EGU2012-12951

Updating of states in operational hydrological models

Oddbjørn Bruland (Statkraft Energy)

S. Kolberg, K. Engeland, L. Tøfte (SINTEF) A. S. Gragne and K. Alfredsen (Norwegian University of Technology and Science) G. Liston (University of Colorado)

Statkraft….

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EGU 2012

… a European leader in renewable energy

Hydropower and Hydrology

How important is hydrology to a hydropower producer?

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Importance….

NO1

64 HBV-models within the NO1 area

Precipitation data

98 Precipitation gaugeswithin the NO1 area- provide input to the HBV-models

Precipitation at Vågsli; summer 2008

• 130% of normal precipitation

• is input to 18 HBV-models

• Surrounding gauges showed 80-90% of normal

0

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21.10

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10.12

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29.01

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19.03

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08.05

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27.06

.08

16.08

.08

En

erg

y (G

Wh

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Snow Accumulation

Normal 1931-2006

Normal 1980-2000

14000 GWh

Snow surveys

Snow storage NO1Week 36/2007 – 16/2008 (as simulated by HBV models)

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How did we act?

Forecast:

Very high risk of flooding

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How did we act?

Forecast:

Very high risk of flooding

Price forecasts indicated price=0

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Week no. /2008

EU

R/M

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Mean 0% 10% 25% 75% 90% 100%

How did we act?

Forecast:

Very high risk of flooding

Price forecasts indicated price=0

=> High production at low prices to reduce risk of flooding

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25 %

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75 %

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Byrt

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Haukeli

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Week no. /2008

EU

R/M

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Mean 0% 10% 25% 75% 90% 100%

-120,0

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Summary – economical

Spill from reservoir

Estimated loss of income: ≈ 270 mill NOK (i.e. 45 mUSD or 30 mEUR)

… in Statkraft alone..

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Observations and Met ForecastsEstablished models for hydrological state and forecasting

Hydropower and Hydrology

How do the Hydropower companies apply hydrological information?

For short term production planningOptimizing use of water at the horizon of 5 to 10 daysDaily updating and replanning

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How do the Hydropower companies apply hydrological information?

For short term production planningOptimizing use of water at the horizon of 5 to 10 daysDaily updating and replanning

For Long term planning of water disposalOptimizing the use of water at a seasonal horizonWeekly updating and replanning

Hydropower and Hydrology

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Short vs Long Term planning

Short termProfile and timing is more important than volume

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Short vs Long Term planning

Long termVolume is more important than profile and timing

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Short vs Long Term planning

Short termProfile and timing is more important than volume

Long termVolume is more important than profile and timing

Different Updating approach

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Updating for Short term forecast

Timing, level and tendency in focusShort time storages must be right

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Updating for Long term forecast

Volume and duration in focusSnowstorage is important, relative to normalClimate adjusted climatology

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How do we approach these issues

The project was established in 2009 as a cooperation between all larger hydropower producers in Norway, SINTEF, NTNU, Powel and Liston and the Norwegian Research Council.

With the goal to develop, improve and implement model updating methods and techniques in operational models

side 27side 27 Fordelt hydrologiskmodell

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Project Outline

Developing and implementing updating methods based on available sources of information

Runoff, snow cover, snow measurements

Combined withObservation uncertaintyModel uncertaintyAnd a combination of these

”An open source distributed

model system”

What is done…

Using snow distribution model combined with snowobservations – Liston

Using Bayesian statistics weighing model state against satelite observation - Sintef

Using particle filtering to benefit from recent information – Sintef

Using errordetection to detect and explain and correct the model in spesific situations - NTNU

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EGU 2012

Snow storage updating 1

Liston’s SnowTran3D snow distribution model is implemented in the model system

Using information from snowcourses to update snowdistribution in simulated snowdistribution from Listons model

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Data averaged over the snow

observation mask.

Precipitation correction factor ~

0.75.

614 mm SWE on 21 April 2008

Snow storage updating 2

Based on modeled snowdistribution, an predefined (apriori) snowdepletion curve, modelled snow melt progress and snow cover information from satelite images

Methodology based on Bayesian statistics by Sjur Kolberg, SINTEF, has shown promising results and is a candidate for implementation in the model system.

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0 200 400

0 0.5 1 1.50 250 500

E[m]

SD[m]

E[cv]

SD[cv]

E[y0]

SD[y0]

E[λ]

SD[λ]

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0.2

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m,cv,y0,λ]E [

Prior

Posterior

0 0.2 0.4

Snow storage updating 2

Bayesian updating of the Snow Depletion Curve

(Sjur Kolberg, 2010)

More images improves the estimate of (m, cv, y0, {λ(t)})

ie uncertainty decreases as snowcover depletes

Calibration based on previous years also improves estimates

R2 from 0.63 - 0.68 (average for 22 catchments using same parametersets)

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Snow storage updating 2

y = 0.69x + 184.50

R2 = 0.82

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Sim

y = 1.04x + 19.21

R2 = 0.92

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Sim

More images improves the estimate of (m, cv, y0, {λ(t)})

ie uncertainty decreases as snowcover depletes

Calibration based on previous years also improves estimates

R2 from 0.63 - 0.68 (average for 22 catchments using same parametersets)

Snow storage updating 2

Snow storage updating 3

Early snowfalls are sensitive to threshold temperatures for snow – rain, and melt – freeze.

