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Imbach, P, Molina, L., Locatelli B., Roupsard O., Mahé G., Neilson R., Corrales L., Scholze M., Ciais P. (2012). Modeling potential equilibrium states of vegetation and terrestrial water cycle of Mesoamerica under climate change scenarios. Journal of Hydrometeorology, 13, 665-680.

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Title 1

Changes and uncertainties in the vegetation and terrestrial water cycle of Mesoamerica under 2

climate change scenarios 3

Authors 4

Pablo Imbach1*

(corresponding author), Luis Molina1, Bruno Locatelli

2, Olivier Roupsard

1,3, Gil 5

Mahé4, Ronald Neilson

5, Lenin Corrales

6, Philippe Ciais

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Contact details 7

1Climate Change Program, CATIE, Costa Rica;

2CIRAD UPR Forest Ecosystem Services, 8

Montpellier, France; CIFOR ENV Program, Bogor, Indonesia; 3CIRAD-Persyst, UPR80, 9

Fonctionnement et Pilotage des Ecosystèmes de Plantations, Montpellier, France; 4 10

IRD/HydroSciences Montpellier, Case MSE, Université Montpellier 2, 34095 Montpellier, 11

Cedex 5, France; 5USDA Forest Service, Pacific Northwest Research Station in Corvallis, 12

Oregon, USA; 6Apdo. 299-11001, San José-Costa Rica;

7IPSL – LSCE, CEA CNRS UVSQ, 13

Centre d’Etudes Orme des Merisiers, 91191 Gif sur Yvette, France. 14

Dateline: 15

*CATIE 7170, Turrialba, Cartago 30501, Costa Rica. E-mail: [email protected] 16

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ABSTRACT 22

We aimed at evaluating the likelihood of the impacts of climate change on potential 23

vegetation and the water cycle in Mesoamerica to support future development of ecosystem 24

based adaptation strategies. Mesoamerica is a global biodiversity hotspot with highly diverse 25

topographic and climatic conditions and is among the tropical regions with the highest expected 26

changes in precipitation and temperature under future climate scenarios. We used the 27

biogeographic soil-vegetation-atmosphere model MAPSS for simulating the integrated changes 28

in leaf area index, vegetation life forms (grass, shrubs and trees), evapotranspiration, and runoff 29

at the end of the 21st century. We assessed the likelihood of changes in vegetation and water 30

cycle under 136 climate scenarios, generated with 23 general circulation models (GCMs) and 31

belonging to three groups of greenhouse gas emission scenarios (low, intermediate and high 32

emissions). Our results showed that potential vegetation will likely shift from humid to dry types 33

and LAI is likely to decrease over 77 – 89% of the region, depending on climate scenario groups. 34

Runoff will decrease across the region even in areas where precipitation increases, as 35

temperature change will increase evapotranspiration. Higher emission scenarios show lower 36

uncertainty in modeled impacts. Although uncertainty is high for future precipitation, the impacts 37

of climate change on vegetation and water cycle are predicted with relatively low uncertainty. 38

Projected climate change will reduce ecosystems leaf area and water balance with large 39

consequences for biodiversity and ecosystem functioning. Future expansion of dry ecosystems 40

indicates that conservation efforts should consider current dry ecosystems as a source of genes 41

and species to facilitate adaptation of ecosystems to climate change while seeking potential 42

refugees for the threatened humid ecosystems. 43


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Keywords Central America, ecosystem services, adaptation to climate change, biodiversity, 44

protected areas, freshwater, biomass 45

1. Introduction 46

There is a need to understand the potential impacts of climate change on ecological systems 47

before defining adaptation measures for ecosystems and the people who depend on their services 48

(Millenium Ecosystem Assessment 2005). The Mesoamerican region, where approximately 60 49

million people depend highly on natural resources, is a global biodiversity hotspot (DeClerck et 50

al. 2010), with more than 5000 endemic vascular plant species (Greenheck 2002). It is a reservoir 51

of the evolutionary history of biodiversity (Sechrest et al. 2002) and a bridge between North and 52

South America for mammals (MacFadden 2006), birds (Weir et al. 2009), and plants (Gentry 53

