Short to medium range probabilistic streamflow forecasting ... · Priya, Satya, William Young,...
Transcript of Short to medium range probabilistic streamflow forecasting ... · Priya, Satya, William Young,...
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Short to medium range probabilistic streamflow forecasting for Tel river using the
Hydrologic Ensemble Forecast Service
M. Sarath, V. V. Srinivas
Department of Civil Engineering , and
Interdisciplinary Center for Water Research (ICWaR)
Indian Institute of Science, Bangalore, India
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Streamflow forecasting - Applications
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Kerala floods 2018 (google)
Overtopping of dam in Kerala
Lead time of forecast
Applications
Short range flood forecasts(up to 3 days)
• Flood warning • Flood control
system management for savings lives and some assets
Medium-range flood forecasts (3–15 days)
• Flood control system management for preserving additional livelihood assets
Long-range flood forecasts (e.g., monthly, seasonal flow outlooks)
• Planning and management of water resources (e.g., for agriculture, hydropower, Industry)
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Streamflow Forecasting Approaches3
ANN
Data-driven/statisticalProcess-driven
Meteorological
forecast
(Source: essc.psu.edu)
Hydrologic models are forced with meteorological forecasts from NWP models, considering basin initial
conditions
StreamflowPredictorsARIMAANN
ANFISk-NN
ANFIS
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Deterministic forecast
Flood
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Forecast uncertainty Causes
Complex nature of system
Imperfect models
Lack of data
Measurement errors, etc.
Quantification of uncertainty
Ensemble (instead of single valued forecast)
Probabilistic forecast
Flood threshold
~25 % probability of flooding
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Forecasting Flow in a River using Hydrologic Ensemble Prediction Systems (HEPS)
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Generate ensemble forecasts of streamflow to quantify forecastuncertainty
Increasingly used by flood forecasting centres
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HEPS name Region
Global Flood Awareness System (GloFAS) Global
European Flood Awareness System (EFAS) Europe
Hydrologic Ensemble Forecast Service (HEFS)
United States
Climate Forecast Applications in Bangladesh (CFAB)
Bangladesh
Joint Flood Forecasting System UKFrench Hydro-meteorological Ensemble
Prediction SystemFrance
Watershed simulation and forecasting system (WSFS)
FinlandOperational/pre-operational HEPS
source: HEPEX
Can the HEPS provide skilful streamflow forecasts for Indian rivers? Do we need India specific ensemble forecasting system?
Forecasting Flow in a River using Hydrologic Ensemble Prediction Systems (HEPS)
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Studies in India
Priya, Satya, William Young, Thomas Hopson, and Ankit Avasthi. 2017. Flood Risk Assessment and Forecasting for the Ganges-Brahmaputra-Meghna River Basins. Washington, DC: World Bank.
Forecasting Flow in a River using Hydrologic Ensemble Prediction Systems (HEPS)
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HEPS name Region
Global Flood Awareness System (GloFAS) Global
European Flood Awareness System (EFAS) Europe
Hydrologic Ensemble Forecast Service (HEFS)
United States
Climate Forecast Applications in Bangladesh (CFAB)
Bangladesh
Joint Flood Forecasting System UKFrench Hydro-meteorological Ensemble
Prediction SystemFrance
Watershed simulation and forecasting system (WSFS)
FinlandOperational/pre-operational HEPS
source: HEPEX
Forecasting River Flow using Hydrologic Ensemble Prediction Systems (HEPS)
Demargne, J., Wu, L., Regonda, S.K., Brown, J.D., Lee, H., He, M., Seo, D.J., Hartman, R.,Herr, H.D., Fresch, M., Schaake, J., Zhu, Y., 2014. The science of NOAA’s operationalhydrologic ensemble forecast service. Bull. Am. Meteorol. Soc. 95, 79–98.
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HEFS (Hydrologic Ensemble Forecast Service)
MEFP: Meteorological Ensemble Forecast Processor
Figure: Schematic diagram of HEFS
Meteorological forecasts
(precipitation, temperature)
Ensemble meteorological
forecasts
Raw ensemble streamflow forecast
Postprocessed ensemble streamflow forecast
I: MEFP
II: Hydrologic Processor
III: EnsPost
Model meteorological uncertainty
Model hydrologic uncertainty
Simulate streamflow, propagate uncertainty
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I: MEFP (Meteorological Ensemble Forecast Processor)
Models meteorological uncertainty
Considers raw forecasts from multiple sources
Corrects bias in raw forecasts
Preserves skill over multiple time scales (Canonical events).
