Global Land Use Research Articles

Plant nutrient-acquisition strategies drive topsoil microbiome structure and function.

Plant nutrient-acquisition strategies drive soil processes and vegetation performance, but their effect on the soil microbiome remains poorly understood. This knowledge is important to predict the shifts in microbial diversity and functions due to increasing changes in vegetation traits under global change. Here we documented the topsoil microbiomes of 145 boreal and temperate terrestrial sites in the Baltic region that broadly differed in vegetation type and nutritional traits, such as mycorrhizal types and symbiotic nitrogen-fixation. We found that sites dominated by arbuscular mycorrhizal (AM) vegetation harbor relatively more AM fungi, bacteria, fungal saprotrophs, and pathogens in the topsoil compared with sites dominated by ectomycorrhizal (EM) plants. These differences in microbiome composition reflect the rapid nutrient cycling and negative plant-soil feedback in AM soils. Lower fungal diversity and bacteria:fungi ratios in EM-dominated habitats are driven by monodominance of woody vegetation as well as soil acidification by EM fungi, which are associated with greater diversity and relative abundance of carbohydrate-active enzymes. Our study suggests that shifts in vegetation related to global change and land use may strongly alter the topsoil microbiome structure and function.

Habitat fragmentation changes top-down and bottom-up controls of food webs.

Top-down and bottom-up controls regulate the structure and stability of ecosystems, but their relative roles in terrestrial systems have been debated. Here we studied a hydro-inundated land-bridge system in subtropical China and tested the relative importance of these two controls in determining the rodent-mediated regeneration of a locally dominant tree species. Our results showed that both controls operated in terrestrial habitats and that their relative importance switched as habitat size changed. Habitat loss initially removed predators of rodents that released rodent populations and triggered massive seed predation (top-down control), leading to reduced seedling establishment. A further reduction in habitat size led to decrease in rodent population that was supposed to increase seedling survival of the tree species, but the decline in habitat size deteriorated the abiotic environments (bottom-up control) that severely prevented seedling recruitment. As the ongoing global land use change is creating increasing number of small-sized forest fragments, our findings provide novel insights into the restoration of seriously fragmented forests.

Probing Soil Electrochemistry: Electrochemical Characterization of Soil Via a Faradaic Approach

Soil is a vital component that holds together the Earth’s ecosystem. It plays an active role in biological events, hydrologic (water) cycle and behaves as a sink-source system for greenhouse agents (gases). More so, soil directly impacts growth of plants, crops and other vegetation. Commercially, soil quality determines the yield and type of farmland-wherein it is essential to have thorough information about soil parameters that is synchronized in terms of geological location (space) and period (time) due to variations associated with global change and land use. Implementation of soil sensors would be fundamentally beneficial to characterize the soil parameters in a local as well as global environmental-impact standpoint. Assessment of these parameters in soil samples in farm setting facilitates quantification and recording of its inherent physical and biochemical characteristics. Sufficient levels of soil nutrients is required for sustainable agricultural practices that typically exists as crop yield, pasture growth, etc. Currently, soil sampling and evaluation methods involve destructive and intrusive approaches to collect and then subsequently test the soil samples. Among this-Combustion method is one of the most widely sought after techniques to look at soil anatomy. Even with the upcoming research breakthroughs in non-destructive approaches like spectroscopy and tomography based models, there is a barrier in-terms of equipment complexity and availability as well as high costs and high logistical overhead. Therefore a sensing based ‘bottom-up’ approach is proposed in this study to probe soil matter and nutrient content. Soils are known to be dynamic systems under natural conditions and implementation of an integrated electrochemical and analytical chemistry methodology offers the added advantage of evaluating soil properties on-demand and in situ. Another main advantage of an electrochemical-analytical approach and the novelty of the proposed platform lies in the ability to profile soil at an interfacial level using a probe system and characterize the matrix in order to provide information in terms of various physico-chemical phenomena occurring at the electrode interface and correlate that to useful data that helps to understand soil fertility and bio-availability of nutrients for plants and other vegetation at the field level. There are a wide number of redox systems present in soil that exist in reduced state under ideal conditions (submerged). Soil is known to contain considerable amounts of these reducing substances as a function of organic matter content. In this work, faradaic measurements are utilized to determine threshold changes in soil electrochemical activity due to presence of various redox active substances present in the matrix. From preliminary data (Fig.1) and literature studies, there seems to be a high-correlation between electrochemical activity and presence of active substances that contribute to the soil-nutrient cycle which in turn differs in terms of various soil types. Therefore, probing these parameters could give further insight as to what in particular in the soil matrix contributes to the overall properties of soil type of a specific region. Results from this experimental question is used to track if confident deductions can be made with respect to constituency of soil matrix, assessing soil type, bulk density (BD), cation exchange capacity (CEC), electrical conductivity (EC), and electrochemical activity as a function of spatial and depth changes among other parameters. Figure 1

