Browsing by Author "Gunawardhana, HGLN"

Now showing 1 - 4 of 4

item: Conference-Abstract
Assessing future low-flow variations in a dry zone river basin under changing climate conditions
(Department of Civil Engineering, University of Moratuwa, 2024) Tharuka, WMS; Gunawardhana, HGLN; Pasindu, HR; Damruwan, H; Weerasinghe, P; Fernando, L; Rajapakse, C
Climate change significantly alters the low-flow regimes of river basins worldwide and presents significant challenges to water-scarce regions, especially in dry regions. This current study investigates the impact of climate change on projected low-flow variations in the Maduru Oya River Basin in Sri Lanka, focusing on the reach to the Padiyathalawa stream-gauge station. The study utilizes a lumped hydrological modeling framework, which used the HEC-HMS rainfall-runoff model to simulate streamflow behavior considering anticipated climate scenarios. Projections for future precipitation were obtained from the CNRM-CM6-1 Global Climate Model (GCM), which is part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), and subsequently downscaled through the Long Ashton Research Station Weather Generator (LARS-WG) according to two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5-8.5. The precipitation data, downscaled to the local scale, were integrated into the HEC-HMS model to forecast future river discharge and investigate possible changes in low-flow characteristics. The 7Q10 low-flow index, which is defined as the minimum average flow in a continuous seven-day period with a recurrence interval of ten years was used for estimating and comparing low-flow characteristics. The model parameters were calibrated and validated using historical data from 1997 to 2019. Three objective functions namely: Nash-Sutcliffe Efficiency (NSE), Mean Relative Absolute Error (MRAE), and Percent Error in Peak Flow (PEPF) were used for optimizing model parameters. Future precipitation was projected for short-term (2021-2040), medium-term (2041-2060), and long-term (2061-2080) durations. The projected precipitation data was subsequently input into the developed HEC-HMS model to obtain future streamflow projections for the specified periods. The results of the climate change scenario analysis showed that precipitation may vary due to climate change within the range of -16 % to -5 % for the 2021-2040 period, - 4 % to 1 % for the 2041-2060 period, and 1 % to 21 % for the 2061-2080 period. The results indicated a likely increase in low-flow values across both SSP scenarios. The flow-duration analysis showed that the Q90 flow, representing the flow level that exceeds 90% of the time, is expected to increase, reflecting an upward change in streamflow for low-flow conditions. These findings are important for water resource managers working in the area to plan for and adapt to the impacts of altered low-flow regimes that can impact water supply, agriculture, and overall ecosystem health. Further studies should consider incorporating the use of hydrological models coupled with diverse climate scenarios to better capture the uncertainties related to climate predictions and land-use changes. These would provide a better understanding of the impacts of climate change on river basin hydrology in dry regions like the Maduru Oya River Basin.
item: Conference-Abstract
Evaluating the impact of drought spatial distribution on river flow dynamics using remote sensing data
(2024) Madhumal, PVRP; Gunawardhana, HGLN; Pasindu, HR; Damruwan, H; Weerasinghe, P; Fernando, L; Rajapakse, C
Drought is a complex and challenging weather-related disaster with significant economic, social, and environmental impacts. Traditional drought monitoring, which primarily relies on ground observations, often falls short due to limited spatial coverage and data scarcity. Most existing drought indices focus on a single variable, which may not adequately capture the full scope of drought conditions. To address this, integrating multiple parameters from remote sensing data presents a promising approach, providing spatially distributed and real-time information for a more accurate and comprehensive drought analysis. This study aims to utilize multiple remote sensing parameters to provide a comprehensive analysis of droughts in the Padiyathalawa catchment area, a dry zone river basin in Sri Lanka covering 171 km². Three satellite-derived indices, namely the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI), derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, and Standardized Precipitation (SP) from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), were integrated using Principal Component Analysis (PCA) to derive a Combined Drought Index (CDI). In this analysis, Principal Component (PC) one, capturing 58% of the total variance, was used to develop the CDI. Validation showed a strong visual correlation between the normalized CDI and river flow over time. However, the Kolmogorov-Smirnov (KS) test revealed that the CDI needs improvement, as it does not fully capture streamflow dynamics during significant rainfall events. Despite this, the CDI effectively reflected seasonal variations, indicating dry conditions from June to August and wetter periods influenced by the northeast monsoon. Using the Hydrologic Engineering Center - Hydrologic Modeling System (HEC-HMS), the area was modelled as a semi-distributed system with five sub-catchments, based on the spatial variation of drought in developed 1 km resolution CDI maps. Model calibration was conducted for the period 1999-2005, followed by validation from 2005-2010, employing the Nash–Sutcliffe model efficiency coefficient (NSE) and mean relative absolute error (MRAE). The results indicated that the HEC-HMS model effectively simulated streamflow, with NSE values of 0.78 and 0.92, and MRAE values of 0.86 and 2.44. However, the model exhibited limitations in simulating low-flow conditions, failing to accurately represent discharges below 0.1 m³/sec. Further analysis of drought-prone areas identified by the CDI was performed using the HEC-HMS, incorporating hypothetical drought scenarios. The study found that river flow decreases as drought severity intensifies, with the impact lessening in sub-catchments farther from the catchment outlet. It highlights the potential of integrating remote sensing data, PCA, and hydrological modelling for effective drought assessment, benefiting farming communities and decision-makers in understanding drought severity on river flow and taking necessary action.
