Climate Change, Implications for Hydrologic Extremes, and What can we do about it?
Global Flood Forecasting Systems
Tom Hopson
Large-scale constraints on
Extreme Precipitation
What we expect – extreme precipitation
• individual storms increase 6-10% /degC (scales
with available moisture)
• high confidence much greater than mean
precipitation, but varies with time-scale, location,
season
South Asian Monsoon
Precip increases in:
• average
• variance
• 5-day seasonal max
• duration
%
Yr 2100
AR5
OutlineI. Precipitation products
• QPE: Rain gauges telemetric systems, Radar, satellite precipitation estimates
• QPE/QPF “nowcasting”
• QPF NWP: mesoscale and ensemble medium-range GCM
II. Global forecast systems
• Satellite-based systems
• Hydrologic Research Center
• NASA GFMS
• NWP Ensemble-based systems
• Unified systems
• USA System, Mid-Atlantic River Forecasting Center
• European EFAS, France
• Climate Forecasting Applications for Bangladesh
II. New river measurement technologies for flood forecasting
•Most gauges are placed near permanent settlements rather than distributed evenly
Measurement of Precipitation – Limits of Rain Gauges:
Sends and receives horizontal &
vertical polarized radiation
Image courtesy Terry Schuur
Dual Polarimetric Radar
• Satellite precipitation estimation useful
in areas with poor radar & rain gauge
coverage
• Although satellite sampling more
consistent than radar sampling, generally
less accurate, with infrared less accurate
than passive microwave sensors
OutlineI. Precipitation products
• QPE: Rain gauges telemetric systems, Radar, satellite precipitation
estimates
• QPE/QPF “nowcasting”
• QPF NWP: mesoscale and ensemble medium-range GCM
II. Global forecast systems
• Satellite-based systems
• Hydrologic Research Center
• NASA GFMS
• NWP Ensemble-based systems
• Unified systems
• USA System, Mid-Atlantic River Forecasting Center
• European EFAS, France
• Climate Forecasting Applications for Bangladesh
II. New river measurement technologies for flood forecasting
Nowcasting definition – description of the current state of the weather in detail and the prediction of changes in a few hours
WHAT IS NOWCASTINGOriginally defined by Browning for the1st Nowcasting Conference in 1981 as:
O-6 hr forecastingby any method
spatial scale of no more than a few kilometers (1-3 km) with frequent updates (5-10 min)
Heavy emphasis on observations
Jim Wilson
East
Nort
h
Storm Echo at Time-1
Time-2
Time-3
Time-4
Nowcast for
Time-5
Storm track
Storm Motion Vector
Extrapolation
Jim Wilson
Increasing Forecast Length
less
more
Nowcast
Schematic Representation of Forecast SkillR
ela
tive
Fo
rec
as
t S
kill
Numerical Models
• Nowcast skill decreases rapidly with leadtime
• High-resolution NWP required for predicting storm
organization.
• Blending optimally combines Nowcast and NWP
Radar data assimilation
CoSPA Technical Review Panel : May 16, 2011
Blending
Some Blending REFS
Golding 1998
Pierce 2001
Lin et al 2005
Bowler 2006
Yeung et al. 2009
Kitzmiller 2010
Atencia et al. 2010
Pinto et al. 2010
James Pinto
Archive Centre
Current Data Provider
NCAR NCEP
CMC
UKMO
ECMWFMeteoFrance
JMAKMA
CMA
BoMCPTEC
IDD/LDM
HTTP
FTP
NCDC
Unique Datasets/Software Created
Thorpex-Tigge
Early May 2011, floods in southwestern Africa
Early May 2011, floods in southwestern Africa-- examine ens forecasts … ECMWF 5-day precip
OutlineI. Precipitation products
• QPE: Rain gauges telemetric systems, Radar, satellite precipitation estimates
• QPE/QPF “nowcasting”
• QPF NWP: mesoscale and ensemble medium-range GCM
II. Global forecast systems
• Satellite-based systems
• Hydrologic Research Center
• NASA GFMS
• NWP Ensemble-based systems
• Unified systems
• USA System, Mid-Atlantic River Forecasting Center
• European EFAS, France
• Climate Forecasting Applications for Bangladesh
II. New river measurement technologies for flood forecasting
• Established in 1993 as a nonprofit research, technology transfer, and training organization. • HRC was created to help bridge gaps between scientific research in hydrology and applications for the solution of important societal problems that involve water.
