PITCHER PLANTS RECORDED FROM BRIS FOREST IN JAMBU BONGKOK, KUALA TRENGGANU, MALAYSIA

1INTRODUCTION NepentheswhichareknownlocallyinPeninsular Malaysiaasperiukkerabelongtoamonogenericfami lyNepenthaceae.Atpresent,atotalofninespecies(excludingnaturalhybrids)arerecordedfromPenin sularMalaysia.Ofthese,fourspeciesareendemic,whilsttheotherfivespeciesarefoundoutsidethepen insula.Thespeciescanarbitrarilybeclassifiedinto thelowlandspeciesandthehighlandspecies(Clarke,2002;Shivas,1984;Kurata,1976).Threespeciesarefoundinthehighlandhabitatscom monlyatanelevationofabove1000mforaboves…     

Grain-size distribution and subsurface mapping at the Setiu wetlands,Setiu, Terengganu

A study was carried out around the lagoon and coastal area of Setiu wetlands to determine the general characteristics and grain-size distribution of their sediments. Sediment samples were collected from 39 stations of the lagoon area and 8 stations of the coastal area, and were analyzed for their sedimentological characteristics (mean, sorting, skewness and kurtosis). The sediment mean size ranged between 0.06–2.52 and 2.12–2.69 phi (Ø) for both lagoon and coastal sea-bottom sediments, respectively. Generally, the sediments in the study area consist mostly of coarse to fine particles. The major portions of the sediment are negatively skewed. This indicates that the study area is under the influence of the rather strong wave and current action. The study also encompassed the geophysical survey that was conducted near the coastal zone of the study area. The high-resolution seismic sub-bottom profiling technique is the standard method employed to map geological features and locate structures below the surface of the seafloor. Data obtained from seismic records were used to interpret the seismic reflectors. Three main reflectors (layers), which consist of the seabed and the seabed multiple, were traced using Sonar Web Pro software, and the results showed a uniform distribution pattern for all stations surveyed. It is envisaged that this method could be an effective way to image the marine subsurface structures; thus, its use is anticipated in the higher degrees of sedimentary research and marine resource exploration.     

Assessing flood inundation mapping through estimated discharge using GIS and HEC-RAS model

Water discharge is the main parameter in hydraulic modeling for flood hazard assessment. However, the unavailability of data on discharge and observed river morphologies resulted in erroneous calculations and irregularities in flood inundation mapping. The objectives of this study are (i) to investigate uncertainties of hydraulic parameters (width, cross-sectional depth, and channel slope) used in discharge equation and (ii) to examine the influence of estimate discharge on water extent and flood depth with different boundary conditions on interferometric synthetic aperture radar (IFSAR) and modified IFSAR DEMs. Sensitivity analysis was conducted with the Monte Carlo simulation method to generate random data combinations. Bjerklie’s equation was used to calculate discharge based on the three variables, and Manning’s n was substituted into the Hydrologic Engineering Center River Analysis System (HEC-RAS) model. TerraSAR-X was used to distinguish existing flood water bodies and normal water extent. The uncertainty of the combined variables was assessed with the likelihood measures such as F-statistic, mean absolute error, root mean square error, and Nash–Sutcliffe efficiency which compares observed and predicted inundated area as well as flood water depth simulated using the HEC-RAS model.     

Tracer-based estimation of temporal variation of water sources: an insight from supra- and subglacial environments

ABSTRACT

The temporal variations in electrical conductivity and the stable isotopes of water, δD and δ¹⁸O, were examined at Chhota Shigri Glacier, India, to understand water sources and flow paths to discharge. Discharge is highly influenced by supraglacially derived meltwater during peak ablation, and subglacial meltwaters are more prominent at the end of the melt season. The slope of the best fit linear regression line for δD versus δ¹⁸O, for both supraglacial and runoff water, is lower than that for precipitation (snow and rain) and surface ice, indicating strong isotopic fractionation associated with the melting processes. The slope of the local meteoric water line (LMWL) is close to that of the global meteoric water line (GMWL), reflecting that the moisture source is predominantly oceanic. The d-excess variation in rainwater confirms that the southwest monsoon is the main contributor during summer while the remainder including winter is mostly influenced by westerlies.     

Temperature variability caused by internal tides in the coastal waters of east coast of Peninsular Malaysia

The effects of tidal currents (i.e., barotropic and internal tides) are important in the biogeochemistry of a coastal shelf sea. The high-frequency of currents and near-bottom temperatures collected in three consecutive southwest monsoon seasons (May, June, July and August of 2013 until 2015) is presented to reveal the role of the tidal currents to the temperature variability in the coastal shelf sea of the east coast of Peninsular Malaysia (ECPM), south of the South China Sea (SCS). The results of a spectral density and harmonic analysis demonstrate that the near-bottom temperature variability and the tidal currents are influenced by diurnal (O₁ and K₁) and semidiurnal (M₂) tidal currents. The spectral density of residual currents (detided data) at 5, 10 and 16 m depth also shows significant peaks at the diurnal tidal frequency (K₁) and small peaks at the semidiurnal tidal frequency (M₂) indicating the existence of internal tides. The result of the horizontal kinetic energy (HKE) shows a strong intermittent energy of internal tides in the ECPM with the strongest energy is found at 16 m depth during a sporadic cooling event in June and July. A high horizontal cross-shore heat flux (16 m) also indicates strong intrusions of cooler water into the ECPM in June and July. During the short duration of cold pulse water observed in June and July, a cross-wavelet analysis also reveals the strong relationship between the near-bottom temperatures and the internal tidal currents at the diurnal tidal frequency. The intrusion of this cooler water is probably related to the monsoon-induced upwelling in June. It is loosely interpreted that the interaction between the strong barotropic tides and the steep slope in the central basin of the SCS under the stratified condition in southwest monsoon has generated these internal tides. The dissipation of internal tides from the slope area probably has driven the cold-upwelled water into the ECPM coastal shelf sea when the upwelling intensity is the highest in June and July.     

