ORCID Profile
0000-0002-0694-7348
Current Organisation
Cardiff University
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Publisher: Wiley
Date: 22-03-2013
DOI: 10.1002/ESP.3404
Publisher: Elsevier BV
Date: 04-2016
Publisher: Springer Science and Business Media LLC
Date: 20-12-2012
Publisher: Elsevier BV
Date: 1998
Publisher: American Geophysical Union (AGU)
Date: 11-2011
DOI: 10.1029/2010JF001942
Publisher: American Geophysical Union (AGU)
Date: 11-2011
DOI: 10.1029/2010JF001943
Publisher: Elsevier BV
Date: 03-2019
Publisher: Copernicus GmbH
Date: 04-11-2021
DOI: 10.5194/GMD-2021-337
Abstract: Abstract. Dust emissions influence global climate while simultaneously reducing the productive potential and resilience of landscapes to climate stressors, together impacting food security and human health. Vegetation is a major control on dust emission because it extracts momentum from the wind and shelters the soil surface, protecting dry and loose material from erosion by winds. Many of the current dust emission models (TEM) assume that the Earth’s land surface is constantly devoid of vegetation, then adjust the dust emission using a vegetation cover reciprocal, and finally calibrate to dust in the atmosphere. We compare this approach with an albedo-based dust emission model (AEM) which calibrates Earth’s land surface shadow to shelter depending on wind speed, to represent aerodynamic roughness spatio-temporal variation. We also compare these dust emission models with estimates of dust in the atmosphere using dust optical depth frequency (DOD). Using existing datasets of satellite observed dust emission from dust point sources (DPS), we show that during the same period, DOD frequency exceeds DPS frequency by up to two orders of magnitude (RMSEDOD = 67 days). Relative to DPS frequency, both models over-estimated dust emission frequency by up to one order of magnitude (RMSETEM = 6 days RMSEAEM = 4 days) but showed strong relations with DPS frequency suitable for calibrating models to observed dust emission. Theoretically, the TEM is incomplete in its formulation, which despite the pragmatic adjustment using the vegetation cover reciprocal, causes dust emission to be highly dependent on wind speed and over-estimates large ( 0.1 kg m−2 a−1) dust emission over vast vegetated areas. Consequently, the TEM produces considerable falsely positive change in dust emission, relative to the AEM. Since the main difference between the dust emission models is the treatment of aerodynamic roughness we conclude that its crude representation in the TEM has caused large, previously unknown, uncertainty in Earth System Models (ESMs). Our results indicate that tuning dust emission models to dust in the atmosphere has hidden for more than two decades, these TEM modelling weaknesses and its poor performance. The AEM overcomes these weaknesses and improves performance without tuning. In ESMs the AEM can be driven by available prognostic albedo to represent the fidelity of drag partition physics to reduce uncertainty of aerosol effects on, and responses to, contemporary and future environmental change.
Publisher: Wiley
Date: 2005
DOI: 10.1002/ESP.1185
Publisher: Elsevier BV
Date: 03-2015
Publisher: Springer Science and Business Media LLC
Date: 26-10-2015
DOI: 10.1038/NCLIMATE2829
Publisher: Wiley
Date: 07-1998
DOI: 10.1002/(SICI)1099-145X(199807/08)9:4<295::AID-LDR293>3.0.CO;2-J
Publisher: Elsevier BV
Date: 06-2015
DOI: 10.1016/J.JENVRAD.2015.02.015
Abstract: The caesium-137 ((137)Cs) technique for estimating net, time-integrated soil redistribution by the processes of wind, water and tillage is increasingly being used with repeated s ling to form a baseline to evaluate change over small (years to decades) timeframes. This interest stems from knowledge that since the 1950s soil redistribution has responded dynamically to different phases of land use change and management. Currently, there is no standard approach to detect change in (137)Cs-derived net soil redistribution and thereby identify the driving forces responsible for change. We outline recent advances in space-time s ling in the soil monitoring literature which provide a rigorous statistical and pragmatic approach to estimating the change over time in the spatial mean of environmental properties. We apply the space-time s ling framework, estimate the minimum detectable change of net soil redistribution and consider the information content and cost implications of different s ling designs for a study area in the Chinese Loess Plateau. Three phases (1954-1996, 1954-2012 and 1996-2012) of net soil erosion were detectable and attributed to well-documented historical change in land use and management practices in the study area and across the region. We recommend that the design for space-time s ling is considered carefully alongside cost-effective use of the spatial mean to detect and correctly attribute cause of change over time particularly across spatial scales of variation.
