•Globally, 271Mha, or 2.06% of ice-free land surface, can be characterized as urban in 2000.•In 2040, these numbers increase to 621 Mha, or 4.71% of global ice-free land.•Urban land is predominantly ...located in areas that are suitable afor crop production.•65 Mton of crop production will be displaced by urbanization between 2000 and 2040.•Impacts of urbanization on cropland differ widely between world regions.
Urban growth has received little attention in large-scale land change assessments, because the area of built-up land is relatively small on a global scale. However, this area is increasing rapidly, due to population growth, rural-to-urban migration, and wealth increases in many parts of the world. Moreover, the impacts of urban growth on other land uses further amplified by associated land uses, such as recreation and urban green. In this study we analyze urban land take in cropland areas for the years 2000 and 2040, using a land systems approach. As of the year 2000, 213Mha can be classified as urban land, which is 2.06% of the earth’s surface. However, this urban land is more than proportionally located on land that is suitable and available for crop production. In the year 2040, these figures increase to 621Mha, or 4.72% of all the earth’s surface. The increase in urban land between 2000 and 2040 is also more than proportionally located on land that is suitable and available for crop production, thus further limiting our food production capacity. The share of urban land take in cropland areas is highest in Europe, the Middle-East and Northern Africa, and China, while it is relatively low in Oceania and Sub-Saharan Africa. Between 2000 and 2040, urban growth caused the displacement of almost 65Mton of crop production, which could yield an expansion of up to 35Mha of new cropland. Land-use planning can influence both the location and the form of urbanization, and thus appears as an important measure to minimize further losses in crop production.
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.
The world's population is growing and demand for food, feed, fiber, and fuel is increasing, placing greater demand on land and its resources for crop production. We review previously published ...estimates of global scale cropland availability, discuss the underlying assumptions that lead to differences between estimates, and illustrate the consequences of applying different estimates in model‐based assessments of land‐use change. The review estimates a range from 1552 to 5131 Mha, which includes 1550 Mha that is already cropland. Hence, the lowest estimates indicate that there is almost no room for cropland expansion, while the highest estimates indicate that cropland could potentially expand to over three times its current area. Differences can largely be attributed to institutional assumptions, i.e. which land covers/uses (e.g. forests or grasslands) are societally or governmentally allowed to convert to cropland, while there was little variation in biophysical assumptions. Estimates based on comparable assumptions showed a variation of up to 84%, which originated mainly from different underlying data sources. On the basis of this synthesis of the assumptions underlying these estimates, we constructed a high, a medium, and a low estimate of cropland availability that are representative of the range of estimates in the reviewed studies. We apply these estimates in a land‐change model to illustrate the consequences on cropland expansion and intensification as well as deforestation. While uncertainty in cropland availability is hardly addressed in global land‐use change assessments, the results indicate a large range of estimates with important consequences for model‐based assessments.
•We present global land change scenarios driven by carbon storage, biodiversity, and food.•Carbon storage and biodiversity demands greatly influence future land change.•Incorporating additional ...demands for land leads to specialization of land systems.•Demand for ecosystem services will increasingly influence land change.
Many global land change scenarios are driven by demand for food, feed, fiber, and fuel. However, novel demands for other ecosystem services give rise to nexus issues and can lead to different land system changes. In this paper we explore the effects of including multiple different demands in land change scenarios. Our reference scenario is driven by demands for crop production, ruminant livestock production, and provisioning of built-up area. We then compare two alternative scenarios with additional demands for terrestrial carbon storage and biodiversity protection, respectively. These scenarios represent possible implementations of globally agreed policy targets. The simulated land system change scenarios are compared in terms of changes in cropland intensity and area, as well as tree and grassland area changes. We find that the carbon and biodiversity scenarios generally result in greater intensification and less expansion of cropland, with the biodiversity scenario showing a stronger intensification effect. However, the impact of setting the targets impacts different world regions in different ways. Overall, both scenarios result in a larger tree area compared to the reference scenario, while the carbon scenario also yields more grassland area. The land systems simulated while accounting for these additional demand types show strong patterns of specialization and spatial segregation in the provisioning of goods and services in different world regions. Our results indicate the relevance of including demands for multiple different goods and services in global land change assessments.
Cultural heritage landscapes are consistently perceived as landscapes of high value. However, these landscapes are very vulnerable to change. In China, rapid land use change, especially urbanization, ...has become one of the main challenges for the conservation of cultural heritage landscapes in rural areas. This paper focuses on the designated cultural villages in rural China by systematically analyzing the spatial distribution of the designated cultural landscape across the country and assessing the threats these traditional landscapes are facing under current and future urbanization and other land use pressures. Current designated cultural heritage landscapes in China are predominantly located in the rural and peri-urban regions of Central and South China and less frequently found in other regions. Especially in these regions risks to land use change are large. These risks are assessed based on observed recent land use change and land use model simulations for scenarios up to 2050. The risk assessment reveals that especially in Southeast China along the sea coast and near the cities along the Yangtze River, high pressures are expected on cultural heritage landscapes due to urbanization. At the same time, in Southwest China, especially in Yunnan and Guizhou provinces, high pressures due to other land use changes are expected, including land abandonment. This assessment gives direction and guidance toward the selection of the most threatened cultural villages for detailed investigation and additional protection measures.
