Land Use And Land Cover Change Data Research Articles

Dynamic Estimation of Mangrove Carbon Storage in Hainan Island Based on the InVEST-PLUS Model

Mangrove ecosystems are pivotal to the global carbon budget. However, there is still a dearth of research addressing the impact of regional mangrove land use and land cover change (LUCC) on carbon sequestration and its associated spatial distribution patterns. To investigate the impact of different development scenarios on the carbon storage capacity of mangrove ecosystems, we focused on Hainan Island. We used LUCC data from 2010 to 2020 from mangrove-inhabited regions. The Markov-PLUS model was applied to predict the spatiotemporal dynamics of mangrove coverage under the natural increase scenario (NIS) and the mangrove protection scenario (MPS) over the next 40 years. Carbon storage was estimated using the InVEST model based on field-measured carbon density data. The outcomes show the following: (1) The Markov-PLUS model, with an overall accuracy of 0.88 and a Kappa coefficient of 0.82, is suitable for predicting mangrove distribution patterns on Hainan Island. (2) Environmental factors were the main drivers of historical mangrove changes on Hainan Island, explaining 54% of the variance, with elevation, temperature, and precipitation each contributing over 13%. (3) From 2025 to 2065, the mangrove area on Hainan Island is projected to increase by approximately 12,505.68 ha, mainly through conversions from forest land (12.73% under NIS and 12.37% under MPS) and agricultural land (39.72% under NIS and 34.53% under MPS). (4) The carbon storage increment within Hainan Island’s mangroves is projected at 2.71 TgC over the whole island, with notable increases expected in the eastern, northern, and northwestern regions, and modest gains in other areas. In this study, we comprehensively investigated the spatiotemporal dynamics and future trends of carbon storage in the mangroves of Hainan Island, offering invaluable guidance for the long-term management of mangrove ecosystems and the realization of carbon neutrality goals by 2060.

Spatiotemporal Characteristics of Land Cover Change in the Yellow River Basin over the Past Millennium

Investigating the ecological and environmental impacts stemming from historical land use and land cover change (LUCC) holds paramount importance in systematically comprehending the fundamental human-land relationship, a pivotal focus within geographical research. The Yellow River Basin (YRB), often referred to as the cradle of Chinese civilization, ranks as the fifth-largest river basin globally. Early inhabitants made significant alterations to the landscape, resulting in substantial damage to natural vegetation, giving rise to prominent regional ecological challenges. By now, the examination of historical LUCC in the YRB over the past millennium remains in the qualitative research stage, primarily due to the limited availability of high-confidence gridded historical LUCC data. This study aims to advance the current historical LUCC research in the YRB from primarily qualitative analysis to an exploration incorporating timing, positioning, and quantification. Based on reconstructed historical cropland, forest, and grassland grid data of 10 km × 10 km from 1000 AD to 2000 AD, the degree of cropland development and the depletion of forests and grasslands were calculated, respectively. Then, the kernel density method was employed for spatiotemporal analysis and interpretation of dynamic changes in land cover. Subsequently, a cartographic visualization depicting the migration trajectories of the land cover gravity centers was generated, allowing for an assessment of the distance and direction of the centroids’ movement of cropland, forest, and grassland. The results indicate that the cropland coverage in the YRB escalated from the initial 11.65% to 29.97%, while the forest and grassland coverage dropped from 63.36% to 44.49%. The distribution of cultivated land continually expanded outward from the southeast of the Loess Plateau and the southwest of the North China Plain. All three types of land cover experienced a westward shift in their gravity centers between 1000 and 2000 AD. Besides the population growth and technological advancements, the regime shifts induced by wars, along with land use policies in distinct periods, always served as the predominant factors influencing the conversion between different land covers. This research will present a paradigmatic regional case study contributing to the investigation of historical changes in land use and land cover. Additionally, it will offer historical perspectives beneficial for the advancement of China’s objectives in “Ecological Conservation and High-Quality Development of the Yellow River Basin”.