1982). Countries in the region have developed national and regional policies for integrating 54

biodiversity conservation and development (e.g., the Central American System of Protected 55

Areas1 and the Puebla-Panama plan

2) that should account for future climate threats to help 56

reduce the vulnerability of the region. 57

Central America could be the tropical region most exposed to climate change (Giorgi 2006). 58

Between 1961 and 2003, temperatures have been increasing and precipitation has been 59

intensifying, with a larger amount of annual precipitation falling during extreme events (Aguilar 60

et al. 2005). Observed trends in annual rainfall differ on signal (positive or negative) and 61

statistical significance depending on the data source used. Aguilar et al. (2005) found a non-62

significant trend based on weather station data while Malhi & Wright (2004) found significant 63

increases in some areas based on spatial interpolation of weather station data over forest covered 64

1 http://www.sica.int/

2 http://www.planpuebla-panama.org/


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areas. Furthermore, Neelin et al. (2006) found an observed decrease in precipitation trends based 65

on remote sensing sources (except for southern Panama that shows the opposite trend), but 66

highlights the difficulty of discerning the natural multi-decadal or inter-annual variability on 67

these observed trends in tropical areas. Precipitation is projected to decrease in the future (Neelin 68

et al. 2006) but uncertainties remain on this development since the year at which the trend for 69

each model is discernible from natural variability and at which several models agree on the trend 70

is highly variable. However, the potential for extreme droughts in the region is becoming 71

increasingly clear (Dai 2010). The impacts of these changes in climate on vegetation and 72

hydrology will affect the availability of natural resources (i.e. water and biomass) with 73

implications for development. For example, the collapse of the Mayan civilization in Northern 74

Mesoamerica, has been linked to multi-decadal droughts and its impact on resource availability 75

(Curtis et al. 1996; Haug et al. 2003). 76

The impacts of climate change on ecosystems depend on non-linear and complex interactions 77

among soils, vegetation, and climate. These interactions can be simulated by process-based 78

models, such as land surface models, which functionally integrate atmospheric, vegetation and 79

hydrologic responses that cannot be accomplished by correlative climate-vegetation models 80

(Yates et al. 2000) and have been applied to modelling the impacts of climate change on 81

vegetation and hydrology for all IPCC reports since 1995 (e.g., Cramer et al. 2001; Neilson; 82

Marks 1994). 83

Uncertainties of impacts are related to model structure, model parameters and input data, 84

including future changes in climate (IPCC 2005). Assessing uncertainties is crucial for informed 85

decision making and can be done using several ecosystem models, General Circulation Models 86

(GCMs), and emission scenarios. Calibrating and validating ecosystem models with past 87


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observations allows the uncertainties arising from the ecosystem model to be analyzed and 88

reduced; however, uncertainties from climate scenarios cannot be diminished since the future 89

will never be precisely predictable. 90

We aimed to assess the impacts of climate change on vegetation and the water cycle in 91

Mesoamerica using the process-based Mapped Atmosphere Plant Soil System (MAPSS) 92

biogeography model (Neilson, 1995) with 136 climate scenarios downscaled from the outputs of 93

23 global GCMs under low (B1), intermediate (A1B) and high (A2) emission scenarios for the 94

2070-2099 period and applied in the IPCC 4th

assessment report. This allowed us to quantify the 95

magnitude and uncertainties of climate change impacts on vegetation life forms, leaf area index 96

(LAI), runoff and evapotranspiration. Due to the complex topography and spatial climate 97

variability in the region, we assessed the impacts at 1 km resolution. 98

2. Materials and methods 99

a. Study area: climate and vegetation 100

The study area spans a one million square kilometer area of land between 76.5 and 99 101

degrees longitude W and 6.5 and 22 degrees latitude N (excluding the Caribbean islands). It 102

extends from Panama in the south to southern Mexico in the north across six other Central 103

American countries (Costa Rica, Nicaragua, Honduras, El Salvador, Guatemala and Belize). The 104

region is biophysically diverse with a topography marked by a mountain range that reaches over 105

