Simple Empirical Model Research Articles

A Semi-analytical Model for Short-Range Near-Ground Continuous Dispersion

We present a semi-analytical model for the dispersion of passive scalars from continuous ground sources up to distances of a few hundred metres. We attempt to cope with problems typical of analytical models, such as the correct representation of the near-ground concentration and lateral dispersion, while avoiding the use of any empirical parameters. A previous analysis of Prairie Grass Project (PGP) data has shown that the near-ground, cross-wind integrated concentration decreases as some power of the distance from the source that is, itself, distance dependent. As the conventional power-law model is incapable of reproducing this behaviour, we propose a model in which the vertical diffusivity depends on both the height as a power law, and on the distance from the source. For this equation, we construct an infinite-series formal solution, with the first term used as an approximation. A set of equations based on this approximation and on Monin–Obukhov similarity theory is proposed for the the vertical diffusivity, from which the cross-wind integrated concentration is derived analytically. We further construct a simple empirical model for the distance-dependent vertical diffusivity. For the plume lateral width, a Langevin stochastic model depending on the plume height is proposed, whose formal analytical solution is used to derive a set of equations for the cloud width, which are easily solved numerically, with the results validated against PGP data. We apply four statistical measures to evaluate the performance of the model, including the computation of the 95% confidence intervals, for which we find very good agreement. Implementation of this model is extremely simple and computationally efficient.

An investigation into the flowability of fine powders used in pharmaceutical industries

This paper investigates into the flow properties of two popular pharmaceutical excipients (Calcium sulphate and Dicalcium phosphate) and their respective feeder hopper design requirements. There are numerous problems related to poor powder flowability that are faced by pharmaceutical industries, due to a lack of fundamental understanding of powder flow properties and their effects on hopper design parameters (Standard, 2008 [1]). Characterization tests confirmed that the Dicalcium phosphate powder displayed a smaller particle size, larger specific surface area and higher value of Hausner ratio compared to Calcium sulphate powder indicating that Dicalcium phosphate powders are markedly more cohesive. This was validated by the instantaneous and time consolidation flow function curves which showed that the Dicalcium phosphate was the more cohesive. Powder flow function and wall friction tests were carried out on a ring type shear tester working on the basis of Jenike's principle. Based on the instantaneous test results, Dicalcium phosphate was found to require a larger hopper outlet dimension for the same hopper half angle compared to Calcium sulphate. The time consolidation test results have shown that both Dicalcium phosphate and Calcium sulphate gained strength during static storage and have become more cohesive. A simple empirical model for powder cohesion and unconfined yield stress has been developed and validated with a number of experimental values derived from common pharmaceutical powders.

An empirical model using dissolved oxygen as an indicator for eutrophication at a regional scale

Simple empirical models can sometimes capture salient patterns without sacrificing predictive capacity when compared to more complex models. Herein we examine dissolved oxygen as an indicator of eutrophication status for shallow estuaries. Dissolved oxygen was measured hourly in the upper estuary of 15 watersheds along a nutrient-loading and geographic gradient. Metrics describing hypoxia and supersaturation were devised and then analyzed using multivariate statistics. Results revealed independent responses for hypoxia and supersaturation with hypoxia-related metrics correlating strongly with water residence. A metric integrating hypoxia and supersaturation effectively discriminated between seagrass and algae-dominated habitats and was significantly correlated with both water residence and nitrate-N loading. Chlorophyll, measured bi-weekly, was not correlated with our predictor variables likely because it does not account for benthic production. Over 70% of the variability in hypoxia was explained by water residence and nitrate-N loading indicating that this model can be of use for managers.

Combining sorption experiments and Time of Flight Secondary Ion Mass Spectrometry (ToF-SIMS) to study the adsorption of propranolol onto environmental solid matrices – Influence of copper(II)

