Hotspots of uncertainty in land‐use and land‐cover change projections: a global‐scale model comparison

Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.     

A Land System representation for global assessments and land‐use modeling

Current global scale land‐change models used for integrated assessments and climate modeling are based on classifications of land cover. However, land‐use management intensity and livestock keeping are also important aspects of land use, and are an integrated part of land systems. This article aims to classify, map, and to characterize Land Systems (LS) at a global scale and analyze the spatial determinants of these systems. Besides proposing such a classification, the article tests if global assessments can be based on globally uniform allocation rules. Land cover, livestock, and agricultural intensity data are used to map LS using a hierarchical classification method. Logistic regressions are used to analyze variation in spatial determinants of LS. The analysis of the spatial determinants of LS indicates strong associations between LS and a range of socioeconomic and biophysical indicators of human‐environment interactions. The set of identified spatial determinants of a LS differs among regions and scales, especially for (mosaic) cropland systems, grassland systems with livestock, and settlements. (Semi‐)Natural LS have more similar spatial determinants across regions and scales. Using LS in global models is expected to result in a more accurate representation of land use capturing important aspects of land systems and land architecture: the variation in land cover and the link between land‐use intensity and landscape composition. Because the set of most important spatial determinants of LS varies among regions and scales, land‐change models that include the human drivers of land change are best parameterized at sub‐global level, where similar biophysical, socioeconomic and cultural conditions prevail in the specific regions.     

Challenges in using land use and land cover data for global change studies

Land use and land cover data play a central role in climate change assessments. These data originate from different sources and inventory techniques. Each source of land use/cover data has its own domain of applicability and quality standards. Often data are selected without explicitly considering the suitability of the data for the specific application, the bias originating from data inventory and aggregation, and the effects of the uncertainty in the data on the results of the assessment. Uncertainties due to data selection and handling can be in the same order of magnitude as uncertainties related to the representation of the processes under investigation. While acknowledging the differences in data sources and the causes of inconsistencies, several methods have been developed to optimally extract information from the data and document the uncertainties. These methods include data integration, improved validation techniques and harmonization of classification systems. Based on the data needs of global change studies and the data availability, recommendations are formulated aimed at optimal use of current data and focused efforts for additional data collection. These include: improved documentation using classification systems for land use/cover data; careful selection of data given the specific application and the use of appropriate scaling and aggregation methods. In addition, the data availability may be improved by the combination of different data sources to optimize information content while collection of additional data must focus on validation of available data sets and improved coverage of regions and land cover types with a high level of uncertainty. Specific attention in data collection should be given to the representation of land management (systems) and mosaic landscapes.     

Synergistic effects of climate and land‐use change influence broad‐scale avian population declines

Climate and land‐use changes are expected to be the primary drivers of future global biodiversity loss. Although theory suggests that these factors impact species synergistically, past studies have either focused on only one in isolation or have substituted space for time, which often results in confounding between drivers. Tests of synergistic effects require congruent time series on animal populations, climate change and land‐use change replicated across landscapes that span the gradient of correlations between the drivers of change. Using a unique time series of high‐resolution climate (measured as temperature and precipitation) and land‐use change (measured as forest change) data, we show that these drivers of global change act synergistically to influence forest bird population declines over 29 years in the Pacific Northwest of the United States. Nearly half of the species examined had declined over this time. Populations declined most in response to loss of early seral and mature forest, with responses to loss of early seral forest amplified in landscapes that had warmed over time. In addition, birds declined more in response to loss of mature forest in areas that had dried over time. Climate change did not appear to impact populations in landscapes with limited habitat loss, except when those landscapes were initially warmer than the average landscape. Our results provide some of the first empirical evidence of synergistic effects of climate and land‐use change on animal population dynamics, suggesting accelerated loss of biodiversity in areas under pressure from multiple global change drivers. Furthermore, our findings suggest strong spatial variability in the impacts of climate change and highlight the need for future studies to evaluate multiple drivers simultaneously to avoid potential misattribution of effects.     

