Seasonal Streamflow Forecasting – Current Practice,
Challenges and Opportunities
Andy Wood
NCAR Research Applications Laboratory
Boulder, Colorado
Water Resources Engineering CVEN 5423/4323
Boulder, CO, Nov 5, 2014
1
Outline
• Water Management in the western US
• Operational Forecasting
– objectives
– decisionmaking
• Hydrologic Predictability
• Opportunities for Improvement
The Arid Lands
Many droughts will occur; many seasons in a long series will be
fruitless; and it may be doubted whether, on the whole, agriculture
will prove remunerative.
John Wesley Powell, 1879
Report on the lands of the arid region of the
United States
‘water supply forecasts’
1-12, even 24 months
large reservoirs
NCAR
statistical/ESP
Boulder, Colorado
‘runoff outlooks’
1-3 months
smaller reservoirs
ESP
Precipitation,
1971-2000
3
Colorado River





25 million people in 7 states rely
on Colorado River water
3.5 million acres of irrigation
85% of runoff comes from above
9000 feet
Mean annual discharge is … (?)
Storage capacity is about 60 MAF
(4-5 times mean annual flow)
Stakeholder
Example
Metropolitan Water
District (California)
• Supplies water to
~20m residents in
southern California
(including L.A.)
• Issues weekly water
supply conditions
map (right) based
on RFC, CA DWR,
and NRCS
forecasts and data
5
Colorado Basin
River Forecast Center
Thirteen River Forecast Centers
Established in the 1940s for water
supply forecasting
Three primary missions:
1. Seasonal Water supply
forecasts for water management
2. Daily forecasts for flood,
recreation, water management
3. Flash flood warning support
Value
Damage from 1/10 AZ storm:
Damage from 6/10 UT flooding:
Damage from 12/10 UT/NV storm:
Damage from 09/13 Boulder flood:
$11ma
$6.5ma
$11ma
$1bc
Colorado River average runoff: 12.4 MAF
Replacement value of $330/AF ->
$4bb
Economic value of water resources far greater than
flooding damages
Sources:
a: WFO, FEMA (via stormdata); b: MWD (via Hasencamp, private communication)
c: Wikipedia
i7
Water allocations
8
Water allocations
 1944 -- Mexican
Water Treaty
allocated Mexico 1.5
MAF
 1928 -- Boulder
Canyon Project first
allocated water
among lower basin
states …
 1964 -- It was
disputed by states
until Act that cleared
way for Central AZ
Project
9
• By law, 16.5 MAF can be
taken from river
• values for inflow depend on
method of calculation
What IS the flow of the Colorado River?

13.2 MAF/YR
H. G. Hidalgo, T. C. Piechota, and J. A. Dracup, 2000: Alternative principal components
regression procedures for dentrohydrological reconstructions, WRR v. 36, p. 3241-3249

13.5 MAF/YR
C. W. Stockton and G. C. Jacoby, 1976: Long-term surface-water supply and streamflow trends in
the Upper Colorado River Basin. Lake Powell Res. Proj. Bulletin no 18, NSF

14.3 MAF/YR
C. A. Woodhouse, S. T. Gray, and D. M. Meko, 2005: Updated streamflow reconstructions for the
Upper Colorado River Basin, WRR v. 42, W05415, doi:10.1029/2005/WR004455, 2006
http://treeflow.info/upco/colo
radoleesmeko.html
Lake Mead Elevation
1935
1975
2010
12
Recent drought has increased interest in forecasts
Colorado River Allocation Law responds
• Interim “Shortage Sharing” Guidelines (2007) – rules now in
force through 2026 that are tied to NWS CBRFC forecasts
Key facts:
 River is over-allocated: original allocation (16.5 MAF) was based
on a series of wet years
 Lower basin states (AZ, CA, NV) use full 7.5 MAF each year
 Mexico uses its full 1.5 MAF
 Upper basin states (CO, WY, UT, NM) are still “developing” their
7.5 MAF
 No shortage has ever been declared on the river
 Shortages would affect lower basin states first (and AZ first of all)
Management using CBRFC Flow Forecasts
Upper Colorado Reservoir Management
Water
transfer
from
upper
to lower
basin states depends on forecast
Major
releasesdecision
depend on
CBRFC
April
1 Forecasts
3,635
1/1/10
Projection
15.4
1/1/010Proj
ection
1,098
1/1/10
Projection
11.4
1/1/10
Projection
15
Management Challenges
infrastructure constraints not always in line with legal objectives
outage
Management Challenges
Management Challenges
a forecast
Management using CBRFC Flow Forecasts
Upper Colorado Reservoir Management
4,035 cfs
Mar 15 Forecast
570 KAF
19
How are forecasts created?
Water Supply Forecast Methods
Statistical Forecasting

Statistical Regression Equations

Primary NOAA/RFC forecast method from 1940’s to mid 1990’s.