Publisher: American Society of Agricultural and Biological Engineers
DOI: 10.13031/2013.4583
Publisher: Elsevier BV
Date: 05-2003
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 06-2017
Publisher: Informa UK Limited
Date: 2001
Publisher: CSIRO
Date: 2013
Publisher: Elsevier BV
Date: 1998
Publisher: CSIRO Publishing
Date: 2014
DOI: 10.1071/SR14078
Abstract: Soil organic carbon (SOC) redistribution by soil erosion is fundamental to the C cycle and is a key component of global soil C accounting. Widespread conversion of cropland to forest and grassland and the adoption of conservation agriculture (minimum-till and no-till practices) worldwide and particularly in China since 2000, may have reduced wind erosion and increased SOC storage and ‘avoided’ CO2 emission. However, few SOC sequestration studies have separated changes in SOC stock caused by changes in land-use and management activity from net SOC redistribution due to reduced SOC erosion and SOC dust accumulation, particularly from in idual or short-term (months) wind erosion events. We used measurements of SOC and the short-lived natural radionuclide beryllium-7 (7Be, half-life 53.3 days) to estimate net SOC redistribution for changes in several land-use and management practices in Fengning County in North China. Compared with conventional tillage (CT), conservation grassland (CG) and minimum tillage (CL) showed enhanced SOC stocks (0–245 mm depth) of ~0.8 ± 0.03 and 2.0 ± 0.06 t C ha–1 year–1 as a consequence of their land-use conversion for 5 and 3 years, respectively. However, SOC erosion on CG (0.46 ± 0.04 t C ha–1 year–1) and CL (0.52 ± 0.04 t C ha–1 year–1) plots was 54% and 47%, respectively, less than on CT (0.99 ± 0.11 t C ha–1 year–1). Net C sequestration (0–245 mm), considering SOC redistribution for CG (0.27 ± 0.12 t C ha–1 year–1 5 years) and CL (1.53 ± 0.13 t C ha–1 year–1 3 years), revealed an overestimate of 196% and 31% without considering SOC redistribution (CG, 0.8 ± 0.03 t C ha–1 year–1 CL, 2.0 ± 0.06 t C ha–1 year–1), respectively, relative to CT. Reduced SOC erosion and/or SOC dust accumulation by vegetation–crop cover must be included when considering SOC sequestration induced by changes in land use and management.
Publisher: Elsevier BV
Date: 02-2013
Publisher: Elsevier BV
Date: 03-2016
Publisher: American Geophysical Union (AGU)
Date: 07-09-2023
DOI: 10.1029/2023JD038584
Abstract: Large‐scale classical dust cycle models, developed more than two decades ago, assume for simplicity that the Earth’s land surface is devoid of vegetation, reduce dust emission estimates using a vegetation cover complement, and calibrate estimates to observed atmospheric dust optical depth (DOD). Consequently, these models are expected to be valid for use with dust‐climate projections in Earth System Models. We reveal little spatial relation between DOD frequency and satellite observed dust emission from point sources (DPS) and a difference of up to two orders of magnitude. We compared DPS data to an exemplar traditional dust emission model (TEM) and the albedo‐based dust emission model (AEM) which represents aerodynamic roughness over space and time. Both models over‐estimated dust emission probability but showed strong spatial relations to DPS, suitable for calibration. Relative to the AEM calibrated to the DPS, the TEM over‐estimated large dust emission over vast vegetated areas and produced considerable false change in dust emission. It is difficult to avoid the conclusion that calibrating dust cycle models to DOD has hidden for more than two decades, these TEM modelling weaknesses. The AEM overcomes these weaknesses without using masks or vegetation cover data. Considerable potential therefore exists for ESMs driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections.
