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Flood is one of the worst natural disasters, which causes significant damage to economy and society. Flood risk assessment helps to reduce flood damage by managing flood risk in flood affected areas. For ungauged catchments, regional flood frequency analysis (RFFA) is generally used for design flood estimation. This study develops a Convolutional Neural Network (CNN) based RFFA technique using data from 201 catchments in south-east Australia. The CNN based RFFA technique is compared with multiple linear regression (MLR), support vector machine (SVM), and decision tree (DT) based RFFA models. Based on a split-sample validation using several statistical indices such as relative error, bias and root mean squared error, it is found that the CNN model performs best for annual exceedance probabilities (AEPs) in the range of 1 in 5 to 1 in 100, with median relative error values in the range of 29–44%. The DT model shows the best performance for 1 in 2 AEP, with a median relative error of 24%. The CNN model outperforms the currently recommended RFFA technique in Australian Rainfall and Runoff (ARR) guideline. The findings of this study will assist to upgrade RFFA techniques in ARR guideline in near future. [ABSTRACT FROM AUTHOR]

The economy and welfare of Iranians are heavily reliant on crops, thus the detrimental effects of drought on crops can have far-reaching consequences for society. In this research, we evaluated the occurrence of meteorological drought and its consequential effect on crop yield (barley, lentils, chickpea, and wheat) in rainfed cultivation within diverse regions of Iran from 2006 to 2020. The results demonstrate that a severe drought affected the majority of regions in Iran in both 2007 and 2016–2017, resulting in a negative impact on crop yields specifically in regions R1 and R4. The findings from the analysis of crop performance using the SYRS index indicated that in the year 2007, there were significant losses observed in rainfed crops. Furthermore, the regions exhibiting values of -2.38 and − 2.35 within the barley crop are the regions characterized by the most significant decline in yield. In 2008, the highest losses in chickpea production were observed in the region R8, with a value of -2.08. Lentils have recorded the highest casualties in the regions of R3 and R7, with − 2.71 and − 2.65 respectively. From 2007 to 2012, there has been a decline in wheat yield, with R4 experiencing higher losses compared to other regions. Based on the findings of SYRS, it can be concluded that wheat and chickpeas experience greater losses compared to lentils and barley. A study investigating the effect of climatic parameters and drought on rainfed crop yield found that rainfall had a greater influence on the yield of most crops. Moreover, the yield of barley is more significantly influenced by drought compared to other crops in 3 and 7 regions. The findings derived from this research can inform the implementation of suitable measures to address future drought challenges in regions susceptible to drought and regions crucial for agricultural production. [ABSTRACT FROM AUTHOR]

Flood is one of the worst natural disasters, which causes the damage of billions of dollars each year globally. To reduce the flood damage, we need to estimate design floods accurately, which are used in the design and operation of water infrastructure. For gauged catchments, flood frequency analysis can be used to estimate design floods; however, for ungauged catchments, regional flood frequency analysis (RFFA) is used. This paper compares two popular RFFA techniques, namely the quantile regression technique (QRT) and the index flood method (IFM). A total of 181 catchments are selected for this study from south-east Australia. Eight predictor variables are used to develop prediction equations. It has been found that IFM outperforms QRT in general. For higher annual exceedance probabilities (AEPs), IFM generally demonstrates a smaller estimation error than QRT; however, for smaller AEPs (e.g. 1 in 100), QRT provides more accurate quantile estimates. The IFM provides comparable design flood estimates with the Australian Rainfall and Runoff—the national guide for design flood estimation in Australia. [ABSTRACT FROM AUTHOR]

Australian summer heat events have become more frequent and severe in recent times. Temperature-duration-frequency (TDF) curves connect the severity of heat episodes of various durations to their frequencies and thus can be an effective tool for analysing the heat extremes. This study examines Australian heat events using data from 82 meteorological stations. TDF curves have been developed under stationary and non-stationary conditions. Generalised Extreme Value (GEV) distribution is considered to estimate extreme temperatures for return periods of 2, 5, 10, 25, 50 and 100 years. Three major climate drivers for Australia have been considered as potential covariates along with Time to develop the non-stationary TDF curves. According to the Akaike information criterion, the non-stationary framework for TDF modelling provides a better fit to the data than its stationary equivalent. The findings can be beneficial in offering new information to aid climate adaptation and mitigation at the regional level in Australia. [ABSTRACT FROM AUTHOR]

Flexible, economical, and low-toxic organic solar cells are becoming highly popular in photovoltaic research. Interestingly, its efficiency of energy conversion remains lower than that of silicon-based solar cells. As a result, it is unavoidable to focus on organic solar cell efficiency enhancement. This article presents a nano-composite thin-film developed using zinc oxide (ZnO) and polyvinyl alcohol (PVA) with a solution casting technique varying weight percentage (wt.%) of ZnO into the PVA matrix. The characterization of the thin-film of ZnO/PVA has been made using SEM, XRD, FTIR, and UV-Vis spectroscopy. The characterization reveals that the ZnO nanoparticle network forms an excellent path for electron flow in the PVA matrix at the optimal ZnO concentration of 16.66 % and a PVA concentration of 83.33 %. The thin film was applied to an organic solar cell of architecture consists of carbon fiber reinforced with ZnO-epoxy resin/CuO-epoxy resin for performance investigation. The solar cell's maximum efficiency was determined to be 9.01 % before and 14.65 % after using the nano-composite film. 5.64 % increase in the efficiency of organic solar cells are observed after the ZnO/PVA nanocomposite thin film is applied. [ABSTRACT FROM AUTHOR]

