action research studies on climate change impacts in the...

Action Research studies on climate change impacts in the Haor Areasand Improving Flash Flood Forecasting

A.K.M. Saiful Islam

National Startup Workshop on Climate Adaptation and Livelihood Improvement Protection (CALIP)

29-31 January 2015, Dhaka, Bangladesh

Institute of Water and Flood Management Bangladesh University of Engineering and Technology (BUET)

Professor

Institute of Water and Flood Management - Bangladesh University of Engineering and Technology (BUET)

Outline

• Climate Change modeling and scenarios for the Haor Areas of Bangladesh

• Possible Impact of the Extreme Climate on the hydrological cycles of the Haor Regions

• Capacity building of the Flash Flood Forecasting of the Haor Regions of Bangladesh through action research and training.

• Overview of some of the on-going climate research at IWFM, BUET.


Green House Gases and Global Warming leading to Climate Change

• CO2 and some other minor gases absorb some of the thermal radiation leaving the surface of the earth.

• Emit radiation from much higher and colder levels out to space. These radiatively active gases are known as greenhouse gases.

• They act as a partial blanket for the thermal radiation from the surface which enables it to be substantially warmer than it would otherwise be, analogous to the effect of a greenhouse.


There is positive correlation of Global temperature and Greenhouse gases


Both Green House Gas and Global Temperature is rising at faster Rate in the last Decade than last 1000 years


NASA: Change in global surface temperature relative to 1951-1980

1884

2014Source: http://climate.nasa.gov/vital-signs/global-temperature/


Possible Changes of Global Mean Temperature and Sea Level Rise

Source: http://www.realclimate.org/index.php/archives/2013/09/the-new-ipcc-climate-report/


Climate Change Impact for Bangladesh

• Increase of intensity and duration of natural disasterssuch as floods, Cyclones and Storm Surges.

• Increase of moisture stress (droughts) due to erratic precipitation

• Salinity intrusion due to Sea Level Rise

• Inundation due to sea level rise leading towards “Climate Refugees”

• Effect on health and livelihood of coastal people.

• Effect on Bio-diversity, Ecology & Sundarbans.

• Hampered Food Security & Social Security.


Possible Consequences for the Haor Regions

• Increase of floods and landslide

• Change of onset of monsoon.

• Changes of rainfall patterns: distribution, frequency, intensity

• Impact on Agriculture due to Erratic Flood (Boro and Aman can be hampered)

• Impact of water bodies and fishers. http://commons.wikimedia.org/wiki/File:Fishi

ng_in_the_haor_of_Bangladesh.jpg


BUET Collaborative Studies on the Impact of CC on Haor Areas

• DFID Funded in collaboration with Hadley Center UK Met office “High-resolution Regional Climate Change Information for Bangladesh to inform Impacts assessments, Vulnerability indicators and Adaptation policies”.

• JRC Funded in collaboration with IWM ”Support Services on Mathematical Modeling for Bangladesh Side of the India-Bangladesh Joint Study Team for Joint Study on Proposed Tipaimukh Hydro-Electric (Multipurpose) Project of India”


Case Study in the Haor Basin Area: An Assessment of Present and Future Condition of Hydrological Cycle


Index Description Definition

R99p Frequencies in mm

Very wet days due to heavy rainfall event

exceeding 99%

R95p Frequencies in mm

Extremely wet days due to heavy rainfall event

exceeding 95%

PRCPTOT Total rainfall Annual total wet day when rain rate >1mm

SDII Daily Intensity index

Annual total rainfall divided by the number of wet

days (mm/day)

CDD Longest spell in days Consecutive dry days when rainfall < 1mm

CWD Longest spell in days Consecutive wet days when rainfall > 1mm

RX1day Intensity in mm One-day maximum rainfall

RX5day Intensity in mm Five-day maximum rainfall

Changes of Extreme Climate using Climate Indices


Regional Climate Change Scenarios

• GCM provides climate change predictions in a coarser resolution (>100km) which often fail to capture the sub-grid scale processes such as cloud formation occurs within 10km.

• Regional climate change modeling is dynamically downscaled using the same physical model but for a limited areas.

• PRECIS regional climate model has been used to dynamically downscaled to generate climate change information at a spatial resolution of 25km.


Future Climate Change Scenarios using Multi-member simulations of PRECIS

• UK Met office Hadley Center’s Regional Climate Model is used for downscaling GCM data at 25km resolution.

• The Quantifying Uncertainty in Model Predictions (QUMP)ensembles of 17 members of A1B scenarios are used to provide probabilistic predictions of future climate over the GBM basins.

