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Different modeling techniques can be used to estimate the saturated conductivity of a porous medium based on computed tomography (CT) images. In this research, two methods are intercompared: direct modeling using the Navier–Stokes (NS) approach and simplified geometry pore network (PN) modeling. Both modeling approaches rely on pore media geometry which was determined using an X-ray CT scans with voxel size 2 μm. An estimate of the saturated conductivity using both methods was calculated for 20 samples prepared from sand with diverse particle size distributions. PN-estimated saturated conductivity was found to be statistically equivalent to the NS-determined saturated conductivity values. The average value of the ratio of the PN-determined conductivity to the NS-determined conductivity (KsatPN/NS) was equal to 0.927. In addition to the NS and PN modeling approaches, a simple Kozeny-Carman (KC) equation-based estimate was made. The comparison showed that the KC estimate overestimated saturated conductivity by more than double (2.624) the NS estimate. A relationship was observed between the porous media specific surface and the KsatPN/NS ratio. The tortuosity of analyzed samples was estimated, the correlation between the porous media tortuosity and the specific surface of the samples was observed. In case of NS modelling approach the difference between pore media total porosity and total porosity of meshes, which were lower, generated for simulations were observed. The average value of the differences between them was 0.01. The method of NS saturated conductivity error estimation related to pore media porosity underestimation by numerical meshes was proposed. The error was on the average 10% for analyzed samples. The minimum value of the error was 4.6% and maximum 19%.

We developed a low-cost method for fabricating "soil-on-a-chip" micromodels with 2D and 2.5D pore structures by stacking layers made with a conventional low-cost tabletop CNC router followed by tape bonding. The pore structure was extracted from an X-ray micro-computed tomography scanning image of a medium-grain sandstone sample. The imbibition experiments performed in the 2D and 2.5D micromodels showed the trends of the residual saturation versus capillary number (Ca). The channels showed opposing trends for low-aspect-ratio 2D and high-aspect-ratio 2.5D micromodels. As the channel aspect ratio increased, the location of air entrapment changed from dead-end pores to transport pores. The sizes of trapped air bubbles in the transport pores decreased as the injection flow rates increased. To show the relationship between the air trapped size and Ca, we derived equations that described the competition between the bulk menisci and the corner flow in the channels for different Ca based on the "supply principle." The relative contributions of the piston displacement and corner film flow, which were dependent on the cross-sectional shapes of the pores and Ca, determined the size and location of the air bubbles trapped in the 2.5D micromodel.

The saturated water flow phenomenon is determined by the soil pore transport processes occurring at a microscale. In this study, saturated water flow was modeled using two different approaches, depending on the existence of the percolating macropore network. The soil material comprised 26 undisturbed soil cores. Soil samples were scanned using an X-ray micro-CT scanner, and saturated hydraulic conductivity (Ksat), bulk density and particle size distribution were measured. The macropore network was percolating in 11 samples, while the remaining cores were not. A typical approach based on Navier–Stokes (NS) equations was used for saturated water flow modeling in the case of a percolating samples. In the case of cores with a non-percolating macropore network, the NS modeling approach could not be used. An alternative method of modeling (NS/Darcy) was used in this case, blending: regular NS flow in the well-defined macropores with the Darcy–Forchheimer flow in the remaining part – the soil matrix. Soil matrix is treated by the NS/Darcy model as a pore medium without well-defined pore geometry but with some intrinsic permeability incorporated in the model using the Darcy–Forchheimer equation. Unlike the NS approach, the NS/Darcy model allowed for the simulation of water flow for all soil samples, including those where the macropore network was not percolating. Based on simulations, the Ksat was estimated used for model validation. The analysis of results leads to the proposal of a new hybrid modeling approach, mixing the NS and NS/Darcy modeling approaches. A good estimation of the Ksat was obtained using the proposed model (R2 = 0.61). The NS/Darcy modeling approach was used for the analysis of the macropore flow in the soil media. The simulations show that water permeates through the core, but macropores are a favorable flow path if they exist, even if they are not directly connected to each other. The areas of the soil cores taking part in the preferential, macropore flow were quantified, showing that only a small fraction of the macropores take part in water flow both for percolating and non-percolating cores. But generally, for most of the analyzed flow-related indices, apparent differences in results between percolating and non-percolating samples were observed. Effective flow area (EFA), i.e., the sample area used for water flow with a velocity higher than the threshold velocity (Utr) was analyzed. Considering the macropore flow, only ~2% of sample volume is responsible for: 82% of the total flux in case of percolated and 34% in case of non-percolated samples. The simulation results for the non-percolating samples revealed the relationship between the simulated saturated conductivity of the whole soil sample and the saturated conductivity of the soil matrix and macroporosity. This allowed for developing a simple multiple linear regression model (R2 = 0.98) of the soil core’s hydraulic conductivity.This work was partially supported by a grant from the Polish National Centre for Research and Development within contract no.: PL- TW/IV/5/2017, and Taiwanese Ministry of Science and Technology: MOST-106-2923-E-009-001-MY3.