Impact of agricultural land conversion on climate change

Climate change and land use conversion are two major global environmental issues. A claim is made that climate change has brought new challenges for global land use, while land use conversion is hardly realized as a major driver for climate change. Using mapping techniques, this study aims to investigate the relationship between climate change and agricultural land conversion (ALC), by which land is converted from agricultural to other uses (e.g., urban areas, national and natural parks, roads, industrial areas, and afforestation projects). CO2 emission is considered as the main impact of climate change, and agricultural land conversion is regarded as the most important global land use. In this study, data are obtained from two databases: the World Bank and the Food and Agriculture Organization (FAO) for the period of 1962–2011. Considering the FAO (2015) classification, the countries are categorized into five different groups (high-income non-OECD, high-income OECD, upper-middle-, lower-middle-, and low-income countries). Economies were divided into several income groups according to 2014 gross national income per capita. The results show that agricultural areas in high-income countries have decreased, while in low- to middle-income countries, they have increased. The highest CO2 emissions can be observed, especially in high-income countries, whereas the lowest CO2 emissions happen in the low- and lower-middle-income countries. The results further show that there is a positive relationship between CO2 emissions and ALC across the world. It can be observed that CO2 emission is increasing where agricultural area is declining. On the contrary, CO2 emission is declining where agricultural area is increasing.

Atmospheric Moisture as a Factor of Land Degradation Neutrality in Forest–Steppe Landscapes

Analysis of data from meteorological observations near the Kursk Biosphere Station, Institute of Geography, Russian Academy of Sciences, for the past 40 years shows a steady increase in temperature against the background of an increasing deficit of precipitation in this area. At the same time, the precipitation is characterized by a high instability and a contrast distribution by year, as well as by an increase in the number and duration of dry periods. Precipitation (in particular, snow accumulation) is an important source of moisture and a significant factor of functioning for forest–steppe landscapes. It is shown that different landscape types can have a two- to fourfold difference in snow accumulation, which plays a decisive role in the formation of moisture reserves. Agrolandscapes are deficient in moisture, which contributes to deeper freezing of soils, as well as to the deterioration of their water permeability and the development of a surface snowmelt runoff from fields, which leads to landscape degradation. The essence of the concept of land degradation neutrality, which mainly concerns the maintenance of global sustainable land use, is to obtain objective parameters for state of landscapes and to assess the proportion of lands exposed to degradation. Atmospheric moisture in forest–steppe landscapes is a significant factor determining their functioning and can be used as a local indicator of the concept.

