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

Projected patterns of land uses in Africa under a warming climate

Land-use change is a direct driver of biodiversity loss, projection and future land use change often consider a topical issue in response to climate change. Yet few studies have projected land-use changes over Africa, owing to large uncertainties. We project changes in land-use and land-use transfer under future climate for three specified time periods: 2021-2040, 2041-2060, and 2081-2100, and compares the performance of various scenarios using observational land-use data for the year 2020 and projected land-use under seven Shared Socioeconomic Pathways Scenarios (SSP): SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5 from 2015 to 2100 in Africa. The observational land-use types for the year 2020 depict a change and show linear relationship between observational and simulated land-use with a strong correlation of 0.89 (P < 0.01) over Africa. Relative to the reference period (1995-2014), for (2021-2040), (2041-2060), (2081-2100), barren land and forest land are projected to decrease by an average of (6%, 11%, 16%), (9%, 19%, 38%) respectively, while, crop land, grassland and urban land area are projected to increase by (36%, 58%, and 105%), (4%, 7% and 11%), and (139%, 275% and 450%) respectively. Results show a substantial variations of land use transfer between scenarios with major from barren land to crop land, for the whole future period (2015-2100). Although SSP4-3.4 project the least transfer. Population and GDP show a relationship with cropland and barren land. The greatest conversion of barren land to crop land could endanger biodiversity and have negative effects on how well the African continent's ecosystem's function.

Non-stationary evaluation of runoff peaks in response to climate variability and land use change in Ferson Creek, Illinois, USA

In this paper, we examine how surface runoff affects public safety and urban infrastructure worldwide and how human activity has significantly altered the frequency and magnitude of these events. We investigate this issue in Ferson Creek, IL, USA. Our study focuses on three specific areas of impact: (1) the primary reasons for a considerable increase in average runoff peaks, using annual maximum runoff discharge and annual maximum precipitation and temperature to evaluate the role of climate variability; (2) the effect of land use change on runoff peaks by coupling dominant land use categories with annual maximum runoff discharge; and (3) the use of return level plots as a reference to explore the watershed's sensitivity to land use change. Our findings indicate that land use change has a greater effect on runoff peak values than climate variability in our region of interest. The agricultural areas of Ferson Creek have been most affected by the rapid transformation of about 20% of their land into developed areas. Although agricultural areas can sometimes intensify runoff peaks, their reduction has led to excessive runoff discharges in Ferson Creek, as they have higher relative infiltration capacity than developed areas. We conclude that each watershed has its own fingerprint in terms of the connection between its land use types and hydrological patterns and that the region is most sensitive to the percentage of forests. These results are essential for improving infrastructure design and risk estimation methods in the region of interest.

Modeling Production-Living-Ecological Space for Chengdu, China: An Analytical Framework Based on Machine Learning with Automatic Parameterization of Environmental Elements

Cities worldwide are facing the dual pressures of growing population and land expansion, leading to the intensification of conflicts in urban productive-living-ecological spaces (PLES). Therefore, the question of "how to dynamically judge the different thresholds of different indicators of PLES" plays an indispensable role in the studies of the multi-scenario simulation of land space changes and needs to be tackled in an appropriate way, given that the process simulation of key elements that affect the evolution of urban systems is yet to achieve complete coupling with PLES utilization configuration schemes. In this paper, we developed a scenario simulation framework combining the dynamic coupling model of Bagging-Cellular Automata (Bagging-CA) to generate various environmental element configuration patterns for urban PLES development. The key merit of our analytical approach is that the weights of different key driving factors under different scenarios are obtained through the automatic parameterized adjustment process, and we enrich the study cases for the vast southwest region in China, which is beneficial for balanced development between eastern and western regions in the country. Finally, we simulate the PLES with the data of finer land use classification, combining a machine learning and multi-objective scenario. Automatic parameterization of environmental elements can help planners and stakeholders understand more comprehensively the complex land space changes caused by the uncertainty of space resources and environment changes, so as to formulate appropriate policies and effectively guide the implementation of land space planning. The multi-scenario simulation method developed in this study has offered new insights and high applicability to other regions for modeling PLES.

Adaptation measures under the impacts of climate and land-use/land-cover changes using HSPF model simulation: Application to Gongola river basin, Nigeria

Recently, there is an upsurge in flood emergencies in Nigeria, in which their frequencies and impacts are expected to exacerbate in the future due to land-use/land cover (LULC) and climate change stressors. The separate and combined forces of these stressors on the Gongola river basin is feebly understood and the probable future impacts are not clear. Accordingly, this study uses a process-based watershed modelling approach - the Hydrological Simulation Program FORTRAN (HSPF) (i) to understand the basin's current and future hydrological fluxes and (ii) to quantify the effectiveness of five management options as adaptation measures for the impacts of the stressors. The ensemble means of the three models derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed for generating future climate scenarios, considering three distinct radiative forcing peculiar to the study area. Also, the historical and future LULC (developed from the hybrid of Cellular Automata and Markov Chain model) are used to produce the LULC scenarios for the basin. The effective calibration, uncertainty and sensitivity analyses are used for optimising the parameters of the model and the validated result implies a plausible model with efficiency of up to 75 %. Consequently, the results of individual impacts of the stressors yield amplification of the peak flows, with more profound impacts from climate stressor than the LULC. Therefore, the climate impact may trigger a marked peak discharge that is 48 % higher as compared to the historical peak flows which are equivalent to 10,000-year flood event. Whilst the combine impacts may further amplify this value by 27 % depending on the scenario. The proposed management interventions such as planned reforestation and reservoir at Dindima should attenuate the disastrous peak discharges by almost 36 %. Furthermore, the land management option should promote the carbon-sequestering project of the Paris agreement ratified by Nigeria. While the reservoir would serve secondary functions of energy production; employment opportunities, aside other social aspects. These measures are therefore expected to mitigate feasibly the negative impacts anticipated from the stressors and the approach can be employed in other river basins in Africa confronted with similar challenges.

Soils in warmer and less developed countries have less micronutrients globally

Soil micronutrients are capital for the delivery of ecosystem functioning and food provision worldwide. Yet, despite their importance, the global biogeography and ecological drivers of soil micronutrients remain virtually unknown, limiting our capacity to anticipate abrupt unexpected changes in soil micronutrients in the face of climate change. Here, we analyzed >1300 topsoil samples to examine the global distribution of six metallic micronutrients (Cu, Fe, Mn, Zn, Co and Ni) across all continents, climates and vegetation types. We found that warmer arid and tropical ecosystems, present in the least developed countries, sustain the lowest contents of multiple soil micronutrients. We further provide evidence that temperature increases may potentially result in abrupt and simultaneous reductions in the content of multiple soil micronutrients when a temperature threshold of 12-14°C is crossed, which may be occurring on 3% of the planet over the next century. Altogether, our findings provide fundamental understanding of the global distribution of soil micronutrients, with direct implications for the maintenance of ecosystem functioning, rangeland management and food production in the warmest and poorest regions of the planet.

Linking Land Use Land Cover change to global groundwater storage

Groundwater storage is facing the constant threat of over-exploitation and irreversible depletion, often attributed to agricultural and industrial usage as well as human mismanagement. While several methodologies, varying from well logs to gravity recovery data, have been successfully adopted over the years to track and mitigate groundwater loss, Land Use and Land Cover (LULC) has never been quantified to evaluate groundwater storage and variability. LULC change alters the hydrological connectivity between the surface and subsurface water. Towards this, we employed a decision tree based Machine Learning model to (a) identify hydrological and terrestrial drivers affecting groundwater resources, (b) predict shallow and deep groundwater variability, (c) rank the drivers according to their impact on groundwater distribution, and (d) understand groundwater distribution as a function of LULC change. The model was developed globally, and then extended to basinal scale observations in the Indus, Ganga and Brahmaputra rivers of the Indian subcontinent. Model output has helped to (a) compute the 'infiltration index' associated with each Land Cover, (b) equate cropland expansion among the three basins with shallow and deep groundwater storage and (c) link LULC-groundwater change to crop yield. RCP 2.6 crop yield estimates for the 21^st century proves detrimental to Indian food and freshwater security, given the strong coupling of groundwater-LULC among the three basins and how Land Cover change translates to groundwater storage.

