ALBERT

Description: Publication date: Available online 29 December 2015 Source: Geodesy and Geodynamics Author(s): Fuqiong Huang, Yong Chen, Ping Ji, Kexin Ren, Fuwang Gao, Lingkong Zhang The Kunlunshan Mountain M s8.1 earthquake, occurred in Nov.14, 2001, is the first event with magnitude more than 8 in the China earthquake monitoring history, specifically at the beginning of digital techniques in precursor monitoring networks. Any investigation of recorded data on this earthquake is very important for testing the operation of the digital monitoring networks and understanding the preparation, occurrence, and adjustment of stress/strain of strong continental earthquakes. In this paper we investigated the co-seismic response changes of well water level of groundwater and volume strain meter of bore hole in digital earthquake monitoring network of Capital area and its vicinity, due to the Nov.14, 2001 M s8.1 Kunlun Mountain earthquake. The responding time, shapes or manners, amplitudes, and lasting time of well water level and strain-meters to seismic wave are studied in comparison. Then we discussed the possibility that the response changes of groundwater to strong distant earthquakes can be understood as one kind of observing evidence of stress/strain changes induced by distant earthquake.

Description: A long-term study of O, H and C stable isotopes has been undertaken on river waters across the 7000 km 2 upper Thames lowland river basin in the southern UK. During the period, flow conditions ranged from drought to flood. A 10-year monthly record (2003–2012) of the main River Thames showed a maximum variation of 3‰ (δ 18 O) and 20‰ (δ 2 H), though inter-annual average values varied little around a mean of –6.5‰ (δ 18 O) and –44‰ (δ 2 H). A δ 2 H/δ 18 O slope of 5.3 suggested a degree of evaporative enrichment, consistent with derivation from local rainfall with a weighted mean of –7.2‰ (δ 18 O) and –48‰ (δ 2 H) for the period. A tendency towards isotopic depletion of the river with increasing flowrate was noted, but at very high flows (〉100 m 3 /s) a reversion to the mean was interpreted as the displacement of bank storage by rising groundwater levels (corroborated by measurements of specific electrical conductivity). A shorter quarterly study (October 2011 – April 2013) of isotope variations in 15 tributaries with varying geology revealed different responses to evaporation, with a well-correlated inverse relationship between Δ 18 O and baseflow index (BFI) for most of the rivers. A comparison with aquifer waters in the basin showed that even at low flow, rivers rarely consist solely of isotopically unmodified groundwater. Long-term monitoring (2003–2007) of carbon stable isotopes in dissolved inorganic carbon (DIC) in the Thames revealed a complex interplay between respiration, photosynthesis and evasion, but with a mean inter-annual δ 13 C-DIC value of –14.8 ± 0.5‰, exchange with atmospheric carbon could be ruled out. Quarterly monitoring of the tributaries (October 2011 – April 2013) indicated that in addition to the above factors, river flow variations and catchment characteristics were likely to affect δ 13 C-DIC. Comparison with basin groundwaters of different alkalinity and δ 13 C-DIC values showed that the origin of river baseflow is usually obscured. The findings show that long-term monitoring of environmental tracers can help to improve the understanding of how lowland river catchments function. This article is protected by copyright. All rights reserved.

Description: Publication date: Available online 29 December 2015 Source: Journal of Rock Mechanics and Geotechnical Engineering Author(s): Yankun Sun, Qi Li, Duoxing Yang, Xuehao Liu Carbon dioxide (CO 2 ) geosequestration in deep saline aquifers has been currently deemed as a preferable and practicable mitigation means for reducing anthropogenic greenhouse gases (GHGs) emissions to the atmosphere, as deep saline aquifers can offer the greatest potential from a capacity point of view. Hence, research on core-scale CO 2 /brine multiphase migration processes is of great significance for precisely estimating storage efficiency, ensuring storage security, and predicting the long-term effects of the sequestered CO 2 in subsurface saline aquifers. This review article initially presents a brief description of the essential aspects of CO 2 subsurface transport and geological trapping mechanisms, and then outlines the state-of-the-art laboratory core flooding experimental apparatus that has been adopted for simulating CO 2 injection and migration processes in the literature over the past decade. Finally, a summary of the characteristics, components and applications of publicly reported core flooding equipment as well as major research gaps and areas in need of further study are given in relevance to laboratory-scale core flooding experiments in CO 2 geosequestration under reservoir conditions.

Description: Publication date: Available online 29 December 2015 Source: Geodesy and Geodynamics Author(s): Hongtao Hao, Jin Wei, Minzhang Hu, Ziwei Liu, Hui Li Using mobile gravity data from the central area of Sichuan and Yunnan Provinces, the relationship between gravity variation and earthquakes was studied based on the Ludian M s6.5 earthquake that occurred on August 3rd, 2014, and the Kangding M s6.3 earthquake that occurred on November 22nd, 2014; the mechanism of gravity variation was also explored. The results are as follows: (1) Prior to both earthquakes, gravity variation exhibited similar characteristics as those observed before both the Tangshan and Wenchuan earthquakes, in which typical precursor anomalies were positive gravity variation near the epicenter and the occurrence of a high-gravity-gradient zone across the epicenter prior to the earthquake. (2) A relatively accurate prediction of the occurrence locations of the two earthquakes was made by the Gravity Network Center of China (GNCC) based on these precursor anomalies. In the gravity study report on the 2014 earthquake trends submitted at the end of 2013, the Daofu-Shimian section at the junction of the Xianshuihe and Longmenshan fault zones was noted as an earthquake-risk region with a predicted magnitude of 6.5, which covered the epicenter of the Kangding M s6.3 earthquake. In another report on earthquake trends in southwestern China submitted in mid-2014, the Lianfeng, Zhaotong fault zone was also classified as an earthquake-risk region with a magnitude of 6.0, and the central area of this region basically overlapped with the epicenter of the Ludian M s6.5 earthquake. (3) The gravity variation characteristics are reasonably consistent with crustal movements, and deep material migration is likely the primary cause of gravity variation.