Wrong values gives simulated ≠ observed twice as both the accumulation and timing of melting get wrong

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Snow storage updating 3

Develop methodology for classification of hydrographs error

Develope methodology for model updating based on these events.

Defined as a main subject in PhD

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Figure 2: Definition of types of error between measured (—) and simulated (---) hydrographs [Source: adopted from Serban and Askew (1991)]

(a) amplitude errors (b) phase errors (c) shape errors

Description of simulation errors

Algorithm for categorization of errors

Error type Subclass Condition

Amplitude (A)

A-p precipitation; m≠1; q≈0; T<<0Oc or T>>0Oc

A-t precipitation; m≠1; q≈0; T≠0Oc

A-pt precipitation; m≠1; q≈0; T≈0Oc

A-m m≠1; q≈0

Phase (P)

P-p precipitation; m≈1; q≠0; T<<0Oc or T>>0Oc

P-t precipitation; m≈1; q≠0; T≠0Oc

P-pt precipitation; m≈1; q≠0; T≈0Oc

P-m m≈1; q≠0

Shape    

Observed data (O) O No explanation

Combination (AP)

AP-p precipitation; m≠1; q≠0; T≠0Oc

AP-t precipitation; m≠1; q≠0; T<<0Oc or T>>0Oc

AP-pt precipitation; m≠1; q≠0; T≈0Oc

Qsim=m*Qobs+q

• Data: hourly simulation of Votna hydropower plant; hydrologic year 1999.• Mismatches totally compounded to 195 local errors; 51 significant.

Error groups identified (overestimation, exact and underestimated).

Preliminary results

Error type Subclass Number Condition

Amplitude (A)

A-p 5 precipitation; m≠1; q≈0; T<<0Oc or T>>0OcA-t 10 precipitation; m≠1; q≈0; T≠0OcA-pt 1 precipitation; m≠1; q≈0; T≈0OcA-m - m≠1; q≈0

Phase (P)

P-p - precipitation; m≈1; q≠0; T<<0Oc or T>>0OcP-t 2 precipitation; m≈1; q≠0; T≠0OcP-pt - precipitation; m≈1; q≠0; T≈0OcP-m - m≈1; q≠0

Shape      Observed data (O) 0 10 No explanation

Combination (AP)AP-p - precipitation; m≠1; q≠0; T≠0OcAP-t - precipitation; m≠1; q≠0; T<<0Oc or T>>0Oc

AP-pt 23 precipitation; m≠1; q≠0; T≈0Oc

Preliminary results

Parameter uncertainty

Parameter uncertainty used in model updating approaches

Autocalibration gives a huge number of parameter sets that objectively are equally good (R2 and waterbalance)But behave significantly different for the same event

For forecasting purpose the model best adapted to the actual situation is assumed to give the best forecast

DYNIA and DREAM are calibration routines relevant for this purpose

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DYNIA

Dynamic Identifiability Analysis (Wagener et. al. 2003)

Applied to a catchment in soutwest norwayTX (snow/rain threshold)

– high sensitivity except from July-Oct, two preferred value ranges

K0 , K1 and K2 had all two preferred value ranges

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Saurdal

Stølsdal

Lauvastøl

OsaliSaurdal

Stølsdal

Lauvastøl

Osali

k0

k1

k2

Q=f(k0,k1,k2,h)

k0

Osali

DREAM

Dream (Vrugt et al, 2008) is a Markov Chain Monte Carlo (MCMC) optimizing algorithm that suits the purpose of detecting the model sensitivity to each parameters value-range

Gives parameters uncertainty/influenceThus also which parameters to test in an updating sequence

Gives apriori distribution of parameters for a calibrated region

Ensemble of models used for weighted updating

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DREAM

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f ieldcapD

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From Dream to ParticleFiltering

Dream gives a ensemble of models with equal likelihood

But also heavily autocorrolated (results)And thus a challange to extract significant information

A likelihood function was used to extract those giving significant information and thus reduce the number of observations.

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Likelihood function

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Likelihood function

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Particle Filtering

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ParticlesFilter(Runoff, windowsize, weigth)

Re-weigthed particles

Apriori for parameters

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Uncertain input(P,T)

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Particle Filtering

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Osali original

Date

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mflo

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2007.0 2007.2 2007.4 2007.6 2007.8 2008.0

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Filtered runoff – 95% confidence interval

Particle Filtering

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Resultats – filtering on snow

Osali original

Date

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e (

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2007.0 2007.2 2007.4 2007.6 2007.8 2008.0

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2007.0 2007.2 2007.4 2007.6 2007.8 2008.0

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Particle Filtering

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Reff

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Lauvastøl

CRPS

Beste partikkel

Aprio fordeling

Filtrert

Filtrert + usikker temp

Filtrert + usikker temp. og nedb.

Further Work

Improve local optimization by allowing parameters to vary over the domain

And still ensure consistent behaviorDevelop methodology to find and use the criteria for the variability

A new response routine is developed/tested in order to both simplify calibrationto reduce model complexityto get more significant correlation parameters/topographyimprove updating

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Contact info

SINTEFSjur Kolberg : sjur.kolberg@sintef.noKolbjørn Engeland: Kolbjorn.Engeland@sintef.noLena Tøfte: Lena.S.Tofte@sintef.no

NTNUAshenafi Seifu Gragne: ashenafi.gragne@ntnu.no

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