Uncertainty in land use obscures global soil organic carbon stock estimates

The impact of land use and land cover change (LULCC) on soil organic carbon (SOC) stock is one of the most uncertain items in estimating the global C budget. Despite the improvements in satellite monitoring techniques and inventory data in recent decades, the uncertainty in modeled LULCC-induced SOC changes stemming from the choice of land use datasets remains largely unknown. Using a process-based model, the Dynamic Land Ecosystem Model (DLEM), we investigated global SOC changes during 1900–2018 driven by six LULCC datasets (i.e., LUH2-GCB2019, ESA CCI-LC, MODIS, GLASS-GLC, HH, and RF), which were generated by varied data sources and methodologies. The simulated global SOC stock was negatively affected by land conversions in all the LULCC datasets; however, the corresponding SOC loss was highly different ranging from 33.11 Pg C to 106.20 Pg C. Such significant differences in the global SOC stock estimates due to LULCC uncertainty were mainly located in boreal and temperate forests of the northern high latitudes and were most likely attributed to the LULCC-induced changes in vegetation net primary production. Meanwhile, regions exhibiting large divergence in relative changes of LULCC-induced SOC loss were mainly located in the low latitudes. When considering the interactive effects of LULCC with other environmental factors, the simulated SOC showed divergent trends, increasing in MODIS-, ESA CCI-LC-, and GLASS-GLC-based estimations, but decreasing in LUH2-GCB2019-, HH-, and RF-based estimations. These results highlight the importance of the accuracy of LULCC data in determining the global carbon budget. Future efforts are required for harmonizing satellite observations and inventory data both spatially and temporally to better represent the land conversion processes in terrestrial ecosystem modeling.

Soil erosion variations along land use and land cover dynamics in Matenchose watershed, Rift Valley Basin, Southern Ethiopia

AbstractThe global community recognizes land use and land cover change (LULC) as a primary cause of ecological modification that has a considerable impact on natural resources, particularly soil and water resources. The aim of this research was to investigate land use change's influences on soil erosion in the Matenchose watershed of Ethiopia in 1991, 2003, and 2020. The maximum likelihood classification (MLC) method was used in the study for supervised image analysis. Soil erosion was estimated using the geographic information system (GIS), remote sensing, and the Revised Universal Soil Loss Equation (RUSLE) model. According to the LULC data from 1991, the watershed was mostly covered by grassland (35%), while in 2003 and 2020, it was typically enclosed by cultivated land (36%) and (52%), respectively. The watershed's mean annual soil erosion rate grew significantly from 13 t/ha in 1991 to 18 t/ha in 2003 to 21 t/ha in 2020. Based on the current soil loss rate result, the Matenchose watershed was divided into five priority groups for soil management practices. In contrast, the watershed is made up of 2052 ha (21%) of areas with high to very high erosion risk, 3304 ha (33%) of areas with moderate erosion risk, and 2866 ha (29%) of areas with severe erosion risk. Based on the average annual rate of soil erosion, several vital subwatersheds were identified for potential future land management‐related actions. Over the 29 years, the area of grassland and forest decreased while agricultural and settlement areas expanded, and they contributed to the enhanced hazards of soil erosion. Particularly vulnerable to erosion are the watershed's hilly and steeper areas. The identified subwatersheds that are most at risk of erosion should be given priority for upcoming LULC initiatives, proper participatory watershed planning and management, and measures to conserve soil and water to preserve the Matenchose watershed's soil resources.

A new estimation of carbon emissions from land use and land cover change in China over the past 300 years

