Potential impact of global climate change on forest distribution in Sri Lanka
POTENTIAL IMPACT OF GLOBAL CLIMATE CHANGE ON FOREST DISTRIBUTION IN SRI LANKA S. SOMARATNE 1 and A.H. DHANAPALA 2 1 Division of Botany, The Open University ofSri Lanka Nawala, Nugegoda, Sri Lanka 2 Participatory Forestry Project Forest Department, Battaramulla, Sri Lanka Abstract. The potential impact of climate change on forest distribution in Sri Lanka was evaluated. The Holdridge Life Zone Classification was used along with current climate and climate change scenarios derived from two general circulation models, the Geophysical Fluid Dynamics Laboratory model and the Canadian Climate Centre Model, at a 0.5 x 0.5 resolution. Current and future distributions of life zones were mapped with a Geographic Information System. These maps were then used to calculate the extent of the impact areas for the climate change scenarios. The current distribution pattern of forest vegetation includes tropical very dry forest (6%), tropical dry forest (56%), and tropical wet forest (38%). Results obtained using the Geophysical Fluid Dynamics Laboratory model show an increase in tropical dry forest (8%) and decrease in tropical wet forest (2%). The Canadian Climate Centre Model scenario predicted an increase intropical very dry forest (5%) and tropical dry forest (7%), and a decrease in tropical wet forest (11%). Both models predicted a northward shift of tropical wet forest into areas currently occupied by tropical dry forest. The application of general circulation models such as the Geophysical Fluid Dynamics Laboratory model and the Canadian Climate Centre Model, as well as the Holdridge Life Zone Classification, to estimate the effect of climate change on Sri Lankan forests in this paper indicates that these methods are suitable as a tool for such investigations in Sri Lanka. Key words: Sri Lanka, forest, CCCM, GFDL, Holdridge Life Zone Classification 1. Introduction The impact of global climate change on terrestrial vegetation distribution patterns has been a major issue over the last decade, and has been analyzed by numerous scientists (e.g., Emmanuel et al., t 985; Woodward, 1987; Smith et al., 1990; Smith et al., 1992). It is a wel l -known fact that the distribution pattern of terrestrial vegetation is a result of bioclimatic interactions. Thus changes in the global climate due to increased atmospheric greenhouse gases could after the present patterns. A number of studies have been carried out to investigate the potential impacts of climate change on terrestrial vegetation distribution on regional scales (e.g., Leemans, 1989; Neilson et al., 1989; Neilson et al., 1991; Halpin and Smith, 1992). The potential impacts of such climate changes on the distribution patterns of Sri Lankan vegetation, and especially its forests, have not yet been examined in detail. There are, however, many detailed studies on classification of Sri Lankan climate and terrestrial vegetation that resulted from the equil ibrium of vegetation and climate interaction (Holmes, 1956; Koelmayer, 1957, 1958; Mueller-Dombois, 1968). This paper presents a preliminary investigation to evaluate the response of Sri Lankan forests to climate change. The objectives of this report were to evaluate the potential impacts of climate change on forests of Sri Lanka, evaluate the suitability of general circulation models in predicting Sri Lankan climate, verify the applicability of the Holdridge Life Zone Classification to Sri Lanka, and document potential climate change impacts on Sri Lankan forests. Water, Air, and Soil Pollution 92: 129-135, 1996 Kluwer Academic Publishers. Printed in the Netherlands. 130 S. SOMARATNE AND A. H. DHANAPALA 2. Methods The Holdridge Life Zone Classification (Holdridge, 1967) is a climate classification scheme that relates the distribution of major ecosystem complexes to the climatic variables of biotemperature, mean armual precipitation, and the ratio of potential evapotranspiration (PET) to precipitation. The life zones are depicted by a series of hexagons in a triangular coordinate system. To construct baseline climate scenarios for Sri Lanka, mean monthly climate data of a 30 year period, 1951-1980, from 21 climate stations were used. These data were interpolated to 0.5 x 0.5 to estimate the gridpoint data on an area defined by longitude 79.5 E- 81.5 E and latitude 6.5N-9.5N. A comparison was made between observed 1 xCO 2 climate data and estimated 1 xCO 2 data from three general circulation models (GeMs), the Geophysical Fluid Dynamics Laboratory (GFDL) model (Mitchell et al., 1990), the Canadian Climate Centre Model (CCCM) (Boer et al., 1992), and the United Kingdom Meteorological Office (UKMO) model (Wilson and Mitchell, 