4000 m.a.s.l. and runs close to the Pacific coast (Fig. 1). 106

Climate is tropical and rainfall follows a bimodal seasonal cycle with high inter-annual 107

variability (Magaña et al. 1999). In the Pacific watersheds and Yucatán, seasonal rainfall occurs 108

from May to October, whereas rainfall occurs year-round in the Atlantic watersheds with a 109


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weaker bimodal seasonal cycle. Pristine areas are covered with savanna and dry forests in the 110

Pacific watershed and Yucatán, and evergreen forests in the Atlantic watershed. Around 42% of 111

the region is covered by crops and pastures, 57% by natural vegetation and 2% by urban areas 112

(DeClerck et al. 2010) (Fig. 1). 113

b. Modelling approach 114

MAPSS is an equilibrium biogeography model that simulates the long-term average water 115

balance and potential vegetation, given an average climate, based on water and energy 116

constraints (Neilson 1995). The model, described in detail by Neilson (1995), has been applied in 117

earlier studies for modeling runoff and vegetation patterns of the United States (Bachelet et al. 118

1998; Bishop et al. 1998), and assessing the impacts of climate change globally (Neilson 1993a; 119

Neilson; Marks 1994) and in North America (Bachelet et al. 2001; Neilson 1993a; Neilson; 120

Drapek 1998; Scott et al. 2002). MAPSS simulates equilibrium runoff, evapotranspiration, leaf 121

area index (LAI), and potential vegetation life forms (trees, shrubs and grasses) depending on 122

climate and soils. MAPSS is a General Vegetation Model (GVM), a relatively new class of 123

models based largely on the fundamental principle that vegetation will continue to amass leaf 124

area at a given location until it utilizes nearly all of the available soil moisture (Horton 1933). 125

The assumption that LAI and vegetation forms adjust according to soil drought allows the model 126

to search for an equilibrium of LAI, evapotranspiration and soil moisture depending on 127

temperature and precipitation. This equilibrium model does not account for transient changes, as 128

would a Dynamic GVM (DGVM) and, therefore, the modeled future vegetation and hydrology 129

represent the equilibrium that would exist if long-term future climate remained as during the 130

simulated period of 2070-2099. The use of an equilibrium GVM is preferred for this study, given 131

the very high spatial resolution and the large number of scenarios. All DGVMs operate under the 132


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same water-balance constraint, but are always approaching that constraint under a constantly 133

changing climate. The use of a GVM allowed the exploration of a much greater range of 134

uncertainty among climate scenarios with a far greater resolution of the extremely high 135

complexity of Central American terrain. 136

MAPSS has been satisfactorily calibrated and validated in Mesoamerica with data on LAI 137

and runoff in 138 catchments (Imbach et al. 2010). Following other studies in tropical areas 138

(Hély et al. 2006), we ignored the effects of elevated atmospheric CO2 concentrations on 139

stomatal conductance, water use efficiency, water balance, and plant growth, because of the lack 140

of knowledge of these processes in tropical areas (Körner 2009) at ecosystem level (Norby; Luo 141

2004) and of the combined effects of elevated CO2 and temperature change (Hickler et al. 2008). 142

Furthermore, relative to changes in precipitation and temperature, these effects might be 143

relatively small (Chambers; Silver 2004; Körner; Arnone 1992). However, MAPSS has been run 144

globally with and without a direct CO2 effect, emulated by a 35% reduction in stomatal 145

conductance, based on literature available at the time (Neilson; Marks 1994). 146

c. Climate scenarios 147

We used climate change scenarios produced by World Climate Research Programme, 148

Coupled Model Intercomparison Project phase 3 (WCRP-CMIP3). The scenarios of this multi-149

model dataset, used in the IPCC Fourth Assessment Report, were downscaled to a 2.5 minute 150

resolution (around 5 km pixel) by The Nature Conservancy-California. The climatology dataset 151

includes 48, 52 and 36 scenarios for low (B1), intermediate (A1B) and High (A2) emission 152

scenarios respectively, from the IPCC-SRES. As in other similar studies (Hulme et al. 1999; 153