Scientific estimation of carbon emissions induced by historical land use and land cover change (LUCC) can improve the accuracy of terrestrial ecosystem carbon budget estimates and deepen understanding of the future carbon-sink potential of terrestrial ecosystems. The present study, using historical-document-based data for provincial cropland, forest, and grassland area in China, and experimental-data-based information for provincial vegetation and soil organic carbon density, re-estimates China's LUCC-induced carbon emissions for 1700–1980 using a bookkeeping model in which we updated tabulated functions for carbon losses and gains. The past 300 years have witnessed a dramatic LUCC in China. The cropland area has increased by 67.11 million ha, while the forest and grassland areas have decreased by 127.96 million ha and 16.72 million ha, respectively. Accordingly, the net carbon emissions for 1700–1980 are 6.17–12.35 Pg C, with 8.55 Pg C in the moderate scenario. Among the contributing factors, deforestation was the largest carbon source, accounting for over 90 % of the total carbon emissions. According to our estimates, over 70 % of carbon emissions were caused by harvesting wood, while <30 % were from converting forest and grassland to cropland. Spatially, for the whole period, carbon emissions in southwestern China (Chuan-Yu, Yunnan, and Guangxi), northeastern China (Liaoning, Jilin, and Heilongjiang), and parts of northwestern China (Gan-Ning, Qinghai, and Xinjiang) were as high as 6.03 Pg C, accounting for 70 % of the total carbon emissions. Extending previous studies, we updated the historical LUCC data, carbon density data, and tabulated functions for carbon losses and gains. The estimation results objectively reveal the historical spatiotemporal changes in LUCC-induced emissions.

Land Use and Land Cover Change Assessment and Future Predictions in the Matenchose Watershed, Rift Valley Basin, Using CA-Markov Simulation

Land use and land cover change (LULC) is known worldwide as a key factor of environmental modification that significantly affects natural resources. The aim of this study was to evaluate the dynamics of land use and land cover in the Matenchose watershed from the years 1991, 2003, and 2020, and future prediction of land use changes for 2050. Landsat TM for 1991, ETM+ for 2003, and Landsat-8 OLI were used for LULC classification for 2020. A supervised image sorting method exhausting a maximum likelihood classification system was used, with the application using ERDAS Imagine software. Depending on the classified LULC, the future LULC 2050 was predicted using CA-Markov and Land Change Models by considering the different drivers of LULC dynamics. The 1991 LULC data showed that the watershed was predominantly covered by grassland (35%), and the 2003 and 2020 LULC data showed that the watershed was predominantly covered by cultivated land (36% and 52%, respectively). The predicted results showed that cultivated land and settlement increased by 6.36% and 6.53%, respectively, while forestland and grassland decreased by 63.76% and 22.325, respectively, from 2020 to 2050. Conversion of other LULC categories to cultivated land was most detrimental to the increase in soil erosion, while forest and grassland were paramount in reducing soil loss. The concept that population expansion and relocation have led to an increase in agricultural land and forested areas was further reinforced by the findings of key informant interviews. This study result might help appropriate decision making and improve land use policies in land management options.

Identifying Land-Use Related Potential Disaster Risk Drivers in the Ayeyarwady Delta (Myanmar) during the Last 50 Years (1974–2021) Using a Hybrid Ensemble Learning Model

Land-use and land-cover change (LULCC) dynamics significantly impact deltas, which are among the world’s most valuable but also vulnerable habitats. Non-risk-oriented LULCCs can act as disaster risk drivers by increasing levels of exposure and vulnerability or by reducing capacity. Making thematically detailed long-term LULCC data available is crucial to improving understanding of those dynamics interlinked at different spatiotemporal scales. For the Ayeyarwady Delta, one of the least studied mega-deltas, such comprehensive information is still lacking. This study used 50 Landsat and Sentinel-1A images spanning five decades from 1974 to 2021 in five-year intervals. A hybrid ensemble model consisting of six machine-learning classifiers was employed to generate land-cover maps from the images, achieving accuracies of about 90%. The major identified potential risk-relevant LULCC dynamics include urban growth towards low-lying areas, mangrove deforestation, and the expansion of irrigated agricultural areas and cultivated aquatic surfaces. The novel area-wide LULCC products achieved through the analyses provide a basis to support future risk-sensitive development decisions and can be used for regionally adapted disaster risk management plans and models. Developed with freely available data and open-source software, they hold great potential to increase research activity in the Ayeyarwady Delta and will be shared upon request.

Assessing Impact of Land Use Change on the Ecosystem Service Value in Yinchuan City from 1980 to 2018

Accurate assessment and response analysis of land use and land cover change (LUCC), and ecosystem service values (ESV), are critical to regional ecological security and economic development. There is a lack of detailed reports on the impact of LUCC on the temporal and spatial evolution of ESV in Yinchuan City, which is inconsistent with the pilot urban design policy. This paper, using the LUCC data of Yinchuan City from 1980 to 2018, calculated the ESV, analyzed the temporal and spatial patterns of LUCC and ESV, and discussed the response of ESV to LUCC. The results show that, from 1980 to 2018, the building land increased significantly in Yinchuan City, as did the cultivated land. Meanwhile, grassland and bare land decreased, while forest and the water body remained stable. The spatial connectivity of the building land showed regular improvements, while the urban landscape developed in a regular and balanced direction. During the study period, the total ESV of Yinchuan City decreased by 0.75 × 109 yuan. This was due to the decrease in grasslands and the increase in building area. The supply, regulation and support of three types of services have a high correlation with different land types. The prosperity and progress of culture reduces the ESV value of cultivated land to some extent, while the change in land use type leads to the significant loss of ESV in Yinchuan City.

Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

Substantially Greater Carbon Emissions Estimated Based on Annual Land-Use Transition Data

Quantifying land-use and land-cover change (LULCC) effects on carbon sources and sinks has been very challenging because of the availability and quality of LULCC data. As the largest estuary in the United States, Chesapeake Bay is a rapidly changing region and is affected by human activities. A new annual land-use and land-cover (LULC) data product developed by the U.S. Geological Survey Land Change Monitoring and Analysis Program (LCMAP) from 2001 to 2011 was analyzed for transitions between agricultural land, developed land, grassland, forest land and wetland. The Land Use and Carbon Scenario Simulator was used to simulate effects of LULCC and ecosystem disturbance in the south of the Chesapeake Bay Watershed (CBW) on carbon storage and fluxes, with carbon parameters derived from the Integrated Biosphere Simulator. We found that during the study period: (1) areas of forest land, disturbed land, agricultural land and wetland decreased by 90, 82, 57, and 65 km2, respectively, but developed lands gained 293 km2 (29 km2 annually); (2) total ecosystem carbon stock in the CBW increased by 13 Tg C from 2001 to 2011, mainly due to carbon sequestration of the forest ecosystem; (3) carbon loss was primarily attributed to urbanization (0.224 Tg C·yr−1) and agricultural expansion (0.046 Tg C·yr−1); and (4) estimated carbon emissions and harvest wood products were greater when estimated with the annual LULC input. We conclude that a dense time series of LULCC, such as that of the LCMAP program, may provide a more accurate accounting of the effects of land use change on ecosystem carbon, which is critical to understanding long-term ecosystem carbon dynamics.

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.

The spatial association of ecosystem services with land use and land cover change at the county level in China, 1995–2015

Ecosystem services are increasingly affected by land use and land cover change (LULCC) in China. However, the spatial association between LULCC and ecosystem services remains unclear, thus constraining effective land use and ecosystem conservation policymaking. This study attempts to fill this gap by examining the association between ecosystem services intensity (ESI) and LULCC from a spatial perspective at the county level in China. The adapted benefit transfer method was employed to measure the ESI based on LULCC data. LULCC was characterized and measured by single land use dynamic degree, integrated land use dynamic degree, land use intensity, and land use diversity. The results indicated that ESI levels in China experienced a continuously decreasing trend from 1995 to 2015, especially in large metropolitan areas. The relationship between ESI and LULCC exhibited both significant spatial dependence and heterogeneity. Overall, ESI and land use intensity had statistically significant negative associations. However, the association between ESI and LULCC varied greatly over space and in different time periods. The results have important implications for future delineation of ecological conservation priority zones, sustainable development of national land, and the integration of ESI into landscape planning.

Land Use and Land Cover Changes, and Environment and Risk Evaluation of Dujiangyan City (SW China) Using Remote Sensing and GIS Techniques

Understanding of the Land Use and Land Cover (LULC) change, its transitions and Landscape risk (LR) evaluation in earthquake-affected areas is important for planning and urban sustainability. In the present study, we have considered Dujiangyan City and its Environs (DCEN), a seismic-prone area close to the 2008 Wenchuan earthquake (8.0 Mw) during 2007–2018. Five different multi-temporal data sets for the years 2007, 2008, 2010, 2015, and 2018 were considered for LULC mapping, followed by the maximum likelihood supervised classification technique. The individual LULC maps were further used in four time periods, i.e., 2007–2018, 2008–2018, 2010–2018, and 2015–2018, to evaluate the Land Use and Land Cover Transitions (LULCT) using combined remote sensing and GIS (Geographical Information System). Furthermore, multi-criteria evaluation (MCE) techniques were applied for LR mapping. The results of the LULC change data indicate that built-up, agricultural area, and forest cover are the prime categories that had been changed by the natural and anthropogenic activities. LULCT, along with multi-parameters, are suggested to avoid development in fault-existing areas that are seismically vulnerable for future landscape planning in a sustainable manner.

