Climate Change Impact on Floods in...

Climate Change Impact on Floods in Bangladesh

A.K.M. Saiful Islam

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

Photo Courtesy: Green Peace

Outline

• Understanding climate change. Predictions ofclimate change for Bangladesh

• Changes of future rainfall extremes and internalfloods through rainfall extreme indicators usingobserved and regional climate model data.

• Changes of the Riverine floods of theBrahmaputra river Basin using hydrological basinmodeling.

Understanding climate change at Global, Regional and Local Scale

Climate Change, Global Warming and Green House Effect

• Co2 and some minor radioactively active gases are (known as greenhouse gases) acted 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

Increasing trends of CO2

Human induced changes of green house gases

CO2 from the measuring station at Mauna Loa (Hawaii) is located at an altitude of 3400 meters

Global temperature and Greenhouse gases

Climate Forcing

Temperature variation past 1,000 years

Increase of Temperature past 140 year

Trends of increase of Temperature

Surface Air temperature (1960-1990)

Trends of Seal Surface temperature

Changes of Sea Surface Temperature

Ice melting

• Images from gathered from the Defense Meteorological Satellite Program of NASA show the minimum Arctic sea ice concentration 1979 (left) and 2003 (right).

1979 2003

Cracks in Ice bars

Sea Level Rise (1980-2000)

Trends of Precipitations

Predictions by Climate Models

• Climate models are computer-based simulations that use mathematical formulas to re-create the chemical and physical processes that drive Earth’s climate.

• To “run” a model, scientists divide the planet into a 3-dimensional grid, apply the basic equations, and evaluate the results.

• Atmospheric models calculate winds, heat transfer, radiation, relative humidity, and surface hydrology within each grid and evaluate interactions with neighboring points.

• Climate models use quantitative methods to simulate the interactions of the atmosphere, oceans, land surface, and ice.

GCM typical horizontal resolution of between 250 and 600 km, 10 to 20 vertical

layers in the atmosphere and sometimes as many as 30 layers in the oceans.

Projected Annual Green House Gas Emission

Future Projection of Green House Gas

Global Average Surface warming

Projected Change in Average Annual Temperature

Near Term projections of global mean temperature

Predicted Arctic sea Ice

Arctic Sea Ice in

2040Arctic Sea Ice in

2000

Results from community climate system models

Prediction of Sea level rise

Changes of Average Precipitation

Impacts of climate change

• Human Health impacts

• Ecosystem Impacts

• Agriculture Impacts

• Water Resources Impacts

• Market Impacts

Possible Climate Change Impact for Bangladesh

• Increase of intensity and duration of naturaldisasters such 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.

Global Emission

Per capita CO2 emission

Per capita emission

Bangladesh

1990 – 0.1 ton

2009 – 0.36 ton

Observed Changes of Rainfall extremes in Bangladesh

Changes of rainfall extremes and floods inside Bangladesh

• Changes of the rainfall extremes has profound impact on the economy, livelihood and ecosystems of Bangladesh.

• The high-intensity rainfall has become more frequent in the recent years, which is evident from the events like 341mm of rainfall in 8 hours in 2004 and 333mm of rainfall in 2009 in Dhaka, and 408mm of rainfall in 2007 in Chittagong.

• These rainfall events indicate a change in extreme rainfall characteristics in Bangladesh.

• A detailed analysis of the changes of the trends of the heavy rainfall, its pattern, magnitude, frequency, and intensity has been conducted and presented in the different hydrological regions of the country.

Climate of Bangladesh

• There are four climatic seasons in Bangladesh.

• Pre-monsoon season characterized by hot weather consist of March, April and May.

• Monsoon season, when almost 80% of rainfall occurs starts from June and end it by September.

• October and November are termed as Post Monsoon and December, January and February represents dry winter season.

http://www.banglapedia.org/httpdocs/Maps/MC_0288.GIF

http://www.banglapedia.org/httpdocs/Maps/MC_0288.GIF

Hydrological Regions of Bangladesh• Eight hydrological planning regions

of Bangladesh classified by Water Resources Planning Organization to facilitate water management of the country. These regions are: North East (NE), North Central (NC), North West (NW), South East (SE), South Central (SC), South West (SW), Eastern Hill (EH) and River and Estuary (RE).

