Impact of high end climate change for Bangladesh on the water resources, agriculture and food security

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

A.K.M Saiful Islam, GM Tarekul Islam, Sujit Kumar Bala

3rd HELIX Annual Meeting on 10-14 October 2016 at Postdam, Germany

Outline of the presentation• Impact on climate extremes – changes of temperature and rainfall

extremes

• Impact of High end climate change on water sector –The high flow (floods) and low flow extremes of the Brahmaputra river that carries two third of the flow was investigated.

• Impact of sea level rise in (SLR) the coastal regions of Bangladesh-inundation patterns due to SLR under normal conditions and cyclonic storm surges

• Impact on Agriculture and food security – changes of the yield of the major crop Boro rice over Bangladesh

• Changes of the vulnerability of the coastal regions of Bangladesh

Introduction

• Bangladesh is one of the most vulnerable countries considering climate change due to its geographic location, high population density, poverty and natural disasters.

• Country is sufferings from a number of natural disasters such as monsoon floods, early pre-monsoon flash floods, heavy rainfall and landslides, cyclones and storm surges, thunderstorms, hail stones, lightening and droughts etc.

• Climate change will pose additional threats to the existing environmental issues of the countries.

Heatwave during 6-30 April 2016Can we link with El Nino & global warming?

The temperature anomaly of 0.84 degrees Celsius above average topped the previous warmest July in 2011 by 0.1 degree, according to NASA's analysis released Monday.

July 2016 Was Earth's Warmest Month on Record

NASA, 2016

A total of 57 people died during 12-13 May 2016 hit by the Lightening

This year Govt. has declared it as disaster

Farmers are working during thunder stormsWithout any protection

Holle and Islam (2016)

GLD 360 data on 12 May 153,621 strokes detected

GLD 360 data on 13 May 242,570 strokes detected

Devastating Flash floods hit the northeast region of Bangladesh during 17-21 April Rubber dam to protect from flash

Flood constructed by LGED

Flash flood and hailstorm has caused extensive damage ato mature and half-mature Boro paddy in haor (large marshy land) areas

Damage of the crops due to Flash flood during April 2016

Meghalaya, Tripura and Barak hilly basins Heavy rainfall during 17-21 April

Cyclone ‘Roanu’ landfalls in Bangladesh on21st May 2016 and killed about 24 people, damage crops, fisheries in the central & southeast coastal regions

Recent Floods in Bangladesh and South Asia

Floods in Pakistan 2010

Floods in Nepal 2016

Floods in India 2016

Floods in Bangladesh 2016 Floods in China 2016

Floods in Bhutan 2016

Global warming will exceed 1.5C by 2025 and 2C by 2040

Near surface global annual mean warming since pre-industrial for simulations from CMIP5, CMIP3 and by a HadCM3 perturbed parameter experiments of SRES A1B and the RCPs. Both concentration and emissions driven simulations.

Betts et al. (2011)

50% models

Regional Climate Modeling (RCM) for Bangladesh using CORDEX-South Asia Experiments

• GCM provides output more than 150km resolution which is not enough to capture mesoscale processes.

• RCM daily output with horizontal resolution 50km are available for South Asia CORDEX domain.

• Predictions are considered for extreme emission scenarios, RCP 8.5

• Climate output data have been bias corrected. Fahad et al. (2016)

RCM Projections using CIMP5 data

Institute GCM RCMDriving Ensemble

MemberRes. RCP

1 CSIRO ACCESS1.0 CCAM-1391M r1 0.5° 8.5

2 CSIRO CCSM4.0 CCAM-1391M r1 0.5° 8.5

3 SMHI CNRM-CERFACS-CNRM-CM5 RCA4 r1i1p1 0.5° 8.5

4 CSIRO CNRM-CM5 CCAM-1391M r1 0.5° 8.5

5 SMHI ICHEC-EC-EARTH RCA4 r12i1p1 0.5° 8.5

6 CSIRO MPI-ESM-LR CCAM-1391M r1 0.5° 8.5

7 MPI-CSC MPI-M-MPI-ESM-LR REMO2009 r1i1p1 0.5° 8.5

8 SMHI MPI-M-MPI-ESM-LR RCA4 r1i1p1 0.5° 8.5

9 SMHI NOAA-GFDL-GFDL-ESM2M RCA4 r1i1p1 0.5° 8.5

10 SMHI IPSL-CM5A-MR RCA4 r1i1p1 0.5° 8.5

11 SMHI MIROC-MIROC5 RCA4 r1i1p1 0.5° 8.5

Fahad et al. (2016)