The nexus between income inequality,economic growth and environmental degradation in Pakistan

This study examines the impact of income inequality and economic growth on environmental degradation in Pakistan using ARDL bounds testing approach for the period 1966–2011. Empirical results for the aggregate CO₂ emissions and its four sources such as CO₂ emissions from solid fuel, liquid fuel and gaseous fuel consumption as well as electricity and heat production confirm the existence of long run co-integrated relationship between income inequality, economic growth and environment degradation. The estimated results indicate that carbon emissions increase as the income gap expands in Pakistan. Besides the negative impact of industrial share and population density on CO₂ emissions, we also confirm that economic growth in Pakistan comes up with higher emissions. Hence, the hypothesis of EKC is not valid for Pakistan during the study period. Our empirical findings are robust as evidenced by dynamic ordinary least squared and the U-tests. Overall, this study suggests that the distribution of income matters to aggregate carbon emissions and focus should be made on sustained economic growth to reduce pollutants and hence CO₂ emission in the study area.     

Prioritization of sub-watersheds based on morphometric and land use analysis using remote sensing and GIS techniques

Watershed prioritization has gained importance in natural resources management, especially in the context of watershed management. Morphometric analysis has been commonly applied to prioritization of watersheds. The present study makes an attempt to prioritize sub-watersheds based on morphometric and land use characteristics using remote sensing and GIS techniques in Kanera watershed of Guna district, Madhya Pradesh. Various morphometric parameters, namely linear and shape have been determined for each sub-watershed and assigned ranks on the basis of value/relationship so as to arrive at a computed value for a final ranking of the sub-watersheds. Land use/land cover change analysis of the sub-watersheds has been carried out using multi-temporal data of IRS LISS II of 1989 and IRS LISS III of 2001. The study demonstrates the significant land use changes especially in cultivated lands, open scrub, open forest, water bodies and wastelands from 1989 to 2001. Based on morphometric and land use/land cover analysis, the sub-watersheds have been classified into three categories as high, medium and low in terms of priority for conservation and management of natural resources. Out of the seven sub-watersheds, two sub-watersheds viz., SW1 and SW6 qualify for high priority, whereas SW7 has been categorised as medium priority based on the integration of morphometric and land use change analysis.     

Regional analysis of annual maximum rainfall using TL-moments method

Information related to distributions of rainfall amounts are of great importance for designs of water-related structures. One of the concerns of hydrologists and engineers is the probability distribution for modeling of regional data. In this study, a novel approach to regional frequency analysis using L-moments is revisited. Subsequently, an alternative regional frequency analysis using the TL-moments method is employed. The results from both methods were then compared. The analysis was based on daily annual maximum rainfall data from 40 stations in Selangor Malaysia. TL-moments for the generalized extreme value (GEV) and generalized logistic (GLO) distributions were derived and used to develop the regional frequency analysis procedure. TL-moment ratio diagram and Z-test were employed in determining the best-fit distribution. Comparison between the two approaches showed that the L-moments and TL-moments produced equivalent results. GLO and GEV distributions were identified as the most suitable distributions for representing the statistical properties of extreme rainfall in Selangor. Monte Carlo simulation was used for performance evaluation, and it showed that the method of TL-moments was more efficient for lower quantile estimation compared with the L-moments.     

Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification

Conditioning complex subsurface flow models on nonlinear data is complicated by the need to preserve the expected geological connectivity patterns to maintain solution plausibility. Generative adversarial networks (GANs) have recently been proposed as a promising approach for low-dimensional representation of complex high-dimensional images. The method has also been adopted for low-rank parameterization of complex geologic models to facilitate uncertainty quantification workflows. A difficulty in adopting these methods for subsurface flow modeling is the complexity associated with nonlinear flow data conditioning. While conditional GAN (CGAN) can condition simulated images on labels, application to subsurface problems requires efficient conditioning workflows for nonlinear data, which is far more complex. We present two approaches for generating flow-conditioned models with complex spatial patterns using GAN. The first method is through conditional GAN, whereby a production response label is used as an auxiliary input during the training stage of GAN. The production label is derived from clustering of the flow responses of the prior model realizations (i.e., training data). The underlying assumption of this approach is that GAN can learn the association between the spatial features corresponding to the production responses within each cluster. An alternative method is to use a subset of samples from the training data that are within a certain distance from the observed flow responses and use them as training data within GAN to generate new model realizations. In this case, GAN is not required to learn the nonlinear relation between production responses and spatial patterns. Instead, it is tasked to learn the patterns in the selected realizations that provide a close match to the observed data. The conditional low-dimensional parameterization for complex geologic models with diverse spatial features (i.e., when multiple geologic scenarios are plausible) performed by GAN allows for exploring the spatial variability in the conditional realizations, which can be critical for decision-making. We present and discuss the important properties of GAN for data conditioning using several examples with increasing complexity.