Lower permian oncolites from South China: implications for equatorial sea-level responses to late palaeozoic Gondwanan glaciation

The oncoid-bearing Chuanshan Formation is a regionally extensive carbonate deposit of predominantly Asselian to early Sakmarian (Early Permian) age in South China, occupying an area of some 500,000 km2. Throughout South China, the oncoid-bearing horizons are generally stable and broadly comparable in lithology, fossil content and the morphology of the oncoid grains. Four types of microfacies are recognized from the oncolite succession and overall they suggest a moderate- to high-energy, wave-agitated shallow marine carbonate platform environment. An analysis of the stratigraphic distribution of oncoid grain size, density, thickness and the bedding structures of the oncolite beds and the number of coating laminae indicate the presence of metre-scale cyclothems, suggestive of possible high-frequency cycles of sea-level fluctuation. Compared to carbonate successions above and below that lack oncolites, and in conjunction with evidence from sequence stratigraphic and isotopic geochemical analyses of coeval carbonate deposits in South China and elsewhere, the origin of the Chuanshan oncolites is linked to a drastic drop in global sea-level at the Pennsylvanian–Permian boundary, that can be correlated closely in timing with the zenith of the Late Palaeozoic Gondwanan glaciation. It is further suggested that the eustatic changes apparent from the deposition of the Chuanshan oncolites and similar coeval deposits in lower palaeolatitudes were coupled with, and influenced by, the contemporaneous high-latitude Gondwanan glaciation, the largest and longest known such event in Phanerozoic Earth history.

Sensor placement and measurement of wind for water quality studies in urban reservoirs

We study the water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the impact of surrounding buildings. In this work, we develop an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir using a limited number of wind sensors. Unlike existing solutions that assume Gaussian process of target phenomena, this study measures the wind that inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons that follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir in real time. Ten wind sensors are deployed. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction scheme provides accurate wind measurement that outperforms the state-of-the-art Gaussian model based on interpolation-based approaches.

Holocene record of Tuggerah Lake estuary development on the Australian east coast: sedimentary responses to sea-level fluctuations and climate variability

We investigated the Holocene palaeo-environmental record of the Tuggerah Lake barrier estuary on the south-east coast of Australia to determine the influence of local, regional and global environmental changes on estuary development. Using multi-proxy approaches, we identified significant down-core variation in sediment cores relating to sea-level rise and regional climate change. Following erosion of the antecedent land surface during the post-glacial marine transgression, sediment began to accumulate at the more seaward location at ~8500. years before present, some 1500. years prior to barrier emplacement and ~4000. years earlier than at the landward site. The delay in sediment accumulation at the landward site was a consequence of exposure to wave action prior to barrier emplacement, and due to high river flows of the mid-Holocene post-barrier emplacement. As a consequence of the mid-Holocene reduction in river flows, coupled with a moderate decline in sea-level, the lake experienced major changes in conditions at ~4000. years before present. The entrance channel connecting the lake with the ocean became periodically constricted, producing cyclic alternation between intervals of fluvial- and marine-dominated conditions. Overall, this study provides a detailed, multi-proxy investigation of the physical evolution of Tuggerah Lake with causative environmental processes that have influenced development of the estuary.

Late Palaeozoic sequence stratigraphy and brachiopod faunas of the Tarim Basin, Northwest China

