1 Introduction G. Thirel and V. Andréassian IAHS Hw15 22 July 2013.
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Transcript of 1 Introduction G. Thirel and V. Andréassian IAHS Hw15 22 July 2013.
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Introduction
G. Thirel and V. Andréassian
IAHS Hw15 22 July 2013
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Modelling is like painting
Catchments are hyper-dynamic systems: they change continuously
For the sake of our ‘portrating’, we need to make simplifying assumptions
The risk: that the simplifying hypotheses cause a catchment non-stationarity artefact
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Non stationarity makes the life of hydrologists miserable
Identifying parameters is already not an easy task within the stationarity hypothesis…
… it is much worse when changes which we have neglected turn out to have a significant impact on the calibration process
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Goal of this workshop
To provide a factual diagnosis: describe / document the problem
based on common catchments
also on other datasets
Can we agree on the problem? On how to assess it, numerically and graphically?
Investigate solutions
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Workshop preparation group
Guillaume Thirel
Valérie Borrell-Estupina
Sandra Ardoin-Bardin
Julien Lerat
Olga Semenova
Francesco Laio
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Outline of this presentation
• The dataset
• The calibration and evaluation protocol
• Some results
IAHS Hw15 22 July 2013
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Outline of this presentation
The dataset
The calibration and evaluation protocol
Some results
IAHS Hw15 22 July 2013
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Need for a dedicated websiteFOR DEFINING THE COMMON FRAMEWORK AND FOR PROVIDING THE COMMON DATABASE
Website address: http://non-stationarities.irstea.fr/
• Description of the dataset for each basin• Description of the calibration and evaluation protocol
Possibility to download the data (password protected)
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The dataset14 RIVER BASINS SHOWING NON-STATIONARITIES
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The dataset14 RIVER BASINS SHOWING NON-STATIONARITIES
Basins sizes from 0.2 km² to 100,000km²
Several types of non-stationarities encountered: • Temperature increase• Precipitation change or high variability• Urbanization• Forest cover modification
Period: variable according to the considered basin
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Which data?DATA COLLECTED FROM MANY PARTNERS
Variables: precipitation (P), temperature (T), potential evapotranspiration (PE), discharge (Q).
What we provided: • basin-wide aggregated values of P, T and PE • Q at the outlet • (repartition of altitude within the basin if available)
Time step: daily
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Temperature increaseTHE KAMP (622 KM²), ALLIER (2267 KM²), DURANCE (2170 KM²) AND GARONNE (9980 KM²) RIVERS
All located in Europe, impacted by snowmelt
Allier Kamp
Durance Garonne
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The case of the Kamp RiverVERY LARGE FLOODS IN 2002
P
QIAHS Hw15
22 July 2013
(Komma et al., 2007; Blöschl et al., 2008; Reszler et al., 2008)
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The case of the Allier RiverCONSTRUCTION OF A DAM IN 1983 FOR SUSTAINING LOW FLOWS
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Impact on low flows
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Precipitation change or high variabilityTHE AXE CREEK (237 KM²) AND THE WIMMERA RIVER (2000 KM²)
Millenium drought in Australia (1997-2008)
Wimmera River
Axe Creek
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Q
Q
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Decrease in rainfall and deep water recharge between before 1970 and after 1971
Precipitation change or high variabilityTHE BANI RIVER (100,000 KM²)
P Q
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Precipitation change or high variabilityTHE GILBERT AND FLINDERS RIVERS (AROUND 1900 KM²)
Arid catchments under cyclonic heavy rainfall influence.
Major flood in 2002.
The Flinders RiverIAHS Hw15
22 July 2013
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UrbanizationTHE FERSON (134 KM²) AND BLACKBERRY CREEKS (182 KM²)
Located in the USA
The urbanization modifies the hydrological response
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
0
10
20
30
40
50
60
70
Ferson Creek
Blackberry Creek
Percentage of urbanization
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Forest cover modificationTHE FERNOW (0,2 KM²) AND MÖRRUMSÅN (97 KM²) RIVERS AND THE REAL COLLOBRIER (1,4 KM²)
The Fernow Experimental watershed: forest cut of the lower part of the basin, then forest cut of the upper part of the basin, then plantation of firtrees.
The Mörrumsån River: a severe storm (Gudrun), led to loss of forest in January 2005.
The Real Collobrier: forest fire in August 1990.