Decomposing the spatial and temporal effects of climate on bird populations in northern European mountains

The relationships between species abundance or occurrence versus spatial variation in climate are commonly used in species distribution models to forecast future distributions. Under "space-for-time substitution", the effects of climate variation on species are assumed to be equivalent in both space and time. Two unresolved issues of space-for-time substitution are the time period for species' responses and also the relative contributions of rapid- versus slow reactions in shaping spatial and temporal responses to climate change. To test the assumption of equivalence, we used a new approach of climate decomposition to separate variation in temperature and precipitation in Fennoscandia into spatial, temporal, and spatiotemporal components over a 23-year period (1996-2018). We compiled information on land cover, topography, and six components of climate for 1756 fixed route surveys, and we modeled annual counts of 39 bird species breeding in the mountains of Fennoscandia. Local abundance of breeding birds was associated with the spatial components of climate as expected, but the temporal and spatiotemporal climatic variation from the current and previous breeding seasons were also important. The directions of the effects of the three climate components differed within and among species, suggesting that species can respond both rapidly and slowly to climate variation and that the responses represent different ecological processes. Thus, the assumption of equivalent species' response to spatial and temporal variation in climate was seldom met in our study system. Consequently, for the majority of our species, space-for-time substitution may only be applicable once the slow species' responses to a changing climate have occurred, whereas forecasts for the near future need to accommodate the temporal components of climate variation. However, appropriate forecast horizons for space-for-time substitution are rarely considered and may be difficult to reliably identify. Accurately predicting change is challenging because multiple ecological processes affect species distributions at different temporal scales.

Pathways to sustainable land use and food systems in Canada

Meeting global sustainability targets under the United Nations Sustainable Development Goals and the Paris Agreement requires paying attention to major land-use sectors such as forestry and agriculture. These sectors play a large role in national emissions, biodiversity conservation, and human well-being. There are numerous possible pathways to sustainability in these sectors and potential synergies and trade-offs along those pathways. This paper reports on the use of a model for Canada's land use to 2050 to assess three different pathways (one based on current trends and two with differing levels of ambition for meeting sustainability targets). This was done as part of a large international consortium, Food, Agriculture, Biodiversity, Land and Energy (FABLE), which allows for incorporating international trade in meeting both national and global sustainability targets. The results show not only the importance of increasingly stringent policies in meeting the targets, but also the role that population and consumption (e.g., diets) play in meeting the targets. Both the medium and high ambition sustainability pathways can drastically reduce greenhouse gas emissions while protecting forestland.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11625-022-01213-z.

Global inventory of suitable, cultivable and available cropland under different scenarios and policies

Where land-use change and particularly the expansion of cropland could potentially take place in the future is a central research question to investigate emerging trade-offs between food security, climate protection and biodiversity conservation. We provide consistent global datasets of land potentially suitable, cultivable and available for agricultural use for historic and future time periods from 1980 until 2100 under RCP2.6 and RCP8.5, available at 30 arc-seconds spatial resolution and aggregated at country level. Based on the agricultural suitability of land for 23 globally important food, feed, fiber and bioenergy crops, and high resolution land cover data, our dataset indicates where cultivation is possible and how much land could potentially be used as cropland when biophysical constraints and different assumptions on land-use regulations are taken into account. By serving as an input for land-use models, the produced data could improve the comparability of the models and their output, and thereby contribute to a better understanding of potential land-use trade-offs.

Impact of Climate-Driven Land-Use Change on O3 and PM Pollution by Driving BVOC Emissions in China in 2050

This study predicted three future land-use type scenarios in 2050 (including the Shared Socioeconomic Pathway SSP126, SSP585, and carbon scenario) based on the Land-Use Harmonization (LUH2) project and the future evolution of land-use types considering China’s carbon neutrality background. The contribution of land-use changes to terrestrial natural source biogenic volatile organic compounds (BVOCs), as well as O3 and PM concentrations, were determined. Under the SSP126 pathway, meteorological changes would increase BVOC emissions in China by 1.0 TgC in 2050, compared with 2015, while land-use changes would increase them by 1.5–7.1 TgC. The impact of land-use changes on O3 and PM concentrations would be less than 3.6% in 2050 and greater in summer. Regional differences must be considered when calculating future environmental background concentrations of pollutants. Due to more afforestation measures under the SSP126 scenario, the impact of land-use change on pollutants was more obvious under the SSP126 pathway than under the SSP585 pathway. Under the carbon scenario, the increase in PM concentration caused by land-use changes would pose a risk to air quality compliance; thus, it is necessary to consider reducing or offsetting this potential risk through anthropogenic emission control measures.

A Comparison of Raster-Based Forestland Data in Cropland Data Layer and the National Land Cover Database

The National Agricultural Statistics Service, the statistical arm of the US Department of Agriculture, and the Multi-Resolution Land Characteristics Consortium, a group of the US federal agencies, collect and publish several land-use and land-cover data sets. The aim of this study is to analyze the consistency of forestland estimates based on two widely used, publicly available products: the National Land-Cover Database (NLCD) and Cropland Data Layer (CDL). Both remote-sensing-based products provide raster-formatted land-cover categorization at a spatial resolution of 30 m. Although the processing of the yearly published CDL non-agricultural land-cover data is based on less frequently updated NLCD, the consistency of large-area forestland mapping between these two datasets has not been assessed. To assess the similarities and the differences between CDL- and NLCD-based forestland mappings for the state of North Carolina, we overlay the two data products for the years 2011 and 2016 in ArcMap 10.5.1 and analyze the location and attributes of the matched and mismatched forestland. We find that the mismatch is relatively smaller for the areas of the state where forests occupy larger shares of the total land, and that the relative mismatch is smaller in 2011 when compared to 2016. We also find that a large portion of the forestland mismatch is attributable to the dynamics of re-growth of periodically harvested and otherwise disturbed forests. Our results underscore the need for a holistic approach to data preparation, data attribution, and data accuracy when performing high-scale map-based analyses using each of these products.

Exploring the spatiotemporal changes in carbon storage under different development scenarios in Jiangsu Province, China

Carbon storage (CS) is closely linked to the global challenge of climate change. Land use/cover (LULC) change is the main factor driving changes in CS, and evaluating the impact of LULC changes on CS is important for carbon balance. Taking Jiangsu Province as an example, we used the Integrated Valuation of Ecosystem Services and Trade-offs model to analyze the spatiotemporal changes in CS during 2000-2015. Then we coupled it with the patch-generating land use simulation model to simulate and predict LULC and CS in 2050 under four different development plans. The results showed that LULC change in Jiangsu Province was manifested mainly as transformation of cropland to construction land (3,485 km²) and cropland to water body (470 km²). The high value area for CS was concentrated mainly in forest land, water body and grassland, whereas the low value area was concentrated mainly in construction land. During 2000-2015, CS decreased by 0.23 Tg, and during 2015-2050, CS was predicted to decrease by 0.16, 1.69, 0.02, and 0.10 Tg under the baseline, fast, slow and harmonious development scenarios. The conversion of a large amount of cropland to construction land was the main cause of CS loss. In all scenarios, the carbon loss was the largest in southern Jiangsu and lowest in central Jiangsu. It is necessary to balance the conflict between economic development and ecological protection during the process of urbanization. This study can provide an important reference for decision makers during the formulation of regional development models and ecological management strategies.

Response of Surface Runoff and Sediment to the Conversion of a Marginal Grassland to a Switchgrass (Panicum virgatum) Bioenergy Feedstock System

The land systems between the humid and arid zones around the globe are critical to agricultural production and are characterized by a strong integration of the land use and water dynamics. In the southern Great Plains (SGP) of the United States, lakes and farm ponds are essential components in the land systems, and they provide unique habitats for wildlife, and critical water resources for irrigation and municipal water supplies. The conversion of the marginal grasslands to switchgrass (Panicum virgatum) biofuel feedstock for energy production has been proposed in the region. However, we have limited experimental data to assess the impact of this potential land-use change on the surface runoff, which is the primary water source for surface impoundments. Here, we report the results from a paired experimental watershed study that compared the runoff and sediment responses that were related to the conversion of prairie to a low-input biomass production system. The results show no significant change in the relationship between the event-based runoff and the precipitation. There was a substantial increase in the sediment yield (328%) during the conversion phase that was associated with the switchgrass establishment (i.e., the site preparation, herbicide application, and switchgrass planting). Once the switchgrass was established, the sediment yield was 21% lower than the nonconverted watershed. Our site-specific observations suggest that switchgrass biofuel production systems will have a minimum impact on the existing land and water systems. It may potentially serve as an environmentally friendly and economically viable alternative land use for slowing woody encroachment on marginal lands in the SGP.