• Results obtained from this study has presented for the stations in one of the eight hydrological (planning) regions of Bangladesh as per NWMP, 2001.

NW

SW

SC

NCNE

SE

RE EH

• Data of 28 stations out of 34 stations of BMD used in the study which passes homogeneity and consistency test.

• Data collected from BMD-

– Rainfall

– Maximum and Minimum Temperature

• Observed data has been divided into the following two time periods each 20 years to detect the changes

– 1971-1990

– 1991-2010

• Seasons:

– Winter (Dec-Feb)

– Pre monsoon (Mar-May)

– Monsoon (Jun-Sep)

– Post monsoon (Oct-Nov)

Collection of observed meteorological data from Bangladesh Meteorological Department

Temperature Data Analysis of last 60 years (1947-2007)

Mean daily temperature of Bangladesh has increased with a rate of 1.03 0C per century

y = 0.0103x + 25.428

R2 = 0.2996

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Relationship with Rainfall

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Data Quality and consistency check of meteorological data

Although Bangladesh Meteorological Department (BMD)has thirty seven ground based stations, but only data of thirty five (35) stations are available. At initial stage, quality of rainfall and temperature data are checked by verifying the following criteria (Peralta-Hernandez et al., 2009; Shahid, 2011)-

• Non-existence of dates• Negative daily precipitation• Daily winter rainfall > 100mm• Consecutive dry days > 10 in Monsoon• Weather stations > 35% missing data• Stations with gaps three or more years in between series

Selection of Meteorological stations

• Stations that are not able to fulfilling the criteria ofquality check are rejected. So, six BMD stationsChittagong(Patenga), Chuadanga, Kutubdia, Mongla,Sayedpur, Tangail are discarded after following thepreceding conditions considering data period from 1961to 2010.

• The investigation has been carried out using daily records of rainfall from 29 ground based stations of Bangladesh Meteorological Department (BMD) distributed over the country during the time period 1961-2010.

Homogeneity Test

• R-based program, RHtest, developed at the Meteorological Service of Canada, is used to detect non-homogeneities in the daily data series.

• This software uses a two phase regression model to check the multiple step-change points that could exist in a time series (Wang, 2003).

• Non-homogeneous data sets are eliminated based on the above test from the trend analysis.

Trend Analysis• To smooth out short-term fluctuations

and highlight longer-term trends orcycles, five-year moving average, a typeof finite impulse response filter, is usedto analyze and compute the trends ofprecipitation records (Gallant et al.,2007).

• The computed trends of indices areused non parametric Kendall’s tau basedslope estimator known as Theil-SenSlope estimator or Sen’s slopeestimator. It is more accurate forskewed distribution (e.g. rainfall) thansimple linear regression.

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Year

Hydrological Regionwise 5 years

Moving Average for SDII

5 years moving average (NE)5 years moving average (NW)5 years moving average (NC)5 years moving average (SE)5 years moving average (SW)5 years moving average (SC)5 years moving average (EH)

Trends of Rainfall (mm/year)

Zones

Pre monsoon Monsoon Post

Monsoon

Winter

North West1.89 4.26 1.99 0.10

North East5.62 -0.69 -0.25 -0.09

North Central1.36 3.29 1.40 0.26

South West3.25 7.05 1.37 0.68

South East2.13 -2.25 -0.16 0.13

South Central2.03 5.88 1.19 0.10

River and

Estuary

3.86 1.28 1.81 -0.08

Eastern Hilly5.12 8.49 1.33 0.32

Decadal Changes of Annual Rainfall

1970-1980 1991-2000 2001-2010

Extreme Climate using Indices

• A total of 11 indices for the precipitation at different thresholds have been calculated. These indices greatly facilitate assessment of the changes in precipitation and temperature patterns, intensities, frequency and extremes.

• Annual and seasonal trends of precipitation indices and their spatial distributions are analyzed. A software RClimDex 2.14, has been used for processing data and calculating indices.