CO2 emissions from land‐use change affected more by nitrogen cycle,than by the choice of land‐cover data

The high uncertainty in land‐based CO₂ fluxes estimates is thought to be mainly due to uncertainty in not only quantifying historical changes among forests, croplands, and grassland, but also due to different processes included in calculation methods. Inclusion of a nitrogen (N) cycle in models is fairly recent and strongly affects carbon (C) fluxes. In this study, for the first time, we use a model with C and N dynamics with three distinct historical reconstructions of land‐use and land‐use change (LULUC) to quantify LULUC emissions and uncertainty that includes the integrated effects of not only climate and CO₂ but also N. The modeled global average emissions including N dynamics for the 1980s, 1990s, and 2000–2005 were 1.8 ± 0.2, 1.7 ± 0.2, and 1.4 ± 0.2 GtC yr^?1, respectively, (mean and range across LULUC data sets). The emissions from tropics were 0.8 ± 0.2, 0.8 ± 0.2, and 0.7 ± 0.3 GtC yr^?1, and the non tropics were 1.1 ± 0.5, 0.9 ± 0.2, and 0.7 ± 0.1 GtC yr^?1. Compared to previous studies that did not include N dynamics, modeled net LULUC emissions were higher, particularly in the non tropics. In the model, N limitation reduces regrowth rates of vegetation in temperate areas resulting in higher net emissions. Our results indicate that exclusion of N dynamics leads to an underestimation of LULUC emissions by around 70% in the non tropics, 10% in the tropics, and 40% globally in the 1990s. The differences due to inclusion/exclusion of the N cycle of 0.1 GtC yr^?1 in the tropics, 0.6 GtC yr^?1 in the non tropics, and 0.7 GtC yr^?1 globally (mean across land‐cover data sets) in the 1990s were greater than differences due to the land‐cover data in the non tropics and globally (0.2 GtC yr^?1). While land‐cover information is improving with satellite and inventory data, this study indicates the importance of accounting for different processes, in particular the N cycle.     

Coupled land use and ecological models reveal emergence and feedbacks in socio‐ecological systems

Understanding the dynamics of socio‐ecological systems is crucial to the development of environmentally sustainable practices. Models of social or ecological sub‐systems have greatly enhanced such understanding, but at the risk of obscuring important feedbacks and emergent effects. Integrated modelling approaches have the potential to address this shortcoming by explicitly representing linked socio‐ecological dynamics. We developed a socio‐ecological system model by coupling an existing agent‐based model of land‐use dynamics and an individual‐based model of demography and dispersal. A hypothetical case‐study was established to simulate the interaction of crops and their pollinators in a changing agricultural landscape, initialised from a spatially random distribution of natural assets. The bi‐directional coupled model predicted larger changes in crop yield and pollinator populations than a unidirectional uncoupled version. The spatial properties of the system also differed, the coupled version revealing the emergence of spatial land‐use clusters that neither supported nor required pollinators. These findings suggest that important dynamics may be missed by uncoupled modelling approaches, but that these can be captured through the combination of currently‐available, compatible model frameworks. Such model integrations are required to further fundamental understanding of socio‐ecological dynamics and thus improve management of socio‐ecological systems.     

Agricultural intensification in Brazil and its effects on land‐use patterns: an analysis of the 1975–2006 period

Does agricultural intensification reduce the area used for agricultural production in Brazil? Census and other data for time periods 1975–1996 and 1996–2006 were processed and analyzed using Geographic Information System and statistical tools to investigate whether and if so, how, changes in yield and stocking rate coincide with changes in cropland and pasture area. Complementary medium‐resolution data on total farmland area changes were used in a spatially explicit assessment of the land‐use transitions that occurred in Brazil during 1960–2006. The analyses show that in agriculturally consolidated areas (mainly southern and southeastern Brazil), land‐use intensification (both on cropland and pastures) coincided with either contraction of both cropland and pasture areas, or cropland expansion at the expense of pastures, both cases resulting in farmland stability or contraction. In contrast, in agricultural frontier areas (i.e., the deforestation zones in central and northern Brazil), land‐use intensification coincided with expansion of agricultural lands. These observations provide support for the thesis that (i) technological improvements create incentives for expansion in agricultural frontier areas; and (ii) farmers are likely to reduce their managed acreage only if land becomes a scarce resource. The spatially explicit examination of land‐use transitions since 1960 reveals an expansion and gradual movement of the agricultural frontier toward the interior (center‐western Cerrado) of Brazil. It also indicates a possible initiation of a reversed trend in line with the forest transition theory, i.e., agricultural contraction and recurring forests in marginally suitable areas in southeastern Brazil, mainly within the Atlantic Forest biome. The significant reduction in deforestation that has taken place in recent years, despite rising food commodity prices, indicates that policies put in place to curb conversion of native vegetation to agriculture land might be effective. This can improve the prospects for protecting native vegetation by investing in agricultural intensification.     

