Adaptive fisheries management in Vietnam: The use of indicators and the introduction of a multi-disciplinary Marine Fisheries Specialist Team to support implementation

Vietnam is in the process of introducing adaptive management based on indicators and time-series to manage its marine fisheries. The development of indicator-based frameworks for fisheries management has accelerated in recent years. This article describes the suite of indicators being suggested or used in Vietnam including the data-collection system. The Vietnamese approach is supported by an institutional structure consisting of a multi-disciplinary advisory system to facilitate the process of using indicators and to introduce knowledge-based management. The article concludes by identifying current problems and weaknesses and by providing suggestions for future improvement of the system.     

Preliminary Results of Anthropogenic Seismicity Monitoring in the Region of Song Tranh 2 Reservoir,Central Vietnam

Song Tranh 2 hydropower plant and the reservoir containing backed up water are located in the Quang Nam province (Central Vietnam). The region experiences unusual seismic activity related to the reservoir impoundment, with earthquakes of magnitude up to 4.7. In result of cooperation between the Institute of Geophysics, Vietnam Academy of Sciences and Technology and the Institute of Geophysics, Polish Academy of Sciences a seismic network has been built to facilitate seismic monitoring of the Song Tranh 2 area. The network, operating since August 2013, consists of 10 seismic stations. Here we show that the network is sufficient for advanced data processing. The first results of monitoring of the earthquake activity in Song Tranh 2 area in the period between 2012 and 2014, especially the completeness of catalogs, study and comparisons between water level and the seismic activity suggest direct connection between reservoir exploitation and anthropogenic seismicity.     

Freshwater stromatolites with the cone-in-cone structure from the Neogene lacustrine sediments of Vietnam

Morphological features of the columnar-stratiform stromatolites, which resemble the cone-in-cone structure, from Neogene lacustrine sediments of northern Vietnam are analyzed. Their structural-textural peculiarities and interrelations with the host terrigenous sediments of a stromatolitic buildup are discussed. It is shown that stages of the growth of columns are recorded in the succession of vertically oriented carbonate plates, which form stromatolites, and in the serrate surface protrusions of the plates at their contacts with host sediments. It is established that replacement of the columnar stromatolites by the stratiform varieties in the section can be related to variations in the intensity of terrigenous material influx to the sedimentation basin. Stratiform stromatolites were formed during relatively slow accumulation of sedimentary material. Appearance of the columnar varieties is attributed to intensification of sedimentation.     

Statistical data fusion of multi-sensor AOD over the Continental United States

This article illustrates two techniques for merging daily aerosol optical depth (AOD) measurements from satellite and ground-based data sources to achieve optimal data quality and spatial coverage. The first technique is a traditional Universal Kriging (UK) approach employed to predict AOD from multi-sensor aerosol products that are aggregated on a reference grid with AERONET as ground truth. The second technique is spatial statistical data fusion (SSDF); a method designed for massive satellite data interpolation. Traditional kriging has computational complexity O(N³), making it impractical for large datasets. Our version of UK accommodates massive data inputs by performing kriging locally, while SSDF accommodates massive data inputs by modelling their covariance structure with a low-rank linear model. In this study, we use aerosol data products from two satellite instruments: the moderate resolution imaging spectrometer and the geostationary operational environmental satellite, covering the Continental United States.     

Are satellite products good proxies for gauge precipitation over Singapore?

The uncertainties in two high-resolution satellite precipitation products (TRMM 3B42 v7.0 and GSMaP v5.222) were investigated by comparing them against rain gauge observations over Singapore on sub-daily scales. The satellite-borne precipitation products are assessed in terms of seasonal, monthly and daily variations, the diurnal cycle, and extreme precipitation over a 10-year period (2000–2010). Results indicate that the uncertainties in extreme precipitation is higher in GSMaP than in TRMM, possibly due to the issues such as satellite merging algorithm, the finer spatio-temporal scale of high intensity precipitation, and the swath time of satellite. Such discrepancies between satellite-borne and gauge-based precipitations at sub-daily scale can possibly lead to distorting analysis of precipitation characteristics and/or application model results. Overall, both satellite products are unable to capture the observed extremes and provide a good agreement with observations only at coarse time scales. Also, the satellite products agree well on the late afternoon maximum and heavier rainfall of gauge-based data in winter season when the Intertropical Convergence Zone (ITCZ) is located over Singapore. However, they do not reproduce the gauge-observed diurnal cycle in summer. The disagreement in summer could be attributed to the dominant satellite overpass time (about 14:00 SGT) later than the diurnal peak time (about 09:00 SGT) of gauge precipitation. From the analyses of extreme precipitation indices, it is inferred that both satellite datasets tend to overestimate the light rain and frequency but underestimate high intensity precipitation and the length of dry spells. This study on quantification of their uncertainty is useful in many aspects especially that these satellite products stand scrutiny over places where there are no good ground data to be compared against. This has serious implications on climate studies as in model evaluations and in particular, climate model simulated future projections, when information on precipitation extremes need to be reliable as they are highly crucial for adaptation and mitigation.     

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.     

The effect of long-term aerial exposure on intertidal mudflat erodibility

Intertidal zones by definition are exposed to air at low tide, and the exposure duration can be weeks (e.g. during neap tides) depending on water level and bed elevation. Here we investigated the effect of varying exposure duration (6 h to 10 days) on intertidal mudflat erosion (measured using the EROMES device), where the effects of water content and biofilm biomass (using chlorophyll-a content as a proxy, Chl-a μg g⁻¹) were taken into account. Sediments were collected between spring and summer (in October 2018, January 2019 and February 2019) from an intertidal site in the Firth of Thames, New Zealand. Longer exposure duration resulted in more stable sediments [higher erosion threshold (Ƭ_cr, N m⁻²) and lower erosion rate (ER, g m⁻² s⁻¹)]. After 10 days, exposure increased Ƭ_cr by 1.7 to 4.4 times and decreased ER by 11.6 to 21.5 times compared with 6 h of exposure. Chl-a and water content changed with exposure duration and were significantly correlated with changes in Ƭ_cr and ER. The stability of sediments after two re-submersion periods following exposure was also examined and showed that the stabilizing effect of exposure persisted even though water content had increased to non-exposure levels. Re-submersion was associated with an increase in Chl-a content, which likely counteracted the destabilizing influence of increased water content. A site-specific model, which included the interplay between evaporation and biofilm biomass, was developed to predict water content as a function of exposure duration. The modelled water content (W_Mod.) explained 98% of the observed variation in water content (W_Obs.). These results highlight how the exposure period can cause subtle changes to erosion regimes of sediments. An understanding of these effects (e.g. in sediment transport modelling) is critical to predicting the resilience of intertidal zones into the future, when sea-level rise is believed to exacerbate erosion in low-lying areas. © 2020 John Wiley & Sons, Ltd.     

Non‐local damage modelling of concrete: a procedure for the determination of model parameters

This paper presents a procedure for the determination of parameters of non‐local damage models. This is to assure a consistent response of a non‐local damage model, as choice of the internal length and other parameters of the model are varied. Correlations between the internal length and other parameters governing the local constitutive behaviour of the model are addressed and exploited. Focus is put on the relationship between the internal length of the non‐local model and the width of the fracture process zone. Numerical examples are used to demonstrate the rigour of the proposed method. Copyright © 2006 John Wiley & Sons, Ltd.