www.hrc-lab.org
NASA Real-time Global Flood Estimation System (GFMS)
• quasi-global (tropics and mid-latitudes)• satellite precipitation from TRMM Multi-satellitePrecipitation
Analysis [TMPA]) -- IR and microwave instruments used• Univ of Oklahoma hydrologic model• flood estimates every three hours• calculates water depth and streamflow at each grid (at 0.125
latitude-longitude) • Flood detection based on water depth thresholds calculated from a
13-year retrospective
Mature ensemble-based systems
European Flood Awareness SystemUS National Weather Service, North Central River Forecast Centre (NCRFC)Climate Forecast Applications in Bangladesh (CFAB)UK Flood Forecast CentreSwedish Meteorological and Hydrological Institute (SMHI)Electricité de France (EDF)Water Management Centre of The Netherlands (WMCN) Meuse forecastsBonneville Power Authority
OutlineI. Precipitation products
• QPE: Rain gauges telemetric systems, Radar, satellite precipitation estimates
• QPE/QPF “nowcasting”
• QPF NWP: mesoscale and ensemble medium-range GCM
II. Global forecast systems
• Satellite-based systems
• Hydrologic Research Center
• NASA GFMS
• NWP Ensemble-based systems
• Unified systems
• USA System, Mid-Atlantic River Forecasting Center
• European EFAS, France
• Climate Forecasting Applications for Bangladesh
II. New river measurement technologies for flood forecasting
U.S.A. Hydrologic Services Overview
Peter Ahnert
“America’s NOAA National Weather Service: Protecting Lives, Livelihoods, and A Way of Life”20
•Office of Hydrologic Development
•Develops hydrologic/hydrometeorologic models and systems
•Manages the development of the web-site
•Maintains Hydrometeorological Automated Data System (HADS)
•Office of Climate, Water, and Weather Services
•Service planning, policy, requirements, coordination
•Hydrologic systems and data network support
•Training
•Office of Science and Technology
•develops Weather Forecast Office software used to produce hydrologic
watch/warning/advisory products
•OOS
•manages operational systems and provides engineering software management, facilities,
communications, and logistical services.
•Regional Headquarters
•oversight of hydrologic service delivery, direct and support improvements to RFC and WFO
hydrologic modeling and forecast operations, facilitates training activities, policy, regional
outreach, requirements
Hydrologic Services Responsibilities
River Forecast Centers
Weather Forecast Offices
“America’s NOAA National Weather Service: Protecting Lives, Livelihoods, and A Way of Life”23
NCEP WFORFC
Local EM
State EM
FEMA Region
Regional
State
Local
Media
23
NWS Service Delivery
Radar Sites across the US
Scott Ellis
25
Gridded Precipitation Estimate
Quality-Controlled
Precipitation Gage
Measurement
GOES
Satellite
Estimate
Radar
Estimate
Climate
patterns
MPE
Software
Forecaster
Analysis
River Forecast Services:Quantitative Precipitation Estimate (QPE)
“America’s NOAA National Weather Service: Protecting Lives, Livelihoods, and A Way of Life”26
Hydrologic Ensemble Forecast
System (HEFS)
Probabilistic information to support risk-based decisions
• Seamless short- to long-term
Implementation Status:
Demonstrating components of short-term capability at 6 RFCs
Will deploy additional prototypes during the next 2 years
Initial version of full capability in 2014
• Incorporates both atmospheric and hydrologic uncertainties
OutlineI. Precipitation products
• QPE: Rain gauges telemetric systems, Radar, satellite precipitation
estimates
• QPE/QPF “nowcasting”
• QPF NWP: mesoscale and ensemble medium-range GCM
