• Energy Research

Short duration intense rainfall causes an increase in rainfall derived infiltration and inflow (RDII) into aging sewer networks, which leads to Sanitary Sewer Overflows (SSOs). This study presents a generalised framework for assessing and mitigating the impacts of intense rainfall on sanitary sewer networks. The first part of the proposed framework involves a detailed hydraulic modelling to evaluate the performance of the sewer network. The second part deals with the development of SSO mitigation strategies based on Water Sensitive Urban Design (WSUD) approaches. This paper also demonstrates the application of the first part of the proposed framework for a case study catchment in Melbourne, Australia. The hydraulic performance of the case study sewer network during a wet and a dry year is presented. The analysis found that for the wet year, 11 manholes had sewer overflows, whereas 53 of 57 manholes in the network of 3.2 km had surcharges. Such a study will benefit the water authorities to develop mitigation strategies for controlling SSOs in their sewer systems.

Blue-green infrastructure (BGI) is defined as a strategically planned network of natural and semi-natural areas with other environmental features designed and managed to deliver a wide range of ecosystem services, which include microclimate regulation and enhanced human thermal comfort. While green infrastructure is widely known to be capable of mitigating the adverse effects of urban heat island, the effect of blue infrastructure to regulate thermal comfort is still poorly understood. This study investigates several blue-green-infrastructure (BGI) scenarios in the central business district (CBD) of Melbourne, Australia to assess their effects on microclimate and human thermal comfort. Three-dimensional microclimatic modelling software, ENVI-met, was used to simulate the microclimate and human thermal comfort. Physiological equivalent temperature (PET) was used to quantify the level of thermal comfort in selected research areas. Ten different scenarios were simulated, which included those based on green roofs, green walls, trees, ponds and fountains. The simulations suggest that green roofs and green walls in the high-rise building environment have a small temperature reduction in its surrounding area by up to 0.47 °C and 0.27 °C, respectively, and there is no noticeable improvement in the level of thermal perception. The tree-based scenarios decrease temperature by up to 0.93 °C and improve the thermal perception from hot to warm. Scenarios based on water bodies and fountains decrease the temperature by up to 0.51 °C and 1.48 °C, respectively, yet they cannot improve the thermal perception of the area. A deeper water body has a better microclimate improvement as compared to a shallow one. The temperature reduction in the fountain scenario tends to be local and the effect could only be felt within a certain radius from the fountain.

Climate change has had a significant impact on the tourism industry in many countries, leading to changes in policies and adaptations to attract more visitors. However, there are few studies on the effects of climate change on Sri Lanka’s tourism industry and income, despite its importance as a destination for tourists. A study was conducted to analyze the holiday climate index (HCI) for Sri Lanka’s urban and beach destinations to address this gap. The analysis covered historical years (2010–2018) and forecasted climatic scenarios (2021–2050 and 2071–2100), and the results were presented as colored maps to highlight the importance of HCI scores. Visual analysis showed some correlation between HCI scores and tourist arrivals, but the result of the overall correlation analysis was not significant. However, a country-specific correlation analysis revealed interesting findings, indicating that the changing climate can be considered among other factors that impact tourist arrivals. The research proposes that authorities assess the outcomes of the study and conduct further research to develop adaptive plans for Sri Lanka’s future tourism industry. The study also investigated potential scenarios for beach and urban destinations under two climate scenarios (RCP 4.5 and RCP 8.5) for the near and far future, presenting the findings to tourism industry stakeholders for any necessary policy changes. As Sri Lanka expects more Chinese visitors in the future due to ongoing development projects, this study could be valuable for policymakers and industry stakeholders when adapting to changing climate and future tourist behavior. While more research is needed to fully understand the effects of climate change on Sri Lanka’s tourism industry, this study serves as a starting point for future investigations.

