Adrian Chappell

Fertilizing the Amazon and equatorial Atlantic with West African dust

Atmospheric mineral dust plays a vital role in Earths climate and biogeochemical cycles. The Bodele Depression in Chad has been identified as the single biggest source of atmospheric mineral dust on Earth. Dust eroded from the Bodele is blown across the Atlantic Ocean towards South America. The mineral dust contains micronutrients such as Fe and P that have the potential to act as a fertilizer, increasing primary productivity in the Amazon rain forest as well as the equatorial Atlantic Ocean, and thus leading to N2 fixation and CO2 drawdown. We present the results of chemical analysis of 28 dust samples collected from the source area, which indicate that up to 6.5 Tg of Fe and 0.12 Tg of P are exported from the Bodele Depression every year. This suggests that the Bodele may be a more significant micronutrient supplier than previously proposed.

Toward more realistic projections of soil carbon dynamics by Earth system models

Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.

Using the ‘Grieving’ Process and Learning Journals to Evaluate Students' Responses to Problem-Based Learning in an Undergraduate Geography Curriculum

Problem-based learning (PBL) works with a series of problems that form the syllabus for a course or the basis of a curriculum. Learning occurs through the definition of the problem and its attempted resolution. PBL is challenging but offers much potential for geography with its interdisciplinary character. The challenge to geography lecturers is to become facilitators of student learning. This paper outlines an approach to PBL in a single module in the first year of an undergraduate degree programme. The approach involved cycles of learning and employed learning journals as a means of encouraging student reflection. These journals show how students struggled to come to terms with PBL and how it challenged their conceptions of learning. Woodss (1994) ‘grieving process’ is a helpful way of understanding this process. The students did seem to make the transition from ‘teach me’ to ‘help me to learn’ and to appreciate the benefits of PBL.

The implications for dust emission modeling of spatial and vertical variations in horizontal dust flux and particle size in the Bodele Depression, Northern Chad

The Bodele Depression has been confirmed as the single largest source of atmospheric mineral dust on Earth. It is a distinctive source because of its large exposure of diatomite and the presence of mega-barchan dunes. Direct measurements of horizontal dust flux and particle size were made to investigate dust emission processes and for comparison with mechanisms of emission assumed in current dust models. More than 50 masts, with traps mounted on each, were located across and downwind of three barchans in 56 km2 study area of the eastern Bodele. The size-distribution of surface material is bi-modal; there are many fine dust modes and a mixed mineralogy with a particle density three times smaller than quartz. Horizontal fluxes (up to 70 m above the playa) of particles, up to 1000 μm in diameter, are produced frequently from the accelerated flow over and around the barchans, even in below-threshold shear conditions on the diatomite playa. Our data on dust sizes do not conform to retrievals of dust size distributions from radiance measurements made in the same area. Dust emission models for the region may need to be revised to account for: saltators in the Bodele, which are a mixture of quartz sand and diatomite flakes; the great spatial and vertical variation in the abundance, mass and density of dust and abraders; and the patterns of surface erodibility. All of these have important local effects on the vertical dust flux and its particle sizes.

The timing of climbing dune formation in southwestern Niger: fluvio-aeolian interactions and the rôle of sand supply

Contemporary gully erosion has exposed sections in a climbing dune which is banked up against ferricrete terraces along the southern bank of the Niger River in southwestern Niger. The main sand transport direction in this area is from northeast to southwest, and the immediate source of the dune sand is the Niger River. Dune stratigraphy contains evidence of episodic, fluvially controlled accretion, separated by two palaeosols. Channel fills and stone stringers suggest occasional alluvial and colluvial reworking. Infra-red stimulated luminescence dating of the aeolian sands shows that dune development occurred episodically during the African Humid Period (15–5 ka), probably in response to an increase in sediment supply from the Niger River. Soil development occurred during the relatively short-lived period of enhanced aridity associated with the Younger Dryas, driven by weakening of the southwesterly monsoon circulation. Climate-driven dune accretion and further soil development occurred during the Holocene period.