Mapping and assessment of future changes in the coastal and marine ecosystem services supply in Lithuania

Assessing and mapping ecosystem services (ES) became an integral part of coastal and marine management practices. Hence, quantitative and validated approaches are lacking, especially to address future conditions. The objective of this study is to apply further existing and develop new methodological frameworks to quantitatively assess and map the current and future supply of 3 ES in the coastal zone of Lithuania: coastal flood protection, nutrient regulation, and maintenance of nursery conditions. For coastal flood ES modelling, 2 time periods (1990 and 2018) and 4 scenarios (A0, A1 A2, A3 - based on future socio-economic changes in Lithuania) were analysed. The coastal flood protection ES model was validated (r² = 0.30) using tree cover density. The results showed spatial differences among the analysed periods but no statistical differences. High supply areas are located in the southern coastal area, while the central part displays a low supply. For nutrient regulation and maintenance of nursery conditions, 7 time periods were analysed: a historical period and 6 scenarios based on Representative Concentration Pathway 4.5 and 8.5 and 3 Shared Socioeconomic Pathways. The nutrient regulation ES model was validated (r² = 0.85) using in situ nutrient. Statistical differences were observed for this ES, but a similar spatial distribution of high and low supply areas. A decrease in the supply was observed comparing the historical period and future scenarios. Maintenance of nursery conditions was validated (r² = 0.72) based on the protection status of the coastal zone. Results show no statistical differences and similar spatial patterns among the periods. Rocky and sandbank areas show a high supply for this ES. Limitations of our work are mainly related to the resolution of the utilised indicators. Nevertheless, the information obtained from our models can support spatial planning and decision-making processes.

Ten facts about land systems for sustainability

Land use is central to addressing sustainability issues, including biodiversity conservation, climate change, food security, poverty alleviation, and sustainable energy. In this paper, we synthesize knowledge accumulated in land system science, the integrated study of terrestrial social-ecological systems, into 10 hard truths that have strong, general, empirical support. These facts help to explain the challenges of achieving sustainability in land use and thus also point toward solutions. The 10 facts are as follows: 1) Meanings and values of land are socially constructed and contested; 2) land systems exhibit complex behaviors with abrupt, hard-to-predict changes; 3) irreversible changes and path dependence are common features of land systems; 4) some land uses have a small footprint but very large impacts; 5) drivers and impacts of land-use change are globally interconnected and spill over to distant locations; 6) humanity lives on a used planet where all land provides benefits to societies; 7) land-use change usually entails trade-offs between different benefits-"win-wins" are thus rare; 8) land tenure and land-use claims are often unclear, overlapping, and contested; 9) the benefits and burdens from land are unequally distributed; and 10) land users have multiple, sometimes conflicting, ideas of what social and environmental justice entails. The facts have implications for governance, but do not provide fixed answers. Instead they constitute a set of core principles which can guide scientists, policy makers, and practitioners toward meeting sustainability challenges in land use.

Representing responses to climate change in spatial land system models

Modelling future change to land use and land cover is done as part of many local and global scenario environmental assessments. Nevertheless, there are still considerable challenges related to simulating land-use responses to climate change. Mostly, climate change is considered by changing the temperature and precipitation, affecting the spatial distribution and productivity of future land use and land cover as result of differential changes in growing conditions. Other climate change effects, such as changes in the water resources needed to support future cropland expansion and intensification, are often neglected. In this study, we demonstrate how including different types of responses to climate change influences the simulation of future changes to land use and land cover, and land management. We study the influence of including different climate change effects in land system modeling step by step. The results show that land system models need to include numerous simultaneous climate change effects, particularly when looking at adaptation options such as implementing irrigation. Otherwise, there is a risk of biased impact estimates leading either to under- or overestimation of the consequences of land use change, including land degradation. Spatial land system models therefore need to be developed accounting for a multitude of climate change impacts, uncertainties related to climate data, and an assessment of the sensitivity of the outcomes toward the decisions of modellers on representing climate change impacts.

Monitoring of Land Use–Land Cover Change and Potential Causal Factors of Climate Change in Jhelum District, Punjab, Pakistan, through GIS and Multi-Temporal Satellite Data

Land use–land cover (LULC) alteration is primarily associated with land degradation, especially in recent decades, and has resulted in various harmful changes in the landscape. The normalized difference vegetation index (NDVI) has the prospective capacity to classify the vegetative characteristics of many ecological areas and has proven itself useful as a remote sensing (RS) tool in recording vegetative phenological aspects. Likewise, the normalized difference built-up index (NDBI) is used for quoting built-up areas. The current research objectives include identification of LULC, NDVI, and NDBI changes in Jhelum District, Punjab, Pakistan, during the last 30 years (1990–2020). This study targeted five major LULC classes: water channels, built-up area, barren land, forest, and cultivated land. Satellite imagery classification tools were used to identify LULC changes in Jhelum District, northern Punjab, Pakistan. The perception data about the environmental variations as conveyed by the 500 participants (mainly farmers) were also recorded and analyzed. The results depict that the majority of farmers (54%) believe in the appearance of more drastic changes such as less rainfall, drought, and decreased water availability for irrigation during 2020 compared to 30 years prior. Overall accuracy assessment of imagery classification was 83.2% and 88.8% for 1990, 88.1% and 85.7% for 2000, 86.5% and 86.7% for 2010, and 85.6% and 87.3% for 2020. The NDVI for Jhelum District was the highest in 1990 at +0.86 and the lowest in 2020 at +0.32; similarly, NDBI values were the highest in 2020 at +0.72 and the lowest in 1990 at −0.36. LULC change showed a clear association with temperature, NDBI, and NDVI in the study area. At the same time, variations in the land area of barren soil, vegetation, and built-up from 1990 to 2020 were quite prominent, possibly resulting in temperature increases, reduction in water for irrigation, and changing rainfall patterns. Farmers were found to be quite responsive to such climatic variations, diverting to framing possible mitigation approaches, but they need government assistance. The findings of this study, especially the causes and impacts of rapid LULC variations in the study area, need immediate attention from related government departments and policy makers.

Forest restoration and support for sustainable ecosystems in the Gandaki Basin, Nepal

Restoring degraded forest is essential if we are to reduce human pressure on natural ecosystems and their biodiversity. Forests were nationalized in 1957 in Nepal and as a consequence, forest cover declined from 45% in 1964 to just 29% in 1994. However, as its response, sectoral plans and policies, particularly introduction of community-based forest management programs since the 1980s and conservation activities resulted in large scale forest cover restoration. Here, we examined the forest cover change in the Gandaki River Basin (GRB), the catchment with the largest altitudinal variation (ranging from ± 93 to 8167 m) and environmental and ecological significance. To see how forests have changed since then, we analyzed snapshots of spatiotemporal, ecological and physiographic changes in forest cover, and forest type at decadal intervals from 1996 to 2016 using Landsat 5 and 8 satellite images. We observed an overall gain in forest cover of 207 km², from 7571 km² (34.4% of the total area) in 1996 to 7778 km² (35.3%) in 2016. Of the 21 forest cover types identified, the greatest forest coverage during 2016 was of Schima-Castanopsis forest (25.9%) and hill sal forests (16.4%). In terms of physiographic zones, land below 500 m (Tarai) where most people live, witnessed gradual declines in forest cover, in contrast to large increases in forests above 500 m. Historical examination of forest cover at ecological and physiographic scales helps to identify the elevation-wise distribution of forest resources, vegetation composition, ecosystem characteristics, anthropogenic pressure upon vegetation, and hence the overall influence of LULC upon the environment. These outputs will assist planners, policy makers, and researchers in their formulation of effective basin wide plans and policies to ensure the protection of basin level biodiversity and ecosystem function.