This thesis deals with the stratigraphy and brachiopod systematic palaeontology of the latest Devonian (Famennian) to Early Permian (Kungurian) sedimentary sequences of the Tarim Basin, NW China. Brachiopod faunas of latest Devonian and Carboniferous age have been published or currently in press in the course of the Ph.D candidature and are herein appendixed, while the Early Permian brachiopod faunas are systematically described in this thesis. The described Early Permian brachiopod faunas include 127 species, of which 29 are new and 12 indeterminate, and six new genera (subgenera) are proposed; Tarimella, Bmntonella, Marginifera (Arenaria), Marginifera (Nesiotia), Baliqliqia and Ustritskia. A new integrated brachiopod biostratigraphical zonation scheme is proposed, for the first time, for the latest Devonian-Early Permian sequences of the entire Tarim Basin on the basis of this study as well as previously published information (including the Candidate's own published papers). The scheme consists of twenty three brachiopod acm biozones, most of which replace previously proposed assemblage or assemblage zones. The age and distribution of these brachiopod zones within the Tarim Basin and their relationships with other important fossil groups are discussed. In terms of regional correlations and biostratigraphical affinities, the Late Devonian to Early Carboniferous brachiopod faunas of the Tarim Basin are closest to those from South China, while the Late Carboniferous faunas demonstrate strong similarities to coeval faunas from the Urals, central Asia, North China and South China. During the Asselian-Sakmarian, strong faunal links between the Tarim Basin and those of the Urals persisted, while at the same time links with central Asia, North China and South China weakened. On the other hand, during the Artinskian-Kungurian times, affinities of the Tarim faunas with the Urals/Russian Platform rapidly reduced, when those with peri-Gondwana (South Thailand, northern Tibet) and South China increased. Thirty lithofacies (or microfacies) types of four facies associations are recognised for the Late Devonian to early Permian sediments. Based on detailed lithostratigraphy, biostratigraphy and facies analysis, 23 third-order sequences belonging to four supcrsequences are identified for the Late Devonian to Early Permian successions, from which sea-level fluctuation curves are reconstructed. The sequence stratigraphical analysis reveals that four major regional regressions, each marking a distinct supersequence boundary, can be recognised; they correspond to the end-Serpukhovian, end-Moscovian, late Artinskian and end-Kungurian times, respectively. The development of these sequences is considered to have been formed and regulated by the interplay of both eustasy and tectonism. Using the system tract of a sequence as the mapping time unit, a succession of 47 palaeogeographical maps have been reconstructed through the Late Devonian to Early Permian. These maps reveal that the Tarim Basin was first immersed by southwest-directed (Recent geographical orientation) transgression in the late Famennian after the Caledonian Orogeny. Since then, the basin had maintained its geometry as a large, southwest-mouthed embayment until the late Moscovian when most areas were the uplifted above sea-level. The basin was flooded again in late Asselian-Artinskian times when a new transgression came from a large epicontinental sea lying to its northwest. Thereafter, marine deposition was restricted to local areas (southwestern and northwestern margins until the late Kungurian, while deposition of continental deposits prevailed and continued through the Middle and late Permian into the Triassic.

On monotonic sufficient conditions of fuzzy inference systems and their applications

An important and difficult issue in designing a Fuzzy Inference System (FIS) is the specification of fuzzy sets and fuzzy rules. In this paper, two useful qualitative properties of the FIS model, i.e., the monotonicity and sub-additivity properties, are studied. The monotonic sufficient conditions of the FIS model with Gaussian membership functions are further analyzed. The aim is to incorporate the sufficient conditions into the FIS modeling process, which serves as a simple (which can be easily understood by domain users), easy-to-use (which can be easily applied to or can be a part of the FIS model), and yet reliable (which has a sound mathematical foundation) method to preserve the monotonicity property of the FIS model. Another aim of this paper is to demonstrate how these additional qualitative information can be exploited and extended to be part of the FIS designing procedure (i.e., for fuzzy sets and fuzzy rules design) via the sufficient conditions (which act as a set of useful governing equations for designing the FIS model). The proposed approach is able to avoid the "trial and error" procedure in obtaining a monotonic FIS model. To assess the applicability of the proposed approach, two practical problems are examined. The first is an FIS-based model for water level control, while the second is an FIS-based Risk Priority Number (RPN) model in Failure Mode and Effect Analysis (FMEA). To further illustrate the importance of the sufficient conditions as the governing equations, an analysis on the consequences of violating the sufficient conditions of the FIS-based RPN model is presented.

Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling

This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate-gradient, gradient descent and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from LM-NN and these results were then compared with those from PSO-based ANNs, including conventional PSO neural network (CPSONN) and improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. Our results show that the PSO-based ANNs performed better than LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing dataset for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multi-parameter (rainfall and water level) inputs, the RMSE of the testing dataset for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels, in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN.

Flood forecasting in large rivers with data-driven models

Results from the application of adaptive neuro-fuzzy inference system (ANFIS) to forecast water levels at 3 stations along the mainstream of the Lower Mekong River are reported in this paper. The study investigated the effects of including water levels from upstream stations and tributaries, and rainfall as inputs to ANFIS models developed for the 3 stations. When upstream water levels in the mainstream were used as input, improvements to forecasts were realized only when the water levels from 1 or at most 2 upstream stations were included. This is because when there are significant contributions of flow from the tributaries, the correlation between the water levels in the upstream stations and stations of interest decreases, limiting the effectiveness of including water levels from upstream stations as inputs. In addition, only improvements at short lead times were achieved. Including the water level from the tributaries did not significantly improve forecast results. This is attributed mainly to the fact that the flow contributions represented by the tributaries may not be significant enough, given that there could be large volume of flow discharging directly from the catchments which are ungauged, into the mainstream. The largest improvement for 1-day forecasts was obtained for Kratie station where lateral flow contribution was 17 %, the highest for the 3 stations considered. The inclusion of rainfall as input resulted in significant improvements to long-term forecasts. For Thakhek, where rainfall is most significant, the persistence index and coefficient of efficiency for 5-lead-day forecasts improved from 0.17 to 0.44 and 0.89 to 0.93, respectively, whereas the root mean square error decreased from 0.83 to 0.69 m.

Comparison between response surface models and artificial neural networks in hydrologic forecasting

Developing an efficient and accurate hydrologic forecasting model is crucial to managing water resources and flooding issues. In this study, response surface (RS) models including multiple linear regression (MLR), quadratic response surface (QRS), and nonlinear response surface (NRS) were applied to daily runoff (e.g., discharge and water level) prediction. Two catchments, one in southeast China and the other in western Canada, were used to demonstrate the applicability of the proposed models. Their performances were compared with artificial neural network (ANN) models, trained with the learning algorithms of the gradient descent with adaptive learning rate (ANN-GDA) and Levenberg-Marquardt (ANN-LM). The performances of both RS and ANN in relation to the lags used in the input data, the length of the training samples, long-term (monthly and yearly) predictions, and peak value predictions were also analyzed. The results indicate that the QRS and NRS were able to obtain equally good performance in runoff prediction, as compared with ANN-GDA and ANN-LM, but require lower computational efforts. The RS models bring practical benefits in their application to hydrologic forecasting, particularly in the cases of short-term flood forecasting (e.g., hourly) due to fast training capability, and could be considered as an alternative to ANN

Simulated impacts of climate change on current farming locations of striped catfish (Pangasianodon hypophthalmus; Sauvage) in the Mekong Delta, Vietnam

In Vietnam, culturing striped catfish makes an important contribution to the Mekong Delta's economy. Water level rise during rainy season and salt intrusion during dry season affect the water exchange and quality for this culture. Sea level rise as a consequence of climate change will worsen these influences. In this study, water level rise and salt water intrusion for three sea level rise (SLR) scenarios (i.e., +30, +50, and +75 cm) were simulated. The results showed that at SLR +50, the 3-m-flood level would spread downstream and threaten farms located in AnGiang, DongThap and CanTho provinces. Rising salinity levels for SLR +75 would reduce the window appropriate for the culture in SocTrang and BenTre provinces, and in TienGiang's coastal districts. Next to increasing dikes to reduce the impacts, the most tenable and least disruptive option to the farming community would be to shift to a salinity tolerant strain of catfish.