Lag Distance Research Articles

In‐Field Variability of Soil Test Phosphorus and Implications for Agronomic and Environmental Phosphorus Management

Spatial and seasonal variability of P presents a challenge for accurate soil sampling for agronomic and environmental management of farm fields. Our objectives were to: (i) evaluate the accuracy of soil sampling protocols (sampling intensity) during the growing season and after harvest, with and without manure application; and (ii) evaluate the impact of temporal and spatial variability on the accuracy of conversion equations for Mehlich‐3 to Morgan P soil test P. Two 4‐ha corn (Zea mays L.) fields were sampled (150 samples per field; 64 in a regular grid, the remainder in a pattern that optimized distribution of lag distances) in July and after corn harvest. Semivariograms were constructed to investigate spatial dependence. Simulations were done using the regular grid samples to determine the 95% confidence interval (CI) and probability of obtaining a mean within the 95% CI as impacted by sampling intensity. Thirty or more samples per 4‐ha field were needed to obtain an estimate within 10% of the population mean with >90% probability independent of the timing of sampling. Manure application reduced spatial dependence and both summer sampling and sampling within a month after manure application greatly reduced the accuracy of Mehlich‐3 to Morgan conversions used in New York (P < 0.001). Additional research is needed to expand the database with more fields, both tilled and hay and pasture fields, but based on current research, we conclude that current guidelines for the timing of sampling and number of samples per hectare should be adjusted for most accurate P guidelines, especially if Mehlich‐3 to Morgan P conversion equations are used.

Use of spatially explicit physicochemical data to measure downstream impacts of headwater stream disturbance

Regulatory agencies need methods to quantify the influence of headwater streams on downstream water quality as a result of litigation surrounding jurisdictional criteria and the influence of mountaintop removal coal mining activities. We collected comprehensive, spatially referenced physicochemical data (pH, dissolved oxygen, temperature, and specific conductance) from the partially mined Buckhorn Creek, KY, watershed in summer 2005 (n = 239 sites) and spring 2006 (n = 494 sites). We found conductivity was >10X higher in mined streams than in forested streams. Semivariograms, which quantify the degree of spatial dependence in chemistry values, indicated summer temperatures in both mined and unmined portions of the watershed had similar lag distances (approximately 5 km). Data for other parameters and seasons, however, violated model assumptions because of strong confluence effects in headwaters. We therefore developed a post hoc predictive model for water physicochemistry downstream of confluences using watershed areas as weighting factors. This weighted average model accurately predicted downstream conductivity (mean absolute error, MAE = 55.34 μS cm−1), pH (MAE = 0.16 units), and temperature (MAE = 0.41°C) for confluences in Buckhorn Creek and two additional watersheds with headwater disturbance in West Virginia and Ohio. Use of semivariograms or predictive confluence models can help regulatory agents identify downstream influence of headwater streams and presence of a “significant nexus” with downstream waters.

A spatially explicit census reveals population structure and recruitment patterns for a narrowly endemic pine, Pinus torreyana

We conducted a census of the rare pine, Pinus torreyana ssp. torreyana, to determine (a) its population size and whether it is stable, growing, or declining; (b) the spatial variation in population structure; (c) the spatial patterning of trees in different life stages; and (d) the environmental factors that are related to seedling recruitment. Trees were classified into four stage classes: adult (>160 cm tall with cones); sub-adult (>160 cm without cones); saplings (30–160 cm); and seedlings (<30 cm). Stem diameter was measured for adults and sub-adults, and height for saplings and seedlings. Stands were defined by spatial clustering of the tree map. Univariate and bivariate point pattern analyses were used to explore spatial patterns for adult and juvenile trees and identify potential stand development processes such as density dependence, dispersal limitations, and patchy recruitment. Logistic regression was used to analyze seedling establishment and survival in relation to environmental variables derived from digital maps. Earlier studies reported little or no recruitment. We mapped 5,394 individuals, and tree size had “reverse J-shaped” distribution suggestive of a recruiting population. However, population structure was variable among stands. The predominant spatial pattern detected for adult and juvenile trees was clustering at lag distances <10 m. Bivariate pattern analysis did not suggest repulsion between adult and juvenile size classes. Seedlings tended to be found close to adults and on certain soil types. This suggests that the clustered patterns resulting from patchy recruitment of juveniles persist over time.

Relationships between correlation lengths and integral scales for covariance models with more than two parameters

In geostatistical applications, the terms correlation length and range are often used interchangeably and refer to a characteristic covariance length ξ that normalizes the lag distance in the variogram or the covariance model. We present equations that strictly define the correlation length (r c ) and integral range (l c ). We derive analytical expressions for r c and l c of the Whittle–Matern, fluctuation gradient curvature and rational quadratic covariances. For these covariances, we show that the correlation length and integral range for a given model are not fully determined by ξ. We define non-trivial covariance functions, and we formulate an ergodicity index based on l c . We propose using the ergodicity index to compare coarse-grained measures corresponding to non-trivial covariance functions with different parameters. Finally, we discuss potential applications of the proposed covariance models in stochastic subsurface hydrology.

A comprehensive framework for exploratory spatial data analysis: Moran location and variance scatterplots

A significant Geographic Information Science (GIS) issue is closely related to spatial autocorrelation, a burning question in the phase of information extraction from the statistical analysis of georeferenced data. At present, spatial autocorrelation presents two types of measures: continuous and discrete. Is it possible to use Moran's I and the Moran scatterplot with continuous data? Is it possible to use the same methodology with discrete data? A particular and cumbersome problem is the choice of the spatial-neighborhood matrix (W) for points data. This paper addresses these issues by introducing the concept of covariogram contiguity, where each weight is based on the variogram model for that particular dataset: (1) the variogram, whose range equals the distance with the highest Moran I value, defines the weights for points separated by less than the estimated range and (2) weights equal zero for points widely separated from the variogram range considered. After the W matrix is computed, the Moran location scatterplot is created in an iterative process. In accordance with various lag distances, Moran's I is presented as a good search factor for the optimal neighborhood area. Uncertainty/transition regions are also emphasized. At the same time, a new Exploratory Spatial Data Analysis (ESDA) tool is developed, the Moran variance scatterplot, since the conventional Moran scatterplot is not sensitive to neighbor variance. This computer-mapping framework allows the study of spatial patterns, outliers, changeover areas, and trends in an ESDA process. All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb© (or, in the near future, myGeooffice.org).

베리오그램을 이용한 중력과 자력 자료의 선형성 및 상관거리 비교 분석

자료가 부족한 지역에서의 타당성 높은 공간 자료 해석을 위해서는 추정의 기본 자료인 공간 분포 특성에 대한 올바른 분석이 선행되어야 한다. 공간 추정과 관련해서 자료에 내재된 공간적 특성 척도로 이용 가능한 베리오그램은 자료의 공백을 보완하는 여러 추정 기법의 기초 자료로 그 이용 영역이 제한되어 있는 경향이 있었다. 이에 보다 신뢰성 있는 자료 추정을 위해서는 베리오그램 값이 갖는 의미에 대한 보다 심도 있는 분석이 필요하다. 이 연구에서는 베리오그램 값이 공간 분포 상에서 갖는 의미에 대하여 고찰하고, 베리오그램 분석을 통해 얻을 수 있는 자료의 공간 분포 특성에 대해 연구하였다. 베리오그램은 방향과 분리거리에 따른 자료간의 상관관계 정보를 제공하며, 거리에 따른 상관관계의 변화 정도에 대한 특성을 알려줄 수 있다. 이를 이용하여 베리오그램의 문턱값과 상관거리를 분석하고, 자기상관 값을 이용한 유사거리 개념을 도입하여 선형성 분석의 가능성을 검증하였으며, 화산칼데라 지역에서 획득된 중력 자료 및 자력 자료의 공간 분포 특성을 비교 분석하였다. 분석 결과, 자료의 분포 폭과 변동 폭에 따라 나타나는 중력 자료와 자력 자료의 상이한 특성 패턴을 확인할 수 있었으며, 화산칼데라 지역에서 중력 및 자력의 선형성 방향이 지형의 선형성 방향과 유사하게 나타나고 있어 중력 및 자력 이상체의 지표 연장성이 좋은 것으로 나타났다. To make reliable interpretations on the sparse spatial data, the spatial distribution characteristics that are inevitable for spatial estimation should be properly analyzed. Variograms have been widely used for obtaining the spatial characteristics inherent to data in spatial estimation problems. But their applications were limited as the basic information for further data estimation. Therefore, the additional analysis of the meaning of variograms is required for more reliable data processing and interpretations. In this paper, we investigated the proper meaning of variogram values and the specific features of distributions which can be obtained through variogram analysis. Variograms can provide the information on both linearity and the strength changes of interrelationships between the data sets according to the direction and lag distance. First, sill and range values, which are main parameters of variograms, were analyzed. Then a similarity range using spatial auto-correlation values was introduced to verify the applicability of linearity analysis through the comparative study of spatial distribution features of gravity and magnetic data collected in Hwasan caldera. Through these analyses, we were able to identify the dissimilar patterns of gravity and magnetic data that became apparent according to the distribution and variation ranges of the data sets. It is inferred that the gravity and magnetic anomalous bodies are extended to the ground because linearity direction of gravity and magnetic data appear similarly with linearity derection of topography in Hwasan caldera.

Evaluating Relationships between Spatial Heterogeneity and the Biotic and Abiotic Environments

While most studies of heterogeneity have focused on describing patterns of species or communities, few have focused on the relationships between biotic and abiotic environmental landscape-level gradients. Our study was designed to determine relationships between grazing (heavy, moderate, ungrazed), topographic position (upland vs. riparian), vegetation structure and the thermal environment (i.e., soil-surface temperatures) and determine the influence on landscape patterns of heterogeneity. Biotic and abiotic patterns of heterogeneity were evaluated by establishing 200-m transects that were centered on and perpendicular to a riparian zone so that spatial patterns of variability could be determined along each transect which transcends the maximum level of landscape heterogeneity. Vegetation cover and structure and soil-surface temperatures were recorded at 1-m intervals along the transect. Bare ground increased and leaf litter, grass cover, vegetation heights and angle of obstruction decreased with grazing intensity. However, mean soil-surface temperatures did not differ between grazing treatments. Tree canopy cover associated with riparian areas generally reduced soil-surface temperatures 20 C below that of upland temperatures. In fact, 96% of observations of riparian soil-surface temperature were ≤ 39 C, while 94% of upland soil-surface temperatures were ≥ 40 C regardless of grazing intensity. Vegetation characteristics and soil-surface temperatures were correlated (P < 0.05), but correlation coefficients were small because soil-surface temperature was highly variable. Grazing influenced patterns of landscape heterogeneity, but effects were inconsistent among biotic and abiotic variables. Although grazing had little influence on moderating mean soil-surface temperatures, results suggest that grazing intensity influences thermal heterogeneity at a variety of spatial scales. For instance, thermal heterogeneity (in moderately grazed treatments) is highest at smaller (lag distances ≤ 20 m) and larger (lag distances ≥ 48 m) spatial scales but was lowest at moderate scales (lag distances 22–45 m). For all variables, other than soil-surface temperature and forb cover, semi-variances of moderately grazed sites generally lie intermediate between heavy and ungrazed sites. Nearly all ungrazed vegetation characteristics, except leaf litter, fit a spherical model that reached a sill at a lag distance ≤ 20 m and became spatially independent thereafter, while heavily and moderately grazed sites typically fit an exponential model, indicating a high degree of continuity. Patterns of thermal variability (on uplands) are not related directly to any one vegetation variable, hence, landscape patterns based on vegetation parameters alone are of limited value since patterns of thermal variability are effected by the integration of vegetation and environmental variables within the ecosystem.

Physical explanations of variations in river meander migration rates from model comparison

AbstractIn meandering rivers, the local channel migration rate increases with increasing bend sharpness until it reaches a maximum at a certain critical value of the bend sharpness. Beyond this critical value, the migration rate decreases if bend sharpness increases. Similarly, reach‐averaged migration rates attain a maximum at a certain river sinuosity. This work investigates the physics of these phenomena by comparing the results of two physics‐based models of different complexity, in which the migration rates are proportional to the near‐bank flow velocity excess. In the computational tests the river was allowed to meander progressively, starting from an almost straight planimetry.Both models reproduced the observed peak in the curve describing the local migration rate as a function of the ratio radius of curvature‐channel width (R/B), with a rising limb at lower R/B values and a falling limb at higher R/B values. The rising limb can be explained by the decrease in relative lag distance between near‐bank flow velocity and forcing curvature as R/B increases. The falling limb results from the decrease in local channel curvature and near‐bank flow velocity excess. Since the models do not include flow separation, the results indicate that this phenomenon is not needed to explain the decrease of channel migration rates in sharp bends. The models reproduced also the peak in the curve describing the reach‐averaged migration rates as a function of river sinuosity The increase and then decrease of reach‐averaged migration rates as sinuosity increases appears to be mainly caused by the variation of the reach‐averaged value of the ratio R/B. Copyright © 2009 John Wiley &amp; Sons, Ltd.

Fractal Analyses of Steady Infiltration and Terrain on an Undulating Agricultural Field

Analysis of spatially distributed quasi‐steady infiltration measurements is important because of the effects of infiltration capacity on spatial patterns of soil water, nutrients, and plant development and production. We explored fractal models of quasi‐steady infiltration rates, emphasizing apparent changes in scaling exponents with domain size. Quasi‐steady infiltration rates were measured using single‐ring (0.30‐m‐diameter) constant‐head infiltrometers at 150 locations over 10 landscape positions (30 by 30 m, or approximately 0.1 ha) across a field (approximately 100 ha). At each landscape position, 15 rings were distributed randomly in nested patterns. The field in northeastern Colorado comprises undulating agricultural terrain cropped with dryland winter wheat (Triticum aestivum L.) under conventional tillage. Data were analyzed for multifractal and monofractal behavior. A monofractal model fit the infiltration data best, while terrain attributes other than elevation deviated from monofractals to different degrees. The Hurst exponent (H) was estimated by fitting power‐law variograms at different scales across the field. Values of H changed with the maximum lag distance, peaking near H = 0.9 at approximately 200 m, which represents a typical hillslope length. Spatial persistence in steady infiltration was not observed at long distances (&gt;300 m). Using dense and sparsely sampled terrain attributes as proxy data indicated that the observed spatial scaling of infiltration was not solely due to the clustering of infiltration measurements. These results shed new light on research needed to address spatial variability of soil hydraulic properties, with implications for hillslope process interactions between land areas when modeling distributed infiltration, runoff and run‐on, and plant water use.

Stand-scale spatial patterns of soil microbial biomass in natural cold-temperate beech forests along an elevation gradient

This study focuses on spatial heterogeneity in the soil microbial biomass (SMB) of typical climax beech ( Fagus crenata) at the stand scale in forest ecosystems of the cold-temperate mountain zones of Japan. Three beech-dominated sites were selected along an altitudinal gradient and grid sampling was used to collect soil samples at each site. The highest average SMB density was observed at the site 1500 m a.s.l. (44.9 gC m −2), the lowest was recorded at the site 700 m a.s.l. (18.9 gC m −2); the average SMB density at the 550 m site (36.5 gC m −2) was close to the overall median of all three sites. Geostatistics, which is specifically designed to take spatial autocorrelation into account, was then used to analyze the data collected. All sites generally exhibited stand-scale spatial autocorrelation at a lag distance of 10–18 m in addition to the small-scale spatial dependence noted at <3.5 m at the 550 m site. Correlation analysis with an emphasis on spatial dependency showed SMB to be significantly correlated with bulk density at the 550 and 1500 m sites, dissolved organic carbon (DOC) at the 700 and 1500 m sites, and nitrogen (N) at the 550 and 700 m sites. However, no soil parameter showed a significant correlation with SMB at every site, and some variables were also differently correlated (negative or positive) with SMB at different sites. This suggests that the factors controlling the spatial distribution of SMB are very complex and responsive to local in situ conditions. SMB regression models were generated from both the ordinary least-squares (OLS) and generalized least-squares (GLS) models. GLS performance was only superior to OLS when cross-variograms were accurately fitted. Geostatistics is preferable, however, since these techniques take the spatial non-stationarity of samples into account. In addition, the sampling numbers for given minimum detectable differences (MDD s) are provided for each site for future SMB monitoring.

TEXTNN—A MATLAB program for textural classification using neural networks

A new MATLAB code that provides tools to perform classification of textural images for applications in the geosciences is presented in this paper. The program, here coined as textural neural network (TEXTNN), comprises the computation of variogram maps in the frequency domain for specific lag distances in the neighborhood of a pixel. The result is then converted back to spatial domain, where directional or omni-directional semivariograms are extracted. Feature vectors are built with textural information composed of semivariance values at these lag distances and, moreover, with histogram measures of mean, standard deviation and weighted-rank fill ratio. This procedure is applied to a selected group of pixels or to all pixels in an image using a moving window. A feed-forward back-propagation neural network can then be designed and trained on feature vectors of predefined classes (training set). The training phase minimizes the mean-squared error on the training set. Additionally, at each iteration, the mean-squared error for every validation is assessed and a test set is evaluated. The program also calculates contingency matrices, global accuracy and kappa coefficient for the training, validation and test sets, allowing a quantitative appraisal of the predictive power of the neural network models. The interpreter is able to select the best model obtained from a k-fold cross-validation or to use a unique split-sample dataset for classification of all pixels in a given textural image. The performance of the algorithms and the end-user program were tested using synthetic images, orbital synthetic aperture radar (SAR) (RADARSAT) imagery for oil-seepage detection, and airborne, multi-polarized SAR imagery for geologic mapping, and the overall results are considered quite positive.

Pathogenic Variation ofPhakopsora pachyrhiziInfecting Soybean in Nigeria

Soybean rust, caused by Phakopsora pachyrhizi, is an important disease in Nigeria and many other soybean-producing countries worldwide. To determine the geographical distribution of soybean rust in Nigeria, soybean fields were surveyed in the Derived Savanna (DS), Northern Guinea Savanna (NGS), and Southern Guinea Savanna (SGS) agroecological zones in Nigeria between 2004 and 2006. Disease severity in each zone was determined and analyzed using geostatistics. Prevalence of infected fields and disease severity in surveyed fields were significantly (P < 0.05) different between geographical zones with both variables being higher in the DS zone than in either NGS or SGS zones. Geostatistical analysis indicated that the spatial influence of disease severity at one location on severity at other locations was between 75 and 120 km. An exponential model best described the relationship between semivariance and lag distance when rust severity was high. Spatial interpolation of rust severity showed that locations in the DS zone were more conducive for the rust epidemic compared to areas in the NGS zone. In the 2005 survey, 116 purified isolates were established in culture on detached soybean leaves. To establish the nature of pathogenic variation in P. pachyrhizi, a set of four soybean accessions with Rpp(1), Rpp(2), Rpp(3), and Rpp(4) resistance genes, two highly resistant and two highly susceptible genotypes were inoculated with single uredinial isolates. Principal component analysis on the number of uredinia per square centimeter of leaf tissue for 116 isolates indicated that an adequate summary of pathogenic variation was obtained using only four genotypes. Of these four, PI 459025B (with Rpp(4) gene) and TGx 1485-1D had the lowest and highest number of uredinia per square centimeter, respectively. Based on cluster analysis of the number of uredinia per square centimeter, seven pathotype clusters were determined. Isolates in cluster III were the most virulent, while those in cluster IV were the least virulent. Shannon's index (H) revealed a more diverse pathogen population in the DS zone (H = 1.21) compared to the rust population in SGS and NGS with H values of 1.08 and 0.91, respectively. This work will be useful in breeding and management of soybean rust by facilitating identification of resistant genotypes and targeting cultivars with specific resistance to match prevailing P. pachyrhizi pathotypes in a given geographical zone.

The detection of spatial structure in populations and communities: An empirical case study

The presence of spatial autocorrelation in abundance and richness patterns has been recognised for some time. Evaluation of the tools to quantify patterning often uses simulated data that may be unrealistic or empirical field data where the presence and cause of structuring are unknown. We examine the efficacy of spatial pattern analysis for detecting pattern in empirical data at a fine scale using a field-based mesocosm experiment of a Drosophilidae community associated with decaying fruit. The mesocosm comprised 2 microclimate treatments that generated a particular expected spatial pattern in abundance and species richness. The magnitude of Moran's autocorrelation coefficients (I) was < 0.3 (i.e., low). However, the detected pattern was unaffected. Low Moran's I values did not result from low sample sizes, neither was the significance of Moran's I falsely inflated by large sample sizes. Examination of published I values revealed that autocorrelation values between 0.1 and 0.3 are common in empirical data, particularly at fine (lag distance ≤ 1 m) spatial scales. Pooling temporal samples strengthened the detected output without affecting the form of spatial pattern. We conclude that “weak” responses provide a valuable basis for mechanistic hypothesis generation, especially at fine spatial scales, and that the strength of spatial structure is likely to be determined by the spatial scale of the study.

Temporal characteristics of semantic perseverations induced by blocked-cyclic picture naming

When unimpaired participants name pictures quickly, they produce many perseverations that bear a semantic relation to the target, especially when the pictures are blocked by category. Evidence suggests that the temporal properties of these “semantic perseverations” may differ from typical lexical perseverations in aphasia. To explore this, we studied semantic perseverations generated by participants with aphasia on a naming task with semantic blocking [Schnur, T. T., Schwartz, M. F., Brecher, A., & Hodgson, C. (2006). Semantic interference during blocked-cyclic naming: Evidence from aphasia. Journal of Memory and Language, 54, 199–227]. The properties of these perseverations were investigated by analyzing how often they occurred at each lag (distance from prior occurrence) and how time (response-stimulus interval) influenced the lag function. Chance data sets were created by re-shuffling stimulus-response pairs in a manner that preserved unique features of the blocking design. We found that the semantic blocking manipulation did not eliminate the expected bias for short-lag perseverations (recency bias). However, immediate (lag 1) perseverations were not invariably the most frequent, which hints at a source of inconsistency within and across studies. Importantly, there was not a reliable difference between the lag functions for perseverations generated with a 5 s, compared to 1 s, response-stimulus interval. The combination of recency bias and insensitivity to elapsed time indicates that the perseveratory impetus in a named response does not passively decay with time but rather is diminished by interference from related trials. We offer an incremental learning account of these findings.

Quantifying a dynamic risk landscape: heterogeneous predator activity and implications for prey persistence

Spatial heterogeneity in predation risk can ameliorate impacts on prey populations, particularly for prey of generalists. Spatially heterogeneous risk implies the existence of refugia, and the spatial scale of those refugia and their persistence over time affect whether prey can avoid predation by aggregating therein. Our objective was to quantify the magnitude, spatial scale, and temporal persistence of heterogeneity in risk of predation by white-footed mice (Peromyscus leucopus), an abundant generalist predator of gypsy moths (Lymantria dispar) and songbirds. We used track plates to measure white-footed mouse activity at > 170 trees in each of three forest plots in upstate New York during summers of 2003-2005. We quantified the mean and coefficient of variation of track activity among trees by fitting the beta-binomial distribution to data from each plot and study period. We measured temporal persistence by disattenuated autocorrelation, and spatial scale by fitting exponential variograms. Mice were much less abundant in 2005 than the other two years, leading to lower overall track activity but higher coefficient of variation among trees. Mouse track activity at individual trees was positively autocorrelated between monthly study periods in 2003 and 2004, and even between the two years, whereas temporal autocorrelation in 2005 was much weaker. Track activity showed positive spatial autocorrelation over lag distances from approximately 30 to > 1000 m. These findings indicate that mouse activity, and hence risk to their prey, varies substantially in space at spatial and temporal scales that appear responsive to mouse population dynamics. The spatial scale and temporal persistence of that variation imply that prey may benefit from returning to, or failing to disperse from, refugia.

Interannual Consistency in Fractal Snow Depth Patterns at Two Colorado Mountain Sites

Abstract Fractal dimensions derived from log–log variograms are useful for characterizing spatial structure and scaling behavior in snow depth distributions. This study examines the temporal consistency of snow depth scaling features at two sites using snow depth distributions derived from lidar datasets collected in 2003 and 2005. The temporal snow accumulation patterns in these two years were substantially different, but both years represent nearly average 1 April accumulation depths for these sites, with consistent statistical distributions. Two distinct fractal regions are observed in each log–log variogram, separated by a scale break, which indicates a length scale at which a substantial change in the driving processes exists. The lag distance of the scale break is 15 m at the Walton Creek site and 40 m at the Alpine site. The datasets show consistent fractal dimensions and scale break distances between the two years, suggesting that the scaling features observed in spatial snow depth distributions are largely determined by physiography and vegetation characteristics and are relatively insensitive to annual variations in snowfall. Directional variograms also show consistent patterns between years, with smaller fractal dimensions aligned with the dominant wind direction at each site.

Thickness distribution of Antarctic sea ice

Ship‐based observations are used to describe regional and seasonal changes in the thickness distribution and characteristics of sea ice and snow cover thickness around Antarctica. The data set comprises 23,373 observations collected over more than 2 decades of activity and has been compiled as part of the Scientific Committee on Antarctic Research (SCAR) Antarctic Sea Ice Processes and Climate (ASPeCt) program. The results show the seasonal progression of the ice thickness distribution for six regions around the continent together with statistics on the mean thickness, surface ridging, snow cover, and local variability for each region and season. A simple ridge model is used to calculate the total ice thickness from the observations of level ice and surface topography, to provide a best estimate of the total ice mass, including the ridged component. The long‐term mean and standard deviation of total sea ice thickness (including ridges) is reported as 0.87 ± 0.91 m, which is 40% greater than the mean level ice thickness of 0.62 m. Analysis of the structure function along north/south and east/west transects revealed lag distances over which sea ice thickness decorrelates to be of the order of 100–300 km, which we use as a basis for presenting near‐continuous maps of sea ice and snow cover thickness plotted on a 2.5° × 5.0° grid.

One-dimensional numerical modelling of dam-break waves over movable beds: application to experimental and field cases

This paper reports a numerical study on dam-break waves over movable beds. A one-dimensional (1-D) model is built upon the Saint-Venant equations for shallow water waves, the Exner equation of sediment mass conservation and a spatial lag equation for non-equilibrium sediment transport. The set of governing equations is solved using an explicit finite difference scheme. The model is tested in various idealized experimental cases, with fairly good agreement between the numerical predictions and measurements. Discrepancies are observed at the earlier stage of the dam-break wave and around the dam location due to no vertical velocity component being taken into account. Sensitivity tests confirm that the friction coefficient is an important parameter for the evaluation of sediment transport processes operating during a dam-break wave. The influence of the non-equilibrium adaptation length (or the lag distance) is negligible on the wavefront celerity and weak on the free surface and bed profiles, which indicates that one may ignore the spatial lag effect in dam-break wave studies. Finally, the simulation of the Lake Ha!Ha! dyke-break flood event shows that the model can provide relevant results if a convenient formula for computing the sediment transport capacity and an appropriate median grain diameter of riverbed material are selected.

Further studies on sex expression in Taxus canadensis Marshall1

Sex expression was examined in two island populations of the monoecious Taxus canadensis at the Apostle Island National Lakeshore, USA. Long-term observations of tagged plants supported earlier published conclusions based on comparisons among populations of the effect of size and age on sex expression, measured as standardized phenotypic gender. As populations of plants increased in size and age, strobilus ratios became more male-biased and gender concordance increased. Male-biased plants remained male-biased and female-biased plants remained female-biased; gender switching was not a feature of either of these populations.The two Taxus canadensis populations were mapped to examine spatial dispersion of plants and plant attributes, such as size, strobilus production, and gender. Frequency distribution of gender in both populations was significantly bimodal supporting earlier studies; this conclusion held even when small (typically male) plants with unstable gender were excluded from the analysis. Distribution of T. canadensis in both populations was clumped. Plant attributes showed some spatial autocorrelation, although the results were not consistent across populations or at different lag distances. Size and male strobilus production tended toward spatial autocorrelation in both populations, female strobilus production showed significant spatial autocorrelation on Manitou Island only at a few lag distances, and gender showed no spatial autocorrelation at any lag distance in either island population. The implication of these results for genetic versus environmental determination of sex expression in T. canadensis are briefly discussed.

Some effects of a buried electricity transmission cable on bulk soil

A case study in NW Italy investigating an underground electric line (1 m depth triple cable at operative voltages 220-380 kV) measured electric fields in the surrounding soil virtually close to zero but magnetic fields (microTs) 20 times the background level. After 6 months, the influence radius around the cable on microbial activity (estimated by soil ATP), organic carbon, and total nitrogen follows exactly the inverse trend of the MF, shifting the biological activity with a lag distance of 5 m from the 220 kV cable.