The increasing atmospheric CO2 concentration is a consequence of human activities leading to severe environmental deteriorations. Techniques are thus needed to sequester and reduce atmospheric carbon. One of the proposed techniques is the transformation or construction of new soils into which more organic carbon can be sequestered and CO2 be consumed by increased weathering. By using a chronosequence of new and transformed soils on crushed limestone (0–48 years) in a Mediterranean area (Sicily), we tried to quantify the amount of organic carbon that could be additionally sequestered and to derive the corresponding rates. A further aim was to trace chemical weathering and related CO2 consumption and the evolution of macropores that are relevant for water infiltration and plant nutrition. Owing to the irrigation of the table grape cultivation, the transformed soils developed fast. After about 48 years, the organic C stocks were near 12 kg m-2. The average org. C sequestration rates varied between 68 and 288 g m-2 yr-1. The C accumulation rates in the transformed soils are very high at the beginning and tend to decrease over (modelled) longer time scales. Over these 48 years, a substantial amount of carbonate was leached and reprecipitated as secondary carbonates. The proportion of secondary carbonates on the total inorganic carbon was up to 50%. Main mineralogical changes included the formation of interstratified clay minerals, the decrease of mica and increase of chloritic components as well as goethite. The atmospheric CO2 consumption due to silicate weathering was in the range of about 44–72 g C m-2 yr-1. Due to the high variability, the contribution of chemical weathering to CO2 consumption represents only an estimate. When summing up organic C sequestration and CO2 consumption by silicate weathering, rates in the order of 110–360 g C m-2 yr-1 are obtained. These are very high values. We estimated that high sequestration and CO2 consumption rates are maintained for about 50–100 years after soil transformation. The macropore volume decreased over the observed time span to half (from roughly 10 to 5 %). The transformation of soils may even amend their characteristics and increase agricultural production. Due to the relatively sandy character, enough macropores were present and no substantial compaction of the soils occurred. However, great caution has to be taken as such measures can trigger deterioration of both soil ecosystem services and soil quality., Geoderma, 402, ISSN:0016-7061, ISSN:1872-6259

This research proposes an innovative Kriging-approximation simulated annealing (KASA) optimization algorithm to increase optimization efficiency and reduce the computation time for the parameter calibration of simulation model. The newly developed KASA optimization algorithm utilizes the simulated annealing algorithm to search the global optimum; meanwhile, Kriging approximation, a statistical estimation method, is incorporated with simulated annealing to interpolate unknown objective values in solution spaces. Furthermore, this research establishes a network-based porous media flow simulation (NET-PFS) model and then KASA is applied in the calibration of the NET-PFS representative pore network. NET-PFS is a pore network based model constructing a representative pore network to approximate soil characteristics and pore geometry with limited access to pore-scale imaging processes. NET-PFS is applied to estimate the permeability of a sand-packing porous media. NET-PFS establishes a framework for simplifying the pore network but remaining the same hydraulic conductivity and the flow status of pore networks with limited information about the pore structure. In the case study, a quartz sand-packing porous media is scanned by X-ray micro computed tomography. The NET-PFS model is applied to estimate the hydraulic conductivity and flow velocity distribution from the original pore network. The results demonstrate the proposed KASA algorithm effectively calibrated the NET-PFS model; in addition, a representative pore network and the determined flow status in the pore network is presented by NET-PFS.

The results of the multifractal analysis performed for meteorological time series coming from four stations in Poland and Bulgaria located in varying climatic zones are presented. To assess climatic shift response (in 2001/2002), the analysis was conducted separately for two subsets. To analyze long-distance power-law correlations within the studied time series and evaluate the differences in dynamics of the climate between the analyzed sites and periods of time, the multifractal detrended fluctuation analysis methodology (MF-DFA) was proposed. It was revealed that the multifractal properties of precipitation differ considerably from other analyzed quantities. The singularity spectra were susceptible to climatic shift, what was indicated by the changes of spectra parameters. It was especially apparent for asymmetry, which changed from being right- to left-skewed, implying the occurrence of more extreme events. Similarities in the dynamics of meteorological processes for each of the climatic zones were proven by the close relation of respective multifractal spectra parameters coming from closely spatially related localizations.

