scholarly journals A fire model with distinct crop, pasture, and non-agricultural burning: use of new data and a model-fitting algorithm for FINAL.1

2018 ◽  
Vol 11 (2) ◽  
pp. 815-842 ◽  
Author(s):  
Sam S. Rabin ◽  
Daniel S. Ward ◽  
Sergey L. Malyshev ◽  
Brian I. Magi ◽  
Elena Shevliakova ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Regional Scale ◽  
Lessons Learned ◽  
Sub Saharan Africa ◽  
Burned Area ◽  
Pasture Management ◽  
Dynamic Global Vegetation Model ◽  
Fire Models ◽  
Marquardt Algorithm ◽  
Agricultural Burning

Abstract. This study describes and evaluates the Fire Including Natural & Agricultural Lands model (FINAL) which, for the first time, explicitly simulates cropland and pasture management fires separately from non-agricultural fires. The non-agricultural fire module uses empirical relationships to simulate burned area in a quasi-mechanistic framework, similar to past fire modeling efforts, but with a novel optimization method that improves the fidelity of simulated fire patterns to new observational estimates of non-agricultural burning. The agricultural fire components are forced with estimates of cropland and pasture fire seasonality and frequency derived from observational land cover and satellite fire datasets. FINAL accurately simulates the amount, distribution, and seasonal timing of burned cropland and pasture over 2001–2009 (global totals: 0.434×106 and 2.02×106 km2 yr−1 modeled, 0.454×106 and 2.04×106 km2 yr−1 observed), but carbon emissions for cropland and pasture fire are overestimated (global totals: 0.295 and 0.706 PgC yr−1 modeled, 0.194 and 0.538 PgC yr−1 observed). The non-agricultural fire module underestimates global burned area (1.91×106 km2 yr−1 modeled, 2.44×106 km2 yr−1 observed) and carbon emissions (1.14 PgC yr−1 modeled, 1.84 PgC yr−1 observed). The spatial pattern of total burned area and carbon emissions is generally well reproduced across much of sub-Saharan Africa, Brazil, Central Asia, and Australia, whereas the boreal zone sees underestimates. FINAL represents an important step in the development of global fire models, and offers a strategy for fire models to consider human-driven fire regimes on cultivated lands. At the regional scale, simulations would benefit from refinements in the parameterizations and improved optimization datasets. We include an in-depth discussion of the lessons learned from using the Levenberg–Marquardt algorithm in an interactive optimization for a dynamic global vegetation model.

scholarly journals A fire model with distinct crop, pasture, and non-agricultural burning: Use of new data and a model-fitting algorithm for FINALv1

2017 ◽  
Author(s):  
Sam S. Rabin ◽  
Sergey L. Malyshev ◽  
Brian I. Magi ◽  
Elena Shevliakova ◽  
Stephen W. Pacala
Keyword(s):  
Carbon Emissions ◽  
Regional Scale ◽  
Model Fitting ◽  
Fire Regimes ◽  
Optimization Method ◽  
Sub Saharan Africa ◽  
Burned Area ◽  
Pasture Management ◽  
Fire Models ◽  
Agricultural Burning

Abstract. This study describes and evaluates the Fire Including Natural & Agricultural Lands model (FINAL) which, for the first time, explicitly simulates cropland and pasture management fires separately from non-agricultural fires. The non-agricultural fire module uses empirical relationships to simulate burned area in a quasi-mechanistic framework, similar to past fire modeling efforts, but with a novel optimization method that improves the fidelity of simulated fire patterns to new observational estimates of non-agricultural burning. The agricultural fire components are forced with estimates of cropland and pasture fire seasonality and frequency derived from observational land-cover and satellite fire datasets. FINAL accurately simulates the amount, distribution, and seasonal timing of burned cropland and pasture over 2001–2009 (global totals: 0.434 × 106 and 2.02 × 106 km2 yr−1 modeled, 0.454 × 106 and 2.04 × 106 km2 yr−1 observed), but carbon emissions for cropland and pasture fire are overestimated (global totals: 0.297 PgC yr−1 and 0.712 PgC yr−1 modeled, 0.194 PgC yr−1 and 0.538 PgC yr−1 observed). The non-agricultural fire module underestimates global burned area (1.66 × 106 km2 yr−1 modeled, 2.44 × 106 km2 yr−1 observed) and carbon emissions (1.33 PgC yr−1 modeled, 1.84 PgC yr−1 observed). The spatial pattern of total burned area and carbon emissions is generally well reproduced across much of sub-Saharan Africa, Brazil, central Asia, and Australia, whereas the boreal zone suffers from underestimates. FINAL represents an important step in the development of global fire models, and offers a strategy for fire models to consider human-driven fire regimes on cultivated lands. At the regional scale, simulations would benefit from refinements in the parameterizations and improved optimization datasets.