Understanding the changes in climate and land use/land cover (LULC) over time is important for developing policies for minimizing the socio-economic impacts of riverine floods. The present study evaluates the influence of hydro-climatic factors and anthropogenic practices related to LULC on floods in the Kelani River Basin (KRB) in Sri Lanka. The gauge-based daily precipitation, monthly mean temperature, daily discharges, and water levels at sub-basin/basin outlets, and both surveyed and remotely sensed inundation areas were used for this analysis. Flood characteristics in terms of mean, maximum, and number of peaks were estimated by applying the peak over threshold (POT) method. Nonparametric tests were also used to identify the climatic trends. In addition, LULC maps were generated over the years 1988–2017 using Landsat images. It is observed that the flood intensities and frequencies in the KRB have increased over the years. However, Deraniyagala and Norwood sub-basins have converted to dry due to the decrease in precipitation, whereas Kithulgala, Holombuwa, Glencourse, and Hanwella showed an increase in precipitation. A significant variation in atmospheric temperature was not observed. Furthermore, the LULC has mostly changed from vegetation/barren land to built-up in many parts of the basin. Simple correlation and partial correlation analysis showed that flood frequency and inundation areas have a significant correlation with LULC and hydro-climatic factors, especially precipitation over time. The results of this research will therefore be useful for policy makers and environmental specialists to understand the relationship of flood frequencies with the anthropogenic influences on LULC and climatic factors.

Coastal landforms are continuously shaped by natural and human-induced forces, exacerbating the associated coastal hazards and risks. Changes in the shoreline are a critical concern for sustainable coastal zone management. However, a limited amount of research has been carried out on the coastal belt of Sri Lanka. Thus, this study investigates the spatiotemporal evolution of the shoreline dynamics on the Oluvil coastline in the Ampara district in Sri Lanka for a two-decade period from 1991 to 2021, where the economically significant Oluvil Harbor exists by utilizing remote sensing and geographic information system (GIS) techniques. Shorelines for each year were delineated using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager images. The Normalized Difference Water Index (NDWI) was applied as a spectral value index approach to differentiate land masses from water bodies. Subsequently, the Digital Shoreline Analysis System (DSAS) tool was used to assess shoreline changes, including Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR). The results reveal that the Oluvil coast has undergone both accretion and erosion over the years, primarily due to harbor construction. The highest SCE values were calculated within the Oluvil harbor region, reaching 523.8 m. The highest NSM ranges were recorded as −317.1 to −81.3 m in the Oluvil area and 156.3–317.5 m in the harbor and its closest point in the southern direction. The maximum rate of EPR was observed to range from 3 m/year to 10.7 m/year towards the south of the harbor, and from −10.7 m/year to −3.0 m/year towards the north of the harbor. The results of the LRR analysis revealed that the rates of erosion anomaly range from −3 m/year to −10 m/year towards the north of the harbor, while the beach advances at a rate of 3 m/year to 14.3 m/year towards the south of the harbor. The study area has undergone erosion of 40 ha and accretion of 84.44 ha. These findings can serve as valuable input data for sustainable coastal zone management along the Oluvil coast in Sri Lanka, safeguarding the coastal habitats by mitigating further anthropogenic vulnerabilities.

Urban green infrastructures (UGI) have been suggested as a natural solution to tackle the problem of human thermal comfort as well as to reduce energy consumption in buildings under the pressures of rapid urbanization and global warming. However, the acceptance of UGI to mitigate the urban heat effect is not yet universal. The development of such an infrastructure is also not consistent across the regions, emphasizing the different objective parameters and methodologies. A systematic review has been conducted to analyze the published research work on UGI, targeting thermal comfort, in the past decade to identify the trends of UGI development around the world. The result shows that most of the studied locations were situated around the Mediterranean Sea region in a temperate climate, and most of the studied cities are within countries with a high gross domestic product, large urban area and urban population, primary energy consumption, and high greenhouse gas and carbon dioxide emissions. Extensive green roofs are the most popular type of UGI and mostly use Sedum plants. In the published studies, experimental setups are the most common methods by which to collect data. EnergyPlus is the most popular software used to conduct energy analysis for buildings, whereas ENVI-met is more commonly used for microclimate analysis. These results indicated that the direction of UGI studies is driven by climate characteristics and the socioeconomic factors of geographical location, which favor low construction cost and maintenance needs, with a minimal irrigation requirement for small-scale UGI projects. Understanding the trend of UGI approaches for thermal comfort allows researchers to standardize practices that help the decision-making process for future researchers while recognizing the limitations and potential of current UGI practices. It is recommended that future studies should include arid and equatorial climate regions, with more focus on large-scale projects including high-rise building environments to comprehensively evaluate the effectiveness of UGIs.