The bioavailability of pharmaceuticals is governed by their sorption in soils/sediments, as the retention processes determine their concentration in surface- and ground-water. The adsorption of these contaminants can involve various solid components such as organic matter, clays and metallic oxides, and their distribution among these solid components depends on contaminant and solid properties. In this paper we studied the adsorption of the pharmaceutical propranolol – a beta-blocker – on eight different solids (six soils, one sediment and one kaolinite-based sample) by batch experiments. The influence of contact time, propranolol concentration and pH was considered, as well as the presence of copper(II). The investigated solids displayed a wide variability in terms of CEC (cationic exchange capacity) and organic carbon and carbonates contents. The influence of pH was negligible in the pH range from 5.5 to 8.6. The adsorbed amounts were greatly dependent on the solid and two groups of solids were evidenced: three soils of high CEC and organic carbon contents which retained high amounts of propranolol, and three soils, the sediment and the kaolinite-based sample (low CEC and organic carbon content) displaying a low adsorption capacity for the beta-blocker. A linear model enabling the determination of the sorption parameters Kd and Koc was pertinent to describe the adsorption isotherms but the Koc values showed a great variability. It was shown that organic carbon content alone could not explain propranolol adsorption. The CEC value was identified as influent parameter and a simple empirical model was proposed to describe propranolol adsorption. At microscopic and molecular scales, ToF-SIMS experiments indicated (i) a decrease of potassium on the surface upon propranolol adsorption with a distribution of the beta-blocker similarly to alumino-silicates, iron and organic carbon on the surface confirming a cation exchange mechanism and (ii) the absence of degradation products and copper-propranolol complexes.

A methodology and a simple engineering fire performance model for Intumescent Fire Retardant coatings

This paper presents a practical engineering model to determine the flammability properties and predict the fire performance of Intumescent Fire Retardant (IFR) coatings by performing experiments on a 5.0 mm inert steel substrate in the Cone Calorimeter. The present model simplifies the complete conservation equations governing the IFR system by utilizing the assumptions that the initial IFR coating layer (virgin plus melt) is thermally thin and its expanding char layer, which includes also the vapor and the foam, has negligible volumetric heat capacity. These simplifications eliminate the requirement in the existing models for determination of numerous expansion and thermal properties for the IFR coating and its resulting char. The thermal insulation effect of the expanded char is represented as a ratio between the heat flux received by the substrate and the heat flux imposed on the substrate surface for the case there is no char layer. This heat flux ratio is expressed as a function of the expanded char height history, which is related to the mass loss rate and total mass loss of the IFR coating as determined from modeling of mass loss rate based on TGA measurements. This type of modeling for the IFR expansion height is based on the observation that the mass loss as measured from the TGA experiments is in fact contributing, through chemistry and diffusion, to the formation of the gas bubbles in the expanded char. The accuracy and robustness of the model are validated by comparing its predictions of expansion height and steel plate temperature in Cone Calorimeter experiments at different heat fluxes for two commercial IFR coatings having different initial masses. Compared to other simple empirical models or complicated modeling efforts, our model includes the basic physics of IFR expansion and insulation in a comprehensive way. Improvements are also anticipated for oxidation or degradation of the expanded char at high temperatures over 1200 K.

Tracking the fate and transport of estrogens following rainfall events.

Surface runoff following rainfall events can transport estrogens from agricultural land to water systems, causing potential risks to aquatic biota. This study adopted two simple models, the wash-off and empirical models, to quantify the pathway of three prevalent manure-borne estrogens, estrone (E1), 17β-estradiol (E2β) and 17α-estradiol (E2α), from agricultural land to the receiving water following rainfall events. The complex interconversion of the three estrogens during attenuation was considered in the models. These two models were calibrated and validated using the data obtained from three artificial rainfall events from the literature. The data from the first two rainfall events were used to quantify key model parameters and the data from the third rainfall event were used to validate the models. The performances of the models were then evaluated through a statistical analysis. Results show that both models can closely reproduce the mass loads of estrogens during rainfall events and that the wash-off model shows a better overall performance than the empirical model for the data used in this study.

Global assessment of the effect of climate change on ammonia emissions from seabirds