Compound extreme natural events cause a significantly larger impact than individual extreme events. Therefore, the urgency of exploring the climatology of compound events is growing. This paper is aimed to identify the current hotspots of compound heatwaves and droughts (CHD) and trends in their occurrence in southeast Australia. In this context, 61 weather stations were selected from the study area, and analyses were carried out over the extended summer season of the time period 1971–2021. The hotspots of CHDs in southeast Australia were identified considering both the total count of CHD days and 90th percentile of CHDs during the study period. The study period was divided into two periods 1971–2000 and 2001–2021, to assess the change in hotspots spatially and temporally. Four different attributes of CHDs based on the number, duration, severity and amplitude of CHDs were also calculated, and Mann–Kendall (MK) test followed by Sen's slope was adopted to detect the trends in all these four attributes of CHDs. Trends in CHD indices were also calculated for the two periods of 1971–2000 and 2001–2021. For calculating the CHD, excess heat factor (EHF) was used to identify the heatwaves. In the case of drought identification, SPEI and SPI drought indices were adopted with aggregation of 3 and 126 antecedent months, and three different threshold values were selected to consider three levels of dry conditions, e.g. 0, − 0.5 and − 1.0. It has been found that more CHDs occurred on the eastern side of NSW and Queensland states. Furthermore, the total count of CHD days increased notably during the last two decades. The trends in CHD indices were found to be significant in the recent period (2001–2021), and there was no trend in the earlier period (1971–2000). The findings of this study will help to plan heat and drought-related emergency management in the study area. [ABSTRACT FROM AUTHOR]

Several climatic variables like precipitation, temperature, fog, dew, humidity, and wind significantly impact agricultural production. While formulating policies for agriculture and other industrial sectors, precise knowledge regarding climatic variables is essential and helpful. The Bureau of Meteorology (BOM) monitored various climatic variables at more than 226 meteorological stations in New South Wales (NSW), Australia. However, the placement of these monitoring stations was not systematic. As a result, predictions for unobserved sites turn out to be erroneous. Inadequate or poorly placed meteorological stations can lead to inaccurate weather forecasts, make it difficult to fully understand local and regional weather patterns, and, lastly, make it impossible to identify the early warnings for severe weather events like hurricanes, tornadoes, and flash floods. Therefore, the study aims to optimize and suggest a monitoring network to minimize the prediction error of these climatic variables. The optimized monitoring network can be found by optimally adding new meteorological monitoring stations or withdrawing existing ones while still ensuring the reliability of weather data. In this study, the meteorological monitoring network of NSW, Australia was optimized using two stochastic search algorithms: Spatial Simulated Annealing (SSA) and Genetic Algorithms (GA). The Average Kriging Variance (AKV) is considered an accuracy measure for SSA and GA. Ordinary kriging (OK) and Universal Kriging (UK), two popular prediction methods, are used using covariation modeled by the Matheron variogram model. The results reveal that the time consumption for SSA and GA are relatively similar, but the SSA utilizing the UK gives a lower AKV than GA. The optimized meteorological monitoring will be useful for ensuring accurate weather forecasts, providing early warnings of severe weather events, and enabling scientific research to understand and mitigate the effects of climate change. Furthermore, this novel optimization strategy will not only be helpful for the government of Australia but will significantly improve prediction accuracy, providing more reliable information for various weather-related activities, such as agriculture, construction, and emergency management. [ABSTRACT FROM AUTHOR]

Flood is one of the most common natural disasters, which also triggers other natural disasters such as erosion and landslides. Flood damage can be minimised by ensuring optimum design of drainage infrastructure and other flood management tasks, which depends largely on reliable estimation of flood quantiles. This study investigates flood quantile estimation in ungauged catchments using a kriging-based regional flood frequency analysis (RFFA) technique. Three main research objectives are addressed in this study. Firstly, kriging-based RFFA models are developed using 558 catchments from eastern Australia in the range of frequent to rare flood quantiles (2, 5, 10, 20, 50 and 100 years of average recurrence intervals (ARIs)). Secondly, a validation of the models by adopting a leave-one-out (LOO) validation technique is undertaken to identify the best and the worst performing catchments across eastern Australia. Finally, a detailed comparison is made for the kriging-based RFFA technique with a generalised least-squares-based quantile regression technique, known as 'RFFE model 2016' using the same dataset to evaluate whether there are general patterns of the performance in different catchments. The study shows that for eastern Australia (a) the developed kriging-based RFFA model is a viable alternative for flood quantile estimation in ungauged catchments, (b) the 10-year ARI model Q10 performs best among the six quantiles, which is followed by the models Q5 and Q20, and (c) the kriging-based RFFA model is found to outperform the 'RFFE model 2016'. [ABSTRACT FROM AUTHOR]