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Scholze et al. 2006), climate scenarios were constructed by adding an interpolated GCM 154

anomaly to our reference climatology (1950-2000) at a 1 km resolution (Hijmans et al. 2005). 155

d. Uncertainty assessment 156

We evaluated the uncertainties of the changes in model outputs between the reference 157

period (1950-2000) and 2070-2099 (the period for the future climatology used). For LAI, runoff, 158

and evapotranspiration, we only considered changes larger than 20% in absolute value to be of 159

potentially large significance and lower changes as no-change scenarios (other thresholds were 160

explored; see supplementary material). For vegetation structure, we considered changes in the 161

dominant life form of the canopy, e.g., from tree to grass or vice versa. Using the terminology 162

recommended by the IPCC (2005), we considered that a change is very likely if it is observed in 163

more than 90% of the scenarios, ―likely‖ if 90> 66%, ―about as likely as not‖ from 33<66%, 164

―unlikely‖ 10< 33%, and ―very unlikely‖ < 10%. We mapped the likelihood of impacts using 165

multicolored maps (Scholze et al. 2006; Teuling et al. 2010). 166

3. Results 167

All climate scenarios forecasted temperature increases in Mesoamerica at the end of the 168

century varying from less than 2.5 °C (average B1 scenarios) to more than 3.5 °C in the 169

northwest part of the region (average A2 scenarios). Precipitation is projected to increase or 170

decrease depending on the location and the scenario (Fig. 2a), and the absolute changes increase 171

with higher emissions (Fig. 2b). On average among the scenarios with decreasing precipitation, 172

the projected change is from 4% to more than 20% (larger reductions are observed in dry areas). 173

Uncertainties are the lowest for the highest emission scenarios (A2 group of scenarios). The 174

trend in decreasing precipitation is more certain in the north of the region than in Panama and 175


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Costa Rica (Fig. 2a), which stand between areas of decreasing precipitation in the north and 176

areas of increasing precipitation south of the study area, i.e. in South America. 177

a. Water cycle 178

Annual runoff is likely to decrease over 61–71% of the area for all emission scenarios (red areas 179

in Fig. 3a) and likely to increase in less than 1% (blue areas in Fig. 3a). Uncertainty in predicted 180

changes in runoff is less with higher emissions, as a likely decrease in runoff is observed in 181

larger areas under scenarios A2 than under the other scenarios. Uncertainties about runoff are 182

higher in the northwest of the region, where opposing trends are observed under different 183

scenarios (see purple areas in Fig. 3a). In most of the south (Costa Rica and Panama) runoff will 184

likely decrease (Fig.3a). It is very unlikely that runoff will change in central Honduras and parts 185

of southern Mexico (white areas in Fig. 3a). 186

Evapotranspiration is likely to increase in 18–22% of the area, particularly in Panama, Costa 187

Rica, El Salvador, and parts of central Guatemala (blue areas in Fig. 3b), and likely to decrease 188

in <1% of the area. No changes in evapotranspiration are observed in the north (white areas in 189

Fig. 3b). Uncertainty in the likelihood of change in evapotranspiration is higher in the northwest 190

(purple areas in Fig. 3b) than in the rest of the region. 191

b. Vegetation 192

LAI is likely to decrease over 77–89% of the area (red areas in Fig. 3c) depending on the 193

scenario and likely to increase over less than 2% of the area (green areas in Fig. 3c). In areas 194

with less certainty in LAI trends (e.g., in central Panama, Costa Rica, and Honduras; coastal 195

Yucatan, and the highlands of El Salvador, Mexico, Guatemala, and Mexico), the scenarios show 196

mixed responses in LAI to these changes in climate (yellow areas in Fig. 3c), as anticipated by 197


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Neilson 1993b) for any areas of rapid change (ecotones). The decrease in LAI is generally driven 198

by a decrease in tree and grass LAI (red areas in Fig. 4a and c), except in some areas of Mexico 199

where the change affects shrub-dominant life forms (Fig. 4b). An increase in grass LAI (green 200

areas in Fig. 4c) is the driver of the increases in total LAI (green areas in Fig. 3c) in savanna and 201

seasonal ecosystems, indicating an increase in grasses as tree density is reduced. 202