Primary NRCS/NWCC forecast method

Historical Relationships between flow, snow, & precipitation (1971-2000)

Tied to a fixed runoff period (inflexible)
 Ensemble Simulation Model Forecasting

A component of a continuous conceptual model (NWSRFS)

Continuous real time inputs (temperature, precipitation, forecasts)

Accounts for soil moisture states (SAC-SMA) - drives runoff efficiency

Builds and melts snowpack (Snow-17) – output feeds SAC-SMA

Flexible run date, forecast period, forecast parameters.

Evolving toward ESP as primary forecast tool at NOAA/RFCs
Statistical Forecast Equations:
Trial Lake SNOTEL
Predictor variables must make sense
Challenge when few observation sites exist within river basin
Challenge when measurement sites are relatively young
Fall & Spring precipitation is frequently used (why?)
Sample Equation for April 1:
April-July volume Weber @ Oakley =
+ 3.50 * Apr 1st Smith & Morehouse (SMMU1) Snow Water Equivalent
+ 1.66 * Apr 1st Trial Lake (TRLU1) Snow Water Equivalent
+ 2.40 * Apr 1st Chalk Creek #1 (CHCU1) Snow Water Equivalent
- 28.27
Source: NRCS
NWS Community Hydrologic Prediction System
New modeling structure at all RFCs
• New wrapper for old models…but...
• Facilitates research to operations
• gives more insight into the model used
Transition completed in 2011
NWS River Forecast Process
Weather and Climate
Forecasts
Hydrologic Model Analysis
hydrologic
expertise &
judgment
Forecast
precip / temp
model
guidance
River
Forecast
Modeling
System
Analysis &
Quality Control
Observed
Data
Outputs
Graphics
parameters
Calibration
River
Forecasts
Rules, values,
other factors,
politics
Decisions
Data Processing
•
•
•
•
•
•
Data collection
collation
Data assimilation
Quality control
Forcing construction (HAS)
Forecast Review
Observed forcings
Precipitation + Temperature
+ Freezing level + ET
•
•
•
•
•
•
Modeling
•
•
•
•
•
•
Dissemination
•
•
•
•
•
•
Model simulations
Hydrologic analysis
Hydraulic analysis
Bias & error adjustments
Regulation Integration
Collaboration
MPE
Multi-Precip Estimator
MM
Mountain Mapper Daily QC
Process methods vary
Interactive Processes
Threshold and spatial comparisons
Systematic estimation
Manual forcing and over-rides
Hourly and max/min data (temperature)
Quality Control (QC) and processing
procedures vary by region and RFC
Interactive methods
GFE
Output qualified for model use
Gridded Forecast Editor
Slide by H. Opitz, NWRFC
Forecast generation
Quality assurance
Dissemination
Web services
Coordination & collaboration
Basin monitoring & review
National Center Support
Forecast Forcings
WFO Weather Forecast Office
National
Model
Guidance
National Model guidance
available to WFO & RFC
WFO & RFC directly share gridded
forecasts via GFE exchange
Grids at 4km or 2.5km
GFE
Gridded Forecast Editor
RFC River Forecast Center
Slide by H. Opitz, NWRFC
RFCs employ grid and point
forcings to hydrologic model
Point or gridded data converted
to mean areal forcings
Data Assimilation
•
•
•
•
•
Data collation
Data assimilation
Quality control
Forcing construction (HAS)
Forecast Review
Modeling
•
•
•
•
•
•
Model simulations
Hydrologic analysis
Hydraulic analysis
Bias & error adjustments
Regulation Integration
Collaboration
NWS: Streamflow Forecasting is an iterative & interactive process
Analysis & Assessment
Bias adjustments and error corrections
Dissemination
•
•
•
•
•
•
Forecast generation
Quality assurance
Dissemination
Web services
Coordination & collaboration
Basin monitoring & review
Also: SNOW-17 Temperature
index model for simulating
snowpack accumulation and melt
Simulation and Analysis
Observed Hydrology
Sacramento Soil Moisture
Accounting Model
Slide by H. Opitz, NWRFC
Streamflow Prediction Challenges:
forecast future weather (hours to seasons)
Past
Historical Data
e.g., SNOW-17 / SAC
Historical Simulation
Future
Forecasts
SNOW-17 / SAC
SWE
SM
Q
General State of Practice
• conduct ensemble simulation at fine-time step (e.g., sub-daily); aggregate to client needs
(e.g., daily to seasonal information)
