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 Brief introduction to global warming, climate variability and climate change Extreme Climate Change scenarios for Bangladesh Drought Risks for Bangladesh Changes of droughts over the last 40 years Future possible risks of droughts
Global Warming and Climate Change
Human induced changes of green house gases
Increasing trends of CO2
Carbon Dioxide Latest Measurement: On March 2016 CO 2 was 403.28 ppm http://climate.nasa.gov/vital-signs/carbon-dioxide/
Global temperature and Greenhouse gases
Green house gases CO 2 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.
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
Global mean land-ocean surface temperature base period: 1951-1980
Rise of temperature This graph illustrates the change in global surface temperature relative to 1951-1980 average LATEST MEASUREMENT: January 2015 0.87 C http://climate.nasa.gov/vital-signs/global-temperature/
Surface Air temperature (1960-1990)
Changes of Sea Surface Temperature https://www3.epa.gov/climatechange/science/indicators/oceans/sea-surface-temp.html
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). 1979 2003 2012 Yellow line represents Area 30 years before http://www.nasa.gov/topics/earth/features/2012-seaicemin.html
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 gigatonnes per year. (Source: GRACE satellite data) http://climate.nasa.gov/vital-signs/land-ice/
Photographs of the 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. 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 2000. http://climate.nasa.gov/vital-signs/sea-level/
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 https://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch3s3-3-2.html
Scenarios RCP scenarios provide estimates for future concentration of greenhouse gases, aerosols, land use changes Global emission (in PgC per year) and (b) atmospheric concentration of CO 2 (in ppm) in four RCP scenarios.
Variability among models IPCC, 2007
1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Temperature Anomaly ( 0 C) relative to 1971-2015 Temperature anomaly based on the observed data of the 24 BMD stations (1971-2015) 0.8 0.6 0.4 0.2 0-0.2-0.4 Solid bold line represents 3 year moving average -0.6-0.8
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. 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)
The spatial distribution of precipitation changes Some changes can be interpreted as an amplification of the existing differences in precipitation minus evaporation (P-E), often referred to as the wet-get-wetter and the dry-get-dryer response. Multi-model mean of average percent change in mean precipitation for the scenarios RCP2.6 and RCP8.5 in 2081 2100 relative to 1986-2005. Figure from IPCC (2013).
Changes in sea ice Changes are larger in summer in the Arctic. February and September CMIP5 multi-model mean sea ice concentrations (%) in the Northern and Southern Hemispheres for the period 2081 2100 under (a) RCP4.5 and (b) RCP8.5. The pink lines show the observed 15% sea ice concentration limits averaged over 1986 2005 (Comiso and Nishio, 2008). Figure from Collins et al. (2013)..
Long-term climate changes: sea level and ice sheets The melting of Greenland ice sheet would take millennia. A complete melting of the Greenland ice sheet would lead then to a sea level rise of about 7m. Greenland ice-sheet evolution in a scenario in which the CO 2 concentration is maintained at a constant level equal to 4 times the pre-industrial value (4 times CO 2 scenario) during 3000 years. Shown is surface elevation. Figure from Huybrechts et al. (2011).
Prediction of Sea level rise
Changes in climate extremes A temperature rise increases the probability of very warm days and decreases the probability of very cold days. Schematic diagram showing the effect of a mean temperature increase on extreme temperatures, for a normal temperature distribution. Figure from Solomon et al. (2007).
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
RCM Projections using CIMP5 data Institute GCM RCM Driving Ensemble Member Res. 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
Temperature Anomaly ( 0 C) relative to 1861-1880 Temperature Anomaly (ᵒC) relative to 1861-1880 for Bangladesh (RCP8.5) Increasing trend ranging between 3.24 C to 5.77 C under RCP 8.5 scenario over Bangladesh. 7 6 5 ACCESS1_CSIRO-CCAM-1391M CNRM-CM5_SMHI-RCA4 EC-EARTH_SMHI-RCA4 MIROC5_SMHI-RCA4 MPI-ESM-LR_MPI-REMO2009 CCSM4_CSIRO-CCAM-1391M CNRM-CM5_CSIRO-CCAM-1391M IPSL-CM5A-MR_SMHI-RCA4 MPI-ESM-LR_CSIRO-CCAM-1391M MPI-ESM-LR_SMHI-RCA4 4 3 2 1 0 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
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.
Change of Rainfall 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
Changes of extreme rainfall in Bangladesh 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.