Large‐scale impact of climate change vs. land‐use change on future biome shifts in Latin America

Climate change and land‐use change are two major drivers of biome shifts causing habitat and biodiversity loss. What is missing is a continental‐scale future projection of the estimated relative impacts of both drivers on biome shifts over the course of this century. Here, we provide such a projection for the biodiverse region of Latin America under four socio‐economic development scenarios. We find that across all scenarios 5–6% of the total area will undergo biome shifts that can be attributed to climate change until 2099. The relative impact of climate change on biome shifts may overtake land‐use change even under an optimistic climate scenario, if land‐use expansion is halted by the mid‐century. We suggest that constraining land‐use change and preserving the remaining natural vegetation early during this century creates opportunities to mitigate climate‐change impacts during the second half of this century. Our results may guide the evaluation of socio‐economic scenarios in terms of their potential for biome conservation under global change.     

A socio‐ecological model for predicting impacts of land‐use and climate change on regional plant diversity in the Austrian Alps

Climate and land‐use change jointly affect the future of biodiversity. Yet, biodiversity scenarios have so far concentrated on climatic effects because forecasts of land use are rarely available at appropriate spatial and thematic scales. Agent‐based models (ABMs) represent a potentially powerful but little explored tool for establishing thematically and spatially fine‐grained land‐use scenarios. Here, we use an ABM parameterized for 1,329 agents, mostly farmers, in a Central European model region, and simulate the changes to land‐use patterns resulting from their response to three scenarios of changing socio‐economic conditions and three scenarios of climate change until the mid of the century. Subsequently, we use species distribution models to, first, analyse relationships between the realized niches of 832 plant species and climatic gradients or land‐use types, respectively, and, second, to project consequent changes in potential regional ranges of these species as triggered by changes in both the altered land‐use patterns and the changing climate. We find that both drivers determine the realized niches of the studied plants, with land use having a stronger effect than any single climatic variable in the model. Nevertheless, the plants' future distributions appear much more responsive to climate than to land‐use changes because alternative future socio‐economic backgrounds have only modest impact on land‐use decisions in the model region. However, relative effects of climate and land‐use changes on biodiversity may differ drastically in other regions, especially where landscapes are still dominated by natural or semi‐natural habitat. We conclude that agent‐based modelling of land use is able to provide scenarios at scales relevant to individual species distribution and suggest that coupling ABMs with models of species' range change should be intensified to provide more realistic biodiversity forecasts.     

Land suitability for energy crops under scenarios of climate change and land‐use

Bioenergy is expected to play a critical role in climate change mitigation. Most integrated assessment models assume an expansion of agricultural land for cultivation of energy crops. This study examines the suitability of land for growing a range of energy crops on areas that are not required for food production, accounting for climate change impacts and conservation requirements. A global fuzzy logic model is employed to ascertain the suitable cropping areas for a number of sugar, starch and oil crops, energy grasses and short rotation tree species that could be grown specifically for energy. Two climate change scenarios are modelled (RCP2.6 and RCP8.5), along with two scenarios representing the land which cannot be used for energy crops due to forest and biodiversity conservation, food agriculture and urban areas. Results indicate that 40% of the global area currently suitable for energy crops overlaps with food land and 31% overlaps with forested or protected areas, highlighting hotspots of potential land competition risks. Approximately 18.8 million km² is suitable for energy crops, to some degree, and does not overlap with protected, forested, urban or food agricultural land. Under the climate change scenario RCP8.5, this increases to 19.6 million km² by the end of the century. Broadly, climate change is projected to decrease suitable areas in southern regions and increase them in northern regions, most notably for grass crops in Russia and China, indicating that potential production areas will shift northwards which could potentially affect domestic use and trade of biomass significantly. The majority of the land which becomes suitable is in current grasslands and is just marginally or moderately suitable. This study therefore highlights the vital importance of further studies examining the carbon and ecosystem balance of this potential land‐use change, energy crop yields in sub‐optimal soil and climatic conditions and potential impacts on livelihoods.     

