1 Streamflow Forecasting from Weather to Climate Scales Current US Practice, Challenges and...
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- Slide 1
- 1 Streamflow Forecasting from Weather to Climate Scales Current
US Practice, Challenges and Opportunities Helmholtz Environmental
Centre Leipzig, Germany, Oct 13, 2014 Andy Wood NCAR Research
Applications Laboratory Boulder, Colorado
- Slide 2
- Outline Operational Seasonal Forecasting western US objectives
processes Predictability Research Opportunities for
Improvement
- Slide 3
- The Arid Lands 3 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
Precipitation, 1971-2000 NCAR Boulder, Colorado
- Slide 4
- Seasonal streamflow prediction is critical 4 One example: Met.
Water Dist. of S. California (MWD) MWD gets: 1.25 MAF OR 0.55 MAF
from B. Hazencamp, MWD ? +$150M gap Surplus
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- How are forecasts created?
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- National Weather Service River Forecast Centers 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
- Slide 7
- Forecast precip / temp Weather and Climate Forecasts River
Forecast Modeling System parameters Observed Data Analysis &
Quality Control Calibration model guidance Hydrologic Model
Analysis hydrologic expertise & judgment Outputs Graphics River
Forecasts Decisions Rules, values, other factors, politics NWS
River Forecast Process
- Slide 8
- MPE Multi-Precip Estimator MPE Multi-Precip Estimator MM
Mountain Mapper Daily QC MM Mountain Mapper Daily QC Data
Processing Modeling Dissemination GFE Gridded Forecast Editor GFE
Gridded Forecast Editor Observed forcings Precipitation +
Temperature + Freezing level + ET 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 Output qualified for model use Slide by H.
Opitz, NWRFC
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- NationalModelGuidanceNationalModelGuidance National Model
guidance available to WFO & RFC WFO & RFC directly share
gridded forecasts via GFE exchange Grids at 4km or 2.5km RFCs
employ grid and point forcings to hydrologic model Point or gridded
data converted to mean areal forcings National Model guidance
available to WFO & RFC WFO & RFC directly share gridded
forecasts via GFE exchange Grids at 4km or 2.5km RFCs employ grid
and point forcings to hydrologic model Point or gridded data
converted to mean areal forcings GFE Gridded Forecast Editor
National Center Support Forecast Forcings WFO Weather Forecast
Office RFC River Forecast Center Slide by H. Opitz, NWRFC
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- Data Assimilation Modeling Dissemination Sacramento Soil
Moisture Accounting Model Data Assimilation Dissemination Observed
Hydrology Simulation and Analysis NWS: Streamflow Forecasting is an
iterative & interactive process Analysis & Assessment Bias
adjustments and error corrections NWS: Streamflow Forecasting is an
iterative & interactive process Analysis & Assessment Bias
adjustments and error corrections Also: SNOW-17 Temperature index
model for simulating snowpack accumulation and melt Slide by H.
Opitz, NWRFC
- Slide 11
- Observed and Simulated not Tracking Result: Improved observed
period simulation 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 OHD- core models MOD capability has been available in
the NWS >30 years Generic MOD capability implemented within FEWS
Extend capability to other users outside of OHD- core models MOD
Interface Calibration Climatology Hydrologist render a Run-Time
modification to the SACSMA Model and increases the lower zone
primary and supplemental states Hydrologist render a Run-Time
modification to the SACSMA Model and increases the lower zone
primary and supplemental states CHPS Slide by H. Opitz, NWRFC
- Slide 12
- 12 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
- Slide 13
- 13 Examples: Nooksack R One Solution -- Double the snowpack
(WECHNG) Other approaches may also have been tried: lower
temperatures, raise soil moisture, etc.
- Slide 14
- 14 Examples: Nooksack R The resulting simulation is better,
hence the forecast is more confident Flows stay elevated, have
diurnal signal of continued melt.