Computer vision-enhanced selection of geo-tagged photos on social network sites for land cover classification

Land cover maps are key elements for understanding global climate and land use. They are often created by automatically classifying satellite imagery. However, inconsistencies in classification may be introduced inadvertently. Experts can reconcile classification discrepancies by viewing satellite and high-resolution images taken on the ground.We present and evaluate a framework to filter relevant geo-tagged photos from social network sites for land cover classification tasks. Social network sites offer massive amounts of potentially relevant data, but its quality and fitness for research purposes must be verified.Our framework uses computer vision to analyse the content of geo-tagged photos on social network sites to generate descriptive tags. These are used to train artificial neural networks to predict a photo's relevance for land cover classification. We apply our models to four African case studies and their neighbours. The framework has been implemented within Geo-Wiki to fetch relevant photos from Flickr.

Projections of global and UK bioenergy potential from Miscanthus × giganteus—Feedstock yield, carbon cycling and electricity generation in the 21st century

AbstractIn this article, we modify bioenergy model MiscanFor investigating global and UK potentials for Miscanthus × giganteus as a bioenergy resource for carbon capture in the 21st century under the RCP 2.6 climate scenario using SSP2 land use projections. UK bioenergy land projections begin in the 2040s, 60 year average is 0.47 Mega ha rising to 1.9 Mega ha (2090s). Our projections estimate UK energy generation of 0.09 EJ/year (60 year average) and 0.37 EJ/year (2090s), under stable miscanthus yields of 12 t ha−1 year−1. We estimate aggregated UK soil carbon (C) increases of 0.09 Mt C/year (60 year average) and 0.14 Mt C/year (2090s) with C capture plus sequestration rate of 2.8 Mt C/year (60 year average) and 10.49 Mt C/year (2090s). Global bioenergy land use begins in 2010, 90 year average is 0.13 Gha rising to 0.19 Gha by the 2090s, miscanthus projections give a 90 year average energy generation of 16 EJ/year, rising to 26.7 EJ/year by the 2090s. The largest national capabilities for yield, energy and C increase are projected to be Brazil and China. Ninety year average global miscanthus yield of 1 Gt/year will be 1.7 Gt/year by the 2090s. Global soil C sequestration increases less with time, from a century average of 73.6 Mt C/year to 42.9 Mt C/year by the 2090s with C capture plus sequestration rate of 0.54 Gt C/year (60 year average) and 0.81 Gt C/year (2090s). M. giganteus could provide just over 5% of the bioenergy requirement by the 2090s to satisfy the RCP 2.6 SSP2 climate scenario. The choice of global land use data introduces a potential source of error. In reality, multiple bioenergy sources will be used, best suited to local conditions, but results highlight global requirements for development in bioenergy crops, infrastructure and support.

Balancing security, resilience, and sustainability of urban water supply systems in a desirable operating space

The security, resilience, and sustainability of urban water supply systems (UWSS) are challenged by global change pressures, including climate and land use changes, rapid urbanization, and population growth. Building on prior work on UWSS security and resilience, we quantify the sustainability of UWSS based on the performance of local sustainable governance and the size of global water and ecological footprints. We develop a new framework that integrates security, resilience, and sustainability to investigate trade-offs between these three distinct and inter-related dimensions. Security refers to the level of services, resilience is the system’s ability to respond to and recover from shocks, and sustainability refers to local and global impacts, and to the long-term viability of system services. Security and resilience are both relevant at local scale (city and surroundings), while for sustainability cross-scale and -sectoral feedbacks are important. We apply the new framework to seven cities selected from diverse hydro-climatic and socio-economic settings on four continents. We find that UWSS security, resilience, and local sustainability coevolve, while global sustainability correlates negatively with security. Approaching these interdependent goals requires governance strategies that balance the three dimensions within desirable and viable operating spaces. Cities outside these boundaries risk system failure in the short-term, due to lack of security and resilience, or face long-term consequences of unsustainable governance strategies. We discuss these risks in the context of poverty and rigidity traps. Our findings have strong implications for policy-making, strategic management, and for designing systems to operate sustainably at local and global scales.

Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping

The multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.

Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature.