Changes in forest areas and land cover and their causes using intensity analysis: the case of Alabarda forest planning unit

The examination of land cover change, as the main driving force of global climate change, and the determination of its economic, ecological, and social effects are necessary for making the right decisions in sustainable development, planning, and management. This study, conducted in the local forest areas of Alabarda, located in Tavşanlı District (Turkey), examined temporal and spatial changes in land cover using an intensity analysis consisting of three levels: interval, category, and transition. Using the three maps of 1994, 2004, and 2015, we analyzed the land use changes during two time intervals (1993-2004 and 2004-2015) in the area for six categories, including three for forest areas (Productive, Degraded, and Treeless) and three for non-forest areas (Others, e.g., mine sites, roads, wetlands; Settlement; and Cultivated). The interval level results of the analysis showed that the land change rate was more rapid in the 2004-2015 time interval compared to 1993-2004. According to the category level results, in both time intervals, the Productive category was dormant in terms of loss and gain, whereas the Cultivated category was active. The level showing transitions between categories indicated that the Productive category targeted the Treeless and Cultivated losses in the first time interval and targeted the Degraded losses most intensely in the second time interval. The successful forestry activities (afforestation and rehabilitation) carried out by the government as of 2006 and the ongoing migration of the population from the region have had a joint effect on the increase of forest areas and the transformation of Degraded forest areas into Productive ones in the study area.

Assessing the Impacts of Urban Land Use Changes on Regional Ecosystem Services According to Urban Green Space Policies Via the Patch-Based Cellular Automata Model

As urbanization affects the quality of regional ecosystems, which are needed to ensure the well-being of residents, urban land management plans should consider ecosystem services. While several studies have assessed urbanization impacts on regional ecosystem services in developing urban areas, they used limited approaches that only considered options pertaining to urban green space management. A modeling approach that simulates changes in urban land use and associated ecosystem services can better support urban land management decisions by comparing the effects of different scenarios. We simulated urban land use change impacts on regional ecosystem services using a patch-based cellular automata model and assessment tools for seven ecosystem service indicators in different urban management scenarios regarding green space policies in Ansan, South Korea. The simulation results showed spatial patterns of land use changes and impacts on associated ecosystem services although their impacts varied according to different service types. While urbanization affected regional ecosystem services to a lesser extent than land use changes, urban green space policies contributed to increasing ecosystem services. Our approach provides useful information for improved policy decisions with regard to coupling urban management with ecosystem services, as urban green spaces are necessary for residents' well-being.

Effect of Complex Road Networks on Intensive Land Use in China’s Beijing-Tianjin-Hebei Urban Agglomeration

Coupled with rapid urbanization and urban expansion, the spatial relationship between transportation development and land use has gained growing interest among researchers and policy makers. In this paper, a complex network model and land use intensity assessment were integrated into a spatial econometric model to explore the spatial spillover effect of the road network on intensive land use patterns in China’s Beijing–Tianjin–Hebei (BTH) urban agglomeration. First, population density, point of interest (POI) density, and aggregation index were selected to measure land use intensity from social, physical, and ecological aspects. Then, the indicator of average degree (i.e., connections between counties) was used to measure the characteristics of the road network. Under the hypothesis that the road network functions in shaping land use patterns, a spatial econometric model with the road network embedded spatial weight matrix was established. Our results revealed that, while the land use intensity in the BTH urban agglomeration increased from 2010 to 2015, the road network became increasingly complex with greater spatial heterogeneity. The spatial lag coefficients of land use intensity were positively significant in both years and showed a declining trend. The spatially lagged effects of sector structure, fixed asset investment, and consumption were also significant in most of our spatial econometric models, and their contributions to the total spillover effect increased from 2010 to 2015. This study contributes to the literature by providing an innovative quantitative method to analyze the spatial spillover effect of the road network on intensive land use. We suggest that the spatial spillover effect of the road network could be strengthened in the urban–rural interface areas by improving accessibility and promoting population, resource, and technology flows.

Disclosing contrasting scenarios for future land cover in Brazil: Results from a high-resolution spatiotemporal model

Gaining information on the dynamics of land cover changes is a valuable step towards improving practical conservation actions. In recent years, the Brazilian presidential elections in 2018 and the recovery from one of the nation's worst economic recessions defined a political scenario that has been causing shifts in the patterns of land cover change. A variety of national plans for the near-future exist and include the construction of new roads connecting remote Amazonian areas and large dams that could flood up to 10 million hectares. These development plans threaten environmental conservation, but the potential effects on the local or regional land cover are mostly unknown. In this work, we construct a model to evaluate the possible consequences of policy actions on land cover dynamics in the near-future at a high-resolution scale. The regression model extracts the historical relationships between land cover and spatial drivers of change, and its extrapolation for the future enables the simulation of scenarios for the national plans currently discussed in Brazil. We also simulate three scenarios based on the Representative Concentration Pathways of the Intergovernmental Panel on Climate Change, which makes contrasting management assumptions. The resulting maps indicate that considerable changes in land cover composition and configuration may occur even in a short period. The historical Brazilian economic forces make the decrease in natural vegetation probabilities challenging to stop even in an environmentally oriented scenario, where plans for the construction of new infrastructure are abruptly interrupted. Our results also indicate that environmental degradation cannot be prevented without coordinated efforts between public agencies with a broad diversity of development viewpoints.

Closely related species show species-specific environmental responses and different spatial conservation needs: Prionailurus cats in the Indian subcontinent

Phylogenetically closely related species are often assumed to have similar responses to environmental conditions, but species-specific responses have also been described. These two scenarios may have different conservation implications. We tested these two hypotheses for Prionailurus cats (P. rubiginosus, P. bengalensis, P. viverrinus) in the Indian subcontinent and show its implications on species current protected area coverage and climatic suitability trends through time. We fitted ecological niche models with current environmental conditions and calculated niche overlap. In addition, we developed a model for the Jungle Cat Felis chaus to compare species responses and niche overlap estimates within Prionailurus with those for a related sympatric small cat species. Then we estimated the proportion of current suitable environment covered by protected area and projected climatic models from past (last interglacial) to future (2070; RCP4.5 and RCP8.5) conditions to show implications on population management and conservation. The hypothesis of a similar response and niche overlap among closely related species is not supported. Protected area coverage was lowest for P. viverrinus (mean = 0.071, SD = 0.012) and highest for P. bengalensis (mean = 0.088, SD = 0.006). In addition, the proportion of the subcontinent with suitable climate varied through time and was species-specific. For P. bengalensis, climatic suitability shrunk since at least the mid-Holocene, a trend that can be intensified by human-induced climate warming. Concerning P. viverrinus, most predictions show stable future climatic suitability, but a few indicated potential loss. Climatic suitability for P. rubiginous was predicted to remain stable but the species exhibited a negative association with intensive agriculture. Similar responses to environmental change by phylogenetically closely related species should not be assumed and have implications on protected area coverage and natural trends of species climatic suitability over time. This should be taken into account during conservation and management actions.

Identifying Agricultural Frontiers for Modeling Global Cropland Expansion

The increasing expansion of cropland is major driver of global carbon emissions and biodiversity loss. However, predicting plausible future global distributions of croplands remains challenging. Here, we show that, in general, existing global data aligned with classical economic theories of expansion explain the current (1992) global extent of cropland reasonably well, but not recent expansion (1992-2015). Deviations from models of cropland extent in 1992 ("frontierness") can be used to improve global models of recent expansion, most likely as these deviations are a proxy for cropland expansion under frontier conditions where classical economic theories of expansion are less applicable. Frontierness is insensitive to the land cover dataset used and is particularly effective in improving models that include mosaic land cover classes and the largely smallholder-driven frontier expansion occurring in such areas. Our findings have important implications as the frontierness approach offers a straightforward way to improve global land use change models.

