climate change on UAE - Stockholm Environment Institute-US Center

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First principles ecosystem models: potential vegetation and disturbance Mechanistic models are designed to simulate the structure, function, and (sometimes) dynamics of ecosystems based on a “first principles” understanding of the components of the ecosystem. Exclusively, these models simulate and follow <strong>change</strong>s in vegetation composition only, and do not consider fauna, except occasionally as numerical agents of disturbance. The bottom-up approach is an effective way of exploring how a biome forms, and which climatic and competitive features drive the equilibrium composition of a biome. There are no standard structures for mechanistic models, but many do share common features. The mechanistic component is usually a set of equations describing carbon balance (photosynthesis and respiration) with available light and water for a small variety of plant functional types (i.e. grasses, shrubs, trees, and deciduous and coniferous species). Various levels of sophistication in these models may describe important ecosystem components, depending on the question at hand: Water availability and transfer: water infiltration into the soil and uptake by roots (CARLUC, Hirsch et al., 2004) Physical structures: tall trees deprive shorter shrubs and grasses of light (ED, Moorcroft et al., 2001) Disturbances: fires, windstorms, and other destructive events (IBIS, Foley et al., 1998; ED) Nutrient dynamics: available nitrogen in soils, roots, stems, and leaves (TEM, Tian et al., 1998) Seasonality: <strong>change</strong>s in leaf density, senescence (CASA, Potter et al., 2004) These mechanistic models are useful for understanding how large scale <strong>change</strong>s in <strong>climate</strong> or other abiotic factors will <strong>change</strong> biome locations or biomass, or tracing complex feedback mechanisms (such as how shifts in vegetation abundance impact <strong>climate</strong> patterns, Wang et al., 2004). These models, however, are difficult to apply at small scales and lack the ability to discriminate <strong>change</strong>s in composition more detailed than basic functional types. In addition, mechanistic models can only describe a limited degree of complexity, and may neglect detailed, yet critical, interactions (such as nutrient or water flow between clustered shrubs and grasses in a semi-arid system, or different phenological responses to seasonality and drought). This class of model may be useful in determining <strong>climate</strong> impacts on the UAE if the predominant question is in regard to biomass, ecosystem feedback cycles, or how an ecosystem might be structured in the UAE without an anthropogenic influence. Bioclimatic envelope models Bioclimatic envelope models are designed to explore how species ranges may shift in response to <strong>climate</strong> <strong>change</strong>. It has long been understood that <strong>climate</strong> (precipitation and temperature) strongly controls the ability of certain species and functional types to survive and thrive (Pearson and Dawson, 2003), and in fact, one of the central tenants of biogeographical niche theory is that an ecological niche can be defined by the environmental variables which affect a species. Major biomes, and even biogeographical boundaries within these biomes, are largely defined climatically. Individual species may have a narrow range of acceptable <strong>climate</strong>s, or an envelope in which they are typically found (the “realized niche”) or should be found based on known biological functions (the “fundamental niche”). Bioclimatic modeling asserts that we can anticipate the ecosystem impact of <strong>climate</strong> <strong>change</strong> on species ranges (at the regional scale) by determining the new bounds on bioclimatic envelopes. Bio<strong>climate</strong> envelope modeling suffers from at least three shortcomings (see Pearson and Dawson, 2003): Competition: how a species might thrive in an environment and how it actually interacts in its community can be very different; if a species is non-competitive within its <strong>climate</strong> envelope, it may not be found in the new environment. Adaptation: it may be more effective for a 178 Climate Change Impacts, Vulnerability & Adaptation
species to adapt to a changing <strong>climate</strong> than for it to migrate and establish in a new physical location. Species dispersal: non-mobile species may be unable to migrate or disperse to new climatic zones, even over the course of many generations, while highly mobile species may be able to exploit much more of their fundamental <strong>climate</strong> range. Empirically-based bio<strong>climate</strong> models share an underlying methodology (Araujo et al., 2005): the physical locations of a species is recorded over a wide range (as presence-absence), and <strong>climate</strong> variables are derived for all locations. Climate variables may include cooling or warming degree days, average temperature over a timeperiod, maximum or minimum temperatures during a critical period, number of days over a temperature threshold, volume of precipitation over a time-period, frequency of rainfall, and drought lengths. Using a variety of classification mechanisms (neural networks, statistical clustering, or decision trees), the variables (and their ranges) which best discriminate species presence or absence are determined on a subset of the data and 70% is a standard (Pearson and Dawson, 2003). The remaining data is used to validate the <strong>climate</strong> envelope assumptions. New <strong>climate</strong> variables are derived for a <strong>climate</strong> <strong>change</strong> scenario, and the derived rules are applied to the new variables to determine the potential species range. This class of model may be useful in determining the impact of <strong>climate</strong> <strong>change</strong> in the UAE if the underlying question is in regard to expected new species ranges or biodiversity. These models require significant field and possibly remote sensing data to run successfully. Patch structure and spatial distribution models Patch structure and spatial distribution models have at least two distinct lineages, but have evolved to answer similar questions: how does the spatial structure of an ecosystem (usually at a landscape scale) impact the function and composition of the ecosystem Similarly to the mechanistic models described above, these models are usually theoretically based and non site-specific, and usually track the dynamics of vegetation, rather than fauna. Patch structure, or gap, models are derived from forest stand models, developed to estimate the rate of growth and height of trees in dense, light-limited environments (such as rainforests). These models simulate the light and water environment for individual stands of trees, and often explicitly model the shape, size, and leaf cover of each tree in the stand, using allometric equations to estimate leaf density, branch size, and tree height from more simply tracked metrics, such as stem width (Busing and Mailly, 2004). Important questions in these models revolve around how quickly gaps (treefalls) are replaced with new vegetation in certain environments. Spatial distribution models are systems developed to explore the dynamic systems in which physical proximity, rather than height, is important. Such models are often seen applied in arid or semi-arid ecosystems where nutrient and water availability are critical limiting factors. The distance between shrubs or clumps of grasses may determine how much water is available to individual plants, how water is transferred between plants, or where pools of nutrients are available. Spatial distribution models may be combined with grazing or fire simulations to determine how herbivory and disturbance <strong>change</strong>s the structure, health, or composition of sparsely vegetated landscapes (i.e. van de Koppel and Rietkerk, 2004; Adler et al., 2001; Weber et al., 1998; Aguiar and Sala, 1999) This class of model may be useful in determining the impact of <strong>climate</strong> <strong>change</strong> in the UAE in the context of evaluating both precipitation frequency and intensity influence on ecosystem composition, and grazing impacts, primarily by camels, on arid ecosystem health. Climate / phenology models Climate-phenology models are a distinct and unique class of model, usually empirically based, which strive to understand the drivers of seasonality of flora and fauna. Many of these models relate various <strong>climate</strong> factors (temperature, precipitation, and available sunlight at key times of the year) to the timing Impacts, Vulnerability & Adaptation for Dryland Ecosystems in Abu Dhabi 179
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Acknowledgements The authors are de
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Foreword There are many levels at w
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Table of Contents (Part 2) Water Re
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9.2. Types of ecosystem models ....