1987), to select the most suitable model or models that best simulated the Sri Lankan climate. Based on the comparison, the CCCM and the GFDL model were selected. Estimated current distributions of life zones were mapped using observed climate data of mean monthly precipitation and temperature at a 0.5 x 0.5 (latitude and longitude) resolution. Simulations of current (1 CO2) and 2xCO 2 climates from two GCMs (Table I) were used to construct climate change scenarios. Changes in mean monthly precipitation and temperature were calculated for each GCM scenario for each computational grid element by taking the difference between simulated current and 2xCOa climates. Temperatures were expressed as absolute difference (2xCQ - 1 xCO2) and precipitation as the ratio of 2xCQ to 1 CO 2. These data from each GCM were interpolated to 0.5 x 0.5 using the same technique as applied in the development of the data base for current climate (Leemans and Cramer, 1990). The technique used was a triangulation of all data points. Changes in monthly precipitation and temperature were then applied to the observed climate data to provide a change scenario. The altered data bases corresponding to each of the two GCM scenarios were then used to reclassify the grid cells (0.5 x 0.5 ) using the Holdridge classification and to create a map of the estimated distribution of life zones under 2 CO 2 conditions. TABLE I Adjustment statistics for monthly precipitation and temperature used in the construction of CCCM and GFDL scenarios for Sri Lanka Rainfall (%) Temperature ( C) Month CCCM GFDL CCCM GFDL January -35 -14 1.6 1.7 February -28 -21 1.5 2.1 March -46 +04 1.4 2.0 April -09 -08 1.7 2.1 May -01 +10 1.6 2.0 June -35 -24 1.8 2.1 July -08 +05 2.1 2.3 August -09 -10 2.2 2.5 September +17 -24 2.2 2.7 October -10 -23 2.1 2.6 November -03 -01 1.9 2.3 December -51 - 15 1.7 2.3 POTENTIAL IMPACT ON FOREST DISTRIBUTION IN SRI LANKA 131 3. Results As a consequence of the need for food, fuel, fiber, and forest products, the indigenous forest cover of the island decreased from approximately 70% at the beginning of twentieth century to less than 25% today. Based on remote sensing data supplemented by field observations, the latest figures for closed-canopy indigenous forest cover indicate that it now occupies only 23.87% of the land area. It is generally expected that to maintain normal local climate conditions and provide ecological security, it would be desirable to have a forest cover of at least 25% of the land area. Under the current climate, of the 24% of existing closed canopy forest, approximately 18% is inthe tropical dry forest zone and 6% is distributed in the tropical mesic forest zone (Figure 1). In the CCCM scenario, 12% of the closed canopy forest is in the tropical mesic forest, 10% in the tropical dry forest, and 2% in the tropical very @ forests (Figure 2). Under the GFDL scenario, the closed canopy forest is distributed in the ratio of 3:1 in tropical mesic forests and tropical dry forest, respectively (Figure 3). Current forest distribution patterns (Figure 1) include four different life zones, i.e., tropical very dry forest, tropical dry forest, tropical moist forest, and tropical wet forest. The changed distribution pattern of forest under 2xCO 2 climate scenarios predicted by CCCM and GFDL are represented in Figures 2 and 3, respectively. Both GCM scenarios show different degrees of scatter distribution of forest vegetation under the changed climate. Under the CCCM scenario (Figure 2), the dry forest area increases and there is a shift of mesic forest toward the noah and northeast part of the island. Further, a strip of a very dry forest that extends from the northwestern province is projected under the CCCM scenario. A tropical dry forest occupies the rest of the island, and extends diagonally from northwest to southeast. N q 3atticoloa Tropical very dry forest ]~ Tropical dry forest ~ Tropical mesic forest Matara Fig. 1. Holdridge life zones ofSri Lankan forests under cmTent climate conditions. S. SOMARATNE AND A. H. DHANAPALA q Put 132 ~atticoloa ~'l 'ropieal very dry forest [~ Tropical dry forest ~Tropieal mesic forest Fig. 2. Holdridge life zones of Sri Lankan forests under CCCM derived climate change scenarios. N Mann~ Puttalatr Battieoloa Tropical dry forest ~ Tropical mesie forest' Coloml~ Matara Fig. 3. Holdridge life zones of Sri Lankan forests under GFDL derived climate change scenarios. POTENTIAL IMPACT ON FOREST DISTRIBUTION IN SRI LANKA TABLE II Approximate areas of potential life zones under current and changed climate conditions in Sri Lanka 133 GFDL CCCM Baseline Area % Change Area % Change Vegetation Type (ha) (ha) from Baseline (ha) from Baseline Tropical very dry forest 387,099 0 -100% 