Assessment of the Future Climate Change Projections on Streamflow Hydrology and Water Availability over Upper Xijiang River Basin, China

Hydrological models are widely applied for simulating complex watershed processes and directly linking meteorological, topographical, land-use, and geological conditions. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated at two monitoring stations, which improved model performance and increased the reliability of flow predictions in the Upper Xijiang River Basin. This study evaluated the potential impacts of climate change on the streamflow and water yield of the Upper Xijiang River Basin using Arc-SWAT. The model was calibrated (1991–1997) and validated (1998–2001) using the Sequential Uncertainty Fitting Algorithm (SUFI-2). Model calibration and validation suggest a good match between the measured and simulated monthly streamflow, indicating the applicability of the model for future daily streamflow predictions. Large negative changes of low flows are projected under future climate scenarios, exhibiting a 10% and 30% decrease in water yield over the watershed on a monthly scale. Overall, findings generally indicated that winter flows are expected to be affected the most, with a maximum impact during the January–April period, followed by the wet monsoon season in the May–September period. Water balance components of the Upper Xijiang River Basin are expected to change significantly due to the projected climate change that, in turn, will seriously affect the water resources and streamflow patterns in the future. Thus, critical problems, such as ground water shortages, drops in agricultural crop yield, and increases in domestic water demand are expected at the Xijiang River Basin.

Spatial influence of ecological networks on land use intensity

Rapid urbanization resulted in widespread urban expansion and the fragmentation and isolation of large-scale ecological sources. Ecological sustainability has propelled the popularity and implementation of intensive land use programs worldwide, in China particularly. In this study, we explored the spatial spillover effect through ecological networks on intensive urban land use and the underlying driving mechanism using the Wuhan urban agglomeration as the case study area. First, we comprehensively measured land use intensity (LUI) from three dimensions: input, output, and landscape aggregation. Second, ecological sources were identified on the basis of land use maps, and ecological networks were constructed using the "minimum cumulative resistance" model. Then, the "gravity model" was applied to measure the spatial interaction among ecological sources and to construct spatial weight matrices for spatial modeling. Lastly, we devised a spatial Durbin model using the designed "ecological" spatial weight matrices to examine the influencing factors and the potential spatial interactions or constraints. The results showed that the average values of LUI in 2017 were almost 70 times higher than that in 2005 and the Jianghan District had the highest increment (91 times) from 2005 to 2017. LUI was primarily driven by socioeconomic development. Gross domestic product and proportion of tertiary sector exerted positive influences, whereas agricultural output value exhibited a negative effect on LUI in 2005 and 2017. A positive spatial autocorrelation of LUI was observed at the county level, and the spatial spillover effect was confirmed through ecological networks during intensive land use, indicating that ecological spatial influence is an important factor in explaining LUI. The findings help in exploring the spatial influence through ecological networks on LUI at the regional level and provide references for formulating relevant policies to achieve the ecological security of terrestrial ecosystems and coordinated and balanced regional sustainable development.

Quantifying the Effect of Land Use Change Model Coupling

Land-use change (LUC) is a complex process that is difficult to project. Model collaboration, an aggregate term for model harmonization, comparison and/or coupling, intends to combine the strengths of different models to improve LUC projections. Several model collaborations have been performed, but to the authors’ knowledge, the effect of coupling has not been evaluated quantitatively. Therefore, for a case study of Brazil, we harmonized and coupled the partial equilibrium model GLOBIOM-Brazil and the demand-driven spatially explicit model PLUC, and then compared the coupled-model projections with those by GLOBIOM-Brazil individually. The largest differences between projections occurred in Mato Grosso and Pará, frontiers of agricultural expansion. In addition, we validated both projections for Mato Grosso using land-use maps from remote sensing images. The coupled model clearly outperformed GLOBIOM-Brazil. Reductions in the root mean squared error (RMSE) for LUC dynamics ranged from 31% to 80% and for total land use, from 10% to 57%. Only for pasture, the coupled model performed worse in total land use (RMSE 9% higher). Reasons for a better performance of the coupled model were considered to be, inter alia, the initial map, more spatially explicit information about drivers, and the path-dependence effect in the allocation through the cellular-automata approach of PLUC.

The environmental consequences of climate-driven agricultural frontiers

Growing conditions for crops such as coffee and wine grapes are shifting to track climate change. Research on these crop responses has focused principally on impacts to food production impacts, but evidence is emerging that they may have serious environmental consequences as well. Recent research has documented potential environmental impacts of shifting cropping patterns, including impacts on water, wildlife, pollinator interaction, carbon storage and nature conservation, on national to global scales. Multiple crops will be moving in response to shifting climatic suitability, and the cumulative environmental effects of these multi-crop shifts at global scales is not known. Here we model for the first time multiple major global commodity crop suitability changes due to climate change, to estimate the impacts of new crop suitability on water, biodiversity and carbon storage. Areas that become newly suitable for one or more crops are Climate-driven Agricultural Frontiers. These frontiers cover an area equivalent to over 30% of the current agricultural land on the planet and have major potential impacts on biodiversity in tropical mountains, on water resources downstream and on carbon storage in high latitude lands. Frontier soils contain up to 177 Gt of C, which might be subject to release, which is the equivalent of over a century of current United States CO₂ emissions. Watersheds serving over 1.8 billion people would be impacted by the cultivation of the climate-driven frontiers. Frontiers intersect 19 global biodiversity hotspots and the habitat of 20% of all global restricted range birds. Sound planning and management of climate-driven agricultural frontiers can therefore help reduce globally significant impacts on people, ecosystems and the climate system.

Comparing the impact of future cropland expansion on global biodiversity and carbon storage across models and scenarios

Land-use change is a direct driver of biodiversity and carbon storage loss. Projections of future land use often include notable expansion of cropland areas in response to changes in climate and food demand, although there are large uncertainties in results between models and scenarios. This study examines these uncertainties by comparing three different socio-economic scenarios (SSP1–3) across three models (IMAGE, GLOBIOM and PLUMv2). It assesses the impacts on biodiversity metrics and direct carbon loss from biomass and soil as a direct consequence of cropland expansion. Results show substantial variation between models and scenarios, with little overlap across all nine projections. Although SSP1 projects the least impact, there are still significant impacts projected. IMAGE and GLOBIOM project the greatest impact across carbon storage and biodiversity metrics due to both extent and location of cropland expansion. Furthermore, for all the biodiversity and carbon metrics used, there is a greater proportion of variance explained by the model used. This demonstrates the importance of improving the accuracy of land-based models. Incorporating effects of land-use change in biodiversity impact assessments would also help better prioritize future protection of biodiverse and carbon-rich areas.

This article is part of the theme issue ‘Climate change and ecosystems: threats, opportunities and solutions’.

Optimization of the National Land Space Based on the Coordination of Urban-Agricultural-Ecological Functions in the Karst Areas of Southwest China

National land spatial planning is dominated by urban-agricultural-ecological functions and has become a Chinese national strategic issue. However, the three functional spaces have serious conflicts in the karst areas, causing inconsistencies in regional development and triggering poverty and a more serious situation for the ecological environment. In this study, we used the gray multi-objective dynamic programming model and the conversion of land use and its effects at small region extent model to simulate the developmental structures of future land use in the karst areas of Southwest China under a socioeconomic development scenario, an arable land protection scenario and an ecological security scenario. Finally, based on the coordination of the urban-agricultural-ecological functions, we used a functional space classification method to optimize the spatial structures of the national land space for 2035 year and to identify different functional areas. The results showed that the three scenarios with different objectives had differences in the quantities and spatial structures of land use but that the area of forestland was the largest and the area of water was the smallest in each scenario. The optimization of the national land space was divided into seven functional areas—urban space, agricultural space, ecological space, urban-agricultural space, urban-ecological space, agricultural-ecological space and urban-agricultural-ecological space. The ecological space was the largest and the urban-ecological space was the smallest among seven functional areas. The different types of functional spaces had significant differentiation characteristics in the layouts. The urban-agricultural space, urban-ecological space, agricultural-ecological space and urban-agricultural-ecological space can effectively alleviate the impacts of human activities and agricultural production activities in karst areas, promote the improvement of rocky desertification and improve the quality of the regional ecological environment. The results of this research can provide support for decisions about the balanced development of the national land space and the improvement of environmental quality in the karst areas.