X-ray computational tomography (CT) is becoming more and more popular research tool in geosciences. Estimation of the saturated conductivity of the porous media based on X-ray CT images is an example of its application. In case of simulations for the pore media, which are approximated by the very complicated meshes, problems might arise when mesh does not follow the shape of pore-space ideally, which may happen due to limitations imposed (e.g. due to some technical constraints) on minimum mesh cell size which usually is bigger than CT scan resolution used for determination of the pore space. If this is the case, the mesh can’t be generated properly in the narrow regions of the pore-space.The work tries to quantify the impact of the limited mesh quality on estimation of the saturated conductivity coefficient. Four mesh generation parameters, resulting in different sizes of the minimum mesh cell size, were compared. For comparison five different pore media (three sandpacks prepared from different sand fractions and two types of sandstones) were used, all of them were used in two repetitions which resulted in 10 studied samples in total. First samples were X-ray CT scanned with resolution 2um. Than images were thresholded to obtain information about pore-space. In the next step, for all of 10 3D images of pore-space, mesh was generated in four repetitions differing with minimum mesh cell size: 2.56, 3.41, 5.12 and 10.25 times greater than voxel size used for CT scanning.Saturated conductivity was simulated based on prepared meshes using finite volume based solver of the Navier-Stokes equations. Estimated for each sample saturated conductivity differed from 12% for coarse media to 200% for fine grain media for different numerical meshes representing with different accuracy pore space geometry.Based on samples studied, one may conclude that for optimal results of saturated conductivity numerical estimation, the smallest numerical mesh’s cell size should be of the level of pore media CT scan resolution. Acknowledgments:This work was partially supported by a grant from the Polish National Centre for Research and Development within the contract no.: PL-TW/IV/5/2017.

Soil chronosequences in Alpine areas have often been applied to trace physical, mineralogical and chemical soil properties over time. How the soil pore system of undisturbed soils evolves, however, has not yet been clarified. We therefore investigated two soil chronosequences in the Swiss Alps spanning a time period from a few decades up to the Early Holocene/Late Pleistocene. One soil sequence developed on siliceous parent material in the Sustenpass (proglacial area of the Stein glacier), with soils aged from 30a to 10 ka while the second developed on calcaric parent material in the Klausenpass, with soils aged from 110a to 14 ka (proglacial area of the Griess glacier). We hypothesised that the overall pore size of the topsoil and consequently the relative proportion of macropores decrease with increasing soil age. The pore size characteristics were determined by using X-ray computed tomography. Independent of the parent material (calcareous vs siliceous), macroporosity decreased with time. The greatest changes seemed to occur between the Little Ice Age (LIA) and about 3–4 kyr of soil evolution. Vegetation composition and the functional richness of the plant community exert an influence on soils that developed on siliceous parent material by giving rise to more small pores. Besides pore sizes, pore characteristics also changed with time. In general, the tortuous structure of the soils became more complex with time. The evolution of the pore characteristics such as tortuosity, number and length of pore networks seem also to be related to biologic factors such as root properties, plant species and soil organic matter composition. It remains, however unanswered how these time-dependent biologic factors interact with and influence in detail the pore network. This aspect certainly merits investigation in greater detail in the future.

The aim of this study was to quantify soil properties, microbial biodiversity and crop yield under two tillage systems used for winter wheat production in monoculture. The study was conducted in the period 2013-2016, on a long-term field experiment on a silt loam at the Krasne Research Station near Rzeszów, Poland. Traditional tillage involved soil inversion whereas reduced tillage was a non-inversion system. The following soil properties: chemical (soil organic carbon, pH, available P, K, Mg), physical (soil bulk density, water content, stability in water), and biological (the diversity of diatoms) were measured on samples collected throughout the growing season and at harvest. Soil organic carbon content, water content and bulk density in the 0-5 and 5-10 cm layers were greater in reduced tillage than in traditional tillage. Under reduced tillage the amount of readily dispersible clay was reduced giving increased soil stability in water. Soil under reduced tillage had greater diversity of diatoms (139 taxa) than that under traditional tillage (102 taxa). Wheat yields were positively correlated with precipitation, soil water content and soil organic carbon, and negatively correlated with readily dispersible clay.

