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Ensemble analogue downscaling of theTwentieth Century Reanalysis overFrance for 140-year-long hydrologicalreconstructions
Jean-Philippe Vidal1, Laurie Caillouet1, EricSauquet1 & Benjamin Graff2
1Irstea, Hydrology-Hydraulics Research Unit (UR HHLY)2Compagnie Nationale du Rhone (CNR)
19 September 2017 – SWGen-Hydro
1 / 40www.irstea.fr
Outline
1. Reconstructing 20th century hydrology
2. SCOPE
3. SCOPE Climate
4. SCOPE Hydro
5. Applications of SCOPE Hydro
6. Beyond SCOPE
2 / 40
Outline
1. Reconstructing 20th century hydrology
2. SCOPE
3. SCOPE Climate
4. SCOPE Hydro
5. Applications of SCOPE Hydro
6. Beyond SCOPE
3 / 40
Reconstructing 20th century hydrologyScarce and sparse hydrometeorological data before the 1960s
0
1000
2000
3000
4000
5000
1900 1950 2000
Num
ber
of s
tatio
ns
VariablesStreamflow
Precipitation
Temperature
The current state of databases does not allow exploring properly thelong-term hydrometeorological variability at the national scale.
4 / 40
Reconstructing 20th century hydrologyNear-natural catchments with long records
662 near-natural catchments
French reference hydrometricnetwork (Giuntoli et al., 2013)
Additional stations selected byCatalogne et al. (2014)
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5 / 40
Reconstructing 20th century hydrology140 years of French hydrology from observations
6 / 40
Reconstructing 20th century hydrologyHydrometeorological modelling chain
Safran(Vidal et al., 2010)
Gridded archive of local meteorological variables
1958-2008
20CR(Compo et al., 2011)
Large-scale atmosphericvariables
1871-2012
7 / 40
Reconstructing 20th century hydrologyHydrometeorological modelling chain
Safran(Vidal et al., 2010)
Gridded archive of local meteorological variables
1958-2008
20CR(Compo et al., 2011)
Large-scale atmosphericvariables
1871-2012
SCOPE Climate(Caillouet et al., 2016,
2017)25 sets of gridded local meteorological variables
1871-2012
SCOPE
Statisticaldownscaling
method
SCOPE: Spatially COherent Probabilistic Extension method7 / 40
Reconstructing 20th century hydrologyHydrometeorological modelling chain
Safran(Vidal et al., 2010)
Gridded archive of local meteorological variables
1958-2008
20CR(Compo et al., 2011)
Large-scale atmosphericvariables
1871-2012
SCOPE Climate(Caillouet et al., 2016,
2017)25 sets of gridded local meteorological variables
1871-2012
SCOPE Hydro(Caillouet et al., 2017)
25 sets of daily streamflow
over 662 catchments1871-2012
SCOPE
Statisticaldownscaling
method
GR6J+ CemaNeige
Hydrologicalmodelling
7 / 40
Reconstructing 20th century hydrologyHydrometeorological modelling chain
Safran(Vidal et al., 2010)
Gridded archive of local meteorological variables
1958-2008
20CR(Compo et al., 2011)
Large-scale atmosphericvariables
1871-2012
SCOPE Climate(Caillouet et al., 2016,
2017)25 sets of gridded local meteorological variables
1871-2012
SCOPE Hydro(Caillouet et al., 2017)
25 sets of daily streamflow
over 662 catchments1871-2012
SCOPE
Statisticaldownscaling
method
GR6J+ CemaNeige
Hydrologicalmodelling
Analysis of spatio-temporal extreme low-
flow events(Caillouet et al., 2017)
7 / 40
Reconstructing 20th century hydrologyHydrometeorological modelling chain
Safran(Vidal et al., 2010)
Gridded archive of local meteorological variables
1958-2008
20CR(Compo et al., 2011)
Large-scale atmosphericvariables
1871-2012
SCOPE Climate(Caillouet et al., 2016,
2017)25 sets of gridded local meteorological variables
1871-2012
SCOPE Hydro(Caillouet et al., 2017)
25 sets of daily streamflow
over 662 catchments1871-2012
SCOPE
Statisticaldownscaling
method
GR6J+ CemaNeige
Hydrologicalmodelling
Analysis of spatio-temporal extreme low-
flow events(Caillouet et al., 2017)
Long-term trend analysis of snow water
equivalent and snowmelt timing(Vidal et al., 2017)
7 / 40
Outline
1. Reconstructing 20th century hydrology
2. SCOPE
3. SCOPE Climate
4. SCOPE Hydro
5. Applications of SCOPE Hydro
6. Beyond SCOPE
8 / 40
SCOPEPrinciple
Ensemble analogue downscaling approach
9 / 40
SCOPESANDHY
Stepwise ANalogue Downscaling method for HYdrology
