Low Spatial Variability Research Articles

The Importance of Systematic Spatial Variability in the Surface Heat Flux of a Large Lake: A Multiannual Analysis for Lake Geneva

AbstractThe spatiotemporal surface heat flux (SurHF) distribution over Lake Geneva, the largest lake in Western Europe, was estimated for a 7‐year period (2008–2014). Data sources included hourly maps of over‐the‐lake assimilated meteorological data from a validated numerical weather model and lake surface water temperature (LSWT) from satellite imagery. A set of bulk algorithms, previously optimized and calibrated at two locations in Lake Geneva, was used. Results indicate a systematic long‐term average spatial range of >40 Wm‐2 between different parts of the lake and little year‐to‐year variability. This variability is mainly due to topographically induced wind sheltering over parts of the lake, which in turn produces spatial variability in the sensible and latent heat fluxes. These results are supported by a systematic spatial heat content variability obtained from long‐term temperature profile measurements in the lake. During spring, a lower SurHF spatial range was evident. Unlike other seasons, the spring spatial variability of air‐water temperature differences and, to a lesser extent, the global radiation variability resulting from sheltering by the mountainous topography were the main drivers of the SurHF spatial variability. Analysis of the atmospheric thermal boundary layer showed stable conditions from March to early June and unstable conditions for the rest of the year. This regime change can explain the low SurHF spatial variability observed during spring. The results emphasize that spatial variability in meteorological and LSWT patterns, and consequently in the spatiotemporal SurHF data, should be considered when assessing the time evolution of the heat budget of large lakes.

Low concentrations and low spatial variability of marine microplastics in oysters (Crassostrea virginica) in a rural Georgia estuary.

Microplastics are an emerging concern for the health of marine ecosystems. In the southeastern US, the filter-feeding Eastern oyster, Crassostrea virginica, is susceptible to microplastic ingestion. We quantified the distribution of microplastics within adult oysters (harvestable size >7.5 cm) from 28 reefs throughout a rural estuary with limited riverine inputs (St. Catherines Sound, Georgia). To determine which variables best predict microplastic concentration in oysters, we also quantified oyster recruitment, distance to ocean, fetch, and water body width. Oysters averaged 0.72 microplastic particles per individual (0.18 particles per gram wet mass); microfragments and microplastics were equally abundant. Although microplastic concentrations were low, multivariate models identified a positive effect of water body width on the site-level concentration of plastic microfibers; average microfragment length was affected by fetch. Our work informs a growing understanding of microplastic distribution in coastal estuaries, providing an important rural contrast to the urbanized estuaries that have been examined.

Ribbed mussel (Geukensia demissa) filtration services are driven by seasonal temperature and site-specific seston variability

Ribbed mussels (Geukensia demissa) are ubiquitous intertidal invertebrates of eastern North American salt marshes, often living in close association with salt marsh cordgrass Spartina alterniflora. They are typically concentrated at the lower extent of the vegetated zone along tidal channels, but are also found across the marsh platform. As filter-feeding bivalves, ribbed mussels remove suspended micro-particulate matter and associated nutrients from the water column, potentially resulting in benefits to water quality. However, the magnitude of such ecosystem services may depend on temporal and spatial variation in the mussel's water clearance and particle filtration rates. The goal of this study was to identify sources of variation regarding the distribution of ribbed mussel filtration services.Seasonal-spring, summer, and fall- water clearance and particle filtration rates were quantified for ribbed mussels collected from three habitats- low marsh along main tidal channels; low marsh along intra-marsh creek networks; and from the high marsh- and replicated in four representative marshes- three in New Jersey and one in Rhode Island, USA, normalized by mussel dry tissue biomass. Clearance rates varied with seasonal temperatures, but exhibited low spatial variability among habitats and marshes, highlighting the importance of understanding the temporal physiological variability when calculating annual rates. In contrast, filtration rates were highly variable, driven largely by spatial variation in seston availability. Annual bulk filtration services provided by ribbed mussels are therefore largely driven by both temporal variability in water processing and spatial variations in food availability, and can therefore be predicted by integrating seasonal clearance rates and site-specific seston composition.