In the years 2008-2011, an environmental study was conducted for Polish soils, focusing on the south-eastern Poland soils, as they exhibit significant acidification. This study aimed at assessing the current pHKCl and the supply of basic macro- (P, K, Mg and S-SO4) and microelements (B, Cu, Fe, Mn and Zn) in the collected soil samples, and also at determining their relationship with the soil agronomic category, humus content and pH class. Soil reaction and humus and macronutrient content were positively correlated with the amount of colloidal clay and particles < 0.02 mm. In the majority of cases, the macro-element content in the soil was positively correlated with soil pH and humus content. As for microelements, a usually significant and positive correlation was found between the soil agronomic category and the content of manganese, iron and zinc, whereas for the content of boron and copper, no such relationship was observed. A significant and positive correlation between soil reaction and the content of manganese, iron and boron was also found. Such correlations were not observed in relation to copper and zinc content. Statistical analysis indicated that the content of boron and manganese depended to the greatest extent on the investigated physicochemical properties.

Large-scale crop simulations with process-based models rely on meteorological input data of coarse spatial resolution. We assess how spatial aggregation of meteorological data to coarser resolutions affects the data’s temporal properties. This is largely unknown as is the impact which this aggregation effect (AE) has on simulations which use such aggregated data as input. In simulations of crop yield AE may exceed 10% in single years. We hypothesize that AE should be analysed with regard to both temporal and spatial input data properties. For this purpose, we analysed changes in temporal multifractal properties of meteorological variables due to spatial averaging from 1 to 100 km resolution. Results show that temporal properties of the time series were affected depending on the meteorological variable. We argue that the magnitude of this effect depends on local orography and climate. Similar impact of spatial aggregation on temporal properties can therefore be expected in regions of comparable orography and climate. These changes in multifractal properties potentially affect results of continuous dynamic simulations.

Hydrophysical characteristics of arctic and temperate zones soils were determined. The soils from the temperate zone showed a greater capability of water retention than those from the arctic zone. In both investigated depths (surface and subsurface layers), the highest water content was observed for the Sądecki Regosol, and the lowest one for Turbic Cryosol formed in the cell forms from Spitsbergen at all soil water potentials. The differences between water content for these soils at the same soil water potentials varied between 20 and 25% vol. in the surface layer, and from 19 to 22% vol. in the subsurface, respectively. The lowest differences (2.7-5.0% vol.) in water content were noticed between the Wyspowy Regosol and Turbic Cryosol (Skeletic) derived in the sorted circles. In both depths, higher values of water conductivity were observed for Regosols than for Cryosols at high soil water potentials, from -0.1 till -7 kJ m-3. These differences were especially high at -0.1 kJ m-3 and they were three or four times higher for soils from the temperate zone than from the arctic ones. For lower water potentials, the differences in water conductivity do not exceed one order in the surface layer and two orders in the subsurface.

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.

Agro-meteorological quantities are often in the form of time series, and knowledge about their temporal scaling properties is fundamental for transferring locally measured fluctua- tions to larger scales and vice versa. However, the scaling analysis of these quantities is compli- cated due to the presence of localized trends and nonstationarities. The objective of this study was to characterise scaling properties (i.e. statistical self-similarity) of the chosen agro-meteorological quantities through multifractal detrended fluctuation analysis (MFDFA). For this purpose, MFDFA was performed with 11 322 measured time series (31 yr) of daily air temperature, wind velocity, rel- ative air humidity, global radiation and precipitation from stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating con- siderable differences in dynamics and development. In log-log plots of the cumulative distribu- tions of all meteorological parameters the linear functions prevailed for high values of the response, indicating that these distributions were consistent with power-law asymptotic behav- iour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent by analysing the corresponding shuffled and surrogate time series. For most of the studied meteorological parameters, the multifractality is due to different long-range correlations for small and large fluctuations. Only for precipitation does the multifrac- tality result mainly from broad probability function. This feature may be especially valuable for assessing the effect of change in climate dynamics.