Analogy onVertical velocity
5000 analogues
170 analogues
70 analogues
25 analogues
Analogy on temperature
Globalreanalysis
Analogy on geopotential
Analogy onhumidity
Localoptimisation
SANDHY
Specificities
Dedicated to precipitation as predictand
Stepwise refinement of the pool of analogues
Local optimisation of geopotential spatial domains
10 / 40
SCOPESANDHY
Stepwise refinement of the pool of analogues
Predictor Pressure level [hPa] &hour [UTC]
Analogycriterion
Number ofanalogues
Temperature 925@+36h, 600@+12h Euclideandistance
5000
Geopotential 1000@+12h, 500@+24h TWS 170
Vertical velocity 850@+6h,+12h,+18h,+24h
Euclideandistance
70
Humidity (Rh×TCW) 850@+12h,+24h Euclideandistance
25
TWS: score comparing the shape of geopotential fields (Teweles andWobus, 1954)
11 / 40
SCOPESANDHY
1. Local optimisation of geopotential spatial domains
5 best domains for each of the 608 climatically homogeneous zones
2. Nearest grid cell for other predictors
3. Look up for yet better domains over all 608 locally optimized domains
12 / 40
SCOPESANDHY
1. Local optimisation of geopotential spatial domains
5 best domains for each of the 608 climatically homogeneous zones
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001 074 127
317 442 493
557 596 615
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60
70
20
30
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50
60
70
20
30
40
50
60
70
−20 −10 0 10 20 −20 −10 0 10 20 −20 −10 0 10 20Longitude (°E)
Latit
ude
(°N
)
Domaines1
2
3
4
5
2. Nearest grid cell for other predictors
3. Look up for yet better domains over all 608 locally optimized domains
12 / 40
SCOPESANDHY
1. Local optimisation of geopotential spatial domains
5 best domains for each of the 608 climatically homogeneous zones
2. Nearest grid cell for other predictors
3. Look up for yet better domains over all 608 locally optimized domains
12 / 40
SCOPESANDHY
1. Local optimisation of geopotential spatial domains
5 best domains for each of the 608 climatically homogeneous zones
2. Nearest grid cell for other predictors
3. Look up for yet better domains over all 608 locally optimized domains
12 / 40
SCOPESANDHY
Stepwise ANalogue Downscaling method for HYdrology
Analogy onVertical velocity
5000 analogues
170 analogues
70 analogues
25 analogues
Analogy on temperature
Globalreanalysis
Analogy on geopotential
Analogy onhumidity
Localoptimisation
SANDHY
Simplified version used operationally by CNR for quantitativeprecipitation forecasts over the Rhone basin for hydropowerproduction (Ben Daoud et al., 2011b,a, 2016)
Full version: 25 × 5 analogues dates for each target date, and foreach of the 608 climatically homogeneous zones (Radanovics et al.,2013)
13 / 40
SCOPEAdditional analogy steps (”stepwise”)
Adaptation to both precipitation and temperature as predictands
Analogy on large-scale sea surface temperature
SANDHY outputs
125 analogues
80 analogues
Analogy on large-scale2m temperature
25 analogues
14 / 40
SCOPEPost-processing steps 1/2
Correction of dry bias
Remove 1 to 3 dates (depending on the zone) with the lowestprecipitation amounts
Resample remaining dates to preserve the 25-member ensemble size
15 / 40
SCOPEPost-processing steps 2/2
Spatial coherence
Schaake Shuffle (Clark et al.,2004): Local reorganisationof ensemble members basedon climatological spatialcoherence
Example
21 September 1890: verystrong convective event overthe Cevennes area(south-east France) leadingto record flood of theArdeche River
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Obs Membre 1 Membre 2 Membre 3 Membre 4
Membre 5 Membre 6 Membre 7 Membre 8 Membre 9
Membre 10 Membre 11 Membre 12 Membre 13 Membre 14
Membre 15 Membre 16 Membre 17 Membre 18 Membre 19
Membre 20 Membre 21 Membre 22 Membre 23 Membre 24
Membre 25 Minimum Médiane Maximum
100
200
300
Precip (mm)
16 / 40
SCOPEPost-processing steps 2/2
Spatial coherence
Schaake Shuffle (Clark et al.,2004): Local reorganisationof ensemble members basedon climatological spatialcoherence
Example