In some scenarios, the woody dominant life form (either trees or shrubs) shifts to grasses (red 203

areas in Fig. 3d) but this trend is uncommon: it is likely in less than 2% of the area. No changes 204

from grasses to shrubs or trees are expected. 205

4. Discussion 206

The changes in the water cycle reflect changes in precipitation and temperature but also in 207

vegetation density and structure and, therefore, hydrological impacts result from diverse 208

equilibrium states between these climate components. 209

Under a general drying trend in climate scenarios (lower precipitation combined with higher 210

temperatures), we found that runoff, evapotranspiration and LAI have mixed (positive and 211

negative) spatial patterns of change. Similar to climate uncertainties, the higher the emissions the 212

lower the uncertainty (between models) of ecosystems and hydrologic responses. 213

In some areas runoff is likely to decrease even though precipitation change is uncertain. We 214

found likely runoff decreases and evapotranspiration increases in most of Panama and southern 215

Costa Rica where 30-60% of scenarios show an increase in precipitation and the complement the 216

opposite signal. The increased certainty of the impacts is due to the increase in temperature 217

(across all scenarios) that drives a non-linear increase in evapotranspiration at the expense of 218

runoff. 219


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Runoff is likely to increase in areas where grass LAI increases and where evapotranspiration 220

decreases, mostly central Honduras, Guatemala, and mountains in Mexico. This trend is 221

observed in relatively dry areas, where current annual runoff and precipitation are less than 200 222

mm and 1000 mm respectively and probably results from the reduction in deep woody roots, 223

which extract deep water. 224

The increase in evapotranspiration in humid areas is driven by the increase in temperatures, 225

which compensates the effects of decreased LAI on reduced evapotranspiration. Therefore, in 226

these areas, the virtually likely increase in temperature results in a likely decrease in runoff, even 227

where the future trend in precipitation is uncertain. In drier areas, runoff can increase even 228

though the future climate appears to be drier because decreased water availability reduces 229

vegetation LAI, evapotranspiration, and woody roots and causes a larger fraction of precipitation 230

to run off (Aber et al. 2001). 231

Future changes in LAI and potential vegetation life forms imply modifications in the density 232

and structure of vegetation, ecosystems and their functions, particularly in areas where a shift 233

from tree to grass dominated vegetation types is expected. Mesoamerican ecosystems will shift 234

to drier types, particularly in areas where runoff and evapotranspiration are both reduced 235

indicating that precipitation falls below the potential evapotranspiration, and the water 236

availability for vegetation is reduced (i.e. central Yucatan). Increased evapotranspiration in other 237

areas (e.g. Costa Rica and Panama) indicate that under climate change the 238

runoff:evapotranspiration ratio decreases and a larger fraction of precipitation can still be used 239

by vegetation. 240


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A global study by Scholze et al. 2006), using the Lund-Potsdam-Jena (LPJ) dynamic 241

vegetation model at a coarser resolution, found qualitatively similar trends for runoff and LAI 242

changes in Mesoamerica but with differences compared to our study; for example, larger areas of 243

forest loss and increased runoff in the southern part of the region as opposed to the likely 244

decreased runoff found in our study. Comparing the two studies is not straightforward because of 245

the vastly different spatial resolutions (ca. 50 km vs. 1 km) and they grouped the climate 246

scenarios differently (by range of temperature increase vs. by emission scenarios in our study). 247

Also, the different results may come from the different thresholds used to estimate changes (33% 248

of observed variability vs. 20% in our study). These different resolutions and thresholds may 249

explain the smaller number of changes in our study. Yet, it is encouraging (regarding 250

uncertainties) to see similarities in the vegetation shifts between the results of the high resolution 251

climate scenarios used in MAPSS and those of the more coarse resolution scenarios in the LPJ. 252

The MAPSS-modeled changes in tree fractional cover, which results in structural ecosystem 253

changes, call for in-depth future studies of populations, species, and community ecology. For 254

example, changes in the dominant form (from trees to grasses) imply changes in seed sizes 255

