Sustainable groundwater development in the coastal Tra Vinh province in Vietnam under saltwater intrusion and climate change

Three-dimensional transient groundwater flow and saltwater transport models were constructed to assess the impacts of groundwater abstraction and climate change on the coastal aquifer of Tra Vinh province (Vietnam). The groundwater flow model was calibrated with groundwater levels (2007–2016) measured in 13 observation wells. The saltwater transport model was compared with the spatial distribution of total dissolved solids. Model performance was evaluated by comparing observed and simulated groundwater levels. The projected rainfalls from two climate models (MIROC5 and CRISO Mk3.6) were subsequently used to simulate possible effects of climate changes. The simulation revealed that groundwater is currently depleted due to overabstraction. Towards the future, groundwater storage will continue to be depleted with the current abstraction regime, further worsening in the north due to saltwater intrusion from inland trapped saltwater and on the coast due to seawater intrusion. Notwithstanding, the impact from climate change may be limited, with the computed groundwater recharge from the two climate models revealing no significant change from 2017 to 2066. Three feasible mitigation scenarios were analyzed: (1) reduced groundwater abstraction by 25, 35 and 50%, (2) increased groundwater recharge by 1.5 and 2 times in the sand dunes through managed aquifer recharge (reduced abstraction will stop groundwater-level decline, while increased recharge will restore depleted storage), and (3) combining 50% abstraction reduction and 1.5 times recharge increase in sand dune areas. The results show that combined interventions of reducing abstraction and increasing recharge are necessary for sustainable groundwater resources development in Tra Vinh province.

     

Lang Trang Cave (Northern Vietnam): Pleistocene Mammalian Locality with Gigantopithecus Finds

Doklady Earth Sciences - Speleological, geological and paleontological characteristics of the Lang Trang cave in northern Vietnam are presented. Primates Gigantopithecus blacki von Koenigswald,...     

Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach

Natural Hazards - Advances in the availability of multi-sensor, remote sensing-derived datasets, and machine learning algorithms can now provide an unprecedented possibility to predict flood events...     

越南西北部Phan Si Pan地区Ngoi Chi岩组的锆石U-Pb年龄和Hf同位素组成

本文对越南西北部Phan Si Pan地区变质地体中的一个Ngoi Chi片麻岩进行了锆石CL内部结构分析、LA-(MC)-ICP-MS锆石U-Pb定年和Hf同位素分析。CL图象和Th/U比值特征显示该片麻岩样品中的锆石主要为岩浆锆石,有少量窄的变质边。岩浆锆石的年龄为~2.9 Ga,表明该样品是越南西北部Phan Si Pan地区的基底岩石。它们的εHf(t)值为–4.70±0.92,二阶段Hf模式年龄为~3.5 Ga,表明其为更古老的(3.5 Ga)冥太古代地壳物质部分熔融作用形成。变质边部锆石给出了~1.8 Ga的年龄,表明变质作用发生在古元古代早期,Phan Si Pan地区在这一时期可能经历了一次重要的构造热事件。     

Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed,China

Natural Hazards - This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China. The comparative shallow...     

Ionosphere probing with simultaneous GNSS radio occultations

GPS Solutions - Radio occultation (RO) is a powerful technique for providing vertical profiles of refractivity, temperature, pressure, and water vapor of the neutral atmosphere and electron density...     

Seismic ground-roll noise attenuation using deep learning

We propose to adopt a deep learning based framework using generative adversarial networks for ground-roll attenuation in land seismic data. Accounting for the non-stationary properties of seismic data and the associated ground-roll noise, we create training labels using local time–frequency transform and regularized non-stationary regression. The basic idea is to train the network using a few shot gathers such that the network can learn the weights associated with noise attenuation for the training shot gathers. We then apply the learned weights to test ground-roll attenuation on shot gathers, that are not a part of training input to obtain the desired signal. This approach gives results similar to local time–frequency transform and regularized non-stationary regression but at a significantly reduced computational cost. The proposed approach automates the ground-roll attenuation process without requiring any manual input in picking the parameters for each shot gather other than in the training data. Tests on field-data examples verify the effectiveness of the proposed approach.     

A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.     

Modeling undertow due to random waves

A numerical model of undertow due to random waves is developed. The model includes three sub-models: (i) a model for multi-directional and multi-frequency random wave transformation, (ii) a surface roller evolution model, and (iii) a model for calculating the vertical distribution and the mean value of the undertow velocity. The calculation of wave trough level is performed based on a theory for the wave asymmetry. The model was successfully validated against small- and large-scale laboratory experiments. Thus, the model is expected to provide reliable input for the modeling of sediment transport and morphological change due to waves and currents.     

越南西北部地区Sinh Quyen岩组副片麻岩的锆石年龄及其地质意义

在大地构造位置上, 越南西北部地区是古特提斯造山带东段重要的组成部分, 并记录了印支陆块和华南板块碰撞拼合历史.其构造归属是厘定该地区古特提斯板块缝合带位置的关键.报道了出露的基底岩石Sinh Quyen岩组副片麻岩的碎屑锆石年龄, 探讨其沉积物源和归属问题.该岩组主要由长英质副片麻岩和混合岩等岩石类型构成, 被认为是越南西北部中元古代—古元古代基底岩石.采用LA-ICP-MS锆石U-Pb定年方法分析了3个长英质副片麻岩样品的116粒碎屑锆石.123个分析点锆石年龄的统计结果显示, 碎屑锆石形成时代主要集中在约1.8 Ga左右, 有少量约2.2~3.0 Ga中太古代碎屑锆石, 暗示Sinh Quyen副片麻岩沉积物源主要为早古元古代基底岩石.基底岩石在形成时间上与华南板块古老基底相似, 推断Sinh Quyen岩组在构造归属上可能来自华南板块.部分碎屑锆石边部记录约250 Ma变质增生作用, 可能与华南板块—印支板块的印支期拼合有关, 记录了古特提斯造山作用.