Climate change poses a critical threat to the Pantanal, the largest wetland in the world. Models indicate an increase in the frequency of extreme precipitation events and extended periods of drought. These changes can amplify consequences for Pantanal’s ecological functioning, which has already experienced intensive human modification of its hydrological system and environmental health. The present study analyzed the spatial and temporal dynamics of rainfall and resulting extremes in the Brazilian area of the Upper Paraguay River Basin (UPRB) along with a co-evaluation of the global Sea Surface Temperature data (SST). The predicted results indicate that wet extreme precipitation events will become more frequent in the highlands, while severe and prolonged droughts triggered by warming SSTs in the Northern Hemisphere (North Atlantic and North Pacific oceans) will affect the Pantanal. The linear relations between precipitation with SST of very specific oceanic regions and even from specific oceanic indexes obtained in the present study significantly improve the forecasting capacity, mainly from a resulting reduction to two months of the lead-time between SST warming to concomitant precipitation impacts, and by explaining 80% of Pantanal´s precipitation variation from major oceanic indexes (e.g., ENSO, PDO, NAO, ATL3). Current SST trends will result in inter- and intra-annual flooding dynamic alterations, drastically affecting the Pantanal ecosystem functioning, with consequences for wildlife diversity and distribution. Regarding the foreseeable global climate and land use change scenarios, the results from the present study provide solid evidence that can be used at different decision-making levels (from local to global) for identifying the most appropriate management practices and effectively achieving sustainability of the anthropic activity occurring in the Pantanal.

Development of a PM2.5 Forecasting System Integrating Low-cost Sensors for Ho Chi Minh City, Vietnam

ABSTRACT Air pollution is a serious concern in urban areas, especially cities such as Ho Chi Minh City (HCMC). Because the air quality directly affects people’s health, air quality monitoring is urgently needed. In this study, the models of Weather Research and Forecasting (WRF), Sparse Matrix Operator Kernel Emission (SMOKE), and Community Multiscale Air Quality (CMAQ) were integrated to develop an air quality forecasting system. Drawing input data from transportation and industrial emission inventories, the forecasting system was calibrated and configured using local parameters to deliver hourly forecasts for HCMC. To increase the accuracy of WRF and the meteorological forecasting, the global DEM and land use data were replaced by Lidar data, and land use data were also retrieved from MODIS. Output from the MOZART model served as the boundary conditions for CMAQ, and AOD values reported by the MODIS Aerosol Product were assimilated to enhance the accuracy of the results. A low-cost PM2.5 sensor connected to a LinkIt ONE, a development board for Internet of things (IoT) devices, was employed for calibration and verification. The strong correlation (R2 = 0.8) between the measured and predicted concentrations indicates that the estimates delivered by the proposed forecasting system are consistent with the values obtained via monitoring.

Relative influence of forest and cropland on fluvial transport of soil organic carbon and nitrogen in the Nen River basin, northeastern China

Riverine transport of carbon and nitrogen strongly impacts terrestrial and aquatic ecosystem functions but is itself affected by changes in land cover and land use. China’s Nen River basin has experienced widespread conversion from forest to cropland in the past century, making it representative of similar global land use changes. We assessed organic carbon and nitrogen transport in this basin during 2018 and 2019, using water samples collected at one-hour intervals during the twelve flood events in two sub-basins (the Yalu and Yin rivers) dominated by forests and cropland, respectively. We also assessed dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) concentrations from 19 sampling sites distributed throughout the Nen River basin, in combination with hydrological records, to investigate the impacts of land cover on riverine carbon and nitrogen fluxes. The forested Yalu river basin showed significantly higher DOC content and lower TDN content than Yin river basin. Riverine DOC concentrations during non-flood periods in the forested Yalu river basin were significantly higher than in the cultivated Yin river basin, but riverine TDN concentrations were lower. Throughout the Nen River basin and its sub-basins, riverine TDN content was proportional to cropland area. During the twelve flood events, riverine DOC and TDN varied widely over time in the Yin and Yalu river basins, possibly due to dilution effects of water originating from different land covers within the basins. Our results demonstrate that land cover is the principal cause of changes in riverine DOC and TDN content in this region.