(Moles et al. 2007) and a modification of migration rate, a capacity that is important for forming 256

new species assemblages depending on where future new climates as well as current analogs will 257

be located in Mesoamerica (Neilson et al. 2005; Williams et al. 2007. Changes in vegetation will 258

also affect forest carbon stocks (Bunker et al. 2005). Since LAI can be used as a proxy for the 259

storage of carbon in soils and vegetation (Bachelet et al. 2001; Neilson 1993a), we can assume 260

that Mesoamerican ecosystems may be atmospheric carbon sources under future climate. Studies 261

with dynamic vegetation models at coarse spatial resolutions suggest that this trend will be 262

apparent in the second half of the century (Cramer et al. 2001). 263


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Changes in vegetation have implications for biodiversity. Our results highlight the potential 264

vulnerability of ecosystems in dry areas and their importance as a source of genes and species 265

that could help ecosystems shifting to drier types, for example, the Mexican dry forests (Dick; 266

Wright 2005). This could mean that changes in the network of protected areas and biological 267

corridors of the Mesoamerican Biological Corridor (MBC) are needed, as it has been suggested 268

for Canada’s national park system (Scott et al. 2002). Changes in the water cycle will also have 269

implications for terrestrial biodiversity. Water availability has been used as a determinant of 270

plant species richness in warm areas (Hawkins et al. 2003), since climate and ecosystem 271

productivity influence species richness (Field et al. 2009; Kreft; Jetz 2007). For example, 272

evapotranspiration was found to be correlated with global richness of vascular plants (Kreft; Jetz 273

2007) as well as terrestrial vertebrates of the Neotropics (Qian 2010). 274

The interactions between gradual climate change and other drivers of changes in ecosystems 275

and the water cycle clearly need more research on: (i) feedbacks between vegetation change and 276

fires, as changes in fire regimes may affect ecosystems and the climate-induced conversion of 277

forests to grasslands can modify fire regimes (Lewis 2006) (MAPSS contains a simplified fire 278

algorithm); (ii) hurricane and extreme events, which can be modified by climate change and have 279

impacts on the structure of forests (i.e. stem density and tree height) (Gillespie et al. 2006); (iii) 280

intra- and inter-annual climate variability, as variability affects the distribution of vegetation 281

types in Mesoamerica (Lozano-García et al. 2007), which is specially enhanced in complex 282

topography (Neilson 1993b); (iv) human disturbances, as deforestation, degradation, and 283

fragmentation influence water cycles (Piao et al. 2007) and increase ecosystem vulnerability to 284

climate change; (v) effects of elevated CO2 on LAI and evapotranspiration (Gedney et al. 2006). 285


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Finally, this regional assessment could provide input for studies on the impacts of future 286

availability of water (for drinking water, irrigation or hydro-energy) and biomass (for household 287

firewood energy or forestry sectors) at scales below country level (Arnell 2004; Döll 2002) to 288

support the development of adaptation strategies to climate change in Mesoamerica. 289

Acknowledgements 290

This work was funded by the MESOTERRA Project of the Mesoamerican Agro-environmental 291

Program at the Tropical Agricultural Research and Higher Education Center (CATIE) and the 292

TroFCCA project executed by CATIE and CIFOR and funded by the European Commission 293

(contract EuropeAid/ENV/2004-81719). We acknowledge the Program for Climate Model 294

Diagnosis and Intercomparison (PCDMI) and the WCRP’s Working Group on Coupled 295

Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. 296

The creation of this dataset was supported by the Office of Science, U.S. Department of Energy. 297

We thank The Nature Conservancy California and Kirk Klausmeyer for downscaling and 298

converting the climate data to ArcGIS format and Marko Scholze for providing the code for the 299

maps legends. 300

Appendix 1 Changes in leaf area index (LAI) and runoff for a range of change thresholds. 301

Insert Figure 1. 302

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431


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Figure captions 432

Figure 1. Region of study: relief, areas with natural vegetation cover (for the year 2000;CCAD; 433

WB 2003), watershed boundaries, and country limits. This and subsequent maps are based on the 434

Mollweide projection. 435

Figure 2. Precipitation and temperature changes in Mesoamerica under low (B1), moderate 436