Effects of contemporary land-use and land-cover change on the carbon balance of terrestrial ecosystems in the United States

Changes in land use and land cover (LULC) can have profound effects on terrestrial carbon dynamics, yet their effects on the global carbon budget remain uncertain. While land change impacts on ecosystem carbon dynamics have been the focus of numerous studies, few efforts have been based on observational data incorporating multiple ecosystem types spanning large geographic areas over long time horizons. In this study we use a variety of synoptic-scale remote sensing data to estimate the effect of LULC changes associated with urbanization, agricultural expansion and contraction, forest harvest, and wildfire on the carbon balance of terrestrial ecosystems (forest, grasslands, shrublands, and agriculture) in the conterminous United States (i.e. excluding Alaska and Hawaii) between 1973 and 2010. We estimate large net declines in the area of agriculture and forest, along with relatively small increases in grasslands and shrublands. The largest net change in any class was an estimated gain of 114 865 km2 of developed lands, an average rate of 3282 km2 yr−1. On average, US ecosystems sequestered carbon at an annual rate of 254 Tg C yr−1. In forest lands, the net sink declined by 35% over the study period, largely a result of land-use legacy, increasing disturbances, and reductions in forest area due to land use conversion. Uncertainty in LULC change data contributed to a ~16% margin of error in the annual carbon sink estimate prior to 1985 (approximately ±40 Tg C yr−1). Improvements in LULC and disturbance mapping starting in the mid-1980s reduced this uncertainty by ~50% after 1985. We conclude that changes in LULC are a critical component to understanding ecosystem carbon dynamics, and continued improvements in detection, quantification, and attribution of change have the potential to significantly reduce current uncertainties.

Cellular automata and Markov Chain (CA_Markov) model-based predictions of future land use and land cover scenarios (2015–2033) in Raya, northern Ethiopia

Little is known about the future land use and land cover (LULC) type in some parts of Ethiopia, but not in the study area. This study aims to predict and analyze the future scenarios of LULC (2015–2033) using cellular automata and Markov Chain model (CA_Markov) by taking into consideration the physical and socio-economic drivers of LULC dynamics. The historical LULC change data of 1984–1995, 1995–2015, and 1984–2015 were used as a baseline. Both transition rules and transition area matrix for the period 1984–1995, 1995–2015, and 1984–2015 were produced quantitatively using the Markov chain model. After that, the physical and socio-economic factors were standardized using fuzzy and then Multi-Criteria Evaluation (MCE) was used to produce the transition suitability image. The CA_Markov model was then applied as a standard contiguity filter of 5 × 5 to predict the 2033 LULC condition using the TerrSet Geospatial Modeling and Monitoring System software. The result indicated that forestland are predicted to increase by 108 sq km (44.5%), shrub/bush lands 710 sq km (20%), built-up area 286.2 sq km (48.3%), and grasslands 31 sq km (15%), respectively. However, significant reductions (losses) in a water body (Wb) 5.2 sq km (11.2%), croplands (Cl) 78.9 sq km (1.3%), barren lands (Bl) 800 sq km (27.4%), and floodplain area (Fp) 251.68 sq km (33.7%), respectively. Furthermore, the Pearson correlation result between the historical and predicted LULC type indicated that there are positive, strongly correlated, and are statistically significant relationships (r = 0.981, p = 0.000). The increase in forest land and reduction in barren and flood plain may benefit the study area. However, the decrease in the water body may contribute to the severity of drought in the area. This study may help to use as useful information to foster better decisions and improve policies in land use within the framework of sustainable land use planning system.

