Assessment of the Risks of Climate Change: A Case of Bangladesh using local and global Climate Model

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Workshop on Capacity Building Workshop of the National Climate

Organized by BCAS, Dhaka Assessment of the Risks of Climate

Model A.K.M. Saiful Islam Professor Institute of Water and

1 Workshop on Capacity Building Workshop of the National Climate Change Impact Survey Team of Nepal 26 th December 2016, Organized by BCAS, Dhaka Assessment of the Risks of Climate Change: A Case of Bangladesh using local and global Climate Model A.K.M. Saiful Islam Professor Institute of Water and Flood Management Bangladesh University of Engineering and Technology (BUET)

2 Outline of the presentation Understanding Global Warming and Climate change. Climate Change Scenarios, Global and regional Climate Change Modeling. Regional Climate Change predictions for Bangladesh Impact of water, agriculture, floods and coastal ecosystems.

3 Understanding Global Warming and Climate Change

partial blanket for the thermal radiation from the surface which enables it to

4 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

5 CO2 constantly increasing after industrializations since 1880 and so does the temperature

6 In 2016, CO2 topped 400ppm

7 Global temperature constantly rising since 1880 This July temperature anomaly of 0.84 degrees Celsius above average , according to NASA.

8 Surface Air temperature ( )

9 Trends of Sea Surface temperature

10 Changes of Sea Surface Temperature

11 Trends of Global Land Precipitations Time series for 1900 to 2005 of annual global land precipitation anomalies (mm) with respect to the 1981 to 2000 base period

http://www.nasa.gov/topics/earth/features/2012-seaicemin.

12 Arctic Sea Ice melting Images gathered from the Defense Meteorological Satellite Program of NASA show the minimum Arctic sea ice concentration 1979 (left) and 2003 (right) Yellow line represents Area 30 years before

13 Arctic Sea Ice Extent (2016)

14 Cracks in Ice bars

Sea level rise Sea level rise is caused primarily by two factors related to global warming: the added water from melting land ice and the expansion of sea water as it warms.

15 Sea level rise Sea level rise is caused primarily by two factors related to global warming: the added water from melting land ice and the expansion of sea water as it warms. The first chart tracks the change in sea level since 1993 as observed by satellites. The second chart, derived from coastal tide gauge data, shows how much sea level changed from about 1870 to

16 Decreasing Land Ice Data from NASA's GRACE satellites show that the land ice sheets in both Antarctica and Greenland are losing mass. The continent of Antarctica has been losing about 134 billion metric tons of ice per year since 2002, while the Greenland ice sheet has been losing an estimated 287 gigatones per year. (Source: GRACE satellite data)

17 Climate Change Scenarios and Climate Modeling

18 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.

20 General Circulation Model (GCM) General Circulation Models (GCMs) are a class of computerdriven models for weather forecasting, understanding climate and projecting climate change, where they are commonly called global Climate Models. Three dimensional GCM's discretise the equations for fluid motion and energy transfer and integrate these forward in time. They also contain parameterizations for processes - such as convection - that occur on scales too small to be resolved directly. Atmospheric GCMs (AGCMs) model the atmosphere and impose sea surface temperatures. Coupled atmosphere-ocean GCMs (AOGCMs, e.g. HadCM3, EdGCM, GFDL CM2.X, ARPEGE- Climate) combine the two models.

21 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.

23 How do we model climate?

24 Grids of Global Climate Models Complicated choices starting from how to grid the globe.

28 Accuracy of climate model predictions Average precipitation Temperature anomaly (a) Observed ( ) (b) Predicted IPCC, 2007

29 How Well Do Climate Models Work? Pretty Good.

30 Importance of anthropogenic contribution

31 Variability exists among models IPCC, 2007

32 Climate models: Limitations Low resolution limits the ability to predict features such as tornadoes, etc Can t predict East Asian or Indian monsoons well Regional scale climatic features are difficult to predict accurately

33 Climate Change Scenarios and Predictions

34 Climate Change Scenarios Scenarios for future changes in external forcing have to be selected. Representative concentration pathways (RCP) scenarios provide a large range of future change in radiative forcing.

35 Projected Annual Green House Gas Emission Changes of CO2 emission for each RCP

36 Future Projection of Green House Gas

Changes in global mean surface temperature The magnitude of the surface warming is strongly different in the RCP scenarios, showing the potential impact of mitigation policies.

37 Changes in global mean surface temperature The magnitude of the surface warming is strongly different in the RCP scenarios, showing the potential impact of mitigation policies. Time series of global annual mean surface air temperature anomalies (relative to ) from an ensemble of model simulations performed in the framework of CMIP5. Figure from Collins et al. (2013).

38 Long term Global Average Surface warming

Spatial distribution of surface temperature changes Multi-model mean of surface temperature change for the scenarios RCP2.6 and RCP8.5 in 2081 2100 relative to 1986-2005.

