Master of Science in Water Resources Engineering & Management

Permanent URI for this collectionhttp://192.248.9.226/handle/123/40

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item: Thesis-Abstract
Analysis of the effect of climate change impacts on floods in Kelani river basin, Sri Lanka
(2023) Kulathunga, SPSP; Rajapakse, RLHL
Sri Lanka is highly vulnerable to climate change impacts, including rising land and sea temperatures, changing precipitation patterns, more extreme weather events, and sea-level rise. Notably, climate change has been observed to increase flood frequency, expand flood areas, and intensify flood damages. Previous research in Sri Lanka has mainly focused on rainfall estimation using weather models and examining climate change scenarios. This study aims to improve flood forecasting by analyzing climate change-induced changes in rainfall depths from Intensity-Duration-Frequency (IDF) curves and considering design sea levels. The objective is to gain insights into future flood characteristics, specifically the projected increases in discharges and water levels. The HEC-HMS Hydrological modelling tool was selected for the hydrological modelling of the entire Kelani Basin, while the HEC-RAS model was used for flood modelling in the Lower Kelani Basin which is downstream from Glencourse. HEC-HMS simulating discharges from rainfall inputs that served as boundary conditions for the HEC-RAS model. The verified models are utilized to simulate the 50-year design rainfall dataset lasting 3 days, incorporating published IDF equations from selected rain gauge locations along with the calibrated models. Rainfall depth multipliers of 1.100, 1.122, and 1.140 were applied to the design rainfall dataset for the RCP4.5, RCP6.0, and RCP8.5 projections, respectively. Simulations also considered sea-level rise values of 0.47 m, 0.48 m, and 0.63 m corresponding to the respective climate change projection scenarios. Calibration and validation of the three HEC-HMS models (Kelani Upper, Kelani Middle, and Kelani Lower) and the HEC-RAS Flood model for Lower Kelani (downstream to Glencourse) Basin were successfully calibrated using 2016 May and validated using 2017 May flood event data. The Nash Efficiency values during calibration were 0.79, 0.95, and 0.85 for the Kelani Upper, Kelani Middle, and Kelani Lower models, respectively. During validation, the Nash Efficiency values were 0.87, 0.85, and 0.25, respectively. The calibration Nash Efficiency values for the HEC-RAS model were 0.57, 0.56, and 0.52, and the validation Nash Efficiency values were 0.80, 0.57, and 0.53 for the respective models considering Hanwella Discharges, Hanwella Water Levels and Nagalagama Street Water levels, respectively. The research concluded that, under climate change projections, the Glencourse Peak Discharge is projected to increase by approximately 13.3% to 16.2%. Similarly, at Hanwella, the peak discharge is expected to increase by approximately 6.4% to 8.8%, while the maximum water level is anticipated to rise by approximately 3.1% to 4.2%. Moreover, the maximum water level at Nagalagama Street is likely to experience an increase of around 16.2% to 21.7% under climate change projections.
item: Thesis-Abstract
Potential of incorporating satellite soil moisture observations in flood modeling in Kelani river basin, Sri Lanka
(2023) Rajapaksha, RMKC; Rajapakse, RLHL
Floods are frequent and devastating natural disasters, causing significant social and economic losses worldwide. Flood modelling plays a crucial role in predicting and mitigating the impacts of floods. In recent years, satellite remote sensing technology has emerged as a valuable tool for flood modelling, especially in estimating soil moisture, a critical parameter for flood forecasting. The potential of incorporating satellite soil moisture observations in flood modelling within the highly flood-prone Kelani River Basin up to Glencourse in Sri Lanka was investigated in this study. The study utilizes ESA CCI Soil moisture data and employs the Pearson correlation coefficient to evaluate the linear correlation between the modified Antecedent Precipitation Index (API) and the European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM). The Hydrologic Engineering Center's Hydrologic Modelling System (HEC-HMS) is taken as the preferred hydrological model, with the Green-Ampt loss method, Clark's unit hydrograph method, and recession constant baseflow method chosen for the lumped daily resolution event-based model. Twelve flood events are carefully selected for model calibration and validation. Events EI to E6 are used for calibration, while events E7 to E12 are utilized for model validation. Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and the ratio of root mean square error to the standard deviation of measured data (RSR) is employed to assess the predictive accuracy of the model in comparison to observed streamflow. The findings indicate a robust association between the modified API and the soil moisture data from ESA CCI SM. The calibrated HEC-HMS model exhibits an NSE of 0.76, an RSR value of 0.45, and a PBIAS of 5% during calibration, while validation yields an average NSE of 0.726, RSR of 0.51, and a PBIAS of 6%. The incorporation of ESA CCI SM data leads to marginal improvements in model performance for some events while showing negative impacts for others. The study reveals that incorporating ESA CCI SM observations results in minor enhancements in the model's predictive accuracy. Furthermore, the integration of ESA CCI SM data has the potential to improve the accuracy of total stream flow predictions and peak flow predictions. These findings contribute to the advancement of flood modelling techniques, providing valuable insights for flood mitigation efforts in the Kelani River Basin and similar flood-prone regions globally.
