Longitudinal vibration characteristics of a tapered pipe pile considering the vertical support of surrounding soil and construction disturbance

This research is concentrated on the longitudinal vibration of a tapered pipe pile considering the vertical support of the surrounding soil and construction disturbance.First,the pile-soil system is partitioned into finite segments in the vertical direction and the Voigt model is applied to simulate the vertical support of the surrounding soil acting on the pile segment.The surrounding soil is divided into finite ring-shaped zones in the radial direction to consider the construction disturbance.Then,the shear complex stiffness at the pile-soil interface is derived by solving the dynamic equilibrium equation for the soil from the outermost to innermost zone.The displacement impedance at the top of an arbitrary pile segment is obtained by solving the dynamic equilibrium equation for the pile and is combined with the vertical support of the surrounding soil to derive the displacement impedance at the bottom of the upper adjacent segment.Further,the displacement impedance at the pile head is obtained based on the impedance function transfer technique.Finally,the reliability of the proposed solution is verified,followed by a sensitivity analysis concerning the coupling effect of the pile parameters,construction disturbance and the vertical support of the surrounding soil on the displacement impedance of the pile.

Development and Prospect of Study on Soil Nonlinear Dynamic Characteristics under Strong-Motion

Ground motions are significantly influenced by dynamic characteristics of overburden soil layers near ground surface,as thick and soft soil layers would obviously amplify the ground motion strength. The conventional research method on soil nonlinear dynamic characteristics under strong motions is based on experiments in laboratories for the deficiency of observation data,but it is difficult to reliably simulate the complex factors of soils in actual earthquake durations,including loading paths,boundary conditions,and drainage conditions. The incremental data of the vertical downhole observation array,which is comprised of at least one observation point on ground surface and one observation point in a downhole rock base, makes it possible to study soil nonlinear dynamics according to in situ observation data,and provides new basic data and development opportunities to soil nonlinear dynamics studies.

A performance evaluation of remotely sensed sea surface salinity products in combination with other surface measurements in reconstructing three-dimensional salinity fields

Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles（S） using multilinear regressions. The performance is evaluated using a total root mean square（RMS） error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.

Spatial variability of soil organic carbon stock in an olive orchard at catchment scale in Southern Spain

Orchards have a high potential for carbon sequestration.However,little research is available on the spatial variability at catchment scale and on the difference between the tree area and the lanes.We analyzed theik spatial variability of soil organic carbon stock,SOCstock at 90 cm depth in an 8-ha catchment in Southern Spain with olives on a vertic soil.Results showed higher soil organic carbon concentration,SOC,in the tree area as compared to the lane up to 60 cm depth,but its impact on SOCstock was negligible since it was compensated by the higher soil bulk density in the lane.SOC at different depths was correlated with that in the top 0-5 cm.The overall SOCstock of the orchard was 4.14 kg m^(-2),ranging between 1.8 and 6.0 kg m^(-2).This SOCstock is in the mid-lower range of values reported for olive orchards,measured at smaller scale,and similar to those other intensive field crops and agroforestry under comparable rainfall conditions.The spatial variability in SOCstock was correlated to several geomorphological variables:elevation,cumulative upstream area,topographic wetness index,sediment transport index,and tillage erosion.Differences in SOC and SOCstock are driven by the sediment redis-tribution downslope,mainly by tillage erosion,and higher soil water availability in lower areas allowing higher biomass production.These topographic indexes and the correlation between SOC in the topsoil and SOCstock up to 90 cm should be further explored in other typology of olive orchards for facilitating the mapping of SOC_(stock).

Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform(MEMIP)

Background:Large uncertainty in modeling land carbon(C)uptake heavily impedes the accurate prediction of the global C budget.Identifying the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models(ESMs).Here we present a Matrix-based Ensemble Model Inter-comparison Platform(MEMIP)under a unified model traceability framework to evaluate multiple soil organic carbon(SOC)models.Using the MEMIP,we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter(SOM)models.By comparing the model outputs from the C-only and CN modes,the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly disentangled.Results:Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation(1900–2000).The SOC difference between the multi-layer models was remarkably higher than between the single-layer models.Traceability analysis indicated that over 80%of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes,while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth representation.Conclusions:The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction,especially between models with similar process representation.Further analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model differences.We stressed the importance of analyzing ensemble outputs from the fundamental model structures,and holding a holistic view in understanding the ensemble uncertainty.