Maintaining and rehabilitating pavement in a timely manner is essential for preserving or improving its condition, with roughness being a critical factor. Accurate prediction of road roughness is a vital component of sustainable transportation because it helps transportation planners to develop cost-effective and sustainable pavement maintenance and rehabilitation strategies. Traditional statistical methods can be less effective for this purpose due to their inherent assumptions, rendering them inaccurate. Therefore, this study employed explainable and supervised machine learning algorithms to predict the International Roughness Index (IRI) of asphalt concrete pavement in Sri Lankan arterial roads from 2013 to 2018. Two predictor variables, pavement age and cumulative traffic volume, were used in this study. Five machine learning models, namely Random Forest (RF), Decision Tree (DT), XGBoost (XGB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were utilized and compared with the statistical model. The study findings revealed that the machine learning algorithms’ predictions were superior to those of the regression model, with a coefficient of determination (R2) of more than 0.75, except for SVM. Moreover, RF provided the best prediction among the five machine learning algorithms due to its extrapolation and global optimization capabilities. Further, SHapley Additive exPlanations (SHAP) analysis showed that both explanatory variables had positive impacts on IRI progression, with pavement age having the most significant effect. Providing accurate explanations for the decision-making processes in black box models using SHAP analysis increases the trust of road users and domain experts in the predictions generated by machine learning models. Furthermore, this study demonstrates that the use of explainable AI-based methods was more effective than traditional regression analysis in IRI prediction. Overall, using this approach, road authorities can plan for timely maintenance to avoid costly and extensive rehabilitation. Therefore, sustainable transportation can be promoted by extending pavement life and reducing frequent reconstruction.

Climate change is one of the biggest challenges of our times, even before the onset of the Coronavirus (COVID-19) pandemic. One of the main contributors to climate change is greenhouse gas (GHG) emissions, which are mostly caused by human activities such as the burning of fossil fuels. As the lockdown due to the pandemic has minimised human activity in major cities, GHG emissions have been reduced. This, in turn, is expected to lead to a reduction in the urban heat island (UHI) effect in the cities. The aim of this paper is to understand the relationship between human activity and the UHI intensity and to provide recommendations towards developing a sustainable approach to minimise the UHI effect and improve urban resilience. In this study, historical records of the monthly mean of daily maximum surface air temperatures collected from official weather stations in Melbourne, New York City, Tokyo, Dublin, and Oslo were used to estimate the UHI intensity in these cities. The results showed that factors such as global climate and geographic features could dominate the overall temperature. However, a direct relationship between COVID-19 lockdown timelines and the UHI intensity was observed, which suggests that a reduction in human activity can diminish the UHI intensity. As lockdowns due to COVID-19 are only temporary events, this study also provides recommendations to urban planners towards long-term measures to mitigate the UHI effect, which can be implemented when human activity returns to normal.

Natural flood management strategies are increasingly recognized as sustainable alternatives to conventional engineered flood control measures. Among these, leaky dams, also known as woody debris dams or log dams, have emerged as effective nature-based solutions for mitigating flood risks while preserving essential ecosystem services. This review traces the historical evolution of leaky dams from ancient water management practices to contemporary applications, highlighting their development and adaptation over time. It presents a comparative examination of leaky dams and conventional flood control structures, outlining their respective strengths and limitations across ecological, hydrological, and economic dimensions. The review also introduces a conceptual classification of leaky dams into naturally occurring, engineered, hybrid, and movable systems, showing how each form aligns with varying catchment characteristics and management objectives. By synthesizing foundational knowledge and strategic insights, this paper establishes a theoretical and contextual framework for understanding leaky dams as distinct yet complementary tools in integrated flood management, laying the groundwork for further technical evaluations. The findings offer valuable insights for end users by highlighting the potential of leaky dams as integral components of sustainable flood management systems, elucidating their roles in mitigating flood risks, enhancing water retention, and supporting ecosystem resilience.