item: Thesis-Abstract
Drought assessment of Kirindi oya and Kelani river basins in Sri Lanka under climate change impacts
(2022) Azmi F; Bamunawala RMJ; Wijayaratna TMN
Drought is a natural phenomenon that occurs because of climate change. Droughts are localized events influenced by climatic variables such as precipitation, evapotranspiration, and temperature. As a result, the characteristics and implications of drought differ depending on the climatic administrations in various regions around the world. Drought is one of the maximum significant intervals in Sri Lanka. Sri Lanka is very sensitive to the effects of climate change. Drought is an extremely considerable interval in Sri Lanka in terms of people concerned and helps provided, and the country also serves as a recent example for drought interval and risk assessment in tropical regions. This research investigates the probable use of drought indices at Kirindi Oya and Kelani River basins and provides drought assessment for future climatic scenarios. This research was directed to perceive the changes in drought, their consistencies according to seasonal analysis in the Kirindi Oya and Kelani River basin in Sri Lanka using normalized difference vegetation index (NDVI), standardized precipitation index (SPI), and streamflow drought index (SDI) for future climate change RCP 8.5 which is one of the worst scenarios according to 5 th assessment report of the intergovernmental panel on climate change (IPCC). The drought assessment has been divided into three-time intervals such as observed period (1985-2015), mid-century (2040-2059), and end-century (2080-2099). Further, future climate rainfall data has been forecasted by bias correction monthly factor of historical climate rainfall and observed rainfall data using linear scaling. The NDVI has been calculated by using Landsat images near-infrared (NIR) and RED bands in GIS 10.3. Initially, SPI and SDI have been calculated for observed rainfall and streamflow data respectively. Hydrological model HEC-HMS was set up and calibrated (2002-2006) with a root mean square error standard deviation ratio (RMSE std dev) value of 0.6, nash sutcliffe (NSE) value of 0.59, and percent bias (PBIAS) of 7.63%. The model was validated from 2010 to 2014 with an RMSE std dev value of 0.7, NSE value of 0.51, and PBIAS of 3.22% for Kirindi Oya basin. Further, for the Kelani basin. the HEC-HMS was set up and calibrated (1990-1995) with an RMSE std dev value of 0.6, NSE value of 0.64, and PBIAS of 0.64% and validated (2007-2011) with RMSE std dev value of 0.7, NSE value of 0.56 and Percent Bias of -3.27% for Kelani basin. Thereafter, mid and end-century SPI and SDI have been calculated for future bias-corrected rainfall data and future simulated streamflow, respectively. To achieve the objectives of this research work, The rate of recurrence of drought occurrences was determined using a combined SPI and SDI evaluation which identified 1989, 1990, 1992, 2001, and 2004 as a severe drought-affected year in the Kirindi Oya river basin in this observed interval. For the Kelani River basin, severe drought has been identified during 1990, 2001, 2012, 2013, and 2014 in the observed interval. According to seasonal analysis, the probability of occurrence of extreme drought according to SPI values in Kirindi Oya basin is decreasing 25% for mid and 50% end-century, in the Kelani basin 93.75% for mid and 68.75% in end-century. According to SDI values in the Kirindi Oya basin is decreasing 25% for mid and 25% end-century, in the Kelani basin 93.75% for mid and 50% in end-century. First inter monsoon has been found more severe to drought for both SPI and SDI combination in Kirindi Oya river basin, the northeast monsoon period is the driest season for the Kelani River basin which is situated in wet zone in Sri Lanka.
item: Thesis-Abstract
Flood risk assessment in Kalu river basin Sri Lanka using geospatial techniques and hydrological modelling
(2022) Gurung R; Wijayaratna TMN
Kalu Ganga basin is one of the main waterway basins in Sri Lanka which gets exceptionally high rainfalls with higher discharges. This report is about how the different zones, having a higher likelihood of a flood in the Kalu Ganga basin can be identified through remote sensing and geospatial approach. RRI model was additionally used to get the flood extents to verify the flood susceptibility map. Six flood influencing parameters (elevation, slope, land use, flow accumulation, soil, and rainfall) were taken for obtaining a flood susceptibility map with the AHP method in ArcMap. In the RRI model, the built-in data (Flow Direction, Flow Accumulation, DEM, Soil, and Land Cover) with observed rainfall data was used for generating a flood inundation map. The flood susceptibility map has a flood susceptibility area under the Very High category of 267 km² (8.4%) in the Kalu Ganga basin which is the most probable flood occurrence area. The flood inundation maps of May 2003, April 2008, and May 2008 have an area of 217.9 km² (6.3%), 193.6 km² (5.6%), and 108.5 km² (3.1%), respectively with flood depths greater than or equivalent to 1 meter in Kalu Ganga basin where the flood depths greater than or equivalent to 1 meter cover all the flood depths. In the AHP method, the rainfall parameter greatly influenced the flood susceptibility map. The built-in data with nr river value and ns slope values greatly influence the flood inundation map in the RRI model. The flood susceptibility map from the AHP method in ArcMap has a higher flood potential area than the flood inundation map obtained from the RRI model, and this is because the simulated or observed flood maps are always a subset of the flood susceptibility map. The flood susceptibility map obtained from the AHP method in ArcMap is a flood probability map and the flood inundation map from the RRI model is a simulated flood map. Therefore, they are different. However, the flood inundation area occurs within the flood susceptibility map. The Analytical Hierarchy Process (AHP) method is an alternative quick method of identifying flood potential areas and it can be applied to any other place around the world for the prevention and management of flood hazards.