Ecosystem services costs of metal mining and pressures on biomes

Metal mining has significant impacts on the land it uses. With increasing demand for metals, these impacts will continue to intensify. One way to look at land use and related environmental impacts is the concept of ecosystem services (ES), defined as the benefits people derive from services provided by ecosystems. This paper estimates the costs of the reduction of ES due to metal mining`s global land use by analysing four key metal ores – bauxite (aluminium), copper, gold and iron, and by doing so, provides also novel information from which biomes those metals are extracted.The overall ES cost caused by metal mining is estimated at about USD 5.4 billion/year (2016), with about two thirds in forested areas. If added to prices, it would lead to increases of between 0.8 % and 7.9 % for the four commodities studied.The authors do not understand ES valuation as a market-based, stand-alone tool to lower the land impact of metal mining. Other policy tools would have to play a leading role, such as zoning regulations, environmental minimum standards or closure legislation. However, it would be a useful support for such policy tools in all stages of mining where land use aspects play a role.

An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems

Representation of ecosystems in protected area networks and conservation strategies is a core principle of global conservation priority setting approaches and a commitment in Aichi Target 11 of the Convention on Biological Diversity. The 2030 Sustainable Development Goals (SDGs) explicitly call for the conservation of terrestrial, freshwater, and marine ecosystems. Accurate ecosystem distribution maps are required to assess representation of ecosystems in protected areas, but standardized, high spatial resolution, and globally comprehensive ecosystem maps have heretofore been lacking. While macroscale global ecoregions maps have been used in global conservation priority setting exercises, they do not identify distinct localized ecosystems at the occurrence (patch) level, and instead describe large ecologically meaningful areas within which additional conservation planning and management are necessary. We describe a new set of maps of globally consistent climate regions and ecosystems at a much finer spatial resolution (250 m) than existing ecological regionalizations. We then describe a global gap analysis of the representation of these ecosystems in protected areas. The new map of terrestrial World Ecosystems was derived from the objective development and integration of 1) global temperature domains, 2) global moisture domains, 3) global landforms, and 4) 2015 global vegetation and land use. These new terrestrial World Ecosystems do not include either freshwater or marine ecosystems, but analog products for the freshwater and marine domains are in development. A total of 431 World Ecosystems were identified, and of these a total of 278 units were natural or semi-natural vegetation/environment combinations, including different kinds of forestlands, shrublands, grasslands, bare areas, and ice/snow regions. The remaining classes were different kinds of croplands and settlements. Of the 278 natural and semi-natural classes, 9 were not represented in global protected areas with a strict biodiversity conservation management objective (IUCN management categories I-IV), and an additional 206 were less than 8.5% protected (half way to the 17% Aichi Target 11 goal). Forty four classes were between 8.5% and 17% protected (more than half way towards the Aichi 17% target), and only 19 classes exceeded the 17% Aichi target. However, when all protected areas (IUCN management categories I-VI plus protected areas with no IUCN designation) were included in a separate global gap analysis, representation of ecosystems increases substantially, with a third of the ecosystems exceeding the 17% Aichi target, and another third between 8.5% and 17%. The overall protection (representation) of global ecosystems in protected areas is considerably less when assessed using only strictly conserved protected areas, and more if all protected areas are included in the analysis. Protected area effectiveness should be included in further evaluations of global ecosystem protection. The ecosystems with the highest representation in protected areas were often bare or sparsely vegetated and found in inhospitable environments (e.g. cold mountains, deserts), and the eight most protected ecosystems were all snow and ice ecosystems. In addition to the global gap analysis of World Ecosystems in protected areas, we report on the representation results for the ecosystems in each biogeographic realm (Neotropical, Nearctic, Afrotropical, Palearctic, Indomalayan, Australasian, and Oceania).

