Integrated assessment models (IAM) are used to predict the landuse conversion for agriculture and pasture that is subsequently applied to full Earth system model simulations. IAMs used in the construction of the RCP scenarios assume that the continued increases in agricultural yield due to improved management, technol- ogy, and economic growth will reduce the rate of deforestation in the future, despite increases in population and shifts in diet [Riahi et al., 2011; Riahl et al., 2007; van Vuuren et al., 2011]. The combined historical and RCP8.5 estimates of current deforestation rates are less than those observed during the past two decades based on Food and Agriculture Organization estimates [Food and Agriculture Organization, 2010; Hurtt et al., 2011; Meyfroidt and Lambin, 2011], though more recent satellite estimates suggest even larger defores- tation rates [Hansen et al., 2013]. While the future yield projections are very sensitive to assumptions about the ability of humans to innovate both agricultural management methods and crop genetic attributes [e. g., Fischer et al., 2005], some evidence suggests that yield improvements may already be decreasing [Ray et al., 2012] and that agricultural crops may be more sensitive to temperature than previously estimated [Lobell et al., 2011]. On the other hand, estimates of net primary production and respiration in Earth system models assume a static response to temperature [e.g., Oleson et al., 2010], although there is evidence that plants can acclimate to higher temperatures on seasonal and interannual timescales [Kattge and Knorr, 2007; Atkin et al., 2008; Sendall et al., 2015]. This is consistent with other evidence that Earth system models may be overly pessimistic about the impact of higher temperatures on landcarbon [Frank et al., 2010; Keenan et al., 2013; Mahecha et al., 2010], and studies which include temperature acclimation indicate that including the effects of acclimation would increase the terrestrial carbon in Earth system models [Arneth et al., 2012; Atkin et al., 2008; King et al., 2006]. Thus, the IAM models used to predict future landuse conversion may be underestimating the threat to forests from deforestation, while Earth system models may be overestimat- ing the threat to forests from higher temperatures. Future studies should consider in more detail the incon- sistency in treatment of temperature on terrestrial ecosystems and the implications for LULCC and terrestrial carbon.
Sampling of topsoil (0 –15 cm) was carried out on 2 m × 2 m “soil plots ” located at the centre of randomly placed 200 m 2 X-plots at 5 loca-
tions within each survey square ( Wood et al., 2017 ). Vegetation and biophysical data associated with each soil plot were collected over the area of the associated X-plot. In 2007, the soil plots from the 256 squares sampled in 1978 were relocated using maps and/or markers placed in the 1978 survey and the topsoil resampled. The repeat soil plots are within 2 –3 m of the original survey locations, and detailed vegetation and biophysical measurements were taken at the same X-plot locations for both surveys. Loose leaf litter was brushed from the soil surface be- fore sampling in both years. In 1978 soil samples were taken from soil pits, whereas soil cores were sampled in 2007. Cross comparisons be- tween pit and coring methods indicated similar bulk densities were achieved ( Emmett et al., 2008 ) however, since bulk density was not measured in 1978, we were not able to analyse change in stock. Rigor- ous cross-comparison of laboratory analytical methods employed in the two survey years was also carried out ( Emmett et al., 2010 ). In some cases the original soil plots could not be located or the data from one of the surveys was incomplete, but the resampling effort nonethe- less produced 783 “repeat soil plots” with complete observations. The tSOC values from these 783 sites form the core of our analyses here.
In addition, it is undoubtedly essential to increase the insight of flood behaviour related to future developments of the Chi River Basin. Merely pointing out the presence of arbitrary landuse changes is not nearly as helpful as following the government landuse planning (i.e. year 2057). In the plan, the amount of forest cover is predicted with a remarkable increase from 17.9% (2000 - 2002) to 37.2% (2057). This implies that the rise in forest cover will result in decline in agricultural areas (75.5% of the total river basin area) by up to 18.9% in 2057. The urban areas will also decrease 0.9% and this decrease will be replaced by the industry for approximately 0.5%. To demonstrate this point, the modelling results suggested that little or no significant potential impact of future landusechange on river basin flood regime, but rather at sub-basin scale. It was apparently greater in some upstream sub-basins where additional forest land helps to buffer the effects of intensifying runoff, whereby there was no significant downstream hydrological impact. Certainly remarking on the massive flood damage, it will occur drastically due to higher density and more centralized urban growth. To contribute to future flood management, a simulation was conducted based jointly on future landuse changes and optimal combination of flood mitigation measures. Their results indicated that optimal intervention could considerably reduce flood damage. Moreover, the 0.1% annual probability of exceedance flood event was also carried out afterwards, which found that its consequences corresponded closely to all the above findings.