item: Thesis-Abstract
Impact of climate change and socio - economic development on water allocation for ecosystem - water -energy -food services :a case study on Mahaweli river basin, Sri Lanka
(2022) Fernando WBDT; Rajapakse L
This study was carried out to assess the impacts of climate change on the dynamics of surface water availability for different present and future users in the Mahaweli river basin. A multi-tier modeling method was applied in the analysis by combining the Soil and Water Assessment Tool (SWAT) and Water Evaluation and Planning Models (WEAP) to mimic stream flow under climate change and evaluate situations of future water accessibility for diverse socio-economic activities by the year 2050. Three standard global circulation models (GCMs), CSIRO Mk3.6, Had, CM2-ES, and MIROCS, were downscaled, rectifying bias using CMHyd. The SWAT model was successfully calibrated with R 2 equals 0.65 for calibration period and for validation the R 2 equals 0.57. The calibrated model shows a Nash- Sutcliff efficiency (NSE) values of 0.68 during the calibration period and 0.73 in validation period. The SWAT model was initially calibrated using available data to forecast future stream flows. Then those stream flows were used as inputs for the WEAP model to assess water availability for various socio-economic activities. Results from GCMs indicate that an increase in annual mean rainfall within a range of 16-18% can be expected by the 2050s, compared to the rainfall during the period between 2006 to 2009. The average temperature is forecasted to increase by about 2°C compared to the temperature baseline period. Further, there will be an increase of about 10% in long-term average stream flow. However, the model predicted a decrease in peak flows in the 2050s compared to the current average flows. The model forecasted that the overall total water demand in the Mahaweli basin will increase to 3,249.69 Mm 3 in the year 2050, compared to the current demand of 1,879.73 Mm 3 . This will create a situation where 51.5% of the total demand amounting to about 1,673.80 Mm 3 will not be met in the 2050s. A severe water shortage is predicted that about 71.12% of future irrigation demand will not be fulfilled in the 2050s. Water for hydropower generation will also be significantly affected as its unmet demand will be around 27.47%. However, the water demand for livestock will be marginally affected by about 1.41% of unmet demand as per the model's forecasts. The modeling results raise the need for paying attention to future water shortages for various socioeconomic activities, which can be caused by climate change, and the need for taking necessary steps to address this situation effectively.
item: Thesis-Abstract
Development of a rainfall-runoff-inundation model and flood monitoring system based on satellite imagery for Kalu ganga basin, Sri Lanka
(2022) Sultana T; Rajapakse RLHL
Floods are getting severe due to climate change and anthropogenic activities which neeimmediate response to lower the risk and decrease the human and financial losses. Floodinundation mapping for flood risk preparedness using satellite data has been widely used imany recent studies. However, satellite imageries may contain some uncertainties. Thereforflood inundation maps from satellite data need to be verified with flood inundation mapgenerated by hydrological models from observed data for accurate estimation of flood risk.Although satellite-generated flood maps are widely used to determine the inundation extentthere are certain challenges to their use such as inaccessibility of imagery due to satellite orbior cloud cover, which hampers accurate measurement of inundation risk. In this study, the rainfall-runoff inundation (RRI) model for the Kalu Ganga basin wasdeveloped, and its applicability to evaluate the discharge and flood inundation areas wasdiscussed. The RRI model could estimate discharge, water levels, and inundation areasimultaneously based on two-dimensional diffusion wave equations. The results and statisticaanalysis indicate that the RRI model could efficiently estimate extreme flood events. Formodel calibration, the R 2 value ranges from 0.72-0.80 and for model validation, the R valueranges from 0.75-0.90, which shows good performance of the model. The simulated inundation extents were verified and compared with Sentinel 1A SA(Synthetic Aperture Radar) satellite imagery data for 2016 and 2017 flood events. Sentinel 1AGRD-IW (Ground Range Detected - Interferometric Wide swath) mode of VV co-polarization,with a spatial resolution of 20 m was acquired and pre-processed using the SentineApplication Platform (SNAP) software toolbox. The pre-processed images were correcteand maximum likelihood supervised classification was performed to produce the floodinundation maps of the study area. The actual flooded area from RRI is found to be 291.9km 2 and that from satellite image is found to be 201.7 km 2 for the 2016 flood event. For th2017 flood event, the actual flooded area from RRI is found to be 371.14 km and that frosatellite image is found to be 297.42 km 2 . Hence, the flooded area difference was found to be35.54 % for 2016 and 22.13 % for 2017 flood events from the total area selected from thmodel. Most of the floodplains from the RRI model and satellite images were along the mairiver in the basin, including the city of Ratnapura (upstream), the city of Kalutar(downstream), and the areas in between. These results with an accuracy level of ~25 % - 30 % are deemed to be within an acceptable range for emergency evacuation and rapid flood damageassessment purposes. Future studies should further investigate and validate the flooinundation mapping ability of Sentinel 1A SAR using ground-based reference flood maps orother satellite data. This study reveals that satellite imagery can be one of the most coseffective ways to capture the flood disaster footprints, identify flood-prone areas, anunderstand the flooding problem in a better way. This methodology can be effectively usefor disaster risk management, where the time factor is very critical. 2 2
item: Thesis-Abstract
Impact of climate change on droughts in Maduru Oya river basin in Sri Lanka over the 21st century
(2022) Kour G; Bamunawala RMJ; Wijayaratna TMN