PUBLICATIONS Water Resources Research RESEARCH ARTICLE 10.1002/2016WR019533 Key Points: The main wetting branch of the soil water retention curve is estimated based on its main drying branch The machine learning method is used for analysis Results are compared with classical methods of estimating the main wetting branch Supporting Information: Supporting Information S1 Data Set S1 Correspondence to: K. Lamorski, k.lamorski@ipan.lublin.pl Citation: Lamorski, K., J. r Sim˚unek, C. Slawi nski, and J. Lamorska (2017), An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method, Water Resour. Res., 53, doi:10.1002/2016WR019533. Received 19 JUL 2016 Accepted 28 JAN 2017 Accepted article online 3 FEB 2017 An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method unek 2 , Cezary Slawi n ski 1 , and Joanna Lamorska 3 Krzysztof Lamorski 1 , Ji r i Sim˚ Institute of Agrophysics, Polish Academy of Sciences, Lublin, Poland, 2 Department of Environmental Sciences, University of California Riverside, Riverside, California, USA, 3 Institute of Agricultural Sciences, State School of Higher Education in Chelm, Chelm, Poland Abstract In this paper, we estimated using the machine learning methodology the main wetting branch of the soil water retention curve based on the knowledge of the main drying branch and other, optional, basic soil characteristics (particle size distribution, bulk density, organic matter content, or soil specific surface). The support vector machine algorithm was used for the models’ development. The data needed by this algorithm for model training and validation consisted of 104 different undisturbed soil core samples collected from the topsoil layer (A horizon) of different soil profiles in Poland. The main wetting and drying branches of SWRC, as well as other basic soil physical characteristics, were determined for all soil samples. Models relying on different sets of input parameters were developed and validated. The analysis showed that taking into account other input parameters (i.e., particle size distribution, bulk density, organic matter content, or soil specific surface) than information about the drying branch of the SWRC has essentially no impact on the models’ estimations. Developed models are validated and compared with well-known models that can be used for the same purpose, such as the Mualem (1977) (M77) and Kool and Parker (1987) (KP87) models. The developed models estimate the main wetting SWRC branch with estimation errors (RMSE 5 0.018 m 3 /m 3 ) that are significantly lower than those for the M77 (RMSE 5 0.025 m 3 /m 3 ) or KP87 (RMSE 5 0. 047 m 3 /m 3 ) models. 1. Introduction Soil water retention curves (SWRCs) are one of the most important soil hydrological characteristics required for both agricultural and environmental research related to the vadose zone. SWRCs link the soil water con- tent with the soil water potential and represent indispensable information for the modeling of soil water flow processes. Although laboratory measurements are the ultimate source of information about retention curves, for many reasons SWRCs are commonly estimated using various statistical models, such as the so- called pedotransfer functions (PTF) [e.g., Vereecken et al., 1989, 2016; Schaap et al., 2001]. The main reason for using PTF estimations of SWRCs instead of direct measurements is their long duration and high cost. Typical steady state equilibrium measurements of the retention curve for a full range of soil water potentials can last several months. PTFs estimate SWRCs based on various physical and chemical soil characteristics. For example, particle size dis- tribution and dry bulk density are commonly used predictor variables in PTFs. Additional soil variables such as organic carbon content, soil-specific surface area, and/or cation exchange capacity can also be used. There were numerous PTF models developed utilizing different statistical and/or soft computing methods for SWRC estimation. Early PTFs were often developed using statistical regression [Rawls et al., 1982; Vereecken et al., € sten et al., 1999; Walczak et al., 2006], leading to some still often used models. Various soft computing 1989; W o methods of statistical inference such as artificial neural networks (ANN) [Schaap et al., 2001; Jana et al., 2008], th et al., the k-nearest neighbors algorithm (k-NN) [Nemes et al., 2006; Botula et al., 2013], regression trees [T o 2012, 2015], or support vector machines (SVM) [Lamorski et al., 2008] were used later on. C 2017. American Geophysical Union. V All Rights Reserved. LAMORSKI ET AL. Soil water retention curves of many soil materials exhibit hysteretic behavior, which means that the depen- dence between the soil water potential (h) and the soil water content (h) is not unique and depends on the ESTIMATION OF WETTING FROM DRYING BRANCH OF RC

Climate dynamics were assessed using multifractal detrended fluctuation analysis (MF-DFA) for sites in Finland, Germany and Spain across a latitudinal transect. Meteorological time series were divided into the two subsets (1980–2001 and 2002–2010) and respective spectra of these subsets were compared to check whether changes in climate dynamics can be observed using MF-DFA. Additionally, corresponding shuffled and surrogate time series were investigated to evaluate the type of multifractality. All time series indicated underlying multifractal structures with considerable differences in dynamics and development between the studied locations. The source of multifractality of precipitation time series was two-fold, coming from the width of the probability function to a greater extent than for other time series. The multifractality of other analyzed meteorological series was mainly due to long-range correlations for small and large fluctuations. These results may be especially valuable for assessing the change of climate dynamics, as we found that larger changes in asymmetry and width parameters of multifractal spectra for divided datasets were observed for precipitation than for other time series. This suggests that precipitation is the most vulnerable meteorological quantity to change of climate dynamics.

