Evaluation of Future Streamflow in the Upper Part of the Nilwala River Basin (Sri Lanka) under Climate Change

Climate change is a serious and complex crisis that impacts humankind in different ways. It affects the availability of water resources, especially in the tropical regions of South Asia to a greater extent. However, the impact of climate change on water resources in Sri Lanka has been the least explored. Noteworthy, this is the first study in Sri Lanka that attempts to evaluate the impact of climate change in streamflow in a watershed located in the southern coastal belt of the island. The objective of this paper is to evaluate the climate change impact on streamflow of the Upper Nilwala River Basin (UNRB), Sri Lanka. In this study, the bias-corrected rainfall data from three Regional Climate Models (RCMs) under two Representative Concentration Pathways (RCPs): RCP4.5 and RCP8.5 were fed into the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model to obtain future streamflow. Bias correction of future rainfall data in the Nilwala River Basin (NRB) was conducted using the Linear Scaling Method (LSM). Future precipitation was projected under three timelines: 2020s (2021–2047), 2050s (2048–2073), and 2080s (2074–2099) and was compared against the baseline period from 1980 to 2020. The ensemble mean annual precipitation in the NRB is expected to rise by 3.63%, 16.49%, and 12.82% under the RCP 4.5 emission scenario during the 2020s, 2050s, and 2080s, and 4.26%, 8.94%, and 18.04% under RCP 8.5 emission scenario during 2020s, 2050s and 2080s, respectively. The future annual streamflow of the UNRB is projected to increase by 59.30% and 65.79% under the ensemble RCP4.5 and RCP8.5 climate scenarios, respectively, when compared to the baseline scenario. In addition, the seasonal flows are also expected to increase for both RCPs for all seasons with an exception during the southwest monsoon season in the 2015–2042 period under the RCP4.5 emission scenario. In general, the results of the present study demonstrate that climate and streamflow of the NRB are expected to experience changes when compared to current climatic conditions. The results of the present study will be of major importance for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset the negative impacts of future changes in climate.publishedVersio

Comparing Combined 1D/2D and 2D Hydraulic Simulations Using High-Resolution Topographic Data: Examples from Sri Lanka—Lower Kelani River Basin

The application of numerical models to understand the behavioural pattern of a flood is widely found in the literature. However, the selection of an appropriate hydraulic model is highly essential to conduct reliable predictions. Predicting flood discharges and inundation extents are the two most important outcomes of flood simulations to stakeholders. Precise topographical data and channel geometries along a suitable hydraulic model are required to accurately predict floods. One-dimensional (1D) hydraulic models are now replaced by two-dimensional (2D) or combined 1D/2D models for higher performances. The Hydraulic Engineering Centre’s River Analysis System (HEC-RAS) has been widely used in all three forms for predicting flood characteristics. However, comparison studies among the 1D, 2D to 1D/2D models are limited in the literature to identify the better/best approach. Therefore, this research was carried out to identify the better approach using an example case study of the Kelani River basin in Sri Lanka. Two flood events (in 2016 and 2018) were separately simulated and tested for their accuracy using observed inundations and satellite-based inundations. It was found that the combined 1D/2D HEC-RAS hydraulic model outperforms other models for the prediction of flows and inundation for both flood events. Therefore, the combined model can be concluded as the better hydraulic model to predict flood characteristics of the Kelani River basin in Sri Lanka. With more flood studies, the conclusions can be more generalized.Comparing Combined 1D/2D and 2D Hydraulic Simulations Using High-Resolution Topographic Data: Examples from Sri Lanka—Lower Kelani River BasinpublishedVersio

Improving Stormwater Infrastructure with Low-Carbon SuDS: A Comparison of Porous Asphalt versus Interlocking Permeable Pavements