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The dataset
River Country Area Period Change Providers
Fernow River USA 0.2km² 1956-2009 Forest USDA Forest Service
Real Collobrier France 1.4km² 1966-2006 Forest Irstea & Météo-France
Mörrumsan River Sweden 97km² 1981-2010 Forest SMHI
Ferson Creek USA 134km² 1980-2011 Urbanization USGS & DayMet
Blackberry Creek USA 182km² 1980-2011 Urbanization
Axe Creek Australia 237km² 1970-2011 P decrease Victoria data Warehouse
Kamp River Austria 622km² 1976-2008 T increase TU Wien, UFZ
Gilbert River Australia 1907km² 1963-1988 P variability Queensland Government
Flinders River Australia 1912km² 1967-2011 P variability
Wimmera River Australia 2000km² 1960-2009 P decrease Victoria data Warehouse
Durance River France 2170km² 1901-2010 T increase EDF
Allier River France 2267km² 1958-2008 T increase Météo-France & Banque HydroGaronne River France 9980km² 1958-2008 T increase
Bani River W Africa 103390km² 1959-1990 P decrease DMM & DNH
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Outline of this presentation
• The dataset
• The calibration and evaluation protocol
• Some results
IAHS Hw15 22 July 2013
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The protocolA COMMON CALIBRATION AND EVALUATION FRAMEWORK
• Common calibration / evaluation periods
• Common minimum set of metrics
• Possibility that I produce a set of metrics and plots for the modellers (providing that they sent to me their simulations)
• Modellers are free to do more!
IAHS Hw15 22 July 2013
Complete periodWarm-up
Time
P1 P4P3P2 P5
Change
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The protocolLEVEL 1: THE BEGINNER LEVEL
• Calibration has to be done on the “Complete period” or the model does not need calibration
• Models are run on the “Complete period”
• Evaluation is done on the “Complete period” + P1 to P5
IAHS Hw15 22 July 2013
Complete periodWarm-up
Time
P1 P4P3P2 P5
Change
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The protocolLEVEL 2: THE NORMAL LEVEL
• Calibration has to be done on each pre-defined sub-period P1 to P5
• Models are run on the “Complete period” for each calibration
• Evaluation is done on the “Complete period” + P1 to P5
IAHS Hw15 22 July 2013
Complete periodWarm-up
Time
P1 P4P3P2 P5
Change
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The protocolLEVEL 3: THE EXPERT LEVEL
The modellers found that their model failed at level 2 to deal with non-stationarities or could do better.
They want to try to solve this issue, or at least to try to test solutions that could solve this issue.
-> all solutions are allowed.
Failing is fine, since it allows to discard a solution.
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The protocolTHE METRICS
Participants were asked to produce the following statistics on each sub-period: • NSE and NSE on low flows (i.e. using 1/Q+ε instead of Q) • Bias (Qsim/Qobs)• Discharge quantiles: Q95, Q85, Q15 and Q05• Frequency of low flows (i.e. when Q<5% of mean Qobs)
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The protocolTHE METRICS
IAHS Hw15 22 July 2013
Participants were asked to produce the following statistics on each sub-period: • NSE and NSE on low flows (i.e. using 1/Q+ε instead of Q) + their
decomposition• Bias (Qsim/Qobs)• Discharge quantiles: Q95, Q85, Q15 and Q05• Frequency of low flows (i.e. when Q<5% of mean Qobs)• KGE and its decomposition• Nash and bias on sliding windows• Flow regime• Ranked discharges
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The protocolTHE GRAPHS
Used for the bias, the Nash criteria, the KGE, and their decompositions
For the quantiles and the frequency of low flows, the observed value is added
Two different ways of showing the same thingIAHS Hw15 22 July 2013
The criterion value
Six curves: one for each calibration
Six values: one for each evaluation period
The criterion value
Six columns: one for each calibration
Six lines: one for each evaluation period
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The protocolTHE GRAPHS
Extension of some graphs for a 1-year frequency evaluation
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The protocolTHE GRAPHS
The discharges regimes and the ranked discharges
-> one graph for each evaluation period
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The protocolTHE COMPARISONS BETWEEN MODELS
IAHS Hw15 22 July 2013
Not values, but differences
between the criteria of two
models
Blue values indicate that this model has a higher criterion
Red values indicate that this model has a higher criterion
Blue values indicate that this model has a higher criterion
Red values indicate that this model has a higher criterion
A column compares a single calibration on each evaluation period
A line compares each calibration on a single evaluation period
Mod 1 Mod 2
Mod 1
Mod 2
Over-estimation from model on top
Δ
Under-estimation from the model on top
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Outline of this presentation
• The dataset
• The calibration and evaluation protocol
• Some results
IAHS Hw15 22 July 2013
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List of models used for the workshop
1k-DHM, AWBM, CLSM, COSERO, ECOMAG, GARDENIA, GR4J, GR5J, HBV, HYDROGEOIS, HYPE, HyMod, IHACRES, MISO, MORDOR, MORDOR6, SAFRAN-ISBA-MODCOU, SimHyd, SpringSim, TOPMODEL, Xinanjiang,…
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Brief presentation of some results from people who participated but could not come
GARDENIA: D. Thiéry, BRGM, France
COSERO: H. Kling, Austria
SpringSim: A. Ramchurn, Australia
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Lumped model with slow compo-
nents reservoirs
Can take into account aquifer
level measurements (not used
here)
4 to 6 parameters
Calibration metrics:
MSE(sqrt(Q))+5%(Qsim-Qobs)
Ran on 11 basins
Ref: Thiéry, D. (2010) Reservoir Models in Hydrogeology, in Mathematical Models, Volume 2 (ed J.-M. Tanguy), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118557853.ch13
GARDENIAUSED BY: Dominique Thiery ([email protected], BRGM, France)
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Soil storage
Vadose zone
Groundwater tank
Effective rainfall
Percolation (Recharge)
Rapid flow
Grounwater flow
Rain Snow
Evapotranspiration (PET)
Aquifer level
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Low bias on calibration periods, but high bias on contrasted periods.
