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)