The use of electronic coil location control for focal magnetic stimulation at the cervical level

BackgroundAccurate re-positioning of the coil is challenging in magnetic stimulation at the cervical spinal level. The applicability of coil location control for this type of stimulation is unexplored. New methodUtilizing a figure-of-eight coil and anatomy-specific models of the magnetic stimulation system, we developed a novel technique that enables probing corticospinal excitability at the cervical spinal level. Magnetic stimulation was performed in 9 healthy subjects at C2-C6 spinal levels using a figure-of-eight coil and a coil tracking system. MEPs were recorded from the abductor digiti minimi muscle. The functioning of the coil tracking system was tested with an estimated electric field maximum (eEFM) above the C1 cervical level (group 1) and below (group 2). Motor-evoked potential (MEP) reproducibility was assessed with intra-class correlation coefficient (ICC). ResultsThe use of coil location control in cervical level focal magnetic stimulation enabled the recording of highly reproducible MEPs. Within one co-registration, the ICC 95% confidence interval (CI) in group 1 was 0.89-0.99 and in group 2 was 0.24-0.85. Comparison with existing methods and conclusionsThis method can be used for accurate maintenance and retrieval of the focal coil position at the cervical level with low spatial variability during stimulation. Existing methodologies employ determination of the coil location based on external landmarks, which makes the procedure cumbersome. This technique can optimize existing stimulation protocols and facilitate development of navigated spinal stimulation.

Spatial variability of PM10, PM2.5 and PM chemical components in an industrialised rural area within a mountainous terrain

We describe the measurement and spatial variability of particulate matter (PM) chemical composition, PM10 and PM2.5 in the Greater Tubatse Municipality, South Africa. Monthly samples were collected over 12 months (July 2015 to June 2016) using the inexpensive and easy to operate passive samplers of the University of North Carolina. Sites for sample collection were located at private residences, a church, a hospital and a school. Concentrations of PM10, PM2.5 and PM chemical components were determined using computer-controlled scanning electron microscopy with energy-dispersive X-ray spectroscopy. The annual observed concentrations at all sites were below the South African National Ambient Air Quality Standards of 40 μg/m3 for PM10 and 25 μg/m3 for PM2.5. The Cr-rich and CrFe-rich particles showed substantial heterogeneity with high concentrations observed near the chrome smelters, and Si-rich particles were highest near the silicon mine. SiAl-rich particles were highest at sites close to busy roads, while SiAlFe-rich particles were less spatially distributed. The low spatial variability of SiAlFe-rich particles indicates that these elements are mainly found in crustal material. Using the synoptic meteorological parameters of The Air Pollution Model, we were unable to effectively determine correlations between PM10 and mixing height, Monin–Obukhov length, air pollution potential, or coefficient of divergence.
 Significance:
 
 We have shown that the use of University of North Carolina passive samplers coupled with computer-controlled scanning electron microscopy is effective in determining the chemical composition of PM.
 The use of passive samplers is a cheap and effective method to collect data in remote areas of South Africa which have limited or no electricity supply.
 Assessment of the spatial distribution of PM and PM chemical components can assist in the development of effective air quality management strategies.

Evaluation of surface shortwave and longwave downwelling radiations over the global tropical oceans