(greenhouse gases, aerosols, landusechange, and volcanoes), the two 1%/yr simulations are only forced with the CO 2 increase; all other forcings are held at their preindustrial levels.
From the CMIP5 models that were available on the ESGF by summer 2013, we selected those that provide both land and ocean carbon ﬂuxes and storage for all three experiments. For details of these models, see Table 1. Table 3 lists details of the land and ocean carbon representation for each model. For comparison, we also analyzed six models from the Coupled Climate-CarbonCycle Model Intercomparison Project (C 4 MIP) [Friedlingstein et al., 2006]. For C 4 MIP, the coupled and uncoupled simulations were forced by anthropogenic CO 2 emissions for the historical period, followed by anthropogenic emissions from the Special Report on
Here, we develop a stylised model of the global carboncycle and its role in the climate system to explore the poten- tial weakening of carboncyclefeedbacks on policy-relevant timescales (< 100 years) up to the year 2100. Whereas com- prehensive Earth system models are generally used for pro- jections of climate, models of the Earth system of low com- plexity are useful for improving mechanistic understanding of Earth system processes and for enabling learning (Randers et al., 2016; Raupach, 2013). Compared to comprehensive Earth system models, our model has far fewer parameters, can be computed much more rapidly, can be more rapidly understood by both researchers and policymakers, and is even sufficiently simple that analytical results about feed- back strengths can be derived. Compared to previous stylised models (Gregory et al., 2009; Joos et al., 1996; Meinshausen et al., 2011a, c; Gasser et al., 2017a), our model features sim- ple mechanistic representations, as opposed to parametric fits to Earth system model output, of aggregated carbon uptake both on land and in the ocean. Our stylised and mechanisti- cally based climate–carboncycle model also offers a work- bench for investigating the influence of mechanisms that are at present too uncertain, poorly defined, or computationally intensive to include in current Earth system models. Such stylised models are valuable for exploring the uncertain but potentially highly impactful Earth system dynamics such as interactionsbetween climatic and social tipping elements (Lenton et al., 2008; Kriegler et al., 2009; Schellnhuber et al., 2016) and the planetary boundaries (Rockström et al., 2009; Steffen et al., 2015).
Fire can be integrated into all of these systems. In a patch-burn grazing system, a different portion of the field is burned every year. In this system, cattle spend a large percentage of their time in recently burned areas of the pasture. Burning releases nutrients and plants growing in these areas are often more nutritious. The productivity and resilience of the grasslands within the region are influenced by management practices, such as grazing intensity and fire (Smart et al., 2013). Depending on the fire temperature and amount of standing biomass, the fire can contribute to the loss of N and C from the system (Hobbs et al., 1991). Interactionsbetween management (fire and grazing intensity) and site characteristics (soil and climatic variability) have the potential to produce landscape position specific impacts on long-term sustainability. For example, in areas with high slopes, fire and heavy grazing can reduce surface cover and increase the risk of erosion and gully formation (Smart et al., 2015).
Gross algorithms account for the possibility that there might be grid cells in which at some time, for example, grass- land is turned into cropland while at the same time elsewhere in the same grid cell cropland is abandoned. In the net al- gorithms such simultaneous, bidirectional land-use changes within a grid cell are not accounted for, and the models only see the net gain or loss of agricultural area during a LULCC time step. In some parts of the world shifting cultivation – clearing a piece of natural land, farming it for some years, abandoning it again while clearing another piece of natural land – is a common practice (Lanly, 1985; Ranjan and Upad- hyay, 1999; Bruun et al., 2006; Lojka et al., 2011). Though shifting cultivation does not change the vegetation distribu- tion, as is seen by global models, it releases carbon from the natural vegetation. These carbon fluxes are not accounted for when using a net algorithm. Therefore also the carbon stocks are different which may cause different behaviour of other modelled effects – e.g. wildfires. Though not addressed in this study, the two methods also leave the vegetation in different states, which affects their canopy structure, growth and biogeophysical properties such as albedo and roughness length.