Drought is an creeping hazard that is least understood and the most complex of all-natural hazards. The drought study requires large historical climatological and meteorological datasets and their complex inter-relationships. Its impacts are prominently observed on a local scale only when the severity becomes high, and the coherent onset and persistence of mild droughts may go unnoticed. The current study investigates the existing drought conditions and future drought risk in the Maduru Oya River Basin over the 21 st -century in terms of meteorological and hydrological drought indices (i.e., SPI, SPEI, RDI, EDI and SRI). The future hydrology over the basin is simulated for this research, using bias-corrected precipitation and potential evapotranspiration outputs under RCP 4.5 and 8.5 of the MPI-M-MPI-ESM-MR. The relevant drought-related indices were computed in monthly and seasonal timescales over the 19512099 period. The time series have been classified for drought characterization, including drought frequency, severity, trend, and probability computation. Further, to assess the impact of these droughts on the basin's response, a hydrological model (i.e., HEC-HMS) was developed to simulate the discharge at the Padiyathalawa outlet considering 2008-2012 as validation period. The results of the monthly timescale for SPI (approximately similar drought frequency and severity by RDI and EDI) depicted that the severe and extreme droughts (45) occurred in March (8), August (5), September (4), October (6) and November (9) in the historical period. Severe and extreme droughts (110 under RCP 4.5,104 under RCP 8.5) are projected in January (17), February (12), April (10), May (12) and December (13) under RCP 4.5 and January (12), February (10), April (13), June (13) and August (10) under RCP 8.5 over the 21 -century. The SPEI at monthly timescale identified highest number of severe and extreme drought (67) events in the historical period and projected highest severe and extreme drought (128 under RCP 4.5,122 under RCP 8.5) events over the 21 st -century in the study area. The hydrological drought index, SRI projected severe and extreme droughts under RCP 4.5 (65) and RCP 8.5 (62) over the 21 st -century that is about 50 % frequency of the meteorological drought indices. The Northeast Monsoon season had the least drought episodes (~20) in the historical period, and on a seasonal time scale, high drought frequency (~30 using meteorological drought indices and ~20 using SRI under RCP 4.5 and RCP 8.5) and severity(severe and extreme droughts) are projected in the Northeast Monsoon. It is also observed that there is a consistent mild drought throughout the mid (~70) and end (~65) century for a maximum duration compared to the historical (~50) period. The accuracy of results obtained from the continuous HEC-HMS model (NSE, RMSE Std. Dev, and R 2 st values of 0.59, 0.72, and 0.60 achieved in validation)highlights the efficient way to simulate a basin's hydrological parameters. The model can project the future variation of streamflow of the Maduru Oya River Basin under varied climatic conditions. The discharge is projected to have a decreasing trend (Sen’s slope=0.008) for future years, identified as droughts. It can be concluded that the impact of climate change on meteorological drought will affect the discharge of the basin. Moreover, due to time lag between meteorological and hydrological drought, about 50 % of meteorological droughts may lead to a severe and extreme hydrological drought in the Maduru Oya River Basin over the mid-century (14) under RCP 8.5 and end-century (13) under RCP 4.5 scenarios. This study will begin with quantitative investigations including streamflow variability and climatology over the basin incorporating the application of regional circulation models.
item: Thesis-Abstract
Evaluation of the effect of loss and transform methods on the performance of HEC-HMS model : a case study in Kelani river basin, Sri Lanka
(2022) Kothalawala CD; Rajapakse RLHL
Hydrological modelling plays a vital role in understanding the hydrological system of any watershed and providing reliable data to manage the water resources of the relevant watershed in a sustainable manner. Among the numerous types of hydrological models, HEC-HMS is very popular among hydrologic modellers due to its user-friendliness. Different methods are available in HEC-HMS to compute the hydrological process in a relevant watershed and the selection of appropriate methods among different available methods plays an important role in the performance of the model. The objective of this study is to select the most suitable loss and transform methods for Kelani river basin, comparing different combinations of selected loss and transform methods available for event-based simulations. Seethawaka subbasin was selected as the study area. From the possible methods embedded in the HEC-HMS, two loss methods (SCS Curve Number method, Initial Constant method) and three transform methods (SCS Unit Hydrograph method, Clark Unit Hydrograph method, Snyder Unit Hydrograph method) were selected for this study. Six (06) different combinations using those loss and transform methods were tried out to evaluate the performance of the model. Percent Error in Peak (PEP) and Percent Error in Volume (PEV) objective functions were selected for this study to determine the performance of the model. Hourly rainfall and streamflow data for four (04) extreme flood events (May 2014, May 2016, May 2017, May 2018) which occurred in the recent past were used in this study as this is an event-based simulation. The 2018 flood event was used for the model calibration and other three events were used for the validation of the calibrated model. Combination 01 (SCS-CN Loss Method and SCS-UH Transform Method) shows a better performance compared to other combinations w.r.t both PEP and PEV objective functions. N.S.E. and RMSE values were reported as 0.816 and 0.400 after calibrated w.r.t PEP objective function and as 0.830 and 0.400 after calibrated w.r.t. PEV objective function. Even considering the validation results, it was confirmed that Combination 01 shows the best results among all the 06 combinations. It can be concluded that the SCS-CN loss method and SCS-UH transform method provides more reliable estimates w.r.t. both PEP and PEV objective functions in streamflow forecasting in Kelani river using HEC-HMS. Considering the ability of the model to predict the peak discharge and the time to peak, this developed model can be used to provide early flood warnings to the Deraniyagala area during extreme rainfall events as well.