Climate and land use change effects on soil erosion in two small agricultural catchment systems Fugnitz – Austria, Can Revull – Spain

Soil erosion represents one of the most important processes of land degradation in the world and is considered a serious threat to the provision of food supply, to human health and to terrestrial ecosystems. In Europe, soil erosion by water and tillage is responsible for the loss of fertile topsoil and therefore productive land. Under Global Change scenarios climate and land use are expected to impact soil loss and sediment discharge rates distinctly in contrasting climatic regions, further influenced by tillage practices. Soil erosion modeling is a valuable tool to estimate future changes and elucidate opportunities to mitigate future threats to soil loss and crop yield, ultimately leading to the development of Best Management Practices (BMPs). In this study, future change of soil erosion processes under the IPCC Representative Concentration Pathways RCP2.6 and RCP6.0, as well as a conventional tillage (CT) and a reduced tillage (RT) practice are investigated in two small agricultural catchments in Europe under contrasting climate; Can Revull in Spain and Fugnitz in Austria. We applied GeoWEPP, the Geospatial Interface for the Water Erosion Prediction Project, to model these two agricultural catchments at a fine spatial resolution. We demonstrate that tillage practice, precipitation and runoff are driving factors for soil erosion at both locations. Furthermore, we illustrate that tillage practices have a greater effect on soil erosion than climate change scenarios. RT could reduce soil erosion by more than 75% compared to CT practices. Under RCP6.0, future changes in runoff, hillslope soil loss and sediment discharge would be greater compared to RCP2.6, with different responses depending on the investigated climatic region. Linking soil erosion models on a fine spatial scale and with different management practices to downscaled global circulation models, can provide valuable input for the development of future BMPs to reduce soil loss in agricultural landscapes.

Evaluation of global land use/land cover products for hydrologic simulation in the Upper Yom River Basin, Thailand

Recently, the availability of global landuse and land cover data has made hydrological simulation studies possible in data scarce regions. However, the suitability of these products as inputs in the hydrological modeling has not been investigated in detail. Therefore, this study aims to evaluate the use of global landuse and land cover products to simulate the hydrology of the Upper Yom River Basin located in the Northern part of Thailand. Three types of global landuse and landcover products i.e. ESACCI, MCD12Q1, GLC2000 were compared with the landuse product from Land Development Department (LDD) of Thailand. All of these products were used as input in hydrological model, Soil and Water Assessment Tool (SWAT), and performance of the model was compared to simulate hydrological regime including high flow, low flow and seasonal discharge at the outlet and upstream of the basin. The results show that the performance of the hydrological model in simulating the discharge at the basin outlet is better than in the upstream areas while using all types of landuse and land cover data. The model well simulated the annual discharge, wet-season discharge and base flow while using landuse and land cover products of ESACCI and MCD12Q1. Similarly, the high flow and dry-season discharge is well simulated while using MCD12Q1 landuse and landcover products compared to other three products. The results of this study is useful in selecting landuse and land cover products in simulating hydrology for water resources planning and management in data scarce regions.

Governing by models: Exploring the technopolitics of the (in)visilibities of land

Achieving food security at a global scale while protecting the environment, as envisioned in the Sustainable Development Goals, will require a complex process of collaboration and the integration of analyses at multiple scales. Agricultural and land use models are increasingly being used to bridge the global/local divide, particularly as a means to envision future land access, use and management in different agricultural production systems. This special issue contributes to our understanding of global modeling, governance and land use change. Specifically, it provides insights into the question of: how do the ways of knowing and governing affect the politics of environmental protection and agriculture? The papers in this special issue bring reflexive questions of knowledge production, public policy and civic engagement into the epistemic spaces of scientific and technological development, both as a means to improve the way we model and to understand the implications of governing by models.

