scholarly journals A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions

2018 ◽  
Vol 15 (9) ◽  
pp. 2909-2930 ◽  
Author(s):  
Sebastian Lienert ◽  
Fortunat Joos
Keyword(s):  
Land Use ◽  
Carbon Emissions ◽  
Latin Hypercube Sampling ◽  
Historical Period ◽  
Model Parameters ◽  
Dynamic Global Vegetation Model ◽  
Order Of Magnitude ◽  
The Difference ◽  
Wood Harvest ◽  
Global Vegetation

Abstract. A dynamic global vegetation model (DGVM) is applied in a probabilistic framework and benchmarking system to constrain uncertain model parameters by observations and to quantify carbon emissions from land-use and land-cover change (LULCC). Processes featured in DGVMs include parameters which are prone to substantial uncertainty. To cope with these uncertainties Latin hypercube sampling (LHS) is used to create a 1000-member perturbed parameter ensemble, which is then evaluated with a diverse set of global and spatiotemporally resolved observational constraints. We discuss the performance of the constrained ensemble and use it to formulate a new best-guess version of the model (LPX-Bern v1.4). The observationally constrained ensemble is used to investigate historical emissions due to LULCC (ELUC) and their sensitivity to model parametrization. We find a global ELUC estimate of 158 (108, 211) PgC (median and 90 % confidence interval) between 1800 and 2016. We compare ELUC to other estimates both globally and regionally. Spatial patterns are investigated and estimates of ELUC of the 10 countries with the largest contribution to the flux over the historical period are reported. We consider model versions with and without additional land-use processes (shifting cultivation and wood harvest) and find that the difference in global ELUC is on the same order of magnitude as parameter-induced uncertainty and in some cases could potentially even be offset with appropriate parameter choice.

scholarly journals A Bayesian Ensemble Data Assimilation to Constrain Model Parameters and Land Use Carbon Emissions

2018 ◽  
Author(s):  
Sebastian Lienert ◽  
Fortunat Joos
Keyword(s):  
Land Use ◽  
Carbon Emissions ◽  
Latin Hypercube Sampling ◽  
Historical Period ◽  
Model Parameters ◽  
Dynamic Global Vegetation Model ◽  
Order Of Magnitude ◽  
The Difference ◽  
Wood Harvest ◽  
Global Vegetation

Abstract. A dynamic global vegetation model (DGVM) is applied in a probabilistic framework and benchmarking system to constrain uncertain model parameters by observations and to quantify carbon emissions from land-use and land-cover change (LULCC). Processes featured in DGVMs include parameters which are prone to substantial uncertainty. To cope with these uncertainties Latin Hypercube Sampling (LHS) is used to create a 1000-member perturbed parameter ensemble, which is then evaluated with a diverse set of global and spatio-temporaly resolved observational constraints. We discuss the performance of the constrained ensemble and use it to formulate a new best-guess version of the model (LPX-Bern v1.4). The observationally constrained ensemble is used to investigate historical emissions due to LULCC (ELUC) and their sensitivity to model parametrization. We find a global ELUC estimate of 158 (108, 211) PgC (median and 90 % confidence interval) between 1800 and 2016. We compare ELUC to other estimates both globally and regionally. Spatial patterns are investigated and estimates of ELUC of the ten countries with the largest contribution to the flux over the historical period are reported. We consider model versions with and without additional land-use processes (shifting cultivation and wood harvest) and find that the difference in global ELUC is on the same order of magnitude as parameter induced uncertainty and in some cases could potentially even be offset with appropriate parameter choice.