• adjust results to compensate for known model biases (practice varies)
Fig. M. Clark (NCAR)
Runtime Modifications River Forecast Centers
•
•
•
MOD capability has been available in the NWS >30
years
Generic MOD capability implemented within FEWS
Extend capability to other users outside of OHDcore models
Slide by H. Opitz,
NWRFC
Calibration Climatology
CHPS
Observed and Simulated not Tracking
MOD Interface
Result: Improved observed period simulation
Hydrologist render a Run-Time
modification to the SACSMA Model
and increases the lower zone
primary and supplemental states
Examples: Nooksack R
Typical situation during snowmelt: the simulation goes awry
 What can a hydrologist deduce from this simulation?
 As it is, blending simulation and obs gives an ‘unrealistic’ forecast
30
Examples: Nooksack R
One Solution -- Double the snowpack (WECHNG)
 Other approaches may also have been tried: lower temperatures, raise soil moisture, etc.
31
Examples: Nooksack R
The resulting simulation is better, hence the forecast is more confident
 Flows stay elevated, have diurnal signal of continued melt.
32
A manual, subjective process
Manual elements
Meteorological
analysis
Quality control of station data
Quality control of radar and radar parameters
WFO+HPC met.
forecast
Water Supply
Forecast
Hydrologic
simulation and flood
forecasting
Daily Flood Forecast
Process
Long-lead forecasting
Coordination with
USDA/NRCS
WFO forecast itself (though based on models)
RFC merge with HPC forecast (similar to WFO)
Sac./Snow17 model states and parameters
Bias-adjustment relative to obs. Flow
Input forcings (2nd chance at adjustment)
Model states as adjusted for flood forecasting
Choice of models (statistical / ESP )
Blend of models
Choice of meteorology: QPF, ENSO, None?
Merging with NRCS statistical forecasts
means, confidence limits (“10-90s”)
33
ESP example -- Feb 1 forecast
Feb 1
Future
Medium chance of this
level snow or higher
Sno
w
Past
Low chance of this
level snow or higher
High chance of this
level snow or higher
Time
• ESP forecasts based on
• (1) initial conditions (e.g. snow pack, base flow, etc)
• (2) historical meteorological scenarios
©The COMET Program
Mar 1 forecast
Mar 1
Future
Medium chance of
this level flow or
higher
Sno
w
Past
Low chance of this
level flow or higher
High chance of this
level flow or higher
Time
©The COMET Program
Apr 1 forecast
Medium chance of this
level snow or higher
High chance of this
level snow or higher
Sno
w
Past
Apr 1
Future
Low chance of this
level snow or higher
Time
©The COMET Program
Forecasts and Water Management
CBRFC ensemble flow
forecasts for Reclamation
water management
Average contribution to Lake Powell
Apr-Jul inflow:
Green RiverStakeholder
34%
Reclamation
Allocations
Colorado River 50%
MidtermSan Juan River 13%
Probabilistic
Model
Graphic from Dr. Katrina Grantz, Bureau of Reclamation
37
37
Seasonal streamflow prediction is critical
One example:
Met. Water Dist. of
S. California (MWD)
MWD gets:
Surplus
1.25 MAF
OR
0.55 MAF
+$150M gap
from B. Hazencamp, MWD
?
38
Water supply forecasting
Both frameworks can be skillful
ESP – model-based
PCR - statistical
Yet both have drawbacks
• raw ESP: fails to account fully for
hydrologic uncertainty
• may have bias
• overconfident
• PCR, ie, flow = f(SWE, acc. PCP)
• cannot provide confidence
bounds (heteroskedastic
error) in many places
• small samples
• used since mid-1990s
• Ad-hoc combination
What are the sources of seasonal
streamflow predictability?
Opportunities for prediction
hydrological predictability
meteorological predictability
Hydrological Prediction: How
well can we estimate the
amount of water stored?
Accuracy in precipitation
estimates
Fidelity of hydro model
simulations
Effectiveness of hydrologic data
assimilation methods
Meteorological predictability:
How well can we forecast the
weather?
Opportunities: Which area
has most potential for
different applications?
Water Cycle (from NASA)
A western US water cycle
Runoff = Precip – ET – ΔSM - ΔSWE
October
April