The effects of organic and inorganic phosphorus amendments on the biochemical attributes and active microbial population of agriculture podzols following silage corn cultivation in boreal climate

Phosphorus (P) is the second most important macronutrient that limits the plant growth, development and productivity. Inorganic P fertilization in podzol soils predominantly bound with aluminum and iron, thereby reducing its availability to crop plants. Dairy manure (DM) amendment to agricultural soils can improve physiochemical properties, nutrient cycling through enhanced enzyme and soil microbial activities leading to improved P bioavailability to crops. We hypothesized that DM amendment in podzol soil will improve biochemical attributes and microbial community and abundance in silage corn cropping system under boreal climate. We evaluated the effects of organic and inorganic P amendments on soil biochemical attributes and abundance in podzol soil under boreal climate. Additionally, biochemical attributes and microbial population and abundance under short-term silage corn monocropping system was also investigated. Experimental treatments were [P₀ (control); P₁: DM with high P₂O₅; P₂: DM with low P₂O₅; P₃: inorganic P and five silage-corn genotypes (Fusion RR, Yukon R, A4177G3RIB, DKC 23-17RIB and DKC 26-28RIB) were laid out in a randomized complete block design in factorial settings with three replications. Results showed that P₁ treatment increased acid phosphatase (AP-ase) activity (29% and 44%), and soil available P (SAP) (60% and 39%) compared to control treatment, during 2016 and 2017, respectively. Additionally, P₁ treatments significantly increased total bacterial phospholipids fatty acids (ΣB-PLFA), total phospholipids fatty acids (ΣPLFA), fungi, and eukaryotes compared to control and inorganic P. Yukon R and DKC 26-28RIB genotypes exhibited higher total bacterial PLFA, fungi, and total PLFA in their rhizospheres compared to the other genotypes. Redundancy analyses showed promising association between P₁ and P₂ amendment, biochemical attributes and active microbial population and Yukon R and DKC 26-28RIB genotypes. Pearson correlation also demonstrated significant and positive correlation between AP-ase, SAP and gram negative bacteria (G⁻), fungi, ΣB-PLFA, and total PLFA. Study results demonstrated that P1 treatment enhanced biochemical attributes, active microbial community composition and abundance and forage production of silage corn. Results further demonstrated higher active microbial population and abundance in rhizosphere of Yukon R and DKC 26-28RIB genotypes. Therefore, we argue that dairy manure amendment with high P₂O₅ in podzol soils could be a sustainable nutrient source to enhance soil quality, health and forage production of silage corn. Yukon R and DKC 26-28RIB genotypes showed superior agronomic performance, therefore, could be good fit under boreal climatic conditions.

Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition

The availability of suitable habitat is a key predictor of the changing status of biodiversity. Quantifying habitat availability over large spatial scales is, however, challenging. Although remote sensing techniques have high spatial coverage, there is uncertainty associated with these estimates due to errors in classification. Alternatively, the extent of habitats can be estimated from ground-based field survey. Financial and logistical constraints mean that on-the-ground surveys have much lower coverage, but they can produce much higher quality estimates of habitat extent in the areas that are surveyed. Here, we demonstrate a new combined model which uses both types of data to produce unified national estimates of the extent of four key habitats across Great Britain based on Countryside Survey and Land Cover Map. This approach considers that the true proportion of habitat per km2 (Zi ) is unobserved, but both ground survey and remote sensing can be used to estimate Zi . The model allows the relationship between remote sensing data and Zi to be spatially biased while ground survey is assumed to be unbiased. Taking a statistical model-based approach to integrating field survey and remote sensing data allows for information on bias and precision to be captured and propagated such that estimates produced and parameters estimated are robust and interpretable. A simulation study shows that the combined model should perform best when error in the ground survey data is low. We use repeat surveys to parameterize the variance of ground survey data and demonstrate that error in this data source is small. The model produced revised national estimates of broadleaved woodland, arable land, bog, and fen, marsh and swamp extent across Britain in 2007.

Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity

With rising demand for biomass, cropland expansion and intensification represent the main strategies to boost agricultural production, but are also major drivers of biodiversity decline. We investigate the consequences of attaining equal global production gains by 2030, either by cropland expansion or intensification, and analyse their impacts on agricultural markets and biodiversity. We find that both scenarios lead to lower crop prices across the world, even in regions where production decreases. Cropland expansion mostly affects biodiversity hotspots in Central and South America, while cropland intensification threatens biodiversity especially in Sub-Saharan Africa, India and China. Our results suggest that production gains will occur at the costs of biodiversity predominantly in developing tropical regions, while Europe and North America benefit from lower world market prices without putting their own biodiversity at risk. By identifying hotspots of potential future conflicts, we demonstrate where conservation prioritization is needed to balance agricultural production with conservation goals.

The increase in needs for agricultural commodities is projected to outpace the growth of farmland production globally, leading to high pressure on farming systems in the next decades. Here, the authors investigate the future impact of cropland expansion and intensification on agricultural markets and biodiversity, and suggest the need for balancing agricultural production with conservation goals.

Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors

Mapping suitable land for development is essential to land use planning efforts that aim to model, anticipate, and manage trade-offs between economic development and the environment. Previous land suitability assessments have generally focused on a few development sectors or lack consistent methodologies, thereby limiting our ability to plan for cumulative development pressures across geographic regions. Here, we generated 1-km spatially-explicit global land suitability maps, referred to as "development potential indices" (DPIs), for 13 sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). To do so, we applied spatial multi-criteria decision analysis techniques that accounted for both resource potential and development feasibility. For each DPI, we examined both uncertainty and sensitivity, and spatially validated the map using locations of planned development. We illustrate how these DPIs can be used to elucidate potential individual sector expansion and cumulative development patterns.

Quantifying the biophysical and socioeconomic drivers of changes in forest and agricultural land in South and Southeast Asia

South and Southeast Asia (SSEA) has been a hotspot for land use and land cover change (LULCC) in the past few decades. The identification and quantification of the drivers of LULCC are crucial for improving our understanding of LULCC trends. So far, the biophysical and socioeconomic drivers of forest change have not been quantified at the regional scale, particularly for SSEA. In this study, we quantify the biophysical and socioeconomic drivers of forest change on a country-by-country basis in SSEA using an integrated quantitative methodology, which systematically accounts for previously published driver information and regional datasets. We synthesize more than 200 publications to identify the drivers of the forest change at different spatial scales in SSEA. Subsequently, we collect spatially explicit proxy data to represent the identified drivers. We quantify the dynamics of forest and agricultural land from 1992 to 2015 using the Climate Change Initiative (CCI) land cover data developed by the European Space Agency (ESA). A geographically weighted regression method is employed to quantify the spatially heterogeneous drivers of forest change. Our results show that socioeconomic drivers are more important than biophysical drivers for the conversion of forest to agricultural land in South Asia and maritime Southeast Asia. In contrast, biophysical drivers are more important than socioeconomic drivers for the conversion of agricultural land to forest in maritime Southeast Asia and less important in South Asia. Both biophysical and socioeconomic drivers contribute approximately equally to both changes in the mainland Southeast Asia region. By quantifying the dynamics of forest and agricultural land and the spatially explicit drivers of their changes in SSEA, this study provides a solid foundation for LULCC modeling and projection.

Key determinants of global land-use projections

Land use is at the core of various sustainable development goals. Long-term climate foresight studies have structured their recent analyses around five socio-economic pathways (SSPs), with consistent storylines of future macroeconomic and societal developments; however, model quantification of these scenarios shows substantial heterogeneity in land-use projections. Here we build on a recently developed sensitivity approach to identify how future land use depends on six distinct socio-economic drivers (population, wealth, consumption preferences, agricultural productivity, land-use regulation, and trade) and their interactions. Spread across models arises mostly from diverging sensitivities to long-term drivers and from various representations of land-use regulation and trade, calling for reconciliation efforts and more empirical research. Most influential determinants for future cropland and pasture extent are population and agricultural efficiency. Furthermore, land-use regulation and consumption changes can play a key role in reducing both land use and food-security risks, and need to be central elements in sustainable development strategies.

There lacks model comparison of global land use change projections. Here the authors explored how different long-term drivers determine land use and food availability projections and they showed that the key determinants population growth and improvements in agricultural efficiency.