In the present study, daily downwelling shortwave (QS) and longwave radiation (QL) data from one satellite and two hybrid products have been evaluated using Global Tropical Moored Buoy Array during 2001–2009 in the tropical oceans. Daily satellite data are used from the Clouds and Earth’s Radiant Energy System (CERES) program. Data are obtained using Moderate Resolution Imaging Spectroradiometer (MODIS) (CM) aboard the Terra and Aqua satellites. Coordinated Ocean Research Experiments (CORE-II) and Tropical Flux data (TropFlux) are the other two hybrid products used in this study. The analysis shows that majority of QS observations as well as derived products lie in 200–300 Wm−2 range in all the three tropical oceans. Both QS and QL in all products overestimated the majority of the observations. Yet, they underestimated the lower (0–100 Wm−2) values in QS and higher (300–440 Wm−2) values in QL. Majority of the QL observations lie within 390–420 Wm−2 range, and CM slightly overestimated this observed distribution in the Pacific and the Atlantic Oceans. But, majority of the observations in the Indian Ocean lie within 420–450 Wm−2 range. This implies that the tropical Indian Ocean receives 30 Wm−2 more energy as compared to the tropical Pacific and the Atlantic in the form of downwelling longwave radiation. Daily observed QS shows dominant seasonal cycle over the central, the eastern Pacific and the eastern Atlantic. On the other hand, the western Pacific, the central Atlantic and the Indian Oceans show intraseasonal variations. All products show this variation with high root-mean-square error (RMSE) values (QS and QL) over the Indian Ocean than in the Pacific and the Atlantic Oceans. Downwelling radiation from CORE-II shows highest RMSE (for both QS and QL) with least correlation coefficient (CC), and TropFlux has lowest RMSE and highest CC among all products in all three tropical oceans. CM has intermediate values of standard deviation, CC and RMSE. These results are not seasonally dependent, since the seasonal statistics are consistent with seasonal changes. Assuming that the SST is only driven by the downwelling shortwave and longwave fluxes, the errors associated with monthly SST can be as large as 0.2–0.3 (0.1–0.2) °C associated with errors in QS (QL). Both QS and QL in CORE-II have lower spatial variability as compared to other datasets. QL in the tropical oceans shows seasonal spatial variability determined by intertropical convergence zone positions. This variability does not change significantly over the Pacific and the Atlantic Oceans. The summer and winter monsoon patterns in the Indian Ocean guide the QL variability. Opposite to QS, higher QL values have lower variability. Thus, this study aims at finding better radiation dataset to use in the numerical models and deduce that satellite data could be an alternative to existing reanalysis products.

Using multi-fractal and joint multi-fractal methods to characterize spatial variability of reconstructed soil properties in an opencast coal-mine dump in the Loess area of China

Opencast coal mining activities have significant effects on the reconstructed soil properties, including its physical, chemical and biological properties. To assess the effects of opencast mining and associated dumping activities on reconstructed soil properties, the spatial variability of soil properties, i.e., soil particle size distribution (PSD), organic matter (OM) and total nitrogen (TN), in an inner dump after dumping and before reclamation in Antaibao opencast coal-mine of Shanxi Pingshuo was analyzed using multi-fractal and joint multi-fractal methods. Soil samples at 78 sampling sites at the depths of 0–20 cm and 20–40 cm were collected using an auger. The singularity spectra of soil properties in the study area were analyzed, and three multi-fractal parameters (i.e., D1, D2 and ∆α) were calculated. The singularity spectra and multi-fractal parameters can quantitatively characterize the spatial variability of soil PSD, OM and TN. The variability of soil PSD, OM and TN at the depth of 20–40 cm was higher than that at the depth of 0–20 cm. At same soil depth, the sand and clay contents showed relatively high spatial variability, and the silt content, OM and TN exhibited relatively low spatial variability. The spatial variability in sand content, OM and TN between 0 and 20 cm and 20–40 cm depths had a relatively high correlation, while the clay and silt contents at the depths of 0–20 cm and 20–40 cm showed a relatively low correlation. Multi-fractal and joint multi-fractal methods exhibited more advantages in characterizing spatial variability of reconstructed soil properties. This study provided a theoretical basis for the soil sampling optimization of in the opencast coal-mine dump in the Loess area of China.