•

RCM domain with 25km resolution


17 Member QUMP ensembles

Atmospheric CO2 concentrations (ppm) for the SRES A1B emissions scenario along with the four RCP scenarios (Rogelj et al., 2012)

Comparing the Global mean temperature change (0C) of the 17 member ensemble of HadCM3 with AR4 GCMs (Collins et al., 2011)


R99p

(mm)

@ 2050s @ 2080s

CDD

(days)

CWD

(days)

@ 2020s

…..changes from the baselineSpatial Distribution


R99p (mm)

CDD (days)

Anomaly

CWD (days)

…..from the baseline mean

-100.00

0.00

100.00

200.00

300.00

400.00

19

71

19

81

19

91

20

01

20

11

20

21

20

31

20

41

20

51

20

61

20

71

20

81

20

91

Years

Rai

nfa

ll 9

9th

Pe

rce

nti

le

An

om

alie

s, m

m

-200.00

0.00

200.00

400.00

600.00

19

71

19

81

19

91

20

01

20

11

20

21

20

31

20

41

20

51

20

61

20

71

20

81

20

91

Years R

ain

fall

95

th P

erc

en

tile

An

om

alie

s, m

m

-400.00-200.00

0.00200.00400.00

600.00800.00

19

71

19

81

19

91

20

01

20

11

20

21

20

31

20

41

20

51

20

61

20

71

20

81

20

91

Years

To

tal P

reci

pit

atio

n

An

om

aly,

mm

-2.00-1.000.001.00

2.003.004.00

19

71

19

81

19

91

20

01

20

11

20

21

20

31

20

41

20

51

20

61

20

71

20

81

20

91

Years

Dai

ly In

ten

sity

An

om

alie

s, m

m/r

ain

y

day

-400.00-200.00

0.00200.00400.00600.00800.00

19

71

19

81

19

91

20

01

20

11

20

21

20

31

20

41

20

51

20

61

20

71

20

81

20

91

Years

Co

nse

cuti

ve D

ry D

ays

An

om

alie

s, d

ays

-8.00-6.00-4.00-2.000.002.004.00

19

71

19

81

19

91

20

01

20

11

20

21

20

31

20

41

20

51

20

61

20

71

20

81

20

91

Years

Co

nse

qu

tive

We

t D

ays

An

om

alie

s, d

ays

R95p (mm) PRCPTOT (mm)

SDII (mm/day)


0

0.05

0.1

0.15

0.2

0.25

30 35 40 45

Consecutive Dry Days in a Year

Prob

abili

ty

Baseline 2020s2050s 2080s

0

0.005

0.01

0.015

0.02

0.025

0.03

90 140 190

Prob

abili

ty


0

0.01

0.02

0.03

0.04

0.05

0.06

40 60 80 100

Prob

abili

ty


0

0.1

0.2

0.3

0.4

0.5

25 27 29 31 33 35

Consecutive Wet Days in a year

Pro

bab

ilit

y


Probability Distribution Function

One-day Maximum Rainfall, mm Five-day Maximum Rainfall, mm


Conclusion

• Significant increase in magnitudes and variability both rainfall and temperature during premonsoon, monsoon and annual rainfall.

• Highest variability in both rainfall and temperature during the premonsoon

• Premonsoon rainfall is expected to be 0.42 - 75% more in 2080s.

• The rainy days are projected to be less frequent and more intense where the deeply flooded haors are situated. Projection on continuous decrease in anomaly of CWD and continuous increase in anomaly of R99p also support the increasing tendency of SDII.

• Significant increase in both RX1 and RX5 during the premonsoonseason shows a tendency towards more occurrences of flash floods with high volumes.

• Except CDD and CWD, all indices show more rightward shifting in future indicating increase in amount.

Reference:

Nowreen, S., Murshed, S.B., Islam, A.S., Bhaskaran, B. and Hasan, M.A. (2014), “Changes of Rainfall Extremes around

the Haor Basin Areas of Bangladesh using Multi-member Ensemble RCM”, Theoretical and Applied Climatology 1-15, (doi:10.1007/s00704-014-1101-7).


BUET’s proposal for CALIP

Developing Flash Flood Early Warning System, Capacity Building and Knowledge Management for the Haor

Region of Bangladesh

• Research on Rainfall Forecasting using WRF model

• Rainfall-Runoff Modeling of the Haor Basin using open source tools and models.

• Capacity building for flood forecasting and early warning through Short Training Courses.