The land-usechange information was adapted from the land-use harmonization project by Hurtt et al. (2011). Al- though common land-use information were provided to all modeling groups, vegetation dynamics, land surface schemes and parameterizations differ substantially among the mod- els leading to different changes in vegetation cover (Fig. S1 in the Supplement). MPI and MIR, for example, simulate LULCC patterns based on annual fractional changes given by a transition matrix (“gross LULCC transitions”), whereas CAN and IPSL only simulate annual LULCC state maps for each grid cell (“net transitions”). Details about participating models can be found in Fig. S1 and Table S1 in the Supple- ment as well as in Brovkin et al. (2013). It needs to be noted that none of the participating models simulated plant growth with respect to nitrogen and phosphorus limitation and thus, landcarbon uptakes by the biosphere and LULCC emissions might be overestimated (Goll et al., 2012).
atmospheric CO 2 growth rate and the simulated land and ocean carbon fluxes, one can diagnose the CO 2 emissions compatible with the prescribed CO 2 concen- trations (Matthews 2005, 2006; Jones et al. 2006, 2013). The magnitude of the emissions compatible with the RCP concentrations would be affected by the carboncyclefeedbacks; a model with a large negative climate– carboncycle feedback would have lower sinks, and hence lower compatible emissions (Jones et al. 2013). Four concentration-driven (C driven) scenarios were proposed for the twenty-first century and beyond, the representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5 (Moss et al. 2010; van Vuuren et al. 2011; Meinshausen et al. 2011c). Integrated assessment models (IAM) simulated the greenhouse gas and aero- sol emissions for these four RCPs scenarios (van Vuuren et al. 2011). These emissions were harmonized with historical estimates and then translated into concentra- tions using the Model for the Assessment of Greenhouse Gas Induced Climate Change, version 6 (MAGICC6) (Meinshausen et al. 2011a,b,c). MAGICC6 is a simple climate model that also includes a representation of the global carboncycle and atmospheric chemistry. The carboncycle is composed of three landcarbon pools, an ocean carbon component, and multiple temperature- dependent terrestrial and oceanic carbon fluxes, as well as a parameterization for the CO 2 fertilization effect. Through the optimization of several parameters, MAGICC6 can closely reproduce the temporal behav- ior of higher-complexity physical climate and climate– carboncycle models [see Meinshausen et al. (2011a) for details on the optimization method]. For the CMIP5 experiments, MAGICC6 used a multimodel average setup of parameters for climate sensitivity, combined with the carboncycle emulation of the Bern-CC (Joos et al. 2001) taken as the ‘‘best estimate’’ for the carboncycle behavior. This is essentially because the Bern-CC and its earlier versions have been used for the consoli- dated concentrations of IPCC SRES scenarios presen- ted in the Third Assessment Report (Prentice et al. 2001; see also appendix II in Houghton et al. 2001).
7 Recommendations and Perspectives
Species richness alone may not be able to reflect the actual ecosystem functioning (Chapin III, et al., 2000) because the presence of a species in an ecosystem does not really reflect its importance especially if the abundance is below a certain level (Michelsen, 2008). This problem could be tackled by the Shannon-Wiener index because both species richness and evenness are considered (Geyer, et al., 2010b). Another way to tackle this problem is the incorporation of the free net primary productivity (fNPP) into biodiversity assessment methodologies because the global biodiversity of plants often depends on ecosystem productivity (Irigoien, et al., 2004). As suggested by Weidema & Lindeijer (2001), free net primary productivity (fNPP) may serve as an indicator for impacts on biodiversity. The fNPP is defined as the net carbon uptake of an ecosystem less the amount of carbon sequestered for human use (Weidema & Lindeijer, 2001). Also, global biodiversity is assumed to grow with global biomass (Rothman, 2001). Properly- derived indicators of biodiversity could also serve as proxies for land occupation or transformation impacts on life-support functions of a given ecosystem (Lindeijer, et al., 2002). If the motivation for protecting biodiversity is to conserve ecosystem processes, then species richness may not be a good indicator of biodiversity because the differences in ecosystem processes experienced from one region to another cannot be explained by the differences in species richness but these differences are mostly driven by climate, resource availability, and disturbance (Millennium Ecosystem Assessment, 2005). According to Tietenberg (2006), due to the interdependence of species within ecological communities, any particular species may have a value to the community far beyond its intrinsic value. Certain species contribute balance and stability to their ecological communities by providing food sources or holding the population of the species in check (Tietenberg, 2006).