39 Spatial distribution of surface temperature changes Multi-model mean of surface temperature change for the scenarios RCP2.6 and RCP8.5 in relative to Hatching indicates regions where the multi model mean change is less than one standard deviation of internal variability. Stippling indicates regions where the multi model mean change is greater than two standard deviations of internal variability and where 90% of models agree on the sign of the change. Figure from Stocker et al. (2013)

40 Predicted Arctic sea Ice Results from community climate system models Arctic Sea Ice in 2000 Arctic Sea Ice in 2040

41 Prediction of Sea level rise

42 Changes of Average Precipitation

43 Understanding climate change at Regional and Local Scale

Regional Climate Modeling (RCM) for Bangladesh over CORDEX: South

44 Regional Climate Modeling (RCM) for Bangladesh over CORDEX: South Asia 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)

45 Fahad et al. (2016) RCM Projections using CIMP5 data Institute GCM RCM Driving Ensemble Member Res. RCP 1 CSIRO ACCESS1.0 CCAM-1391M r CSIRO CCSM4.0 CCAM-1391M r SMHI CNRM-CERFACS-CNRM- CM5 RCA4 r1i1p CSIRO CNRM-CM5 CCAM-1391M r SMHI ICHEC-EC-EARTH RCA4 r12i1p CSIRO MPI-ESM-LR CCAM-1391M r MPI-CSC MPI-M-MPI-ESM-LR REMO2009 r1i1p SMHI MPI-M-MPI-ESM-LR RCA4 r1i1p SMHI NOAA-GFDL-GFDL- ESM2M RCA4 r1i1p SMHI IPSL-CM5A-MR RCA4 r1i1p SMHI MIROC-MIROC5 RCA4 r1i1p

46 Temperature Anomaly ( 0 C) relative to Fahad et al. (2016) Temperature Anomaly (ᵒC) relative to for Bangladesh (RCP8.5) Increasing trend ranging between 3.24 C to 5.77 C under RCP 8.5 scenario over Bangladesh. 7 6 ACCESS1_CSIRO-CCAM-1391M CCSM4_CSIRO-CCAM-1391M CNRM-CM5_SMHI-RCA4 CNRM-CM5_CSIRO-CCAM-1391M EC-EARTH_SMHI-RCA4 IPSL-CM5A-MR_SMHI-RCA4 MIROC5_SMHI-RCA4 MPI-ESM-LR_CSIRO-CCAM-1391M MPI-ESM-LR_MPI-REMO2009 MPI-ESM-LR_SMHI-RCA4 GFDL-ESM2M_SMHI-RCA

47 Temperature Anomaly ( C) relative to 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)

48 Change of Rainfall in the 2020s, 2050s and 2080s from 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 Hasan et al. (2016) minimum temperature It means extremity of temperature would become more prominent from the mid to end of the 21st Century.

49 Changes of extreme maximum and Hasan et al. (2016) 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- maximum of daily maximum temperature TNn- minimum of daily minimum temperature

50 Changes of extreme 1mm and 50mm Hasan et al. (2016) daily 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- number of days When rainfall > 1 mm Rx50- number of days when rainfall > 50mm

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 km 2 and crosses four different

51 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 km 2 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 m 3 /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 Bahdurabad station in Brahmaputra Calibration (2001-2004) Validation

52 Calibration and validation at Bahdurabad station in Brahmaputra Calibration ( ) Validation ( ) NSE R RSR PBIAS (-) Mohammed et al. (2016)

53 Mohammed et al. (2016) Changes of mean 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

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.

54 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 (%) Mohammed et al. (2016)

55 Key Messages The uncertainty for future changes of flow of the Brahmaputra 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 mean monsoon flow will be increase by 5 to 10%. Extreme peak flows will be increased in the future, i.e. floods will become more severe. Extreme low flows will be increased in the future, i.e. droughts will become less severe

56 Impact of Climate Change on the Sundarbans..Mangrove forest?

flow and transport phenomena that result from tidal and meteorological

57 Coastal modeling using Delft3D 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. Tazkia et al. (2016)

Tidal Validation: Complex Error of tidal

58 Tidal Validation: Complex Error of tidal constituents Complex Error(cm) Hiron Point FES2014: 41.9 cm DELFT3D: 17.5 cm Cox's Bazar Char Changa FES2014: 52.6 cm DELFT3D: 44.8 cm Cox s Bazar FES2014: 37.6 cm DELFT3D: 40.2 cm M2 S2 K1 O1 total Harmonic Constituents FES2012 FES2014 DELFT3D Tazkia et al. (2016)

59 Inundation map for 1.0m and 0.5m Sea Level Rise (SLR) Inundation area will be increased under increased SLR 0.5m SLR 1.0m SLR Tazkia et al. (2016)