Seabird colonies alter the biogeochemistry of nearby ecosystems, while the associated emissions of ammonia (NH3) may cause acidification and eutrophication of finely balanced biomes. To examine the possible effects of future climate change on the magnitude and distribution of seabird NH3 emissions globally, a global seabird database was used as input to the GUANO model, a dynamic mass-flow process-based model that simulates NH3 losses from seabird colonies at an hourly resolution in relation to environmental conditions. Ammonia emissions calculated by the GUANO model were in close agreement with measured NH3 emissions across a wide range of climates. For the year 2010, the total global seabird NH3 emission is estimated at 82 [37–127] Gg year−1. This is less than previously estimated using a simple temperature-dependent empirical model, mainly due to inclusion of nitrogen wash-off from colonies during precipitation events in the GUANO model. High precipitation, especially between 40° and 60° S, results in total emissions for the penguin species that are 82% smaller than previously estimated, while for species found in dry tropical areas, emissions are 83–133% larger. Application of temperature anomalies for several IPCC scenarios for 2099 in the GUANO model indicated a predicted net increase in global seabird NH3 emissions of 27% (B1 scenario) and 39% (A2 scenario), compared with the 2010 estimates. At individual colonies, the net change was the result of influences of temperature, precipitation and relative humidity change, with smaller effects of wind-speed changes. The largest increases in NH3 emissions (mean: 60% [486 to −50] increase; A2 scenario for 2099 compared with 2010) were found for colonies 40°S to 65°N, and may lead to increased plant growth and decreased biodiversity by eliminating nitrogen sensitive plant species. Only 7% of the seabird colonies assessed globally (mainly limited to the sub-polar Southern Ocean) were estimated to experience a reduction in NH3 emission (average: −18% [-50 to 0] reduction between 2010 and 2099, A2 scenario), where an increase in precipitation was found to more than offset the effect of rising temperatures.

Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes.

Pressure-varying CO2 distribution affects the ultrasonic velocities of synthetic sandstones

We performed a novel experiment in which three synthetic sandstones – manufactured using a common method but having different porosities – were saturated with brine and progressively flooded with CO2 under constant confining pressure. The fluid pressure was varied around the critical pressure of CO2 and repeated measurements were made of resistivity, in order to assess the saturation, and elastic wave velocity during the flood. The measured saturated bulk moduli were higher than those predicted by the Gassmann–Wood theory, but were consistent with behaviour described by a recently derived poroelastic model which combines “patch” and “squirt” effects. Measurements on two of the samples followed a patch-based model while those on the highest porosity sample showed evidence of squirt-flow behaviour. Our analysis suggests that the appropriate fluid mixing law is pressure dependent, which is consistent with the notion that the effective patch size decreases as fluid pressure is increased. We derive simple empirical models for the patch dependence from fluid pressure which may be used in seismic modelling and interpretation exercises relevant to monitoring of CO2 injection.

Reference Evapotranspiration Variation Analysis and Its Approaches Evaluation of 13 Empirical Models in Sub-Humid and Humid Regions: A Case Study of the Huai River Basin, Eastern China

Accurate and reliable estimations of reference evapotranspiration (ET0) are imperative in irrigation scheduling and water resource planning. This study aims to analyze the spatiotemporal trends of the monthly ET0 calculated by the Penman–Monteith FAO-56 (PMF-56) model in the Huai River Basin (HRB), eastern China. However, the use of the PMF-56 model is limited by the insufficiency of climatic input parameters in various sites, and the alternative is to employ simple empirical models. In this study, the performances of 13 empirical models were evaluated against the PMF-56 model by using three common statistical approaches: relative root-mean-square error (RRMSE), mean absolute error (MAE), and the Nash–Sutcliffe coefficient (NS). Additionally, a linear regression model was adopted to calibrate and validate the performances of the empirical models during the 1961–2000 and 2001–2014 time periods, respectively. The results showed that the ETPMF increased initially and then decreased on a monthly timescale. On a daily timescale, the Valiantzas3 (VA3) was the best alternative model for estimating the ET0, while the Penman (PEN), WMO, Trabert (TRA), and Jensen-Haise (JH) models showed poor results with large errors. Before calibration, the determination coefficients of the temperature-based, radiation-based, and combined models showed the opposite changing trends compared to the mass transfer-based models. After calibration, the performance of each empirical model in each month improved greatly except for the PEN model. If the comprehensive climatic datasets were available, the VA3 would be the recommended model because it had a simple computation procedure and was also very well correlated linearly to the PMF-56 model. Given the data availability, the temperature-based, radiation-based, Valiantzas1 (VA1) and Valiantzas2 (VA2) models were recommended during April–October in the HRB and other similar regions, and also, the mass transfer-based models were applicable in other months.

Model - Based Design and Control of Lead-Acid Batteries: Is There Any More Juice Left in a System That Is 158 Years Old?

