江苏灌河入海口附近重金属元素环境地球化学质量分析

以往对灌河口附近海域进行地球化学调查发现,海底沉积物及海水质量总体良好,但局部沉积物中Cr含量高于一类标准,Cu、As、Pb、Hg等其他重金属元素含量也明显高于其他地区,个别重金属元素已接近一类标准最高限,存在潜在生态风险。为进一步了解其与人类活动是否有关,对灌河靠近入海口40 km长的河水进行了地球化学调查,发现灌河河水中Hg、Cu、Pb等重金属元素存在一定程度的超标现象。结合其他证据认为,人类活动不仅对灌河下游河水质量产生了负面影响,还对入海口附近的海域环境质量产生了潜在威胁。借鉴内梅罗综合指数评价思路对灌河下游及入海口的重金属元素环境质量进行评价,并对如何降低、消除生态风险提出了若干建议。     

综合物探方法在广西妙皇隐伏多金属矿勘查中的应用

妙皇多金属矿区位于来宾凹陷东缘与大瑶山隆起西侧交接部位,广西大瑶山及其西侧铅锌成矿带中部成矿条件优越,历年来开展过多种研究和找矿方法,但找矿效果不佳。通过研究妙皇铜铅锌银矿区的地质条件和成矿背景,运用综合物探方法,对区内部分地质问题及矿产特征进行了推断和预测。2012年,依据地质物探综合成果布置了ZK22701号验证孔,取得了突破性的进展。同时,依据物探成果推测了矿区成矿机理和成矿模式,在找矿方向和找矿潜力方面进行了分析探讨,对中深部找矿具有很好的指导意义。     

内蒙古翁公敖包航磁异常的推断与解释

文章对内蒙古四子王旗—土默特右旗中北部近年发现的翁公敖包航磁异常进行推断与解释,在对找矿有望的航磁异常进行筛选后,对翁公敖包航磁异常进行了野外地质-地球物理查证,确定异常为铁矿所致;采用向上延拓、垂向导数、斜导数及小波变换等方法对航磁异常进行数据转换处理,以获取铁矿体的横向及纵向分布信息,取得了较好的效果。     

IPCC AR6报告解读：气候系统对太阳辐射干预响应

IPCC第六次评估报告（AR6）第一工作组报告评估了太阳辐射干预（Solar radiation modification,SRM）对气候系统和碳循环的影响。在大幅度减排基础上,太阳辐射干预有潜力作为应对气候变化的备用措施。目前,对于太阳辐射干预气候影响的评估都是基于模式模拟结果。评估主要结论如下：太阳辐射干预可以在全球和区域尺度上抵消一部分温室气体增加造成的气候变化（高信度);但是太阳辐射干预无法在全球和区域尺度上完全抵消温室气体增加引起的气候变化（几乎确定);有可能通过适当的太阳辐射干预设计,同时实现多个温度变化减缓目标（中等信度);在高强度温室气体排放情景下,如果太阳辐射干预实施后突然终止,并且这种终止长时间持续,将会造成快速的气候变化（高信度);如果在减排和CO₂移除的情况下,太阳辐射干预的实施强度逐渐减小至零,将显著降低太阳辐射干预突然终止产生的快速气候变化风险（中等信度);太阳辐射干预会通过降温作用,促进陆地和海洋对大气CO₂的吸收（中等信度),但是太阳辐射干预无法缓解海洋酸化（高信度);太阳辐射干预对其他生物化学循环影响的不确定性大。由于对云-气溶胶-辐射过程的相互作用和微物理过程认知有限,目前对平流层气溶胶注入、海洋低云亮化、高层卷云变薄等太阳辐射干预方法的冷却潜力和气候效应的认知还有很大的不确定性。     

The Key Supply Source of Long-Distance Moisture Transport for the Extreme Rainfall Event on July 21, 2012 in Beijing

In this study, the Weather Research and Forecasting (WRF) model and meteorological observation data were used to research the long-distance moisture transport supply source of the extreme rainfall event that occurred on July 21, 2012 in Beijing. Recording a maximum rainfall amount of 460 mm in 24 h, this rainstorm event had two dominant moisture transport channels. In the early stage of the rainstorm, the first channel comprised southwesterly monsoonal moisture from the Bay of Bengal (BOB) that was directly transported to north China along the eastern edge of Tibetan Plateau (TP) by orographic uplift. During the rainstorm, the southwesterly moisture transport was weakened by the transfer of Typhoon Vicente. Moreover, the southeasterly moisture transport between the typhoon and western Pacific subtropical high (WPSH) became another dominant moisture transport channel. The moisture in the lower troposphere was mainly associated with the southeasterly moisture transport from the South China Sea and the East China Sea, and the moisture in the middle troposphere was mainly transported from the BOB and Indian Ocean. The control experiment well reproduced the distribution and intensity of rainfall and moisture transport. By comparing the control and three sensitivity experiments, we found that the moisture transported from Typhoon Vicente and a tropical cyclone in the BOB both significantly affected this extreme rainfall event. After Typhoon Vicente was removed in a sensitivity experiment, the maximum 24-h accumulated rainfall in north China was reduced by approximately 50% compared with that of the control experiment, while the rainfall after removing the tropical cyclone was reduced by 30%. When both the typhoon and tropical cyclone were removed, the southwesterly moisture transport was enhanced. Moreover, the sensitivity experiment of removing Typhoon Vicente also weakened the tropical cyclone in the BOB. Thus, the moisture pump driven by Typhoon Vicente played an important role in maintaining and strengthening the tropical cyclone in the BOB through its westerly airflow. Typhoon Vicente was not only the moisture transfer source for the southwesterly monsoonal moisture but also affected the tropical cyclone in the BOB, which was a key supply source of long-distance moisture transport for the extreme rainfall event on July 21, 2012 in Beijing.     

