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Water Systems RetreatBoulder, January 14, 2015

Observations on short range streamflow forecasting

Andy Wood NCAR Research Applications LaboratoryHydrometeorological Applications Program

Outline

• Traditional flood forecasting• New Opportunities• Strategies for development

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”)

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Process

Forecasting viewed as a ‘human intelligence task’

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example

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)

Enabling hydrologic analysis across scalesex. in Colombia

flow, Mexico flow, S. America

User-oriented products can be derived … ex. in Colombia, fine scale national domain

… and disseminated via slick websites

mockup

Flow Duration Curve

But are the results any good?

Major cha• mean annual flow

(what could possibly go wrong?)

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

Questions?