Comparative Performance of the LUR, ANN, and BME Techniques in the Multiscale Spatiotemporal Mapping of PM2.5 Concentrations in North China

Fine particle matter (PM2.5) has been receiving increasing attention by the government due to its considerable adverse health effects, especially in the north part of China. Even though a number of techniques of estimating PM2.5 exposure have been developed, what is still lacking is a systematic comparison of commonly used techniques based on classical statistics, artificial intelligence, and geostatistics. To address this need, the land use regression (LUR), the artificial neural networks (ANN), and the Bayesian maximum entropy (BME) techniques were all used to map the space–time PM2.5 concentration distribution in the highly polluted Jing-Jin-Ji region (Huabei plain of North China) during the period June 2015–May 2016. The tenfold cross-validation analysis and the entropic information theory were used to evaluate numerically the performance of the three techniques at monthly, seasonal, and annual time scales. Our results showed that the performance of each mapping technique was affected by the temporal scale and the degree of spatial heterogeneity. All three techniques were suitable for low temporal resolution (annual) datasets with low spatial variability. BME also showed a noticeable ability to analyze higher temporal resolution (monthly) datasets exhibiting high spatial heterogeneity. BME involved a single dependent variable (PM2.5) and generated complete (full-coverage) space–time PM2.5 maps, whereas LUR and ANN produced incomplete maps because of lacking independent variables (such as satellite data). Due to its self-learning feature, ANN showed better modeling performance than LUR and produced more informative maps. Overall, the ANN and BME techniques perform better than the LUR technique.

Environmental factors controlling potentially toxic element behaviour in urban soils, El Tebbin, Egypt

This study focuses on the assessment of surface soils from industrially polluted region (El Tebbin) of southern Cairo, Egypt. The impact of agricultural, residential and industrial land use on soils developed from Nile river sediments has significantly compromised their function. Previous evidence has shown that the food chain is contaminated and enhances risk of contaminant exposure of the residential communities. This study investigates factors controlling potentially toxic element (PTE) distribution (Co, Ni, Pb, Cd, Zn, Cr and Cu) in El Tebbin soils and provide estimates of their mobility and bioavailability. The PTE concentrations are characterised by high variability as result of the variety of natural and anthropogenic influences. Highest spatial variability is found for Zn, Cd, Pb and Cu (C.V = 260.0%, 280.4%, 140.8% and 159.6% respectively) and enrichment factors indicate strong anthropogenic inputs. For Co and Ni, relatively low spatial variability (C.V = 65.8% and 45.0% respectively) with depletion in Ni suggests a relatively minor contribution from anthropogenic sources. For Cr, a more uniform distribution pattern showing depletion to minimal enrichment across the study area (C.V = 19.2%) reflects almost exclusive lithogenic control. Using principle component analysis (PCA) to explore concentration data reveals that the major inputs affecting PTE distribution are modified by primary soil properties (texture and pH). Their relative bioavailability (identified through sequential chemical extraction) relates strongly to local input sources. Those elements dominated by lithogenic input (Ni and Co) were found predominantly in soil residual fractions (95.6% and 90.5% respectively), while elements with stronger anthropogenic contributions (Cd, Zn, Pb and Cu) showed much higher portion in the more mobile and bioavailable fractions obtained from sequential chemical extraction, with average proportions of the totals being 62.6%, 57%, 40.7% and 39.2% respectively. Those PTEs with strong anthropogenic influence are potentially much more mobile for bioaccumulation in food chain with increased health risk for exposed residents and are confirmed by elevated concentrations of Cd, Zn, Pb and Cu recorded in local plant species. The main pollution sources were further highlighted by cluster analysis and showed vehicle traffic and specific industrial activities but which varied significantly from site to site. The identification of sources through the approach developed here allows prioritisation of monitoring and regulatory decisions by the local government to reduce further environmental exposure of the local population.

Potential to reduce the concentrations of short-lived climate pollutants in traffic environments: A case study in a medium-sized city in Brazil