item: Thesis-Abstract
Forecasting dry weather flow to assess future water extraction capacities at Koleimodara intake in Kuda ganga, Kalu ganga
(2022) Fernando WRS; Ratnasooriya AHR
Kalu Ganga is the primary source of potable water supply in the greater Colombo area and total Kalutara District. Kethhena water treatment is supposed to cover the water demand in the middle and southern parts of the Kalutara district, which is estimated as 1.5 m /s, including the subsequent explanation to covet 2030 to 2060 design horizon. The new intake at Koleimodara in Kuda Ganga is supposed to extract water during the dry weather period the as the old intake at Thebuwana is impacted by salinity intrusion. Therefore, this study was formulated to assess the possibility of extracting water from the Koleimodara intake during the subsequent design horizon. A hydrological model was developed using Hydrologic Engineering Centre’s Hydrologic Modelling System (HEC HMS) to estimate river discharge at Koleimodara with Deficit and Constant loss method, linear reservoir baseflow method, Snyder Unit Hydrograph transform method, and Muskingum routing method. The calibration and validation events were selected as the water cycle having prolonged dry spells i.e.,2006/2007 and 2011/2012 for calibration and 2008/2009, 2009/2010, 2013/2014 and, the continuous stimulation from 2005 to 2015 for validation. Kukule Ganga run-off-the-river plant operations were included for the model with elevation-capacity-discharge relationship considering environmental flow (0.5 m /s) and maximum turbine discharge. The objective functions, Relative Nash-Sutcliff (NSE rel ), Mean Ratio of Absolute Error (MRAE), Root mean square error (RMSE), and Percent bias (PBIAS) were used to evaluate model performance. Future precipitation projections were derived from Regional Climate Model (RCM) ICTP-RegCM4-7 based on NCC-NORESM1-M Global Climate Model (GCM) under Coupled Model Intercomparison Project Phase 5 (CMIP5) project. Two future scenarios of Representative Concentration Pathways (RCP) 2.6 and 8.5 were used to assess the future precipitation in the basin and streamflow at the intake location. The Standard Precipitation Index (SPI) and low flow indices i.e., Probability exceedance flow of 90 th percent (Q 90 ) and 50 th percent (Q 50 ), Mean 7-day annual minima (MAM7) and Mean 30-day annual minima (MAM30), Baseflow index (BFI), deficit duration, deficit volume, and intensity were applied to assess the future (2030-2060) climatic and low flow conditions of the project area relative to the observed data simulations of the 2005 to 2020 period. The SPI indicated a possibility of the dry months becoming drier (June, July, and August under RCP 2.6 and July and August under RCP 8.5) or prevail the same dry conditions (January and February under both RCPs), and the wet month May receives more precipitation (under RCP 8.5). All indices indicated a possibility of low flows decreasing with deficit durations becoming more prolonged under both RCPs particularly during 2030-2040. Deficit analysis results and MAM7, MAM30 results indicated that the first inter-monsoon and Northwest monsoon periods continue to be the dry period. The intake is projected as facing a maximum deficit volume of 4.9 MCM for 47 days with the intensity of 105 thousand m /day and with a deficit volume of 4.4 MCM for 42 days with the intensity of 105 thousand m 3 /day respectively, under RCP 2.6 and 8.5 during 2030 to 2040. Deficit events are projected as two during the base period (2005-2020) and nine and twelve respectively, under RCP 2.6 and 8.5 from 2030 to 2060. Based on the results of this study, it is recommended to select another water source for the next design horizon extractions or maintain storage of about 4.9 MCM to cater to the dry period water deficit to provide an uninterrupted water supply.
item: Thesis-Abstract
Assessment of climate change impact on water availability in upper Mahaweli river basin, Sri Lanka
(2022) Musadiq F; De Silva PKC
Climate change, population increase, and economic development will all have an impact on future water availability for drinking water supply, agriculture, and recreation activities, with different effects in different regions. The present study investigates the potential impact of climate change on future water availability in the Peradeniya sub-catchment of the Upper Mahaweli river basin. The hydrological modeling of this study was performed by Hydrologic Engineering Centre Hydrological Modelling systems (HEC-HMS). In this study, the entire catchment area was divided into three sub-basins to simulate runoff at the outlet of the catchment and the model results were calibrated and validated using historical streamflow data. Future runoff based on calibrated parameters was estimated after bias correction of climate rainfall data for representative concentration pathways (RCP) 4.5 and RCP 8.5 scenarios. Further, an assessment of water availability based on annual and seasonal periods was carried out from the model results. The model calibration carried out from 1990 to 1994, indicated good model results in terms of objective functions where root mean square error (RMSE) is 0.60, Nash-Sutcliffe (NSE) is 0.62, and Percent Bias is -15%. Further, validation of model results from 1994 to 2000 yielded RMSE of 0.60, NSE of 0.52, and Percent Bias of 13.9 % indicating good model results. From the results obtained, it was identified that the water availability will increase for both scenarios RCP 4.5 and RCP 8.5 during the mid-century (2040-2060) and end-century (2080-2100) period. The annual water availability concerning the historical period will increase by 27.34 % during the mid-century period and will further increase by 42.06 % during the end-century period in the RCP 8.5 scenario. The seasonal water availability in mid-century compared to the historical period will be more affected during the first inter-monsoon (FIM) period with an average increase of 69 % and 83 % in RCP 4.5 and RCP 8.5 scenario, respectively. Whilst the seasonal water availability will decrease during the first inter-monsoon (FIM) in the endcentury compared to the mid-century period by 26 % and 27 % in RCP 4.5 and RCP 8.5 scenarios, respectively. The findings of this study can be useful for the water managers and stakeholders to manage future water needs in the basin and reduce the future vulnerabilities associated with the increasing water availability in the basin.