Index Definition

R99p Very wet days due to heavy rainfall event exceeding 99%

R95p Extremely wet days due to heavy rainfall event exceeding 95%

PRCPTOT Annual total wet day when rain rate >1mm

SDII Annual total rainfall divided by the number of wet days (mm/day)

CDD Consecutive dry days when rainfall < 1mm

CWD Consecutive wet days when rainfall > 1mm

RX1day One-day maximum rainfall

RX5day Five-day maximum rainfall

RX10 No of rainy days when rainfall > 10mm

RX20 No of rainy days when rainfall > 20mm

RX 100 No of rainy days when rainfall > 100mm

Precipitation related Climate Indices

North East, Central and West Regions

Hydrologic

Region Stations

RX1

day

RX5

Day SDII R10mm R20mm R100mm CDD CWD R95P R99P PRCPTOT

North EastSreemongal -0.396 -0.738 -0.041 -0.018 -0.026 -0.003 0.336 0.073 -1.042 1.878 -0.258

Sylhet -0.394 -0.447 -0.043 -0.084 -0.034 -0.013 0.583 -0.07 -4.868 -2.422 -5.36

North West

Bogura -0.091 0.07 0.011 0.15 0.077 0.004 0.701 -0.042 -0.033 -1.328 3.856

Dinajpur 0.744 1.066 -0.01 0.151 0.048 0.044 0.471 0.104 5.82 3.543 9.687

Ishardi -0.773 -0.566 -0.022 0.043 -0.012 -0.016 0.273 0.004 -2.898 -2.03 -2.459

Rajshahi 0.214 0.536 -0.098 -0.077 -0.066 -0.015 0.459 -0.008 -3.086 -0.75 -3.696

Rangpur 0.87 1.69 0.047 0.175 0.144 -0.006 0.372 -0.041 0.963 2.84 5.914

Sayedpur 1.149 2.507 -0.164 -0.247 -0.281 -0.085 3.253 0.002 0 0 -19.859

North Central

Dhaka 0.013 0.406 0.024 0.044 0.02 -0.02 0.599 -0.028 -1.727 0.483 1.605

Mymensingh 0.775 1.106 -0.001 0.011 0.06 -0.01 0.494 0.057 1.667 1.806 5.177

Tangail 6.159 6.447 0.048 -0.165 -0.088 -0.046 3.072 -0.14 -2.653 0.228 -1.841

Faridpur -0.568 -0.028 -0.054 -0.004 -0.033 0.002 0.428 -0.001 -0.996 -1.7 -2.392

Trends that are significant as per Mann-Kendall test

South East, Central and West Regions

Hydrolo

gic

Region Stations RX1 day RX5 Day SDII R10mm R20mm R100mm CDD CWD R95P R99P

PRCPTO

T

South East Comilla -0.527 -0.256 -0.154 -0.022 -0.069 -0.025 0.526 0.032 -4.132 -4.438 -6.203

Feni -0.836 0.26 -0.007 -0.231 -0.209 -0.019 1.438 -0.128 -4.982 0.155 -9.211

Maijdicourt0.704 0.694 -0.146 -0.005 -0.041 -0.031 0.315 0.13 -3.728 1.609 -3.15

South West Chuadanga 3.703 6.148 0.055 -0.306 -0.127 0.029 0.617 -0.097 4.187 5.889 -2.297

Jessore -0.057 0.683 0.049 0.177 0.115 0.024 0.454 -0.031 3.904 2.163 8.109

Mongla 0.127 1.923 0.125 0.214 0.155 0.024 3.199 0.465 0 0 4.81

South

Central

Chandpur -1.419 -1.731 -0.143 0.028 -0.113 -0.082 0.062 0.082 -11.493 -4.356 -9.478

Barisal -0.551 -0.57 -0.032 0.038 0.021 -0.003 -0.05 -0.013 -2.678 -1.585 -0.382

Khepupara 1.08 5.495 0.008 0.343 0.207 0.034 0.537 0.021 5.42 3.316 12.987

Madaripur 0.345 1.494 -0.113 -0.36 -0.25 -0.006 1.172 0.046 -2.732 -0.622 -14.39

Patuakhali 0.015 2.355 -0.185 -0.11 -0.07 -0.067 0.975 0.089 -6.839 -4.634 -8.687