The calibration on the complete period allows an « intermediary » solution but does not prevent from biased simulations.
Calibrations on dryer periods gave higher soil reservoir capacities: the model tries to allow more evapotranspiration for compensating the lower Q.
GARDENIAUSED BY: Dominique Thiery ([email protected], BRGM, France)
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Bani
Regime change from
1971
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High bias on contrasted periods.
Calibrations on dryer periods gave higher soil reservoir capacities: try of the model to allow more evapotranspiration for compensating the lower Q.
GARDENIAUSED BY: Dominique Thiery ([email protected], BRGM, France)
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Wimmera
MilleniumDrought
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COSERO
Continuous, semi-distributed rainfall-runoff model.
• Snow processes• Soil moisture accounting (HBV-type)• Surface-flow, inter-flow, base-flow
(linear reservoirs)
Nachtnebel et al. (1993)
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USED BY: Harald Kling ([email protected], Pöyry Energy GmbH, Austria)
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COSERO
Objective function: KGE on Q.
Ran on 11 basins (i.e. all except US basins).
Dam module (affects low flows) added for the Allier River.Riparian zone (affects evaporation) added for Australian rivers.
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USED BY: Harald Kling ([email protected], Pöyry Energy GmbH, Austria)
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COSERO
Allier
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USED BY: Harald Kling ([email protected], Pöyry Energy GmbH, Austria)
New dam built in 1983
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COSEROUSED BY: Harald Kling ([email protected], Pöyry Energy GmbH, Austria)
Wimmera
COSERO GR4JSimilar behaviour: clear over-estimation for the Millenium DroughtDifference: no clear under-estimation of wet years for Cosero when calibrated on dry years
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MilleniumDrought
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SpringSIM
New model, implemented to deal specifically with incorporation of long term droughts in the routine response to rainfall/evaporation of rainfall-runoff models
12 parameters
IAHS Hw15 22 July 2013
USED BY: Avijeet Ramchurn ([email protected], Bureau of Meteorology, Australia)
Interception store
Quick response layer
Unsa
turate
dzo
neSa
turate
dzo
ne
Soil p
rofile
de
pth (
D)
Saturation flow
Quickflow
Interflow
Subsurface loss from saturated zone
Subsurface loss from unsaturated zone
Rainfall Evaporation
Outletheight (OH)
Spill
Leakage to saturated zone
Unavailableto quickflow
GWD
UNZD
Channel routing
Impervious area flow
Rainfall
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SpringSIM
IAHS Hw15 22 July 2013
USED BY: Avijeet Ramchurn ([email protected], Bureau of Meteorology, Australia)
0
100
200
300
400
500
600
700
800
900
1000So
il M
oist
ure
Stor
e
Soil moisture content of unsaturated zone
Saturated zone
Outlet level
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SpringSIM
Good simulations of the water volume
IAHS Hw15 22 July 2013
USED BY: Avijeet Ramchurn ([email protected], Bureau of Meteorology, Australia)
Bani
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SpringSIM
Low bias in contrasted periods
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USED BY: Avijeet Ramchurn ([email protected], Bureau of Meteorology, Australia)
Bani
Wet Dry
Wet
Dry
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Thank you!
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The protocolTHE COMPARISONS BETWEEN MODELS
Comparisons between the models
Comparisons with observations
Mod 1 Mod 2M
od 1M
od 2
Over-estimation from model on top
Δ
Under-estimation from the model on top
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The protocolTHE GRAPHS
The 10-year sliding windows plots
IAHS Hw15 22 July 2013