The transparency,reliability and utility of tropical rainforest land‐use and land‐cover change models

Land‐use and land‐cover (LULC) change is one of the largest drivers of biodiversity loss and carbon emissions globally. We use the tropical rainforests of the Amazon, the Congo basin and South‐East Asia as a case study to investigate spatial predictive models of LULC change. Current predictions differ in their modelling approaches, are highly variable and often poorly validated. We carried out a quantitative review of 48 modelling methodologies, considering model spatio‐temporal scales, inputs, calibration and validation methods. In addition, we requested model outputs from each of the models reviewed and carried out a quantitative assessment of model performance for tropical LULC predictions in the Brazilian Amazon. We highlight existing shortfalls in the discipline and uncover three key points that need addressing to improve the transparency, reliability and utility of tropical LULC change models: (1) a lack of openness with regard to describing and making available the model inputs and model code; (2) the difficulties of conducting appropriate model validations; and (3) the difficulty that users of tropical LULC models face in obtaining the model predictions to help inform their own analyses and policy decisions. We further draw comparisons between tropical LULC change models in the tropics and the modelling approaches and paradigms in other disciplines, and suggest that recent changes in the climate change and species distribution modelling communities may provide a pathway that tropical LULC change modellers may emulate to further improve the discipline. Climate change models have exerted considerable influence over public perceptions of climate change and now impact policy decisions at all political levels. We suggest that tropical LULC change models have an equally high potential to influence public opinion and impact the development of land‐use policies based on plausible future scenarios, but, to do that reliably may require further improvements in the discipline.     

GHG emissions and other environmental impacts of indirect land use change mitigation

The implementation of measures to increase productivity and resource efficiency in food and bioenergy chains as well as to more sustainably manage land use can significantly increase the biofuel production potential while limiting the risk of causing indirect land use change (ILUC). However, the application of these measures may influence the greenhouse gas (GHG) balance and other environmental impacts of agricultural and biofuel production. This study applies a novel, integrated approach to assess the environmental impacts of agricultural and biofuel production for three ILUC mitigation scenarios, representing a low, medium and high miscanthus‐based ethanol production potential, and for three agricultural intensification pathways in terms of sustainability in Lublin province in 2020. Generally, the ILUC mitigation scenarios attain lower net annual emissions compared to a baseline scenario that excludes ILUC mitigation and bioethanol production. However, the reduction potential significantly depends on the intensification pathway considered. For example, in the moderate ILUC mitigation scenario, the net annual GHG emissions in the case study are 2.3 MtCO₂‐eq yr^?1 (1.8 tCO₂‐eq ha^?1 yr^?1) for conventional intensification and ?0.8 MtCO₂‐eq yr^?1 (?0.6 tCO₂‐eq ha^?1 yr^?1) for sustainable intensification, compared to 3.0 MtCO₂‐eq yr^?1 (2.3 tCO₂‐eq ha^?1 yr^?1) in the baseline scenario. In addition, the intensification pathway is found to be more influential for the GHG balance than the ILUC mitigation scenario, indicating the importance of how agricultural intensification is implemented in practice. Furthermore, when the net emissions are included in the assessment of GHG emissions from bioenergy, the ILUC mitigation scenarios often abate GHG emissions compared to gasoline. But sustainable intensification is required to attain GHG abatement potentials of 90% or higher. A qualitative assessment of the impacts on biodiversity, water quantity and quality, soil quality and air quality also emphasizes the importance of sustainable intensification.     

Effect of land‐use and land‐cover change on mangrove blue carbon: A systematic review

Mangroves shift from carbon sinks to sources when affected by anthropogenic land‐use and land‐cover change (LULCC). Yet, the magnitude and temporal scale of these impacts are largely unknown. We undertook a systematic review to examine the influence of LULCC on mangrove carbon stocks and soil greenhouse gas (GHG) effluxes. A search of 478 data points from the peer‐reviewed literature revealed a substantial reduction of biomass (82% ± 35%) and soil (54% ± 13%) carbon stocks due to LULCC. The relative loss depended on LULCC type, time since LULCC and geographical and climatic conditions of sites. We also observed that the loss of soil carbon stocks was linked to the decreased soil carbon content and increased soil bulk density over the first 100 cm depth. We found no significant effect of LULCC on soil GHG effluxes. Regeneration efforts (i.e. restoration, rehabilitation and afforestation) led to biomass recovery after ~40 years. However, we found no clear patterns of mangrove soil carbon stock re‐establishment following biomass recovery. Our findings suggest that regeneration may help restore carbon stocks back to pre‐disturbed levels over decadal to century time scales only, with a faster rate for biomass recovery than for soil carbon stocks. Therefore, improved mangrove ecosystem management by preventing further LULCC and promoting rehabilitation is fundamental for effective climate change mitigation policy.     