Climate change resulting in frequent flooding events have caused catastrophic effects to Small Island Developing States (SIDS) in recent times. Rainfall events have become less predictable in recent years. This paper presented the findings of a project that evaluated permeable pavement systems (PPS) using low-carbon materials, recycled aggregates, and carbon-negative aggregates within its structure as a sustainable drainage system (SuDS). It replicates typical drainage systems, reducing the surface runoff volumetric rates and retaining stormwater pollutants from downstream runoff. Low carbon permeable pavements and Carbon Negative pavement systems are novel structural pavements implementing materials which can withstand the same axial loading as the conventional pavements and can enhance stormwater quality by water treatment through filtration and infiltration between sub-base layers. Carbon-negative aggregates utilise patented technology that converts secondary waste products into high-quality aggregates based on a process that absorbs CO2 into the pavement materials. This paper evaluates two pilot-scaled Low-Carbon-Porous Asphalt Pavement (LC-PAP) systems versus two carbon-negative interlocking concrete block permeable pavement systems (CN-ICB-PPS) on the overall environmental and structural performance. It was found that the pavement systems achieved similar permeability for stormwater remediation results using a combination of virgin aggregates, recycled aggregates, and carbon-negative aggregates for the CN-ICB-PPS and LC-PAP. The pavement systems utilising greater content of carbon-negative aggregates displayed a higher water infiltration rate when compared to the CN-ICB-PPS because of the sub-base design implemented. The LC-PAP systems could achieve the necessary strength at a lower cost, implementing low-carbon recycled materials and carbon-negative aggregates forming 70 % of sub-base layer of the pavements. For the LC-PAP system, ammonium, nitrates, colour, BOD and COD from the stormwater influent decreased significantly when compared to outflow water samples from the CN-ICB-PPS. Due to the variations in the top layers of the pavements with very small pore-spaces, this ensured a greater pollutant retention rate improving the overall stormwater quality being discharged from the pavement. The CN-ICB-PPS displaced a slight decrease in ammonium, nitrates, and colour over the period of study. Moreover, the LC-PAP contained higher content of low-carbon materials and recycled aggregates, placed above the saturation zone of the pavement, allowing some stormwater pollutanta to filtrate easily through the pavement structure

A comparison of artificial intelligence approaches in predicting discharge coefficient of streamlined weirs

In the present research, three different data-driven models (DDMs) are developed to predict the discharge coefficient of streamlined weirs (C-dstw). Some machine-learning methods (MLMs) and intelligent optimization models (IOMs) such as Random Forest (RF), Adaptive NeuroFuzzy Inference System (ANFIS), and gene expression program (GEP) methods are employed for the prediction of C-dstw. To identify input variables for the prediction of C-dstw by these DMMs, among potential parameters on C-dstw, the most effective ones including geometric features of streamlined weirs, relative eccentricity (lambda), downstream slope angle (beta), and water head over the crest of the weir (h(1)) are determined by applying Buckingham pi-theorem and cosine amplitude analyses. In this modeling, by changing architectures and fundamental parameters of the aforesaid approaches, many scenarios are defined to obtain ideal estimation results. According to statistical metrics and scatter plot, the GEP model is determined as a superior method to estimate C-dstw with high performance and accuracy. It yields an R-2 of 0.97, a Total Grade (TG) of 20, RMSE of 0.032, and MAE of 0.024. Besides, the generated mathematical equation for C-dstw in the best scenario by GEP is likened to the corresponding measured ones and the differences are within 0-10%

Geospatial assessment of a severe flood event in the Nilwala River basin, Sri Lanka

Lessons learned from previous flood disasters are significant in mitigating future flood damages. Therefore, this study aims at understanding the disastrous damage caused by the 2017 flood that occurred in southern Sri Lanka. Most of the recent studies related to natural disasters have incorporated geospatial analysis to produce more convincing maps. However, generated flood maps using geospatial analysis for major flood events are limited in Sri Lanka. In order to fill the research gap, this study explores flood-affected areas using geospatial data for a severe flood event that occurred in May 2017 in the Nilwala River Basin, southern Sri Lanka. This is the first-ever study of the river basin even if it is annually flooded causing significant damage. We utilized Sentinel-2 images to identify the land use and land cover (LULC) of the downstream area of the basin. The study focused on two divisional secretariat (DS) divisions, specifically Matara and Thihagoda, which experienced significant impacts. The satellite images for the pre-flood and flood-affected areas were identified and compared to showcase that 46 km2 of area out of 109 km2 tested were inundated. Results found from the research paved to present applicable disaster management practices to mitigate the damages from future floods to the basin. In addition, a predominant influence has also been noted in the chosen DS divisions. Therefore, it is crucial to concentrate more research in this area to reduce the severity of the damage