mosphere into vegetation and the associated soil. Preventing de- forestation, decreasing the impact of logging or preventing the drainage of wetlands or peat lands are practices that decrease emis- sions. In contrast, planting trees, changing agricultural tillage or cropping practices, or re-establishing grasslands sequester carbon. The Kyoto Protocol recognised the role that changes in the use of land and forests have on the global carboncycle. Parties to the Protocol can use credits generated either by sequestering carbon or by reducing carbon emissions from landuse to help them reach their reduction targets. Carbon credits can be produced within the emission-source country or in an alternative industrialised na- tion (Joint Implementation [JI], Article 6). In addition, the Pro- tocol includes a mechanism by which industrialised (Annex I) nations can offset some of their emissions by investing in projects in non-industrialised (non-Annex I) nations (CDM, Article 12).
The potential gains from greater coupling are threefold. First, the use of many of the Earth’s resources by humans al- ters the state and trajectory of the Earth system (Zalasiewicz et al., 2015; Waters et al., 2016; Bai et al., 2015). Therefore, representing and quantifying the impact of humans on the natural system can determine their magnitude relative to pro- cesses endogenous to the natural system as well as provide insight into how to mitigate those impacts through changes in human behaviour. Second, the natural system (e.g. cli- mate variability and change, extreme weather events, pro- cesses affecting soil fertility) also affects human processes. Therefore, interactions and feedbacks within the social and in socio-ecological systems must be better quantified (Ver- burg et al., 2016). Achieving substantive gains in our under- standing of coupled human–natural systems requires a crit- ical assessment of the different modelling approaches used to couple representations of human systems with natural sys- tems that range from local ecological and biophysical pro- cesses (e.g. erosion, hydrology, vegetation growth) through to global processes (e.g. climate). Third, coupled models will be most useful if we can use them to test possible interven- tions (e.g. policies or technologies) in the human or natural system and identify feedbacks that amplify or dampen sys- tem responses, thus garnering a better understanding about how human impacts on the environment can be mitigated and
To quantify the response of an ecosystem to changes in landuse, it is necessary to track changes in the ter- restrial carbon pools over time based on known rates of landusechange. Thereby the net exchange of carbonbetween the atmosphere and the biosphere can be esti- mated (see e.g. [37,38]). The carbon book-keeping model employed in this study has been used to estimate the effect of landusechange on terrestrial carbon fluxes since the beginning of the 1980 s (see e.g. [11,37-40]). The term “ book-keeping ” stems from the fact that the model tracks carbon stocks from year to year rather than trying to model the individual biological processes that constitute the carboncycle, i.e. photosynthesis and respiration . As a consequence, it is not possible to verify the model results with direct atmospheric mea- surements such as flux tower measurements. Further- more, there is no attempt to include inter-annual variability in forest growth. Instead, the model makes use of forest harvest and clearing estimates and average forest growth rates; and takes into account the time lags associated with decomposition of wood products. It also takes the depletion/accumulation of soil carbon into account. For the above-mentioned reasons, the model is well suited for estimating the effects of landusechange on terrestrial carbon budgets on a national scale over time periods ranging from years to decades. The use of
Use of biomass as a renewable source of energy and materials is an important option in climate change mitigation. However, in some cases it can cause substantial emissions from terrestrial carbon stocks – even without change in land-use category, e.g. final felling in forestry – compensated by the re-growth of biomass only in the long term. Ambitious climate targets such as the 2°C stabilization target, which requires that global GHG emissions peak within one decade, has lead the timing of net GHG emissions to become an important indicator for evaluation of bioenergy systems. Thus the true climatic consequences of landusechange as a function of time should be considered, but there is no clear choice for the optimal time frame, related to the success of global climate policy in general. An illustrative indicator for the warming impact in time is the cumulative radiative forcing (CRF) of the emissions. Calculating CRF for the emissions annualized (=amortized) over 20 years (PAS 2050, RED) clearly undermines the climatic impacts compared with the calculation of CRF for the actual instant land-use-change emissions. Moreover, another challenging task is to estimate the actual baseline landuse over time, i.e. the development of terrestrial C stocks without biomass use, needed in a consequential climate impact analysis of specific biomass use cycles. PAS 2050 and RED assume that the baseline is constant, but in reality forests could continue sequestering carbon for a long time, e.g. far beyond their economically feasible rotation lengths.