Land use for animal production in global change studies: Defining and characterizing a framework

Land use for animal production influences the earth system in a variety of ways, including local‐scale modification to biodiversity, soils, and nutrient cycling; regional changes in albedo and hydrology; and global‐scale changes in greenhouse gas and aerosol concentrations. Pasture is furthermore the single most extensive form of land cover, currently comprising about 22–26% of the earth's ice‐free land surface. Despite the importance and variable expressions of animal production, distinctions among different systems are effectively absent from studies of land use and land cover change. This deficiency is improving; however, livestock production system classifications are rarely applied in this context, and the most popular global land cover inventories still present only a single, usually poorly defined category of “pasture” or “rangeland” with no characterization of land use. There is a marked lack of bottom‐up, evidence‐based methodology, creating a pressing need to incorporate cross‐disciplinary evidence of past and present animal production systems into global change studies. Here, we present a framework, modified from existing livestock production systems, that is rooted in sociocultural, socioeconomic, and ecological contexts. The framework defines and characterizes the range of land usage pertaining to animal production, and is suitable for application in land use inventories and scenarios, land cover modeling, and studies on sustainable land use in the past, present, and future.     

An empirical test of the relative and combined effects of land‐cover and climate change on local colonization and extinction

Land‐cover and climate change are two main drivers of changes in species ranges. Yet, the majority of studies investigating the impacts of global change on biodiversity focus on one global change driver and usually use simulations to project biodiversity responses to future conditions. We conduct an empirical test of the relative and combined effects of land‐cover and climate change on species occurrence changes. Specifically, we examine whether observed local colonization and extinctions of North American birds between 1981–1985 and 2001–2005 are correlated with land‐cover and climate change and whether bird life history and ecological traits explain interspecific variation in observed occurrence changes. We fit logistic regression models to test the impact of physical land‐cover change, changes in net primary productivity, winter precipitation, mean summer temperature, and mean winter temperature on the probability of Ontario breeding bird local colonization and extinction. Models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local colonization for 30%, 27%, and 29% of species, respectively. Conversely, models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local extinction for 61%, 7%, and 9% of species, respectively. The quantitative impacts of land‐cover and climate change variables also vary among bird species. We then fit linear regression models to test whether the variation in regional colonization and extinction rate could be explained by mean body mass, migratory strategy, and habitat preference of birds. Overall, species traits were weakly correlated with heterogeneity in species occurrence changes. We provide empirical evidence showing that land‐cover change, climate change, and the combination of multiple global change drivers can differentially explain observed species local colonization and extinction.     

Anthropogenic disturbance and streams: land use and land‐use change affect stream ecosystems via multiple pathways

1. Ecosystems are strongly influenced by land use practices. However, identifying the mechanisms behind these influences is complicated by the many potential pathways (often indirect) between land use and ecosystems and by the long‐lasting effects of past land use. To support ecosystem restoration and conservation efforts, we need to better understand these indirect and lasting effects. 2. We constructed structural equation models (SEM) to evaluate the direct and indirect effects of contemporary (2002) land use (agriculture and development) and change in land use from 1952 to 2002 on present‐day streams (n = 190) in Maryland, U.S.A. Additional variables examined included site location, system size, altitude, per cent sand in soils, riparian condition, habitat quality, stream water NO₃‐N and benthic macroinvertebrate and fish measures of stream condition. Our first SEM (2002 Land Use) included the proportions of contemporary agriculture and development in catchments in the model. The second SEM (Land Use Change) included five measures of land use change (proportion agricultural in both times, developed in both times, agricultural in 1952 and developed in 2002, forested in 1952 and developed in 2002 and agricultural in 1952 and forested in 2002). 3. The data set fit both SEMs well. The 2002 Land Use model explained 71% of variation in NO₃‐N and 55%, 42% and 38% of variation in riffle quality, macroinvertebrate condition and fish condition, respectively. The Land Use Change model explained similar amounts of variation in NO₃‐N (R² = 0.72), riffle quality (R² = 0.57) and macroinvertebrate condition (R² = 0.44) but slightly more variation in fish condition (R² = 0.43). 4. Both models identified pathways through which landscape variables affect stream responses, including negative direct effects of latitude on macroinvertebrate and fish conditions and positive direct and indirect effects of altitude on NO₃‐N, riffle quality and macroinvertebrate and fish conditions. The 2002 Land Use model showed contemporary development and agriculture had positive total effects on NO₃‐N (both through direct pathways); contemporary development had negative effects on macroinvertebrate condition. The Land Use Change model showed that contemporary developed land that was forested in 1952 had no effects on NO₃‐N; current developed land that was developed or agricultural in 1952 showed positive effects on NO₃‐N. Forests that were agricultural in 1952 had negative effects on NO₃‐N, suggesting reduced NO₃‐N export with reforestation. The Land Use Change model also showed negative total effects of all types of contemporary developed land (developed, agricultural or forested in 1952) on benthic condition. Developed land that was forested in 1952 had negative effects on fish condition. Forest sites that were agricultural in 1952 had negative effects on fish and macroinvertebrate conditions, suggesting a long‐term imprint of abandoned agriculture in stream communities. 5. Our analyses (i) identified multiple indirect effects of contemporary land use on streams, (ii) showed that current land uses with different land use histories can exhibit different effects on streams and (iii) demonstrated an imprint of land use lasting >50 years. Knowledge of these indirect and long‐term effects of land use will help to conserve and restore streams.     