Understanding the spatio-temporal variation of short lived climate pollutants (SLCP) over cities is critical to protect public health and mitigate climate change. There is a lack of knowledge about the distribution of SLCP in South America, mainly in medium-sized cities (<1 million inhabitants). This work reports on results of a campaign conducted at several sites (street canyon, urban background, and suburban) in Londrina, Brazil. The pollution datasets (black carbon BC, nitrogen oxides NOx, ozone O3, fine particles PM2.5, and particle number PN) were analyzed by using descriptive statistics, diurnal cycles, time-space correlations, and establishing linkages with traffic rates and with wind conditions. Hourly mean (±standard deviation) BC concentrations ranged from 0.58 (±0.54) in the suburban area to 3.43 (±2.69) µg m−3 at the street canyon site (10,150 vehicles d−1 on weekdays), showing a high spatio-temporal variability even at short scales. PN (mean of 17,469 cm−3) and NOx (mean of 51.58 µg m−3) concentrations tracked BC levels and traffic rates, particularly the number of diesel buses at the canyon site. PM2.5 and O3 levels presented a lower spatial variability, with poor correlation with traffic rates in the canyon and were more tied to regional sources. NOx, BC, and PN showed the highest reduction potentials connected to the abatement of traffic emissions in the city center, particularly by targeting heavy-duty diesel vehicles. The reduction of PM2.5 and O3 concentrations is more challenging due to a significant regional contribution that requires cutting down emissions at state, national or even transboundary scales.

ГЕОСТАТИСТИЧНИЙ АНАЛІЗ АТМОГЕОХІМІЧНОГО ПОЛЯ В ПРИЗЕМНОМУ ШАРІ АТМОСФЕРИ ПІВНІЧНОЇ УКРАЇНИ (ЗА ДАНИМИ БРІОГЕОХІМІЧНОЇ ІНДИКАЦІЇ)

The bryogeochemical indication of atmospheric pollution in the central part of northern Ukraine is based on the use of moss P.schreberi species. The sampling system provided an empirical reference the moss sample data to a particular physical and geographical zone, taking into account the distance from the source of atmospheric pollution. This approach allowed developing a gradation of natural and man-caused conditions for the accumulation of chemical elements in moss samples. The data on the content of chemical elements in the moss-indicator were analysed and checked for comprehensive geostatistics (factor analysis methods, mapping of spatial correlations of significant factors, centered triangular charts and composite biplot). Results made possible to form a mutual hyperspace of geochemical factors and signs for its subsequent interpretation.The leading factor of the bryogeochemical indication is air pollution by a large dust of natural terrigenous and man-caused origin. Second factor connected with a finely dispersed man-caused-condensation aerosols impact; the third factor is identified by the behavior of titanium and vanadium as the effect of heat and power (TPP, boiler room) on atmospheric emissions; the fourth factor is аn impact of biogenesis, which is determined by active role of trace elements – sulfur and phosphorus; the fifth factor is connected with biogenic migration of trace elements – boron, copper and cobalt. The sixth factor is weak, and probably due to the behavior of manganese and chromium, associated with Eh-pH parameters of atmospheric precipitation and hydrometeors. The regional geostatistical analysis of the bryogeochemical data shows that the factor of the terrigenous dust uplift within the studied territory exhibits a spatial variability due to differences in the granulometric composition of the Quaternary deposits, the degree of soil retention and plowing, and differences in surface wind speeds. Spatial heterogeneity is obvious due to aman-caused factor showing a distribution of the dispersion of the Fe-S-Pb and V-Cd-Pb clusters on bipolar charts. Instead, such a geochemical factor as "biogenesis" shows a low spatial variability. The bryogeochemical indication is a good method for the assessment and monitoring of atmospheric pollution of large and varied natural and man-causedarea as well as the proposed geostatistical mapping of the atmogeochemic field showing that it is efficient todivide the territories by type of pollution.

Delineating soil management zones using a proximal soil sensing system in two commercial potato fields in New Brunswick, Canada

Stagnating potato (Solanum tuberosum L.) yields in eastern Canada have resulted in loss of competitive advantage in global potato markets. Therefore, there is a need to investigate the potential to increase yield by adopting precision agriculture technology. This study evaluated the efficiency of an apparent soil electrical conductivity (ECa) sensor to delineate management zones (MZs) in two commercial potato fields in New Brunswick, Canada, using an unsupervised fuzzy k-means clustering algorithm. Georeferenced soil samples from 0 to 15 cm depth were analyzed for physicochemical properties. Tuber yields were recorded using a yield monitor. The two MZs delineated using soil ECa differed significantly in soil physicochemical properties for both fields; however, tuber yield differed significantly between MZs only in Field 1. The yield difference (7.1 Mg ha−1) in Field 1 was attributed to a difference in soil moisture (23.5% vs 28.5%) resulting from a difference in clay content (141 vs 189 g kg−1). The lack of a yield difference between MZs in Field 2 may reflect relatively low within-field spatial variability. The soil ECa sensor showed promise for use in commercial potato production in New Brunswick, especially in fields with high spatial variability.

