US CLIVAR ASP Researcher ColloquiumBoulder, Co June 13-17, 2011

Andy Wood NOAA/NWS

Colorado Basin River Forecast Center

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Physical and practical requirements in downscaling for hydrologic assessment and prediction 

Outline

Hydrologic simulation of extremes

Hydrologic sensitivities

‘Simple’ Downscaling (in an ideal world)

Typical Downscaling for Hydrologic Assessment Suggested strategies / priorities

Notable:

Memory- snowpack- soil layers

Spatial connectivity- river network

Quick Primer on Hydrologic SystemsNotable: multi-variate forcings

CBRFC Watershed Models

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RFCs use a snow model and a rainfall-runoff model:

SNOW-17: Temperature index model for simulating snowpack accumulation and melt

Sacramento Soil Moisture Accounting Model: Conceptual hydrologic model used to generate runoff

Hydrologic simulation of seasonality

Simulation ExampleLittle Cottonwood Creek, Utah

For monthly flows:

Average Observed 69.3 cfsAverage Simulated 67.5 cfsRMSE = 22.83 RMSE/Obs mean = .33R2 = 0.94

dry anomalies also simulated well

questions about lack of groundwater in LSMs are valid in some areas

From the UW Surface Water Monitor (Wood, 2008)

Hydrologic models and drought

hydrology slidesScales that matter in Hydrology

Hydrologic simulation of flooding extremes

http://www.tennessean.com/

Nashville, May 1-3, 2010: A univariate event

Stationary front Mid – MississippiValley May 1 - 2

Deep moistureadvected from Gulf

Saturated Water Vapor

Wasatch Range Creeks: A multivariate event record snowpack built

from months of rain, then cool temperature anomalies

should not be skiable through late July

Salt Lake City Watersheds

Weber/Provo canal

Little Cottonwood Canyon

(photo courtesy PRWUA)

5500’

11800’

5 km

Modeling scales in hydrologic applications

forecastpoint

elevation gradients exert control on weather, even climate

also influences hydrology determines moisture

storage, fate

must account for these effects

Modeling scales in hydrologic applications RFC lumped models

recognize this physical consideration – land surface variation

recognize 3 response zones

ignores other variation, e.g, N/S slopes

Meteorological forcings are married to hydrologic analysis zones

Captures just enough diurnal/spatial variability to support flood forecasting 5500’

11800’

5 km

Modeling scales in hydrologic applications

forecastpoint

The making of a hydrologic extreme Months of prior weather patterns (filling storages – snow, soil) A terrain-driven pattern of melt, linked by stream network

Note close agreement (obs, sim) across 3 orders of magnitude

Dettinger et al, 2004

Hydrologic simulation across response range

Dettinger et al., Clim. Cng 2004

Outline

Hydrologic simulation of extremes

Hydrologic sensitivities

‘Simple’ Downscaling (in an ideal world)

Typical Downscaling for Hydrologic Assessment Suggested strategies / priorities

Modeling scales in hydrologic applications Errors in temperature

estimation of just a few degrees can cross important thresholds

Sensitivity to TemperatureFraser R nr. Winter Park

in snowmelt regimes, temperature forcing is as sensitive as precipitation

intensity partitioning between

runoff and infiltration

spatial/temporal pattern synchronization

accuracy Q = P – E + ΔS ΔS means biases

accumulate Q = P – E means

relative errors in P are magnified in Q

Sensitivity to Precipitation

Streamflow – Climate Sensitivity: MeansEmigration Canyon – Lower Elevation -16% flow / degree C win-spr warming +25% flow / 10% change win-spr precip

(C)

large scale synchronization matters

main-stem river extremes result from effects that accumulate across the basin, so spatial gradients matter (e.g, blue, below freezing, green-red, above)

Patterns over large scales matter

Outline

Hydrologic simulation of extremes

Hydrologic sensitivities

‘Simple’ Downscaling (in an ideal world)

Typical Downscaling for Hydrologic Assessment Suggested strategies / priorities

A simple downscaling approach

Simulated climatepast, present, future

fromGCM (or RCM)

Simulated climatepast, present, future

fromGCM (or RCM)

Hydrologic model that can simulate flow given

well constructed meteorology

Hydrologic model that can simulate flow given

well constructed meteorology

Simulated hydrologypast, present, future

informed byclimate simulation

Simulated hydrologypast, present, future

informed byclimate simulation

from which to derive period change statistics

interpolationnearest cellhydrology model timestep

Hope quickly fades for direct GCM output use

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No surprise…Prohibitive GCM climatology biases exist even at large scales in time/space

from BOR Westwide Study

What about RCMs?

e.g., NARCCAP

Cannot argue that RCMs do not respond to orographic features

Large scale view hides climatology failings

How to interpret projected changes, e.g., increased extremes?

