but less so for infrastructure development. Plan for staged development of infrastructure, but more costly in the end

BUILDING STRONG ® Hydrologic Engineering Center How can we do better frequency analysis?  If our gaged flood records are not IID, how should we do frequency analysis? o Fit parameters that vary as a function of time? o Other covariates? o Attempt to adjust all historical floods to an estimated present or future condition? o Run climate models longer to capture a stationary (current or future) condition? o Attempt frequency analysis on shorter periods?

BUILDING STRONG ® Hydrologic Engineering Center

BUILDING STRONG ® Hydrologic Engineering Center 1909 – years Return Period

BUILDING STRONG ® Hydrologic Engineering Center Return Period

BUILDING STRONG ® Hydrologic Engineering Center Return Period

BUILDING STRONG ® Hydrologic Engineering Center Random 30-year Samples from full-period Frequency Curve Return Period

BUILDING STRONG ® Hydrologic Engineering Center Short-record Frequency Curves  It’s difficult to make assumptions based on frequency curves from short records  The uncertainty in those short-record frequency curves overwhelms any change we might see in the short-term  Need more data to infer probability distributions, to reduce uncertainty so that it’s less than the change due to climate.