Rezaul K. Chowdhury

Multicriteria decision analysis in water resources management: the malnichara channel improvement

Multicriteria decision analysis tool is used in many water resources and environmental management projects. The Malnichara is one of the natural channels in Sylhet city (Bangladesh) responsible for storm runoff conveyance to the downstream Surma river. The channel is found to be encroached at many locations of the city and found to be very vulnerable. The authority has taken decision to improve natural channels by a traditional approach, e.g. constructing box culvert. In most cases, stakeholders’ participation is ignored in such type of decision making. Hence, efforts were made to evaluate three common alternatives viz. sodding natural channel, lined natural channel and box culvert for the channel improvement. The channel is hydrologically divided into two parts: the upper portion (Choukidekhi-Kanishail) and the lower portion (Kanishail-Topoban). Both parts were separately analyzed. Small groups of stakeholders were interviewed for the selection of criteria and for the assignment of weighed factor and scores. Experts’ opinions were also taken through consultation. Nine criteria from four categories such as technical, economic, environment and social aspects were selected. The relative performances of alternatives were evaluated using the weighed sum technique of multicriteria decision analysis. It was found that the sodding natural channel is the best alternative for both portions of the channel. However, the choice is very sensitive to the social criteria.

Influence of SOI, DMI and Niño3.4 on South Australian rainfall

The influences of climate drivers (SOI, DMI and Niño3.4) on South Australian (SA) rainfall are investigated in this study. Recent records of monthly rainfall and climate driver index values from 1981 to 2010 were analysed for 53 rainfall stations, located across eight SA natural resources management (NRM) regions. The Pearson, Kendall and Spearman correlation tests were applied between rainfall and climate drivers and between the climate drivers themselves. Both SA summer (December to February) and autumn (March to May) rainfalls were found not significantly influenced by climate indices. Winter rainfall in the south and east parts of SA was found strongly influenced by both SOI and DMI, particularly in July and August. Both SOI and DMI are inter-correlated in winter. Spring rainfall was found significantly influenced by DMI in the south and east parts of SA, particularly in September and October. In terms of ENSO phenomena, whilst both SOI and Niño3.4 are correlated, SOI was found more to be influential than Niño3.4 for SA winter and spring rainfall. Outcomes of the study are useful for stochastic rainfall generation and for developing downscaling techniques to generate rainfall projections in the region.

Statistical downscaling of CMIP5 outputs for projecting future changes in rainfall in the Onkaparinga catchment

A generalized linear model was fitted to stochastically downscaled multi-site daily rainfall projections from CMIP5 General Circulation Models (GCMs) for the Onkaparinga catchment in South Australia to assess future changes to hydrologically relevant metrics. For this purpose three GCMs, two multi-model ensembles (one by averaging the predictors of GCMs and the other by regressing the predictors of GCMs against reanalysis datasets) and two scenarios (RCP4.5 and RCP8.5) were considered. The downscaling model was able to reasonably reproduce the observed historical rainfall statistics when the model was driven by NCEP reanalysis datasets. Significant bias was observed in the rainfall when downscaled from historical outputs of GCMs. Bias was corrected using the Frequency Adapted Quantile Mapping technique. Future changes in rainfall were computed from the bias corrected downscaled rainfall forced by GCM outputs for the period 2041-2060 and these were then compared to the base period 1961-2000. The results show that annual and seasonal rainfalls are likely to significantly decrease for all models and scenarios in the future. The number of dry days and maximum consecutive dry days will increase whereas the number of wet days and maximum consecutive wet days will decrease. Future changes of daily rainfall occurrence sequences combined with a reduction in rainfall amounts will lead to a drier catchment, thereby reducing the runoff potential. Because this is a catchment that is a significant source of Adelaides water supply, irrigation water and water for maintaining environmental flows, an effective climate change adaptation strategy is needed in order to face future potential water shortages.

