http://repository.ou.ac.lk/handle/123456789/4271 2026-10-09T16:15:24Z 2026-10-09T16:15:24Z Divyanjalee, K.M.T.S. Weerabangsa, M.Z. Iresh, A.D.S. Athapattu, B.C.L. http://repository.ou.ac.lk/handle/123456789/4329 2026-09-29T09:42:38Z 2026-01-01T00:00:00Z

A Review of Dam Break Risk Assessment and Hydraulic Modelling for Climate-Resilient Reservoir Management: A Review with Application to Basnagoda Reservoir, Sri Lanka Divyanjalee, K.M.T.S.; Weerabangsa, M.Z.; Iresh, A.D.S.; Athapattu, B.C.L. Dam failures can cause severe downstream flooding, infrastructure damage, environmental impacts, and loss of life. Climate related changes in extreme rainfall further emphasize the need for robust dam break risk assessment and flood hazard modelling. This review examines dam failure mechanisms, hydraulic modelling approaches, extreme rainfall assessment, and risk-assessment methods relevant to the Basnagoda Reservoir in the Aththanagalu Oya Basin, Sri Lanka. Published studies were reviewed and grouped according to failure mechanisms, hydrological and hydraulic modelling, uncertainty assessment, and Sri Lankan dam-safety applications. The reviewed literature identifies overtopping, piping and internal erosion, structural instability, and seismic or other natural hazards as important failure mechanisms. Hydrological assessment, including probable maximum precipitation (PMP) and probable maximum flood (PMF), is relevant to defining extreme inflow conditions, while HEC-RAS 1D and 2D models are widely applied to simulate breach flood propagation and estimate inundation depth, velocity, and extent. Case specific dam break modelling and inundation information for Basnagoda Reservoir were not identified in the reviewed literature. Therefore, the reviewed evidence provides a methodological basis for a Basnagoda focused assessment that integrates extreme rainfall analysis, breach scenarios, hydraulic modelling, GIS based hazard assessment, and uncertainty analysis. Moreover, Geographic Information System (GIS)- based Land Use and Land Cover (LULC) analysis is included to assess environmentally sensitive areas and vulnerable infrastructure that may be subject to inundation. The present paper investigates a significant aspect related to dam overtopping features, which are the result of overflowing associated with excessive inflow over the normal capacity in any reservoir system in order to develop an appropriate identification procedure for assessing potential risks linked with various extreme flood scenarios and proposes a comprehensive methodology for integrating hydrological and hydraulic analyses into reservoir risk assessment While numerically simulating results is outside the capabilities of this preliminary investigation data, the suggested framework lays out a systematic evaluation for dam safety under projected in extreme weather conditions. this review is expected to contribute to improved reservoir design, emergency preparedness, dam breach, and sustainable water resource management in Sri Lanka.

2026-01-01T00:00:00Z Lakmali, Dinusha Himanujahn, Sivaperumaan Athapattu, Bandunee http://repository.ou.ac.lk/handle/123456789/4328 2026-09-29T09:40:36Z 2026-01-01T00:00:00Z

Machine Learning Framework for Urban Domestic Water Demand Forecasting: A Case Study of the Aththidiya South GN Division Lakmali, Dinusha; Himanujahn, Sivaperumaan; Athapattu, Bandunee This study examines domestic water demand forecasting in the Aththidiya South Grama Niladhari Division, Colombo, Sri Lanka, where urbanization, climate variability, and ageing infrastructure are straining water supply systems. Accurate demand prediction is critical for effective planning and resource management. Traditional methods such as linear regression and ARIMA are limited by assumptions of linearity and stationarity, restricting their ability to capture the complex, dynamic behaviour of urban water consumption. To address this, the study develops a two-stage forecasting framework combining machine learning with classical time-series modelling. First, a Random Forest regression model identifies and ranks key predictors of domestic water demand using socio-economic, climatic, and infrastructure-related variables. Second, a Long Short-Term Memory (LSTM) neural network forecasts monthly water demand, capturing temporal dependencies and non-linear consumption patterns. ARIMA(1,1,1) and SARIMA(1,1,1)(1,1,1,7) models were developed for benchmarking. Data were sourced from the National Water Supply and Drainage Board, the Department of Meteorology, and a household survey conducted in the study area. Results show monthly household water consumption ranging from 3–37 m3, averaging 11.3 m3. Household size was the most influential predictor (52% relative importance), followed by rainfall (25%) and temperature (22%). The LSTM model outperformed classical benchmarks, achieving an RMSE of 0.78 m3 and MAPE of 21.32% on the test dataset. As a case study, the framework offers promising practical value for water resource planning in the study area, though the limited sample and single-division scope mean that broader generalization will require validation across additional GN divisions and larger datasets.