scholarly journals African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data

2021 ◽  
Vol 118 (9) ◽  
pp. e2011160118
Author(s):  
Ruben Ramo ◽  
Ekhi Roteta ◽  
Ioannis Bistinas ◽  
Dave van Wees ◽  
Aitor Bastarrika ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Spatial Resolution ◽  
Carbon Emissions ◽  
Sub Saharan Africa ◽  
Burned Area ◽  
Fire Emissions ◽  
Coarse Spatial Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sub Saharan ◽  
Sentinel 2

Fires are a major contributor to atmospheric budgets of greenhouse gases and aerosols, affect soils and vegetation properties, and are a key driver of land use change. Since the 1990s, global burned area (BA) estimates based on satellite observations have provided critical insights into patterns and trends of fire occurrence. However, these global BA products are based on coarse spatial-resolution sensors, which are unsuitable for detecting small fires that burn only a fraction of a satellite pixel. We estimated the relevance of those small fires by comparing a BA product generated from Sentinel-2 MSI (Multispectral Instrument) images (20-m spatial resolution) with a widely used global BA product based on Moderate Resolution Imaging Spectroradiometer (MODIS) images (500 m) focusing on sub-Saharan Africa. For the year 2016, we detected 80% more BA with Sentinel-2 images than with the MODIS product. This difference was predominately related to small fires: we observed that 2.02 Mkm2 (out of a total of 4.89 Mkm2) was burned by fires smaller than 100 ha, whereas the MODIS product only detected 0.13 million km2 BA in that fire-size class. This increase in BA subsequently resulted in increased estimates of fire emissions; we computed 31 to 101% more fire carbon emissions than current estimates based on MODIS products. We conclude that small fires are a critical driver of BA in sub-Saharan Africa and that including those small fires in emission estimates raises the contribution of biomass burning to global burdens of (greenhouse) gases and aerosols.

scholarly journals New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets

2021 ◽  
Vol 13 (15) ◽  
pp. 2892
Author(s):  
Zhongbing Chang ◽  
Sanaa Hobeichi ◽  
Ying-Ping Wang ◽  
Xuli Tang ◽  
Gab Abramowitz ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Aboveground Biomass ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Random Forest Regression ◽  
Multiple Datasets ◽  
Optimal Weighting ◽  
Two Hybrid ◽  
Forest Aboveground Biomass

Mapping the spatial variation of forest aboveground biomass (AGB) at the national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several gridded forest AGB products have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products in their estimated AGB carbon, varying from 5.04 to 9.81 Pg C. To reduce this uncertainty, here, we first compiled independent, high-quality field measurements of AGB using a systematic and consistent protocol across China from 2011 to 2015. We applied two different approaches, an optimal weighting technique (WT) and a random forest regression method (RF), to develop two observationally constrained hybrid forest AGB products in China by integrating five existing AGB products. The WT method uses a linear combination of the five existing AGB products with weightings that minimize biases with respect to the field measurements, and the RF method uses decision trees to predict a hybrid AGB map by minimizing the bias and variance with respect to the field measurements. The forest AGB stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two hybrid AGB products had a lower RMSE (29.6 and 24.3 Mg/ha) and bias (−4.6 and −3.8 Mg/ha) than all five participating AGB datasets. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGB maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGB maps of China can be used to improve estimates of carbon emissions and removals at the national and subnational scales.

scholarly journals Community-driven citizen science approach to explore cardiovascular disease risk perception, and develop prevention advocacy strategies in sub-Saharan Africa: a programme protocol

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Kufre Joseph Okop ◽  
Kathy Murphy ◽  
Estelle Victoria Lambert ◽  
Kiya Kedir ◽  
Hailemichael Getachew ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Citizen Science ◽  
Risk Perceptions ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Data Gathering ◽  
Lessons Learned ◽  
Sub Saharan Africa ◽  
Rural And Urban ◽  
Sub Saharan

Abstract Background In sub-Saharan Africa (SSA), which experiences a disproportionately high cardiovascular disease (CVD) burden, population-based screening and prevention measures are hampered by low levels of knowledge about CVD and associated risk factors, and inaccurate perceptions of severity of risk. Methods This protocol describes the planned processes for implementing community-driven participatory research, using a citizen science method to explore CVD risk perceptions and to develop community-specific advocacy and prevention strategies in the rural and urban SSA settings. Multi-disciplinary research teams in four selected African countries will engage with and train community members living in rural and urban communities as citizen scientists to facilitate conceptualization, co-designing of research, data gathering, and co-creation of knowledge that can lead to a shared agenda to support collaborative participation in community-engaged science. The emphasis is on robust community engagement, using mobile technology to support data gathering, participatory learning, and co-creation of knowledge and disease prevention advocacy. Discussion Contextual processes applied and lessons learned in specific settings will support redefining or disassembling boundaries in participatory science to foster effective implementation of sustainable prevention intervention programmes in Low- and Middle-income countries.