Assessment of land use and land cover datasets for Brazil and impact on C emissions

2021 ◽  
Author(s):  
Thais M. Rosan ◽  
Kees Klein Goldewijk ◽  
Raphael Ganzenmüller ◽  
Michael O'Sullivan ◽  
Julia Pongratz ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Carbon Dioxide Emissions ◽  
Dynamic Global Vegetation Model ◽  
Global Carbon Budget ◽  
National Estimates ◽  
National Trends ◽  
Global Vegetation ◽  
Land Cover Maps ◽  
The Impact

<p>Brazil is responsible for about one third of the global land use and land cover change (LULCC) carbon dioxide emissions. However, there is a disagreement among different methodologies on the magnitude and trends in emissions and their geographic distribution. One of the main uncertainties is associated with different LULCC datatasets used as input in the different approaches. In this work we perform an evaluation of LULCC datasets for Brazil, including the global dataset (HYDE 3.2) used in the annual Global Carbon Budget (GCB), and national Brazilian dataset (MapBiomas) over the period 2000-2018. We also analyze the latest global HYDE 3.3 dataset based on new FAO inventory estimates and multi-annual ESA CCI satellite-based land cover maps. Results show that the new HYDE 3.3 can represent well the observed spatial variation in cropland and pastures areas over the last decades compared to national data (MapBiomas) and shows an improvement compared to HYDE 3.2 used in GCB. However, the magnitude of LULCC assessed with HYDE 3.3 is lower than national estimates from MapBiomas. Finally, we used HYDE 3.3 as input to two different approaches included in GCB, a global bookkeeping model (BLUE) and a process-based Dynamic Global Vegetation Model (JULES-ES) to determine the impact of the new version of HYDE dataset on Brazil’s land-use emissions trends over the period 2000-2017. Both JULES-ES and BLUE now simulate a negative land-use emissions trend for the last two decades. This negative trend is in agreement with Brazilian INPE-EM, global H&N bookkeeping models, FAO and as reported in National GHG inventories (NGHGI), although magnitudes differ among approaches. Overall, the inclusion of the multi-annual ESA CCI Land Cover dataset to allocate spatially the FAO statistical data has improved spatial representation of agricultural area change in Brazil in the last two decades, contributing to improve global model capability to simulate Brazil’s LULCC emissions in agreement with national trends estimates and spatial distribution.</p>

Radial heat and water transport across the airway wall

1990 ◽  
Vol 69 (1) ◽  
pp. 222-231 ◽  
Author(s):  
C. L. Tsai ◽  
G. M. Saidel ◽  
E. R. McFadden ◽  
J. M. Fouke
Keyword(s):  
Initial Conditions ◽  
Variable Parameter ◽  
Blood Perfusion ◽  
Breath Hold ◽  
Model Parameters ◽  
Airway Wall ◽  
Healthy Human ◽  
Asthmatic Patients ◽  
Order Of Magnitude ◽  
The Difference

The thermal profiles in the airways of healthy human volunteers and patients with asthma differ after cessation of hyperpnea. The asthmatic patients rewarm their airways more rapidly. To identify thermal properties and processes that could account for the difference between these populations, we developed a model describing the radial transport of heat and water across the trachea. A distinctive feature of the model is a variable parameter describing blood supply to the mucosal and submucosal layers. Simulations performed with the model are initiated by a breath-hold maneuver and are propagative in time. Blood perfusion rates in the airway wall, the thickness of the layer of airway surface liquid, and the mucosa-submucosa thickness, all thought to be more pronounced in asthmatic patients, were varied by changing model parameters and initial conditions. Increasing the thickness of the liquid layer by more than an order of magnitude had little effect on the temperature or water content in the airway lumen. Doubling the blood flow to the mucosa-submucosa resulted in a slight increase in airway temperature. When this effect was coupled, however, with an increase in the thickness of the mucosa-submucosa layer, the increase in temperature was more pronounced. Because the bronchial circulation is the major source of heat to the airway, these results indicate that differences in airway wall thickness coupled with differences in the magnitude or responsiveness of the bronchial microcirculation could account for the differences in intra-airway temperature between the two populations.

scholarly journals Carbon emissions from land use and land-cover change

2012 ◽  
Vol 9 (12) ◽  
pp. 5125-5142 ◽  
Author(s):  
R. A. Houghton ◽  
J. I. House ◽  
J. Pongratz ◽  
G. R. van der Werf ◽  
R. S. DeFries ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Emissions ◽  
Data Uncertainty ◽  
Tropical Peatlands ◽  
Net Flux ◽  
The Mean ◽  
Standard Deviations ◽  
Wood Harvest

