A case study of temperature fluctuations within and above a wheat field before and after sunset

Temperature fluctuations in the stable air layer before and after sunset were measured at 4 heights within and above a wheat field. Large positive temperature fluctuations were frequently observed within the plant canopy. The standard deviations, skewness factors and flatness factors of temperature fluctuations within the canopy showed peculiar time variations, having remarkable positive skewness factors. The occurrence of large positive temperature fluctuations was probably related to the difference of temperature gradients below and above the observation height, i.e., these fluctuations frequently occurred when the temperature gradient above the observation height was greater than that below the observation height. Furthermore, the vertical mixing associated with the penetration of downdrafts from the air layer above the canopy was requisite for the occurrence of the phenomenon.     

Rainfall estimation using raingages and radar — A Bayesian approach: 1. Derivation of estimators

Procedures for estimating rainfall from radar and raingage observations are constructed in a Bayesian framework. Given that the number of raingage measurements is typically very small, mean and variance of gage rainfall are treated as uncertain parameters. Under the assumption that log gage rainfall and log radar rainfall are jointly multivariate normal, the estimation problem is equivalent to lognormal co-kriging with uncertain mean and variance of the gage rainfall field.The posterior distribution is obtained under the assumption that the prior for the mean and inverse of the variance of log gage rainfall is normal-gamma 2. Estimate and estimation variance do not have closed-form expressions, but can be easily evaluated by numerically integrating two single integrals. To reduce computational burden associated with evaluating sufficient statistics for the likelihood function, an approximate form of parameter updating is given. Also, as a further approximation, the parameters are updated using raingage measurements only, yielding closed-form expressions for estimate and estimation variance in the Gaussian domain.     

Effect of barley straw biochar application on greenhouse gas emissions from upland soil for Chinese cabbage cultivation in short-term laboratory experiments

Chinese cabbage was cultivated in upland soil with the addition of biochar in order to investigate the potential for reduction of greenhouse gas emissions. Barley straw biochar (BSB) was introduced in a Wagner pot (1/5000a) in amounts of 0 (BSB0, control), 100 (BSB100), 300 (BSB300), and 500 (BSB500) kg 10a^-1. After the addition of BSB into the upland soil, carbon dioxide (CO₂) emission increased while methane (CH₄) and nitrous oxide (N₂O) emissions decreased. The highest CO₂ flux was measured for the BSB500 sample, (84.6 g m^-2) followed by BSB300, BSB100, and BSB0 in decreasing order. Relative to those of control, the total CH₄ flux and N₂O flux for the BSB500 treatment were lower by 31.6% and 26.1%, respectively. The global warming potential (GWP) of the treatment without biochar was 281.4 g CO₂ m^-2 and those for treatments with biochar were in the range from 194.1 to 224.9 g CO₂ m^-2. Therefore, introducing BSB into upland soil to cultivate Chinese cabbages can reduce the global warming potential.     

Determination of Soil Parameters to Analyze Mechanical Behavior Using Lade's Double-Surface Work-Hardening Model

In this study, Lade's double-surface work-hardening constitutive model was adopted which uses the elasto-plasticity model as a basic conceptual framework. The model can analyze work hardening and work softening of nonlinear stress-strain behavior, and is regarded as superior to other elasto-plasticity constitutive models in terms of estimation. In the double-surface work-hardening constitutive model, 14 soil parameters are needed to estimate soil behaviors. To determine them, laboratory tests—isotropical consolidation test and conventional compression test—were conducted. Determining of soil parameters is highly complicated and time-consuming; randomness cannot be ruled out in determining parameters that are sensitive to stress-strain estimation, and error may occur. For this reason, a linear and nonlinear regression analysis was used to determine soil parameters. In estimation of undrained behavior, the estimated stress-strain behavior based on the two constitutive models largely overlapped with the test results. However, in estimating drained behavior, the outcome of the two models and the test results were mostly the same, but between the two models, the double-surface work-hardening constitutive model had a sharper slope in initial stress state, and a smaller maximum deviatoric stress.     

Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices

ABSTRACT

Fractional green vegetation cover (FVC) is a useful indicator for monitoring grassland status. Satellite imagery with coarse spatial but high temporal resolutions has been preferred to monitor seasonal and inter-annual FVC dynamics in wide geographic area such as Mongolian steppe. However, the coarse spatial resolution can cause a certain uncertainty in the satellite-based FVC estimation, which calls attention to develop a robust statistical test for the relationship between field FVC and satellite-derived vegetation indices. In the arid and semi-arid Mongolian steppe, nadir pointing digital camera images (DCI) were collected and used to produce a FVC dataset to support the evaluation of satellite-based FVC retrievals. An optimal DCI processing method was determined with respect to three color spaces (RGB, HIS, L*a*b*) and six green pixel classification algorithms, from which a country-wide dataset of DCI-FVC was produced and used for evaluating the accuracy of satellite-based FVC estimates from MODIS vegetation indices. We applied three empirical and three semi-empirical MODIS-FVC retrieval models. DCI data were collected from 96 sites across the Mongolian steppe from 2012 to 2014. The histogram algorithm using the hue (H) value of the HIS color space was the optimal DCI method (r² = 0.94, percent root-mean-square-error (RMSE) = 7.1%). For MODIS-FVC retrievals, semi-empirical Baret model was the best-performing model with the highest r² (0.69) and the lowest RMSE (49.7%), while the lowest MB (+1.1%) was found for the regression model with normalized difference vegetation index (NDVI). The high RMSE (>50% or so) is an issue requiring further enhancement of satellite-based FVC retrievals accounting for key plant and soil parameters relevant to the Mongolian steppe and for scale mismatch between sampling and MODIS data.     

