CHANGES OF THE SEASONAL SALINITY DISTRIBUTION AT THE SUNDARBANS COAST DUE TO IMPACT OF CLIMATE CHANGE

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1 CHANGES OF THE SEASONAL SALINITY DISTRIBUTION AT THE SUNDARBANS COAST DUE TO IMPACT OF CLIMATE CHANGE Mohammad Asad Hussain 1, A. K. M. Saiful Islam 2, Mohammad Alfi Hasan 3 and Balakrishnan Bhaskaran 4 1 Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh, asadh@iwfm.buet.ac.bd 2 Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh, akmsaifulislam@iwfm.buet.ac.bd 3 Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh, mdalfihasan19@gmail.com 4 Met Office Hadley Centre, Exeter, UK, balakrishnan.bhaskaran@metoffice.gov.uk ABSTRACT The overall objective of the study is to investigate the future salinity distributions along the coast of Sundarbans Mangrove forest in the northern Bay of Bengal taking into consideration of the changed hydrological and meteorological parameters. A total of seventeen ensembles of projected meteorological data by PRECIS RCM have been employed to generate present and future hydroclimatic scenarios. Salinity levels have been calculated for four seasons and at four time slices. Numerical investigations show the changes in salinity distributions due to the combined effect of sea level rise, altered discharge and also meteorology in future during four different seasons. Maximum increase of salinity has been been found during the monsoon season (average 4 psu), followed by the winter (average 2.4 psu), the post-monsoon (average 1.8 psu) and the premonsoon (average 1.7 psu) season for the 2080 time slice compared to base conditions along the Sundarbans coast of Bangladesh. Keywords: Bay of Bengal, climate change, salinity 3D hydrodynamic model 1. BACKGROUND Bangladesh has been identified as one of the 27 countries, which are the most vulnerable to the impacts of global warming induced accelerated sea level rise. With increase in sea surface level, salinity intrusion is expected to aggravate in low lying coastal areas throughout the world (Bates et al., 2008). For Bangladesh, the salinity ingress is likely to be more severe with decreasing fresh water flow from Himalayan rivers predicted for dry season (dry season getting drier) and the gradual rise in sea level (Cruz et al., 2007). The Sundarbans mangrove forest, one of the largest such forests in the world (140,000 ha), lies in the vast delta on the Bay of Bengal formed by the super confluence of the Padma, Brahmaputra and Meghna rivers across the southern Bangladesh. The Sundarbans is intersected by a complex network of tidal waterways, mudflats and small islands of salt-tolerant mangrove forests. UNESCO (2007) reported that 45-cm rise in sea level (likely by the end of the 21st century, according to the IPCC), combined with other forms of anthropogenic stress on the Sundarbans, and could lead to the destruction of 75% of the Sundarbans mangroves. Objective of the present study is to investigate the uncertainties of seasonal salinity distribution near the Sundarbans coast of Bangladesh due to the impact of climate change. 637