Fig. 1. Conceptual definition of land–atmosphere carbon fluxes representing all individual fluxes included in different model approaches to quantify the net LULCC flux. Note that no approach models every flux. (a) Illustration of the effects of LULCC and changing environmental conditions on carbon stocks and fluxes. Colours indicate steps in the causal chain set off by a LULCC event. Black: initial state. Dark red: effects of changes in vegetation distribution due to LULCC (dashed rectangle being transformed from actual natural to managed land) on carbon stocks (“direct LULCC effects”). Green: feedbacks of LULCC via environmental conditions on carbon stocks and fluxes. The LULCC feedback is put in parenthesis for potential natural vegetation because in coupled (ESM) simulations this flux will not occur due to the absence of LULCC; however, in uncoupled (DGVM) simulations the atmospheric CO 2 concentration can be prescribed so as to include both LULCC and fossil-fuel effects, and δ l E p occurs. Orange: subsequent changes in carbon fluxes from changes in vegetation due to LULCC-induced environmental changes (“indirect LULCC effects”). Blue: changes in carbon fluxes due to other externally induced environmental changes, primarily due to fossil-fuel burning. (b) Summary of carbon fluxes related to LULCC from (a), distinguished by the environmental conditions under which they occur (u: undisturbed environmental conditions; l: LULCC-induced changes in environmental conditions; f : other externally induced changes in environmental conditions, primarily due to fossil-fuel burning) and by the vegetation state of the area the fluxes occur on (m: managed land; n: actual natural land; p: potential vegetation on the area of managed land). Note that in an individual model simulation, the vegetation state will be composed of either actual natural and managed land, when LULCC is accounted for, or actual natural and potential natural (instead of managed) land, when no LULCC is considered; because the net LULCC flux is derived as the difference between with- and without-LULCC simulations, fluxes on potential natural vegetation occur only as subtrahend, marked by square brackets here. I: instantaneous emissions from LULCC, L: legacy flux; E: changes in carbon stocks as a response to environmental changes. δ u fluxes are 0 by definition and not depicted. Note that all fluxes apart from instantaneous emissions (I) may act as a source or sink of carbon on land; the arrow directions indicated here loosely refer to historical evidence for global fluxes, but in fact depend on region, assumed scenario of LULCC and environmental conditions, and model.
Complex landuse and land cover change (LULCC) processes modify ecosystems' ability to store and sequester carbon and regulate the climate, resulting in thermally uncomfortable climates and even more carbon emissions in an unchecked cycle. The value of potential loss of such climate ecosystem services remains understudied in urbanization planning and development. Using ecosystem modeling, this research quantifies potential changes of carbon storage and sequestration for a case of future LULCC in a tropical country by building an initial baseline carbon account of the existing forest. This study looked at a unique case of planned local-scale LULCC in Singapore where a secondary forest, Punggol Forest, is slated for conversion into a mixed-use residential neighborhood, Punggol Eco-Town. Carbon accounting is conducted to determine the carbon footprint of the LULCC, specifically for carbon storage and rate of carbon sequestration, using a sampled tree inventory with primary data collection. The results suggest that considerations of urban tree species selection in urban forestry are important in planning in order to reduce climate ecosystem services loss as a result of development. It is also a first step in using urban forestry tools for carbon accounting in decision-making for urban planning.