• Providing Support to the Master’s Students of BUET through Research Assistantship


Flow Chart of Methodology

• WRF Data Assimilation

• WRF Rainfall forecasting

• Rainfall Runoff modeling

• Hydraulic Modeling

• Flood Inundation Modeling

• Flood information at the Gauge stations and flood maps


Two Recently completed Masters at IWFM on WRF and basin modeling relevant to the BUET proposal on CALIP

• Alfi Hasan’s MSc thesis on “Improvement of forecasting heavy rainfall events using Weather Research and Forecasting (WRF) model”

• Supriya Paul’s MSc theis on Impact of Climate Change Hydrological Modeling of Brahmaputra using SWAT


Improvement of forecasting heavy rainfall events using Weather Research and Forecasting (WRF) model

• There are several physical options in WRF, which are responsible for generating rainfall in the model.

• With a selection of suitable physical schemes, the forecast skill of heavy rainfall events can be increased effectively.

• Therefore, this study is conducted to evaluate the high impact rainfall events over Bangladesh using WRF model.


Model domain

• Domain Extant: 71°East to 97° East; 9°North to 32°North

• Spatial Resolution: 30 km by 30km (domain 1) and 10km by 10km (domain 2)

• Static data resolution: 30second (~900m resolution)

• Parent grid ration 1:3

• Grid size: 91 by 91 (D1); 115 by 115 (D2).

• Map Projection: Lambert Conformal Projection

• Reference Latitude: 21N

• Reference Longitude: 83.5E

• True Median Latitude: 21N


Data used

Name of Data Spatial

Resolution

Time

frequency

Extent Temporal

Coverage

Global Precipitation

Climatology Project (GPCP)

50km day Global 1981-2010

Tropical Rainfall Measuring

Mission (TRMM)

25km 3 hour Global 1998-present

Asian Precipitation Highly-

Resolved Observational Data

Integration Towards

Evaluation (APHRODITE)

25km daily Asia 1981-2007


Selected Convective and Microphysics schemes

Convective Schemes

• Kain–Fritsch Scheme

• Betts–Miller–Janjic Scheme

• Grell 3D Scheme

• Tiedtke Scheme

• New Simplified Arakawa–Schubert Scheme

Microphysics Schemes

• Kessler Scheme

• Lin et al. Scheme

• WRF Single–moment 6–class Scheme

• Stony–Brook University Scheme


Selected Microphysics and Cumulus schemes

ID Name Micro-physics scheme Cumulus schemes01 KEKF Kessler (1) Kain-Fritsch (new) (1)02 KEBJ Kessler (1) Betts-Miller-Janjic (2)03 KEGR Kessler (1) New Grell 3D (5)04 KETD Kessler (1) Tiedtke (6)05 LNKF Lin et al. (2) Kain-Fritsch (Eta) (1)06 LNBJ Lin et al. (2) Betts-Miller-Janjic (2)07 LNGR Lin et al. (2) New Grell 3D (5)08 LNTD Lin et al. (2) Tiedtke (6)09 WMKF WRF Single–moment 6–class (6) Kain-Fritsch (Eta) (1)10 WMBJ WRF Single–moment 6–class (6) Betts-Miller-Janjic (2)11 WMGR WRF Single–moment 6–class (6) New Grell 3D (5)12 WMAS WRF Single–moment 6–class (6) New Arakawa–Schubert (14)13 SUKF Stony–Brook University (13) Kain-Fritsch (Eta) (1)14 SUBJ Stony–Brook University (13) Betts-Miller-Janjic (2)15 SUGR Stony–Brook University (13) New Grell 3D (5)16 SUAS Stony–Brook University (13) New Arakawa–Schubert (14)17 SUTD Stony–Brook University (13) Tiedtke (6)18 WMTD WRF Single–moment 6–class (6) Tiedtke (6)19 LNAS Lin et al. (2) New Arakawa–Schubert (14)


Spatial RainfallPatterns

• Spatial pattern of rainfall in (a)KEKF, (b)KEBJ, (c)KEGR, (d)KETD, (e)LNKF, (f)LNBJ, (g)LINGR3D, (h)LNTD (i)WMKF (j)WMBJ, (k)WMGR, (l)WMAS, (m)SUKF (n)SUBJ, (o)SUGR, (p)SUAS, (q)SUTD, (r)WMTD, (s)LNAS, scheme and (T)

• TRMM observed data during 26th June 2012.


Comparing observed vs WRF model for various schemes


Performance Indicators

• Threat Score 𝑇𝑆 =𝑎

𝑎 + 𝑏 + 𝑐

The threat score is the number of

correct “yes” forecasts divided by the

total number of occasions on which

that event was forecast and/or

observed



• False Aram 𝐹𝐴𝑅 =𝑏

𝑎 + 𝑏

FAR is the fraction of “yes” forecasts that

turn out to be wrong, or that proportion of

the forecast events that fail to materialize.



• Hit Score 𝐻 =𝑎

a + 𝑏

The hit rate (H) is the ratio of

correct forecasts to the number of

times this event occurred.