In flood frequency analysis (FFA), annual maximum (AM) model is widely adopted in practice due to its straightforward sampling process. However, AM model has been criticized for its limited flexibility. FFA using peaks-over-threshold (POT) model is an alternative to AM model, which offers several theoretical advantages; however, this model is currently underemployed internationally. This study aims to bridge the current knowledge gap by conducting a scoping review covering several aspects of the POT approach including model assumptions, independence criteria, threshold selection, parameter estimation, probability distribution, regionalization and stationarity. We have reviewed the previously published articles on POT model to investigate: (a) possible reasons for underemployment of the POT model in FFA; and (b) challenges in applying the POT model. It is highlighted that the POT model offers a greater flexibility compared to the AM model due to the nature of sampling process associated with the POT model. The POT is more capable of providing less biased flood estimates for frequent floods. The underemployment of POT model in FFA is mainly due to the complexity in selecting a threshold (e.g., physical threshold to satisfy independence criteria and statistical threshold for Generalized Pareto distribution – the most commonly applied distribution in POT modelling). It is also found that the uncertainty due to individual variable and combined effects of the variables are not well assessed in previous research, and there is a lack of established guideline to apply POT model in FFA. [ABSTRACT FROM AUTHOR]

This study investigates rainfall and drought characteristics in southeastern Australia (New South Wales and Victoria) using data from 45 rainfall stations. Four homogeneity tests are adopted to determine inhomogeneities in the annual total rainfall (ATR) and monthly rainfall data, namely The Pettitt test, the SNHT, the Buishand range test and the Von Neumann ratio test at significance levels of 1%, 5%, and 10%. Temporal trends in rainfall (ATR, monthly, and seasonal) and droughts are examined using autocorrelated Mann–Kendall (A-MK) trend test at 1%, 5%, and 10% significance levels. We also assess meteorological droughts by using multiple drought indices (3-, 6-, 9-, 12-, 24-, and 36-month Standardized Precipitation Index (SPI) and Effective Drought Index (EDI)). Furthermore, spatial variability of temporal trends in rainfall and drought are investigated through interpolation of Sen's slope estimator. The results represent an increasing trend in ATR between 1920 and 2019. However, southeast Australia is highly dominated by a significant negative trend in the medium term between 1970 and 2019. Winter is found to be dominated by a significantly negative trend, whereas summer and spring seasons are dominated by a positive trend. April is detected as the driest month according to magnitude of Sen's slope and the A-MK test result. Positive trends on droughts are observed at inner parts of the study area, whereas a negative trend is detected in the south, southeast, and northeast of the study area based on SPIs and EDI. The findings of this study help to understand changes in rainfall and droughts in southeastern Australia. [ABSTRACT FROM AUTHOR]

Flood frequency analysis (FFA) involves fitting of a probability distribution to observed flood data. Two main models, annual maximum (AM) and peaks over threshold (POT), are generally adopted in FFA. The POT model is underemployed due to its complexity and uncertainty associated with threshold selection and meeting independence criteria in selecting POT data series. This study evaluates the POT and AM models using data from 188 gauged stations in southeast Australia. The POT model adopted in this study applies different average numbers of events per year fitted with generalized Pareto (GP) distribution with an automated threshold detection method. For the AM model, the GP distribution is also adopted, and flood quantiles estimated by the two models are compared. It has been found that there are notable differences in design flood estimates between the AM and POT models. The study uses catchment characteristics data to understand the differences in quantile estimates between the AM and POT models. The percentage differences between the AM and POT models can be explained by mean annual rainfall (MAR) and mean annual evapotranspiration (MAE) (ARIs of 1.01–5 years), MAR (10-year ARI), stream density (SDEN) (for 20- and 50-year ARIs) and SDEN, main stream slope (S1085) and MAE (for 100-year ARI). Stations showing smaller % differences have relatively higher MAR and smaller MAE (indicating wetter condition); conversely, stations showing higher % differences have smaller MAR and higher MAE (drier condition). [ABSTRACT FROM AUTHOR]

An understanding on different aspects of droughts is crucial for effective water resources management. Australia has experienced notable droughts in recent years. The present study utilises Effective Drought Index (EDI) and Standardized Precipitation Index (SPI) based on six time horizons (3, 6, 9, 12, 24, and 36 months) to evaluate drought parameters such as magnitude, duration, and intensity with their onset and end in south-east Australia using data from 45 rainfall stations. Maximum drought quantities determined using SPI and EDI, with average data length of 125 years, have been mapped by spline interpolation technique. Furthermore, average drought parameters for each of the SPIs and EDI have been investigated and spatial distributions of drought quantities have been exhibited. In the study area, SPIs based on different time scales (either short term or long term) have detected the maximum drought severity, duration, and/or intensity (at least one of the parameters) between 2013 and 2019. It has been found that south-east Australia had faced one of the most intense droughts during 2013 to 2019. It has also been found that the longest duration of drought has been in southern and eastern coasts of the study area. Generated maps based on average EDI values have depicted that the north-western and central parts of the study area have experienced the longest and strongest drought, whereas higher-intensity drought has been found to be dominating the eastern and southern parts of the study area. Higher frequency of the number of droughts has been found mostly in the east and south of the study area in terms of moderate, severe, and extreme droughts. The generated spatial maps of SPIs and EDI in this study help to identify the most drought-prone and vulnerable parts within south-east Australia. The drought maps developed for south-eastern Australia by this spatiotemporal analysis can be a key tool to policymakers for mitigation, risk assessment, and drought preparedness planning. [ABSTRACT FROM AUTHOR]