Current challenges of implementing anthropogenic land-use and land-cover change in models contributing to climate change assessments

Abstract. Land-use and land-cover change (LULCC) represents one of the key drivers of global environmental change. However, the processes and drivers of anthropogenic land-use activity are still overly simplistically implemented in terrestrial biosphere models (TBMs). The published results of these models are used in major assessments of processes and impacts of global environmental change, such as the reports of the Intergovernmental Panel on Climate Change (IPCC). Fully coupled models of climate, land use and biogeochemical cycles to explore land use–climate interactions across spatial scales are currently not available. Instead, information on land use is provided as exogenous data from the land-use change modules of integrated assessment models (IAMs) to TBMs. In this article, we discuss, based on literature review and illustrative analysis of empirical and modeled LULCC data, three major challenges of this current LULCC representation and their implications for land use–climate interaction studies: (I) provision of consistent, harmonized, land-use time series spanning from historical reconstructions to future projections while accounting for uncertainties associated with different land-use modeling approaches, (II) accounting for sub-grid processes and bidirectional changes (gross changes) across spatial scales, and (III) the allocation strategy of independent land-use data at the grid cell level in TBMs. We discuss the factors that hamper the development of improved land-use representation, which sufficiently accounts for uncertainties in the land-use modeling process. We propose that LULCC data-provider and user communities should engage in the joint development and evaluation of enhanced LULCC time series, which account for the diversity of LULCC modeling and increasingly include empirically based information about sub-grid processes and land-use transition trajectories, to improve the representation of land use in TBMs. Moreover, we suggest concentrating on the development of integrated modeling frameworks that may provide further understanding of possible land–climate–society feedbacks.

Variability and predictability of West African monsoon on seasonal and decadal scales

Clim Dyn (2016) 47:3391–3392 DOI 10.1007/s00382-016-3429-4 EDITORIAL Variability and predictability of West African monsoon on seasonal and decadal scales Yongkang Xue 1 · Serge Janicot 2 · William K.‑M. Lau 3 Published online: 26 November 2016 © Springer-Verlag Berlin Heidelberg 2016 Recent observational evidence supports the notion that there are strong decadal climate variabilities in the Sahel and sur- rounding areas from the 1950s to the 2000s, not only in pre- cipitation, but also in sea surface temperature (SST), vegeta- tion cover, land use and land cover change (LULCC), and aerosol loading and spatial distributions (Xue et al. 2016; Boone et al. 2016; Gu et al. 2016). In the 2010 Climate Dynamics Special Issue “West African Monsoon and its Modeling,” the skills of general circulation models (GCMs) and regional climate models (RCMs) in properly simulating the West African monsoon (WAM) were extensively evalu- ated, and possible causes that contribute to model deficien- cies were investigated. While these studies provided basic understanding of the seasonal variations of WAM climatol- ogy, understanding of the complex interactions of WAM variability associated with SST, land surface, and aerosol forcings at different scales, particularly with respect to sea- sonal to decadal scale forcing, is still lacking. This special issue “Decadal variability of the West Afri- can monsoon, external surface forcings, and their mode- ling” aims to provide the latest research on the main WAM dynamic and physical processes, model predictability, and possible interactions between the WAM and external forc- ings at seasonal to decadal scales. The main themes of this * Yongkang Xue yxue@geog.ucla.edu Department of Geography, Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, USA Laboratoire Atmospheres Milieux Observations Spatiales, Sorbonne-Universites, CNRS-UVSQ-UPMC, Paris, France Earth System Science Interdisciplinary Center, Joint Global Change Research Institute, University of Maryland, College Park, MD, USA special issue are: (1) intercomparison and evaluation of skills of GCMs and RCMs in simulating seasonal and dec- adal variability of WAM rainfall and relevant processes, and its association with SST, LULCC, and aerosol processes, as well as identification of major challenges, deficiencies, and uncertainties that remain in modeling WAM variabil- ity; (2) representations of major WAM characteristics and possible underlying physical and dynamic mechanisms; (3) application of observational and reanalyses data as a path- way for model physics evaluation and improvement, and in understanding the WAM variability; and (4) evaluation of RCM downscaling ability in terms of WAM decadal vari- ability. The papers in the special issue are mainly based on research conducted in conjunction with the second phase of the international West African Monsoon Modeling and Evaluation (WAMME II) project and the African Multidis- ciplinary Monsoon Analysis (AMMA) project. Most papers in this issue are from the WAMME II project focusing on the interactions between the WAM and external forcings. In the WAMME II experiment, realistic but ideal- ized forcings (setting to maximum forcing amplitude) are imposed to examine WAM responses in the WAMME II mod- els. Among them, Xue et al. (2016) investigate relative effects of the global and several major individual ocean basins on the WAM decadal variability and compare the SST’s role and the LULCC effect in initiating and modulating the decadal WAM variability and the 1980s Sahel drought. Boone et al. (2016) use recently available LULCC data to evaluate the sensitiv- ity of WAM seasonal variability to land surface processes and physical mechanisms involved. In addition to multi-model experiments, some of the individual models’ results are also presented. Using the aerosol data from the Goddard Chemis- try Aerosol Radiation and Transport (GOCART) model, Gu et al. (2016) compare aerosol effects over the WAM region and the Asian monsoon regions.

Downscaling land use and land cover from the Global Change Assessment Model for coupling with Earth system models

Abstract. The Global Change Assessment Model (GCAM) is a global integrated assessment model used to project future societal and environmental scenarios, based on economic modeling and on a detailed representation of food and energy production systems. The terrestrial module in GCAM represents agricultural activities and ecosystems dynamics at the subregional scale, and must be downscaled to be used for impact assessments in gridded models (e.g., climate models). In this study, we present the downscaling algorithm of the GCAM model, which generates gridded time series of global land use and land cover (LULC) from any GCAM scenario. The downscaling is based on a number of user-defined rules and drivers, including transition priorities (e.g., crop expansion preferentially into grasslands rather than forests) and spatial constraints (e.g., nutrient availability). The default parameterization is evaluated using historical LULC change data, and a sensitivity experiment provides insights on the most critical parameters and how their influence changes regionally and in time. Finally, a reference scenario and a climate mitigation scenario are downscaled to illustrate the gridded land use outcomes of different policies on agricultural expansion and forest management. Several features of the downscaling can be modified by providing new input data or changing the parameterization, without any edits to the code. Those features include spatial resolution as well as the number and type of land classes being downscaled, thereby providing flexibility to adapt GCAM LULC scenarios to the requirements of a wide range of models and applications. The downscaling system is version controlled and freely available.

Land cover change and carbon emissions over 100years in an African biodiversity hotspot.

Agricultural expansion has resulted in both land use and land cover change (LULCC) across the tropics. However, the spatial and temporal patterns of such change and their resulting impacts are poorly understood, particularly for the presatellite era. Here, we quantify the LULCC history across the 33.9million ha watershed of Tanzania's Eastern Arc Mountains, using geo-referenced and digitized historical land cover maps (dated 1908, 1923, 1949 and 2000). Our time series from this biodiversity hotspot shows that forest and savanna area both declined, by 74% (2.8million ha) and 10% (2.9million ha), respectively, between 1908 and 2000. This vegetation was replaced by a fivefold increase in cropland, from 1.2million ha to 6.7million ha. This LULCC implies a committed release of 0.9PgC (95% CI: 0.4-1.5) across the watershed for the same period, equivalent to 0.3MgCha(-1) yr(-1) . This is at least threefold higher than previous estimates from global models for the same study area. We then used the LULCC data from before and after protected area creation, as well as from areas where no protection was established, to analyse the effectiveness of legal protection on land cover change despite the underlying spatial variation in protected areas. We found that, between 1949 and 2000, forest expanded within legally protected areas, resulting in carbon uptake of 4.8 (3.8-5.7) MgCha(-1) , compared to a committed loss of 11.9 (7.2-16.6) MgCha(-1) within areas lacking such protection. Furthermore, for nine protected areas where LULCC data are available prior to and following establishment, we show that protection reduces deforestation rates by 150% relative to unprotected portions of the watershed. Our results highlight that considerable LULCC occurred prior to the satellite era, thus other data sources are required to better understand long-term land cover trends in the tropics.