2 1.1 Sea Level Rise For regional sea level rise, all scenarios have so far been mostly based on expert judgment, not based on any modelling (Salehin and Hossain, 2008). Mahtab (1989) speculated a range of 0 to 150 cm SLR for the year A median value was considered by taking the mean of the two limits and adding 10 cm for local subsidence, which provided a 100 cm net sea level rise by the year In a later study on future climate change scenarios by DOE (1993), a potential future sea level rise of cm by 2050 was projected for Bangladesh. A number of other studies carried out on impact of sea level rise and climate change in Bangladesh proposed values ranging from 15 to 100 cm SLR, likely to occur by the year 2100 (ADB, 1994; Huq et al., 1996; Ahmed et al., 1996). Ahmed and Alam (1998) projected a SLR of 100 cm by the middle of 21st century; the projection combined a 90 cm rise in sea level and about 10 cm local rise due to subsidence. The IPCC-TAR provides a globally averaged sea level change scenario that projects a rise of 9 to 88 cm by the year 2100 (IPCC, 2001), and in the 4th assessment it projects a maximum rise of 59 mm during the last decade of 21st century (IPCC, 2007). National Adaptation Programme for Action (WARPO, 2005) adopted the projects of the third assessment. There have not been any studies providing information in observed sea level rise for the last few decades. However, IPCC-4AR study (IPCC, 2007) provides figures of sea level rise occurring globally in respect to the average level for as baseline. Global average sea level rose at an average rate of 1.8 [1.3 to 2.3] mm per year over 1961 to 2003 and at an average rate of about 3.1 [2.4 to 3.8] mm per year from 1993 to IPCC-4AR study has referred to various researchers who have reported that in the coastal areas of Asia, the current rate of SLR (1 to 3mm/yr) is marginally greater than the global average. In addition to this, the rate of sea level rise of 3.1 mm/yr as reported over the past decade has been accelerated relative to the long-term average taken over the 20th century as a whole. Long term sea level data along the coast of Bangladesh is not available. The SAARC Meteorological Research Centre (SMRC) analysed sea level changes of 22 years historical tide data at three tide gauge locations in the coast of Bangladesh (SMRC, 2000). The study revealed that the rate of sea level rise during previous 22 years is many fold higher than the mean rate of global sea level rise over 100 years. Following the trends, SMRC projected sea level rise of 18 cm, 30cm and 60 cm for the years 2020, 2050 and 2100, respectively. Institute of Water Modelling (Mohal et al., 2006) used a two-dimensional model of the northern Bay of Bengal and Meghna Estuary and a one-dimensional model for the rivers in the coastal area to assess the impact of sea level rise. The result showed that in the year 2100 with 88 cm SLR, about 11% area (4,107 km 2 ) of the coastal zone will be inundated in addition to the inundated area in the year 2000 under same upstream flows. Seawater will enter at Chandpur, about 80 km upstream from estuary. This rise at Chandpur will be about 50 cm for 88 cm SLR at the coast and 15 cm for 32 cm SLR at the coast. 1.2 Salinity Intrusion Saline water intrusion is mostly seasonal in Bangladesh; in winter months the saline front begins to penetrate inland, and the affected areas rise sharply from 10 percent in the monsoon to over 40 percent in the dry season (CCC, 2009a). Coastal districts such as Satkhira, Khulna, Bagerhat, Barguna, Patuakhali, Barisal as well as Sundarbans are the victims of salinity intrusion. Agricultural production, fisheries, livestock, and mangrove forests are affected by higher salinity in the dry season. 638

3 Salinity data from the Land Reclamation Programme (LRP) and the Meghna Estuary Study (MES) indicated an enormous seasonal effect due to the influence of huge fresh water discharge from the Lower Meghna River on the horizontal distribution of salinity in the estuary (CCC, 2009b). Chowdhury and Haque (1990) on the basis of mathematical model studies recommended minimum fresh water discharge of 3000 m 3 /s for the Lower Meghna to keep salinity in control. River water salinity has also important implications for the natural environment, such as functioning of the ecosystem in the Sundarbans, sedimentation rates in tidal rivers, and human health. Increased salinity has resulted in enhanced sedimentation rate in the rivers of the SW region causing enhanced siltation of the river bed and subsequent drainage congestion (CCC, 2009a). Institute of Water Modelling (IWM) performed a model study (Mohal et al, 2006; CEGIS, 2006) employing SLR scenarios of 32 cm and 88 cm for the year 2050 and 2100, which was based on IPCC 3rd assessment report (IPCC, 2001). It shows that the saline front is likely to reach as far as Jessore in the southwest region. 1.3 Salinity in Context of Sundarbans Mangrove Forest The Sundarbans forest health depends upon a number of ecological parameters of which, salinity, inundation, and sedimentation are the most important physical parameters. The soil of Sundarbans is moderately saline to saline in the east and highly saline in the west. Soil salinity of Sundarbans is low in comparison with other mangrove areas of the world where soil salinity exceeds that of sea-water. The Sundarbans forest has been divided into 3 zones on the basis of salinity: Less saline zone (5-15 ppt), Moderately Saline Zone (15-25 ppt) and Strong Saline Zone (25-30 ppt) based on degree of salinity (CEGIS, 2006). It has been observed that species of trees vary according to these salinity zones. For example, species like Sundari tress grows in the areas where salinity is only 5~10 ppt. Gewa grows basically in the moderate saline water of ppt. and low height Goran grows in high saline zones of over 25ppt. As the salinity of Sundarbans increases from east to west, density of vegetation growth and Canopy closure decreases from the east to the west. Height and growth of different species in the Sundarbans are related with salinity (CEGIS, 2006). Height classes of vegetation in different parts of the Sundarbans are different. Three height classes are recognized in the Sundarbans. These are 1(height> 15m), 2(>10m but <15m) and 3(>5m but <10m). The height of the forest is greater in the east. The height decreases as one proceeds towards the west. Siddique et al. (2001) showed that a significant decrease in regeneration and growth with an increase in the level of salinity in the Sundarbans. It indicates that salinity plays a vital role in the distribution of species in the Sundarbans as germination is linked to salinity. Hassan (1990) discussed about the soil and water salinity at four sampling stations in the Sundarbans: Bogi (less saline zone), Chandpai (moderately sline zone) and Burigualini (strongly saline zone) with 20 years of measurement data. The mean water salinity at four stations were 5.13 mmohs/cm at Bogi, mmohs/cm at Chandpai, 14.2 mmohs/cm at Kassiabad and mmohs/cm at Burigoalini. On the other hand, the mean values of soil salinity of these stations were 1.9, 3.4, 5.56 and 5.9 mmhos/cm, respectively. Both the soil and water salinity varied over the months in all the stations. 639