Range size predicts the risk of local extinction from habitat loss

AbstractAimThe geographical range size of species is a strong predictor of vulnerability to global extinction. However, it remains unclear whether range size is also a good predictor of extinction risk at much smaller scales. Here, we reconstruct biodiversity time series to ask whether species with small ranges have declined preferentially with habitat loss at the local scale.LocationGlobal.Time period1500–2015.Major taxa studiedVascular plants.MethodWe collated 70 million occurrence records of 180,000 species of vascular plants from three biodiversity data‐sharing networks. We combined these with data on changes in global land use to find locations (0.25° grid cells) with biodiversity data before and after loss of natural habitat. First, we examined the change in community median range size before and after habitat loss. Second, we quantified the probabilities of local persistence of small‐ and large‐ranged species at different levels of habitat loss.ResultsCommunity median range size was higher after habitat loss, on average. Species with small ranges had lower probabilities of persistence than species with large ranges at already moderate habitat loss (≤50%).Main conclusionsThe loss of natural habitat has a differential effect on the local extinction risk of species with different range sizes. Given that species with small ranges decline preferentially, habitat loss can create a linkage between temporal and spatial species turnover, in that changes within communities decrease compositional differences between communities.

Spatial Sequential Modeling and Predication of Global Land Use and Land Cover Changes by Integrating a Global Change Assessment Model and Cellular Automata

AbstractCharacterizing land use and land cover change (LUCC) is critical for understanding the interaction between human activities and global environmental changes, such as in biological diversity and the carbon cycle. Both natural cycles and human activities can be better examined with more accurate sources of land use data with higher spatial resolution. More importantly, it is crucial to consider spatial heterogeneity to simulate future changes in LUCC. In this paper, a modeling strategy (hereinafter referred to as GCAM‐CA) that combines a global change assessment model (GCAM) with cellular automata (CA) is proposed. This modeling strategy is designed to sequentially spatialize global LUCCs with 1‐km spatial resolution and 5‐year temporal resolution from 2010 to 2100. The GCAM model is employed to predict the land use and land cover area demands for 283 world regions, which are divided by intersecting 32 geopolitical and socioeconomic regions and 18 agroecological zones. The spatialization rules of CA is trained separately for each world region to distinguish global land use and land cover types. The different spatialization rules and trends in land use and land cover demand for each of the 283 regions reflect the spatial heterogeneity in the GCAM‐CA model. We implement and validate the model for the simulation from 2000 to 2010. Next, the model is used to simulate three future scenarios, REF, G26, and G45, demonstrating that the GCAM‐CA model is effective for future global‐scale simulation of LUCCs. GCAM‐CA is freely available at the open geographic modeling and simulation platform (OpenGMS, http://geomodeling.njnu.edu.cn/GCAM‐CA.jsp).

Largely underestimated carbon emission from land use and land cover change in the conterminous United States.

Carbon (C) emission and uptake due to land use and land cover change (LULCC) are the most uncertain term in the global carbon budget primarily due to limited LULCC data and inadequate model capability (e.g., underrepresented agricultural managements). We take the commonly used FAOSTAT-based global Land Use Harmonization data (LUH2) and a new high-resolution multisource harmonized national LULCC database (YLmap) to drive a land ecosystem model (DLEM) in the conterminous United States. We found that recent cropland abandonment and forest recovery may have been overestimated in the LUH2 data derived from national statistics, causing previously reported C emissions from land use have been underestimated due to the definition of cropland and aggregated LULCC signals at coarse resolution. This overestimation leads to a strong C sink (30.3±2.5TgC/year) in model simulations driven by LUH2 in the United States during the 1980-2016 period, while we find a moderate C source (13.6±3.5TgC/year) when using YLmap. This divergence implies that previous C budget analyses based on the global LUH2 dataset have underestimated C emission in the United States owing to the delineation of suitable cropland and aggregated land conversion signals at coarse resolution which YLmap overcomes. Thus, to obtain more accurate quantification of LULCC-induced C emission and better serve global C budget accounting, it is urgently needed to develop fine-scale country-specific LULCC data to characterize the details of land conversion.