item: Thesis-Abstract
Assessment of deforestation and land cover change impacts on flood peak discharge in Maduru oya basin, Sri Lanka
(2022) Nab AW; Ratnasooriya AHR
Population growth raises demand and competition for water resources and food stocks while it changes the landuse types by anthropogenic activities to adopt applicable measures for supplying water for domestic, agricultural, and industrial purposes. These changes alter the hydrological response of the river basins and can impose the communities to severe environmental risks like floods and landslides. Therefore, understanding of landuse change is crucial to study river basins’ behavior and take mitigatory measures. The study presented here quantifies and analyzes the historical deforestation and landuse/landcover (LULC) change impacts on flood peak discharge of the Maduru Oya river basin, Sri Lanka using Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS) and remote sensing techniques. The Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager-thermal Infrared Sensor (OLI-TIRS) images are acquired in 1976, 1994, 2009, 2021 and classified using maximum likelihood algorithm of supervised classification. The analysis of LULC change revealed that LU change was faster and in high magnitude from 1976 to 1994 compared to the remaining period to 2021. The LULC change quantification by analyzing each scenario revealed a 24.9% deforestation while a 2.2%, 9.8%, 8.4%, and 4.5% increase in homestead/garden, paddy, scrubland, and water body between 1976 to 1994, respectively. The deforestation further continued to a rate of 4.1% and a 2.0% decrease in water bodies was also found in 2009 while homestead/garden, paddy, and scrubland continued to increase by 3.5%, 1.4%, and 1.5% compared to 1994 landuse scenario, respectively. In contrast, the 2021 landuse scenario indicated a 7.6% decrease in scrubland while 3.6%, 0.5%, 1.5%, and 1.8% increase in forests, homestead/garden, paddy, and water bodies. The classified images were subjected to accuracy assessment. The overall accuracy of 82%, 84%, 88%, and 91% are found for 1976, 1994, 2009, and 2021 LU scenarios while having kappa coefficients of 0.78, 0.80, 0.85, and 0.89 for respective years. The Normalized Difference Vegetation Index (NDVI) assessment of scenarios corresponds to the landuse classified images. An event-based HEC-HMS model is used to simulate the flood events in the Welikanda catchment of the Maduru Oya river basin. The model is calibrated and validated using the 1976 landuse and then the subsequent landuses are applied to study LU change impact on flood peak discharge. For model performance evaluation, the Nash-Sutcliffe, RMSE Observations Standard Deviation Ratio (RSR) Percent Bias (PBIAS), and the Coefficient of determination (R 2 ) were exploited. The average NSE, RSR, PBIAS, and R 2 values of 0.92, 0.25, 17.60, and 0.94 achieved in calibration and 0.73, 0.50, -3.03, and 0.78 are found in the validation which all can be rated very good performance except for PBIAS as satisfactory in calibration and NSE as good in the validation. The land cover change resulted in an increase (22.3%) in flood peak from 842 m 3 /s in 1976 to 1,030 m 3 /s in 2021. As a result of the landcover changes, the volume is also increased (42.3%) from 178.16 MCM in 1976 to 253.52 MCM in 2021. This study provides useful information for land and water managers, forests conservation units, and hydrologist to understand the LULC change impacts on floods and paves the way for broad LU and hydrological studies in Sri Lanka which are rarely conducted. The same approach can be applied in different parts of Sri Lanka which are exposed to severe LU changes.
item: Thesis-Abstract
Modelling streamflow variability in dry and wet zone river basins in Sri Lanka using satellite soil moisture data
(2022) Phuyal U; Rajapakse RLHL
Streamflow variability is important in basin water resources management to analyze and plan for the present and future hazards and vulnerabilities affecting effective water management. The unique feature of soil to hold the moisture regulates the precipitation falling on its surface generating the variability in streamflow. The lack of extensive data for distributed hydrological models restrains modelers to accurately simulate temporal and spatial variability of streamflow associated with the soil moisture (SM) in basin-scale. The present study is focused on the use of a simple hydrologic model to assess the impact of SM on the generation of streamflow variability in selected dry and wet zone river basins in Sri Lanka and enhance the model accuracy through the use of satellite soil moisture (SSM) data. The wet and dry zone river basins, Kalu Ganga and Kirindi Oya basins, respectively with a diverse streamflow variability were selected for this study. A semi-distributed hydrologic model was developed to model various events using Hydrologic Engineering Centre’s Hydrologic Modelling System (HECHMS) with soil moisture accounting (SMA) as the loss method. The results obtained from the model are compared with model results forced with soil moisture active passive (SMAP) SM data to assess the impact of antecedent moisture on watershed hydrology. Events of varying magnitude in terms of discharge and precipitation from both Maha and Yala seasons were selected considering different return period discharge to calibrate and validate the model performance. Both models developed for Kirindi Oya and Kalu Ganga performed well with an average Nash-Sutcliffe Efficiency (NSE) of above 0.73 for calibration and above 0.75 for validation along with average root mean square error (RMSE) and observed standard deviation ratio (RMSE std dev) below 0.55 for calibration and below 0.48 for validation. The average coefficient of determination (R 2 ) was obtained above 0.80 indicating a strong correlation. Initial use of SSM improved the model performance of the Kalu Ganga basin whereas deteriorated the performance of the Kirindi Oya basin. The performance was further enhanced by optimizing the soil storage and groundwater parameters yielding an average NSE higher than 0.80, an average R 2 of above 0.90 along with an average RMSE std dev below 0.35 in both basins. Further, the average variation in peak discharge and runoff volume was reduced to 6 % and 2 %, respectively for Kirindi Oya and 15 % and 10 %, respectively for Kalu Ganga basins. The overestimated peak discharge and runoff volume were reduced by 28 % and 18%, respectively upon increasing the soil storage parameters whereas the underestimated peak discharge and runoff volume were increased by 37 % and 43%, respectively by decreasing the soil storage parameters. A minor adjustment in soil storage allowed to manipulate and fine-tune the peak discharge and runoff volume in the basin which substantiates that the runoff is directly associated with the basin SM. The findings of this study can be useful in basins with similar hydrological characteristics to understand the role of SM in runoff generation and for sustainable water management in the basin.