II. Global forecast systems
• Satellite-based systems
• Hydrologic Research Center
• NASA GFMS
• NWP Ensemble-based systems
• Unified systems
• USA System, Mid-Atlantic River Forecasting Center
• European EFAS, France
• Climate Forecasting Applications for Bangladesh
II. New river measurement technologies for flood forecasting
Operational Flood Forecasting for Bangladesh:
Tom Hopson, RAL-NCAR
Peter Webster, Georgia Tech
A. R. Subbiah and R. Selvaraju, Asian Disaster
Preparedness Centre
Climate Forecast Applications for Bangladesh (CFAB):
USAID/CARE/ECMWF/NASA/NOAA
Bangladesh Stakeholders: Bangladesh Meteorological Department, Flood Forecasting and Warning Center,
Bangladesh Water Development Board, Department of Agriculture Extension, Disaster Management Bureau,
Institute of Water Modeling, Center for Environmental and Geographic Information Services, CARE-
Bangladesh
(World Food Program)
Damaging Floods:
large peak or extended duration
Affect agriculture: early floods in May, late floods in September
Recent severe flooding: 1974, 1987, 1988, 1997, 1998, 2000, 2004, and 2007
1998: 60% of country inundated for 3 months, 1000 killed, 40 million homeless, 10-20% total food production
2004: Brahmaputra floods killed 500 people, displaced 30 million, 40% of capitol city Dhaka under water
2007: Brahmaputra floods displaced over 20 million
River Flooding
CFAB Project: Improve flood warning lead time
Problems:
1. Limited warning of upstream
river discharges
2. Precipitation forecasting in
tropics difficult
Assets:
1. good data inputs: weather forecasts, satellite rainfall
2. Large catchments => weather forecasting skill “integrates” over large spatial
and temporal scales
3. Partnership with Bangladesh’s Flood Forecasting Warning Centre (FFWC)
=> daily border river readings
Forecast Trigger: ECMWF forecast files
Updated TRMM-
CMORPH-CPC
precipitation estimates
Updated distributed
model parameters
Updated outlet
discharge estimates
Above-critical-level
forecast probabilities
transferred to Bangladesh
Lumped Model Hindcast/Forecast
Discharge Generation
Distributed Model Hindcast/Forecast
Discharge Generation
Multi-Model Hindcast/Forecast Discharge Generation
Discharge Forecast PDF Generation
Calibrate model
Statistically correct
downscaled forecasts
Generate forecasts Generate hindcasts Generate forecasts Generate hindcasts
Update soil moisture
states and in-stream flows
Generate hindcasts
Calibrate AR error model
Calibrate multi-model
Generate forecasts Generate hindcasts
Generate model error PDF
Convolute multi-model forecast
PDF with model error PDF
E O
F
M
Q P
B D
L
F
C
Generate forecasts
Transforming (Ensemble) Rainfall into
(Probabilistic) River Flow Forecasts
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0 1 2 3 4 5 6
Rainfall Probability
Rainfall [mm]
Discharge Probability
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
10,000 30,000 50,000 70,000 90,000
Discharge [m3/s]
Above danger level probability 36%Greater than climatological seasonal risk?
Above-Critical-Level
Cumulative Probability
7 day 8 day
9 day 10 day
3 day 4 day
5 day
7 day 8 day
9 day 10 day
Brahmaputra Discharge
Forecast Ensembles
2004 Brahmaputra Ensemble Forecasts and
Danger Level Probabilities
Five Pilot Sites chosen in
2006 consultation
workshops based on
biophysical, social criteria:
Rajpur Union
-- 16 sq km
-- 16,000 pop.
Uria Union
-- 23 sq km
-- 14,000 pop.
Kaijuri Union
-- 45 sq km
-- 53,000 pop.
Gazirtek Union
-- 32 sq km
-- 23,000 pop.
Bhekra Union
-- 11 sq km
-- 9,000 pop.