Publisher: American Geophysical Union (AGU)
Date: 11-04-2008
DOI: 10.1029/2007JD009233
Publisher: Springer Netherlands
Date: 2001
Publisher: Elsevier BV
Date: 07-1998
Publisher: Proceedings of the National Academy of Sciences
Date: 31-05-2016
Abstract: The role of soil erosion as a net sink or source of atmospheric CO 2 remains highly debated. This work quantifies national-scale land−atmosphere CO 2 fluxes induced by soil erosion. Severe water erosion in China has caused displacement of 180 ± 80 Mt C⋅y -1 of soil organic carbon during the last two decades, and the consequent land−atmosphere CO 2 flux from water erosion is a net CO 2 sink of 45 ± 25 Mt C⋅y -1 , equivalent to 8–37% of the terrestrial carbon sink previously assessed in China. This closes an important gap concerning large-scale estimation of lateral and vertical CO 2 fluxes from water erosion and highlights the importance of reducing uncertainty in assessing terrestrial carbon balance.
Publisher: Elsevier BV
Date: 07-2010
Publisher: Copernicus GmbH
Date: 12-05-2014
Abstract: Abstract. The debate about soil erosion substantially offsetting fossil fuel emissions and acting as an important source or sink of CO2 remains unresolved. There is little historical land use and management context to this debate which is central to Australia's recent past of European settlement, agricultural expansion and agriculturally-induced soil erosion. We use "catchment" scale (∼25 km2) estimates of 137Cs-derived net (1950s–1990) soil redistribution of all processes (wind, water and tillage) to calculate the net soil organic carbon (SOC) redistribution across Australia. We approximate the selective removal of SOC at net eroding locations and SOC enrichment of transported sediment and net depositional locations. We map net (1950s–1990) SOC redistribtion across Australia and estimate erosion by all processes ∼4 Tg SOC yr−1 which represents a~loss of ∼2% of the total carbon stock (0–10 cm) of Australia. Assuming this net SOC loss is mineralised, the flux (∼15 Tg CO2-e yr−1) represents an omitted 12% of CO2-e emissions from all carbon pools in Australia. Although a small source of uncertainty in the Australian carbon budget, the mass flux interacts with energy and water fluxes and its omission from land surface models likely creates more uncertainty than has been previously recognised.
Publisher: Wiley
Date: 30-01-2009
DOI: 10.1002/HYP.7214
Publisher: Elsevier BV
Date: 2018
Publisher: Elsevier BV
Date: 2009
Publisher: Elsevier BV
Date: 06-2013
Publisher: Wiley
Date: 04-2004
DOI: 10.1002/JOC.1021
Publisher: Informa UK Limited
Date: 25-04-2007
Publisher: Elsevier BV
Date: 06-2006
Publisher: Springer Science and Business Media LLC
Date: 1999
Publisher: CSIRO Publishing
Date: 2018
DOI: 10.1071/SR16157
Abstract: Traditional manual tillage using hand tools is widely used by local farmers in hilly and mountainous regions in China and many South-east Asian countries. Manual tillage could result in severe soil erosion, redistributing slopes from upslope areas (erosion) to lower slopes (deposition). This soil redistribution process may potentially affect the soil carbon cycle, but few studies have quantified soil CO2 emission under different manual tillage practices. In the present study we evaluated the soil redistribution and its effects on in situ CO2 emission as affected by manual tillage of different intensities on three short slopes representing typical cultivated landscapes on the Loess Plateau. Soils were removed at 2, 6 and 10 cm depths by three types of hand tools, namely a hoe, mattock and spade respectively, from the upslope and subsequently accumulated at the downslope to simulate soil erosion and deposition processes by traditional manual tillage. Across the tilled hillslopes, soil CO2 emission was reduced at sites of erosion but enhanced at sites of deposition. Tillage with greater intensity (i.e. hoeing mattocking spading) resulted in greater change in CO2 emission. This change in soil CO2 emission was largely associated with the depletion of soil organic carbon (SOC) stocks at erosion sites and the increments of SOC available for decomposition at deposition sites. Moreover, with increasing tillage intensity, soil redistribution by manual tillage shifted the hillslope from a C sink to C neutral or even a C source. Furthermore, manual tillage resulted in substantial changes in soil CO2 emission and redistributed soil in amounts that dwarf animal-powered tillage. The results of the present study imply that manual tillage-induced soil redistribution could have a large effect on the C balance across the local landscape and therefore may have considerable implications for estimates of regional and global C budgets.