(A1B) and high (A2) emission scenarios for 2070-2099 (48, 52 and 36 scenarios for B1, A1B 437

and A2). The likelihood of precipitation change is estimated as the percentage of models 438

showing a decrease in precipitation (column a). Bi-variate color maps (b) combine projected 439

average precipitation and temperature changes across all scenarios. 440

Figure 3. Ratio of simulations in low (B1), moderate (A1B) and high (A2) emission scenarios 441

showing at least 20% change in runoff (a), 20% in evapotranspiration (b), 20% in leaf area index 442

(c) or change in dominant life form (d). The horizontal (vertical) axis of the color map is the 443

percentage of scenarios showing an increase (decrease) in runoff, evapotranspiration or LAI, or a 444

change from grass to shrub/tree (tree/shrub to grass). Legend values show mean range value for 445

each color class. 446


showing a change of at least 20% change in the leaf area index of trees (a), shrubs (b) and 448

grasses (c). The horizontal (vertical) axis of the color map is the percentage of scenarios showing 449

an increase (decrease). Legend values show mean range values for each color class. 450

Figure captions– Appendix 1 451

452


19

Figure 1. Percentage of area under different likelihoods of change in runoff and leaf area index 453

(LAI) in Mesoamerica in 2070-2099, depending on the threshold above which changes are 454

observed. Low (B1) (left hand column a, b, c, d, e, and f) and high (A2) (right-hand column g, h, 455

i, j, k, and l) emissions scenarios are presented. Area for each likelihood category is presented for 456

decreases (a-g, c-i, and e-k) and increases (b-h, d-j, and f-l) in runoff, evapotranspiration and 457

LAI. 458

459


20

Figure 1 460

461

Figure 1. Region of study: relief, areas with natural vegetation cover (for the year 2000;CCAD; 462

WB 2003), watershed boundaries, and country limits. This and subsequent maps are based on the 463

Mollweide projection. 464

465

466


21

Figure 2 467

468

Figure 2. Precipitation and temperature changes in Mesoamerica under low (B1), moderate 469 (A1B) and high (A2) emission scenarios for 2070-2099 (48, 52 and 36 scenarios for B1, A1B 470 and A2). The likelihood of precipitation change is estimated as the percentage of models 471 showing a decrease in precipitation (column a). Bi-variate color maps (b) combine projected 472

average precipitation and temperature changes across all scenarios. 473

474


22

Figure 3 475

476


showing at least 20% change in runoff (a), 20% in evapotranspiration (b), 20% in leaf area index 478 (c) or change in dominant life form (d). The horizontal (vertical) axis of the color map is the 479 percentage of scenarios showing an increase (decrease) in runoff, evapotranspiration or LAI, or a 480


23

change from grass to shrub/tree (tree/shrub to grass). Legend values show mean range value for 481

each color class. 482

483

484


24

Figure 4 485

486


showing a change of at least 20% change in the leaf area index of trees (a), shrubs (b) and 488 grasses (c). The horizontal (vertical) axis of the color map is the percentage of scenarios showing 489

an increase (decrease). Legend values show mean range values for each color class. 490

491


25

Appendix 1 492

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

Runoff decrease (%)

A2

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

Are

a (

%)

Runoff increase (%)

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

Runoff increase (%)

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

Are

a (%

)

Runoff decrease (%)

B1

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

Evapotranspiration decrease (%)

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

Are

a (%

)

Evapotranspiration decrease (%)

493


26

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

Virtualy likely very likely likely about as likely as not

unlikely very unlikely exceptionally unlikely

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

LAI increase (%)

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

Are

a (%

)

LAI decrease (%)

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

LAI decrease (%)

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

Are

a (%

)

Evapotranspiration increase (%)

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

Evapotranspiration increase (%)

0%

20%

40%

60%

80%

100%

10 20 30 40 50 60 70 80 90

Are

a (%

)

LAI increase (%)

494


27

Figure 1. Percentage of area under different likelihoods of change in runoff and leaf area index 495 (LAI) in Mesoamerica in 2070-2099, depending on the threshold above which changes are 496

observed. Low (B1) (left hand column a, b, c, d, e, and f) and high (A2) (right-hand column g, h, 497 i, j, k, and l) emissions scenarios are presented. Area for each likelihood category is presented for 498 decreases (a-g, c-i, and e-k) and increases (b-h, d-j, and f-l) in runoff, evapotranspiration and 499

LAI. 500