Sulphur contamination impact on seasonal and surface water chemistry on a reforested area of a former sulphur mine

AbstractThis work presents an assessment of seasonal changes and a chemometric study of surface water parameters on reclaimed and reforested areas of a former sulphur mine where the mineral was extracted using the Frasch method. Water samples were collected four‐times at 15‐monitored sampling points located on streams and ponds. Base chemical parameters of water (such as pH, EC, ion concentrations, and total P, S, and Al) were determined in laboratory conditions. A chemometric study with the application of the principal component analysis PCA method using the EM (expectation‐maximisation algorithm) procedure was employed to determine seasonal changes and similarities or dissimilarities between the water samples. The obtained results indicate an overall low seasonal and spatial variability of the investigated surface water parameters: mean pH for seasons from 6.2 to 6.7, EC 1,819–1,990 μS cm−1, S 380.7–484.2 mg L−1. However, the occurrence of hot spots was found (in three out of 15 monitoring points), which typically displayed very low pH (below 4.0), high EC (above 2,000 μS cm−1) and sulphur content (above 400 mg L−1), or a high proportion of calcium ions associated with the neutralisation of sulphurous soils. Trace elements occur in very low concentrations because most of the sampling points displayed high pH. The research was carried out in a unique location where high‐sulphur concentration in soils was not correlated with acidification but rather with alkalisation (hotspots). This phenomenon was related to the specificity of reclamation where post‐flotation lime were used.

SOIL ATTRIBUTES AND WHEAT PERFORMANCE IN A GEOREFERENCED SEED PRODUCTION FIELD

ABSTRACT The aim of this research was to study the linear correlations and spatial distribution between soil attributes and productivity and physiological quality of wheat seeds. Soil samples were collected at georeferenced points in an area of 41 ha with a rectangular grid of 100 × 100 m to determine soil fertility. Grain productivity, number of ears m-2, protein content, and physiological seed quality were assessed. The results were submitted to Pearson's correlation analysis, descriptive statistics, and geostatistics. Phosphorus content presented a high spatial variability, with a coefficient of variation of 97.79%. Protein content, number of ears, and SMP index presented low values of coefficient of variation, indicating their low spatial variability. Physiological seed quality was not correlated with soil attributes. Wheat productivity showed a strong correlation between calcium and soil organic matter contents, as verified by analyzing the spatial distribution maps. The analysis of the spatial distribution maps of soil nutrients is a tool that can contribute to farmers’ decision making in the management of crop fertilization aiming at increasing productivity.

Land Use Regression Modelling of Outdoor NO₂ and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa.

Air pollution can cause many adverse health outcomes, including cardiovascular and respiratory disorders. Land use regression (LUR) models are frequently used to describe small-scale spatial variation in air pollution levels based on measurements and geographical predictors. They are particularly suitable in resource limited settings and can help to inform communities, industries, and policy makers. Weekly measurements of NO2 and PM2.5 were performed in three informal areas of the Western Cape in the warm and cold seasons 2015–2016. Seasonal means were calculated using routinely monitored pollution data. Six LUR models were developed (four seasonal and two annual) using a supervised stepwise land-use-regression method. The models were validated using leave-one-out-cross-validation and tested for spatial autocorrelation. Annual measured mean NO2 and PM2.5 were 22.1 μg/m3 and 10.2 μg/m3, respectively. The NO2 models for the warm season, cold season, and overall year explained 62%, 77%, and 76% of the variance (R2). The PM2.5 annual models had lower explanatory power (R2 = 0.36, 0.29, and 0.29). The best predictors for NO2 were traffic related variables (major roads, bus routes). Local sources such as grills and waste burning sites appeared to be good predictors for PM2.5, together with population density. This study demonstrates that land-use-regression modelling for NO2 can be successfully applied to informal peri-urban settlements in South Africa using similar predictor variables to those performed in Europe and North America. Explanatory power for PM2.5 models is lower due to lower spatial variability and the possible impact of local transient sources. The study was able to provide NO2 and PM2.5 seasonal exposure estimates and maps for further health studies.