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APF AML CARA CO 2 CReSIS CSI DEM EG
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Table ‎1‐1. Climate Change and
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2. Sea-level Rise Impacts on Coasta
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differences in observed sea levels
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Sea Level Change (mm) 20 15 10 5 0
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Impacts, Vulnerability & Adaptation
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Gulf is that the shallower depth al
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ago when sea level was a few meters
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Scientists have every reason to bel
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amongst others. Sea level rise may
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Figure ‎4‐2. Data displaying ar
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processed SRTM topographic dataset
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water height of each tidal day obse
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Table ‎4‐3. UAE Coastal Cities.
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Figure ‎4-10: 1 meters above mean
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Figure ‎4-12: 3 meters above mean
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as well as parts of the Industrial
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up to help to assess technology nee
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framework that has relevance to the
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to be estimated reliably and hence
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6. Conclusions and recommendations
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7. List of References ADEA (2006)
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Assessment Report of the Intergover
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8. Glossary Adaptation: Adjustment
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To calculate areas inundated by lan
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Annex 1: Elevation Data Sensitivity
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Annex 2: Inundation maps 3. Urban V
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Figure A2-3 3 meters sea level rise
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Figure A2-6 1,3,9 meters sea level
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Page Figure 4‐6. WEAP estimates o
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in storage occurs in the Liwa lens
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Figure‎ 2‐1. Percentage of tota
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Total installed capacity equals 648
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Without demand management strategie
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Figure 3-1 Global average temperatu
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The CO 2 storylines include both
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of topography on the region’s pre
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Temperature Change (degrees C) Temp
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neglected infrastructure, regional
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and magnitude of the ENSO (Haines e
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Table ‎4‐1. Irrigated Hectares
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including estimates of population a
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4.4. Representing Irrigation Demand
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Table 4-5. Comparison of Historical
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4.6. Scenarios and Key Assumptions
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Avg. Monthly Air Temperature - MOR
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Table ‎4‐8. Summary of scenario
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5. Results and Discussion The <stro
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total water demand estimate at abou
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for the Optimistic and Pessimistic
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eport of the IPCC holds out hope th
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Table 6-1. Adaptation options for w
Page 127 and 128: CIA, (1990), Middle East Area Oil a
Page 129 and 130: Figure A1‐2. Map of Abu Dhabi key
Page 131 and 132: Data Sources And Assumptions We rel
Page 133 and 134: Table A1-3. continued Liwa- Hammim
Page 135 and 136: Table A2-1. Summary of 36 emission
Page 137 and 138: Impacts, Vulnerability & Adaptation
Page 139 and 140: 139
Page 143 and 144: List of Tables Page Table 3-1: Majo
Page 145 and 146: 2. Potential risks to dryland ecosy
Page 147 and 148: 4. Major terrestrial ecosystems in
Page 149 and 150: Peninsula in the north to close to
Page 151 and 152: 5. Important flora in Abu Dhabi Abu
Page 153 and 154: over a one third of the known speci
Page 155 and 156: 6. Fauna of the terrestrial ecosyst
Page 157 and 158: include the urban development and i
Page 159 and 160: (Arnell, 1999; Milly et al., 2005).
Page 161 and 162: camels and other large herbivores i
Page 163 and 164: 8. Biodiversity, ecosystem threshol
Page 169 and 170: Alternatively, established shrublan
Page 171 and 172: e functionally deficient. We projec
Page 173 and 174: Adaptation can be planned or it can
Page 177: is based on a theoretical understan
Page 181 and 182: that remediation steps begin soon t
Page 183 and 184: 10 References Adler, PB, DA Raff, W
Page 185 and 186: Nordad, (2007), Climate Change Fact
Page 187 and 188: Annex 2: Expected effects of global
Page 189 and 190: Annex 4: Main drivers of ecosystem
Page 191 and 192: Sn Species Scientific name Family S
Page 193 and 194: Sn Species Scientific name Family S
Page 195 and 196: Name of species/sub-species Habitat
Page 197: Annex 8: Native species list of ter
Page 200 and 201: The cover picture represents a Brid
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