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- A manual, subjective process 15 WFO+HPC met. forecast
Hydrologic simulation and flood forecasting Long-lead forecasting
Coordination with USDA/NRCS Meteorological analysis Quality control
of station data Quality control of radar and radar parameters
Manual elements 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
(2 nd 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) Daily Flood
Forecast Water Supply Forecast Process
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- Water Supply Forecast Methods Statistical Forecasting
Statistical Regression Equations Primary NOAA/RFC forecast method
from 1940s to mid 1990s. 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
- Slide 17
- NWS Community Hydrologic Prediction System New forecasting
platform at all RFCs New wrapper for old modelsbut... Facilitates
research to operations gives more insight into the model used
Transition completed in 2011
- Slide 18
- Sno w Time ESP example -- Feb 1 forecast Future Feb 1 Past Low
chance of this level snow or higher High chance of this level snow
or higher Medium chance of this level snow or higher The COMET
Program ESP forecasts based on (1) initial conditions (e.g. snow
pack, base flow, etc) (2) historical meteorological scenarios
- Slide 19
- Forecasts and Water Management Average contribution to Lake
Powell Apr-Jul inflow: Green River34% Colorado River50% San Juan
River13% 19 Reclamation Midterm- Probabilistic Model Stakeholder
Allocations Graphic from Dr. Katrina Grantz, Bureau of Reclamation
CBRFC ensemble flow forecasts for Reclamation water management 19
Forecast impacts, e.g.: if WY12 is wet enough, Lake Mead hits
surplus levels, MWD gets water ~ $150M
- Slide 20
- Water supply forecasting
- Slide 21
- Runoff forecast for this period Example flow volume prediction
increasing watershed moisture reduces prediction spread
- Slide 22
- Statistical Forecast Equations: 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?) Source: NRCS Sample
Equation for April 1: April-July volume Weber @ Oakley = + 3.50 *
Apr 1 st Smith & Morehouse (SMMU1) Snow Water Equivalent + 1.66
* Apr 1 st Trial Lake (TRLU1) Snow Water Equivalent + 2.40 * Apr 1
st Chalk Creek #1 (CHCU1) Snow Water Equivalent - 28.27 Trial Lake
SNOTEL
- Slide 23
- What are the sources of seasonal streamflow
predictability?
- Slide 24
- Opportunities for prediction 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 hydrological
predictability Water Cycle (from NASA) Meteorological
predictability: How well can we forecast the weather?
Opportunities: Which area has most potential for different
applications? meteorological predictability
- Slide 25
- Seasonal predictor importance depends on hydroclimate humid
basin uniform rainfall no snow small cycle driven by ET cold basin
drier summers deep snow large seasonal cycle April snowmelt
dominates May-June runoff
- Slide 26
- Data & Methods automated system, large number of gages
Basin Selection: Used GAGES-II, Hydro-climatic data network
(HCDN)-2009 minimal human influence, long period of record large
diversity of landscapes, energy- to water-limited ~670
rwatersheds
- Slide 27
- Assessing the sources of flow forecast skill w=0.5
http://www.ral.ucar.edu/staff/wood/weights/
- Slide 28
- Snow-Driven Basin in the Western US Wide seasonal variations in
influence of different skill sources cold and/or dry forecast
period forecast skill depends strongly on initial condition
influences warmer wet/snowmelt forecast period forecast skill
depends strongly on met. forecast skill IHC: initial Hydrologic
Conditions SDF: Seasonal Climate Forecasts
- Slide 29
- Streamflow Forecast Skill Elasticities The % change in flow
forecast skill versus per % change in predictor source skill Can
help estimate the benefits of effort to improve forecasts in each
area
- Slide 30
- Opportunities for Improvement of Operational Forecasts
- Slide 31
- Improved modeling (ie, IHCs) 31 flexible platform
- Slide 32
- Example: point observations and lumped model zones did not
capture difference between these two years 2006 Snowbird SNOTEL
trace almost identical to 2010 trace from June 1-10 However,
distributed satellite/model analysis indicates south facing slopes
were melted out in 2006 much lower runoff as a result 2010 June 1
2006 June 1
- Slide 33
- From lumped conceptual models 33 SNOW17 SWE Sacramento
- Slide 34
- To finer resolution physically-based models 34 Distributed
modeling of water and energy balance
- Slide 35
- Improved long-lead flow forecasts via climate prediction
Conditioned on IRI seasonal forecast Get the prediction
(A:N:B=40:35:25) Divide historical (seasonal) total 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)
- Slide 36
- 43 For seasonal time scales, skill is lower some seasons may
have better skill, varying little across leads East R at Almont,
Co, precip CFSv2 precipitation forecast skill
- Slide 37
- 37 Short- Range Medium- Range Merging Joining Blending NWS has
a new system for incorporating climate forecasts Under development
/ MMEFS Planned Long- Range Calibrated and seamless short- to
long-term forcing input ensembles Day 1-5 ensembles Day 1-14
ensembles Day 15+ ensembles HPC/RFC single-valued forecast GFS/GEFS
ens. mean CFS/CFSv2 forecast Other ensembles (SREF, NAEFS) Other
info (climate indices)
- Slide 38
- Is it time to start shifting the forecast paradigm? 38
Advantages of a manual process handles data and model deficiencies
in ways that current objective approaches cannot incorporates the
knowledge of the expert forecaster in ways current models and
procedures may not capable of producing the best forecasts at times
Disadvantages limits addition of new techniques that assume
objectivity or automation data assimilation, post-processing,