Natural hazards like floods and droughts affect many aspects of life. The study in particular examined the impacts of droughts on under-five mortality rate in Southern Africa, adjusting for gross domestic product (GDP) and literacy rate. Despite drought and child mortality being key public health concerns in Southern Africa over the past few decades, there have hardly been any studies examining the relationships between them. The study used publicly available data from 1980 to 2012. The Standard Precipitation Index (SPI) was calculated for 3-, 6-, 9-, and 12-monthly time scales for ten southern African countries. The wetter and drier states are represented by positive and negative SPI values, respectively. SPI, GDP, and literacy rate were considered for predicting child mortality rate using both Multiple Linear Regression techniques and nonlinear methods (Generalized Additive Model), on a leave-one-year-out cross validation approach for model evaluation. Child mortality increased as the drought worsened for five countries in this region, namely Angola, Malawi, Mozambique, Namibia, and Zambia. We found that child mortality can be predicted with a high degree of accuracy using three predictor variables—drought index, GDP and literacy rate. Statistical modelling based on early warning system can complement regional capacities for drought response systems to increase child survival rate in drought-prone areas [ABSTRACT FROM AUTHOR]

The yellow banded wasp, Vespa cincta F. is one of the most destructive pests of honeybee colonies in India. Despite high species richness, there is diminutive information on the intraspecific variations occurring in the wasp from different geographical regions. The present study was undertaken to ascertain if any intraspecific variability existed in populations of V. cincta from four geographical regions of Assam, India. The adult specimens from each region were studied using 24 morphological characters, as well as with genetic markers subjected to PCR. No significant variation was recorded for 15 morphological characters; however, nine characters viz., body length, thorax length and width, mandible length and width, fore wing width, fore leg width, hind leg width, and sting width were significantly larger in specimens collected from Barak Valley and Hill Zone (BVHZ) as compared to specimens from Upper Brahmaputra Valley Zone (UBVZ), Lower Brahmaputra Valley Zone (LBVZ) and North Bank Plain Zone (NBPZ). Variability in morphometrics further revealed that species from UPVZ, LBVZ, and NBPZ occupied similar positions, with a clear distinction from the species of BVHZ. Molecular analyses further supplemented this information with the SSR based dendrogram readily separating V. cincta into two main clusters, A and B. Cluster A included the species from LBVZ, NBPZ, and UBVZ while cluster B comprised of species from BVHZ only. Within NBPZ, specimens from Nagaon and Morigaon regions differed to some extent from specimens of other regions. Specimens of Jorhat region of UBVZ were found to more closely related to specimens of Sonitpur region of NBPZ while specimens of Golaghat region of UBVZ formed a separate sub-cluster with specimens of Karbi Anglong region of BVHZ. The present findings suggest that intraspecific diversity in V. cincta could be a result of variation in evolutionary lineage as well as environmental aspects of their bio-geographical area of origin. [ABSTRACT FROM AUTHOR]

Groundwater resources are often the main source of drinking water for remote communities, but they are increasingly found to be unsuitable, and a source of ill health in many parts of the world. High annual rainfall in monsoonal regions makes rainwater harvesting an attractive alternative, but lack of infrastructure for capturing and storing sufficient quantities is often restrictive. This study focuses on the coastal region of Bangladesh where groundwater supplying tubewells are progressively found to contain arsenic and high salinity, and where cyclones are a common cause of damage to infrastructure. The aim of this study is to evaluate the potential of a village scale rainwater harvesting scheme as a solution to water security concerns. Analysis of various size rainwater storage systems (RSS) is conducted using daily rainfall data from Khulna Station in Bangladesh. It was found that a village scale RSS with 3 m deep and 100 m by 100 m surface area could supply 100 L/p/d for 85% of the year. The reliability could feasibly be increased to 100% with seasonal water restrictions. The village scale RSS is compared with an individual household level RSS. Advantages of the village scale RSS include the opportunity for improved management and water quality monitoring, and the potential for public-private partnerships. The proposed methodology can be adapted to other monsoonal delta regions to enhance water supply. [ABSTRACT FROM AUTHOR]

Design rainfall, often known as intensity-frequency-duration (IFD) data, is an important input in rainfall runoff modelling exercise. IFD data are derived by fitting a probability distribution to observed rainfall data. Although there are many researches on IFD curves in the literature, there is a lack of systematic comparison among the IFD curves obtained by different distributions and methods. This study compares the latest IFD curves in Australia, published in 2013, as a part of the new Australian rainfall and runoff (ARR) with the at-site IFD curves to examine the expected degree of variation between the at-site and regional IFD data. Ten pluviography stations from eastern New South Wales (NSW) are selected for this study. The IFD curves generated by the two most commonly adopted probability distributions, generalised extreme value (GEV) and log Pearson type 3 (LP3) distributions are also compared. Empirical and polynomial regression methods in smoothing the IFD curves are compared. Based on the three goodness-of-fit tests, it has been found that both GEV and LP3 distributions fit the annual maximum rainfall data (at 1% significance level) for the ten selected stations. The developed IFD curves based on the second-degree polynomial present better fitting than the empirical method. It has been found that the ARR87 and ARR13 IFD curves are generally higher than the at-site IFD curves derived here. The median difference between the at-site and regional ARR-recommended IFD curves is in the range of 13-19%. It is expected that the outcomes of this research will provide better guidance in selecting the correct IFD data for a given application in NSW. The methodology developed here can be adapted to other parts of Australia and other countries. [ABSTRACT FROM AUTHOR]

Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The online version of the original article can be found at https://doi.org/10.1007/s11269-023-03507-y. B Correction to: Water Resour Manag b DOI https://doi.org/10.1007/s11269-023-03507-y In this article, the second affiliation details of Mohammed M. A. Almazah was removed and the Acknowledgement statement should be " B The authors extend Their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the Large Groups Research Project under grant number (RGP.2/23/44). b " The original article has been corrected. [Extracted from the article]

Design rainfall is widely used in urban infrastructure planning and design such as culverts and urban drainage systems. In design rainfall estimation, one of the primary steps is the selection of a suitable probability distribution that fits the observed rainfall data adequately. This study examines the selection of the best fit probability distribution in design rainfall estimation. The annual maximum (AM) rainfall data from 29 rainfall stations in Qatar are used in this study. The rainfall record lengths of these stations are in the range of 24-49 years (average of 36 years). Fourteen different distributions and three goodness-of-fit tests (Kolmogorov-Smirnov, Anderson-Darling and Chi-squared) are considered. Based on a relative scoring method, the GEV distribution is found to be the best fit distribution. Results from bootstrapping and simulation analyses show that sample estimates of skewness of the AM rainfall series are subject to a higher degree of sensitivity to data length compared with standard deviation and mean as expected. Since the rainfall quantile estimates of higher return periods are greatly influenced by skewness, a longer data length is needed in reducing the uncertainty in rainfall quantile estimates for higher return periods, which is currently unavailable in Qatar. [ABSTRACT FROM AUTHOR]

The focus of this study is to analyze the level of knowledge, awareness, and attitude toward plastic waste and to distinguish the key drivers that encourage the households in Kuala Lumpur, Malaysia, to participate in 'No plastic campaign,' This study used the logistic regression model to explain the factors that may affect the willingness to participate (WTP) of households in the campaign. In this study, it is found that 35 % of households are willing to participate in the campaign. The results of the study also indicate that people who are more informed and more convinced of their knowledge have a more positive attitude toward recycling than their counterparts do. Furthermore, this study provides additional evidence of the level and classification of importance of motivating factors for plastic recycling, using the modified average and coefficient of variation of the models. From the analysis, the factor 'helps reduce landfill use' is found as the most important factor and the factor of 'raising money for charity' is found as the least important factor that motivates households to participate in recycling. The determinations of the study suggest some strategies that could hold implications for government and households to boost them to participate in the campaign 'No Plastic Bag.' [ABSTRACT FROM AUTHOR]

Water demand modelling is an active field of research. The modelling and forecasting tools are useful to get the estimation of forecasted water demand for different forecast horizons (e.g. 1 h to 10 years) in order to achieve more efficient and sustainable water resources management systems. However, modelling and forecasting of accurate water demand are challenging and difficult tasks. Several issues make the demand forecasting challenging such as the nature and quality of available data, numerous water demand variables, diversity in forecast horizons and geographical differences in modelling catchments. These issues have motivated a number of studies to be conducted to produce better water demand modelling and forecasting tools in order to improve forecast reliability. A variety of techniques have been adopted in water demand forecasting, however, application of independent component regression (ICR) technique has not been investigated yet. Hence, this study explores, for the first time, the use of the ICR technique in medium term urban water demand forecasting. This uses data from the city of Aquidauana, Brazil. It also compares the performance of the developed ICR model with two other commonly modeling methods, principal component regression and multiple linear regression models. It has been found that ICR model perform better than the other two models in modelling water demand with a higher performance indices (i.e. R , RMSE, NSE and MARE) for the independent validation period. The results indicate that the ICR technique has the potential to develop water demand models successfully. The methodology adopted in this paper can be applied to other countries to develop water demand forecasting model. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the spatial and temporal distributions of rainfall in Qatar, which falls in the arid region of the middle-east. We use rainfall data from 29 rain gauges covering the period of 1962-2010. Fifteen different rainfall indices are used in the assessment. A combination of Mann-Kendall and Spearman's Rho tests is adopted to identify trends in the rainfall data. The average annual rainfall values are found to be in the range of 55.5-99 mm. A sharp gradient in average annual rainfall is noticed, with north having much higher values than the south. A mixed trend, both increasing (upward) and decreasing (downward) for most of the rainfall indices, is identified. Annual total and maximum daily rainfalls show mixed trends, while rainy days show an increasing trend. For the rainy seasons, the total rainfall during the months of December-January-February shows an increasing trend and March-April rainfall shows a decreasing trend, reflecting that seasonal rainfall in Qatar is changing. The findings of this study provide important insights into the nature of rainfall variability in Qatar, which will be useful in water resources planning tasks in Qatar and nearby countries. [ABSTRACT FROM AUTHOR]