Publisher: Wiley
Date: 25-05-2011
Publisher: Elsevier BV
Date: 07-2016
Publisher: Springer Netherlands
Date: 2001
Publisher: Wiley
Date: 05-09-2017
DOI: 10.1002/FEE.1530
Publisher: Elsevier BV
Date: 11-2007
Publisher: Elsevier BV
Date: 09-2023
Publisher: CSIRO
Date: 2011
Publisher: CSIRO
Date: 2010
Publisher: Elsevier BV
Date: 12-2016
Publisher: Elsevier BV
Date: 07-2003
Publisher: Elsevier BV
Date: 12-2015
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-8203
Abstract: & & Atmospheric mineral dust has a significant impact on many Earth& #8217 s systems, including the energy budget, with subsequent alterations to the climate. These impacts occur directly, by altering the radiative properties of the atmosphere, or indirectly, through changes in cloud formation and precipitation rates. Accordingly, dust emission processes, determined by dust emission models are required to accurately predict climate response in climate change scenarios. Dust emission models remain poorly constrained either by parameterisations or available data and measurements of dust in the atmosphere have long been used for their calibration. However, there is growing recognition that this calibration to atmospheric dust confounds the magnitude and frequency of emission from dust sources and hides potential weaknesses in dust emission model formulation. In the satellite era, dichotomous (presence=1 or absence=0) observations of dust emission point sources (DPS) provide a valuable inventory of regional dust emission. We used these DPS data to evaluate dust emission model performance using coincidence of simulated and observed dust emission (or lack of emission). We evaluated the recently developed albedo-based dust emission model (AEM), with some inherited constraints it dynamically represents land surface roughness (vegetation to grain scale), varying over space (500m resolution) and through time (daily), improving predictions of wind friction velocity at the land surface (). & Using a& total of 37,352 unique DPS locations aggregated into 1,945 1& #176 grid boxes to harmonise data across the studies we identified a total of 59,688 dust emissions.& The DPS data alone revealed that dust emission rarely recurs at the same location, even in North Africa and the Middle East (occurring 1.8% of the time), indicating that dust emission is an extreme, large wind speed event. The AEM over-estimated the occurrence of dust emission by up to 2 orders of magnitude, coincided with dichotomous observations 71% of the time but incorrectly simulated dust emission 27%. Our analysis indicates that a key constraint to dust emission modelling is that entrainment threshold is typically too small, needed to vary over space and time and at a scale consistent with the model. During observed dust emission, & & was often too small because modelled wind speeds (ERA5 11 km) were too small. The absence of any limit to sediment supply caused the AEM to simulate dust emission whenever & em& P& /em& (& em& u& /em& & em& & sub& s*& /sub& & /em& & & & & em& u& /em& & em& & sub& *ts& /sub& & /em& ), producing many false positives when and where wind speeds were frequently large. These results demonstrate the wind range of constraints to existing dust emission modelling, which will continue to hinder dust-climate projections. The improvement of dust emission modelling is essential against dust emission point source data to provide a consistent, reproducible, and valid framework for routine evaluation and potential model optimisation.& & & & & & &
Publisher: Wiley
Date: 2003
DOI: 10.1002/ESP.463
Publisher: Wiley
Date: 06-07-2022
Publisher: Elsevier BV
Date: 08-1999
Publisher: Elsevier BV
Date: 2020
Publisher: Wiley
Date: 30-03-2012
Publisher: MDPI AG
Date: 24-04-2015
DOI: 10.3390/SU7054870
Publisher: Informa UK Limited
Date: 11-2001
Publisher: Elsevier BV
Date: 02-2003
Publisher: Springer Netherlands
Date: 2000
Publisher: Copernicus GmbH
Date: 29-09-2014
Abstract: Abstract. The debate remains unresolved about soil erosion substantially offsetting fossil fuel emissions and acting as an important source or sink of CO2. There is little historical land use and management context to this debate, which is central to Australia's recent past of European settlement, agricultural expansion and agriculturally-induced soil erosion. We use "catchment" scale (∼25 km2) estimates of 137Cs-derived net (1950s–1990) soil redistribution of all processes (wind, water and tillage) to calculate the net soil organic carbon (SOC) redistribution across Australia. We approximate the selective removal of SOC at net eroding locations and SOC enrichment of transported sediment and net depositional locations. We map net (1950s–1990) SOC redistribution across Australia and estimate erosion by all processes to be ∼4 Tg SOC yr−1, which represents a loss of ∼2% of the total carbon stock (0–10 cm) of Australia. Assuming this net SOC loss is mineralised, the flux (∼15 Tg CO2-equivalents yr−1) represents an omitted 12% of CO2-equivalent emissions from all carbon pools in Australia. Although a small source of uncertainty in the Australian carbon budget, the mass flux interacts with energy and water fluxes, and its omission from land surface models likely creates more uncertainty than has been previously recognised.