Abstract. The net flux of carbon from land use and land-cover change (LULCC) accounted for 12.5% of anthropogenic carbon emissions from 1990 to 2010. This net flux is the most uncertain term in the global carbon budget, not only because of uncertainties in rates of deforestation and forestation, but also because of uncertainties in the carbon density of the lands actually undergoing change. Furthermore, there are differences in approaches used to determine the flux that introduce variability into estimates in ways that are difficult to evaluate, and not all analyses consider the same types of management activities. Thirteen recent estimates of net carbon emissions from LULCC are summarized here. In addition to deforestation, all analyses considered changes in the area of agricultural lands (croplands and pastures). Some considered, also, forest management (wood harvest, shifting cultivation). None included emissions from the degradation of tropical peatlands. Means and standard deviations across the thirteen model estimates of annual emissions for the 1980s and 1990s, respectively, are 1.14 ± 0.23 and 1.12 ± 0.25 Pg C yr−1 (1 Pg = 1015 g carbon). Four studies also considered the period 2000–2009, and the mean and standard deviations across these four for the three decades are 1.14 ± 0.39, 1.17 ± 0.32, and 1.10 ± 0.11 Pg C yr−1. For the period 1990–2009 the mean global emissions from LULCC are 1.14 ± 0.18 Pg C yr−1. The standard deviations across model means shown here are smaller than previous estimates of uncertainty as they do not account for the errors that result from data uncertainty and from an incomplete understanding of all the processes affecting the net flux of carbon from LULCC. Although these errors have not been systematically evaluated, based on partial analyses available in the literature and expert opinion, they are estimated to be on the order of ± 0.5 Pg C yr−1.

scholarly journals Smaller global and regional carbon emissions from gross land use change when considering sub-grid secondary land cohorts in a global dynamic vegetation model

2017 ◽  
Author(s):  
Chao Yue ◽  
Philippe Ciais ◽  
Wei Li
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Carbon Emissions ◽  
Shifting Cultivation ◽  
Secondary Forests ◽  
Forest Age ◽  
Priority Rules ◽  
Forest Clearing ◽  
Historical Land Use ◽  
Wood Harvest

Abstract. Several modeling studies reported elevated carbon emissions from historical land use change (LUC) by including bi-directional transitions at the sub-grid scale (termed gross land use change). This has implication on the estimation of so-called residual land CO2 sink over undisturbed lands. However, in most dynamic global vegetation models (DGVM), forests and/or other land use types are represented with a single sub-grid tile, without accounting for secondary lands that are often involved in shifting cultivation or wood harvest. As a result, land use change emissions (ELUC) are likely overestimated, because it is high-biomass mature forests instead of low-biomass secondary forests that are cleared. Here we investigated the effects of including sub-grid forest age dynamics in a DGVM on historical ELUC over 1501–2005. We run two simulations, one with no forest age (Sageless) and the other with sub-grid secondary forests of different age classes whose demography is driven by historical land use change (Sage). Estimated global ELUC for 1501–2005 are 179 Pg C in Sage compared to 199 Pg C in Sageless. The lower emissions in Sage arise mainly from shifting cultivation in the tropics, being of 27 Pg C in Sage against 46 Pg C in Sageless. Estimated cumulative ELUC from wood harvest in the Sage simulation (31 Pg C) are however slightly higher than Sageless (27 Pg C), because secondary forests simulated in Sage are insufficient to meet the prescribed harvest area, leading to the harvest of old forests. This result depends on pre-defined forest clearing priority rules given a simulated portfolio of differently aged forests in the model. Our results highlight that although gross land use change as a former missing emission component is included by a growing number of DGVMs, its contribution to overall ELUC tends to be overestimated, unless low-biomass secondary forests are properly represented.

scholarly journals Land-use based modeling approach for determining freshwater nitrate loadings from small agricultural watersheds

2020 ◽  
Author(s):  
Pierre Grizard ◽  
Kerry T. B. MacQuarrie ◽  
Yefang Jiang
Keyword(s):  
Land Use ◽  
Prince Edward Island ◽  
Agricultural Land ◽  
Latin Hypercube Sampling ◽  
Potato Production ◽  
Lumped Parameter Model ◽  
Model Parameters ◽  
Agricultural Watersheds ◽  
Lumped Model ◽  
Transport Delay