Analytical and artificial neural network models to estimate the discharge coefficient for ogee spillway

In this study, analytical and Artificial Neural Network (ANN) model were used for determine the discharge coefficient of Ogee Spillways. For this aim, discharge coefficients of 11 different heads were calculated by using a test flume having 7.5 cm width, 15 cm depth and 5 m length, in the laboratory. Discharge coefficients were also computed by the formula for the same heads measured in the laboratory to investigate the accuracy of experimental setup. An ANN model was set by using the experimental results in order to estimate the discharge coefficient. Then, the performance of the ANN model was investigated. As the result, the coefficient of determination between ANN model and experimental setup is found R2= 0.98. ANN model is show a good consistency with experimental results

Machine Learning Techniques to Predict the Air Quality Using Meteorological Data in Two Urban Areas in Sri Lanka

The effect of bad air quality on human health is a well-known risk. Annual health costs have significantly been increased in many countries due to adverse air quality. Therefore, forecasting air quality-measuring parameters in highly impacted areas is essential to enhance the quality of life. Though this forecasting is usual in many countries, Sri Lanka is far behind the state-of-the-art. The country has increasingly reported adverse air quality levels with ongoing industrialization in urban areas. Therefore, this research study, for the first time, mainly focuses on forecasting the PM10 values of the air quality for the two urbanized areas of Sri Lanka, Battaramulla (an urban area in Colombo), and Kandy. Twelve air quality parameters were used with five models, including extreme gradient boosting (XGBoost), CatBoost, light gradient-boosting machine (LightBGM), long short-term memory (LSTM), and gated recurrent unit (GRU) to forecast the PM10 levels. Several performance indices, including the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute relative error (MARE), and the Nash–Sutcliffe efficiency (NSE), were used to test the forecasting models. It was identified that the LightBGM algorithm performed better in forecasting PM10 in Kandy (R2 = 0.99, MSE = 0.02, MAE = 0.002, RMSE = 0.1225, MARE = 1.0, and NSE = 0.99) . In contrast, the LightBGM achieved a higher performance (R2 = 0.99, MSE = 0.002, MAE = 0.012, RMSE = 1.051, MARE = 0.00, and NSE = 0.99) for the forecasting PM10 for the Battaramulla region. As per the results, it can be concluded that there is a necessity to develop forecasting models for different land areas. Moreover, it was concluded that the PM10 in Kandy and Battaramulla increased slightly with existing seasonal changes

Data exploration on the factors associated with cost overrun on social housing projects in Trinidad and Tobago

This data article explores the factors that contribute to cost overrun on public sector projects within Trinidad and Tobago. The data was obtained through literature research, and structured questionnaires, designed using open-ended questions and the Likert scale. The responses were gathered from project actors and decision-makers within the public and private construction industry, mainly, project managers, contractors, engineers, architects, and consultants. The dataset was analysed using frequency, simple percentage, mean, risk impact, and fuzzy logic via the fuzzy synthetic evaluation method (FSE). The significance of the analysed data is to determine the critical root causes of cost overrun which affect public sector infrastructure development projects (PSIDPs), from being completed on time and within budget. The dataset is most useful to project and construction management professionals and academia, to provide additional insight into the understanding of the leading factors associated with cost overrun and the critical group in which they occur (political factors). Such understanding can encourage greater decisions under uncertainty and complexity, thus accounting for and reducing cost overrun on public sector projects

Modelling Capabilities of Two Physically Based Hydrologic Models for Streamflow Simulations