Particle size distribution (PSD) is a major soil characteristic, which is essential and commonly used for the development of pedotransfer functions (PTFs) to estimate the water retention of soils. The laser diffraction method (LDM) became a popular alternative to the standard sieve-hydrometer method (SHM) of PSD measurement. Unfortunately, PSDs determined with LDM and SHM methods differ sometimes substantially. Moreover, it is claimed that the laser diffraction method underestimates finer fractions in favor of coarser fractions. Several authors have tried to elaborate on methods to recalculate LDM PSD into its SHM counterparts, but no universal methodology has been developed to this date. In this paper, we use PSD determined by LDM directly for PTF development and compare it with the classical PTF approach based on PSD measured by SHM. Four different PTF models based on LDM particle size distribution data were developed, with different PSD characteristics taken as the models' input variables. The possibility of using alternative PSD characteristics, such as deciles, area moment mean and volume moment mean, for PTF development was examined. The accuracy of PTF models constructed on the basis of LDM-measured PSD was comparable with that of the developed models using texture data obtained from SHM, giving approximately the same RMSE and R2 values. Our study shows that LDM-measured particle size distribution may be directly used for PTF developments without any recalculations to their sieve-hydrometer counterparts.

In this paper studies on the impact of the location of Time-Domain Reflectometry probes in soil samples on the moisture measurement are presented. In particular, we were interested if the commonly accepted assumption that moisture measurements performed by TDR probes correctly average the value of the soil water content in a soil sample. Soil samples having different physical properties, both undisturbed and disturbed, were used. Our results show that the soil moisture measurement depends on the placement of the TDR probe in the sample, and a TDR probe placed vertically measures the arithmetic mean of soil moisture for the whole sample, for both the wetting and the drying cycles. This result is general and does not depend on the analyzed sample.

A b s t r a c t. The database of Polish arable mineral soils is presented. The database includes a lot of information about the basic properties of soils and their dynamic characteristics. It was elaborated for about 1 000 representative profiles of soils in Poland The database concerns: particle size distribution, organic carbon content, acidity-pH, specific surface area, hydrophobicity – solidliquid contact angle, static and dynamic hydrophysical properties, oxidation-reduction properties and selected biological (microbiological) properties of soils. Knowledge about soil characteristics is indispensable for description, interpretation and prediction of the course of physical, chemical and biological processes, and modelling these processes requires representative data. The utility of simulation and prediction models describing phenomena which take place in the soil-plant-atmosphere system greatly depends on the precision of data concerning characteristics of soil. On the basis of this database, maps of chosen soil properties are constructed. The aim of maps is to provide specialists in agriculture, ecology, and environment protection with an opportunity to gain knowledge of soil properties and their spatial and seasonal variability. K e y w o r d s: database, Polish mineral soils, physicochemical properties, biological properties

Impact of diverse tillage on soil moisture dynamicsThe influences of traditional and reduced tillage on the water content dynamics of two soils were investigated in a long-term field experiment under nearly the same meteorological conditions for a winter wheat monoculture during three years. In addition to the moisture changes, the basic physicochemical properties, water retention, differential porosity and hydraulic conductivity of the investigated soils were measured. The results have shown the dependence between moisture and the tillage system applied for both types of soil. The soil water content was higher under reduced tillage in comparison to traditional management.

The study is based on a hypothesis that internal defects and physiological disorders of fruit lead to changes in tissue thermal properties. During thermal stimulation, heterogeneities of thermal properties lead to the occurrence of thermal contrasts on the surface of fruit material, which can be successfully registered with the use of a thermographic device. A method was developed to detect apple bruising early using pulsed-phase thermography (PPT). In PPT the studied object is heated with an individual thermal pulse (most frequently a rectangular pulse) and the temperature decay on the surface is analysed on a pixel-by-pixel basis as a mixture of harmonic waves, thus enabling the computation of phase and amplitude images. The fast Fourier transform was used to obtain ampligrams and phasegrams of fruit heat response to defects occurring at different depths. The automatic segmentation procedure made it possible to select areas of bruised tissue in thermograms. A comparison of PPT results and visual inspection of bruising was performed, indicating high possibilities of the active thermography method for detecting defects up to several millimetres.