21 September 1890: verystrong convective event overthe Cevennes area(south-east France) leadingto record flood of theArdeche River
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Obs Membre 1 Membre 2 Membre 3 Membre 4
Membre 5 Membre 6 Membre 7 Membre 8 Membre 9
Membre 10 Membre 11 Membre 12 Membre 13 Membre 14
Membre 15 Membre 16 Membre 17 Membre 18 Membre 19
Membre 20 Membre 21 Membre 22 Membre 23 Membre 24
Membre 25 Minimum Médiane Maximum
100
200
300
Precip (mm)
16 / 40
SCOPEComplete downscaling process
SCOPE
Bias-correction
StepwiseSchaake Shuffle
25 analogues
Globalreanalysis
Localoptimisation
SANDHY125
analogues
25 analogues
25 analogues
17 / 40
Outline
1. Reconstructing 20th century hydrology
2. SCOPE
3. SCOPE Climate
4. SCOPE Hydro
5. Applications of SCOPE Hydro
6. Beyond SCOPE
18 / 40
SCOPE ClimatePrecipitation: 1958-2008 bias
SAN
DH
Y
Year Spring Summer Autumn Winter
+ Stepw
iseSC
OP
E
Bias (%)
19 / 40
SCOPE ClimatePrecipitation
Paris-Montsouris1871-2012
Year
Spring
Summer
Autumn
Winter
400
600
800
1000
100
200
300
100
200
300
400
100
200
300
100
200
300
1873 1883 1893 1903 1913 1923 1933 1943 1953 1963 1973 1983 1993 2003 2013
Pre
cip
itatio
n (
mm
)
Minimum & Maximum Quantiles 0.25 & 0.75 Safran Homogenized Median
20 / 40
SCOPE ClimateTemperature: 1958-2008 bias
Summer Autumn Winter
SAN
DH
Y
Year Spring
SCO
PE
Bias (°C)
21 / 40
SCOPE ClimateTemperature: 1958-2008 interannual correlation
Summer Autumn Winter
SANDHY
Year Spring
SCOPE
22 / 40
SCOPE ClimateTemperature
Paris-Montsouris1871-2012
Year
Spring
Summer
Autumn
Winter
10
11
12
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10
12
14
17
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21
10
12
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1873 1883 1893 1903 1913 1923 1933 1943 1953 1963 1973 1983 1993 2003 2013
Tem
pe
ratu
re (
°C)
Minimum & maximum Quantiles 0.25 & 0.75 Safran Homogenized Median
23 / 40
Outline
1. Reconstructing 20th century hydrology
2. SCOPE
3. SCOPE Climate
4. SCOPE Hydro
5. Applications of SCOPE Hydro
6. Beyond SCOPE
24 / 40
SCOPE HydroHydrological modelling
Hydrological models
Lumped conceptual modelGR6J (Pushpalatha et al.,2011)
Conceptual snow routineCemaNeige (Valery et al.,2014)
Calibration
Against observations from the662 near-natural catchments
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1600
1800
2000
2200
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0 400 800 1200X Lambert [km]
Y L
ambe
rt [k
m]
KGE( Q)●
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25 / 40
SCOPE Hydro140-year annual streamflow
Corrèze
Ubaye
0
1
2
3
4
0
1
2
3
4
1871 1881 1891 1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001 2011
Q (
mm
/day
)
Observation Safran Hydro SCOPE Hydro
26 / 40
SCOPE Hydro1972 low-flow period
Corrèze
Ubaye
5
10
15
5
10
15
20
1971−03 1971−06 1971−09 1971−12 1972−03 1972−06 1972−09 1972−12
Q (
mm
/day
)
Observation Safran Hydro SCOPE Hydro
27 / 40
SCOPE Hydro140 years of French hydrology from observations
28 / 40
SCOPE Hydro140 years of French hydrology from SCOPE Hydro (member 1)
28 / 40
SCOPE Hydro140 years of French hydrology from SCOPE Hydro (member 2)
28 / 40
Outline
1. Reconstructing 20th century hydrology
2. SCOPE
3. SCOPE Climate
4. SCOPE Hydro
5. Applications of SCOPE Hydro
6. Beyond SCOPE
29 / 40
Applications of SCOPE HydroSpatio-temporal extreme low-flow events (Caillouet et al., 2017)
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Duration − 1878
Duration − 1893
Duration − 1976
Duration − 1990
Return period of duration (years)
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< 2 2−5 5−10
10−20 20−50 > 50
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Duration − 1878
Duration − 1893
Duration − 1976
Duration − 1990
Return period of duration (years)
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< 2 2−5 5−10
10−20 20−50 > 50
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Duration − 1878
Duration − 1893
Duration − 1976
Duration − 1990
Return period of duration (years)
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< 2 2−5 5−10
10−20 20−50 > 50
