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.     

Improving Indigenous Technologies for Sustainable Land Use in Northern Mountainous Areas of Vietnam

More than 30 ethnic groups are now living in northern mountainous regions, Vietnam, mainly relying on shifting cultivation with the fallow period being shortened from time to time. Naturally, soil fertility reduces from cycle to cycle, entailing the reduction of productivity. Large areas of moderately sloping lands suitable for upland agriculture have become bare after many cultivation-fallow cycles. The soils there have been severely degraded with more toxicity, low porosity, low water retention capacity and poor floral diversity. Normally, these lands cannot be used for food crop cultivation. So farmers in uplands have to rely on slash-and-burn practices for their livelihood. As there is no more forest with good soil in medium slopes, farmers go to cut forests in watershed, high slope lands and old forests up to the mountains‘ top. There are ecologically and environmentally very sensitive areas, so their destruction will inevitably cause hazardous consequences in the whole basin. Meanwhile,cultivation in these areas has low economic efficiency and sustainability because the crop yield may decrease very fast due to severe erosion as the higher the slope, the more serious erosion. Consequently living standards of highland farmers remain low and unstable. Sustainable farming on these lands in the perspective of a seriously deteriorated ecology and environmental is not an easy task. There have been many projects trying to help mountainous farmers get out of their vicious circle. However, due to different reasons, the results gained are low, and in some cases,things ceased to move after the projects phased out. During past few years, based on the farmer experiences, the Vietnam Agricultural Science Institute has cooperated with local and international partners to implement different projects in order to solve the problems by developing simple, easy and cheap cultivation technologies, which can be accepted and applied by local poor farmers for sustainable agricultural production. The first results of our activities offered good opportunities for sustain food production, improve soil health, recharge of aquifers,and enhanced household income for better rural lively hoods in the upland eco-regions of northern Vietnam.     

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.     

Biogenic carbon flows through the planktonic food web of the Amundsen Gulf (Arctic Ocean): A synthesis of field measurements and inverse modeling analyses

Major pathways of biogenic carbon (C) flow are resolved for the planktonic food web of the flaw lead polynya system of the Amundsen Gulf (southeast Beaufort Sea, Arctic Ocean) in spring-summer 2008. This period was relevant to study the effect of climate change on Arctic marine ecosystems as it was characterized by unusually low ice cover and warm sea surface temperature. Our synthesis relied on a mass balance estimate of gross primary production (GPP) of 52.5 ± 12.5 g C m⁻² calculated using the drawdown of nitrate and dissolved inorganic C, and a seasonal f-ratio of 0.64. Based on chlorophyll a biomass, we estimated that GPP was dominated by phytoplankton (93.6%) over ice algae (6.4%) and by large cells (>5 μm, 67.6%) over small cells (<5 μm, 32.4%). Ancillary in situ data on bacterial production, zooplankton biomass and respiration, herbivory, bacterivory, vertical particle fluxes, pools of particulate and dissolved organic carbon (POC, DOC), net community production (NCP), as well as selected variables from the literature were used to evaluate the fate of size-fractionated GPP in the ecosystem. The structure and functioning of the planktonic food web was elucidated through inverse analysis using the mean GPP and the 95% confidence limits of every other field measurement as lower and upper constraints. The model computed a net primary production of 49.2 g C m⁻², which was directly channeled toward dominant calanoid copepods (i.e. Calanus hyperboreus 20%, Calanus glacialis 10%, and Metridia longa 10%), other mesozooplankton (12%), microzooplankton (14%), detrital POC (18%), and DOC (16%). Bacteria required 29.9 g C m⁻², a demand met entirely by the DOC derived from local biological activities. The ultimate C outflow comprised respiration fluxes (82% of the initial GPP), a small sedimentation (3%), and a modest residual C flow (15%) resulting from NCP, dilution and accumulation. The sinking C flux at the model limit depth (395 m) supplied 60% of the estimated benthic C demand (2.8 g C m⁻²), suggesting that the benthos relied partly on other C sources within the bottom boundary layer to fuel its activity. In summary, our results illustrate that the ongoing decline in Arctic sea ice promotes the growth of pelagic communities in the Amundsen Gulf, which benefited from a ∼80% increase in GPP in spring-summer 2008 when compared to 2004 – a year of average ice conditions and relatively low GPP. However, 53% of the secondary production was generated within the microbial food web, the net ecological efficiency of zooplankton populations was not particularly high (13.4%), and the quantity of biogenic C available for trophic export remained low (6.6 g C m⁻²). Hence it is unlikely that the increase in lower food web productivity, such as the one observed in our study, could support new harvestable fishery resources in the offshore Beaufort Sea domain.     

A new portable in situ flume for measuring critical shear stress on river beds

A new portable in situ flume(PISF)for measuring critical bed shear stress(CBSS)was developed in this study.The PISF consists of an open bottom sediment erosion chamber and an electrically-driven pump.Unlike most existing in situ flumes with similar designs,the new PISF does not rely on monitoring the flow conditions or particle density in the sediment erosion chamber;instead,it is a pre-calibrated flume.The calibration was performed by first determining CBSS of various selected sediment samples of known particle size and density(using the law of the wall),based on flow velocity-depth profiles measured in a 6 m straight open-channel flume using a Particle Image Velocimetry(PIV)system.These same particles of known CBSS were then used in the new in-situ flume under controlled lab conditions to obtain a series of calibration curves of CBSS vs.pump electrical power.A wide variety of particle types and sizes(simulated sediments)were used in this two-step calibration procedure to widen CBSS measurement range and simulate cohesive force effects.The size of the PISF is much smaller and more practical than other similar devices as lamellar flow conditions are not required and it can be applied to a wide range of sediment types including cohesive sediments.     

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.     

Floodplain hydrology of the Mekong Delta,Vietnam

The Mekong Delta is one of the largest and most intensively used estuaries in the world. Each year it witnesses widespread flooding which is both the basis of the livelihood for more than 17 million people but also the major hazard. Therefore, a thorough understanding of the hydrologic and hydraulic features is urgently required for various planning purposes. While the general causes and characteristics of the annual floods are understood, the inundation dynamics in the floodplains in Vietnam which are highly controlled by dikes and other control structures have not been investigated in depth. Especially, quantitative analyses are lacking, mainly due to scarce data about the inundation processes in the floodplains. Therefore, a comprehensive monitoring scheme for channel and floodplain inundation was established in a study area in the Plain of Reeds in the northeastern part of the Vietnamese Delta. This in situ data collection was complemented by a series of high‐resolution inundation maps derived from the TerraSAR‐X satellite for the flood seasons 2008 and 2009. Hence, the inundation dynamics in the channels and floodplains, and the interaction between channels and floodplains, could be quantified for the first time. The study identifies the strong human interference which is governed by flood protection levels, cropping patterns and communal water management. In addition, we examine the tidal influence on the inundation in various parts of the Delta, since it is expected that climate change‐induced sea level rise will increase the tidal contribution to floodplain inundation. Copyright © 2011 John Wiley & Sons, Ltd.