Publisher: Elsevier BV
Date: 09-1996
Publisher: Elsevier BV
Date: 03-2011
Publisher: Wiley
Date: 06-08-2012
Publisher: American Geophysical Union (AGU)
Date: 07-2010
DOI: 10.1029/2010GL043486
Publisher: Elsevier BV
Date: 09-2016
Publisher: Elsevier BV
Date: 03-2015
DOI: 10.1016/J.JENVRAD.2014.12.007
Abstract: The (137)Cs technique for estimating net time-integrated soil redistribution is valuable for understanding the factors controlling soil redistribution by all processes. The literature on this technique is dominated by studies of in idual fields and describes its typically time-consuming nature. We contend that the community making these studies has inappropriately assumed that many (137)Cs measurements are required and hence estimates of net soil redistribution can only be made at the field scale. Here, we support future studies of (137)Cs-derived net soil redistribution to apply their often limited resources across scales of variation (field, catchment, region etc.) without compromising the quality of the estimates at any scale. We describe a hybrid, design-based and model-based, stratified random s ling design with composites to estimate the s ling variance and a cost model for fieldwork and laboratory measurements. Geostatistical mapping of net (1954-2012) soil redistribution as a case study on the Chinese Loess Plateau is compared with estimates for several other s ling designs popular in the literature. We demonstrate the cost-effectiveness of the hybrid design for spatial estimation of net soil redistribution. To demonstrate the limitations of current s ling approaches to cut across scales of variation, we extrapolate our estimate of net soil redistribution across the region, show that for the same resources, estimates from many fields could have been provided and would elucidate the cause of differences within and between regional estimates. We recommend that future studies evaluate carefully the s ling design to consider the opportunity to investigate (137)Cs-derived net soil redistribution across scales of variation.
Publisher: American Meteorological Society
Date: 2021
Abstract: Global reanalysis products are important tools across disciplines to study past meteorological changes and are especially useful for wind energy resource evaluations. Studies of observed wind speed show that land surface wind speed declined globally since the 1960s (known as global terrestrial stilling) but reversed with a turning point around 2010. Whether the declining trend and the turning point have been captured by reanalysis products remains unknown so far. To fill this research gap, a systematic assessment of climatological winds and trends in five reanalysis products (ERA5, ERA-Interim, MERRA-2, JRA-55, and CFSv2) was conducted by comparing gridcell time series of 10-m wind speed with observational data from 1439 in situ meteorological stations for the period 1989–2018. Overall, ERA5 is the closest to the observations according to the evaluation of climatological winds. However, substantial discrepancies were found between observations and simulated wind speeds. No reanalysis product showed similar change to that of the global observations, although some showed regional agreement. This discrepancy between observed and reanalysis land surface wind speed indicates the need for prudence when using reanalysis products for the evaluation and prediction of winds. The possible reasons for the inconsistent wind speed trends between reanalysis products and observations are analyzed. The results show that wind energy production should select different products for different regions to minimize the discrepancy with observations.