Methods to Assess the Impacts and Indirect Land Use Change Caused by Telecoupled Agricultural Supply Chains: A Review

The increasing international trade of agricultural products has contributed to a larger diversity of food at low prices and represents an important economic value. However, such trade can also cause social, environmental and economic impacts beyond the limits of the countries directly involved in the exchange. Agricultural systems are telecoupled because the impacts caused by trade can generate important feedback loops, spillovers, rebound effects, time lags and non-linearities across multiple geographical and temporal scales that make these impacts more difficult to identify and mitigate. We make a comparative review of current impact assessment methods to analyze their suitability to assess the impacts of telecoupled agricultural supply chains. Given the large impacts caused by agricultural production on land systems, we focus on the capacity of methods to account for and spatially allocate direct and indirect land use change. Our analysis identifies trade-offs between methods with respect to the elements of the telecoupled system they address. Hybrid methods are a promising field to navigate these trade-offs. Knowledge gaps in assessing indirect land use change should be overcome in order to improve the accuracy of assessments.

Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies

Urbanization is a rapid global trend, leading to consequences such as urban heat islands and local flooding. Imminent climate change is predicted to intensify these consequences, forcing cities to rethink common infrastructure practices. One popular method of adaptation is green infrastructure implementation, which has been found to reduce local temperatures and alleviate excess runoff when installed effectively. As cities continue to change and adapt, land use/landcover modeling becomes an important tool for city officials in planning future land usage. This study uses a combination of cellular automata, machine learning, and Markov chain analysis to predict high resolution land use/landcover changes in Philadelphia, PA, USA for the year 2036. The 2036 landcover model assumes full implementation of Philadelphia’s green infrastructure program and past temporal trends of urbanization. The methodology used to create the 2036 model was validated by creating an intermediate prediction of a 2015 landcover that was then compared to an existing 2015 landcover. The accuracy of the validation was determined using Kappa statistics and disagreement scores. The 2036 model successfully met Philadelphia’s green infrastructure goals. A variety of landscape metrics demonstrated an overall decrease in fragmentation throughout the landscape due to increases in urban landcover.

Evaluating Potential Sources of Aggregation Bias with a Structural Optimization Model of the U.S. Forest Sector

Structural economic optimization models of the forestry and land use sectors can be used to develop baseline projections of future forest carbon stocks and annual fluxes, which inform policy dialog and investment in programs that maintain or enhance forest carbon stocks. Such analyses vary in terms of the degree of spatial, temporal, and activity-level aggregation used to represent forest resources, land cover, and markets. While the statistical and econometric modeling communities widely discuss the effects of aggregation bias and have developed correction techniques, there is limited prior research investigating how aggregation bias may affect structural optimization models. This paper explores potential aggregation bias using the Land Use and Resource Allocation model (LURA), a detailed spatial allocation partial equilibrium model of the U.S. forest sector. We ran a series of projections representing alternative aggregation approaches including averaging forest stocks at plot, county, state, and regional levels, across one-, five, or ten-year age classes, and by two or fourteen forest types. We compared the resulting projections of forest carbon stocks and harvesting activities across each aggregation scenario. This allows us to isolate the effect of aggregation on key variables of interest (e.g., GHG emissions and supply costs), while holding all other structural characteristics of the modeling framework constant. We find that age-class and forest type aggregations have the greatest impact on modeling results, with the potential to substantially impact market and greenhouse gas projections. On the other hand, spatial aggregation has a small impact on national carbon stock projections. Importantly, regional results are greatly impacted by different aggregation approaches, with projected regional cumulative carbon stocks differing by more than 25% across scenarios.

Assessing the efficiency of changes in land use for mitigating climate change

Land-use changes are critical for climate policy because native vegetation and soils store abundant carbon and their losses from agricultural expansion, together with emissions from agricultural production, contribute about 20 to 25 per cent of greenhouse gas emissions^1,2. Most climate strategies require maintaining or increasing land-based carbon³ while meeting food demands, which are expected to grow by more than 50 per cent by 2050^1,2,4. A finite global land area implies that fulfilling these strategies requires increasing global land-use efficiency of both storing carbon and producing food. Yet measuring the efficiency of land-use changes from the perspective of greenhouse gas emissions is challenging, particularly when land outputs change, for example, from one food to another or from food to carbon storage in forests. Intuitively, if a hectare of land produces maize well and forest poorly, maize should be the more efficient use of land, and vice versa. However, quantifying this difference and the yields at which the balance changes requires a common metric that factors in different outputs, emissions from different agricultural inputs (such as fertilizer) and the different productive potentials of land due to physical factors such as rainfall or soils. Here we propose a carbon benefits index that measures how changes in the output types, output quantities and production processes of a hectare of land contribute to the global capacity to store carbon and to reduce total greenhouse gas emissions. This index does not evaluate biodiversity or other ecosystem values, which must be analysed separately. We apply the index to a range of land-use and consumption choices relevant to climate policy, such as reforesting pastures, biofuel production and diet changes. We find that these choices can have much greater implications for the climate than previously understood because standard methods for evaluating the effects of land use^4-11 on greenhouse gas emissions systematically underestimate the opportunity of land to store carbon if it is not used for agriculture.

Reconstruction of the cropland cover changes in eastern China between the 10th century and 13th century using historical documents

To evaluate and improve datasets of anthropogenic land cover change used in local and global climate models, great efforts were made to reconstruct historical land use, including the LandCover 6k project which dedicated to reconstructing human land use over the past 10,000 years. In this study, we utilized historical records, including taxed-cropland and cropland measurement areas, and data on the number of households in eastern China between the 10^th century and 13^th century in concert with coefficient calibration, model allocation, and per capita cropland estimation to reconstruct areas of provincial cropland for 22 provinces over five time periods. Our reconstructions indicate that total cropland areas of eastern China for AD 1000, 1066, 1078, 1162, and 1215 are 34.74 × 10⁶ ha, 49.42 × 10⁶ ha, 51.62 × 10⁶ ha, 35.21 × 10⁶ ha, and 51.21 × 10⁶ ha, respectively. And the cropland area fluctuated because of dynasty shift and went through three phases. Cropland expansion and contraction mainly occurred in the middle and lower reaches of the Yangtze and Yellow Rivers as well as the Huaihe River Basin, while in some regions far away from battlefields, including northeastern and southern China, cropland area expanded continuously throughout the study period.

Adaptation of global land use and management intensity to changes in climate and atmospheric carbon dioxide

Land use contributes to environmental change, but is also influenced by such changes. Climate and atmospheric carbon dioxide (CO₂ ) levels' changes alter agricultural crop productivity, plant water requirements and irrigation water availability. The global food system needs to respond and adapt to these changes, for example, by altering agricultural practices, including the crop types or intensity of management, or shifting cultivated areas within and between countries. As impacts and associated adaptation responses are spatially specific, understanding the land use adaptation to environmental changes requires crop productivity representations that capture spatial variations. The impact of variation in management practices, including fertiliser and irrigation rates, also needs to be considered. To date, models of global land use have selected agricultural expansion or intensification levels using relatively aggregate spatial representations, typically at a regional level, that are not able to characterise the details of these spatially differentiated responses. Here, we show results from a novel global modelling approach using more detailed biophysically derived yield responses to inputs with greater spatial specificity than previously possible. The approach couples a dynamic global vegetative model (LPJ-GUESS) with a new land use and food system model (PLUMv2), with results benchmarked against historical land use change from 1970. Land use outcomes to 2100 were explored, suggesting that increased intensity of climate forcing reduces the inputs required for food production, due to the fertilisation and enhanced water use efficiency effects of elevated atmospheric CO₂ concentrations, but requiring substantial shifts in the global and local patterns of production. The results suggest that adaptation in the global agriculture and food system has substantial capacity to diminish the negative impacts and gain greater benefits from positive outcomes of climate change. Consequently, agricultural expansion and intensification may be lower than found in previous studies where spatial details and processes consideration were more constrained.