Estuary and Hilly Regions Hydrolog

ic Region Stations RX1 day RX5 Day SDII R10mm R20mm R100mm CDD CWD R95P R99P

PRCPTO

T

River and

Estuary

Bhola 4.779 5.357 -0.044 -0.279 -0.183 -0.005 -0.237 -0.144 1.965 2.133 -7.096

Hatia 0.979 2.261 0.035 0.121 0.115 0.015 0.656 0.012 4.818 8.308 8.746

Sandwip 1.179 4.644 -0.189 0.143 -0.014 0.002 0.092 0.073 6.612 9.949 7.349

Eastern

Hilly

Region

Chittagong 0.49 1.232 0.05 0.074 0.058 -0.018 0.383 -0.029 -2.828 -1.725 1.69

Cox'sbazar 0.589 -0.179 -0.074 0.105 0.01 -0.053 -0.251 -0.027 -6.201 -4.335 -2.529

Kutubdia 2.967 5.051 0.105 0.525 0.482 0.048 0.408 0.082 5.216 4.053 23.774

Rangamati 1.183 1.245 -0.007 0.023 0.034 -0.002 -0.088 -0.002 3.142 2.566 3.424

Sitakundo -0.135 3.313 0.035 0.141 0.199 -0.002 0.01 0.132 0.329 -0.995 8.135

Teknaf 3.222 5.965 0.224 0.391 0.449 0.082 0.312 -0.029 15.138 10.141 32.636


CDD and CWD – (dry and wet spells)

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Trends of Maximum 1-day and 5-day precipitation – (Intensity)

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R95 and R99 (Frequency)

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R95

• Monthly maximum one day precipitation (RX1) and the monthly maximum five days precipitation (RX5) exhibit non-significant increasing trends at 65% and 75% BMD stations, respectively.

• The total amount of annual precipitation (PRCPTOT) isincreasing for all the eight regions along with increasingtrends in consecutive dry days (CDD). It is prominent in theEH region with the highest increasing trend of 6.12mm/year of PRCPTOT and 0.157 day/year of CDD. Thisindicates that a higher amount of rainfall will occur within ashorter period of time.

Key findings

Key findings

• Annual total precipitation greater than the 95th percentile(R95) also exhibits an increasing trend except in the NEhydrological region. Rainfall greater than 100 mm (R100) isalso decreasing in the NE region. Although the trend inPRCPTOT is increasing, this trend (0.1576 mm/year) isrelatively less significant than others in this particular region.

• CDD is also found to be increasing. Therefore it is predictedthat a longer drier condition will prevail in the NE region,where the highest rainfall occurs at present. The SW regionshows the highest significant change in precipitation indiceswhereas the RE region exhibits the least significant variationin precipitation indices. It is revealed from this study thatshort duration high intensity rainfall is increasing inBangladesh.

Regional Climate Scenarios through Dynamic Downscaling

• Impact assessors need regional detail to assess vulnerability and possible adaptation strategies

• AOGCM projections lack that regional detail due to coarse spatial resolution

• Downscaling for climate change assessment differs from downscaling of seasonal climate prediction

Climate downscaling and Regional Climate

Chagne Scenios

Going from global to local climate …

• They take coarse resolution

information from a GCM

and then develop

temporally and spatially

fine-scale information

consistent with this using

their higher resolution

representation of the

climate system.

• The typical resolution of an

RCM is about 50 km in the

horizontal and GCMs are

typically 500~300 km

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.

• The user-relevant and policy oriented climate change information suitable for decision makers in Bangladesh.

RCM domain with 25km resolution

© Crown copyright Met

Office

PRECIS – Regional climate model developed by Hadley Centre, UK Met office

Choice of model domain and resolution

RCM, GCM/ Reanalysis and scenario

Experiment start date and run length (with spin-up)

Output data configurations

Run

Monitor

Stop

Map of Region

Fine scale configurations to region

Topography of Experiment Domain

17 Member QUMP ensemblesAtmospheric 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)

Predictions of future changes using Regional Climate Model results

• Decadal changes in annual rainfalls in the future are also determined. Regional climate model PRECIS is used to predict various climatic parameters such as temperature and rainfall over Bangladesh.

• The data of the Special Report on Emission Scenarios (SRES) A1B, which is a moderate emission scenario (a balance across all sources), have been used to generate the PRECIS model. Results of PRECIS simulation for 2020s (2011-2040), 2050s (2041-2070) and 2080s (2071-2100) are used.