2026-01-01T00:00:00Z Arsath, Abdul Rasak Mohamed Athapattu, Bandunee Iresh, A. D. S. http://repository.ou.ac.lk/handle/123456789/4327 2026-09-29T09:39:07Z 2026-01-01T00:00:00Z

ESTIMATING FLOW INEFFICIENCIES IN THE KADDUMURIVU IRRIGATION SYSTEM THROUGH INTEGRATED HYDRODYNAMIC MODELING AND REAL-TIME ON-SITE MEASUREMENTS Arsath, Abdul Rasak Mohamed; Athapattu, Bandunee; Iresh, A. D. S. Irrigation water scarcity in Sri Lanka is intensified by conveyance losses in unlined tank-cascade canals. This study quantifies flow inefficiencies in a 650 m reach of the Kaddumurivu Irrigation System (KIS) by integrating field measurements with a one-dimensional steady-flow HEC-RAS model. Eight canal stations were surveyed using a Total Station, while discharge and water-surface elevations were measured under steady operating conditions using a current meter and staff gauges. The model was calibrated by adjusting Manning's roughness coefficient until simulated water-surface elevations agreed with observations within ±0.05 m and was subsequently validated using an independent flow dataset. The measured discharge decreased from 1.0052 m3/s upstream to 0.7681 m3/s downstream, giving a volumetric loss of 0.2371 m3/s equivalent to 23.6% of inflow, and a conveyance efficiency of 76.4%. Energy- based analysis gave a total head loss of 0.30 m, of which 0.12 m was attributed to friction and 0.18 m (60%) to non- friction inefficiency. The calibrated Manning's n values were 0.039 for the upper unlined reach, 0.012 for the concrete-lined reach, and 0.020 for the lower unlined reach. Spatial analysis identified the lower unlined reach and short structural-transition segments as critical loss locations. The results suggest that non-friction mechanisms, potentially including seepage, are significant contributors to the observed inefficiency, supporting targeted lining, maintenance, structural inspection, and continued monitoring rather than uniform rehabilitation of the entire canal.

2026-01-01T00:00:00Z Akmel, M.A.M. Athapattu, B.C.L. http://repository.ou.ac.lk/handle/123456789/4326 2026-09-29T09:37:14Z 2026-01-01T00:00:00Z

Atmospheric Low-Level Disturbances, Rainfall, and Downstream Flood Risk in the Gal Oya River Basin, Sri Lanka: A Review Akmel, M.A.M.; Athapattu, B.C.L. The Gal Oya River basin is an important agricultural and water-resource system in eastern Sri Lanka, but its downstream low-lying areas remain exposed to rainfall-triggered flooding. Flood generation in the basin is controlled by seasonal rainfall as well as atmospheric low-level disturbances, including low- pressure systems, monsoon lows and troughs, easterly waves, convergence zones, and organized convective systems. These processes can modify moisture convergence and convection and thereby influence rainfall intensity, duration, and spatial distribution. This review synthesizes literature on tropical atmospheric processes, Sri Lankan rainfall systems, Gal Oya basin hydrology and morphometry, rainfall– runoff transformation, flood inundation, exposure, vulnerability, and reservoir operation. A structured narrative review identified three principal evidence gaps: limited event-based attribution of Gal Oya extreme rainfall to atmospheric disturbances; inadequate integration of atmospheric indicators with rainfall–runoff and inundation modelling; and limited basin-specific analysis of downstream hazard, exposure and vulnerability. The review also shows that satellite precipitation products should not be used as an unvalidated substitute for gauge observations during extreme events because upper-tail rainfall estimates can contain substantial bias [40]. A review-based analytical framework is therefore proposed in which disturbance identification, rainfall attribution, hydrological response, inundation modelling, and risk assessment are linked. The framework is intended for future empirical testing and is not presented as an already validated predictive model.

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