scholarly journals Conducting clinical trials in sub-Saharan Africa: challenges and lessons learned from the Malawi Cryptosporidium study

Trials ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Neema Toto ◽  
Elaine Douglas ◽  
Markus Gmeiner ◽  
Lynn K. Barrett ◽  
Robert Lindblad ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Lessons Learned ◽  
Sub Saharan Africa ◽  
Sub Saharan

scholarly journals Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models

2019 ◽  
Vol 16 (19) ◽  
pp. 3883-3910 ◽  
Author(s):  
Lina Teckentrup ◽  
Sandy P. Harrison ◽  
Stijn Hantson ◽  
Angelika Heil ◽  
Joe R. Melton ◽  
...  
Keyword(s):  
Land Use ◽  
Co2 Concentration ◽  
Fire Regimes ◽  
Atmospheric Co2 ◽  
Anthropogenic Factors ◽  
Burned Area ◽  
Earth System ◽  
Atmospheric Co2 Concentration ◽  
Fire Models ◽  
The Impact

Abstract. Understanding how fire regimes change over time is of major importance for understanding their future impact on the Earth system, including society. Large differences in simulated burned area between fire models show that there is substantial uncertainty associated with modelling global change impacts on fire regimes. We draw here on sensitivity simulations made by seven global dynamic vegetation models participating in the Fire Model Intercomparison Project (FireMIP) to understand how differences in models translate into differences in fire regime projections. The sensitivity experiments isolate the impact of the individual drivers on simulated burned area, which are prescribed in the simulations. Specifically these drivers are atmospheric CO2 concentration, population density, land-use change, lightning and climate. The seven models capture spatial patterns in burned area. However, they show considerable differences in the burned area trends since 1921. We analyse the trajectories of differences between the sensitivity and reference simulation to improve our understanding of what drives the global trends in burned area. Where it is possible, we link the inter-model differences to model assumptions. Overall, these analyses reveal that the largest uncertainties in simulating global historical burned area are related to the representation of anthropogenic ignitions and suppression and effects of land use on vegetation and fire. In line with previous studies this highlights the need to improve our understanding and model representation of the relationship between human activities and fire to improve our abilities to model fire within Earth system model applications. Only two models show a strong response to atmospheric CO2 concentration. The effects of changes in atmospheric CO2 concentration on fire are complex and quantitative information of how fuel loads and how flammability changes due to this factor is missing. The response to lightning on global scale is low. The response of burned area to climate is spatially heterogeneous and has a strong inter-annual variation. Climate is therefore likely more important than the other factors for short-term variations and extremes in burned area. This study provides a basis to understand the uncertainties in global fire modelling. Both improvements in process understanding and observational constraints reduce uncertainties in modelling burned area trends.

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 Calibration and evaluation of a semi-distributed watershed model of Sub-Saharan Africa using GRACE data

2012 ◽  
Vol 16 (9) ◽  
pp. 3083-3099 ◽  
Author(s):  
H. Xie ◽  
L. Longuevergne ◽  
C. Ringler ◽  
B. R. Scanlon
Keyword(s):  
Regional Scale ◽  
Water Storage ◽  
Hydrologic Model ◽  
Sub Saharan Africa ◽  
Model Parameters ◽  
Total Water ◽  
Discharge Data ◽  
Model Based ◽  
Grace Data ◽  
Sub Saharan

Abstract. Irrigation development is rapidly expanding in mostly rainfed Sub-Saharan Africa. This expansion underscores the need for a more comprehensive understanding of water resources beyond surface water. Gravity Recovery and Climate Experiment (GRACE) satellites provide valuable information on spatio-temporal variability in water storage. The objective of this study was to calibrate and evaluate a semi-distributed regional-scale hydrologic model based on the Soil and Water Assessment Tool (SWAT) code for basins in Sub-Saharan Africa using seven-year (July 2002–April 2009) 10-day GRACE data and multi-site river discharge data. The analysis was conducted in a multi-criteria framework. In spite of the uncertainty arising from the tradeoff in optimising model parameters with respect to two non-commensurable criteria defined for two fluxes, SWAT was found to perform well in simulating total water storage variability in most areas of Sub-Saharan Africa, which have semi-arid and sub-humid climates, and that among various water storages represented in SWAT, water storage variations in soil, vadose zone and groundwater are dominant. The study also showed that the simulated total water storage variations tend to have less agreement with GRACE data in arid and equatorial humid regions, and model-based partitioning of total water storage variations into different water storage compartments may be highly uncertain. Thus, future work will be needed for model enhancement in these areas with inferior model fit and for uncertainty reduction in component-wise estimation of water storage variations.