60 Changes of Inundation area due SLR SLR Inundated Area (sq.km) Percent of total Bangladesh Percent of Coastal Zone Affected population (million) 0.5m m m m (if polder fails) Tazkia et al. (2016)

61 Inundation statistics for the Sundarbans SLR (m) Inundated Area (km 2 ) % of inundation Area 0.5m m m Tazkia et al. (2016)

Changes of the inundation and impact of the coastal cyclones (SIDR, AILA and Roanu) due to SLR Cyclone SIDR Nov 15

1 0-1 2 1 0-1 WL during Roanu at Hiron point e f Model WL during Roanu at Khepupara Model Observed Pressure Field

19/5/2016 12:00 20/5/2016 0:00 20/5/2016 12:00 21/5/2016 0:00 Calibration and Validation of Model for SIDR, AILA and

62 Changes of the inundation and impact of the coastal cyclones (SIDR, AILA and Roanu) due to SLR Cyclone SIDR Nov WL during SIDR at Hiron point -2 5/19/2016 5/19/2016 0:00 12:00 5/20/2016 5/20/2016 0:00 12:00 5/21/2016 0: WL during Roanu at Hiron point e f Model WL during Roanu at Khepupara Model Observed Pressure Field during SIDR Pressure Field during AILA WL during SIDR at Khepupara WL during AILA at Hiron point -2 19/5/2016 0:00 19/5/ :00 20/5/2016 0:00 20/5/ :00 21/5/2016 0:00 Calibration and Validation of Model for SIDR, AILA and ROANU at Hiron point and Khepupara. Suffix a,b,c for SIDR. Suffix d for AILA. Suffix e,f for ROANU. Shaha et al. (2016)

Changes of inundation patterns or cyclone SIDR (2007),

ROANU SIDR AILA Roanu Inunda tion area % of Count ry

Affected Populati on Inund ation area % of Countr y

5m SLR cyclone and 1m SLR cyclone and 1.5m SLR 1484 1.

3 5.1 2752 2.19 1.77 5777 4.4 7.0 6216 4.8 7.5 7504 6.

63 Changes of inundation patterns or cyclone SIDR (2007), AILA (2009) and Roanu (2016) Conditi ons SIDR AILA ROANU SIDR AILA Roanu Inunda tion area % of Count ry Affected Populati on Inundat ion area % of Cou ntry Affected Populati on Inund ation area % of Countr y Affected Populatio n Only cyclone cyclone and 0.5m SLR cyclone and 1m SLR cyclone and 1.5m SLR Shaha et al. (2016)

Crop Modeling using DSSAT (Decision Support System for Agro-technology

Real Name Height Duration of growth Grain quality Brridhan29 95 cm 160

64 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 Height Duration of growth Grain quality Brridhan29 95 cm 160 days Medium Yield (Kg/hectares) 7500 Developed on 1994 Hasan et al. (2016) Developed by Bangladesh Rice Research Institute (BRRI)

65 Change of Rice Yield in the near future ( ) Hasan et al. (2016)

66 Change of Rice Yield in the near future ( ) Hasan et al. (2016)

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

an alarming rate. Hasan et al. (2016) Under high emission RCP 8.

67 Changes of the Yield of Boro rice in Bangladesh in 2030 s ( ) and 2080 s ( ) The yield of Boro crop trend is gradually decreasing at an alarming rate. Hasan et al. (2016) Under high emission RCP 8.5 scenarios the mean yield of Boro will decrease about 10% in 2030 s to 20% in 2080 s.

Coastal vulnerability assessment using indicators based multivariate analysis Coastal areas of Bangladesh is very much prone to various natural disasters such as cyclone, storm

68 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 change follows

based measures of vulnerability: Principle component

variables (indexed) 5 SENSITIVITY INDICATORS 7

69 Coastal vulnerability due to climate change follows IPCC Framework of assessing vulnerability Indicator based measures of vulnerability: Principle component analysis conducted to determine weight of the variables (indexed) 5 SENSITIVITY INDICATORS 7 EXPOSURE INDICATORS 19 ADAPTIVE CAPACITY INDICATORS Bala et al. (2016)

upazilas under low and 27 upazilas under very low category of vulnerability Present (2013) Future (2050s) In future, 73 upazilas are mapped

70 Coastal Vulnerability in preset and in the future (2050) 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 Present (2013) Future (2050s) 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)

71 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 and 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.

72 Thank you

73 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 Burgen University where BCAS is a partner. 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 (ESPAdeltas)

74 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 Proceedings of the 8th Conference on the Meteorological Applications of Lightning Data American Meteorological Society Annual Meeting Seattle, Washington, January 2017.

Water Stress, Droughts under Changing Climate

Water Stress, Droughts under Changing Climate Professor A.K.M. Saiful Islam Institute of Water and Flood Management Bangladesh University of Engineering and Technology (BUET) Outline of the presentation