Both observation and predictions indicate constant rise of temperature throughout the century

Increasing trend ranging between 3.24°C to 5.77°C under RCP 8.5

0

1

2

3

4

5

6

7

2000 2020 2040 2060 2080 2100Tem

pera

ture

Anom

aly

(0C

) re

lative to 1

861

-1

88

0

ACCESS1_CSIRO-CCAM-1391M CCSM4_CSIRO-CCAM-1391M CNRM-CM5_SMHI-RCA4 CNRM-CM5_CSIRO-CCAM-1391MEC-EARTH_SMHI-RCA4 IPSL-CM5A-MR_SMHI-RCA4MIROC5_SMHI-RCA4 MPI-ESM-LR_CSIRO-CCAM-1391MMPI-ESM-LR_MPI-REMO2009 MPI-ESM-LR_SMHI-RCA4

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

19

71

19

73

19

75

19

77

19

79

19

81

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83

19

85

19

87

19

89

19

91

19

93

19

95

19

97

19

99

20

01

20

03

20

05

20

07

20

09

20

11

20

13

Tem

pera

ture

Anom

aly

(0C

) re

lative to

19

71

-20

15

Temperature anomaly based on the observed data of the 24 BMD stations (1971-2015)

Fahad et al. (2016)

Temperature Anomaly (°C) relative to 1861-1880 for 2020s, 2050s and 2080s

Highest increase of temperature in February during 2080s

ranging between 3.6°C and 9.8°C. July, August and September

temperature increase ranging between 0.7° and 4°C.

Fahad et al. (2016)

Change of Precipitation in the 2020s, 2050s and 2080s from 1971-2000

Highest increase in rainfall to be occurred during the pre-monsoon period (i.e. March, April and May) ranging between 125mm–615mm.

Pre-monsoon and Monsoon rain increasing

Winter rain decreasing

Fahad et al. (2016)

Changes of extreme maximum and minimum temperature• It means extremity of temperature would become more prominent from the mid to

end of the 21st Century.

• From distribution of minimum temperature, TNn (minimum of daily minimum temperature) shows a reduction of its extremity in future years.

TXx- maximumof daily maximumtemperature

TNn- minimumof daily minimumtemperature

Hasan et al. (2016)

Changes of extreme 1-day maximum and 50mm rainfall• A clear shift of Rx1 has been observed from the 2020s time period. Annual

Rx1 will increase up to 30 days per year in the 21st Century.

• Rx50 will drastically increase over the hilly region than flatter part of the country. an increasing shift in mean probability at 2050s and 2080s time period.

Rx1- maximum1-day rainfall

Rx50- number of dayswhen rainfall > 50mm

Hasan et al. (2016)

Changes of extremes of Rainfall and Temperature will spatially varied over the country

Tx90 – 90th percentile of daily temperature

Rx50- number of dayswhen rainfall > 50mm

Hasan et al. (2016)

Water Resources Impact Assessment:SWAT Modeling for the Brahmaputra basin

• The Brahmaputra is a major transboundary river which drains an area of around 530,000 km2 and crosses four different countries: China (50.5% of total catchment area), India (33.6%), Bangladesh (8.1%) and Bhutan (7.8%) (Gain et al. 2013).

• Average discharge of the Brahmaputra is approximately 20,000 m3/s. The climate of the basin is monsoon driven with a distinct wet season from June to September, which accounts for 60–70% of the annual rainfall (Immerzeel, 2008).

Mohammed et al. (2016)

Calibration and validation at Bahadurabad station in Brahmaputra

Calibration (2001 - 2004) Validation (2006 - 2009)

NSE 0.806 0.769

R2 0.892 0.859

RSR 0.058 0.400

PBIAS(-) 0.002 0.012

Mohammed et al. (2016)

Uncertainty in the Changes of Future Flow

• increasing tendency of thedischarge of Brahmaputra River atBahadurabad station duringmonsoon when flood usuallyoccurs, while some other modelsshow a decreasing tendencytowards the end of the 21st

century.

• During the pre-monsoon period(MAM), some of the models showsignificant increases of thedischarge peaks, while most ofthe models show that the peakduring this season will remainrelatively unchanged.