Directional‐oriented wavefield imaging: a new wave‐based subsurface illumination imaging condition for reverse time migration

The key objective of an imaging algorithm is to produce accurate and high‐resolution images of the subsurface geology. However, significant wavefield distortions occur due to wave propagation through complex structures and irregular acquisition geometries causing uneven wavefield illumination at the target. Therefore, conventional imaging conditions are unable to correctly compensate for variable illumination effects. We propose a generalised wave‐based imaging condition, which incorporates a weighting function based on energy illumination at each subsurface reflection and azimuth angles. Our proposed imaging kernel, named as the directional‐oriented wavefield imaging, compensates for illumination effects produced by possible surface obstructions during acquisition, sparse geometries employed in the field, and complex velocity models. An integral part of the directional‐oriented wavefield imaging condition is a methodology for applying down‐going/up‐going wavefield decomposition to both source and receiver extrapolated wavefields. This type of wavefield decomposition eliminates low‐frequency artefacts and scattering noise caused by the two‐way wave equation and can facilitate the robust estimation for energy fluxes of wavefields required for the seismic illumination analysis. Then, based on the estimation of the respective wavefield propagation vectors and associated directions, we evaluate the illumination energy for each subsurface location as a function of image depth point and subsurface azimuth and reflection angles. Thus, the final directional‐oriented wavefield imaging kernel is a cross‐correlation of the decomposed source and receiver wavefields weighted by the illuminated energy estimated at each depth location. The application of the directional‐oriented wavefield imaging condition can be employed during the generation of both depth‐stacked images and azimuth–reflection angle‐domain common image gathers. Numerical examples using synthetic and real data demonstrate that the new imaging condition can properly image complex wave paths and produce high‐fidelity depth sections.     

Summer Variations of Macrobenthic Community Structures in Gwangyang Bay,Korea

Ocean Science Journal - We investigated the structure change of the macrobenthic community in Gwangyang Bay (Korea) in response to habitat alteration, using the summer fauna data collected between...     

Harmful Dinoflagellate Prorocentrum donghaiense Lu is Widely Distributed Along the East China Sea and Korean Coastal Area

Ocean Science Journal - There have been controversies regarding the taxonomy of Prorocentrum donghaiense, however Zhang et al. (2016) recently developed a probe with a quantitative real-time PCR...     

Resistance and propulsion characteristics of various commercial ships based on CFD results

This paper uses computational tools to examine the speed performance of various types of commercial ships including resistance and propulsion characteristics. Eight commercial ships built in the last decade were selected for the study. They include four large-sized container carriers, one bulk carrier, one VLCC, and two LNG carriers. The Reynolds averaged Navier-Stokes equation has been utilized, and the computations were executed under the same conditions of the model tests to predict the speed performance, i.e., resistance and self-propulsion. The self-propulsion point was obtained from load-varying tests. The speed performance was predicted based on the model-ship performance analysis method of the revised ITTC’78 method. The limiting streamlines on the hull, wave characteristics around the model ship, and the wake characteristics on the propeller plane were also investigated. After completing the computations, a series of model tests were conducted to evaluate the accuracy of the computational predictions. The predictions clearly reveal the differences in the resistance and propulsion characteristics regarding the various types of commercial ships, and may be applicable to hull-form design.     

Four-dimensional reflectivity data comparison between two ground-based radars: methodology and statistical analysis

Abstract

A methodology is proposed to compare radar reflectivity data obtained from two partially overlapping ground-based radars in order to explain relative differences in radar-rainfall products and establish sound merging procedures for multi-radar observing networks. To identify radar calibration differences, radar reflectivity is compared for well-matched radar sampling volumes viewing common meteorological targets. Temporal separation and three-dimensional matching of two different sampling volumes were considered based on the original polar coordinates of radar observation. Since the proposed method assumes radar beam propagation under standard atmospheric conditions, anomalous propagation cases were eliminated from the analysis. The reflectivity comparison results show systematic differences over time, but the variability of these differences is surprisingly large due to the sensitive nature of the radar reflectivity measurement.

Editor D. Koutsoyiannis/Z.W. Kundzewicz; Guest editor R.J. Moore

Citation Seo, B.-C., Krajewski, W.F., and Smith, J.A., 2013. Four-dimensional reflectivity data comparison between two ground-based radars: methodology and statistical analysis. Hydrological Sciences Journal, 59 (7), 1312–1326. http://dx.doi.org/10.1080/02626667.2013.839872