4 2. METHODOLOGY In the present research, following the study by Chowdhury et al., (1997) on climatic conditions of Bangladesh, four seasons have been considered, namely pre-monsoon (March - May), monsoon (June - September), post-monsoon (October - November) and winter (December - February). For the future projections of salinity distributions four time-slices: base (1991~2010), 2020s (2011~2030), 2050s (2031~2070) and 2080s (2071~2100) have been considered. The hydrodynamic model was run for 272 cases (4 time slices, 4 seasons and 17 ensembles) to capture future possible scenarios. 2.1 Data Meteorological data Input data for meteorological parameters have been obtained from the high resolution (25 km) RCM results obtained from the 17 ensemble runs (run at Met Office Hadley Centre, UK) for each season as space varying inputs over the calculation domain for the hydrodynamic model. Table 1 shows the maximum and minimum of the spatial-average values for different meteorological parameters over the domain at different time slices along with seasonal changes. An important point to note from the projected meteorological parameters is the increase of wind speed during the winter, which may alter the residual circulation and also salinity distribution in future. Table 1: Range of values for different meteorological parameters at four time slices during four seasons. 2080s 2050s 2020s Base Sea- Air Temp. ( 0 C) Relative Solar Rad. Cloud Cover Wind son Humidity (%) (Cal/cm 2 /min) (%) Speed.(m/s) Max Min Max Min Max Min Max Min Max Min DJF MAM JJAS ON DJF MAM JJAS ON DJF MAM JJAS ON DJF MAM JJAS ON Hydrological data Although several large and tiny rivers discharge into the Bay of Bengal, discharge is not measured at any of the tidal rivers in Bangladesh. Some of the measurements done during the Meghna 640

5 Estuary Study (MES) project (MoWR,1997) suggested that the discharge through the estuary was as much as fifteen times during the monsoon season compared to that during the dry season. It reports that in an average year, the discharge through the estuary is about 20,000 m 3 /s during the dry season whereas it is 100,000 m 3 /s during the monsoon season. Discharge (m 3 /s) through the Meghna Estuary has been adopted from Islam et al., (2002) as 12,300 m 3 /s for the pre-monsoon, 69,250 m 3 /s for the monsoon, 31,000 m 3 /s for the post-monsoon and 9,300 m 3 /s for the winter season. These discharges have been implemented as boundary condition for the numerical investigation for the present study. Nohara et.al. (2006) suggests that the discharge from the rivers in the Asian monsoon region (Changjiang, Ganges, and Mekong) is sensitive to the seasonal cycle in precipitation. The amounts of the discharge from these rivers are estimated to increase by 7.8%, 18.0%, and 9.9% in the future through the application of number of global models. So, to make a reasonable estimate, future river discharge into the Bay of Bengal has been obtained by increasing the present discharge by 18%. Long term discharge data for many of the rivers are not available. Discharge data for these rivers are obtained either estimated or obtained from CEGIS (2006). Input data for sea level rise during the time slices of 2020s, 2050s and 2080s have been considered as 14cm, 32cm and 88cm, respectively following NAPA (2005) Bathymetry data Bathymetry data used for the present study is the GEBCO gridded bathymetry data provided by the British Oceanographic Data Centre (BODC). GEBCO is a 30 arc-second global relief model of Earth's surface that integrates land topography and ocean bathymetry. Figure 1 shows the bathymetry of calculation domain in the Bay of Bengal along with the Sundarbans (dark green area) inside Bangladesh. The shaded triangles (located between 89 0 E and 90 0 E) along the southern coast of Sundarbans show the location of observation points at the calculation domain for salinity comparisons Depth (m) Figure 1: Bathymetry of calculation domain in the Bay of Bengal along with the Sundarbans part (dark green area) inside Bangladesh Regional climate model In this study, PRECIS (Providing Regional Climates for Impact Studies) regional climate model has been applied to simulate regional climate over a large domain which covers Bangladesh and south Asia. PRECIS is based on the Hadley Centre's regional climate modelling system which has been developed in order to help generate high-resolution climate change information. PRECIS simulation has been done using the lateral boundary data of the prognostic variables generated by 641

