We thank Vienken and Dietrich for their comment on our recent paper (Vienken and Dietrich 2014). As these researchers point out, there are a number of limitations on the accuracy of the various empirical methods developed for the estimation of hydraulic conductivity from grain-size distribution including the general lack of experimental database size to the physical aspects of the aquifer hydrogeology. Even relatively uniform deposits, such as beach sand, contain heterogeneities related to bedding with horizontal hydraulic conductivity commonly being three to five times greater than vertical hydraulic conductivity. In most cases, the empirical methods using grain-size distribution to estimate hydraulic conductivity produce lower values than found using slug, aquifer performance (pumping), and tracer tests, which is to be
Over 400 unlithified sediment samples were collected from four different depositional environments in global locations and the grain-size distribution, porosity, and hydraulic conductivity were measured using standard methods. The measured hydraulic conductivity values were then compared to values calculated using 20 different empirical equations (e.g., Hazen, Carman-Kozeny) commonly used to estimate hydraulic conductivity from grain-size distribution. It was found that most of the hydraulic conductivity values estimated from the empirical equations correlated very poorly to the measured hydraulic conductivity values with errors ranging to over 500%. To improve the empirical estimation methodology, the samples were grouped by depositional environment and subdivided into subgroups based on lithology and mud percentage. The empirical methods were then analyzed to assess which methods best estimated the measured values. Modifications of the empirical equations, including changes to special coefficients and addition of offsets, were made to produce modified equations that considerably improve the hydraulic conductivity estimates from grain size data for beach, dune, offshore marine, and river sediments. Estimated hydraulic conductivity errors were reduced to 6 to 7.1 m/day for the beach subgroups, 3.4 to 7.1 m/day for dune subgroups, and 2.2 to 11 m/day for offshore sediments subgroups. Improvements were made for river environments, but still produced high errors between 13 and 23 m/day.
The subtropical forests of the Pacific slope of Ecuador lie within a region of wide biodiversity due to the biogeographic influence of Chocó. However, the diversity of small non-volant mammals in these forests is poorly understood. We conducted surveys at seven localities in 2020 and 2021 in Lita, northwestern Imbabura province, Ecuador. Sampling was done on an altitudinal range of 1,314–1,812 m. We used a combination of techniques (Sherman, Tomahawk, and pitfall traps) to capture non-volant small mammals. Our accumulated trapping effort was 2,724 trap/nights. We recorded 180 individuals of 23 species, of which rodents were the most diverse with 17 species, representing 73.9% of the composition. The record of Pattonimus musseri Brito et al. (2020) stands out, representing both latitudinal and elevational altitudinal range extensions. Finally, our results indicate that Lita is a natural area with a high concentration of non-volant mammals in the northwestern Ecuadorian subtropics.
The CCN51 cocoa bean variety is known for being highly resistant to diseases and temperature variation and for having a relatively low cultivation risk for the producers. In this work, a computational and experimental study is performed to analyze the mass and heat transfer within the bean when dried by forced convection. A proximal composition analysis is conducted on the bean testa and cotyledon, and the distinct thermophysical properties are determined as a function of temperature for an interval between 40 and 70 °C. A multidomain CFD simulation, coupling a conjugate heat transfer with a semiconjugate mass transfer model, is proposed and compared to the experimental results based on the bean temperature and moisture transport. The numerical simulation predicts the drying behavior well and yields average relative errors of 3.5 and 5.2% for the bean core temperature and the moisture content versus the drying time, respectively. The moisture diffusion is found to be the dominant mechanism in the drying process. Moreover, a diffusion approximation model and given kinetic constants present a good prediction of the bean’s drying behavior for constant temperature drying conditions between 40 and 70 °C.
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