1 seasonal streamflow forecasting – current practice, challenges and opportunities hydrologic...

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Seasonal Streamflow Forecasting – Current Practice, Challenges and Opportunities

Hydrologic Sciences & Water Resources Engineering Seminar SeriesBoulder, CO, Sep 30, 2014

Andy WoodNCAR Research Applications Laboratory

Boulder, Colorado

Outline

Operational Forecasting – western USobjectivesprocesses

Predictability ResearchOpportunities for Improvement

The Arid Lands

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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

Seasonal streamflow prediction is critical

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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

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:Predictor variables must make senseChallenge when few observation sites exist within river basinChallenge when measurement sites are relatively youngFall & Spring precipitation is frequently used (why?)

Source: NRCS

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

Trial Lake SNOTEL

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

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

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

Sno

w

Time

Apr 1 forecast

FutureApr 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

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

Flo

w

Time

Future

Feb 1

Past

IC signal

Wx/Climate signal

e.g., Wood & Lettenmaier, 2008 GRL

e.g., storyline:• big storm in Feb• very wet April• cool May/June

IC

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) Other approaches may also have been tried: lower temperatures, raise soil moisture, etc.

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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.

A manual, subjective process

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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 elements

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

Forecasts and Water Management

Average contribution to Lake Powell Apr-Jul inflow:

Green River 34%Colorado River 50%San Juan River 13%

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ReclamationMidterm-Probabilistic Model

StakeholderAllocations

Graphic from Dr. Katrina Grantz, Bureau of Reclamation

CBRFC ensemble flow forecasts for Reclamation water management

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Forecast impacts, e.g.:if WY12 is wet enough, Lake Mead hits surplus levels, MWD gets water ~ $150M

Water supply forecasting

Runoff forecast for this period

Example flow volume prediction

increasing watershed moisture reduces prediction spread

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

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




‘ST Doc’ user needs

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Other Categories

What are the sources of seasonal streamflow predictability?

A western US water cyclew

ater

dep

th

water year

Runoff = Precip – ET – ΔSM - ΔSWE

October SeptemberApril

SM

Evap Runoff

SWE

Precip

Seasonal Variation in Predictor Importance

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

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

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

Assessing the sources of flow forecast skill

w=0.5w=0.5

http://www.ral.ucar.edu/staff/wood/weights/

http://www.ral.ucar.edu/staff/wood/weights/

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

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

Flow Forecast Skill SensitivitiesA 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

Flow 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

opportunities for improvement

Improved IHCs through modeling

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Snow Distribution – corroborates presence

of lower elevation snow in 2010 at start

of melt

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

2010 June 1

2006 June 1

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SNOW17 SWE

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Distributed SWE(eg SNODAS, use of MODIS SCA)

Improved SCFs through 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)

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Short-Range

Medium-Range

Merging Joining Blending

Input forcing uncertainty via generated ensembles

Under development / MMEFS

Planned

Long-Range

Calibrated and seamless short- to long-termforcing input ensembles

Day 1-5 ensembles

Day 1-14 ensembles

Day 15+ ensembles

Day 15+ ensembles

HPC/RFC single-valued forecast

GFS/GEFS ens. mean

CFS/CFSv2forecast

Other ensembles(SREF, NAEFS…)

Other info(climate indices…)

April 28, 2010 43

Precipitation Skill, CD 48

Satish Regonda, OHD

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• 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

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

• Is

April 1 Forecast of April-July streamflow volume, North Fork Gunnison (SOMC2)

90th

50th 10th

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

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Questions?

Dr. Andy Wood

CBRFC Development and Operations

Questions?

http://en.wikipedia.org/wiki/Lake_Powell

[email protected]

MPEMulti-Precip Estimator

MMMountain Mapper Daily QC

End-to-End Operational Forecast Process

Data Processing• Model simulations• Hydrologic analysis• Hydraulic analysis• Bias & error adjustments• Regulation Integration• Collaboration

• Forecast generation• Quality assurance• Dissemination• Web services• Coordination & collaboration• Basin monitoring & review

Modeling Dissemination• Data collection• collation• Data assimilation• Quality control• Forcing construction (HAS)• Forecast Review

GFEGridded Forecast Editor

Observed forcingsPrecipitation + 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 RFCInteractive methodsOutput qualified for model use


NationalModel

Guidance

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

GFEGridded Forecast Editor

National Center SupportForecast Forcings

WFO Weather Forecast Office

RFC River Forecast Center


End-to-End Operational Forecast Process

Data Assimilation• Data collation• Data assimilation• Quality control• Forcing construction (HAS)• Forecast Review

Modeling Dissemination• Model simulations• Hydrologic analysis• Hydraulic analysis• Bias & error adjustments• Regulation Integration• Collaboration

• Forecast generation• Quality assurance• Dissemination• Web services• Coordination & collaboration• Basin monitoring & review

Sacramento Soil MoistureAccounting Model

Data Assimilation Dissemination

Observed Hydrology

Simulation and Analysis

NWS: Streamflow Forecasting is an iterative & interactive processAnalysis & Assessment

Bias adjustments and error corrections Also: SNOW-17 Temperature index model for simulating snowpack accumulation and melt


Historical Simulation

Q

SWESM

Historical Data ForecastsPast Future

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)

Assessing regional variations

– Based on ~420 watersheds, 30 year hindcasts

Motivation Development & Performance Predictability SummaryEnsemble Forcing


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?


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Atmospheric Pre-Processor: calibrationOff line, model joint distribution between single-valued forecast and

verifying observation for each lead time

X

Y

Forecast

Obs

erve

d

0

Joint distributionSample Space

PDF of Observed PDF of Obs. STD Normal

NQT

Schaake et al. (2007), Wu et al. (2011)

ForecastO

bser

ved

Joint distributionModel Space

X

YCorrelation (X,Y)

Archive of observed-forecast pairs

PDF of Forecast PDF of Fcst STD Normal

NQT

NQT: Normal Quantile Transform

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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

Obs

erve

d

Joint distributionModel Space

xfcst X

Y

Given single-valued forecast,obtain conditional distribution

xi xn

Conditional distribution given xfcst

Ensemble forecast

Prob

abili

ty

0

1

x1

Schaake et al. (2007), Wu et al. (2011)

…

Ensemble members for that

particular time step

x1

xn

NQT-1

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 theregulation 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 ofprediction skill. The talk will also highlight strengths and weaknesses of the current practice and suggest potential methodological opportunities for WSF improvement.

1 seasonal streamflow forecasting – current practice, challenges and opportunities hydrologic...

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