6 the HadCM3Q model to produce diagnostic variables (e.g. spatial precipitation, orographic height etc.) over the simulation domain during Simulation grids are of 25km resolution over the Indian sub-continent which includes Bangladesh (Islam et al. 2011). Projected meteorological data obtained from this simulation has been employed to generate future scenarios Hydrodynamic model The open source Delft3D model including salinity and temperature constituents along with wind and tidal forcing have been applied for the simulation of residual currents in the Northern Bay of Bengal. In the present model, sigma coordinate system with k- ε turbulence model has been employed. In this model, boundary conditions are considered for the wind stress at surface, friction at bottom (with friction coefficient ) and frictionless along the lateral boundaries. Heat balance and moisture balance at the sea surface have been considered for temperature and salinity boundary conditions, respectively. Constant values for horizontal eddy viscosity (1m 2 /sec) and horizontal eddy diffusivity (10m 2 /sec) are used. This model has been calibrated and validated using observed data from Meghna Estuary Study during 1997 (Hussain et al., 2012,a). The hydrodynamic model has been applied using a single-layer sigma coordinate system. To simulate the salinity distributions during the four above mentioned seasons, steady seasonal wind stress with non-uniform distribution over the calculation domain and a constant river discharge of that season have been applied. Other meteorological parameters have been used considering their spatial variation over the calculation domain. A uniform grid size of 925m 925m has been employed in a calculation domain with a total domain size of 777km in the east-west direction and 333km in the north-south direction. 3. RESULTS AND DISCUSSION 3.1 Seasonal Variation of Salinity Fronts Figure 2 shows the calculated maximum depth average salinity during the monsoon for the four different time slices. From the figure it clearly appears that during the monsoon along the southern coast of the Sundarbans, salinity increases from the east to the west and it is true from base to all the future time slices. The competing processes of sea level rise and increased discharge from the GBM basin can also be observed from the four figures. The smaller fresh water plume during 2080s compared to base conditions suggests that eventually the rise in sea water level dominates over the increased discharge. Figure 3 shows the calculated maximum depth average salinity during the post-monsoon for the four different time slices. From the figure it appears that some of the areas in the Patuakhali and Chittagong coast will become more saline in future during the post-monsoon period. Figure 4 shows the calculated maximum depth average salinity during the winter for the four different time slices. From the figure it clearly appears that fresh water plume is the smallest during this season and it will get even smaller in future due to changing climates. It appears that salinity level may increase along the Urir Char, Sandwip, Chittagong and Sitakunda coasts. It also appears that salinity may reduce along the southern coast of Bhola island which might be due to alteration in residual circulation caused by the stronger north wind in this season. 642

7 4th International Conference on Water & Flood Management (ICWFM-2013) (a) (b) (c) (d) \ Figure 2: Salinity distribution at the northern Bay of Bengal During the monsoon at different time slices: (a) base, (b) 2020s, (c) 2050s and (d) 2080s (a) (b) (c) (d) Figure 3: Salinity distribution at the northern Bay Of Bengal during the post-monsoon at different time slices: (a) base, (b) 2020s, (c) 2050s and (d) 2080s 643

8 4th International Conference on Water & Flood Management (ICWFM-2013) (a) (b) (c) (d) Figure 4: Salinity distribution at the northern Bay of Bengal during the winter at different time slices: (a) base, (b) 2020s, (c) 2050s and (d) 2080s (a) (b) (c) (d) Figure 5: Salinity distribution at the northern Bay of Bengal during the pre-monsoon at Different time slices: (a) base, (b) 2020s, (c) 2050s and (d) 2080s Figure 5 shows the calculated maximum depth average salinity during the pre-monsoon for the four different time slices. From the figure, it clearly appears that fresh water plume is getting smaller in future in the Meghna Estuary area. It appears that salinity level may increase along the Urir Char, Sandwip, Hatiya, Chittagong and Sitakunda coasts. In this season, salinity may also reduce slightly along the southern coast of Bhola island. 644

9 3.2 Seasonal Variation of Salinity along the Sundarbans Coast Figure 6 shows the calculated maximum and minimum salinity level at the four seasons along the southern coast of the Sundarbans at the eight selected locations, location 1 being the western most and location 8 being the eastern. Except during the monsoon season, all the figures show similar tendency with a drop of salinity level at the location 5 which is located at the Rupsha-Passur river confluence. During the monsoon season, overall salinity level is lower and it reduces from the west to the east which is primarily due to the huge fresh water plume created by the large river discharge from the Meghna River, being carried by the anticlockwise residual circulation (Hussain et al. 2012,b). (a) (b) (c) (d) Figure 6: Salinity variation at different time slices along the southern coast of the Sundarbans during the (a) monsoon, (b) post-monsoon, (c) winter and (d) pre-monsoon seasons. Tables 2 to 5 show the maximum and minimum salinity level at the eight locations for different seasons and for different time slices. Table 2: Calculated salinity (psu) along the southern cost of the Sundarbans during the monsoon season. Locations: Base Max Min Max Min Max Min Max Min