The low initial cost, ease of manufacture and relatively mature state of technology make lead-acid batteries the most popular energy storage choice for many applications. Lead-acid batteries exist in different configurations, each suitable for different applications and performance requirements. It is worth addressing the question as to whether model-based strategies can help realize even greater improvements in interrelated overall performance characteristics such as rate performance, cycle life and cost, and in overcoming factors that limit operational performance besides reducing product development time and cost by reducing the amount of testing required for development and validation. Some potential uses of model-based approaches are listed below: For phenomena within lead-acid batteries for which no reliable or physically meaningful models exist, model development to provide a quantitative description may be useful. This may be a new physics-based model based on existing theory, a combination of existing models (e.g. extension to multiple spatial dimensions) or an empirical model with adjustable parameters which may be obtained by fitting experimental data. These mathematical models may then be used to simulate the time evolution of the relevant variables. Several continuum-level models with varying predictive capabilities have been reported in the literature1-2. At the same time, there is scope for more complete descriptions of degradation phenomena such as irreversible sulfation, which is a major cause of cell failure.3 For situations where mathematical models exist, computationally efficient simulations may be performed by exploiting the mathematical structure of model equations to create suitable formulations.4-5 Existing models may also be used to obtain useful qualitative and quantitative design insights. Parametric optimization of design parameters for a certain objective function is also possible, but is especially suitable with computationally efficient physics-based models.6 Different objective functions may be defined – total charge, local overpotentials etc. The optimization problem formulation can include mathematically equivalent constraints to prevent undesirable phenomena (e.g. sulfation, gas evolution). The use of physics-based models gives us predictions of state variables internal to the cell, which allows us to formulate a far more complete description of the problem than would be possible by simple empirical models. Acknowledgements The authors acknowledge financial support from the Department of Chemical Engineering and the Clean Energy Institute at the University of Washington. References H. Gu, T.V. Nguyen, and R.E. White, J. Electrochem. Soc., 134, 2953 – 2960 (1987).M. Cugnet, S. Laruelle, S. Grugeon, B. Sahut, J. Sabatier, J-M Tarascon, and A. Oustaloup, J. Electrochem. Soc., 156, A974 – A985 (2009).K.S. Gandhi, J. Electrochem. Soc., 164, E3092 – E3101 (2017).A.B. Ansari, V. Esfahanian, and F. Torabi, Appl. Energy, 173, 152-167 (2016).V. Esfahanian, F. Torabi, and A. Mosahebi, J. Power Sources, 176, 373-380 (2008).G. Kujundzic, S. Iles, J. Matusko, and M. Vasak, Appl. Energy, 187, 189-202 (2017).

The Dependence of the Soil Microwave Attenuation on Frequency and Water Content in Different Types of Vegetation: an Empirical Model

A layer of vegetation above the surface of the soil absorbs the microwave radiation emitted by the soil and some objects under the vegetation canopy. This paper explores the microwave propagation process within the vegetation layer and evaluates the respective attenuation coefficients. For this purpose, an experimental laboratory test bench is constructed which is based on the special rectangular camera-waveguide with the transmission and reception of broadband antennas. This allows the study of the dependence of the attenuation coefficient on the 0.8–10 GHz frequency range and the water content in different types of vegetation. Evaluations of attenuation coefficients are achieved on a wide range of frequencies for deciduous and coniferous forests, which separate the branches and trunks as canopy fragments. These experimental results are important because they can fulfill the requirement for verification and validation of existing theoretical models that describe the attenuation effect of microwave propagation in vegetation canopy. In addition, the development of a simple empirical model based on the results obtained can give a brief indication of their importance.

Improved keeping quality of Dendrobium “Bom” orchids using nutrients entrapped in a biodegradable hydrogel

Abstract Poly-γ-glutamic acid (γ-PGA) produced by Bacillus licheniformis TISTR 1010 was blended with polyvinyl alcohol (PVA) and irradiated with cobalt-60 γ-rays to produce hydrogels. The most absorbent hydrogels were formed using a γ-PGA to PVA mass ratio of 90:10 and an irradiation dose of 40 kGy. The optimally made hydrogel had a gelation of 60.3 ± 2.4% and a deionized water absorption of 13.6 ± 0.4 g g−1. These hydrogels filled to capacity with a liquid fertilizer solution were used to make a ‘solid’ fertilizer for potting the Dendrobium “Bom” cut orchids for improving the shelf-life for export. The fertilizer absorption by the hydrogel was 2.0 ± 0.8 g g−1. In deionized water, the prepared solid fertilizer released the nutrient sucrose with average release rates of 0.92 ± 0.01 g L−1 h−1 (measured over a 0–10 h interval) and 0.08 ± 0.00 g L−1 h−1 (measured over a 10–168 h interval). The hydrogel solid fertilizer kept the orchids fresh for ∼8 days at 4 °C and ∼7 days at 25 °C. Relative to liquid fertilizer, the solid fertilizer delayed flower opening by 4 days at 25 °C. Use of a γ-PGA (0.5 g L−1) solution alone also delayed flower senescence and opening. Simple empirical models were shown to satisfactorily predict opening and senescence of flowers with cut stems potted in the solid fertilizer and other media.