Design rainfall is needed in the design of numerous engineering infrastructures such as urban drainage systems, bridges, railways, metro systems, highways and flood levees. Design rainfall is derived using regional frequency analysis approach based on observed rainfall data from a large number of stations within a homogeneous region. This paper provides an assessment of the possible impacts of climate change on design rainfalls in Qatar. The future climate conditions are established based on AR4 and A2 categories of emission scenarios (SRES) specified by the Intergovernmental Panel on Climate Change. Predicted 24-h annual maximum rainfall series for both the wet (NCAR-CCSM) and dry scenarios (CSIRO-MK3.5) for the Qatari grid points are extracted for three different periods, which are current (2000-2029), medium-term (2040-2069) and end-of-century climates (2080-2099). Using an L-moments-based index frequency approach, homogeneous regions are established and the best-fit distribution is then used to derive rainfall quantiles for average recurrence intervals (ARIs) of 2, 5, 10, 25, 50 and 100 years. The results show that there is no significant change in the design rainfalls in Qatar in the short term covering 2040-2069; however, a significant change is predicted at the end of century covering 2080-2099. Updated design rainfalls are estimated considering climate change scenarios for the period of 2080-2099 by averaging results from the wet and dry climate scenarios. The increase in 24-h annual maximum rainfall for the period 2080-2099 (compared with the current period 2000-2029) is found to be in the range of 68 and 76 % for 100-year ARI. For the typical design ARIs of 10-20 years, the increase in design rainfall is found to be in the range of 43 and 54 %. The method presented in this study can be applied to other arid regions, in particular to the Middle Eastern countries. [ABSTRACT FROM AUTHOR]

The magnetorheological (MR) damper is one of the utmost progressive applications of asemi-active damper. Uninterrupted controllability in both on and off state is an important factor of its plenitude application. Current research is attempting to make the damper more effective and efficient by minimizing the existing limitations such as MR fluid's sedimentation, power consumption and temperature rising, and design optimization. We have broadly analyzed the optimization of MR dampers design with finite element simulation where various parameters of the MR damper have been considered for more accurate results. A prototype MR fluid has been prepared by coating the carbonyl iron particles with xanthan gum to reduce sedimentation. The SEM and Turbiscan results noticeably verify the improved sedimentation stability. In addition, a power-saving MR damper model has been developed by finite element analysis using ANSYS software. Prolonged operation raises the damper's body temperature and degrades the performance. However, in this energy-saving MR damper model the temperature is not rising to a higher value compared to the conventional dampers, and consequently promotes damper efficiency. [ABSTRACT FROM AUTHOR]

With the potentially devastating consequences of flooding, it is crucial that uncertainties in the modelling process are quantified in flood simulations. In this paper, the impact of uncertainties in design losses on peak flow estimates is investigated. Simulations were carried out using a conceptual rainfall-runoff model called RORB in four catchments along the east coast of New South Wales, Australia. Monte Carlo simulation was used to evaluate parameter uncertainty in design losses, associated with three loss models (initial loss-continuing loss, initial loss-proportional loss and soil water balance model). The results show that the uncertainty originating from each loss model differs and can be quite significant in some cases. The uncertainty in the initial loss-proportional loss model was found to be the highest, with estimates up to 2.2 times the peak flow, whilst the uncertainty in the soil water balance model was significantly less, with up to 60 % variability in peak flows for an annual exceedance probability of 0.02. Through applying Monte Carlo simulation a better understanding of the predicted flows is achieved, thus providing further support for planning and managing river systems. [ABSTRACT FROM AUTHOR]

Design rainfall intensity-frequency-duration data are a basic input to many water-related development projects. To derive design rainfalls, one needs long period of recorded rainfall data. Although daily rainfall data are generally widely available, short-duration rainfall data are scarce. For many urban applications, design rainfalls for much shorter durations are needed, which cannot be obtained directly from daily read rainfall data. This paper presents a simple approach that can be adopted to derive design rainfalls of short durations using daily rainfall data and other physio-climatic characteristics using a novel 'index frequency combined with parameter regression technique'. This uses L moments to reduce the impacts of sampling variability in the analysis. Furthermore, this adopts generalised least squares regression to account for the inter-station correlation of the rainfall data in the analysis. The proposed method is applied to a pilot data set consisting of 203 rainfall stations across Australia. An independent Monte Carlo cross-validation test shows that the proposed method is capable of generating consistent and accurate design rainfall estimates from 6-min to 12-h duration. The developed technique can be adapted to other countries where there is a scarcity of short-duration rainfall data, but daily rainfall data are abundant. [ABSTRACT FROM AUTHOR]

The currently adopted rainfall-based design flood estimation method in Australia, known as design event approach (DEA), has a flaw that is widely criticized by the hydrologists. The DEA is based on the assumption that a rainfall depth of a certain frequency can be transformed to a flood peak of the same frequency by adopting the 'representative values' of other model input variables, such as temporal patterns and losses. To overcome the limitation associated with the DEA, this paper develops stochastic model inputs to apply Monte Carlo simulation technique (MCST) for design flood estimation. This uses data from 86 pluviograph stations and six catchments from eastern New South Wales (NSW), Australia, to regionalize the distributions of various input variables (e.g., rainfall duration, inter-event duration, intensity and temporal patterns and loss and routing characteristics) to simulate thousands of flood hydrographs using a nonlinear runoff routing model. The regionalized stochastic inputs are then applied with the MCST to two catchments in eastern NSW. The results indicate that the developed MCST provide more accurate flood quantile estimates than the DEA for the two test catchments. The particular advantage of the new MCST and stochastic design input variables is that it reduces the subjectivity in the selection of model input values in flood modeling. The developed MCST can be adapted to other parts of Australia and other countries. [ABSTRACT FROM AUTHOR]