Preserves space-time correlation structure (Shaake Shuffle)
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Climatology
Short-range
Long-range
Medium-range
RFC
GEFSv2
CFSv2
Bias correctedseamless/mergedensemble forecast
MEFP
Single-valued raw forecasts(ensemble mean)
Figure: Modified from Wu (2014)
RFC: River forecast centreCFS: Climate Forecast SystemGEFS: Global Ensemble Forecast System
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Canonical events
Time windows over which forecasts and observations are aggregated
Used to capture skill of raw forecasts at multiple time scales
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Lead
time
(days)
Temp. Precipitation
base
events
base
events
modulation
events
1 1 1
12 2 2
3 3 3
24 4 4
5 5 5
36 6 6
7 7 7
48
88
9 9
510
9
10
11 11
6
12 12
13
10
13
14 14
15 15
16 16
I: MEFP (Meteorological Ensemble Forecast Processor)
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Figure: Regression-based statistical processing method used in MEFP (Li et.al, 2017)
I: MEFP (Meteorological Ensemble Forecast Processor)
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MEFP – statistical model13
Temperature
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HEFS (Hydrologic Ensemble Forecast Service)
MEFP: Meteorological Ensemble Forecast Processor
Figure: Schematic diagram of HEFS
Meteorological forecasts
(precipitation, temperature)
Ensemble meteorological
forecasts
Raw ensemble streamflow forecast
Postprocessed ensemble streamflow forecast
I: MEFP
II: Hydrologic Processor
III: EnsPost
Model meteorological uncertainty
Model hydrologic uncertainty
Simulate streamflow, propagate uncertainty
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II: Hydrologic processor Propagates meteorological
uncertainty
Model initialised with basin conditions
Forced with forecasts from MEFP
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GR4J model (Perrin et.al, 2003)
GR4J hydrologic model
Lumped daily rainfall-runoff model
A hybrid metric-conceptual model (Wheater et al., 1993; Young, 2001)
Four parametersRaw ensemble streamflow
forecast (HEFS-RAW)
+Contributes
to runoff
PET Precipitation
(Net Precipitation)
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Re : Extra-terrestrial radiation (MJ m−2 day−1)λ : Latent heat flux (MJ kg−1)ρ : Density of water (kg m−3)Ta : Mean daily air temperature (°C)
PET (Oudin et al., 2005)
(mm/day)
Pn: Net excess precipitationEn: Net deficit in meeting PET demand
Soil moisture accounting store
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HEFS (Hydrologic Ensemble Forecast Service)
MEFP: Meteorological Ensemble Forecast Processor
Figure: Schematic diagram of HEFS
Meteorological forecasts
(precipitation, temperature)
Ensemble meteorological
forecasts
Raw ensemble streamflow forecast
Postprocessed ensemble streamflow forecast
I: MEFP
II: Hydrologic Processor
III: EnsPost
Model meteorological uncertainty
Model hydrologic uncertainty
Simulate streamflow, propagate uncertainty
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III: EnsPost (Hydrologic Ensemble Postprocessor)
Account for hydrologic uncertainty
Correct bias in streamflow simulation
Account for autocorrelation
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Simu
late
d fl
ow (mm
/day
)Observed flow (mm/day)
Hydrologic error
HEFS-RAW EnsPostRaw
ensemble streamflow forecast
Postprocessed ensemble streamflow forecast
HEFS-POST
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EnsPost – statistical model
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Hydrologic error
Observed streamflow at
forecast initialisation
time
Raw streamflow forecast
Postprocessed streamflow forecast
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Case study using HEFS – Tel river in Mahanadi basin
Daily streamflow forecasts are generated at Kantamal gauge (CA: 20,235 km2)
Lead times: 1-day to 1-month
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Study area
Mahanadi basin
HEFS: Hydrologic Ensemble Forecast Service
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Data
Daily Precipitation (IMD): 1901-2013 (0.25°resolution)
Daily Temperature (IMD): 1951-2013 (1° resolution)
Daily forecasts from Global Ensemble Forecast System (GEFSv2) for 1 to 16 day lead times Precipitation (1° resolution)
Temperature (1° resolution)
Period of record: 1985-2013
Daily streamflow at Kantamal gauge (India-WRIS)
Period of record: 1972-2011
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HEFS (Hydrologic Ensemble Forecast Service )
Schematic diagram of HEFS
Meteorological forecasts
(precipitation, temperature)