September
water depth
SWE
SM
Precip
Evap
Runoff
water year
Seasonal Variation in Predictor Importance
Assessing the sources of flow forecast skill
w=0.5
http://www.ral.ucar.edu/staff/wood/weights/
w=0.5
Snow-Driven Basin
in the Western US
• Wide seasonal
variations in influence of
different skill sources
• cold and dry forecast
period forecast skill
depends strongly on
initial condition
influences
• warm and wet forecast
period forecast skill
depends strongly on
met. forecast skill
opportunities for improvement
Improved SCFs through Climate Prediction
Conditioned on IRI seasonal
forecast
Get the prediction
(A:N:B=40:35:25)
Divide historical sequencies into 3
tercile categories
Bootstrap 40, 35 and 25 sample of
historical years from wet, normal
and dry categories
Apply the ensembles in ESP instead
of historical weather sequences
(from Balaji Rajagopalan, CU)
CFSv2 precipitation forecast skill
• For seasonal time scales,
skill is lower
• some seasons may
have better skill,
varying little across
leads
East R at Almont, Co, precip
43
Improved Human Resources
The forecasting and water management enterprise
needs people with a strengths in:
•
•
•
•
•
•
watershed science
land surface modeling
atmospheric and/or climate science
probability and statistics
objective analysis (time-series, spatial)
programming (UNIX, R, Python, ‘traditional’, web,
database)
• technical communication
• systems engineering
• water resources planning, management and policy
50
Questions?
Questions?
Dr. Andy Wood
CBRFC
Development and Operations
Andy.Wood@noaa.gov
http://en.wikipedia.org/wiki/Lake_Powell
Abstract
Seasonal Streamflow Forecasting - Current Practice, Challenges and Opportunities
Several US agencies are charged with operationally producing seasonal streamflow forecasts, which
are a critical input to water management throughout the US, and particularly in the western US, where
flood-prone winters and dry summers necessitate careful decision-making over water allocation and
use. Water supply forecasts (WSFs) that predict the volume of snowmelt-driven runoff in the spring
and early summer, for example, are a critical product informing the
regulation of major storage projects such as Lakes Powell and Mead. This presentation focuses on the
tradition of water supply forecasting and describes an approach for diagnosing the sources of
prediction skill. The talk will also highlight strengths and weaknesses of the current practice and
suggest potential methodological opportunities for WSF improvement.
Improved IHCs through modeling
53
Snow Distribution –
corroborates presence
of lower elevation
snow in 2010 at start
of melt
2010 June 1
2006 Snowbird SNOTEL trace
almost identical to 2010 trace
from June 1-10
However, NOHRSC indicates
south facing slopes had already
melted out in 2006
2006 June 1
SNOW17 SWE
55
Distributed SWE
(eg SNODAS,
use of MODIS SCA)
56
Improved post-processing
• Statistical post-processing of forecasts can correct systematic bias and spread
problems (particularly ESP overconfidence and bias).
• Require long track record of consistent forecasts for training
• Would need to change the current operational ESP approach
April 1 Forecast of April-July streamflow volume, North Fork Gunnison
(SOMC2)
90th
50th
10th
Rain-Driven Basin in
the Southeastern US
•
For 6-month forecast,
seasonal variation in flow
forecast skill sensitivities
(met. forecast or IC) is
minimal
•
Effectiveness of skillful
met. forecasts diminishes
if initial conditions are not
well-captured
day-to-day changes in seasonal flow forecasts
• water managers / traders don’t like rapid changes in seasonal forecasts
• at least, causes should be explicit
?!
1-day change in median ESP forecast (period Apr-July)
bars = no weather forecast (mostly state changes)
lines = 3 and 10 day weather forecasts included in ESP
• sudden changes can come from:
• storm hits basin (rise in forecast)
• change in short range weather forecasts
• forecaster modification to states (or auto-DA)
(clear)
(less clear)
(hidden)