Long term water demand forecasting is needed for the efficient planning and management of water supply systems. A Monte Carlo simulation approach is adopted in this paper to quantify the uncertainties in long term water demand prediction due to the stochastic nature of predictor variables and their correlation structures. Three future climatic scenarios (A1B, A2 and B1) and four different levels of water restrictions are considered in the demand forecasting for single and multiple dwelling residential sectors in the Blue Mountains region, Australia. It is found that future water demand in 2040 would rise by 2 to 33 % (median rise by 11 %) and 72 to 94 % (median rise by 84 %) for the single and multiple dwelling residential sectors, respectively under different climatic and water restriction scenarios in comparison to water demand in 2010 (base year). The uncertainty band for single dwelling residential sector is found to be 0.3 to 0.4 GL/year, which represent 11 to 13 % variation around the median forecasted demand. It is found that the increase in future water demand is not notably affected by the projected climatic conditions but by the increase in the dwelling numbers in future i.e. the increase in total population. The modelling approach presented in this paper can provide realistic scenarios of forecasted water demands which would assist water authorities in devising appropriate management strategies to enhance the resilience of the water supply systems. The developed method can be adapted to other water supply systems in Australia and other countries. [ABSTRACT FROM AUTHOR]

A continuously variable transmission (CVT) system transmits the engine/battery power to the car driving wheel smoothly and efficiently. Several types of CVT already been developed to improve the transmission losses while maintaining acceleration time. However, most of the CVT has some constraints in the actuation mechanism which led us to develop an innovative electromagnetic actuator for CVT. Simplified mathematical equations have been developed for the kinematics analysis of clamping forces of the CVT and electromagnetic forces of EMA. The EMA has been developed for ¼ scale car with two sets of solenoid. Each of the two sets has been equipped with primary and secondary pulleys for pushing and pulling the movable sheave. The solenoid is operated by controlling the supply current with a fuzzy logic controller. A simulation based fuzzy logic controller has been introduced here for identifying the desired current of the EMA actuation. The experimental results show that the EMA develops electromagnetic forces 301 N for the supply current of 3.37 amp, which makes the acceleration time of the car in the range of 2.5∼3.5 sec and electromagnetic actuated CVT system highly energy efficient. [ABSTRACT FROM AUTHOR]

The most direct method of design flood estimation is at-site flood frequency analysis, which relies on a relatively long period of recorded streamflow data at a given site. Selection of an appropriate probability distribution and associated parameter estimation procedure is of prime importance in at-site flood frequency analysis. The choice of the probability distribution for a given application is generally made arbitrarily as there is no sound physical basis to justify the selection. In this study, an attempt is made to investigate the suitability of as many as fifteen different probability distributions and three parameter estimation methods based on a large Australian annual maximum flood data set. A total of four goodness-of-fit tests are adopted, i.e., the Akaike information criterion, the Bayesian information criterion, Anderson–Darling test, and Kolmogorov–Smirnov test, to identify the best-fit probability distributions. Furthermore, the L-moments ratio diagram is used to make a visual assessment of the alternative distributions. It has been found that a single distribution cannot be specified as the best-fit distribution for all the Australian states as it was recommended in the Australian rainfall and runoff 1987. The log-Pearson 3, generalized extreme value, and generalized Pareto distributions have been identified as the top three best-fit distributions. It is thus recommended that these three distributions should be compared as a minimum in practical applications when making the final selection of the best-fit probability distribution in a given application in Australia. [ABSTRACT FROM AUTHOR]

The traditional rainfall-runoff modelling based on the Design Event Approach has some serious limitations as this ignores the probabilistic nature of the key flood producing variables in the modelling except for rainfall depth. A more holistic approach of design flood estimation such as the Joint Probability Approach/Monte Carlo simulation can overcome some of the limitations associated with the Design Event Approach. The Monte Carlo simulation technique is based on the principle that flood producing variables are random variables instead of fixed values. This allows accounting for the inherent variability in the flood producing variables in the rainfall-runoff modelling. This paper applies the Monte Carlo simulation technique and hydrologic model URBS to a large catchment with multiple pluviograph and stream gauging stations. It has been found that it is quite feasible to apply the Monte Carlo simulation technique to large catchments. The Monte Carlo simulation technique has much greater flexibility than the Design Event approach and can provide more realistic design flood estimates with multiple scenarios, which is likely to replace the Design Event Approach. The method developed here can be applied to other catchments in Australia and other countries. [ABSTRACT FROM AUTHOR]

An electromagnetic actuated continuously variable transmission (EMA-CVT) system is developed by two sets of electromagnetic actuators (solenoid) located on primary and secondary pulley. A set of solenoids are attached to the primary and secondary pulley to develop the attraction and repulsive forces. The relationships between the speed ratio and electromagnetic actuation and clamping force and output torque of the CVT are established based on the kinematics of the EMA-CVT system. A fuzzy logic controller (FLC) is developed to control the EMA precisely based on the feedback of the RPM sensor and slope sensor. The EMA-CVT performance with controller has found 28% more than the performance of the EMA-CVT without controller. The solenoids of the EMA were activated by varying the current supply with the Fuzzy-Proportional-Derivative-Integrator (FPID) to maintain the non-linearity of the CVT in response of the vehicle traction torque demand. Result shows that the solenoid is able to pull the plunger in the desired distance with supply current of 12.5 amp while push the plunger to the desired distance with 14.00 amp current supply to the windings when the vehicle is considered in 10% grade. The acceleration time of the ¼ scale car has been recorded as 5.5 s with the response of drive wheels torque. [ABSTRACT FROM AUTHOR]