Relation between Convective Rainfall Properties and Antecedent Soil Moisture Heterogeneity Conditions in North Africa

Recent observational studies have demonstrated the relevance of soil moisture heterogeneity and the associated thermally-induced circulation on deep convection and rainfall triggering. However, whether this dynamical mechanism further influences rainfall properties—such as rain volume or timing—has yet to be confirmed by observational data. Here, we analyze 10 years of satellite-based sub-daily soil moisture and precipitation records and explore the potential of strong spatial gradients in morning soil moisture to influence the properties of afternoon rainfall in the North African region, at the 100-km scale. We find that the convective rain systems that form over locally drier soils and anomalously strong soil moisture gradients have a tendency to initiate earlier in the afternoon; they also yield lower volumes of rain, weaker intensity and lower spatial variability. The strongest sensitivity to antecedent soil conditions is identified for the timing of the rain onset; it is found to be correlated with the magnitude of the soil moisture gradient. Further analysis shows that the early initiation of rainfall over dry soils and strong surface gradients yet requires the presence of a very moist boundary layer on that day. Our findings agree well with the expected effects of thermally-induced circulation on rainfall properties suggested by theoretical studies and point to the potential of locally drier and heterogeneous soils to influence convective rainfall development. The systematic nature of the identified effect of soil moisture state on the onset time of rainstorms in the region is of particular relevance and may help foster research on rainfall predictability.

Adapting the Caesium-137 technique to document soil redistribution rates associated with traditional cultivation practices in Haiti

Large-scale deforestation, intensive land use and unfavourable rainfall conditions are responsible for significant continuous degradation of the Haitian uplands. To develop soil conservation strategies, simple and cost-effective methods are needed to assess rates of soil loss from farmland in Haiti. The fallout radionuclide caesium-137 (137Cs) provides one such means of documenting medium-term soil redistribution rates. In this contribution, the authors report the first use in Haiti of 137Cs measurements to document soil redistribution rates and the associated pattern of erosion/sedimentation rates along typical hillslopes within a traditional upland Haitian farming area. The local 137Cs reference inventory, measured at an adjacent undisturbed flat area, was 670 Bq m−2 (SD = 100 Bq m−2, CV = 15%, n = 7). Within the study area, where cultivation commenced in 1992 after deforestation, three representative downslope transects were sampled. These were characterized by 137Cs inventories ranging from 190 to 2200 Bq m−2. Although, the study area was cultivated by the local farmers, the 137Cs depth distributions obtained from the area differed markedly from those expected from a cultivated area. They showed little evidence of tillage mixing within the upper part of the soil or, more particularly, of the near-uniform activities normally associated with the plough layer or cultivation horizon. They were very similar to that found at the reference site and were characterized by high 137Cs activities at the surface and much lower activities at greater depths. This situation is thought to reflect the traditional manual tillage practices which cause limited disturbance and mixing of the upper part of the soil. It precluded the use of the conversion models normally used to estimate soil redistribution rates from 137Cs measurements on cultivated soils and the Diffusion and Migration conversion model frequently used for uncultivated soils was modified for application to the cultivated soils of the study area, in order to take account of the unusual local conditions. The model was also modified to take account of the fact that cultivation in the study area commenced in 1992, rather than predating the period of weapons test fallout which extended from the mid 1950s to the 1970s. Erosion rates on the upper parts of the hillside involved in the study were found to be relatively high and ca. −23 t ha−1 y−1 with low spatial variability. In the lower, flatter areas at the bottom of the slope, deposition occurred. Deposition rates were characterized by high spatial variability, ranging from 6.0 to 71 t ha−1 y−1. Soil redistribution rates of this magnitude are a cause for concern and there is an urgent need to implement soil conservation measures to ensure the longer-term sustainability of the local agricultural practices.