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Member 1 Member 2 Member 3 Member 4 Member 5
Member 6 Member 7 Member 8 Member 9 Member 10
Member 11 Member 12 Member 13 Member 14 Member 15
Member 16 Member 17 Member 18 Member 19 Member 20
Member 21 Member 22 Member 23 Member 24 Member 25
Return period (years)●●
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< 22−5
5−1010−20
20−50> 50 31 / 40
Applications of SCOPE HydroSpatio-temporal extreme low-flow events (Caillouet et al., 2017)
Maximum spatial extent of spatio-temporal events
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07/1893 11/192109/1943
10/1945
08/1949
10/1955
11/1971
08/1976
12/1978
11/1983
11/1985
12/1989
11/2007
10/2009
10/2011
0
25
50
75
100
1871 1881 1891 1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001 2011Date
Spa
tial e
xten
t (%
Fra
nce)
32 / 40
Applications of SCOPE HydroTrends in SWE and snowmelt runoff timing (Vidal et al., 2017)
Snow water equivalent
1912-2012(Mann-Kendall at95% level)
1st April Maximum
1st February
Probability ofsignificanttrend (%)
0
25
50
75
100
500
1000
1500
2000
Altitude Trenddirection
●
decreasing
no direction
increasing
33 / 40
Applications of SCOPE HydroTrends in SWE and snowmelt runoff timing (Vidal et al., 2017)
Snowmelt runofftiming
1912-2012(Mann-Kendall at95% level)
centre end
start
Probability ofsignificanttrend (%)
0
25
50
75
100
500
1000
1500
2000
Altitude Trenddirection
●
earlier
no direction
later
34 / 40
Outline
1. Reconstructing 20th century hydrology
2. SCOPE
3. SCOPE Climate
4. SCOPE Hydro
5. Applications of SCOPE Hydro
6. Beyond SCOPE
35 / 40
Beyond SCOPEObjectives
0
1000
2000
3000
4000
5000
1900 1950 2000
Num
ber
of s
tatio
ns
VariablesStreamflow
Precipitation
Temperature
Combining sources of information
SCOPE Climate with precipitation and temperature observations
SCOPE Hydro with streamflow observations
36 / 40
Beyond SCOPEFYRE: French Hydrometeorological REanalysis
On-going PhD work (A. Devers)
Formal multivariate data assimilation with:
Ensemble Kalman Filter in an offline setting
SCOPE Climate as background information
Preliminary reanalysis of the 2003 heat wave (Devers et al., 2017)
37 / 40
Beyond SCOPEFYRE: French Hydrometeorological REanalysis
On-going PhD work (A. Devers)
Formal multivariate data assimilation with:
Ensemble Kalman Filter in an offline setting
SCOPE Climate as background information
Preliminary reanalysis of the 2003 heat wave (Devers et al., 2017)
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1893 2003
200 600 1000 200 600 1000
1800
2200
2600
X Lambert [km]
Y L
ambe
rt [k
m]
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Beyond SCOPEFYRE: French Hydrometeorological REanalysis
On-going PhD work (A. Devers)
Formal multivariate data assimilation with:
Ensemble Kalman Filter in an offline setting
SCOPE Climate as background information
Preliminary reanalysis of the 2003 heat wave (Devers et al., 2017)
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●
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●
● ●
●
●15
20
25
30
07−27 08−01 08−06 08−11 08−16 08−21 08−26 08−31
Date
T (
°C)
●
●
●
●
●
Analysis
Background
Obs assimilated
Obs not assimilated
Safran
assimilating all observations
37 / 40
Beyond SCOPEFYRE: French Hydrometeorological REanalysis
On-going PhD work (A. Devers)
Formal multivariate data assimilation with:
Ensemble Kalman Filter in an offline setting
SCOPE Climate as background information
Preliminary reanalysis of the 2003 heat wave (Devers et al., 2017)
●
●●
● ●
●●
●
●
●
●● ● ●
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●
●●
●
●
●● ●
● ●
● ●
●
●●
●●
●
● ●
●
●15
20
25
30
07−27 08−01 08−06 08−11 08−16 08−21 08−26 08−31
Date
T (
°C)
●
●
●
●
●
Analysis
Background
Obs assimilated
Obs not assimilated
Safran
assimilating only stations with the density of 1893
37 / 40
References I
Ben Daoud, A., Sauquet, E., Bontron, G., Obled, C., and Lang, M. (2016). Daily quantitative precipitationforecasts based on the analogue method: Improvements and application to a French large river basin.Atmospheric Research, 169, Part A:147–159.