Changes of Meteorological Drought of Bangladesh using SPI methods
What is Drought? According to Palmer Drought is an interval of time, generally of the order of months of years in duration, during which the actual moisture supply at a given place rather consistently falls short of the climatically expected or climatically appropriate moisture supply.
Drought According to Mc Mohan and Diaz Arena (1982), Drought is a period of abnormally dry weather sufficiently for the lack of precipitation to cause a serious hydrological imbalance and carries connotations of a moisture deficiency with respect to man s usage of water.
Sequence of drought occurrence and impacts (NDMC, 2007)
Comparison of Rainfall of drought prone area with the average rainfall of the country Rajshahi Bangladesh MEAN MONTHLY RAINFALL 536 470 427 3500 3000 2500 Mean Annual Rainfall (mm) - Bangladesh vs Rajshahi y = 4.2753x - 6047.1 R² = 0.0421 About 1,000mm less rainfall 53 10 9 14 23 23 125 60 296 130 259 306 322 251 263 183 113 41 14 10 8 2000 1500 1000 500 y = -6.5135x + 14443 R² = 0.0911 0 1960 1970 1980 1990 2000 2010 2020 Rajshahi Bangladesh Linear (Rajshahi) Linear (Bangladesh)
Occurrence of Drought Drought events have severe impact on country s agricultural economy in past years. Between 1960 and 1991, droughts occurred in Bangladesh 19 times. Very severe droughts hit the country in 1951, 1961, 1975, 1979, 1981, 1982, 1984, and 1989. The Standard Precipitation Index (SPI) is a very useful to predict meteorological drought over a region
Drought prone areas of Bangladesh
Point map of 6 rainfall stations in N-W region was prepared from the lat/long file.
Collection of observed meteorological data from Meteorological Department Data of 28 stations out of 34 stations of BMD used in the study which passes homogeneity and consistency test. Daily data of following variables are collected from, BMD- Rainfall Maximum and Minimum Temperature Wind speed and direction Sunshine hour Relative humidity data are collected from 1971 to 2010.
Observation of changes Observed data has been divided into the following two time periods each 20 years to detect the changes 1971-1990 1991-2010 Analysis has been conducted annually and seasonally Winter (Dec-Feb) Pre monsoon (Mar-May) Monsoon (Jun-Sep) Post monsoon (Oct-Nov)
Methods Drought Index: Standardized Precipitation Index (SPI), a tool derived by McKee et al. (1993), a measure of meteorological drought has been calculated from the available rainfall data. Mathematically, SPI is calculated based on the following equation SPI ( Xi Xm) where, Xi is monthly rainfall record of the station; Xm is rainfall mean; and σ is the standard deviation
SPI based Drought Severity Index Classification of SPI values (McKee et al., 1993) Range Condition SPI -2 Extremely dry -2 < SPI -1.5 Severely dry -1.5 < SPI -1 Moderately dry -1 < SPI 1 Near normal 1 < SPI 1.5 Moderately wet 1.5 < SPI 2 Severely wet SPI 2 Extremely wet
Calculation of SPI 5 time period has considered to calculate SPI. 1- month (monthly SPI) 3- month (Seasonal SPI) 6-month (Short time SPI) 9 month (Medium time SPI) 12 month (Long term SPI) SPI is calculated both temporally and spatially.
Number of extreme drought events during 1971-1990 and 1991-2010 3-month SPI Extreme Droughts during Rabi season
Number of severe drought events during 1971-1990 and 1991-2010 3-month SPI Severe Droughts during Rabi season
Number of moderate drought events during 1971-1990 and 1991-2010 3-month SPI Moderate Droughts during Rabi season
Meteorological Drought: Dry Year: 3 month interpolated SPI for 2000 (dry year) - August, September, October
Meteorological Drought: Dry Year 3 month interpolated SPI for 2006 (dry year)- August, September, October
Meteorological Drought: Wet Year: 3 month interpolated SPI for 2004 (wet year) - August, September, October
Mean Rainfall (mm) and NDVI from MODIS data during 2000-2008
Changes of drought severity Seasonal SPI or 3-month SPI is useful to understand the soil moisture condition of an area. Considering 3-month SPI it has found that, frequency of extreme drought increased in the north western part of Bangladesh. Using SPI-6 month, long term seasonal extreme drought increased rapidly from 1980s to 2000s time period, especially over entire northwestern region of Bangladesh.
Global Emission
Per capita CO 2 emission
Per capita responsibility of Co2
Bangladesh 1990 0.1 ton 2009 0.36 ton
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