Janis Kreiselmeier

Bio: Janis Kreiselmeier is an academic researcher from United Nations University. The author has contributed to research in topics: Soil water & Hydraulic conductivity. The author has an hindex of 7, co-authored 11 publications receiving 114 citations. Previous affiliations of Janis Kreiselmeier include Dresden University of Technology.

Why We Should Include Soil Structural Dynamics of Agricultural Soils in Hydrological Models

Abstract: Surface soil structure is sensitive to natural and anthropogenic impacts that alter soil hydraulic properties (SHP). These alterations have distinct consequences on the water cycle. In this review, we summarized published findings on the quantitative effects of different agricultural management practices on SHP and the subsequent response of the water balance components. Generally, immediately after tillage, soils show a high abundance of large pores, which are temporally unstable and collapse due to environmental factors like rainfall. Nevertheless, most hydrological modeling studies consider SHP as temporally constant when predicting the flow of water and solutes in the atmosphere-plant-soil system. There have been some developments in mathematical approaches to capture the temporal dynamics of soil pore space. We applied one such pore evolution model to two datasets to evaluate its suitability to predict soil pore space dynamics after disturbance. Lack of knowledge on how dispersion of pore size distribution behaves after tillage may have led to over-estimation of some values predicted by the model. Nevertheless, we found that the model predicted the evolution of soil pore space reasonably well (r2 > 0.80 in most cases). The limiting factor to efficiently calibrate and apply such modeling tools is not in the theoretical part but rather the lack of adequate soil structural and hydrologic data.

Quantification of Soil Pore Dynamics During a Winter Wheat Cropping Cycle Under Different Tillage Regimes

Abstract: The water retention characteristic (WRC) and the hydraulic conductivity characteristic (HCC) vary in time due to tillage system, weather conditions and biological activity. These changes in WRC and HCC are a result of varying pore size distributions (PSD). Considering these alterations in soil hydrological models has been shown to improve simulations of water dynamics. An important prerequisite for such an approach is the periodic quantification of WRC and HCC, e.g., over a cropping cycle. Therefore, our study frequently quantified WRC and HCC together with other soil physical and chemical properties on a long-term (23 years) tillage experiment with a silt loam soil. The aim was to identify differences between the three treatments conventional tillage (CT) with a moldboard plow, reduced mulch tillage (RT) with a cultivator and no tillage (NT) with direct seeding. WRC and HCC were parameterized using the bimodal version of the well-known Kosugi retention model together with the Mualem conductivity model to account explicitly for both textural and structural pores. Consequently, bimodal PSD were inferred using the Kosugi parameters. The structural part of the bimodal Kosugi model clearly showed a shift in the PSD on CT and RT from larger to smaller pores throughout the winter wheat growing season with a recovery later in the season on RT. Saturated hydraulic conductivity was positively correlated with the abundance of transmission pores (diameter 50–500 μm) which has implications for infiltration processes under the influence of seasonal PSD changes. Overall, a frequent experimental quantification of PSD may be warranted for modeling soil water on short time scales, e.g., during a cropping cycle, while for longer time frames one to two measurement campaigns per year may be sufficient to describe soil hydraulic behaviour.