715,149 +85% Tropical dry forest 3,674,160 4,199,040 + 14% 4,100,625 + 12% Tropical mesic forest ~ 2,460,375 2,355,399 -4% 1,738,665 -29% Tropical mesic forest = Tropical moist forest + tropical wet forest + sub tropical moist forest. The forest distribution pattern under the OFDL scenario is different from that of the CCCM scenario. Under the GFDL scenario, tropical very dry forest is not indicated, and most of the area in the south is covered by tropical dry forest (Figure 3). Intermediate forest types are not reflected on either of the maps developed from the Holdridge classification and the GCM scenarios. The approximate areas of current and potential life zones under climate change conditions are shown in Table II. The areas of potential life zones under climate change conditions were estimated using the Holdridge classification and 2xCO 2 climate scenarios generated from the GFDL and CCCM. Percent change from baseline is also shown for each life zone type. 4. Discussion The following trends were identified as potential results of climate change in Sri Lanka. A northward shift ofmesic forest over existing dry forest, indicated by both the GFDL and CCCM scenarios, would result from increased temperature and rainfall in the northern region of the island. Most of the forest area is currently distributed in the northern and southeastern parts of the island, where population density is low. Climate change would convert the current forest in these areas to a mesic forest. Tropical mesic forests in the south part of the island would be replaced by tropical dry forests. The most vulnerable tbrest areas would be the Sinharaj a Forest Reserve, a national heritage of Sri Lanka, and Peak Wilderness Forest Reserve. Most of the Sri Lankan endemic species find their refuge in these forest patches, and change in the forest type due to climate change would probably eliminate most of these species from Sri Lanka. These findings indicate that, although there are minor differences, the CCCM and GFDL could be used in assessment of climate change impacts on different sectors other than forest in future studies. The Holdridge Life Zone Classification is useful, but for future studies in-depth investigations on carbon cycling, biomes, biodiversity, and other factors should be undertaken. The current life zones identified in this study based on climate data (Figure 1) to a certain extent agreed with Thornthwaite (1931) and the vegetation map of Mueller-Dombois (1968). In addition, the findings associated with current climate conditions do not deviate significantly from findings by the Asian Development Bank (1994), which pointed out that climate change scenarios developed for Sri Lanka indicate significant change in rainfall and temperature in the island. As a consequence, the Asian Development Bank (1994), showed an increase in rainfall 134 S. SOMARATNE AND A. H. DHANAPALA and temperature in both the dry and wet zone of Sri Lanka during the northeast monsoon. However, in this study both CCCM and GFDL scenarios showed a transformation of existing dry vegetation in just the northern part of the island. Although the GeM (CCCM and GFDL) data simulate the current climate with a certain extent of accuracy, these models are limited due to their inherent characteristics. Further, the low resolution of the GCM data and the assumption that terrain is smooth during GCM funs pose difficulties in generating scenarios for a small country like Sri Lanka. Topographically Sri Lanka is heterogenous and that heterogeneity could not be incorporated in the GCM scenarios. Thus these limitations of the GCM data should be taken into account in the interpretation of climate change scenarios and the results of this study. When the results from the Holdridge Classification are considered it must be noted that this methodology is suitable for exploration of broad-scale vegetation distribution pattern and the influence of climate changes on the suitability of a region to support different vegetation types. However, the resolution of the data used in the generation of Holdridge life zones are very low, and such resolution gives an approximate estimation of life zones. Further studies on the forest sector, especially studies combined with a Gap model, would add to the understanding of the extent of potential climate change impacts on distribution of Sri Lankan forests. Acknowledgments The authors would like to express their sincere thanks to Dr. Robert Dixon and Joe Wisniewski for their critical review of the manuscript and valuable suggestions. Thanks are also due to Ms. Liz Madden, U.S. Country Studies Program Library, Washington, DC, for providing relevant reference materials. 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