us clivar asp researcher colloquium boulder, co june 13-17, 2011
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Physical and practical requirements in downscaling for hydrologic assessment and prediction . US CLIVAR ASP Researcher Colloquium Boulder, Co June 13-17, 2011. Andy Wood NOAA/NWS Colorado Basin River Forecast Center. Outline. Hydrologic simulation of extremes Hydrologic sensitivities - PowerPoint PPT PresentationTRANSCRIPT
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)
Hydrologic model that can simulate flow given
well constructed meteorology
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 simple practical downscaling approach
Simulated climatepast, present, future
fromGCM (or RCM)
Hydrologic model that can simulate flow given
well constructed meteorology
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
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 CanyonLet’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…