基于土地利用/覆被变化的荒漠绿洲碳储量动态评估

以典型的荒漠绿洲区为研究对象,基于不同时期土地利用/覆被类型图,运用Bookkeeping模型,结合土壤、植被碳密度基础资料及调查数据,评估了近30年临泽绿洲土地利用/覆被变化特征及其对碳储量的影响。结果表明:(1)临泽荒漠绿洲区的土地利用/覆被变化特征主要表现为:居民及建设用地、耕地、林地呈增加趋势,增幅分别为90.2%、75%、46.5%;盐碱地、水体、沙地、荒漠草地则呈减少趋势,减幅分别为73.9%、67.8%、46.2%、5.5%。(2) 30 a耕地面积增加了269.38 km~2,其中耕地开垦面积为372.57 km~2,开垦主要来源于盐碱地、荒漠草地和沙地,分别占耕地开垦面积的24.7%、24.4%和21.05%。耕地转变为其他土地覆被类型的面积为103.19 km~2,转变后的主要去向分别是居民及建设用地、盐碱地和荒漠草地,分别占耕地转变为其他土地覆被类型面积的32.78%、17.8%和15.37%。(3)土地利用/覆被变化导致总碳储量增加5.89×10~5t,其中土壤碳储量增加量为4.02×10~5t,植被碳储量增加量为1.86×10~5t;耕地变化使碳储量增加4.91×10~5t,其中使碳储量增加的转变分别是荒漠草地-耕地、沙地-耕地、盐碱地-耕地、耕地-林地,相反的转变则使碳储量减少。总体来看,临泽荒漠绿洲土地利用/覆被面积和结构均发生了变化,耕地开垦为最主要的土地利用/覆被变化,土地利用/覆被变化导致碳储量总体呈增加趋势,耕地变化是影响碳储量变化的主要因素。     

Agent农业土地变化模型研究进展

农业土地变化是全球变化与可持续研究的热点,当前研究虽取得了长足进展,但仍存在诸多不足,集中表现在对农业土地系统复杂性与动态性的认识不够.近年来,基于Agent的农业土地变化研究(农业ABM/LUCC,Agent-based agricultural land change modeling)逐渐兴起,极大的丰富了传统研究的理论与方法,具体表现在:(1)农业ABM/LUCC将微观层面的人类个体行为整合进土地变化研究框架,有助于更加清楚的认识农业土地系统的“人类-自然”综合复杂性问题.(2)农业ABM/LUCC能够动态表达土地系统变化的内生反馈机制,有助于弥补传统的静态土地变化驱动机制分析的不足.(3)基于ABM/LUCC的农业土地利用格局动态研究是整合“人类-自然”综合研究的关键桥梁,农业ABM/LUCC能够与其他生物地球物理模型或经济模型动态嵌套,使多尺度、多维度综合模型研究成为可能.然而,农业ABM/LUCC研究也存在诸多挑战,如理论研究滞后于应用研究,大尺度应用难以开展,以及农户行为的模拟结果很难得到校验等.