Ben Daoud, A., Sauquet, E., Lang, M., Bontron, G., and Obled, C. (2011a). Precipitation forecastingthrough an analog sorting technique: a comparative study. Advances in Geosciences, 29:103–107.
Ben Daoud, A., Sauquet, E., Lang, M., and Ramos, M.-H. (2011b). Can we extend flood forecastinglead-time by optimising precipitation forecasting based on analogs? Application to the Seine river basin.La Houille Blanche, (1):37–43.
Caillouet, L., Vidal, J.-P., Sauquet, E., Devers, A., and Graff, B. (2017). Ensemble reconstruction ofspatio-temporal extreme low-flow events in France since 1871. Hydrology and Earth System Sciences,21(6):2923–2951.
Caillouet, L., Vidal, J.-P., Sauquet, E., and Graff, B. (2016). Probabilistic precipitation and temperaturedownscaling of the Twentieth Century Reanalysis over France. Climate of the Past, 12(3):635–662.
Catalogne, C., Sauquet, E., and Lang, M. (2014). Valorisation des donnees de jaugeages episodiques pourl’estimation du debit de reference d’etiage QMNA5. Houille Blanche, pages 78–87.
Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., and Wilby, R. (2004). The Schaake Shuffle: Amethod for reconstructing space-time variability in forecasted precipitation and temperature fields.Journal of Hydrometeorology, 5(1):243–262.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose,R. S., Rutledge, G., Bessemoulin, P., Bronnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N.,Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y.,Nordli, O., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J. (2011). TheTwentieth Century Reanalysis Project. Quarterly Journal of the Royal Meteorological Society,137(654):1–28.
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References II
Devers, A., Vidal, J.-P., Lauvernet, C., and Graff, B. (2017). Reanalysis of the 1893 heat wave in Francethrough offline data assimilation in a downscaled ensemble meteorological reconstruction. InGeophysical Research Abstracts, volume 19, EGU2017-5618.
Giuntoli, I., Renard, B., Vidal, J.-P., and Bard, A. (2013). Low flows in France and their relationship tolarge-scale climate indices. Journal of Hydrology, 482:105–118.
Pushpalatha, R., Perrin, C., Le Moine, N., Mathevet, T., and Andreassian, V. (2011). A downwardstructural sensitivity analysis of hydrological models to improve low-flow simulation. Journal ofHydrology, 411(1-2):66–76.
Radanovics, S., Vidal, J.-P., Sauquet, E., Ben Daoud, A., and Bontron, G. (2013). Optimising predictordomains for spatially coherent precipitation downscaling. Hydrology and Earth System Sciences,17(10):4189–4208.
Teweles, S. and Wobus, H. B. (1954). Verification of prognostic charts. Bulletin of the AmericanMeteorological Society, 35(10):455–463.
Valery, A., Andreassian, V., and Perrin, C. (2014). As simple as possible but not simpler: What is useful ina temperature-based snow-accounting routine? Part 2. Sensitivity analysis of the Cemaneige snowaccounting routine on 380 catchments. Journal of Hydrology, 517:1176–1187.
Vidal, J.-P., Caillouet, L., Sauquet, E., Graff, B., Gouttevin, I., Thirel, G., and Devers, A. (2017).Hydrometeorological reconstruction of snow-influence streamflow series in France since 1871. InProceedings of the 34th International Conference on Alpine Meteorology.
Vidal, J.-P., Martin, E., Franchisteguy, L., Baillon, M., and Soubeyroux, J.-M. (2010). A 50-yearhigh-resolution atmospheric reanalysis over France with the Safran system. International Journal ofClimatology, 30(11):1627–1644.
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