Northward shift of the agricultural climate zone under 21st-century global climate change

As agricultural regions are threatened by climate change, warming of high latitude regions and increasing food demands may lead to northward expansion of global agriculture. While socio-economic demands and edaphic conditions may govern the expansion, climate is a key limiting factor. Extant literature on future crop projections considers established agricultural regions and is mainly temperature based. We employed growing degree days (GDD), as the physiological link between temperature and crop growth, to assess the global northward shift of agricultural climate zones under 21^st-century climate change. Using ClimGen scenarios for seven global climate models (GCMs), based on greenhouse gas (GHG) emissions and transient GHGs, we delineated the future extent of GDD areas, feasible for small cereals, and assessed the projected changes in rainfall and potential evapotranspiration. By 2099, roughly 76% (55% to 89%) of the boreal region might reach crop feasible GDD conditions, compared to the current 32%. The leading edge of the feasible GDD will shift northwards up to 1200 km by 2099 while the altitudinal shift remains marginal. However, most of the newly gained areas are associated with highly seasonal and monthly variations in climatic water balances, a critical component of any future land-use and management decisions.

Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C

This article investigates projected changes in temperature and water cycle extremes at 1.5°C of global warming, and highlights the role of land processes and land-use changes (LUCs) for these projections. We provide new comparisons of changes in climate at 1.5°C versus 2°C based on empirical sampling analyses of transient simulations versus simulations from the 'Half a degree Additional warming, Prognosis and Projected Impacts' (HAPPI) multi-model experiment. The two approaches yield similar overall results regarding changes in climate extremes on land, and reveal a substantial difference in the occurrence of regional extremes at 1.5°C versus 2°C. Land processes mediated through soil moisture feedbacks and land-use forcing play a major role for projected changes in extremes at 1.5°C in most mid-latitude regions, including densely populated areas in North America, Europe and Asia. This has important implications for low-emissions scenarios derived from integrated assessment models (IAMs), which include major LUCs in ambitious mitigation pathways (e.g. associated with increased bioenergy use), but are also shown to differ in the simulated LUC patterns. Biogeophysical effects from LUCs are not considered in the development of IAM scenarios, but play an important role for projected regional changes in climate extremes, and are thus of high relevance for sustainable development pathways.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

Modelling regional cropping patterns under scenarios of climate and socio-economic change in Hungary

Impacts of socio-economic, political and climatic change on agricultural land systems are inherently uncertain. The role of regional and local-level actors is critical in developing effective policy responses that accommodate such uncertainty in a flexible and informed way across governance levels. This study identified potential regional challenges in arable land use systems, which may arise from climate and socio-economic change for two counties in western Hungary: Veszprém and Tolna. An empirically-grounded, agent-based model was developed from an extensive farmer household survey about local land use practices. The model was used to project future patterns of arable land use under four localised, stakeholder-driven scenarios of plausible future socio-economic and climate change. The results show strong differences in farmers' behaviour and current agricultural land use patterns between the two regions, highlighting the need to implement focused policy at the regional level. For instance, policy that encourages local food security may need to support improvements in the capacity of farmers to adapt to physical constraints in Veszprém and farmer access to social capital and environmental awareness in Tolna. It is further suggested that the two regions will experience different challenges to adaptation under possible future conditions (up to 2100). For example, Veszprém was projected to have increased fallow land under a scenario with high inequality, ineffective institutions and higher-end climate change, implying risks of land abandonment. By contrast, Tolna was projected to have a considerable decline in major cereals under a scenario assuming a de-globalising future with moderate climate change, inferring challenges to local food self-sufficiency. The study provides insight into how socio-economic and physical factors influence the selection of crop rotation plans by farmers in western Hungary and how farmer behaviour may affect future risks to agricultural land systems under environmental change.

Winners and losers of national and global efforts to reconcile agricultural intensification and biodiversity conservation

Closing yield gaps within existing croplands, and thereby avoiding further habitat conversions, is a prominently and controversially discussed strategy to meet the rising demand for agricultural products, while minimizing biodiversity impacts. The agricultural intensification associated with such a strategy poses additional threats to biodiversity within agricultural landscapes. The uneven spatial distribution of both yield gaps and biodiversity provides opportunities for reconciling agricultural intensification and biodiversity conservation through spatially optimized intensification. Here, we integrate distribution and habitat information for almost 20,000 vertebrate species with land-cover and land-use datasets. We estimate that projected agricultural intensification between 2000 and 2040 would reduce the global biodiversity value of agricultural lands by 11%, relative to 2000. Contrasting these projections with spatial land-use optimization scenarios reveals that 88% of projected biodiversity loss could be avoided through globally coordinated land-use planning, implying huge efficiency gains through international cooperation. However, global-scale optimization also implies a highly uneven distribution of costs and benefits, resulting in distinct "winners and losers" in terms of national economic development, food security, food sovereignty or conservation. Given conflicting national interests and lacking effective governance mechanisms to guarantee equitable compensation of losers, multinational land-use optimization seems politically unlikely. In turn, 61% of projected biodiversity loss could be avoided through nationally focused optimization, and 33% through optimization within just 10 countries. Targeted efforts to improve the capacity for integrated land-use planning for sustainable intensification especially in these countries, including the strengthening of institutions that can arbitrate subnational land-use conflicts, may offer an effective, yet politically feasible, avenue to better reconcile future trade-offs between agriculture and conservation. The efficiency gains of optimization remained robust when assuming that yields could only be increased to 80% of their potential. Our results highlight the need to better integrate real-world governance, political and economic challenges into sustainable development and global change mitigation research.

Global change and the distributional dynamics of migratory bird populations wintering in Central America

Understanding the susceptibility of highly mobile taxa such as migratory birds to global change requires information on geographic patterns of occurrence across the annual cycle. Neotropical migrants that breed in North America and winter in Central America occur in high concentrations on their non-breeding grounds where they spend the majority of the year and where habitat loss has been associated with population declines. Here, we use eBird data to model weekly patterns of abundance and occurrence for 21 forest passerine species that winter in Central America. We estimate species' distributional dynamics across the annual cycle, which we use to determine how species are currently associated with public protected areas and projected changes in climate and land-use. The effects of global change on the non-breeding grounds is characterized by decreasing precipitation, especially during the summer, and the conversion of forest to cropland, grassland, or peri-urban. The effects of global change on the breeding grounds are characterized by increasing winter precipitation, higher temperatures, and the conversion of forest to peri-urban. During spring and autumn migration, species are projected to encounter higher temperatures, forests that have been converted to peri-urban, and increased precipitation during spring migration. Based on current distributional dynamics, susceptibility to global change is characterized by the loss of forested habitats on the non-breeding grounds, warming temperatures during migration and on the breeding grounds, and declining summer rainfall on the non-breeding grounds. Public protected areas with low and medium protection status are more prevalent on the non-breeding grounds, suggesting that management opportunities currently exist to mitigate near-term non-breeding habitat losses. These efforts would affect more individuals of more species during a longer period of the annual cycle, which may create additional opportunities for species to respond to changes in habitat or phenology that are likely to develop under climate change.

Ecosystem services from southern African woodlands and their future under global change

Miombo and mopane woodlands are the dominant land cover in southern Africa. Ecosystem services from these woodlands support the livelihoods of 100 M rural people and 50 M urban dwellers, and others beyond the region. Provisioning services contribute $9 ± 2 billion yr⁻¹ to rural livelihoods; 76% of energy used in the region is derived from woodlands; and traded woodfuels have an annual value of $780 M. Woodlands support much of the region's agriculture through transfers of nutrients to fields and shifting cultivation. Woodlands store 18–24 PgC carbon, and harbour a unique and diverse flora and fauna that provides spiritual succour and attracts tourists. Longstanding processes that will impact service provision are the expansion of croplands (0.1 M km²; 2000–2014), harvesting of woodfuels (93 M tonnes yr⁻¹) and changing access arrangements. Novel, exogenous changes include large-scale land acquisitions (0.07 M km²; 2000–2015), climate change and rising CO₂. The net ecological response to these changes is poorly constrained, as they act in different directions, and differentially on trees and grasses, leading to uncertainty in future service provision. Land-use change and socio-political dynamics are likely to be dominant forces of change in the short term, but important land-use dynamics remain unquantified.

This article is part of the themed issue ‘Tropical grassy biomes: linking ecology, human use and conservation’.