Study of infrared reflection characteristics of aerial target using MODIS data on GPU

To study of the infrared signature of an aerial target, it is required to precisely model the background radiation. Simple empirical models or standard atmospheric models in LOWTRAN/MODTRAN were used in earlier studies. To further precisely model the thermal radiation of earth’s surface and atmospheric radiance/transmittance, the atmospheric profile, land surface temperature, and emissivity, the sea surface temperature retrieved from moderate-resolution imaging spectroradiometer, and the sea surface emissivity model developed by Wu and Smith are utilized in this study. Meanwhile, considering that the reflection of background radiation incident from different directions in each spectral wavelength can be calculated in parallel, implementations using open multi-processing and compute unified device architecture on a multi-core CPU and many-core graphics processing unit (GPU) are presented and speedups of 9 $$\times$$ and 258 $$\times$$ are obtained on a platform with dual Xeon E5-2652 CPU and an NVIDIA Tesla K80 GPU card, respectively.

Vulnerability of the Great Barrier Reef to climate change and local pressures.

Australia's Great Barrier Reef (GBR) is under pressure from a suite of stressors including cyclones, crown-of-thorns starfish (COTS), nutrients from river run-off and warming events that drive mass coral bleaching. Two key questions are: how vulnerable will the GBR be to future environmental scenarios, and to what extent can local management actions lower vulnerability in the face of climate change? To address these questions, we use a simple empirical and mechanistic coral model to explore six scenarios that represent plausible combinations of climate change projections (from four Representative Concentration Pathways, RCPs), cyclones and local stressors. Projections (2017-2050) indicate significant potential for coral recovery in the near-term, relative to current state, followed by climate-driven decline. Under a scenario of unmitigated emissions (RCP8.5) and business-as-usual management of local stressors, mean coral cover on the GBR is predicted to recover over the next decade and then rapidly decline to only 3% by year 2050. In contrast, a scenario of strong carbon mitigation (RCP2.6) and improved water quality, predicts significant coral recovery over the next two decades, followed by a relatively modest climate-driven decline that sustained coral cover above 26% by 2050. In an analysis of the impacts of cumulative stressors on coral cover relative to potential coral cover in the absence of such impacts, we found that GBR-wide reef performance will decline 27%-74% depending on the scenario. Up to 66% of performance loss is attributable to local stressors. The potential for management to reduce vulnerability, measured here as the mean number of years coral cover can be kept above 30%, is spatially variable. Management strategies that alleviate cumulative impacts have the potential to reduce the vulnerability of some midshelf reefs in the central GBR by 83%, but only if combined with strong mitigation of carbon emissions.

Temporal stability of within-field variability of total soluble solids of grapevine under semi-arid conditions: a first step towards a spatial model

Aims: This work focuses on the study of the intra- and inter-annual Temporal Stability of Within-Field Variability (TSWFV) of Total Soluble Solids (TSS) as an estimate of grape maturity.
 Methods and results: The experiment was carried out between 2009 and 2015 in four fields located in the Maule Valley, Chile, under irrigated conditions. Each field corresponded to a different cultivar (namely Cabernet-Sauvignon, Chardonnay, Sauvignon blanc and Carménère), and data collection ranged over two to four years depending on the field. A regular sampling grid was designed within each field, and TSS was measured at each site of the grid on different dates (from veraison to harvest). A Kendall test (W) was used to analyse the TSWFV of TSS between all dates for each cultivar and season. A Spearman’s rank correlation coefficient (rs) was used to analyse the relationships between each sampling date and the date of harvest considered as the reference. Results of the study highlighted high within-field variability in TSS. The W test showed significant intra- and inter-annual TSWFV, and rs values showed a high and significant correlation between sampling dates.
 Conclusion: These results are of interest for precision viticulture since, under the conditions of the experiment, the spatial patterns of the TSS maps obtained 40 days before harvest remain the same until harvest. Therefore, early target sampling of TSS may provide a good estimate of the spatial variability of grape maturity at harvest.
 Significance and impact of the study: The inter-annual stability of the TSS spatial patterns makes it possible to propose a simple empirical spatial model that allows estimation of TSS values for the whole field using only one reference measurement, provided that historical data are available.