On RCMs

F. Dominguez et al. (in preparation, 2011)

relative regional signal is okay

local magnitudes are quite biased in some GCM-RCM combinations

note regional averaging

F. Dominguez et al. (in preparation, 2011)

RCMs still challenged in simulating extremes

20 year precip 50 year precip

Implication: applications prefer large ensembles of GCM scenarios

Another ‘requirement’

via a L. Mearns presentation

Water applications culture and tough tests

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Outline

Hydrologic simulation of extremes

Hydrologic sensitivities

‘Simple’ Downscaling (in an ideal world)

Typical Downscaling for Hydrologic Assessment Suggested strategies / priorities

A statistical adjustment scheme

A statistical adjustment scheme

A simple practical downscaling approach

Simulated climatepast, present, future

fromGCM (or RCM)

Simulated climatepast, present, future

fromGCM (or RCM)

Hydrologic model that can simulate flow given

well constructed meteorology

Hydrologic model that can simulate flow given

well constructed meteorology

Simulated hydrologypast, present, future

informed byclimate simulation

Simulated hydrologypast, present, future

informed byclimate simulation

from which to derive period change statistics

now use coarser resolution~ monthly, GCM-scale

(just reconstruct forcings at required scales)

An observed forcing climatology that works

for hydrologic modeling

An observed forcing climatology that works

for hydrologic modeling

Prescribed change approach

Emigration Canyon

projected mean changes for SLC area

2040-2070 versus 1970-2000

112 GCM projections

Downscaled via Wood (2004) method- From LLNL CMIP3 112 Projections 1/8o CONUS archive

Current climate mean

Streamflow – Future Climate Response

Emigration Canyon

Let’s take a look at two simple change scenarios

•No change in precip

•+2, +4 deg C uniform

•can also use monthly varying, for given decade

This is the so-called “Delta method” or “perturbation method”

Current climate mean

Sensitivity of Flow to Projected Temp Changes

Mean annual cycles are well calibrated Even at +2 degrees, annual cycle diminishes flow +4 degrees: annual cycle progressively more altered (time, volume) Emigration Creek (lower elevation) more vulnerable than Little

Cottonwood Creek (higher elevation)

Expanding correction, adding transient signalAdjustments can go further: * correct whole CDF of output, not just means * apply corrections month-by-month to use time varying GCM

output, incorporate GCM sequencing of climate

BCSD (Wood et al., 2002, 2004), used in recent DOI western US studies.

But this approach may take too much information from GCMs…

•GCM sequencing is not always plausible

•recent work re-sequences GCM wet/dry periods using paleo spectrum

GCM-based power spectra for Lees Ferry flow

•Left Observed

•Lower left ECHAM 5

•Lower right NCAR CCSM3.0

from Ken Nowak, CU

For water management uses, tough grading

And do these approaches inform about extremes? Assume met. extreme info is contained in means

hydrologic process still provides non-linear response

Require resampling, scaling, analogue approaches to reconstruct daily meteorology scalings can blow up (esp. in dry, hence water scarce regions)

Extremes at fine time scales can be poor depends on underlying distributions of met. variables high skew or presence of regimes (intermittency) is a problem pathological results may be rare (but represent the extremes!)

Likely leave information on the table CMs probably DO have real information about changes in climate

parameters (e.g., min temperature, precip. intensity, storm track)

Other approaches exist such as stochastic downscaling, CCA, weather typing many apps. are univariate or also have trouble reproducing obs. climatology

Outline

Hydrologic simulation of extremes

Hydrologic sensitivities

‘Simple’ Downscaling (in an ideal world)

Typical Downscaling for Hydrologic Assessment Suggested strategies / priorities

Suggested emphases in downscaling for applications*** Hydrologic extremes often result from complex time/space

phenomena ***Hydrologic applications will rely on statistical downscaling schemes for

years• Essential: a high-quality, high-resolution climatology of land surface

meteorology (e.g., AOR – sub-daily, < 5 km, multi-variate)• Must move beyond reliance only on familiar fields: P, TRCMs have valuable role to play, but have challenges to overcome• We will typically need more runs than RCMs can provide• Can we build RCM sensitivities (‘missing from GCMs’) into statistical

downscaling approaches? Extreme value theory can be helpful in shaping downscaling• Multivariate context is needed (space, time, cross-variable)• Perhaps more physical guidance on application (fit without data)Our applications frameworks must allow for CM climatology error• Avoid an endless chain of corrections…