Water Stress, Droughts under Changing Climate
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1 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)
2 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
3 Global Warming and Climate Change
4 Human induced changes of green house gases
5 Increasing trends of CO2
6 Carbon Dioxide Latest Measurement: On March 2016 CO 2 was ppm
7 Global temperature and Greenhouse gases
8 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.
9 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
10 Global mean land-ocean surface temperature base period:
11 Rise of temperature This graph illustrates the change in global surface temperature relative to average LATEST MEASUREMENT: January C
12 Surface Air temperature ( )
13 Changes of Sea Surface Temperature
14 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
15 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)
16 Photographs of the Cracks in Ice bars
17 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
18 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
19 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.
20 Variability among models IPCC, 2007
21
22 Temperature Anomaly ( 0 C) relative to Temperature anomaly based on the observed data of the 24 BMD stations ( ) Solid bold line represents 3 year moving average
23 Long term Global Average Surface warming
24 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)
25 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 relative to Figure from IPCC (2013).
26 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 under (a) RCP4.5 and (b) RCP8.5. The pink lines show the observed 15% sea ice concentration limits averaged over (Comiso and Nishio, 2008). Figure from Collins et al. (2013)..
27 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).
28 Prediction of Sea level rise
29 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).
30 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
31 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
32 Temperature Anomaly ( 0 C) relative to 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 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-RCA
33 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.
34 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
35 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.
36 Changes of Meteorological Drought of Bangladesh using SPI methods
37 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.
38 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.
39 Sequence of drought occurrence and impacts (NDMC, 2007)
40 Comparison of Rainfall of drought prone area with the average rainfall of the country Rajshahi Bangladesh MEAN MONTHLY RAINFALL Mean Annual Rainfall (mm) - Bangladesh vs Rajshahi y = x R² = About 1,000mm less rainfall y = x R² = Rajshahi Bangladesh Linear (Rajshahi) Linear (Bangladesh)
41 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 The Standard Precipitation Index (SPI) is a very useful to predict meteorological drought over a region
42 Drought prone areas of Bangladesh
43 Point map of 6 rainfall stations in N-W region was prepared from the lat/long file.
44 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.
45 Observation of changes Observed data has been divided into the following two time periods each 20 years to detect the changes Analysis has been conducted annually and seasonally Winter (Dec-Feb) Pre monsoon (Mar-May) Monsoon (Jun-Sep) Post monsoon (Oct-Nov)
46 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
47 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
48 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.
49 Number of extreme drought events during and month SPI Extreme Droughts during Rabi season
50 Number of severe drought events during and month SPI Severe Droughts during Rabi season
51 Number of moderate drought events during and month SPI Moderate Droughts during Rabi season
52 Meteorological Drought: Dry Year: 3 month interpolated SPI for 2000 (dry year) - August, September, October
53 Meteorological Drought: Dry Year 3 month interpolated SPI for 2006 (dry year)- August, September, October
54 Meteorological Drought: Wet Year: 3 month interpolated SPI for 2004 (wet year) - August, September, October
55 Mean Rainfall (mm) and NDVI from MODIS data during
56 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.
57 Global Emission
58 Per capita CO 2 emission
59 Per capita responsibility of Co2
60 Bangladesh ton ton
61 Thank you
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