Development of New Mathematical Model for Compressional and Shear Sonic Times from Wireline Log Data Using Artificial Intelligence Neural Networks (White Box)

Compressional (P-wave) and shear (S-wave) velocities are used to estimate the dynamic geomechanical properties including: Poisson’s ratio, Young’s modulus, and Lame parameters. These parameters are mainly used in estimating the static properties of the formation rocks as well as the in situ stresses. The sonic logs are not always available, epically for old wellbores. Also, in several occasions when the sonic logs are available, missing sections found in the well logs might affect the analysis results. To the authors’ knowledge, there is no single straightforward correlation that can be used to accurately estimate both P- and S-wave travel times directly from the well log data. Most of the existing correlations use the P-wave velocity to measure the S-wave velocity. The main purpose of this study is to develop accurate and simple empirical models using wireline log data (bulk density, gamma ray, and neutron porosity) to predict the sonic travel times (P-wave and S-wave). These wireline logs are slandered wireline log data that are commonly recorded in most of the wells. Three robust artificial intelligence techniques, namely: support vector machine (SVM), artificial neural network (ANN), and adaptive neurofuzzy interference systems (ANFIS), were employed and compared based on their prediction performance. Ultimately, using the weights and biases of optimized ANN model, a simple generalized empirical correlation is derived that can be used without the need of costly commercial software’s to run the AI models. The obtained results showed that ANN, ANFIS, and SVM can be used to estimate P-wave and S-wave travel times. ANN outperformed the ANFIS and SVM by yielding the lowest average absolute percentage error (AAPE) and the highest coefficient of determination $$\left( R^{2}\right) $$ for predicting P-wave and S-wave travel times. ANN model could predict the P-wave and S-wave travel times from wireline log data with high accuracy giving $$R^{2}$$ of 0.98 when compared to actual field data. In addition, the developed empirical correlations prediction completely matched the ANNs prediction. The AAPE of the predicted P and S-waves travel times was less than 5%. The developed correlations are very accurate and can help geomechanical engineers to determine the dynamic geomechanical properties (Poisson’s ratio and Young’s modulus) and propose any operation in case where sonic logs are missing.

Fire forbids fifty-fifty forest.

Recent studies have interpreted patterns of remotely sensed tree cover as evidence that forest with intermediate tree cover might be unstable in the tropics, as it will tip into either a closed forest or a more open savanna state. Here we show that across all continents the frequency of wildfires rises sharply as tree cover falls below ~40%. Using a simple empirical model, we hypothesize that the steepness of this pattern causes intermediate tree cover (30‒60%) to be unstable for a broad range of assumptions on tree growth and fire-driven mortality. We show that across all continents, observed frequency distributions of tropical tree cover are consistent with this hypothesis. We argue that percolation of fire through an open landscape may explain the remarkably universal rise of fire frequency around a critical tree cover, but we show that simple percolation models cannot predict the actual threshold quantitatively. The fire-driven instability of intermediate states implies that tree cover will not change smoothly with climate or other stressors and shifts between closed forest and a state of low tree cover will likely tend to be relatively sharp and difficult to reverse.

Measurement of flow noise generation and pressure loss of nets and screens

Nets, screens and grids are often used to either reduce or generate turbulence for aeroacoustic testing in wind tunnel facilities. It is hence important that the self noise generated by such devices due to the flow is considerably lower than the noise sources to be examined.In order to obtain a basic understanding of the aerodynamic noise generated by nets and screens, a detailed experimental study was performed on a large number of nets of varying parameters. This included the measurement of the pressure loss and the determination of the sound power generated by the nets. The resulting one third octave band sound power levels were then used to derive a simple empirical prediction model for the noise generated by such devices.

On the predictability of land surface fluxes from meteorological variables

Abstract. Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.