Model Evaluation: Annual Cycle of Temperature

• Temperature pattern adjust with model simulations.

• Spatial pattern also captured well by the model simulations.

15

17

19

21

23

25

27

29

31

33

35

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Tem

per

etu

re (°C

)

Qump0

Qump1

Qump2

Qump3

Qump4

Qump5

Qump6

Qump7

Qump8

Qump9

Qump10

Qump11

Qump12

Qump13

Qump14

Qump15

Qump16

Qump_All

Observe

Warm bias

Coldbias

Inter-Seasonal Changes of Temperature

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

20s 50s 80s

0.0

1.0

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7.0

20s 50s 80s

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1.0

2.0

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6.0

7.0

20s 50s 80s

0.0

1.0

2.0

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4.0

5.0

6.0

7.0

20s 50s 80s

Winter Summer MonsoonPost-

monsoon

More increase

More increase

Model Evaluation: Annual Cycle of Rainfall

• Model capture similar annual rainfall pattern for all the ensembles.

• Even, observational data sets are not similar to each other due to grid generation methodology.

0

2

4

6

8

10

12

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18

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Da

ily

ra

infa

ll (

mm

/da

y)

Qump0

Qump1

Qump2

Qump3

Qump4

Qump5

Qump6

Qump7

Qump8

Qump9

Qump10

Qump11

Qump12

Qump13

Qump14

Qump15

Qump16

Qump_All

APH

CRU

GPCP

Observe

Dry bias

Changes of Future Rainfall • Three time slices have used :

– 2020s as short period (2011–2040)– 2050s as medium period (2041–2070) – 2080s as long period (2071–2098) – 1980s as baseline (1971-2000)

-150

-100

-50

0

50

100

150

200

250

300

350

400

450

500

550

20

11 t

o

20

40

20

41 t

o

20

70

20

71 t

o

21

00

Ca

hn

ge

of

an

nu

al

rain

fall

(m

m)

Increase of rainfall in Monsoon

Decrease of rainfall in post monsoon

Probability Distribution Functions of SDII, CDD, CWD, RX5

SDII RX5

CWDR20

Changes of one day maximum precipitation, RX1 for 2050s and 2080s from baseline for the pre-monsoon, monsoon

and post monsoon seasons

2050s

2080s

Pre-monsoon Monsoon Post-monsoon

Pre-monsoon Monsoon Post-monsoon

Future changes of the Number of Days above Rainfall 20mm (RX20)

2020s 2050s 2080s

Inter-annual changes of Rainfall from CMIP5 models over CORDEX domain

Inter-seasonal changes of rainfall from CMIP5 models over CORDEX domain

Monsoon rain increase

Winter rain decrease

Changes of extreme rainfall due to climate change

• Probabilities of the intensity of precipitation, consecutive 5 day precipitation and heavy precipitation show positive trends of precipitation extremes for all three future time slices. Higher changes are found in the 2080s than 2050s and 2020s.

• On the other hand, probabilities of consecutive wet days will be reduced in future. The reduction of the probabilities of CWDs represents than the length of monsoon will be shorter but intensified.

• Among those, five stations show significant negative trends. The probabilities of SDII with respect to four time spans (i.e., 1970s, 2020s, 2050s and 2080s) are analyzed. Such findings show a rapidly increasing trend of present SDII (1971-2000) from 8.0 to 9.5 mm/day.

Changes of Riverine Monsoon Floods of Bangladesh focusing

Brahmaputra Basin

Monsoon or Riverine Floods in Bangladesh under the changing climate

• Floods is a regular phenomenon for Bangladesh.

• Every year one-third areas of Bangladesh are flooded by a water carried from GBM basins during monsoon seasons.

• Major floods occurred in 1988, 1998, 2004 and 2007.

Indian Summer Monsoon (Southwest monsoon)

Onset dates and prevailing wind currents of the southwest summer monsoons in India.

• The southwestern summer monsoons occur from July through September.

• The Thar Desert and adjoining areas of the northern and central Indian subcontinent heats up considerably during the hot summers.

• This causes a low pressure area over the northern and central Indian subcontinent. To fill this void, the moisture-laden winds from the Indian Ocean rush in to the subcontinent.