Statistical downscaling of rainfall: a non-stationary and multi-resolution approach

A novel downscaling technique is proposed in this study whereby the original rainfall and reanalysis variables are first decomposed by wavelet transforms and rainfall is modelled using the semi-parametric additive model formulation of Generalized Additive Model in Location, Scale and Shape (GAMLSS). The flexibility of the GAMLSS model makes it feasible as a framework for non-stationary modelling. Decomposition of a rainfall series into different components is useful to separate the scale-dependent properties of the rainfall as this varies both temporally and spatially. The study was conducted at the Onkaparinga river catchment in South Australia. The model was calibrated over the period 1960 to 1990 and validated over the period 1991 to 2010. The model reproduced the monthly variability and statistics of the observed rainfall well with Nash-Sutcliffe efficiency (NSE) values of 0.66 and 0.65 for the calibration and validation periods, respectively. It also reproduced well the seasonal rainfall over the calibration (NSE = 0.37) and validation (NSE = 0.69) periods for all seasons. The proposed model was better than the tradition modelling approach (application of GAMLSS to the original rainfall series without decomposition) at reproducing the time-frequency properties of the observed rainfall, and yet it still preserved the statistics produced by the traditional modelling approach. When downscaling models were developed with general circulation model (GCM) historical output datasets, the proposed wavelet-based downscaling model outperformed the traditional downscaling model in terms of reproducing monthly rainfall for both the calibration and validation periods.

Can stormwater harvesting restore pre-development flows in urban catchments in South East Queensland?

Increases in the impervious area due to urbanisation have been shown to have negative impacts on the physical and ecological condition of streams, primarily through increased volume and frequency of runoff. The harvesting and detention of runoff has a potential to decrease this impact. This paper describes the effects of urbanisation on catchment flow and of stormwater harvesting on reducing those adverse impacts on a stream in South East Queensland (SEQ), Australia. A largely undeveloped catchment located southeast of Brisbane city was calibrated and validated using the Stormwater Management Model (SWMM). This model was used to investigate the effect of a range of future increases in urbanisation (represented by impervious area) on stream hydrology as well as the potential of stormwater harvesting to return the catchments to predevelopment flow conditions. Stormwater harvesting was modelled according to flow frequency measures specified in current SEQ development guidelines. These guidelines stipulate the capture of the first 10 mm of runoff from impervious areas of 0-40% and the first 15 mm from impervious areas of 40% or greater for urban developments. We found that increases in the impervious area resulted in increases in the mean, frequency and duration of high flows, and an increase in the mean rate of rise and fall for storm events in the catchment. However, the predevelopment (non-urbanised) flow distribution was very flashy in comparison with all urbanised scenarios; i.e. it had the quickest response to rainfall indicated by a high rate of rise to and fall from peak flow volume, followed by a return to zero flow conditions. Capturing the runoff according to the development guidelines resulted in a reduction in flow towards the flow distribution of a lower impervious area, however this was insufficient to meet predevelopment conditions. This suggests a stronger influence of impervious areas in this catchment on the volume of runoff than flow frequency measures are able to ameliorate.

Simulation of extreme rainfall and projection of future changes using the GLIMCLIM model

In this study, the performance of the Generalized LInear Modelling of daily CLImate sequence (GLIMCLIM) statistical downscaling model was assessed to simulate extreme rainfall indices and annual maximum daily rainfall (AMDR) when downscaled daily rainfall from National Centers for Environmental Prediction (NCEP) reanalysis and Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCM) (four GCMs and two scenarios) output datasets and then their changes were estimated for the future period 2041–2060. The model was able to reproduce the monthly variations in the extreme rainfall indices reasonably well when forced by the NCEP reanalysis datasets. Frequency Adapted Quantile Mapping (FAQM) was used to remove bias in the simulated daily rainfall when forced by CMIP5 GCMs, which reduced the discrepancy between observed and simulated extreme rainfall indices. Although the observed AMDR were within the 2.5th and 97.5th percentiles of the simulated AMDR, the model consistently under-predicted the inter-annual variability of AMDR. A non-stationary model was developed using the generalized linear model for local, shape and scale to estimate the AMDR with an annual exceedance probability of 0.01. The study shows that in general, AMDR is likely to decrease in the future. The Onkaparinga catchment will also experience drier conditions due to an increase in consecutive dry days coinciding with decreases in heavy (>long term 90th percentile) rainfall days, empirical 90th quantile of rainfall and maximum 5-day consecutive total rainfall for the future period (2041–2060) compared to the base period (1961–2000).