Ensemble meteorological
forecasts
Raw ensemble streamflow forecast
Postprocessed ensemble streamflow forecast
I: MEFP
II: Hydrologic Processor
III: EnsPost
Model meteorological uncertainty
Model hydrologic uncertainty
Simulate streamflow, propagate uncertainty
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Results – Temperature from GEFS after bias correction by MEFP
median
25%
95%
5%
75%
GEFS temperature forecast has skill over the study area
MEFP: Meteorological Ensemble Forecast ProcessorGEFS: Global Ensemble Forecast System
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Results – Precipitation from GEFS after bias correction by MEFP24
median
25%
95%
5%
75%
GEFS precipitation forecast has low skill over the study area
MEFP: Meteorological Ensemble Forecast ProcessorGEFS: Global Ensemble Forecast System
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Split sample approachParameter estimation – data up to
1999
Forecast verification – 2000-2011
(wet season, June-Oct)
Verification metrics
Mean Error (ME)
Pearson correlation coefficient
CRPSS (Continuous Ranked Probability Skill Score)
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CRPS (www.met-learning.eu)
Validation/verification of forecasts
Ref: Climatological forecast Main: HEFS/ESP/ARIMA
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Streamflow simulation (GR4J model) using observed rainfall26
MeasureCalibratio
nValidatio
n
Correlation
0.90 0.92
RMSE (mm/day)
1.76 2.22
NSE 0.80 0.80
KGEmod 0.90 0.79
flow
(mm
/day
) rain (mm
/day)
timeSi
mul
ated
flow
(mm
/day
)
Observed flow (mm/day)2005
flow (sim)flow (obs)rain (obs)
flow (sim)flow (obs)rain (obs)
(Validation period)
KGEmod : Modified Kling-Gupta efficiency criterionX1=104.5 mm; x2=-1.2 mm; x3=186.7 mm; x4=1.4 days
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Results – Streamflow forecast27
MODIS images (NASA)
Fig: Streamflow forecast generated using HEFS on September 15, 20082008
2008
HEFS: Hydrologic Ensemble Forecast Service
September 20, 2008
During flood
September 7, 2008
Mahanadi
Before flood
Brahmani
18th Sep
19th Sep
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Results - Streamflow forecasts28
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CLIM: climatology/climatological forecast
HEFS: Hydrologic Ensemble Forecast Service
Results – Streamflow forecasts
CRPSS: (Continuous Ranked Probability Skill Score)
ARIMA (4,0,1)
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HEFS (Hydrologic Ensemble Forecast Service )
Schematic diagram of HEFS
Meteorological forecasts
(precipitation, temperature)
Ensemble meteorological
forecasts
Raw ensemble streamflow forecast
Postprocessed ensemble streamflow forecast
I: MEFP
II: Hydrologic Processor
III: EnsPost
Model meteorological uncertainty
Model hydrologic uncertainty
Simulate streamflow, propagate uncertainty
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ESP: GR4J model is forced with CLIM forecasts of precipitation andPETHEFS-RAW: GR4J model is forced with MEFP-GEFS forecasts ofprecipitation and PETHEFS-POST: Postprocessed ensemble streamflow forecast generated usingEnsPost
Comparison of Ensemble Streamflow Forecasts
Forecast Basin initial conditions
Meteorologicalforecast
Hydrologicpost-processing
CLIM
ARIMA
ESP
HEFS-RAW
HEFS-POST MEFPMEFPCLIM
Basin initial conditions ESP
Ensemble streamflow prediction(source: Stokes, 2014)
MEFP: Meteorological Ensemble Forecast Processor
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HEFS: Hydrologic Ensemble Forecast
Service
ESP: Ensemble streamflow prediction
MEFP: Meteorological Ensemble Forecast
Processor
GEFS: Global Ensemble Forecast
System
CRPS
S(C
onti
nuou
s Ra
nked
Pro
babi
lity
Sk
ill Sc
ore)
Comparison of Ensemble Streamflow Forecasts
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Conclusions & Future scope33
Conclusions • HEFS forecasts have higher skill than ARIMA forecasts
• At the daily time scale, most of the skill of HEFS at short lead times is due to the contribution from basin initial conditions.
Future scope • Alternate hydrologic, hydraulic models• Rainfall forecasts from other sources
(IMD; WRF)• Data assimilation• Other basins
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KGEmod : Modified Kling-Gupta efficiency criterion
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Normal quantile transform (NQT)37
(Brown, 2014)
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NCAR flood forecasting38
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ARIMA forecast39
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ARIMA forecast40