Abstract Nitrate released from a variety of land-use activities is a major factor in the degrading conditions observed in many watersheds and estuaries. In this research a spatially lumped model is developed to estimate annual nitrate loads and concentrations from over 100 small watersheds in the Canadian province of Prince Edward Island (PEI). Nitrate source concentrations are associated with major land-use categories, and nitrate attenuation, based on the width of riparian zones, and transport delay due to groundwater residence time are simulated. To investigate the uncertainty of the results, model parameters were selected using a Latin hypercube sampling method. Nitrate concentrations from 12 watersheds were used for model calibration (R2 = 0.91), while 118 other watersheds were used for verification purposes (R2 = 0.82). Overall, the lumped parameter model is shown to be a useful tool for simulating annual nitrate loadings from agricultural watersheds when detailed spatiotemporal agricultural land-use data are available. For PEI the model results indicate that nitrate loadings to estuaries are strongly related to agricultural land, especially the land area in potato production.

scholarly journals Holocene carbon emissions as a result of anthropogenic land cover change

The Holocene ◽  
2010 ◽  
Vol 21 (5) ◽  
pp. 775-791 ◽  
Author(s):  
Jed O. Kaplan ◽  
Kristen M. Krumhardt ◽  
Erle C. Ellis ◽  
William F. Ruddiman ◽  
Carsten Lemmen ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Emissions ◽  
Land Surface ◽  
Simple Relationship ◽  
Data Set ◽  
Dynamic Global Vegetation Model ◽  
The Holocene ◽  
The World

Humans have altered the Earth’s land surface since the Paleolithic mainly by clearing woody vegetation first to improve hunting and gathering opportunities, and later to provide agricultural cropland. In the Holocene, agriculture was established on nearly all continents and led to widespread modification of terrestrial ecosystems. To quantify the role that humans played in the global carbon cycle over the Holocene, we developed a new, annually resolved inventory of anthropogenic land cover change from 8000 years ago to the beginning of large-scale industrialization (ad 1850). This inventory is based on a simple relationship between population and land use observed in several European countries over preindustrial time. Using this data set, and an alternative scenario based on the HYDE 3.1 land use data base, we forced the LPJ dynamic global vegetation model in a series of continuous simulations to evaluate the impacts of humans on terrestrial carbon storage during the preindustrial Holocene. Our model setup allowed us to quantify the importance of land degradation caused by repeated episodes of land use followed by abandonment. By 3 ka BP, cumulative carbon emissions caused by anthropogenic land cover change in our new scenario ranged between 84 and 102 Pg, translating to c. 7 ppm of atmospheric CO2. By ad 1850, emissions were 325–357 Pg in the new scenario, in contrast to 137–189 Pg when driven by HYDE. Regional events that resulted in local emissions or uptake of carbon were often balanced by contrasting patterns in other parts of the world. While we cannot close the carbon budget in the current study, simulated cumulative anthropogenic emissions over the preindustrial Holocene are consistent with the ice core record of atmospheric δ13CO2 and support the hypothesis that anthropogenic activities led to the stabilization of atmospheric CO2 concentrations at a level that made the world substantially warmer than it otherwise would be.

scholarly journals Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

2017 ◽  
Vol 24 (3) ◽  
pp. 553-567 ◽  
Author(s):  
Hazuki Arakida ◽  
Takemasa Miyoshi ◽  
Takeshi Ise ◽  
Shin-ichiro Shima ◽  
Shunji Kotsuki
Keyword(s):  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Model Parameters ◽  
Vegetation Model ◽  
Dynamic Global Vegetation Model ◽  
Area Index ◽  
The Real ◽  
Global Vegetation ◽  
The Impact

Abstract. We developed a data assimilation system based on a particle filter approach with the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM). We first performed an idealized observing system simulation experiment to evaluate the impact of assimilating the leaf area index (LAI) data every 4 days, simulating the satellite-based LAI. Although we assimilated only LAI as a whole, the tree and grass LAIs were estimated separately with high accuracy. Uncertain model parameters and other state variables were also estimated accurately. Therefore, we extended the experiment to the real world using the real Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data and obtained promising results.

Sign in / Sign up

Export Citation Format

Share Document