An Algorithm for Inter-calibration of Time-Series DMSP/OLS Night-Time Light Images

The night-time lights (NTL) captured by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) are widely used as proxy for studying human activities. Lack of on-board calibration necessitates inter-calibration of time-series images acquired by different satellites before using them in long-term studies. Pseudo-invariant features (PIFs) i.e., stable night-light features over time, are often used as references for calibrating the temporal images. This study presents an algorithm for inter-calibration of DMSP/OLS time-series images using a semi-automatic PIFs identification procedure through the combined use of Getis statistic (Gi*) and coefficient of variation (CV). Annual stable NTL images over India from 1992 to 2006 are used in this process. While Gi* helps finding lit clusters of spatial homogeneity, CV is useful in finding areas of low spatial variability. The combined use of both these statistic across the time-series images thus helps identifying the PIFs. The identified PIFs are then used to develop calibration models for all the uncalibrated images taking one year image as a reference. Among the three parameter estimation methods evaluated in this study, least trimmed squares is most suitable for inter-calibration. The quality of calibrated images is evaluated using three metrics, sum of lights, gross domestic product and urban population. The calibration outputs are also compared with that of Elvidge et al. (Energies, 2009. https://doi.org/10.3390/en20300595 ). We find that the algorithm developed in this study for inter-calibration of time-series NTL images provides good results. The PIFs identification procedure proposed here is primarily data dependent and thus minimizes the human errors introducible by manual selection method.

Interannual and spatial variability of solar radiation energy potential in Kenya using Meteosat satellite

Abstract Kenya is faced with a rising demand in electricity resulting from a rapidly growing economy and an increasing population. Being a tropical country, lying astride the equator, solar energy is one of the readily available renewable energy resource options to meet this need. Unfortunately, there is still very low adoption of solar systems in the country which could be majorly attributed to lack of adequate solar resource assessment. Besides, past studies on this area in Kenya only focused on the available amount of solar resource leaving out the issue of variability. To bridge this gap, the temporal and spatial variability of global horizontal irradiance (GHI) and direct normal Irradiance (DNI) is analyzed using 19-year long (1995–2013) Meteosat satellite dataset. GHI interannual variability is low in most parts of the country but DNI has a clearly higher variability except a few locations in the East and Northern desert. Low spatial variability for GHI was recorded for locations within 1225 km2 while DNI variability was double that of GHI. The results offer readers a quick reference of variability of solar resource at different locations in Kenya which is useful in guiding measurement requirements and consequently in promoting deployment of solar systems.

Spatial variability of CO2 concentrations and biogeochemistry in the Lower Columbia River

Carbon dioxide (CO2) emissions from rivers and other inland waters are thought to be a major component of regional and global carbon cycling. In large managed rivers such as the Columbia River, contemporary ecosystem changes such as damming, nutrient enrichment, and increased water residence times may lead to reduced CO2 concentrations (and emissions) due to increased primary production, as has been shown in another large North American river (Upper Mississippi). In this work, spatial patterns of water quality, including dissolved CO2 concentrations, were assessed in the Lower Columbia River (LCR) and major tributaries using underway measurements from a small research vessel during July 2016. We observed near-equilibrium CO2 conditions and overall weak supersaturation of CO2 in the main channel (average 133.8% saturation) and tributaries. We observed only weak correlations between CO2 saturation, chlorophyll a fluorescence, and turbidity, thus not strongly supporting our hypothesis of primary productivity controls. In general, the LCR was clear (low turbidity, mean = 1.48 FNU) and had low chlorophyll fluorescence (mean = 0.177 RFU) during the sampling period. As a whole, the LCR was homogeneous with respect to biogeochemical conditions and showed low spatial variability at >100 km scales. Overall, we find that the LCR is likely a weak summertime source of CO2 to the atmosphere, in line with findings from other altered rivers such as the Upper Mississippi.