Description: Publication date: Available online 29 December 2015 Source: Journal of Rock Mechanics and Geotechnical Engineering Author(s): Mojtaba Asadi Development of accurate and reliable models for predicting the strength of rocks and rock masses is one of the most common interests of geologists, civil and mining engineers and many others. Due to uncertainties in evaluation of effective parameters and also complicated nature of geological materials, it is difficult to estimate the strength precisely using theoretical approaches. On the other hand, intelligent approaches have attracted much attention as novel and effective tools of solving complicated problems in engineering practice over the past decades. In this paper, a new method is proposed for mining descriptive Mamdani fuzzy inference systems to predict the strength of intact rocks and anisotropic rock masses containing well-defined through-going joint. The proposed method initially employs a genetic algorithm (GA) to pick important rules from a preliminary rule base produced by grid partitioning and, subsequently, selected rules are given weights using the GA. Moreover, an information criterion is used during the first phase to optimize the models in terms of accuracy and complexity. The proposed hybrid method can be considered as a robust optimization task which produces promising results compared with previous approaches.

Description: Current characterization of the Urban Heat Island (UHI) remains insufficient to support the effective mitigation and adaptation of increasing temperatures in urban areas. Planning and design strategies are restricted to the investigation of temperature anomalies at a city scale. By focusing on Land Surface Temperature of Wuhan, China, this research examines the temperature variations locally where mitigation and adaptation would be more feasible. It shows how local temperature anomalies can be identified morphologically. Technically, the MODerate-resolution Imaging Spectroradiometer satellite image products are used. They are first considered as noisy observations of the latent temperature patterns. The continuous latent patterns of the temperature are then recovered from these discrete observations by using the non-parametric Multi-Task Gaussian Process Modeling. The Multi-Scale Shape Index is then applied in the area of focus to extract the local morphological features. A triplet of shape, curvedness and temperature is formed as the criteria to extract local heat islands. The behavior of the local heat islands can thus be quantified morphologically. The places with critical deformations are identified as hotpots. The hotspots with certain yearly behavior are further associated with land surface composition to determine effective mitigation and adaptation strategies. This research can assist in the temperature and planning field on two levels: (1) the local land surface temperature patterns are characterized by decomposing the variations into fundamental deformation modes to allow a process-based understanding of the dynamics; and (2) the characterization at local scale conforms to planning and design conventions where mitigation and adaptation strategies are supposed to be more practical. The weaknesses and limitations of the study are addressed in the closing section.

Description: Remote sensing is a key technology that enables us to scale up our empirical, in situ measurements of carbon uptake made at the site level. In low leaf area index ecosystems typical of semi-arid regions however, many assumptions of these remote sensing approaches fall short, given the complexities of the heterogeneous landscape and frequent disturbance. Here, we investigated the utility of remote sensing data for predicting gross primary production (GPP) in piñon-juniper woodlands in New Mexico (USA). We developed a simple model hierarchy using climate drivers and satellite vegetation indices (VIs) to predict GPP, which we validated against in situ estimates of GPP from eddy-covariance. We tested the influence of pixel size on model fit by comparing model performance when using VIs from RapidEye (5 m) and the VIs from Landsat ETM+ (30 m). We also tested the ability of the normalized difference wetness index (NDWI) and normalized difference red edge (NDRE) to improve model fits. The best predictor of GPP at the undisturbed PJ woodland was Landsat ETM+ derived NDVI (normalized difference vegetation index), whereas at the disturbed site, the red-edge VI performed best (R2adj of 0.92 and 0.90 respectively). The RapidEye data did improve model performance, but only after we controlled for the variability in sensor view angle, which had a significant impact on the apparent cover of vegetation in our low fractional cover experimental woodland. At both sites, model performance was best either during non-stressful growth conditions, where NDVI performed best, or during severe ecosystem stress conditions (e.g., during the girdling process), where NDRE and NDWI improved model fit, suggesting the inclusion of red-edge leveraging and moisture sensitive VI in simple, data driven models can constrain GPP estimate uncertainty during periods of high ecosystem stress or disturbance.

Description: Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)—height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems.

Description: Mapping cropland distribution over large areas has attracted great attention in recent years, however, traditional pixel-based classification approaches produce high uncertainty in cropland area statistics. This study proposes a new approach to map fractional cropland distribution in Mato Grosso, Brazil using time series MODIS enhanced vegetation index (EVI) and Landsat Thematic Mapper (TM) data. The major steps include: (1) remove noise and clouds/shadows contamination using the Savizky–Gloay filter and temporal resampling algorithm based on the time series MODIS EVI data; (2) identify the best periods to extract croplands through crop phenology analysis; (3) develop a seasonal dynamic index (SDI) from the time series MODIS EVI data based on three key stages: sowing, growing, and harvest; and (4) develop a regression model to estimate cropland fraction based on the relationship between SDI and Landsat-derived fractional cropland data. The root mean squared error of 0.14 was obtained based on the analysis of randomly selected 500 sample plots. This research shows that the proposed approach is promising for rapidly mapping fractional cropland distribution in Mato Grosso, Brazil.