Islam et al. (2016)

Changes of flow in terms of percentage (left) and total flow (right)

Monsoon (June-Sep) will be more wetter than present time which will increase chances of floods

Mohammed et al. (2016)

Changes in annual peak flow and low flow

Return

Period

(years)

Change

in Flow

of 2020s

Compar

ed to

Baseline

Period

(%)

Change

in Flow

of 2050s

Compar

ed to

Baseline

Period

(%)

Change

in Flow

of 2080s

Compar

ed to

Baseline

Period

(%)

2 5.96 6.35 14.89

5 7.06 8.17 16.31

10 7.62 10.14 17.18

20 8.07 12.40 17.97

50 8.55 15.74 18.93

100 8.86 18.50 19.61

200 9.15 21.47 20.25

500 9.49 25.71 21.06

Mohammed et al. (2016)

Coastal modeling using Delft3D hydrodynamic model• DELFT3D- FLOW is a multi-dimensional (2D or 3D) hydrodynamic (and transport)

simulation program which calculates unsteady flow and transport phenomena that result from tidal and meteorological forcing on a rectilinear or a curvilinear, boundary fitted grid.

Tidal Validation: Complex Error of tidal constituents

0

10

20

30

40

50

M2 S2 K1 O1 total

Co

mp

lex

Erro

rs(c

m)

Harmonic Constituents

Hiron Point

FES2012 FES2014 DELFT3D

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M2 S2 K1 O1 total

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mp

lex

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r(cm

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Harmonics Constituents

Char Changa

FES2012 FES2014 DELFT3D

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M2 S2 K1 O1 total

Co

mp

lex

Erro

r(cm

)

Harmonic Constituents

Cox's Bazar

FES2012 FES2014 DELFT3D

Hiron Point

FES2014: 41.9 cm

DELFT3D: 17.5 cm

Char Changa

FES2014: 52.6 cm

DELFT3D: 44.8 cm

Cox’s Bazar

FES2014: 37.6 cm

DELFT3D: 40.2 cm

Tazkia et al. (2016)

Inundation map for 1.0m and 0.5m Sea Level Rise (SLR)

Inundation area will be increased under increased with SLR

1.0m SLR0.5m SLR Tazkia et al. (2016)

Changes of Inundation area due SLR

SLRInundated Area

(sq.km)

Percent of total

Bangladesh

Percent of

Coastal Zone

Affected

population

(million)

0.5m 2000 1.6 4.3 2.5

1m 3930 3.8 8.4 6.0.0

1.5m 5300 5.1 11.3 8.0

1m (without

Polder) 8500 8.3 18.0 13

Tazkia et al. (2016)

Inundation statistics for the Sundarbans – the world largest mangrove forest

SLR (m) Inundated Area

(km2)

% of inundation

Area

0.5m 491 11.37

1m 1847 42.78

1.5m 2635 61.04

Tazkia et al. (2016)

Changes of inundation patterns or cyclone SIDR (2007), AILA (2009) and Roanu (2016)Conditi

ons

SIDR AILA ROANU

Inu

nda

tion

are

a

% of

Count

ry

Affecte

d

Popula

tion

Inun

dati

on

area

% of

Count

ry

Affecte

d

Popula

tion

Inun

datio

n

area

% of

Count

ry

Affected

Populati

on

Only

cyclone

14

841.2 1.9

199

91.5 2.3 501 0.34 0.54

cyclone

and 0.5m

SLR

33

802.6 4.1

422

63.3 5.1 2752 2.19 1.77

cyclone

and 1m

SLR

57

774.4 7.0

621

64.8 7.5 7504 6.30 4.82

cyclone

and 1.5m

SLR

75

885.8 9.1

749

75.8 9.0

1243

29.80 7.99

SIDR AILA Roanu

Shaha et al. (2016)

Crop Modeling using DSSAT (Decision Support System for Agro-technology Transfer) • Extreme climate change will

pose threat on various dimensions and Agriculture is one of them.

• About 75% of our agricultural land is rice and it covers 28% of GDP.

Real Name Brridhan29

Height 95 cm

Duration of growth 160 days

Grain quality Medium

Yield (Kg/hectares) 7500

Developed on 1994

Developed by Bangladesh Rice Research Institute (BRRI)Hasan et al. (2016)

Change of Rice Yield in the near future (2021-2050)

Hasan et al. (2016)

Change of Rice Yield in the near future (2070-2099)

Hasan et al. (2016)

Changes of the Yield of Boro rice in Bangladesh in 2030’s (2021-2050) and 2080’s (2070-2099)

The yield of Boro crop trend is gradually decreasing at an alarming rate.

Under high emission RCP 8.5 scenarios the mean yield of Boro will decrease about 10% in 2030’s to 20% in 2080’s.

Hasan et al. (2016)

Coastal vulnerability assessment using indicators based multivariate analysis

Coastal areas of Bangladesh is very much prone to various natural disasters such as cyclone, storm surge, river erosion, flood, salinity intrusion, erratic weather condition, etc.