Land systems are described based on various characteristics, including land cover composition and agricultural production. However, it is uncertain to what extent livestock, particularly monogastric ...livestock, determines land systems. We included monogastrics in a land system classification, and statistically analyzed the land cover composition and agricultural production of otherwise similar land systems with and without monogastric livestock. The results indicate that land systems with monogastrics are statistically different from their counterparts in the classification without monogastrics in terms of grassland area and crop yields, but are less different in terms of tree area, crop area, and ruminant livestock production. We then used a land systems map that includes monogastrics in the classification and a similar map that does not include monogastrics to project future changes in a novel manner that integrates livestock as a determinant of land systems. The results show that including monogastrics in otherwise similar projections yields less cropland intensification and more cropland expansion in several world regions, including Northern Africa and the Middle East. Other regions, such as Europe and Australia, were characterized by less decrease or more increase in tree area in the application with monogastrics, mainly due to the occurrence of open forests with monogastrics. This study prompts a call for improved characterization of land systems for land use and cover change (LUCC) assessments in order to better represent LUCC driven by monogastric livestock.
Understanding uncertainties in land cover projections is critical to investigating land‐based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the ...impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro‐economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.
•A spatially dynamic modelling approach to predict functional ecological patterns.•Innovative integration of existing ecological databases from coarse to fine resolutions.•A multi-scale approach that ...combines top-down and bottom-up LU/LC drivers.•A step forward to understand complex emergent ecological patterns under LU/LC changes.•A promising tool to assess local ecological indicators for regional conservation.
Land-use/land-cover (LU/LC) change is one of the main drivers of global biodiversity change. However, the lack of detailed data on species’ local distributions is frequently a major constraint to identify effective indicators of impact and to prescribe effective conservation and management measures. Here we aim to describe and demonstrate the applicability of a novel looping approach to predict spatially dynamic ecological responses to LU/LC changes. The methodology integrates statistical downscaling, multi-model inference, stochastic-dynamic modelling and simulations, and spatial projections under a common and interactive framework. We illustrate the approach with a study of passerine foraging groups and their potential indicator role under LU/LC change scenarios. Based on the coarse occurrence data from published atlases, this approach allowed transposing species richness to fine resolutions in order to assess regional ecological integrity by up scaling the local responses of those indicators again at the landscape level. Overall, our proposed framework was able to provide realistic patterns of passerine foraging responses to LU/LC changes, highlighting the usefulness of existing databases for model-based research in addressing complex emergent problems across scales. Comparative analysis between simulations and independent field data showed a promising model performance, with consistent projections of the local passerine functional composition for a significant number of point-counts tested. Our approach represents a contribution for more universal applications in the scope of conservation and landscape planning, especially when fine resolution data is difficult to obtain due to resources constraints.
This method development paper outlines an integrative stochastic dynamic methodology (StDM) framework to anticipate land use (LU) change effects on the ecological status of monitored and ...non-monitored lotic surface waters under the Water Framework Directive (WFD). Tested in the Alto Minho River Basin District in North West Portugal, the model is an innovative step towards developing a decision-making and planning tool to assess the influence impacts such as LU change and climate change on these complex systems. Comprising a series of sequential steps, a Generalized Linear Model based, competing model Multi Model Inference (MMI) approach was used for parameter estimation to identify principal land use types (distal factors) driving change in biological and physicochemical support elements (proximal factors) in monitored water bodies. The framework integrated MMI constants and coefficients of selected LU categories in the StDM simulations and spatial projections to simulate the ecological status of monitored and non-monitored lotic waterbodies in the test area under 2 scenarios of (1) LU intensification and (2) LU extensification. A total of 100 simulations were run for a 50year period for each scenario. Spatially dynamic projections of WFD metrics were obtained, taking into account the occurrence of stochastic wildfire events which typically occur in the study region and are exacerbated by LU change. A marked projected decline to “Moderate” ecological status for most waterbodies was detected under intensification but little change under extensification; only a few waterbodies fell to “moderate” status. The latter scenario describes the actual regional socio-economic situation of agricultural abandonment due to rural poverty, partly explaining the projected lack of change in ecological status. Based on the WFD “one out all out” criterion, projected downward shifts in ecological status were due to physicochemical support elements, namely increased phosphorus levels. Little or no change in status was driven by Intercalibrated Biological Quality Elements, indicating innate resilience and raising questions concerning uncertainty, the effect of pressures other than land use and metric redundancy and the WFD classification process.
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•The dynamic model framework tests land change scenario effects on surface water ecological status.•Spatial projection of output from simulations using open source Geographical Information System.•Model provides both temporal and spatial patterns of change in surface water bodies.•The two tested land use scenarios produce difference degrees of response•Dynamic tool suitable for Water Framework Directive planning and extrapolation.
Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the ...impacts of land cover change on the climate system. In this work, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.
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