Summary

• In summary, it has found that Stoony Brook University microphysics scheme with Tiedtke cumulus (SUTD) can produce best rainfall forecast for the high intensive rainfall events over the eastern hilly region of Bangladesh.

• The existing default physical scheme (Kessler and Kain-Fritsch (new) scheme) that is commonly used by Bangladesh Meteorological Department (BMD) has poorly performed in capturing high impact rainfall over the Chittagong Division


Masters on Impact of Climate Change Hydrological Modeling of Brahmaputra using SWAT

SWAT is a open source spatially distributed, continuous time scale watershed scale. It was developed to predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, landuse and management conditions over long periods of time.

schematic representation of hydrologic cycle


DEM and Landuse Map of Brahmputra basin

Digital Elevation model of the Brahmaputra basin

Land use map of Brahmaputra Basin

Paul and Islam et al. (2014)



Observational and Gridded Data

Satellite Data Res.

(deg.)

Duration

TRMM 0.25 2000-2009

APRODITE 0.25 1998-2007

GPCP 0.25 1998-2007

ERA-Intrim

(temp data)

0.25 1998-2009

Location of precipitation point

Daily gridded rainfall data sets



SWAT- Model Calibration and Validation using TRMM data

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

Oct-00 Apr-01 Nov-01 May-02 Dec-02 Jun-03 Jan-04 Aug-04 Feb-05 Sep-05

Dis

cha

rge(

m3

/s)

Date

Calibration period (2001-2004)

observed runoff from TRMM

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

May-05 Oct-06 Feb-08 Jul-09 Nov-10

Dis

cha

rge

(m3

/s)

Date

Validation period (2006-2009)

observed runoff from TRMM

Calibration period 2001-20014

Validation period 2006-2009



Statistical parameter of calibration and validation using different rainfall datasets

Aphrodite GPCP TRMMTime period

1999-2002

2004-2007

1999-2002

2004-2007

2001-2004

2006-2009

R2 0.92 0.72 0.88 0.74 0.83 0.85NSE 0.77 0.13 0.78 0.235 0.77 0.62RSR 0.27 0.5 0.26 0.5 0.27 0.33PBIAS 30 52 25 50 -1 25

Aphrodite GPCP TRMMTime period

1999-2002

2004-2007

1999-2002

2004-2007

2001-2004

2006-2009

R2 0.96 0.75 0.92 0.77 0.91 0.91NSE 0.79 0.09 0.82 0.214 0.87 0.63RSR 0.43 0.93 0.4 0.91 0.32 0.35PBIAS 30 52 25 50 -1 25

Calibration

Validation TRMM provides best result !



Future Monthly Precipitation and temperature (changes from baseline)

% change of Temperaturefrom baseline

In three future periods: 2020s, 2050s and 2080s

% change of Rainfallfrom baseline



Seasonal Changes of flow

Change of seasonal flow

% Change of seasonal flow



Changes of flow in 2020s

Change of monthly flow

% Change of monthly flow


Summary

• The uncertainty for pre-monsoon flow maintain through the end century which is very high. But the confidence level for increasing monsoon flow are more prominent.

• Majority of the model predicts future monsoon flow will be increase by 5 to 10%.


Some relevant on-going studies on climate Change at BUET lead by my research group (Hydrology and Climate Change, HCL)

• EU funded “High End Climate Impact and Extremes (HELIX)” lead by Exeter University, UK

• Norwegian Ministry Funded “Transforming Climate Knowledge with and for Society: mobilizing knowledge on climate variability with communities in northeast Bangladesh (TRACKS)” lead by Burgen University.

• DANIA Funded “Combating Cholera caused by Climate changes of Bangladesh”, lead by University of Copenhagen.

http://helixclimate.eu/home

http://web9.swayam-hosted.co.uk/tracks/

http://drp.dfcentre.com/project/combatting-cholera-caused-climate-changes


On-going projects at IWFM, BUET

• AFACI funded research in collaboration with BARC on “Production and Service of Agro-meteorological Information for the Adaptation to Climate Change in Bangladesh”.

• DOE funded research in collaboration with CEGIS on “Assessment of Sea Level Rise and Vulnerability in the Coastal Zone of Bangladesh through Trend Analysis”.

• (PI- Prof Munsur Rahman, IWFM for Bangladesh) Funded by NERC, DFID, ESRC in collaboration with Southampton university “Ecosystem Services for Poverty Alleviations”

http://www.espadelta.net/partners/bangladesh/


Thank you

Questions ?

HCL Research Group at IWFM, BUEThttp://teacher.buet.ac.bd/akmsaifulislam/group/index.html

http://teacher.buet.ac.bd/akmsaifulislam/group/index.html

action research studies on climate change impacts in the...

Documents