Publisher: Copernicus GmbH
Date: 10-01-2022
DOI: 10.5194/GMD-2021-423
Abstract: Abstract. Measurements of dust in the atmosphere have long been used to calibrate dust emission models. However, there is growing recognition that atmospheric dust confounds the magnitude and frequency of emission from dust sources and hides potential weaknesses in dust emission model formulation. In the satellite era, dichotomous (presence = 1 or absence = 0) observations of dust emission point sources (DPS) provide a valuable inventory of regional dust emission. We used these DPS data to develop an open and transparent framework to routinely evaluate dust emission model (development) performance using coincidence of simulated and observed dust emission (or lack of emission). To illustrate the utility of this framework, we evaluated the recently developed albedo-based dust emission model (AEM) which included the traditional entrainment threshold (u*ts) at the grain scale, fixed over space and static over time, with sediment supply infinite everywhere. For comparison with the dichotomous DPS data, we reduced the AEM simulations to its frequency of occurrence in which soil surface wind friction velocity (us*) exceeds the u*ts, P(us* u*ts). We used a global collation of nine DPS datasets from established studies to describe the spatio-temporal variation of dust emission frequency. A total of 37,352 unique DPS locations were aggregated into 1,945 1° grid boxes to harmonise data across the studies which identified a total of 59,688 dust emissions. The DPS data alone revealed that dust emission does not usually recur at the same location, are rare (1.8 %) even in North Africa and the Middle East, indicative of extreme, large wind speed events. The AEM over-estimated the occurrence of dust emission by between 1 and 2 orders of magnitude. More diagnostically, the AEM simulations coincided with dichotomous observations ~71 % of the time but simulated dust emission ~27 % of the time when no dust emission was observed. Our analysis indicates that u*ts was typically too small, needed to vary over space and time, and at the grain-scale u*ts is incompatible with the us* scale (MODIS 500 m). During observed dust emission, us* was too small because wind speeds were too small and/or the wind speed scale (ERA5 11 km) is incompatible with the us* scale. The absence of any limit to sediment supply caused the AEM to simulate dust emission whenever P (us* u*ts), producing many false positives when and where wind speeds were frequently large. Dust emission model scaling needs to be reconciled and new parameterisations are required for u*ts and to restrict sediment supply varying over space and time. Whilst u*ts remains poorly constrained and unrealistic assumptions persist about sediment supply and availability, the DPS data provide a basis for the calibration of dust emission models for operational use. As dust emission models develop, these DPS data provide a consistent, reproducible, and valid framework for their routine evaluation and potential model optimisation. This work emphasises the growing recognition that dust emission models should not be evaluated against atmospheric dust.
Publisher: Elsevier BV
Date: 2023
Publisher: Wiley
Date: 10-2003
DOI: 10.1002/ESP.1036
Publisher: Copernicus GmbH
Date: 08-06-2015
Abstract: Abstract. Atmospheric deposition of iron (Fe) plays an important role in controlling oceanic primary productivity. However, the sources of Fe in the atmosphere are not well understood. In particular, the combustion sources of Fe and the subsequent deposition to the oceans have been accounted for in only few ocean biogeochemical models of the carbon cycle. Here we used a mass-balance method to estimate the emissions of Fe from the combustion of fossil fuels and biomass by accounting for the Fe contents in fuel and the partitioning of Fe during combustion. The emissions of Fe attached to aerosols from combustion sources were estimated by particle size, and their uncertainties were quantified by a Monte Carlo simulation. The emissions of Fe from mineral sources were estimated using the latest soil mineralogical database to date. As a result, the total Fe emissions from combustion averaged for 1960–2007 were estimated to be 5.3 Tg yr−1 (90% confidence of 2.3 to 12.1). Of these emissions, 1, 27 and 72% were emitted in particles 1 μm (PM1), 1–10 μm (PM1-10), and 10 μm (PM 10), respectively, compared to a total Fe emission from mineral dust of 41.0 Tg yr−1 in a log-normal distribution with a mass median diameter of 2.5 μm and a geometric standard deviation of 2. For combustion sources, different temporal trends were found in fine and medium-to-coarse particles, with a notable increase in Fe emissions in PM1 since 2000 due to an increase in Fe emission from motor vehicles (from 0.008 to 0.0103 Tg yr−1 in 2000 and 2007, respectively). These emissions have been introduced in a global 3-D transport model run at a spatial resolution of 0.94° latitude by 1.28° longitude to evaluate our estimation of Fe emissions. The modelled Fe concentrations as monthly means were compared with the monthly (57 sites) or daily (768 sites) measured concentrations at a total of 825 s ling stations. The deviation between modelled and observed Fe concentrations attached to aerosols at the surface was within a factor of 2 at most s ling stations, and the deviation was within a factor of 1.5 at s ling stations dominated by combustion sources. We analysed the relative contribution of combustion sources to total Fe concentrations over different regions of the world. The new mineralogical database led to a modest improvement in the simulation relative to station data even in dust-dominated regions, but could provide useful information on the chemical forms of Fe in dust for coupling with ocean biota models. We estimated a total Fe deposition sink of 8.4 Tg yr−1 over global oceans, 7% of which originated from the combustion sources. Our central estimates of Fe emissions from fossil fuel combustion (mainly from coal) are generally higher than those in previous studies, although they are within the uncertainty range of our estimates. In particular, the higher than previously estimated Fe emission from coal combustion implies a larger atmospheric anthropogenic input of soluble Fe to the northern Atlantic and northern Pacific Oceans, which is expected to enhance the biological carbon pump in those regions.
