Physical and practical requirements in
downscaling for hydrologic
assessment and prediction
Andy Wood
NOAA/NWS
Colorado Basin River Forecast Center
US CLIVAR
ASP Researcher Colloquium
Boulder, Co June 13-17, 2011
1
Outline

Hydrologic simulation of extremes

Hydrologic sensitivities

‘Simple’ Downscaling (in an ideal world)

Typical Downscaling for Hydrologic Assessment

Suggested strategies / priorities
Quick Primer on Hydrologic Systems
Notable: multi-variate forcings
Notable:
Memory
- snowpack
- soil layers
Spatial
connectivity
- river
network
CBRFC Watershed Models
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
4
Hydrologic simulation of seasonality
Simulation Example
Little Cottonwood
Creek, Utah
For monthly flows:
Average Observed 69.3 cfs
Average Simulated 67.5 cfs
RMSE = 22.83
RMSE/Obs mean = .33
R2 = 0.94
Hydrologic models and drought
 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 simulation of flooding extremes

hydrology slides
Scales that matter in
Hydrology
http://www.tennessean.com/
Nashville, May 1-3, 2010: A univariate event
Saturated Water Vapor
Stationary front
Mid – Mississippi
Valley May 1 - 2
Deep moisture
advected from
Gulf
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
(photo courtesy PRWUA)
Little Cottonwood Canyon
Modeling scales in hydrologic applications
 elevation gradients exert
control on weather, even
climate
 also influences hydrology
 determines moisture
storage, fate
 must account for these
effects
11800’
forecast
point
5500’
5 km
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
Modeling scales in hydrologic applications
 Meteorological
forcings are
married to
hydrologic analysis
zones
 Captures just
enough
diurnal/spatial
variability to
support flood
forecasting
11800’
forecast
point
5500’
5 km
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
Hydrologic simulation across response range

Note close agreement (obs, sim) across 3 orders of magnitude
Dettinger et al., Clim. Cng 2004
Dettinger et al, 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 Temperature
 in snowmelt
regimes,
temperature forcing
is as sensitive as
precipitation
Fraser R nr. Winter Park
Sensitivity to 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
Streamflow – Climate Sensitivity: Means
Emigration Canyon – Lower Elevation
 -16% flow / degree C win-spr warming
 +25% flow / 10% change win-spr precip
(C)
Patterns over large scales matter

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)
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 climate
past, present, future
from
GCM (or RCM)
interpolation
nearest cell
hydrology model timestep
Hydrologic model that
can simulate flow given
well constructed
meteorology
Simulated hydrology
past, present, future
informed by
climate simulation
from which to derive period
change statistics
Hope quickly fades for direct GCM output use
No surprise…
Prohibitive GCM climatology biases exist even at large scales in time/space
24
from BOR
Westwide Study
What about RCMs?
e.g., NARCCAP
On RCMs

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?
F. Dominguez et al. (in preparation, 2011)
RCMs still challenged in simulating extremes

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)
20 year precip
50 year precip
Another ‘requirement’
Implication: applications prefer large ensembles of GCM scenarios
via a L. Mearns presentation
Water applications culture and tough tests
29
Outline

Hydrologic simulation of extremes

Hydrologic sensitivities

‘Simple’ Downscaling (in an ideal world)

Typical Downscaling for Hydrologic Assessment

Suggested strategies / priorities
A simple practical downscaling approach
Simulated climate
past, present, future
from
GCM (or RCM)
now use coarser resolution
~ monthly, GCM-scale
(just reconstruct forcings at
required scales)
A statistical adjustment
scheme
An observed forcing
climatology that works
for hydrologic modeling
Hydrologic model that
can simulate flow given
well constructed
meteorology
Simulated hydrology
past, present, future
informed by
climate simulation
from which to derive period
change statistics
Prescribed change approach
Emigration Canyon
projected mean changes
for SLC area
2040-2070
versus
1970-2000
112 GCM projections
Current
climate mean
Downscaled via Wood
(2004) method
- From LLNL CMIP3 112
Projections 1/8o CONUS
archive
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
Current
climate mean
This is the so-called
“Delta method” or
“perturbation method”
Sensitivity of Flow to Projected Temp Changes
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
8
obs
7
obs
sim
6
sim
+2degC
+4degC
5
+2degC
4
+4degC
3
2
1
0
1
2
3
4
5
6
7
month




Mean Streamflow -- Little Cottonwood
Flow (CMSD)
Flow (CMSD)
Mean Streamflow -- Emigration Creek
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
month
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)
12
Expanding correction, adding transient signal
Adjustments 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
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
BCSD (Wood et al., 2002, 2004), used in
recent DOI western US studies.
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, T
RCMs 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…