対数正規分布(Lognormal Distribution)のあてはめについて

Abstract: Ecological data are often lognormally distributed. Nutrient concentrations, population densities and biomasses, rates of production and other flows are always positive, and generally have standard deviations that increase as the mean increases. Lognormally distributed variables have these characteristics, whereas normally distributed variables can be negative and have a standard deviation that does not change as the mean changes. Lognormal errors arise when sources of variation accumulate multiplicatively, whereas normal errors arise when sources of variation are additive. Given a normally distributed random variable Y, one can calculate a lognormally distributed random variable Z = exp(Y) where exp stands for the exponential function (exp(x) = e x). The mean Z and the standard deviation s Z of the lognormal variable are related to the mean Y and standard deviation s Y of the normal variable by) 2 / exp() exp(2 Y s Y Z = [1] 5. 0 2 ] 1) [exp(− = Y Z s Z s [2] Equation 1 can be used to correct for transformation bias in logarithmic regression. Suppose that lognormally-distributed observations Z have been log transformed as Y = log(Z) to fit a regression model such as ε + =) , (ˆ b X f Y [3] where Y is the log-transformed response variable which is predicted to be Y ˆ computed from the function f, X is a matrix of predictors, b is a vector of parameters, and the errors ε are normally distributed with mean zero and standard deviation s ε. Predictions Z ˆ in the original units are calculated using equation 1 as ] 2) ˆ exp[(ˆ 2 ε s Y Z + = [4] Note that estimates the median prediction of Z, which will be smaller than the mean for a lognormally distributed variate. Thus it makes sense to adjust the median upward, as in equation 4.) ˆ exp(Y Equation 1 is also used in drawing random numbers from a lognormal distribution. Generators for normally-distributed random variables Y are common. Suppose we draw many values of Y with mean zero and standard deviation s Y. Then from equation 1, the mean of exp(Y) will not be 1 = e 0 ; instead the mean of exp(Y) will be. Generally, however, one would prefer to have the mean of a set of lognormally distributed random numbers be 1. This can be accomplished by shifting the random numbers to Y) 2 / exp(2 Y …

Simplified evaporation method for determining soil hydraulic properties: a reinvestigation of linearization errors

Abstract: Accurate knowledge of the soil hydraulic properties is a prerequisite for reliable modelling of soil water dynamics. As a consequence, many methods have been developed to derive these constitutive relationships either under field or laboratory conditions. Among these methods, the simplified evaporation method conducted on soil samples in the laboratory has found widespread use and application, mainly due to its relative ease of implementation and its straightforward evaluation of the experimental data. This method, however, relies on various simplifying assumptions. A common approach to assess the validity of these assumptions and to explore potential linearization errors associated with them is the use of synthetic data. In the past, such synthetic data were generated using rather simplistic models considering liquid water flow in capillaries only. In this study, we reinvestigated the accuracy of the simplified evaporation method using a more realistic process description of evaporative drying of the soil sample, including both liquid water flow in capillaries and films, as well as isothermal water vapour diffusion. In contrast to previous results reported in the literature, our results show that the simplifying assumptions used to evaluate the experimental data may result in biased estimates of the soil hydraulic properties, particularly for coarse textured soils. The bias typically increased progressively during stage-two evaporation, which is characterized by the development of a dry surface layer in which water flow is dominated by diffusion of water vapour, resulting in strongly nonlinear pressure head and water content profiles. We investigated various strategies for correcting for this bias caused by simplifying assumptions.

Principles of Soil Physics.

Abstract: R. LAL and M.J. SHUKLA. Marcel Dekker, Inc., New York. 2004. Hardbound, 716 pp. $94.95. ISBN 0-8247-5324-0. In the preface to their book, Principles of Soil Physics , authors Lal and Shukla state that the text is intended to focus on “soil's physical, mechanical, and hydrological properties, such

Simulation of Fluid Flow Through a Granular Bed

Abstract: Numerical study of flow through random packing of non-overlapping spheres in a cylindrical geometry is investigated. Dimensionless pressure drop has been studied for a fluid through the porous media at moderate Reynolds numbers (based on pore permeability and interstitial fluid velocity), and numerical solution of Navier-Stokes equations in three dimensional porous packed bed illustrated in excellent agreement with those reported by Macdonald [1979] in the range of Reynolds number studied. The results compare to the previous work (Soleymani et al., 2002) show more accurate conclusion because the problem of channeling in a duct geometry. By injection of solute into the system, the dispersivity over a wide range of flow rate has been investigated. It is shown that the lateral fluid dispersion coefficients can be calculated by comparing the concentration profiles of solute obtained by numerical simulations and those derived analytically by solving the macroscopic dispersion equation for the present geometry.Copyright © 2006 by ASME