Publisher: Elsevier BV
Date: 06-2015
Publisher: Wiley
Date: 16-10-2012
DOI: 10.1111/GCB.12030
Abstract: The movement of soil organic carbon (SOC) during erosion and deposition events represents a major perturbation to the terrestrial carbon cycle. Despite the recognized impact soil redistribution can have on the carbon cycle, few major carbon accounting models currently allow for soil mass flux. Here, we modified a commonly used SOC model to include a soil redistribution term and then applied it to scenarios which explore the implications of unrecognized erosion and deposition for SOC accounting. We show that models that assume a static landscape may be calibrated incorrectly as erosion of SOC is hidden within the decay constants. This implicit inclusion of erosion then limits the predictive capacity of these models when applied to sites with different soil redistribution histories. Decay constants were found to be 15-50% slower when an erosion rate of 15 t soil ha(-1) yr(-1) was explicitly included in the SOC model calibration. Static models cannot account for SOC change resulting from agricultural management practices focused on reducing erosion rates. Without accounting for soil redistribution, a soil s ling scheme which uses a fixed depth to support model development can create large errors in actual and relative changes in SOC stocks. When modest levels of erosion were ignored, the combined uncertainty in carbon sequestration rates was 0.3-1.0 t CO2 ha(-1) yr(-1) . This range is similar to expected sequestration rates for many management options aimed at increasing SOC levels. It is evident from these analyses that explicit recognition of soil redistribution is critical to the success of a carbon monitoring or trading scheme which seeks to credit agricultural activities.
Publisher: MDPI AG
Date: 06-11-2017
DOI: 10.3390/RS9111134
Publisher: Wiley
Date: 10-2004
DOI: 10.1002/JOC.1072
Publisher: Springer Science and Business Media LLC
Date: 18-11-2019
Publisher: Elsevier BV
Date: 07-2023
Publisher: American Geophysical Union (AGU)
Date: 18-08-2020
DOI: 10.1029/2019JD031978
Abstract: Obtaining reliable estimates of aerodynamic roughness is necessary to interpret and accurately predict aeolian sediment transport dynamics. However, inherent uncertainties in field measurements and models of surface aerodynamic properties continue to undermine aeolian research, monitoring, and dust modeling. A new relation between aerodynamic shelter and land surface shadow has been established at the wind tunnel scale, enabling the potential for estimates of wind erosion and dust emission to be obtained across scales from albedo data. Here, we compare estimates of wind friction velocity ( u * ) derived from traditional methods (wind speed profiles) with those derived from the albedo model at two separate scales using bare soil patch (via net radiometers) and landscape (via Moderate Resolution Imaging Spectroradiometer [MODIS] 500 m) data sets. Results show that profile‐derived estimates of u * are highly variable in anisotropic surface roughness due to changes in wind direction and fetch. Wind speed profiles poorly estimate soil surface (bed) wind friction velocities necessary for aeolian sediment transport research and modeling. Albedo‐based estimates of u * at both scales have small variability because the estimate is integrated over a defined, fixed area and resolves the partition of wind momentum between roughness elements and the soil surface. We demonstrate that the wind tunnel‐based calibration of albedo for predicting wind friction velocities at the soil surface ( u s* ) is applicable across scales. The albedo‐based approach enables consistent and reliable drag partition correction across scales for model and field estimates of u s* necessary for wind erosion and dust emission modeling.
