Data-driven spatial modeling of historic and future land change at global scale

Assessing the historic and future impacts of land-use and land-cover change (LULCC) on climate requires spatially and temporally explicit data sets on LULCC spanning several decades to centuries, because climate change is a long-term problem. Though remote sensing data provides a globally consistent picture of land cover, these data are only available from the past four decades. Therefore, existing LULCC reconstructions are modeled estimates that combine remote sensing data with relatively coarser-resolution inventory statistics that covers longer historical period. The uncertainties in modeling assumptions, and limited availability and inconsistencies across inventory datasets among other reasons introduce uncertainties in LULCC reconstructions. These uncertainties not only limit our ability to model future LULCC, but also translate as uncertainties in both historic and future environmental assessments. The objectives of my PhD work are as follows: (1) systematically investigate the causes of uncertainties in existing historical LULCC datasets, (2) test the sensitivity of LULCC quantification uncertainty in estimating CO2 emissions from LULCC (historic and future) using a process-based land-surface model, the Integrated Science Assessment Model (ISAM), (3) compare the relative uncertainties from various drivers (e.g. LULCC datasets, model processes e.g. nitrogen cycle, environmental factors such as climate) in estimating historic and future LULCC emissions, and (4) explore statistical techniques to model future LULCC that takes into account the uncertainties in quantifying the spatial and temporal patterns of LULCC, and (5) as a case-study, identify a key regional hotspot of historic LULCC quantification uncertainty (here, India), and reduce uncertainty through improved understanding of the dynamics and drivers of land change in the case-study region. I address the above goals by integrating land-surface modeling (ISAM), remote sensing and GIS, data collected through ground transects, and geospatial data on socioeconomics. ISAM simulations show that the estimated net global emissions from LULCC (mean and range) across three different historical LULCC reconstructions are 1.88 (1.7 to 2.21) GtC/yr for the 1980’s, 1.66 (1.48 to 1.83) GtC/yr for the 1990's, and 1.44 (1.22 to 1.65) for the 2000's. The estimates are higher than other published estimates that range from 0.80 to 1.5 GtC/yr for the 1990’s and 1.1 GtC/yr for the 2000’s. These results are higher than other published estimates because they include the effects of nitrogen limitation on regrowth of forests following wood harvest and agricultural abandonment. The estimated LULUC emissions for the tropics are 0.79±0.25 for the 1980’s, 0.78±0.29 for the 1990’s and 0.71±0.33 GtC/yr for the 2000’s, and for the non-tropics regions are 1.08±0.52, 0.90±0.19 and 0.69±0.12 GtC/yr for the three decades. The model results indicate that failing to account for the nitrogen cycle underestimates LULCC emissions by about 40% globally (0.66 GtC/yr), 10% in the tropics (0.07 GtC/yr) and 70% in the non-tropics (0.59 GtC/yr). If LULCC emissions are higher than assessed, it means fossil fuel emissions would have to be even lower to meet the same mitigation target. Extending ISAM simulations to the 21st century resulted in two key insights. First, nitrogen limitation of CO2 uptake is substantial and sensitive to nitrogen inputs. In ISAM, excluding nitrogen limitation underestimated global total LULUC emissions by 34-52 PgC (~21-29%) during the 20th century and by 128-187 PgC (90-150%) during the 21st century. The difference increases with time because nitrogen limitation will progressively down-regulate the magnitude of CO2 fertilization effect on regrowing forests, due to decreasing supply of plant-usable mineral nitrogen. Second, historically, the indirect effects of anthropogenic activity through environmental changes in land experiencing LULCC (indirect emissions) are small compared to direct effects of anthropogenic LULCC activity (direct emissions). As a result, including or excluding indirect emissions had a minor influence on the estimated total LULUC emissions historically. In contrast, the indirect LULCC emissions for the 21st century are a much larger source to the atmosphere, in simulations with nitrogen limitation. This is because of the gradual weakening of the photosynthetic response to elevated (CO2) caused by nitrogen limitation. Therefore, what fluxes are including in LULCC emissions across different models is a crucial source of uncertainty in future LULCC emissions estimates. A detailed investigation of the sensitivity of different global-scale LULCC modeling techniques show that land use allocation approaches based solely on previous land use history (but disregarding the impact of driving factor), or those based on mechanistically fitting models for the spatial processes of land use change do not reproduce well long-term historical land use patterns. With an example application to the terrestrial carbon cycle, I show that such inaccuracies in land use allocation can translate into significant implications for global environmental assessments. In contrast to previous approaches, I present a statistical land use downscaling model and show that the model can reproduce the broad spatial features of the past 100 years of evolution of cropland and pastureland patterns. Therefore, the modeling approach and its evaluation provide an example that can be useful to the land use, Integrated Assessment, and the Earth system modeling communities

Increased influence of nitrogen limitation on CO₂ emissions from future land use and land use change