Average Damage (Tk.) per Household in Pilot Union
7,255
28,745
60,99364,000
4058
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
Uria Gazirtek Kaijuri Rajpur Bekra
Union
Av
era
ge
Da
ma
ge
(T
k)
pe
r
Ho
us
eh
old
2007 Brahmaputra Ensemble Forecasts and
Danger Level Probabilities
7-10 day Ensemble Forecasts 7-10 day Danger Levels
7 day 8 day
9 day 10 day
7 day 8 day
9 day 10 day
OutlineI. Precipitation products
• QPE: Rain gauges telemetric systems, Radar, satellite precipitation estimates
• QPE/QPF “nowcasting”
• QPF NWP: mesoscale and ensemble medium-range GCM
II. Global forecast systems
• Satellite-based systems
• Hydrologic Research Center
• NASA GFMS
• NWP Ensemble-based systems
• Unified systems
• USA System, Mid-Atlantic River Forecasting Center
• European EFAS, France
• Climate Forecasting Applications for Bangladesh
II. New river measurement technologies for flood forecasting
-- Advanced Microwave Scanning Radiometer -
Earth Observing System (AMSR-E) & NASA
TRMM
(Future: Global Precipitation Measurement
System)
-- Utilizing 36-37Ghz (unaffected by cloud)
-- pixel size ~20km
-- ~2day complete global coverage (night-time
brightness temperatures)
-- data range: 1997 to present
Objective Monitoring of River Stage and Flow:
Satellite-based Passive Microwave Radiometer
Other Approaches: satellite altimeter-derived water level (and discharge derived
through rating curve):
e.g. Birkett, 1998; Alsdorf et al. 2000; Jung et al. 2010, Papa et al. 2010, Alsdorf et al.
2011, Biancamaria et al. 2011
One day of data collection
(high latitudes revisited most frequently)
=> On average, global coverage 1-2 days
MODIS sequence of 2006 Winter Flooding
2/24/2006 C/M: 1.004 3/15/2006 C/M: 1.029 3/22/2006 C/M: 1.095
Hardinge Bridge gauge
(Ganges)
Gauging data reference: Hopson 2005; Hopson and Webster 2010
Bahadurabad gauge
(Brahmaputra)
1-, 5-, 15-day Forecasts
Satellite Altimetry – Jason 2Traditionally used for sea level
Satellite Altimetry – now used for river
heights with potential for downstream flood
forecasts for Bangladesh FFWC
37
738
Figure 6. Ground tracks or virtual stations of JASON-2 (J2) altimeter over the GB basin shown 739
in yellow lines. The locations where the track crosses a river and used for deriving forecasting 740
rating curves is shown with a circle and station number. Circles without a station number 741
represent the broader view of sampling by JASON-2 if all the ground tracks on main stem rivers 742
and neighboring tributaries of Ganges and Brahmaputra are considered. 743
744
NCAR
Summary
1. Utility of flood forecast systems dictated by the precipitation product at their core
2. Effective flash flood guidance (FFG) dominated by skillful estimates of local rainfall processes with spatial precision
3. FFG traditionally based on telemetric rain (and stream) gage networks
NCAR
Summary (cont)
1. More recently, FFG utilizes dual-polar radar with greater spatial sampling and “nowcasting” capabilities – but requiring more “overhead” to maintain
2. River flood forecasting (RFF) (medium to large catchments) requires less “local” and more “regional” knowledge of rainfall-runoff processes, and upstream catchment conditions
NCAR
Summary (cont)
1. RFF also benefit from lower requirements in rainfall spatial precision, and can thus utilize numerical weather prediction (NWP)
2. Larger catchments can benefit from long-lead weather forecasts (5-15 days), but which are inherently probabilistic (ensembles)
3. Ensemble RFF must account for uncertainties introduced throughout the “forecasting chain” to truly be effective in user decision making
NCAR
Summary (cont)
1. Indirect satellite measurements of river discharge (changes in river width or height) provide new potential for flood warnings by travel time lags in upstream water flow
“I have a very strong feeling that science exists to serve human welfare. It’s wonderful to have the opportunity given us by society to do basic research, but in return, we have a very important moral responsibility to apply that research to benefiting humanity.” Dr. Walter Orr Roberts (NCAR founder)