Statistical behaviour of Adelaide’s rainfall - is climate change detectable?

The South Australian climate is changing towards less rainfall and consequently water security problems will be intensified (IPCC and CSIRO, 2007). In this study we attempted to identify the statistical behaviour of point rainfall measured at the Adelaide airport. Statistical moments, lag1 autocorrelation, the Buishands Q test for homogeneity, the Mann-Kendall test for trend and wavelet analyses for shift and temporal variability were carried out at fine (< 12 hour) and at coarse (monthly) temporal resolutions. Series of rainfall intensities at finer temporal scales and rainfall depths and proportion dry ratios at monthly scales were accumulated from the high resolution rainfall dataset. Homogeneity of rainfall intensity was found to increase as the temporal scale increases. Both rainfall intensities and monthly rainfall depths were found to be serially correlated. Except for July, the 2nd, 3rd and 4th statistical moments of rainfall intensities decrease as the scale increases. While no statistically significant trends were found at finer and monthly scales using the Mann-Kendall test, there were indications that trends are more likely as the temporal scale increases. Wavelet power spectra also showed this scenario noticeably. At very fine temporal scales (< 1 hour) rainfall intensities have large temporal variability and no trends or shifts in frequency level were detected. At the 12 hour and monthly temporal resolutions, statistically significant changes of dominant frequency level were observed in some months. Interestingly these changes were observed to happen after 1990.

An Experimental Study of Greywater Irrigated Green Roof Systems in an Arid Climate

Green roofs provide multi-functional benefits to the built environment. They minimize urban heat island effects, enhance biodiversity, reduce carbon footprints…

Greywater quality changes in laboratory scale vegetative biofiltration prototypes

Greywater recycling and reuse is becoming a popular choice in many water-scarce regions. Greywater is the largest component of domestic indoor wastewater. Howe…

Potential and Challenges of Project-Based Learning in Engineering Education at the United Arab Emirates University

Learning styles of most engineering students and teaching styles of most engineering instructors are incompatible in several dimensions. Many or most engineering students are visual, sensing, inductive, and active, whereas engineering education is mostly verbal (auditory), abstract (intuitive), deductive, passive, and sequential. These mismatches lead to poor pupil performance, professorial frustration and a barrier to producing many potentially excellent engineers. The modern method of engineering education involves contemporary teaching and learning practices using project- and program-based learning, work-integrated learning and integrative learning approaches. The project-based learning (PBL) is acknowledged as a collaborative, progressive, student-centered, interactive, active, and deep-learning approach. The chapter describes the learning styles of engineering students, teaching approaches of engineering instructors, role of smart technologies, and their applications for the implementation of PBL in engineering education at the United Arab Emirates University (UAEU). Students and instructors were surveyed to understand their learning and instruction manners. Most engineering students at UAEU are observed to be active, visual, sensory, and sequential, whereas surveyed engineering instructors exhibited their strong preference for the expert and delegator style of instruction. Instructors also exhibited their preference for a facilitator style of teaching. Previous studies discovered that the PBL approach prefers the facilitator style of education rather than expert or delegator styles. It was observed that both students and instructors at UAEU are aware of, have adequate institutional supports, and are using advanced technologies in their learning and teaching. Thus, in spite of some manageable constraints, there is potential for implementing PBL in engineering instruction at the UAEU.