In the latest projections of future greenhouse gas emissions for the Intergovernmental Panel on Climate Change (IPCC), few Earth System Models included the effect of nitrogen limitation, a key process limiting forest regrowth. Few included forest management (wood harvest). We estimate the impacts of nitrogen limitation on the CO2 emissions from land use and land use change (LULUC), including wood harvest, for the period 1900–2100. We use a land surface model that includes a fully coupled carbon and nitrogen cycle and accounts for forest regrowth processes following agricultural abandonment and wood harvest. Future projections are based on the four Representation Concentration Pathways used in the IPCC Fifth Assessment Report, and we account for uncertainty in future climate for each scenario based on ensembles of climate model outputs. Results show that excluding nitrogen limitation will underestimate global LULUC emissions by 34–52 PgC (20–30%) during the 20th century (range across three different historical LULUC reconstructions) and by 128–187 PgC (90–150%) during the 21st century (range across the four IPCC scenarios). The full range for estimated LULUC emissions during the 21st century including climate model uncertainty is 91 to 227 PgC (with nitrogen limitation included). The underestimation increases with time because (1) projected annual wood harvest rates from forests summed over the 21st century are 380–1080% higher compared to those of the 20th century, resulting in more regrowing secondary forests; (2) nitrogen limitation reduces the CO2 fertilization effect on net primary production of regrowing secondary forests following wood harvest and agricultural abandonment; and (3) nitrogen limitation effect is aggravated by the gradual loss of soil nitrogen from LULUC disturbance. Our study implies that (1) nitrogen limitation of CO2 uptake is substantial and sensitive to nitrogen inputs; (2) if LULUC emissions are larger than previously estimated in studies without nitrogen limitation, then meeting the same climate mitigation target would require an equivalent additional reduction of fossil fuel emissions; (3) the effectiveness of land-basedmitigation strategies will critically depend on the interactions between nutrient limitations and secondary forests resulting from LULUC; and (4) it is important for terrestrial biosphere models to consider nitrogen constraint in estimates of the strength of future land carbon uptake.NASA (NNX14AD94G)U.S. National Science Foundation (NSF-AGS- 12-43071)U.S. Department of Energy (DOE-DE-SC0006706)Ope

Development of decadal (1985–1995–2005) land use and land cover database for India

India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study

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

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

Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years

Earth’s land cover has been extensively transformed over time due to both human activities and natural causes. Previous global studies have focused on developing spatial and temporal patterns of dominant human land-use activities (e.g., cropland, pastureland, urban land, wood harvest). Process-based modeling studies adopt different strategies to estimate the changes in land cover by using these land-use data sets in combination with a potential vegetation map, and subsequently use this information for impact assessments. However, due to unaccounted changes in land cover (resulting from both indirect anthropogenic and natural causes), heterogeneity in land-use/cover (LUC) conversions among grid cells, even for the same land use activity, and uncertainty associated with potential vegetation mapping and historical estimates of human land use result in land cover estimates that are substantially different compared to results acquired from remote sensing observations. Here, we present a method to implicitly account for the differences arising from these uncertainties in order to provide historical estimates of land cover that are consistent with satellite estimates for recent years. Due to uncertainty in historical agricultural land use, we use three widely accepted global estimates of cropland and pastureland in combination with common wood harvest and urban land data sets to generate three distinct estimates of historical land-cover change and underlying LUC conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and the extent to which different ecosystems have undergone changes. The annual land cover maps and LUC conversion maps are reported at 0.5°×0.5° resolution and describe the area of 28 landcover types and respective underlying land-use transitions. The reconstructed data sets are relevant for studies addressing the impact of land-cover change on biogeophysics, biogeochemistry, water cycle, and global climate.Ope

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

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

Spatial modeling of agricultural land use change at global scale

Long-term modeling of agricultural land use is central in global scale assessments of climate change, food security, biodiversity, and climate adaptation and mitigation policies. We present a global-scale dynamic land use allocation model and show that it can reproduce the broad spatial features of the past 100 years of evolution of cropland and pastureland patterns. The modeling approach integrates economic theory, observed land use history, and data on both socioeconomic and biophysical determinants of land use change, and estimates relationships using long-term historical data, thereby making it suitable for long-term projections. The underlying economic motivation is maximization of expected profits by hypothesized landowners within each grid cell. The model predicts fractional land use for cropland and pastureland within each grid cell based on socioeconomic and biophysical driving factors that change with time. The model explicitly incorporates the following key features: (1) land use competition, (2) spatial heterogeneity in the nature of driving factors across geographic regions, (3) spatial heterogeneity in the relative importance of driving factors and previous land use patterns in determining land use allocation, and (4) spatial and temporal autocorrelation in land use patterns. We show that land use allocation approaches based solely on previous land use history (but disregarding the impact of driving factors), or those accounting for both land use history and driving factors by mechanistically fitting models for the spatial processes of land use change do not reproduce well long-term historical land use patterns. With an example application to the terrestrial carbon cycle, we show that such inaccuracies in land use allocation can translate into significant implications for global environmental assessments. The modeling approach and its evaluation provide an example that can be useful to the land use, Integrated Assessment, and the Earth system modeling communities.Ope

Assessing uncertainties in land cover projections

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.JRC.B.3-Territorial Developmen

Assessing uncertainties in land cover projections.

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover