Predicting Threshold Exceedance by Local Block Means in Soil Pollution Surveys

Soil contamination by heavy metals and organic pollutants around industrial premises is a problem in many countries around the world. Delineating zones where pollutants exceed tolerable levels is a necessity for successfully mitigating related health risks. Predictions of pollutants are usually required for blocks because remediation or regulatory decisions are imposed for entire parcels. Parcel areas typically exceed the observation support, but are smaller than the survey domain. Mapping soil pollution therefore involves a local change of support. The goal of this work is to find a simple, robust, and precise method for predicting block means (linear predictions) and threshold exceedance by block means (nonlinear predictions) from data observed at points that show a spatial trend. By simulations, we compared the performance of universal block kriging (UK), Gaussian conditional simulations (CS), constrained (CK), and covariance-matching constrained kriging (CMCK), for linear and nonlinear local change of support prediction problems. We considered Gaussian and positively skewed spatial processes with a nonstationary mean function and various scenarios for the autocorrelated error. The linear predictions were assessed by bias and mean square prediction error and the nonlinear predictions by bias and Peirce skill scores. For Gaussian data and blocks with locally dense sampling, all four methods performed well, both for linear and nonlinear predictions. When sampling was sparse CK and CMCK gave less precise linear predictions, but outperformed UK for nonlinear predictions, irrespective of the data distribution. CK and CMCK were only outperformed by CS in the Gaussian case when threshold exceedance was predicted by the conditional quantiles. However, CS was strongly biased for the skewed data whereas CK and CMCK still provided unbiased and quite precise nonlinear predictions. CMCK did not show any advantages over CK. CK is as simple to compute as UK. We recommend therefore this method to predict block means and nonlinear transforms thereof because it offers a good compromise between robustness, simplicity, and precisio

Generalized cross-covariances and their estimation

Generalized cross-covariances describe the linear relationships between spatial variables observed at different locations. They are invariant under translation of the locations for any intrinsic processes, they determine the cokriging predictors without additional assumptions and they are unique up to linear functions. If the model is stationary, that is if the variograms are bounded, they correspond to the stationary cross-covariances. Under some symmetry condition they are equal to minus the usual cross-variogram. We present a method to estimate these generalized cross-covariances from data observed at arbitrary sampling locations. In particular we do not require that all variables are observed at the same points. For fitting a linear coregionalization model we combine this new method with a standard algorithm which ensures positive definite coregionalization matrices. We study the behavior of the method both by computing variances exactly and by simulating from various model

A Fractal Approach to Model Soil Structure and to Calculate Thermal Conductivity of Soils

Heat transport in soils depends on the spatial arrangement of solids, ice, air and water. In this study, we present a modified fractal approach to model the pore structure of soils and to describe its influence on the thermal conductivity. Three different fractal generators were sequentially applied to characterize a wide range of particle- and pore-size distributions. The given porosity and particle-size distribution of a clay, clay loam, silt loam and loamy sand were successfully modeled. The thermal conductivity of the fractal soil model was calculated using a network of resistors. We applied a renormalization approach to include the effects of smaller scale structures. The predictions were compared with the empirical Johansen' model (Johansen, 1975), that postulates a simple linear relationship between ice content and thermal conductivity. For high ice-saturated conditions, the calculated thermal conductivity agrees well with the empirical model. To describe partial ice saturation, we assumed that some pores were coated by ice films enclosing the air-filled center. In addition, we introduced a reduced heat exchange coefficient of the particles for unsaturated conditions. The ice-saturated and -unsaturated thermal conductivity calculated with this approach was very similar to that estimated by the empirical model. The variation of the thermal conductivities for different spatial arrangements of pores and particles in the prefractals were determined. Extreme values deviate more than 50% from the mean value

Some considerations on aggregate sample supports for soil inventory and monitoring

Soil monitoring and inventory require a sampling strategy. One component of this strategy is the support of the basic soil observation: the size and shape of the volume of material that is collected and then analysed to return a single soil datum. Many, but not all, soil sampling schemes use aggregate supports in which material from a set of more than one soil cores, arranged in a given configuration, is aggregated and thoroughly mixed prior to analysis. In this paper, it is shown how the spatial statistics of soil information, collected on an aggregate support, can be computed from the covariance function of the soil variable on a core support (treated as point support). This is done via what is called here the discrete regularization of the core-support function. It is shown how discrete regularization can be used to compute the variance of soil sample means and to quantify the consistency of estimates made by sampling then re-sampling a monitoring network, given uncertainty in the precision with which sample sites are relocated. These methods are illustrated using data on soil organic carbon content from a transect in central England. Two aggregate supports, both based on a 20 m 20 m square, are compared with core support. It is shown that both the precision and the consistency of data collected on an aggregate support are better than data on a core support. This has implications for the design of sampling schemes for soil inventory and monitoring

Organic Wheat Farming Improves Grain Zinc Concentration

Zinc (Zn) nutrition is of key relevance in India, as a large fraction of the population suffers from Zn malnutrition and many soils contain little plant available Zn. In this study we compared organic and conventional wheat cropping systems with respect to DTPA (diethylene triamine pentaacetic acid)-extractable Zn as a proxy for plant available Zn, yield, and grain Zn concentration. We analyzed soil and wheat grain samples from 30 organic and 30 conventional farms in Madhya Pradesh (central India), and conducted farmer interviews to elucidate sociological and management variables. Total and DTPA-extractable soil Zn concentrations and grain yield (3400 kg ha-1) did not differ between the two farming systems, but with 32 and 28 mg kg-1 respectively, grain Zn concentrations were higher on organic than conventional farms (t = -2.2, p = 0.03). Furthermore, multiple linear regression analyses revealed that (a) total soil zinc and sulfur concentrations were the best predictors of DTPA-extractable soil Zn, (b) Olsen phosphate taken as a proxy for available soil phosphorus, exchangeable soil potassium, harvest date, training of farmers in nutrient management, and soil silt content were the best predictors of yield, and (c) yield, Olsen phosphate, grain nitrogen, farmyard manure availability, and the type of cropping system were the best predictors of grain Zn concentration. Results suggested that organic wheat contained more Zn despite same yield level due to higher nutrient efficiency. Higher nutrient efficiency was also seen in organic wheat for P, N and S. The study thus suggests that appropriate farm management can lead to competitive yield and improved Zn concentration in wheat grains on organic farms

Characterising the local and intense water cycle during a cold air outbreak in the Nordic Seas

Air masses in marine cold air outbreaks (CAOs) at high latitudes undergo a remarkable diabatic transformation because of the uptake of heat and moisture from the ocean surface, and the formation of precipitation. In this study, the fundamental characteristics of the water cycle during an intense and persistent, yet archetypal basinwide CAO from Fram Strait into the Nordic seas are analyzed with the aid of the tracer-enabled mesoscale limited-area numerical weather prediction model COSMO. A water budget of the CAO water cycle is performed based on tagged water tracers that follow moisture picked up by the CAO at various stages of its evolution. The atmospheric dynamical factors and boundary conditions that shape this budget are thereby analyzed. The water tracer analysis reveals a highly local water cycle associated with the CAO. Rapid turnover of water vapor results in an average residence time of precipitating waters of about one day. Approximately one-third of the total moisture taken up by the CAO falls as precipitation by convective overturning in the marine CAO boundary layer. Furthermore, precipitation efficiency increases as the CAO air mass matures and is exposed to warmer waters in the Norwegian Sea. These properties of the CAO water cycle are in strong contrast to situations dominated by long-range moisture transport that occur in the dynamically active regions of extratropical cyclones. It is proposed that CAOs in the confined Nordic seas provide a natural laboratory for studying local characteristics of the water cycle and evaluating its representation in models.publishedVersio

Nested sampling and spatial analysis for reconnaissance investigations of soil: an example from agricultural land near mine tailings in Zambia

A reconnaissance survey was undertaken on soil near mine tailings to investigate variation in the content of copper, chromium and uranium. A nested sampling design was used. The data showed significant relations between the content of copper and uranium in the soil and its organic matter content, and a significant spatial trend in uranium content with distance from the tailings. Soil pH was not significantly related to any of the metals. The variance components associated with different scales of the sample design had large confidence intervals, but it was possible to show that the random variation was spatially dependent for all spatial models, whether for variation around a constant mean, or with a mean given by a linear effect of organic matter or distance to the tailings. For copper, we showed that a fractal or multifractal random model, with equal variance components for scales in a logarithmic progression, could be rejected for the model of variation around the fixed mean. The inclusion of organic matter as an explanatory factor meant that the fractal model could no longer be rejected, suggesting that the effect of organic matter results in spatial variation that is not scale invariant. It was shown, taking uranium as a case study, that further spatially nested sampling to estimate scale-dependent variance components, or to test a non-fractal model with adequate power, would require in the order of 200–250 samples in total

Characteristics and dynamics of extreme winters in the Barents Sea in a changing climate

The Barents Sea is experiencing large declines in sea ice and increasing surface temperatures while at the same time it is a key region of weather variability in the Arctic. In this study, we identify extreme winter seasons in the Barents Sea, based on a multivariate method, as winters with large seasonal anomalies in one or several surface parameters encompassing surface temperature, precipitation, surface heat fluxes, and surface net radiation. The analyses are based on large-ensemble climate model data for historical (S2000) and end-of-century (S2100) projections following an RCP8.5 emission scenario. In the phase space of the considered seasonal-mean surface weather parameters, we find distinct clusters of extreme winters that are characterized by similar combinations of anomalies in these parameters. In particular, during extreme winters in S2000 simulations, anomalies in surface air temperature during extreme seasons tend to be spatially extended with their maximum amplitude over sea ice. This maximum shifts towards the continental land masses in a warmer climate (S2100), as the formation of intense warm or cold anomalies is damped by the increasing area of open ocean. Our results reveal that large anomalies in surface parameters during extreme seasons are characterized by distinct patterns of anomalous frequencies in cyclones, anticyclones, and cold air outbreaks because these weather systems are responsible for temperature and moisture advection, the formation or suppression of precipitation, and intense surface fluxes. We further show that anomalous surface boundary conditions at the beginning of a season – that is, sea ice concentration and sea surface temperatures – facilitate the formation of persistent anomalous surface conditions or further enhance atmospherically driven anomalies due to anomalous surface heat fluxes. However, a decrease in the variability of both sea ice and sea surface temperatures in S2100 indicates a decreasing importance of anomalous surface boundary conditions for the formation of future extreme winters in the Barents Sea, while the robust link shown for surface weather systems persists in a warmer climate.</p