Abstract Hydrologic processes in a watershed are typically simulated through hydrologic models due to their availability in the public domain and improved computational capacities. However, choosing a suitable model among the many available for a region of interest is challenging. In our work, we compared streamflow generated by the Soil and Water Assessment Tool (SWAT) and the Hydrological Engineering Centre-Hydrologic Modeling System (HEC-HMS) in the Kalu River Basin (KRB), Sri Lanka, frequently impacted by floods. Meteorological data including rainfall and temperature from 1990 to 2000 were used to force the hydrologic models. In addition, we used soil, land use data and a digital elevation model (DEM) for model development. During the calibration phase (1993-1996) of the SWAT model we achieved a coefficient of determination (R²) of 0.93 and a Nash-Sutcliffe Efficiency (NSE) of 0.87. In the validation phase (1997–2000), these indices yielded values of 0.87 and 0.66, respectively. In the HEC-HMS model, during the calibration phase, R2 and NSE yielded values of 0.89 and 0.91 while in the validation phase, these indices yielded values of 0.77 and 0.56, respectively. The exceedance probabilities at 10%, 50%, and 90% derived from flow duration curves (FDCs) from HEC-HMS and SWAT models were 395, 159, 54.5 and 400.5, 148, 29.11 (all in m3/s), respectively. Similarly, for observed flow, these values were 344.40, 138.98, and 65.35 m3/s, respectively. Thus, the FDCs suggest that the HEC-HMS model captures low flows reasonably. Neither model accurately resembled high flows. During the first inter-monsoon season (March-April) the HEC-HMS and SWAT underpredicted 3%, and 4% while during the northeast monsoon season (December-February) the models underpredicted 9%, and 2%, respectively. Similarly, during the second inter-monsoon season (October-November) and the southwest monsoon season (May-September), HECHMS and SWAT models overestimated observed flow by 11%, 5%, and 8%, 17%, respectively. Both models performed reasonably well on a seasonal basis with slight over-predictions and under-predictions. Overall, it is clear that both models can generally capture the hydrology of the KRB.Abstract Hydrologic processes in a watershed are typically simulated through hydrologic models due to their availability in the public domain and improved computational capacities. However, choosing a suitable model among the many available for a region of interest is challenging. In our work, we compared streamflow generated by the Soil and Water Assessment Tool (SWAT) and the Hydrological Engineering Centre-Hydrologic Modeling System (HEC-HMS) in the Kalu River Basin (KRB), Sri Lanka, frequently impacted by floods. Meteorological data including rainfall and temperature from 1990 to 2000 were used to force the hydrologic models. In addition, we used soil, land use data and a digital elevation model (DEM) for model development. During the calibration phase (1993-1996) of the SWAT model we achieved a coefficient of determination (R²) of 0.93 and a Nash-Sutcliffe Efficiency (NSE) of 0.87. In the validation phase (1997–2000), these indices yielded values of 0.87 and 0.66, respectively. In the HEC-HMS model, during the calibration phase, R2 and NSE yielded values of 0.89 and 0.91 while in the validation phase, these indices yielded values of 0.77 and 0.56, respectively. The exceedance probabilities at 10%, 50%, and 90% derived from flow duration curves (FDCs) from HEC-HMS and SWAT models were 395, 159, 54.5 and 400.5, 148, 29.11 (all in m3/s), respectively. Similarly, for observed flow, these values were 344.40, 138.98, and 65.35 m3/s, respectively. Thus, the FDCs suggest that the HEC-HMS model captures low flows reasonably. Neither model accurately resembled high flows. During the first inter-monsoon season (March-April) the HEC-HMS and SWAT underpredicted 3%, and 4% while during the northeast monsoon season (December-February) the models underpredicted 9%, and 2%, respectively. Similarly, during the second inter-monsoon season (October-November) and the southwest monsoon season (May-September), HECHMS and SWAT models overestimated observed flow by 11%, 5%, and 8%, 17%, respectively. Both models performed reasonably well on a seasonal basis with slight over-predictions and under-predictions. Overall, it is clear that both models can generally capture the hydrology of the KRB