scholarly journals Biomass burning fuel consumption dynamics in the tropics and subtropics assessed from satellite

2016 ◽  
Vol 13 (12) ◽  
pp. 3717-3734 ◽  
Author(s):  
Niels Andela ◽  
Guido R. van der Werf ◽  
Johannes W. Kaiser ◽  
Thijs T. van Leeuwen ◽  
Martin J. Wooster ◽  
...  
Keyword(s):  
South America ◽  
Fuel Consumption ◽  
Spatial Patterns ◽  
Field Measurements ◽  
Sub Saharan Africa ◽  
Burned Area ◽  
Return Periods ◽  
Sub Saharan ◽  
In Fire ◽  
Consumption Dynamics

Abstract. Landscape fires occur on a large scale in (sub)tropical savannas and grasslands, affecting ecosystem dynamics, regional air quality and concentrations of atmospheric trace gasses. Fuel consumption per unit of area burned is an important but poorly constrained parameter in fire emission modelling. We combined satellite-derived burned area with fire radiative power (FRP) data to derive fuel consumption estimates for land cover types with low tree cover in South America, Sub-Saharan Africa, and Australia. We developed a new approach to estimate fuel consumption, based on FRP data from the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) and the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) in combination with MODIS burned-area estimates. The fuel consumption estimates based on the geostationary and polar-orbiting instruments showed good agreement in terms of spatial patterns. We used field measurements of fuel consumption to constrain our results, but the large variation in fuel consumption in both space and time complicated this comparison and absolute fuel consumption estimates remained more uncertain. Spatial patterns in fuel consumption could be partly explained by vegetation productivity and fire return periods. In South America, most fires occurred in savannas with relatively long fire return periods, resulting in comparatively high fuel consumption as opposed to the more frequently burning savannas in Sub-Saharan Africa. Strikingly, we found the infrequently burning interior of Australia to have higher fuel consumption than the more productive but frequently burning savannas in northern Australia. Vegetation type also played an important role in explaining the distribution of fuel consumption, by affecting both fuel build-up rates and fire return periods. Hummock grasslands, which were responsible for a large share of Australian biomass burning, showed larger fuel build-up rates than equally productive grasslands in Africa, although this effect might have been partially driven by the presence of grazers in Africa or differences in landscape management. Finally, land management in the form of deforestation and agriculture also considerably affected fuel consumption regionally. We conclude that combining FRP and burned-area estimates, calibrated against field measurements, is a promising approach in deriving quantitative estimates of fuel consumption. Satellite-derived fuel consumption estimates may both challenge our current understanding of spatiotemporal fuel consumption dynamics and serve as reference datasets to improve biogeochemical modelling approaches. Future field studies especially designed to validate satellite-based products, or airborne remote sensing, may further improve confidence in the absolute fuel consumption estimates which are quickly becoming the weakest link in fire emission estimates.

scholarly journals Identifying required model structures to predict global fire activity from satellite and climate data

2016 ◽  
Author(s):  
Matthias Forkel ◽  
Wouter Dorigo ◽  
Gitta Lasslop ◽  
Irene Teubner ◽  
Emilio Chuvieco ◽  
...  
Keyword(s):  
Satellite Observations ◽  
Burned Area ◽  
Human Society ◽  
Fire Emissions ◽  
Fire Models ◽  
Fire Activity ◽  
Process Oriented ◽  
Density And Biomass ◽  
Global Vegetation ◽  
Model Structures

Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. In particular, extreme fire conditions can cause devastating impacts on ecosystems and human society and dominate the year-to-year variability in global fire emissions. However, the climatic, environmental and socioeconomic factors that control fire activity in vegetation are only poorly understood and consequently it is unclear which components, structures, and complexities are required in global vegetation/fire models to accurately predict fire activity at a global scale. Here we introduce the SOFIA (Satellite Observations for FIre Activity) modelling approach, which integrates several satellite and climate datasets and different empirical model structures to systematically identify required structural components in global vegetation/fire models to predict burned area. Models result in the highest performance in predicting the spatial patterns and temporal variability of burned area if they account for a direct suppression of fire activity at wet conditions and if they include a land cover-dependent suppression or allowance of fire activity by vegetation density and biomass. The use of new vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. The SOFIA approach implements and confirms conceptual models where fire activity follows a biomass gradient and is modulated by moisture conditions. The use of datasets on population density or socioeconomic development do not improve model performances, which indicates that the complex interactions of human fire usage and management cannot be realistically represented by such datasets. However, the best SOFIA models outperform a highly flexible machine learning approach and the state-of-the art global process-oriented vegetation/fire model JSBACH-SPITFIRE. Our results suggest using multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with model-data integration approaches to guide the future development of global process-oriented vegetation/fire models and to better understand the interactions between fire and hydrological, ecological, and atmospheric Earth system components.

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