item: Conference-Abstract
Incorporating rainfall projections into hydrological modeling for enhanced design hydrograph estimation
(Department of Civil Engineering, University of Moratuwa, 2024) Heshani, PHTD; Gunawardhana, HGLN; Sirisena, J; Pasindu, HR; Damruwan, H; Weerasinghe, P; Fernando, L; Rajapakse, C
In the context of changing climate conditions, the design of hydrographs faces increasing uncertainties due to shifts in precipitation patterns, hydrological regimes, and a rise in extreme weather events. This study assesses potential uncertainties in design hydrographs linked to future climate change in the Kalu River Basin, Sri Lanka, focusing on the Ellagawa sub-basin. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) was selected based on a comprehensive literature review to account for anticipated changes in rainfall patterns and their impact on streamflow. Seven precipitation gauging stations (Alupolla, Balangoda, Galatura, Halwathura, Pussella, Ratnapura, and Wellandura) were chosen following World Meteorological Organization (WMO) guidelines, based on data availability and percentages of missing data. Streamflow data for Ellagawa and Ratnapura stations were obtained from the Sri Lankan Irrigation Department, with daily precipitation and streamflow data from 1980 to 2017 used for analysis. The model was calibrated and validated using data from four extreme events identified through frequency analysis, each associated with daily precipitation levels corresponding to a 100-year return period. Future changes in precipitation extremes were evaluated using outputs from three General Circulation Models (GCMs): CNRM-CM6-1, HadGEM3-GC31-LL, and MRI-ESM2-0, under two Shared Socioeconomic Pathways (SSP2 and SSP5) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), downscaled to local scale, focusing on the period from 2081-2100. The annual maximum daily precipitation for both observed and projected scenarios was analyzed using Generalized Extreme Value (GEV), Weibull, and Gamma distribution functions. The Nash-Sutcliffe efficiency (NSE) coefficients, ranging from 0.79 to 0.85 during calibration and validation, indicated a close match between simulated and observed river flows. Different GCMs and SSPs predicted varying changes in rainfall regimes and design hydrographs. Specifically, factors such as the frequency and intensity of extreme precipitation events, changes in the seasonal distribution of rainfall, and prolonged dry spells were identified as critical drivers affecting peak flow in the future. Compared to the baseline period (1980-2017), annual total rainfall is projected to increase by -8% to 40% under SSP2-4.5 and -10% to 36% under SSP5-8.5. The maximum daily precipitation is expected to rise from 79 mm to 139 mm under SSP2-4.5 and from 82 mm to 138 mm under SSP5-8.5. Consequently, the peak flow of the design hydrograph may increase by 3% to 106%. These findings underscore the importance of considering climate change uncertainties in hydrological and hydraulic design. By integrating future climate projections into design processes, engineers and policymakers can better adapt infrastructure and planning to evolve conditions, enhancing resilience and sustainability in water management systems.
item: Conference-Abstract
Use of streamflow and satellite remote sensing soil moisture data for jointly calibrating the tank model
(Department of Civil Engineering, University of Moratuwa, 2024) Pabasara, GK; Gunawardhana, HGLN; Pasindu, HR; Damruwan, H; Weerasinghe, P; Fernando, L; Rajapakse, C
Hydrological modelling in arid river basins is particularly complex due to the pronounced seasonal variability in water levels fluctuating between aridity and inundation. Solely relying on a single parameter, such as streamflow data, to calibrate hydrological models in these basins can be insufficient to capture intricate interdependencies of hydrological processes. This study aimed to optimize the lumped hydrological Tank Model to accurately simulate the complex hydrological behaviour of the Maduru Oya River Basin in Sri Lanka. Further, the research investigated the use of satellite (remote sensing)-derived soil moisture data in the hydrological modelling framework, highlighting the capability of advanced technologies to enhance the reliability of hydrological predictions. The study commenced with the collection and preprocessing of climatic data, followed by the imputation of missing values using the Closest Station Patching Technique. Root zone soil moisture data derived from the Soil Moisture Active Passive Level 4 (SMAP L4) product were acquired and pre-processed using the Cumulative Distribution Function (CDF) Matching method. The primary focus of the study was the optimization of the Tank Model through a sequential joint calibration technique with the Kling-Gupta Efficiency (KGE) chosen as the optimization criterion. This process involved optimizing the model using both single-variable calibration with streamflow data and multi-variable calibration with streamflow and soil moisture data. Multi-variable optimization was conducted using a weighted approach that assigned different contributions to soil moisture α and streamflow -α in determining model performance. This approach was implemented across 11 distinct calibration scenarios, with the parameter α varying systematically from 0 to 1 in increments of 0.1. The results demonstrated satisfactory streamflow simulation performance under single-variable optimization, with KGEQ values of 0.872 and 0.848 for calibration and validation, respectively. These findings underscored the Tank model's ability to accurately represent the hydrological processes within the Maduru Oya - Padiyathalawa sub-watershed. The inclusion of root zone soil moisture data (RSRZSM) significantly improved model performance, as evidenced by KGEQ values exceeding 0.850 for all calibration scenarios except α = 1. Multi-variable optimization techniques further reinforced the potential for enhanced overall model performance. The most accurate and reliable streamflow simulations (KGEQ = 0.890) were achieved with a minimal 10% and 90% contributions from soil moisture and streamflow respectively (α = 0.1 calibration scenario). Furthermore, the study emphasized the critical role of remote sensing data, specifically SMAP L4 retrievals, in characterizing the soil moisture intricacies of the study area, particularly in regions with limited in-situ measurements. The study further recommends continued validation to ensure robust model predictions. Due to the short calibration and validation periods used to minimize climatic data discrepancies, long-term validation was deemed essential for assessing model performance. In addition, the study recommended investigating alternative multi-objective optimization approaches, such as Genetic Algorithms, and incorporating more satellite data for other hydrological processes.