10 Table 3: Calculated salinity (psu) along the southern cost of the Sundarbans during the postmonsoon season. Locations: Base Max Min Max Min Max Min Max Min Table 4: Calculated salinity (psu) along the southern cost of the Sundarbans during the winter season. Locations: Base Max Min Max Min Max Min Max Min Table 5: Calculated salinity (psu) along the southern cost of the Sundarbans during pre-monsoon Locations: Base Max Min Max Min Max Min Max Min From the tables, it appears that compared to the base conditions during the 2080s, the maximum salinity increases during the monsoon (average 4 psu), followed by the winter (average 2.4 psu), the post-monsoon (average 1.8 psu) and the pre-monsoon (average 1.7 psu). The authors are aware that for future scenario generation, salinity level along the Sundarbans coast may be highly influenced by human interventions which control the flow through the Gorai river. Considering flow variation through this river some important scenarios may be generated. But to reduce the number of cases for numerical experiments and also considering the objectives of the project, such scenarios have been avoided. 646

11 4. CONCLUSIONS A total of seventeen ensembles of projected meteorological data by PRECIS RCM have been employed to generate present and future hydro-climatic scenarios and calculate salinity levels for the four seasons and the four so called time slices in the Northen Bay of Bengal. Numerical investigations show the changes in salinity distributions due to the combined effect of sea level rise, altered discharge and also meteorology in future during the four different seasons. Maximum increase of salinity has been found during the monsoon season (average 4 psu), followed by the winter (average 2.4 psu), the post-monsoon (average 1.8 psu) and the pre-monsoon (average 1.7 psu) for 2080s time slice compared to base condition along the Sundarbans coast of Bangladesh. ACKNOWLEDGEMENTS This research has been a joint collaboration project between IWFM, BUET and Met Office Hadley Centre, UK and it has been funded by DFID. The authors sincerely appreciate the help of other members of the research group. REFERENCES ADB (1994). Climate Change in Asia: Bangladesh Country Report. Asian Development Bank (ADB), Manila. Ahmed, A.U., Huq, S., Karim, Z., Asaduzzaman, M., Rahman, A.A., Alam, M., Ali, Y., and Choudhury, R.A. (1996). Vulnerability and Adaptation Assessment in Bangladesh. In Vulnerability and Adaptation to Climate Change, J.B. Smith, S. Huq, S. Lenhart, L.J. Mata, I. Nemesova and S. Toure (Eds.). Kluwer Academic Publisjers, Dordrecht, pp Bates, B.C., Kundzewicz, Z.W., Wu, S., and Palutikof, J.P. (2008). Climate Change and Water. Technical Paper VI of the Intergovernmental Panel on Climate Change, IPCC Secretariat, Geneva, 210 pp. CCC (2009a). Characterizing Country Settings: Development of a Base Document in the Backdrop of Climate Change Impacts. Climate Change Cell, DoE, MoEF; Component 4b, CDMP, MoFDM, November 2008, Dhaka. CCC (2009b). Impact Assessment of Climate Change and Sea Level Rise on Monsoon Flooding. Climate Change Cell, DoE, MoEF; Component 4b, CDMP, MoFDM. November 2009, Dhaka CEGIS (2006). Impacts of Sea Level Rise on Landuse Suitability and Adaptation Options, Draft Final Report. Submitted to the Ministry of Environment and Forest, Government of Bangladesh and United Nations Development Programme (UNDP) by Centre for Environmental Geographic Information Services (CEGIS), Dhaka. Chowdhury, J.U., and Haque, A. (1990). Permissible Water Withdrawal based upon Prediction of Salt-water Intrusion in the Meghna Delta, in The Hydrological Basis for Water Resources Management, ed. U. Shamir & C. Jiaqi, IAHS Pub. No. 197, pp , IAHS Press,Institute of Hydrology, UK. Chowdhury, J.U., Rahman, M.R. and Salehin, M. (1997). Flood Control in a Floodplain Country, Experiences of Bangladesh, Report prepared for ISESCO, Morocco. Cruz, R.V., Harasawa, H., Lal, M., Wu, S., Anokhin, Y., Punsalmaa, B., Honda, Y., Jafari, M., Li, C., and Huu, N. (2007). Asia. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK,

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