Regional flood frequency analysis (RFFA) is often used in hydrology to estimate flood quantiles when there is a limitation of at-site recorded flood data. One of the commonly used RFFA methods is the index flood method, which is based on the assumptions that a region satisfies criterion of simple scaling and it can be treated homogeneous. Another RFFA method is quantile regression technique where prediction equations are developed for flood quantiles of interest as function of catchment characteristics. In this paper, the scaling property of regional floods in New South Wales (NSW) State in Australia is investigated. The results indicate that the annual maximum floods in NSW satisfy a simple scaling assumption. The application of a heterogeneity test, however, reveals that NSW flood data set does not satisfy the criteria for a homogeneous region. Finally, a set of prediction equations are developed for NSW using quantile regression technique; an independent test shows that these equations can provide reasonably accurate design flood estimates with a median relative error of about 27%. [ABSTRACT FROM AUTHOR]

This paper presents the control system of cushion pressure for the developed intelligent air-cushion track vehicle (IACTV) for operating on swamp terrain and wet fields. A novel auto-adjusting supporting system is designed for the vehicle's intelligent air-cushion system. Focusing on minimizing the total power demand of the vehicle, an optimization model has been established, for examining the effects of vehicle parameters and load distribution on power consumption by controlling air-cushion pressure. Then optimum cushion pressure is determined based on the developed optimum pressure - sinkage relationship and the pressure in the cushion chamber is controlled by the Fuzzy controller by maintaining volume flow rate and continuously monitored by the pressure sensor attached with the cushion chamber. The ultrasonic displacement sensor is used to measure the sinkage of the vehicle. The output voltages of the ultrasonic displacement are used to operate the pull-in solenoid switch through the microcontroller which closes the circuit of the compressor motor. Distribution of vehicle load to the air-cushion system is controlled by Fuzzy Logic controller by maintaining the inside pressure of the cushion. [ABSTRACT FROM AUTHOR]

Selection of a flood frequency distribution and associated parameter estimation procedure is an important step in flood frequency analysis. This is however a difficult task due to problems in selecting the best fit distribution from a large number of candidate distributions and parameter estimation procedures available in the literature. This paper presents a case study with flood data from Tasmania in Australia, which examines four model selection criteria: Akaike Information Criterion (AIC), Akaike Information Criterion-second order variant (AIC), Bayesian Information Criterion (BIC) and a modified Anderson-Darling Criterion (ADC). It has been found from the Monte Carlo simulation that ADC is more successful in recognizing the parent distribution correctly than the AIC and BIC when the parent is a three-parameter distribution. On the other hand, AIC and BIC are better in recognizing the parent distribution correctly when the parent is a two-parameter distribution. From the seven different probability distributions examined for Tasmania, it has been found that two-parameter distributions are preferable to three-parameter ones for Tasmania, with Log Normal appears to be the best selection. The paper also evaluates three most widely used parameter estimation procedures for the Log Normal distribution: method of moments (MOM), method of maximum likelihood (MLE) and Bayesian Markov Chain Monte Carlo method (BAY). It has been found that the BAY procedure provides better parameter estimates for the Log Normal distribution, which results in flood quantile estimates with smaller bias and standard error as compared to the MOM and MLE. The findings from this study would be useful in flood frequency analyses in other Australian states and other countries in particular, when selecting an appropriate probability distribution from a number of alternatives. [ABSTRACT FROM AUTHOR]

This paper reviews the current status of permeable pavement research and limitations of its applicability. This discusses the influence of design factors such as permeable pavement type, mix design of porous concrete/asphalt, aggregate materials, particle size and distribution, sub-base depth, and layer setting on hydraulic, structural, and environmental performances of the pavement. Findings of this review demonstrate that the uptake of permeable pavement systems as a stormwater best management practice is relatively limited and slow due to lack of in-depth scientific understanding and economic uncertainties. It confirms the necessity of undertaking further research to fill the knowledge gap by providing practical solutions supported by new knowledge and innovations on permeable pavemen systerms. Followings have been identified as challengers and needs for future research on permeable pavement systems: (a) unavailability of cost data and difficulties of estimation of intangible benefits; (b) co-optimising environmental, hydraulic, and structural performances by modifying design; (c) difficulties of simulating actual field condition to investigate the clogging phenomena via laboratory experiments; (d) modelling the relationship of design variations with structural, hydraulic, and environmental performance; (e) developing a standard maintenance procedure to restore infiltration capacity; and (f) improving the bearing capacity of the structure to withstand higher vehicular loads and speeds. [ABSTRACT FROM AUTHOR]

The original article was published with two typographical errors found in the unit of Y-axis in Figs. 4 and 5 of the original article. The author would like readers to know that the units should be changed from "mm/hr" to "mm/day". This correction stands to correct the original article. [ABSTRACT FROM AUTHOR]