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NCAR Networking DayBoulder, April 17, 2015

Advancing streamflow forecast science to support water management

Andy Wood NCAR Research Applications LaboratoryHydrometeorological Applications Program

Acknowledgements & Website

NCAR• Martyn Clark• Andy NewmanUniversity of Washington• Bart Nijssen

http://www.ral.ucar.edu/projects/hap/flowpredict/

Reclamation• Levi BrekkeUS Army Corps of Engineers• Jeff Arnold

Themes

• Motivation• An Evolution of Streamflow Forecasting Approaches• New Science Begets A New Challenge

Key reports

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The Short Term user needs targeted forecasting

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Other Categories

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

Forecastprecip / temp

Wea

ther

and

Clim

ate

For

ecas

ts RiverForecastModelingSystem

parameters

Observed Data

Analysis &Quality Control

Calibration

modelguidance

Hydrologic Model Analysis

hydrologicexpertise &judgment

OutputsGraphics

River Forecasts

Decisions

Rules, values, other factors, politics

NWS River Forecast Process

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 Interface

Calibration Climatology

Hydrologist render a Run-Timemodification to the SACSMA Modeland increases the lower zone primary and supplemental states

CHPS

Slide by H. Opitz, NWRFC

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Examples: Nooksack RTypical 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

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Examples: Nooksack ROne Solution -- double the snowpack (WECHNG modification)

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Examples: Nooksack RThe resulting simulation is better, hence the forecast is more confident Flows stay elevated, have diurnal signal of continued melt.

Paradigm Limitations

For major events• it is difficult to assess forecast

system skill

• influence of forecasters cannot be separated from influence of data, models methods

• forecast service scales with FTEs, not computers

For the development process• new science that requires

reproducibility cannot be integrated

• the value of science infusion is hard to quantify Gochis et al, BAMS, 2015

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

global meteorological and climate datasets, eg, precip timeseries & analyses (TMPA+GPCC)

global hydrologic modeling simulation (VIC)

global, yet fine resolution land cover and terrain analyses for routing and hydraulic features (SRTM)

eg. channel slope 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

Workflow/Data Management Platform

hindcasting, ensembles (uncertainty), benchmarking, real-time operations

Hydrologic Prediction Science now has a framework

Historical Forcings

We are now automating, to the extent possible, what forecasters did before

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

Continental-domain fine-scale flood forecasting systems have arrived

NCAR/Gochis

EFAS

Scottish Flood

Forecasting Service

NSSL

‘End Member’ Forecast System Approaches

Operational, 1980s => • Provide tailored, but limited

(mostly deterministic) forecasts that are inputs to water management

• Simple conceptual models that can be adjusted manually, in real-time

• Heavy use of model calibration• Reliance on human expertise at the

model/data system level.• Non-reproducible, non-scalable

forecast process• Run on small number of

workstations

Research/experimental 2000s =>• Provide non-tailored, centrally

produced forecasts, typically in form of percentile or frequency analyses

• ‘Physically-based’ high-dimensional models

• Little or no calibration• Automated forecast process,

reproducible• Ensemble outputs desired, leverage

supercomputing

grid2grid

End Member Forecast System Philosophies

Operational, 1980s => • “The models, data and systems will

always be inadequate, thus human expertise is needed to fix performance on the fly”

• “If the decisions using your model outputs require a certain answer, the models must be simple so that they can be adjusted to provide that answer”

Research/experimental 2000s =>• “The superior physics in new models

and datasets will yield good quality results”

• “Most problems can be fixed with higher resolution and even more detailed process representation”

• “Research-grade results should be operationally useful”

grid2grid

But are we moving too fast?

Major cha• mean annual flow

(what could possibly go wrong?)

Flow Forecasting Catch-22

There is a key tradeoff in forecast system design- as the model resolution (time/space) becomes finer, the

uncertainty at the model scales increases … but the ability to characterize uncertainty falls

system scale/complexity

ability to assess uncertainty

localuncertainty

lowlow

high

high

low complexitycan run ensembles,calibrate, hindcast, post-process,run many thousands of variations

hyper complexitycannot calibrate,

no ensembles, hindcasting, or full verificationcan only run tens of variations

Short Range Flow Forecasting ObservationsThe ‘new forecasting’ in fact encounters many of the traditional paradigm 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

water regulation issues

and so on…

model issues

Sophisticated systems are not immune to the same broad range of uncertainty

system by Amy Sansone, Matt Wiley, 3TIER

New Paradigm Limitations

For major events• it is difficult to assess forecast system skill (also cannot be run for long

enough to see track record)

• the complex system may be off track for reasons that cannot be easily detected or corrected (science, data and technology gap)

For the development process• options may now be limited due to computational requirements

• a cumbersome system may make it hard to test new variations andinfuse new science

For extreme events, we likelystill can’t diagnose forecasterrors and describe forecast skill!

R2O/O2R – a trek requiring tradeoffs

Research

Operations

How can I carry my hyper-resolution

ESM data across this valley?

Still haven’t seen anything better over on

that far side … why cross over?

Two water agencies (USACE and Reclamation) are supporting NCAR (A. Wood) to assess and demonstrate (in

real time) the adequacy of new hydrologic prediction science for operational forecasting

R2O/O2R – elements of the path forward

Research

Operations

• Flexible, intermediate-complexity approaches are needed• Resolution choices must support key methods in hydrological prediction science• Development efforts must integrate people and knowledge from both R and O• International communities of practice can provide insight on choices

Thank You

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andywood@ucar.edu