Publisher: American Geophysical Union (AGU)
Date: 27-02-2008
DOI: 10.1029/2007JD009032
Publisher: Elsevier BV
Date: 06-2018
Publisher: Springer Netherlands
Date: 2005
Publisher: Elsevier BV
Date: 02-2007
Publisher: American Geophysical Union (AGU)
Date: 2016
DOI: 10.1002/2015GB005239
Publisher: Elsevier BV
Date: 03-2020
Publisher: Elsevier BV
Date: 09-1999
Publisher: IEEE
Date: 2002
Publisher: Elsevier BV
Date: 02-2022
Publisher: American Geophysical Union (AGU)
Date: 13-09-2012
DOI: 10.1029/2012JD017718
Publisher: American Meteorological Society
Date: 15-08-2007
DOI: 10.1175/JCLI4233.1
Abstract: With few exceptions, spatial estimation of rainfall typically relies on information in the spatial domain only. In this paper, a method that utilizes information in time and space and provides an assessment of estimate uncertainty is used to create a gridded monthly rainfall dataset for the United Kingdom over the period 1980–87. Observed rainfall profiles within the region were regarded as the sum of a deterministic temporal trend and a stochastic residual component. The parameters of the temporal trend components established at the rain gauges were interpolated in space, accounting for their auto- and cross correlation, and for relationships with ancillary spatial variables. Stochastic Gaussian simulation was then employed to generate alternative realizations of the spatiotemporal residual component, which were added to the estimated trend component to yield realizations of rainfall (after distributional corrections). In total, 40 realizations of rainfall were generated for each month of the 8-yr period. The methodology resulted in reasonably accurate estimates of rainfall but underestimated in northwest and north Scotland and northwest England. The cause for the underestimation was identified as a weak relationship between local rainfall and the spatial area average rainfall, used to estimate the temporal trend model in these regions, and suggestions were made for improvement. The strengths of this method are the utilization of information from the time and space domain, and the assessment of spatial uncertainty in the estimated rainfall values.
Publisher: Wiley
Date: 29-07-2013
DOI: 10.1111/GCB.12305
Abstract: Soil erosion redistributes soil organic carbon (SOC) within terrestrial ecosystems, to the atmosphere and oceans. Dust export is an essential component of the carbon (C) and carbon dioxide (CO(2)) budget because wind erosion contributes to the C cycle by removing selectively SOC from vast areas and transporting C dust quickly offshore augmenting the net loss of C from terrestrial systems. However, the contribution of wind erosion to rates of C release and sequestration is poorly understood. Here, we describe how SOC dust emission is omitted from national C accounting, is an underestimated source of CO(2) and may accelerate SOC decomposition. Similarly, long dust residence times in the unshielded atmospheric environment may considerably increase CO(2) emission. We developed a first approximation to SOC enrichment for a well-established dust emission model and quantified SOC dust emission for Australia (5.83 Tg CO(2)-e yr(-1)) and Australian agricultural soils (0.4 Tg CO(2)-e yr(-1)). These amount to underestimates for CO(2) emissions of ≈10% from combined C pools in Australia (year = 2000), ≈5% from Australian Rangelands and ≈3% of Australian Agricultural Soils by Kyoto Accounting. Northern hemisphere countries with greater dust emission than Australia are also likely to have much larger SOC dust emission. Therefore, omission of SOC dust emission likely represents a considerable underestimate from those nations' C accounts. We suggest that the omission of SOC dust emission from C cycling and C accounting is a significant global source of uncertainty. Tracing the fate of wind-eroded SOC in the dust cycle is therefore essential to quantify the release of CO(2) from SOC dust to the atmosphere and the contribution of SOC deposition to downwind C sinks.
Publisher: American Geophysical Union (AGU)
Date: 04-2010
DOI: 10.1029/2019JF005042
Publisher: Springer Netherlands
Date: 1997
Publisher: Informa UK Limited
Date: 03-2006
Publisher: Elsevier BV
Date: 05-2022
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.SCITOTENV.2018.04.146
Abstract: Soil erosion by water is accelerated by a warming climate and negatively impacts water security and ecological conservation. The Tibetan Plateau (TP) has experienced warming at a rate approximately twice that observed globally, and heavy precipitation events lead to an increased risk of erosion. In this study, we assessed current erosion on the TP and predicted potential soil erosion by water in 2050. The study was conducted in three steps. During the first step, we used the Revised Universal Soil Equation (RUSLE), publicly available data, and the most recent earth observations to derive estimates of annual erosion from 2002 to 2016 on the TP at 1-km resolution. During the second step, we used a multiple linear regression (MLR) model and a set of climatic covariates to predict rainfall erosivity on the TP in 2050. The MLR was used to establish the relationship between current rainfall erosivity data and a set of current climatic and other covariates. The coefficients of the MLR were generalised with climate covariates for 2050 derived from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models to estimate rainfall erosivity in 2050. During the third step, soil erosion by water in 2050 was predicted using rainfall erosivity in 2050 and other erosion factors. The results show that the mean annual soil erosion rate on the TP under current conditions is 2.76tha
Publisher: Springer Netherlands
Date: 2005
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2019
End Date: 2022
Funder: Natural Environment Research Council
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