Lateral transport of soil carbon and land−atmosphere CO2 flux induced by water erosion in China

Significance The role of soil erosion as a net sink or source of atmospheric CO2 remains highly debated. This work quantifies national-scale land−atmosphere CO2 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 CO2 flux from water erosion is a net CO2 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 CO2 fluxes from water erosion and highlights the importance of reducing uncertainty in assessing terrestrial carbon balance. Soil erosion by water impacts soil organic carbon stocks and alters CO2 fluxes exchanged with the atmosphere. The role of erosion as a net sink or source of atmospheric CO2 remains highly debated, and little information is available at scales larger than small catchments or regions. This study attempts to quantify the lateral transport of soil carbon and consequent land−atmosphere CO2 fluxes at the scale of China, where severe erosion has occurred for several decades. Based on the distribution of soil erosion rates derived from detailed national surveys and soil carbon inventories, here we show that water erosion in China displaced 180 ± 80 Mt C⋅y−1 of soil organic carbon during the last two decades, and this resulted a net land sink for atmospheric CO2 of 45 ± 25 Mt C⋅y−1, equivalent to 8–37% of the terrestrial carbon sink previously assessed in China. Interestingly, the “hotspots,” largely distributed in mountainous regions in the most intensive sink areas (>40 g C⋅m−2⋅y−1), occupy only 1.5% of the total area suffering water erosion, but contribute 19.3% to the national erosion-induced CO2 sink. The erosion-induced CO2 sink underwent a remarkable reduction of about 16% from the middle 1990s to the early 2010s, due to diminishing erosion after the implementation of large-scale soil conservation programs. These findings demonstrate the necessity of including erosion-induced CO2 in the terrestrial budget, hence reducing the level of uncertainty.

Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia

Soil loss causes environmental degradation and reduces agricultural productivity over large areas of the world. Here, we use the latest earth observation data and soil visible-near infrared (vis-NIR) spectroscopy to estimate the factors of the Revised Universal Soil Loss Equation (RUSLE) and to model soil loss by water erosion in Australia. We estimate rainfall erosivity (R) using the Tropical Rainfall Measuring Mission (TRMM); slope length and steepness (L and S) using a 3-arcsec Shuttle Radar Topography Mission (SRTM) digital elevation model; cover management (C) and control practice (P) using the national dynamic land cover dataset (DLCD) of Australia derived from the moderate-resolution imaging spectroradiometer (MODIS); and soil erodibility (K) using vis-NIR estimates of the contents of sand, silt, clay and organic carbon in Australian soil. We model K using a machine-learning algorithm with environmental predictors selected to best capture the factors that influence erodibility and produced a digital map of K. We use the derived RUSLE factors to estimate soil loss at 1-km resolution across the whole of Australia. We found that the potential gross average soil loss by water erosion in Australian is 1.86?t?ha-1?y-1 (95% confidence intervals of 1.78 and 1.93?t?ha-1?y-1), equivalent to a total of 1242?×?106 tonnes of soil lost annually (95% confidence intervals of 1195 and 1293?t?×?106?y-1). Our estimates of erosion are generally smaller than previous continental estimates using the RUSLE, but particularly in croplands, which might indicate that soil conservation practices effectively reduced erosion in Australia. However we also identify localized regions with large erosion in northern Australia and northeastern Queensland. Erosion in these areas carries sediments laden with nitrogen, phosphorus and pollutants from agricultural production into the sea, negatively affecting marine ecosystems. We used the best available data and our results provide better estimates compared to previous assessments. Our approach will be valuable for other large, sparsely sampled areas of the world where assessments of soil erosion are needed. Display Omitted Remote sensing, spectroscopy and digital soil mapping were combined to model and map erosion in Australia using the RUSLE.The potential average soil loss by water erosion in Australia is 1.86 (?0.6) t?ha-1?y-1.The estimates are the most current for Australia.Our approach is novel and will be valuable for other large and sparsely sampled areas of the world.

Spatiotemporal Stochastic Simulation of Monthly Rainfall Patterns in the United Kingdom (1980–87)

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.

Enhancing Wind Erosion Monitoring and Assessment for U.S. Rangelands

On the Ground Wind erosion is a major resource concern for rangeland managers because it can impact soil health, ecosystem structure and function, hydrologic processes, agricultural production, and air quality. Despite its significance, little is known about which landscapes are eroding, by how much, and when. The National Wind Erosion Research Network was established in 2014 to develop tools for monitoring and assessing wind erosion and dust emissions across the United States. The Network, currently consisting of 13 sites, creates opportunities to enhance existing rangeland soil, vegetation, and air quality monitoring programs. Decision-support tools developed by the Network will improve the prediction and management of wind erosion across rangeland ecosystems.

Cost-effective sampling of 137Cs-derived net soil redistribution: part 1 – estimating the spatial mean across scales of variation

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 individual 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 sampling design with composites to estimate the sampling 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 sampling 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 sampling 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 sampling design to consider the opportunity to investigate (137)Cs-derived net soil redistribution across scales of variation.