Bentonite, biotite, illite, kaolin, vermiculite and zeolite were acidified or alkalized with hydrochloric acid or sodium hydroxide of concentrations 0.0, 0.1, 1.0 and 5.0 mol dm− 3 at a room temperature during two weeks. The treated minerals were transferred to calcium homoionic forms from which aggregates were formed by hand (if possible) and subjected to a few cycles of wetting–drying to stabilize the structure. Aggregates of bentonite, illite, kaolin and zeolite, and loose beds of biotite and vermiculite (no aggregation) were studied by mercury porosimetry. From mercury intrusion curves pore volumes, size distributions, average radii and surface fractal dimensions were calculated for pores ranged from c.a. 0.01 to 10 μm in radius. In most cases with an increase of the concentration of acid and alkali the pore volumes increased indicating a loosening of the minerals texture. Depending on the mineral and the treatment both broadening of pore dimensions and formation of new fine pores was observed, therefore the average pore radii changed in different directions. No well defined trends in the pore surface fractal dimension were noted under both treatments, however in most cases they were lower for treated than for nontreated minerals.

Aggregate size distribution and pore structure affect many soil functions and root growth. The aggregate structure is associated with soil genesis and management practices applied. In this study the effects of various size ranges of aggregates (

This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to + 9 degrees C) and precipitation (-50 to + 50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development.

Samples of peat soils characterized by different degree of the secondary transformation are studied by means of nitrogen and water vapor sorption. Moreover, we have also determined the water retention curves and the dependence of the hydraulic conductivity on water potential. The experimental data are interpreted in terms of fractal theories of adsorption and retention, yielding the values of the corresponding fractal dimensions. We discuss the relation between surface fractal dimensions obtained from nitrogen and water sorption data, as well as between the mass fractal dimension resulting from retention data. We have also tried to correlate the fractal dimensions to other physico-chemical properties of investigated soils.

In this paper, we evaluated the WAVE 2.1 and the EURO-ACCESS-II models for predicting crop water consumption, water losses by drainage and volumetric soil water content in a cropped soil under Mediterranean conditions. A detailed dataset was constructed for a furrow-irrigated experimental plot on a homogeneous sandy soil, cropped with maize during the seasons 1992 and 1993. The calibration and the validation results of the models were evaluated by using both simulation graphics and two specific modelling evaluation statistics: the root mean square error (RMSE) and the modelling efficiency. Results showed that both models are able to predict the fate of water in this coarse textured irrigated soil subjected to semi-arid Mediterranean environmental and agronomical conditions. Reasonably good predictions of the seasonal evolution both of the crop evapotranspiration and the water losses by drainage were obtained with both models. Soil moisture was also well predicted by the models when considering total soil moisture storage in the root zone of plants. However, the deviations between the predicted and observed moisture values increased when considering each soil layer separately. Sensitivity analysis showed that the input values of the crop coefficient have a significant influence on predictions made by the WAVE 2.1 model. The value of the hydraulic conductivity close to saturation had a significant influence on the predicted water losses by drainage in the EURO-ACCESS-II model. Our results showed that calibration for the specific environmental conditions of the field sites is required before using any of the two models in a purely prediction model. During this calibration, special attention must be given to the input values of those variables which are most affected by spatio-temporal variability within the field.

A b s t r a c t. The water conductivity of arctic zone soils derived in different micro-relief forms was determined. The greatest water conductivity at the 0-5 cm depth for the higher values of water potentials (> -7 kJ m -3 ) was shown by tundra polygons (Brunic-Turbic Cryosol, Arenic) – 904-0.09 cm day -1 , whereas the lowest were exhibited by Turbic Cryosols – 95-0.05 cm day -1 . Between -16 and -100 kJ m -3