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Streamflow variability under climate change scenarios in Kelani river basin, Sri Lanka
(2022) Farhat F; Bamunuwala RMJ; Wijayaratna TMN
In recent years, the downstream floodplain of the Kelani River Basin has been suffering from frequent floods. With the current climate change trend, flood-related damages are expected to amplify in future. Therefore, understanding how such extreme events behave in the future is quite essential for the basin planners and managers. Using the output from three global climate models (CNRM-CM6-1, MRI-ESM2-0, EC-Earth3-CC, and their ensemble) from the Sixth Phase of Coupled Model Intercomparison Project (CMIP6), variation of streamflow was evaluated from historical (1985-2014) to mid-century (2030-2059) and late-century (20702100) periods under Shared Socioeconomic Pathways (SSPs 2-4.5 and 5-8.5). Bias-corrected precipitation and temperature through linear scaling and power transformation, respectively were fed into the HEC-HMS model to project future river discharge. Based on the ensemble model, changes in average annual discharge indicates an increasing trend in future periods under both scenarios considered. Significantly high changes were projected in the late century (25-40 m 3 /s) compared to the mid-century (15-25 m 3 /s) under both scenarios. Projected changes in monthly streamflow indicate an increasing trend for wet months (June-December) while a decreasing trend for dry months (January-May). Further, the highest changes in streamflow were identified in the monthly changes (-5 to 60 and -5 to 100 m /s under SSP24.5 and SSP5-8.5, respectively). Comparison of seasonal changes shows the highest increase for Southwest Monsoon (40-60 m 3 /s under both scenarios and future periods) while the highest decrease for First-Inter monsoon (<-10 m 3 /s under both scenarios and future periods). Moreover, changes in high, median, and low flows (5%, 50%, and 95% percentiles of flow duration curve, respectively) indicate significant changes in high flows compared to the median and low flows. The findings of this study suggest a significant increase in high flows during the Southwest Monsoon that can further threaten the basin with the devastating floods. The results provide important insight for planners and other stakeholders dealing with water resources management in the basin. 3
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Flood hazard and vulnerability assessment in upper Gin river basin in Sri Lanka under climate change
(2022) Kumar V; Wijayaratna T.MN
Floods are a frequent major disaster throughout the world, usually resulting in fatalities andmassive economic and environmental damage. Seasonal and localized flooding is one of theextremely common natural disasters in Sri Lanka. There are two monsoon seasons (Southwestand Northwest monsoon) and two inter- monsoons (First Inter and Second Inter monsoon) inSri Lanka, each of these monsoon seasons are followed by floods induced by heavy rainfall.The Southwest monsoon, which comes between May and September, has the greatest impacton the southern region of Sri Lanka. This research is developed to assess the flood hazard,vulnerability, and risk of the Thawalama watershed for climate change in future representativeconcentration pathways (RCP) 8.5. The Research methodology begins with selecting Events,which was determined by different statistical approaches. The Gumbel method was the bestfit for determining the event's return periods. A 12-year return period (2003) was selected forcalibration, and a 5-year return period (1999) was selected for validation. Further, the future climate rainfall data was bias-corrected using the linear scaling method. The future climaterainfall data was divided into two centuries: mid-century (2040-2070) and end-century (20702099). Thereafter, the 5-year Return period and 12-year Return period were estimated throughthe Gumbel method for both mid and end centuries. The Hydrologic Engineering Centre'sHydrologic Modeling System (HEC-HMS) was calibrated for 2003 and validated for 1999 atthe gauging station of the Thawalama catchment to obtain lateral flows and inflow inside thecatchment. Thereafter, Hydrological Engineering Centre's River Analysis System (HEC-RAS)was calibrated and validated for the lateral flows and inflows obtained from HEC-HMS for2003 and 1999 respectively. Similarly, the future lateral flows and inflow were derived usingHEC-HMS by importing climatic rainfall data for selected events of 5-year and 12-year returnperiods in both mid and end centuries. Thereafter, HEC-RAS was used to get flood inundation,flood depth, and flood velocity maps. Finally, to achieve objectives, flood depth and floodvelocity were imported to the Arc-GIS interface to develop flood hazards, and populationdensity was used to develop flood vulnerability. Hence, a flood risk map was prepared bymultiplying flood hazards and flood vulnerability. The HEC-HMS was calibrated with NashSutcliffe (NSE)=0.80, Root mean square error standard deviation (RMSE st dev.) =0.40, andPercent Bias (P-bias) =17.65% and Validated with NSE=0.67, RMSE st dev.=0.60 and Pbias=15%. Thereafter, HEC-RAS was calibrated with NSE=0.66, Coefficient of determination(R²) =0.83 and P-bias=3.98% and Validated with NSE=0.62, Coefficient of determination (R²) =0.79 and P-bias=3.28%. The results show an increasing trend of flood inundation area forboth the 5-year return period (17.36 km²,17.40 km²,19.77 km² for years 1999,2052,2091,respectively) and 12-year return period (19.55 km², 20.06 km², 21.18 km² for years 2003, 2058,2098, respectively). Thereafter, sudden increment of flood hazard, flood vulnerability, andflood risk was obtained after mid-century in both 5-year and 12- year return periods. Almost22 Grama Niladhari Division (GND) were found to be a very high-risk category and 21 GNDwere found to be at a high-risk category at the end century of the 12-year Return period in the year 2098 whereas 19 GND were found to be a very high-risk category and 23 GND werefound to be at a high-risk category at the end century of 5-year Return period in the year 2091.Flood hazard, flood vulnerability, and flood risk is increasing suddenly after mid-century inboth 5-year and 12- year return periods. Hence, from the viewpoint of disaster reduction, theinformation derived from this study can help to estimate the probability of flood damage forthe local population.