Modelling tropical cyclone risks for present and future climate change scenarios using geospatial techniques

Tropical cyclones and their devastating impacts are of great concern to coastal communities globally. An appropriate approach integrating climate change scenarios at local scales is essential for producing detailed risk models to support cyclone mitigation measures. This study developed a simple cyclone risk-modelling approach under present and future climate change scenarios using geospatial techniques at local scales, and tested using a case study in Sarankhola Upazila from coastal Bangladesh. Linear storm-surge models were developed up to 100-year return periods. A local sea level rise scenario of 0.34?m for the year 2050 was integrated with surge models to assess the climate change impact. The resultant storm-surge models were used in the risk-modelling procedures. The developed risk models successfully identified the spatial extent and levels of risk that match with actual extent and levels within an acceptable limit of deviation. The result showed that cyclone risk areas increased with the increase of return period. The study also revealed that climate change scenario intensified the cyclone risk area by 5–10% in every return period. The findings indicate this approach has the potential to model cyclone risk in other similar coastal environments for developing mitigation plans and strategies.     

Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models

This paper presents a spatial autoregressive (SAR) method-based cellular automata (termed SAR-CA) model to simulate coastal land use change, by incorporating spatial autocorrelation into transition rules. The model captures the spatial relationships between explained and explanatory variables and then integrates them into CA transition rules. A conventional CA model (LogCA) based on logistic regression (LR) was studied as a comparison. These two CA models were applied to simulate urban land use change of coastal regions in Ningbo of China from 2000 to 2015. Compared to the LR method, the SAR model yielded smaller accumulated residuals that showed a random distribution in fitting the CA transition rules. The better-fitting SAR model performed well in simulating urban land use change and scored an overall accuracy of 85.3%, improving on the LogCA model by 3.6%. Landscape metrics showed that the pattern generated by the SAR-CA model has less difference with the observed pattern.     

Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia

Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover.     

Subsidence in the Kathmandu Basin,before and after the 2015 Mw 7.8 Gorkha Earthquake,Nepal Revealed from Small Baseline Subset-DInSAR Analysis

Land subsidence in densely urbanized areas is a global problem that is primarily caused by excessive groundwater withdrawal. The Kathmandu Basin is one such area where subsidence due to groundwater depletion has been a major problem in recent years. Moreover, on 25 April 2015, this basin experienced large crustal movements caused by the Gorkha earthquake (Mw 7.8). Consequently, the effects of earthquake-induced deformation could affect the temporal and spatial nature of anthropogenic subsidence in the basin. However, this effect has not yet been fully studied. In this paper, we applied the SBAS-DInSAR technique to estimate the spatiotemporal displacement of land subsidence in the Kathmandu Basin before and after the Gorkha earthquake, using 16 ALOS-1 Phased Array L-band Synthetic Aperture Radar (PALSAR) images during the pre-seismic period and 26 Sentinel-1 A/B SAR images during the pre- and post-seismic periods. The results showed that the mean subsidence rate in the central part of the basin was about ?8.2 cm/year before the earthquake. The spatial extents of the subsiding areas were well-correlated with the spatial distributions of the compressible clay layers in the basin. We infer from time-series InSAR analysis that subsidence in the Kathmandu basin could be associated with fluvio-lacustrine (clay) deposits and local hydrogeological conditions. However, after the mainshock, the subsidence rate significantly increased to ?15 and ?12 cm/year during early post-seismic (108 days) and post-seismic (2015–2016) period, respectively. Based on a spatial analysis of the subsidence rate map, the entire basin uplifted during the co-seismic period has started to subside and become stable during the early-post-seismic period. This is because of the elastic rebound of co-seismic deformation. However, interestingly, the localized areas show increased subsidence rates during both the early-post- and post-seismic periods. Therefore, we believe that the large co-seismic deformation experienced in this basin might induce the local subsidence to increase in rate, caused by oscillations of the water table level in the clay layer.     

Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules

The dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Our GWR-CA model is characterized by spatially nonstationary transition rules that fully address local interactions in land use change. More importantly, each driving factor in our GWR model contains effects that both promote and resist land use change. We applied GWR-CA to simulate rapid land use change in Suzhou City on the Yangtze River Delta from 2000 to 2015. The GWR coefficients were visualized to highlight their spatial patterns and local variation, which are closely associated with their effects on land use change. The transition rules indicate low land conversion potential in the city’s center and outer suburbs, but higher land conversion potential in the inner near suburbs along the belt expressway. Residual statistics show that GWR fits the input data better than logistic regression (LR). Compared with an LR-based CA model, GWR-CA improves overall accuracy by 4.1% and captures 5.5% more urban growth, suggesting that GWR-CA may be superior in modeling land use change. Our results demonstrate that the GWR-CA model is effective in capturing spatially varying land transition rules to produce more realistic results, and is suitable for simulating land use change and urban expansion in rapidly urbanizing regions.