Integration of these data sources in models allows bridging the gaps in observation across space and time, and permits simulation of processes that are not directly observable. Testing of multiple scenarios may make it possible to separate the influences of different processes (e.g., land management compared to climate change or weather extremes), as they influ- ence ecosystems and human activities. New methods of analysis, in which entire time series of images can be analyzed at once, provide new possibilities in classifying land cover change as it is occurring (Zhu et al. 2012). It is possible that the algorithms used in such analyses could be adapted to analyze joint time series of climate change, weather extremes, and land cover change to separate and investigate the interactions of these variables. Knowledge of such interactions could be included in coupled models of the climate and be used to forecast scenarios of future system behavior (e.g., tipping points). Such forecasts could help identify critical weaknesses in existing planning for mitigation and adaptation. The assess- ment system should include continued contact with groups that represent decision makers for urban and regional planning, agricultural and forest land man- agement, biodiversity conservation, and ecological research, so that the models are sensitive to the types of policy choices that will be needed in the future. The research should be coordinated with national and international campaigns that have complementary interests, such as NEON, GEWEX, and the Global Earth Observation System of Systems (GEOSS). Data sharing among these groups and relevant idea devel- opment should be part of the activities.
application of a Taylor expansion, β should formally be evaluated with no climate change and surface temperature kept constant, while γ should be evaluated with atmospheric CO 2 kept constant. The carbon-cycle feedback parameters, β and γ , are estimated from the bulk land and ocean carbon changes diagnosed in coupled climate-carbon model experiments with different elements of the carbon-cycle or radiative forcing switched on or off [ 12 – 15 ]; there is also an alternative definition of carbon-cycle feedback parameters based upon the carbon flux to the atmosphere [ 20 ], rather than in terms of inventory changes. To diagnose the carbon-cyclefeedbacks, three model versions have been traditionally used: a fully coupled, a radiatively coupled and a biogeochemically coupled version:
Eleven villages of the division were selected based on soil characteristics (occurrence, area, type of soil) and distribution from previous studies within the division. In the selected villages, the soil surveyed was targeted at two types of landuse: groundnut field and savanna. Study sites were selected according to age (years after land clearing) and soil texture. The soils were classified as ferric Luvisols . The soil profile was homogene- ous, with massive structure and without coarse elements. They are formed on sandy material of dunes or sandstone deposits. Two textural classes were selected, sandy soils (SAS) and sandy clayey soils (SCS). Furthermore, these two soil texture classes represented the range of soils in terms of landuse and texture found within the study area (Table 1). 26 sites were surveyed, with 5 per cultivation duration and soil texture type and 6 sites in wooded sa- vanna.
The increase in population and the improvement of life standards are stretching the boundaries between water-energy-land management and demanding innovative and holistic solutions. This article proposes an approach for increasing the water availability of two or more water basins taking into consideration landuse and wind patterns, and was named Land, Water and Wind Watershed Cycle (L3WC). This approach can be applied to one watershed or a combination of watersheds. In the first case, if wind patterns blow mainly in the opposite direction of the main river flow, plantations with high water demand should be focused on the lowest part of the basin. The transpired moisture would then return to the basin with the wind and possibly increase the water availability of the basin. Applying this method to a series of basins, water is transposed from one basin to another, used for irrigated agriculture, returned to the atmosphere with evapotranspiration and pushed back to the basin where the water was extracted by the wind. Case studies of this methodology are presented in the São Francisco basin and between the Tocantins, Amazonas and Paraná basins and the São Francisco basin in Brazil. The São Francisco basin was the selected because it is located in a dry region, its flow has considerably reduced in the past decade and because the trade winds blow constantly from the ocean into the continent all year around. L3WC is a strategy to plan the allocation of water consumption in a watershed, taking into account wind patterns to support the sustainable development of a region. It has the potential of increasing water availability, and creating a climate change adaptation mechanism to control the climate and reduce vulnerability to climatic variations.