verification, ensemblesdata assimilation, post-processing,
verification, ensembles labor intensive poor scaling limits
opportunity for expanded services cannot quantify uncertainty of an
individual forecast cannot quantify skill of forecasting system and
set baseline for systematic improvements ie benchmarking the system
is partly in the forecaster brain, thus system knowledge can be
lost as forecasters retire or move away Such a shift would require
broad institutional change training, expertise, center staffing,
partner interactions
- Slide 39
- Questions? Dr. Andy Wood CBRFC Development and Operations
Questions? http://en.wikipedia.org/wiki/Lake_Powell
Andy.Wood@noaa.gov
- Slide 40
- April 28, 201040 Precipitation Skill, CD 48 Satish Regonda,
OHD
- Slide 41
- Historical Simulation Q SWE SM Historical DataForecasts
PastFuture e.g., SNOW-17 / SAC Fig. M. Clark (NCAR) Streamflow
Prediction Challenges: forecast future weather (hours to seasons)
SNOW-17 / SAC 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)
- Slide 42
- Assessing regional variations Based on ~420 watersheds, 30 year
hindcasts MotivationDevelopment &
PerformancePredictabilitySummaryEnsemble Forcing Results are
preliminary and subject to change
- Slide 43
- Assessing regional variations Based on ~420 watersheds, 30 year
hindcasts MotivationDevelopment &
PerformancePredictabilitySummaryEnsemble Forcing Results are
preliminary and subject to change
- Slide 44
- Assessing regional variations Based on ~420 watersheds, 30 year
hindcasts MotivationDevelopment &
PerformancePredictabilitySummaryEnsemble Forcing Results are
preliminary and subject to change
- Slide 45
- Flow Forecast Skill Sensitivities A Snowmelt Basin Wide
seasonal variations in influence of different skill sources
Cold/Warm and Dry/Wet patterns are clear Skill increases in
meteorology produce non- linear skill changes in flow Assuming no
skill in one source (model accuracy for watershed states, or
forecasts)what are the benefits of adding skill in the other
source? Results are preliminary and subject to change
- Slide 46
- Flow Forecast Skill Sensitivities A Rain-Driven Basin IC skill
matters most for 1 month forecasts For longer forecasts, met
forecast skill dominates Assuming no skill in one source (model
accuracy for watershed states, or forecasts)what are the benefits
of adding skill in the other source? Results are preliminary and
subject to change
- Slide 47
- 47 Atmospheric Pre-Processor: calibration Off line, model joint
distribution between single-valued forecast and verifying
observation for each lead time X Y Forecast Observed 0 Joint
distribution Sample Space PDF of ObservedPDF of Obs. STD Normal NQT
Schaake et al. (2007), Wu et al. (2011) Forecast Observed Joint
distribution Model Space X Y Correlation (X,Y) Archive of
observed-forecast pairs PDF of Forecast PDF of Fcst STD Normal NQT
NQT: Normal Quantile Transform
- Slide 48
- 48 Atmospheric Pre-Processor: ensemble generation (1) In
real-time, given single-valued forecast, generate ensemble traces
from the conditional distribution for each lead time Forecast
Observed Joint distribution Model Space x fcst X Y Given
single-valued forecast, obtain conditional distribution xixi xnxn
Conditional distribution given x fcst Ensemble forecast Probability
0 1 x1x1 Schaake et al. (2007), Wu et al. (2011) Ensemble members
for that particular time step x1x1 xnxn NQT -1
- Slide 49
- Abstract Streamflow Forecasting from Weather to Climate Scales
Current US 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 highlight strengths
and weaknesses of the current practice, which is highly manual and
regionalized, and potential opportunities for WSF improvement. The
presentation also describes the current practice in flood
forecasting, and new developments toward a more centralized
modeling and flood prediction paradigm.
- Slide 50
- 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
- Slide 51
- Calibration Results Areas with seasonal snow perform best
(using NSE benchmark) More arid regions, high plains perform worst
Need in-depth examination of poor performing basins Forcing data
issues? ~670 basins included in initial calibration
- Slide 52
- 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
- Slide 53
- Analysis effort outside the forecast process is critical to the
forecast service
- Slide 54
- Sno w Time Mar 1 forecast Future Mar 1 Past Low chance of this
level flow or higher High chance of this level flow or higher
Medium chance of this level flow or higher The COMET Program
- Slide 55
- Sno w Time Apr 1 forecast Future Apr 1 Past Low chance of this
level snow or higher High chance of this level snow or higher
Medium chance of this level snow or higher The COMET Program
- Slide 56
- Story line of an extreme year Each hydrologic anomaly has a
story line. As water year progresses: past wx/climate (hydrologic
initial conditions) are an increasing part of the plot future
climate is a diminishing part of the plot extremes often involve
pattern persistence but can be more complicated Flow Time Future
Feb 1 Past IC signal Wx/Climat e signal e.g., Wood &
Lettenmaier, 2008 GRL e.g., storyline: big storm in Feb very wet
April cool May/June IC
- Slide 57
- 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
- Slide 58
- 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 I s April 1 Forecast of
April-July streamflow volume, North Fork Gunnison (SOMC2) 90 th 50
th 10th
- Slide 59
- Premise: We can improve short- term water decisions and
increase long-term adaptation capacity under climate change by
improving monitoring & forecasting Improving how we use this
information Report summarizes needs monitoring forecasting
information use http://www.ccawwg.us/docs/Short-
Term_Water_Management_Decisions_Final_3_Jan_2013.pdf
- Slide 60
- ST Doc user needs 60 Other Categories