1 water systems retreat boulder, january 14, 2015 observations on short range streamflow forecasting...
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Water Systems RetreatBoulder, January 14, 2015
Observations on short range streamflow forecasting
Andy Wood NCAR Research Applications LaboratoryHydrometeorological Applications Program
Are operational forecasts improving?
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Since 1997, there have been notable advances in capabilities supporting hydrologic prediction. Are we harnessing those advances?
http://www.srh.noaa.gov/abrfc/fcstver/
Traditional Resources
1980s construct:• parsimonious watershed models
run on single PCs (or card decks + VAX)
• phone/mail transmission of data, forecast output bulletins
• manual synoptic weather analyses, rudimentary NWP
grey = inactive
Traditional forecaster effort
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Met data QC -- RRS data QC -- HAS fcst1-3 people
Automated input processing / briefings
CHPS deterministic forecasting
Agency coordination
CHPS deterministic forecastingProduct generation
Data/systems cleanup
Forecast T0 (used for 24 hours)
HAS fcst updateEditing of old forecast before new T0
CHPS deterministic forecasting
and so on…
Traditional flow forecasting
WFO+HPC met. forecast
Hydrologic simulation and flood
forecasting
Long-lead forecasting
Coordination with USDA/NRCS
Meteorological analysis
Quality control of station dataQuality control of radar and radar parameters
Manual/subjective elements (examples)
WFO forecast itself (though based on models)RFC merge with HPC forecast (similar to WFO)
Sac./Snow17 model states and parametersBias-adjustment relative to obs. flowInput forcings (2nd chance at adjustment)
Model states as adjusted for flood forecastingChoice of models (statistical / ESP )Blend of modelsChoice of meteorology: QPF, ENSO, None?
Merging with NRCS statistical forecastsmeans, confidence limits (“10-90s”)
Dai
ly F
lood
For
ecas
t
Wat
er S
uppl
yF
orec
ast
Process
Example
Libby Dam backs water into Canada
Provides flood control for Bonner’s Ferry
Also plays role in larger Columbia River water management (power, ag, navigation, fish, etc.)
Situation Lake Koocanusa is
full, causing flood impacts
Bonner’s Ferry is already flooding
Forecasted inflows are well above current damaging outflow
Kootenai R. Flooding
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Examples: Kootenai R. Flooding
River forecast operations Run watershed models to forecast watershed inflows -- relatively unimpaired place but
inflows and input data are not well measured.
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Examples: Kootenai R. FloodingWater management operations NWRFC calls USACE, USACE logs into RFC system to test regulation options, calls
Canadian authorities, negotiates a rise in Lake, enters outflows from the project The process takes an hour to most of the day, depending on the juncture
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Examples: Kootenai R. FloodingRiver forecast operations NWRFC simulates the resulting flows at Bonners’ Ferry, which is well over flood stage,
and moves on downstream. Forecast errors: hydrologic + inaccurate operation projections + flow obs errors
Forecasting resources have greatly expanded
1980s construct:• parsimonious watershed models
run on single PCs (or card decks + VAX)
• phone/mail transmission of data, forecast output bulletins
• manual synoptic weather analyses, rudimentary NWP
Since then:• supercomputing, desktop clusters• web data services and connectivity• GIS • high-res satellite DEMs & land cover• real-time remote sensing• dozens of complex land surface
schemes at fine scales• ESM at large scales• dozens of (better) NWP outputs &
ensembles
Since the late 1990s, this cornucopia of new resources has been applied toward increasingly extensive hydrologic analyses at increasingly fine scales.
grey = inactive
New resources create new capabilities
Personal example (circa 2008)• mean annual flow and gross small
hydropower potential• every 270 m• globally
global precip timeseries & analyses (TMPA+GPCC)
global hydrologic simulations (VIC)
global, yet fine resolution terrain analyses for routing and hydraulic features (SRTM)
ex. in Pakistan
A myriad of new ingredients exist for multi-scale hydrologic analysis (and forecasting)
Challenges in Short Range Flow Forecasting
First, what is not a challenge? wiring together data, models, websites- no shortage of datasets, models, computing resources, software- this is only phase 1
flow, Mexico flow, S. America
Workflow/Data Management Platform
real-time operations
Phase 1: Making a Auto Forecasting System Work Models, Data, Systems
Spinup Forcings
Forecast Forcings
Hydro/Other Models
Hydro/Other Observations
Streamflow & Other Outputs Products, Website
adjustmentsHistorical Forcings?
continental/fine-scale flood forecasting systems
NCAR/Gochis
EFAS
Scottish Flood
Forecasting Service
NSSL
Short Range Flow Forecasting ObservationsThe groups beyond ‘phase 1’ with ‘new forecasting’ are wrestling with many of the traditional challenges, e.g.,
- uncertain initial conditions (watershed moisture and energy, amt & distribution- depends on quality of spinup forcing, the model, flow obs, regulation info
- inconsistent real-time and retrospective forcings and analysis- uncertain future forcings (quality of met forecasts)
MOD name Count Descriptionaescchng 190 Snow areal extent changechgblend 578 Blend simulation with last observationignorets 8 Throw out timeseries input datamfc 133 Melt factor correction (change melt rate)sacco 529 Soil moisture content changessarreg 921 Reservoir regulation changetschng 8554 Alter a timeseries (ie, redraw a flow
forecast or obs)tschng_MAP 2136 Change precipitation forcings (obs,
forecast)tschng_MAT 461 Change temperature forcingsuadj 7 Change threshold for rain on snow to
cause melttotal/watershed/day
~1.25 For ~360 watersheds, for ~30 days
NWRFC Mods for 1 Month
parameter issues
input data issues
regulation issues
and so on…
Sophisticated systems are not immune to the same broad range of uncertainty
system by Amy Sansone, Matt Wiley, 3TIER
Workflow/Data Management Platform
hindcasting, ensembles (uncertainty), benchmarking, real-time operations
Reducing error through supporting methods
Historical Forcings
Phase 2: Making a Auto Forecasting System Work Well Methods & Tradeoffs
Spinup Forcings
Forecastand
Hindcast Forcings
Appropriate Hydro/Other
Models
Hydro/OtherObservations
Streamflow & Other Outputs
Products, Website
verification
post-processing,
forecast calibration
objective DA
(regional)parameter estimation
calibrated downscaling
feedback into component
improvements
auto QC
http://www.hepex.org/Since 2004, HEPEX has highlighted progress in key methods to make new systems work well
Flow Forecasting Catch-22
The leading groups recognize a key challenge in system requirements:- as the model resolution (time/space) becomes finer, the uncertainty at
model scales increases … but the ability to characterize uncertainty falls
system scale/complexity
ability to assess uncertainty
epistemic uncertainty
lowlow
high
high
low complexitycan run ensembles,calibrate, hindcast, post-process,run many thousands of variations
high complexitylimited calibration,
ensembles, hindcasting, verificationrun tens of variations