19 coastal districts were selected for the analysis where 140 Upazilas are included

Bala et al. (2016)

Coastal vulnerability due to climate changefollows IPCC Framework of assessing vulnerability

Indicator based measures of vulnerabilityPrinciple component analysis conducted to determine weight of the variables (indexed)

7 EXPOSURE

INDICATORS

5 SENSITIVITY

INDICATORS

19 ADAPTIVE CAPACITY

INDICATORS Bala et al. (2016)

Coastal Vulnerability in preset and in the future (2050)

Present (2013) Future (2050s)

A total of 140 upazilas (administrative unit) under 19 coastal districts of Bangladesh has been selected as study At present, 6 upazilas come under very high, 13 upazilas under high, 59 upazilas under moderate, 35 upazilas under low and 27 upazilas under very low category of vulnerability

In future, 73 upazilas

are mapped as very

high, 27 upazilas as

high, 17 upazilas as

moderate, 5 upazilas

as low and 18

upazilas as very low

scale of vulnerability

Bala et al. (2016)

A few key messages

• In Bangladesh, both mean maximum and minimum temperature will rise and rainfall will increase slightly. Extreme events (heatwave, extreme on day rainfall etc.) will be more frequent.

• Floods will be more frequent and a 100 year return period flood will have about 8% more discharge than present.

• The 0.5m SLR will inundate additional 4.3% of the coastal areas of the country and 11.37% of Sundarbans area.

• Under high emission RCP 8.5 scenarios the mean yield of Boro rice will decrease about 10% during 2030’s znd 20% during 2080s.

• Analysis of coastal vulnerabilities for the coastal regions, the number of high vulnerable coastal Upazilas has been increased from 6 to 73.

BUET Research Team

• Prof. A.K.M. Saiful Islam

• Prof. G.M. Tarekul Islam

• Prof. Sujit Kumar Bala

• Md. Alfi Hassan

• Supria Paul

• Mohan Kumar Das

• Md. Jamal Uddin Khan

Thank you!

▪ Mustasim Billah

▪ Abdur Rahman Tazkia

▪ Golam Rabbani Fahad

▪ Sudipta Adhikary

▪ Nasir Uddin Ahmed

▪ Khaled Mohammed

▪ Ahmed Sajid Hasan

References• Bala SK, Islam AKMS, Uddin MN, Adhikary S, Islam GMT, Fahad MGR, Sutradhar LC (2016) Composite

vulnerability mapping of coastal Bangladesh using multivariate statistical approach. Ocean & Coastal Management (Under review).

• Mohammed K, Islam AKMS, Islam GMT, Bala SK, Khan MJU (2016) Climate change will increase floods and low flows of the Brahmaputra River. Journal of Hydrologic Engineering (Under Review).

• Islam AKMS, Paul S, Mohammed K, Billah M, Fahad MGR, Hasan MA, Islam GMT, Bala SK (2016) Hydrological response to climate change of the Brahmaputra basin using CMIP5 General Circulation Model ensemble. Journal of Water and Climate (Under Review).

• Fahad MG, Islam AKMS, Nazari R, Hasan MA, Islam GMT, Bala SK (2016) Regional changes of precipitation and temperature over Bangladesh using bias corrected multi-model ensemble projections considering high emission pathways. International Journal of Climatology (Under Review).

• Hasan MA, Islam AKMS, Akanda AS (2016) Climatic extremes from dynamically downscaled CMIP5 models over Bengal Delta under RCP scenarios: An advanced bias-correction approach with new gridded data. International Journal of Climatology (Under Review).

• Tazkia AR, Islam AKMS, Rahman MM, Krien Y, Durand F, Testut L, Islam GMT, Bala SK (2016) Sea level rise induced possible inundation patterns of the world's densely populated delta. Climatic Change (Under Review).

• Shaha PK, Tazkia AR, Islam AKMS, Rahman MM, Krien Y, Durand F, Testut L, Islam GMT, Bala SK (2016) Sea level rise induced possible inundation patterns of the world's densely populated delta. Climatic Change (Submitted).

• Hasan AS, Islam AKMS, Bala SK (2016) Impact of climate change on the production of Boro rice in Bangladesh using DSSAT crop model (In preparation).

• Holle RL and Islam, AKMS (2017) Lightning Fatalities in Bangladesh in May 2016. Proceedings of the 8th Conference on the Meteorological Applications of Lightning Data. 2017 American Meteorological Society Annual Meeting Seattle, Washington, 22-26 January 2017.