• These winds, rich in moisture, are drawn towards the Himalayas. The Himalayas act like a high wall, blocking the winds from passing into Central Asia, and forcing them to rise.

• As the clouds rise their temperature drops and precipitation occurs. Some areas of the subcontinent receive up to 10,000 mm (390 in) of rain annually.

Monsoon circulations

Ganges, Brahmaputra and Meghna(GBM) Basins

• Bangladesh is a delta formed by the three major rivers, namely the Ganges, Brahmaputra and Meghnawith a total area of just over 1.7 million km2, distributed between India (64%), China (18%), Nepal (9%), Bangladesh (7%) and Bhutan (3%)

Flood Hydrographs of major rivers in Bangladesh Delta and Climate Observatory

Flood Forecasting and Warning Center (FFWC) of Bangladesh

Flood Warning Map of Today : 10 July 2014

Impact of Climate Change Hydrological Modeling of Brahmaputra using SWAT

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

schematic representation of hydrologic cycle

DEM and Landuse Map of Brahmputrabasin

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

Changes of the future flows using CMIP5 CORDEX-SA data

-20.00%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

January February March April May June July August September October November December

Percent Change of Mean Discharge in Brahmaputra River under RCP4.5 and RCP8.5 Scenarios

rcp45_20s rcp85_20s rcp45_50s rcp85_50s rcp45_80s rcp85_80s

0

5000

10000

15000

20000

25000

30000

35000

40000

45000


Future Projection of Mean Discharge in Brahmaputra River under RCP4.5 Scenario

historical rcp45_20s rcp45_50s rcp45_80s

0

5000

10000

15000

20000

25000

30000

35000

40000

45000


Future Projection of Mean Discharge in Brahmaputra River under RCP8.5 Scenario

historical rcp85_20s rcp85_50s rcp85_80s

Predicted seasonal discharge of Brahmaputra River under RCP 8.5 scenarios of different RCMs

RCM Model

1 ACCESS1-0_CSIRO-CCAM-1391M

2 CCSM4_CSIRO-CCAM-1391M

3 CNRM-CERFACS-CNRM-CM5_SMHI-RCA4

4 CNRM-CM5_CSIRO-CCAM-1391M

5 ICHEC-EC-EARTH_SMHI-RCA4

6 MPI-ESM-LR_CSIRO-CCAM-1391M

7 MPI-M-MPI-ESM-LR_MPI-CSC-REMO2009

8 MPI-M-MPI-ESM-LR_SMHI-RCA4

9 NOAA-GFDL-GFDL-ESM2M_SMHI-RCA4

10 IPSL-CM5A-MR_SMHI-RCA4

11 MIROC-MIROC5_SMHI-RCA4

Summary of changes of riverine floods of Bangladesh in Brahmaputra river basin

• 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 on-going studies on climate Change at BUET in collaboration with EU universities/institutes

• 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 BurgenUniversity.

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

• Funded by NERC, DFID, ESRC in collaboration with Southampton university “Ecosystem Services for Poverty Alleviations (ESPA-deltas)”

http://helixclimate.eu/home

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

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

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

Why look at > 2°C?

2°C

Emissions limit

to stay below

2°C

We are

here

Source: IPCC (2013)Courtesy: R. Betts

How do the impacts of a specific warming level depend on timing?

IPCC

(2013)

30 more years to adapt

Courtesy: R. Betts

17 Partners

• University of Exeter, UK

• Met Office Hadley Centre, UK

• Tyndall Centre, University of East Anglia, UK

• Potsdam Institute for Climate Impact Research, Germany

• Institute Pierre-Simon Laplace, France

• Rossby Centre, Sweden

• Joint Research Centre, Spain

• Joint Research Centre, Italy

• World Food Programme, Italy

• IGAD Climate Prediction and Applications Centre, Kenya

• Bangladesh University of Engineering and Technology, Bangladesh

• Indian Institute of Technology Delhi, India

• National Agency for Civil Aviation and Meteorology, Senegal

• University of Liege, Belgium

• University College London, UK

• Technical University of Crete, Greece

• Free University Amsterdam, Netherlands

Courtesy: R. Betts

Thank you

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