short to medium range probabilistic streamflow forecasting ... · priya, satya, william young,...

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

Streamflow forecasting - Applications

2

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)

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

Deterministic forecast

Flood

4

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

Forecasting Flow in a River using Hydrologic Ensemble Prediction Systems (HEPS)

5

Generate ensemble forecasts of streamflow to quantify forecastuncertainty

Increasingly used by flood forecasting centres

6

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?


7

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.


8

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.

9

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

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)

10

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

Canonical events

Time windows over which forecasts and observations are aggregated

Used to capture skill of raw forecasts at multiple time scales

11

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


12

Figure: Regression-based statistical processing method used in MEFP (Li et.al, 2017)


MEFP – statistical model13

Temperature

14







forecasts



I: MEFP


III: EnsPost




II: Hydrologic processor Propagates meteorological

uncertainty

Model initialised with basin conditions

Forced with forecasts from MEFP

15

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)

16

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

17







forecasts



I: MEFP


III: EnsPost




III: EnsPost (Hydrologic Ensemble Postprocessor)

Account for hydrologic uncertainty

Correct bias in streamflow simulation

Account for autocorrelation

18

Simu

late

d fl

ow (mm

/day

)Observed flow (mm/day)

Hydrologic error

HEFS-RAW EnsPostRaw

ensemble streamflow forecast


HEFS-POST

EnsPost – statistical model

19

Hydrologic error

Observed streamflow at

forecast initialisation

time

Raw streamflow forecast

Postprocessed streamflow forecast

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

20

Study area

Mahanadi basin

HEFS: Hydrologic Ensemble Forecast Service

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

21

22

HEFS (Hydrologic Ensemble Forecast Service )

Schematic diagram of HEFS




forecasts



I: MEFP


III: EnsPost




23

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

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

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)

25

CRPS (www.met-learning.eu)

Validation/verification of forecasts

Ref: Climatological forecast Main: HEFS/ESP/ARIMA

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

Results – Streamflow forecast27

MODIS images (NASA)

Fig: Streamflow forecast generated using HEFS on September 15, 20082008

2008


September 20, 2008

During flood

September 7, 2008

Mahanadi

Before flood

Brahmani

18th Sep

19th Sep

Results - Streamflow forecasts28

29

CLIM: climatology/climatological forecast


Results – Streamflow forecasts

CRPSS: (Continuous Ranked Probability Skill Score)

ARIMA (4,0,1)

30

HEFS (Hydrologic Ensemble Forecast Service )

Schematic diagram of HEFS




forecasts



I: MEFP


III: EnsPost




31

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)


32

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

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

35

KGEmod : Modified Kling-Gupta efficiency criterion

Normal quantile transform (NQT)37

(Brown, 2014)

NCAR flood forecasting38

ARIMA forecast39

ARIMA forecast40

short to medium range probabilistic streamflow forecasting ... · priya, satya, william young,...

Documents