10 Pags.- 3 Tabls.- 4 Figs.- Copyright © 2014 Krzysztof Lamorski et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited., This work presents point pedotransfer function (PTF) models of the soil water retention curve. The developed models allowed for estimation of the soil water content for the specified soil water potentials: –0.98, –3.10, –9.81, –31.02, –491.66, and –1554.78 kPa, based on the following soil characteristics: soil granulometric composition, total porosity, and bulk density. Support Vector Machines (SVM) methodology was used for model development. A new methodology for elaboration of retention function models is proposed. Alternative to previous attempts known from literature, the -SVM method was used for model development and the results were compared with the formerly used the -SVM method. For the purpose of models’ parameters search, genetic algorithms were used as an optimisation framework. A new form of the aim function used for models parameters search is proposed which allowed for development of models with better prediction capabilities. This new aim function avoids overestimation of models which is typically encountered when root mean squared error is used as an aim function. Elaborated models showed good agreement with measured soil water retention data. Achieved coefficients of determination values were in the range 0.67–0.92. Studies demonstrated usability of -SVM methodology together with genetic algorithm optimisation for retention modelling which gave better performing models than other tested approaches., The work was partly financed from budget of National Science Centre under Contract no. UMO-2011/01/B/ST10/07544.

Elements of design and a field application of a TDR-based soil moisture and electrical conductivity monitoring system are described with detailed presentation of the time delay units with a resolution of 10 ps. Other issues discussed include the temperature correction of the applied time delay units, battery supply characteristics and the measurement results from one of the installed ground measurement stations in the Polesie National Park in Poland.

This paper presents the statistical-physical model (pedotransfer function) relating soil water content at defined values of soil water potential to selected parameters of soil solid phase, developed for eight arable soils representative for south-west Spain. The model contains two equations for which independent variables include the content of soil granulometric fractions 2.0-0.2 mm and 0.2-0.02 mm, and bulk density or total porosity. Similar correlation coefficients between measured and predicted water contents were found for both equations: 0.89 < R < 0.96 for the first and 0.88 < R < 0.96 for the second one. The model was validated for 46 Polish soils of a wide range of textures, yielding also good correlations of measured and predicted data (R values over 0.9 in both cases). That the model elaborated on the base of soil samples from mediterranean climate (Spain) was applicable for soil samples from temperate climate (Poland), proves its general usefulness for soil moisture content estimation from basic physical parameters of soil solid phase. The presented model is simpler than the other commonly used ones described in the literature.

Publisher Summary This chapter presents the investigations of the impact of soil physical and chemical parameters on the water retention curve and water conductivity curve that were initiated in Poland. This research aimed at finding relations between the contents of granulometric fractions and the water contents at various values of the soil water potential. Soil organic matter affects water retention because it can retain water by itself and it strongly affects soil microstructure. It was difficult to establish direct relationships between the organic matter content and the water content at a given soil water potential. However, organic matter content is frequently taken into account as a complementary parameter in soil hydraulic property estimations. The chapter performs investigations that aim at estimating the soil hydraulic conductivity of undisturbed structure. The impact of soil physical parameters on the unsaturated hydraulic conductivity at selected soil water potential values is also investigated in the chapter.

Comparison of long-term daily prediction of soil water content at different depths of soil coming from two models, one physically based (HYDRUS-1D) and the other being soft-computing technique (Support Vector Machine), has been done. The models used provide quite accurate prediction for soil moisture content, especially in the most important layers for crop growth—those close to the surface. The RMSE of estimations of the volumetric soil water content in topsoil made by the Support Vector Machine (ν-SVM) based model was 0.035, 0.030, and 0.021 cm 3 cm −3 for depths of 5, 10, and 25 cm, respectively, while the physically based model had RMSE values of 0.042, 0.049, and 0.045 cm 3 cm −3 for the same depths. Our results showed that the ν-SVM modeling approach can be used for estimations of soil water content changes in time with the accuracy comparable of to the one of the physically based models.

The aim of the paper is to compare results of the instantaneous profile method (IPM) for measurement and calculation of unsaturated hydraulic conductivity k(Ψ) of soils obtained with different measurement data resolution. The application of IPM allows to realize a great number of k(Ψ) measurements for the purpose of mapping soil properties on large areas. Application of shorter samples i.e. less sensors makes the method even more quick and cheap. The calculation of unsaturated soil water conductivity by the IPM method bases on measurements of time and space variability of water content and water pressure within the soil sample in a cylinder. The spatial resolution of data depends on the number of probes applied in the core, The question arises how the number of compartments within one core influences the calculation of soil hydraulic conductivity. Application of three sensors instead of five reduced the accuracy of calculation but allowes to use 5 cm long standard cores during unsaturated flow experiment.