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Evaluation of gridded precipitation products for streamflow modelling in GIN watershed, Sri Lanaka
(2022) Doya PD; De Silva PKC
Watershed, Sri Lanka An accurate representation of spatial precipitation is significant for hydrological studies. Spatial precipitation is also the basic input for distributed hydrological models and the accuracy of spatial precipitation affects the performance of hydrological models. In many parts of the world, ground-based observation networks are inadequate to capture spatial precipitation because gauge stations cannot be set up anywhere as financial and geographical factors play a vital role in the establishment. To overcome those challenges two existing gridded precipitation data (TRMM and APHRODITE) are used to simulate discharge in the Gin watershed of Sri Lanka. The coefficient of determination improves to 0.78 and 0.65 respectively for TRMM and APHRODITE data after bias correction. While comparing two gridded precipitation data to observed data, the TRMM data shows superior to APHRODITE with the same value of daily and a monthly average rainfall of 11.15 mm and 339.29 mm respectively. The standard deviation shows 21.16 for daily and 167.72 for a monthly scale with the difference of 31.00 % and -0.06 % to observed the data set. The HEC-HMS model is used for generating streamflow from the two gridded and observed data against gauge data. From the other four-parameter (SCS Unit Hydrograph, Simple Canopy, SCS Method, Simple Surface, and Recession) soil moisture accounting parameter calculation was challenging as it has to be carefully determined. The three most sensitive parameters are soil percolation, tension zone storage, and impervious area while the groundwater storage two (GW2) is the least sensitive parameter. Model performance criteria such as RMSE, NSE, and PBIAS are carried out for calibration and validation. The observed data performed good in the simulation of streamflow compared to two gridded precipitation data with an NSE value of 0.70, RMSE Std Dev value of 0.50, and PBIAS of -8.40 % for calibration and NSE value of 0.66, RMSE Std Dev value of 0.66, and PBIAS of -2.34 % for validation. The result shows that the TRMM data is more suitable to be used for hydrological modelling for and water resources management in ungauged areas in Sri Lanka.
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Analysis of rainfall trend and ITS impact on future hydropower generation - case study on victoria reservoir.
(2021) Lakmali JWR; Rajapakse RLHL
Mahaweli river basin is the major river basin for hydropower generation in Sri Lanka and it supplies about 1800 GWh annually to the national grid, but the expected generation is about 2400 GWh (2019). The annual hydropower generation in Sri Lanka is decreasing and the contribution of other nonrenewable sources are continuously increasing accordingly. There are eight reservoirs in the upper catchment of Mahaweli Basin which generate hydropower under the Mahaweli Complex. These reservoirs experience both drought periods and high flood periods as well throughout the year. As hydropower generation totally relies on the rainfall amount of the sub-catchment of the reservoirs, the planned hydropower generation cannot be achieved during the drought periods due to the failure in receiving expected rainfall to the sub-catchments of reservoirs. Hence, identifying the rainfall pattern, its peaks and troughs, and possible trend in future rainfall are crucial for managing and optimizing the reservoir operations such that hydropower generation can be maintained at the maximum possible capacity This study is focused on the analysis of rainfall trends in the upper catchment of Mahaweli Basin and its impact on hydropower generation in Victoria reservoir according to the possible variations in future rainfall. The rainfall trend was analyzed for the Mahaweli Upper catchment considering rainfall data of seven rainfall stations with 30 years of monthly rainfall data. The base period for rainfall trend analysis was selected from the year 1981 to 2010 as per World Meteorological Organization (WMO) guideline. The missing rainfall data in selected rainfall stations were filled with the linear regression method. Rainfall trend was analyzed with the Mann Kendall test and the magnitude of the trend was estimated by Sen’s Slope method which were performed using RStudio Software. According to the trend analysis, the rainfall trend is negative in dry periods and a positive trend is observed in rainy seasons and the negative trend is higher than the positive trend. It could be expected that dryer periods getting dryer with a high degree of variation and rainy periods getting even more rainfall to a lesser degree. This implies that overall annual rainfall has a negative trend in the study area. The future rainfall was estimated for further 30 years from 2020 to 2050 as monthly data with parameters obtained from Sens’ slope method and Mann Kendall test. The average annual rainfall was about 2,390 mm in the study area for the selected base period and the estimated future mean annual rainfall for next 30 years will be around 1973 mm with a decrease of 18% compared to the last 30 years. The catchment runoff was calibrated for Victoria reservoir with HEC HMS model for the five years from 2001 to 2005 and the model was validated for the period 2006-2010. The future inflows were predicted for the period 2021 - 2025 with generated monthly future rainfall data. The future annual inflow of Victoria reservoir in next 5 years will be reduced by 10% compared to recent 5 years of inflows of Victoria reservoir. The HEC ResSim model was developed and applied for Victoria reservoir to obtain the potential power generation and the analysis of reservoir operations of Victoria reservoir. HEC ResSim model was calibrated with reservoir operational data in the year 2015 and validated with reservoir operational data in the year 2016. Future power generation was obtained for the time period of 2021 - 2025. It was found that the future annual power generation of the Victoria power plant will be reduced by 23% compared to the last five years due to the predicted decrease in rainfall. This future scenario was analyzed based on monthly data, hence the peak events were not taken into account. Since the hydropower generation in the Victoria reservoir is decreased yearly, optimization of reservoir operations is necessary considering the variation of future rainfall trends.
item: Thesis-Full-text
Evaluation of the changes in the climatic parameters affecting water resources in the Kelani river basin
(2021) Wangmo K; Rajapakse RLHL
The impact of climate change on the freshwater resources of Sri Lanka is most likely to affect the Sri Lankan economy since most sectors are vulnerable to climate change. There is limited research on Climate Change in Sri Lanka and the studies related to climate change impacts on water resources are not definite on their rates and impacts. Most of them have pointed out the need for further investigation and strengthening of the methodologies. The impacts of global-scale climate change on local climate is ambiguous and there is a disparity between global climate models or large climate models and the climate at catchment scale. It is thus necessary to carry out investigations of climate change on the catchment scale. The main objective of this study is the investigation of climate change impacts on water resources in relation to two sub watersheds in the Kelani river basin using parameters such as rainfall, and temperature that are major drivers of water availability at a monthly resolution and evaluating the significance of these changes to global changes while assessing their spatial and temporal variation and influence on water resources. Present work evaluated the climate of the Kelani river basin with monthly rainfall, temperature, and streamflow data. The trends in the climate parameters were studied using Linear Regression models, Mann-Kendall’s trend test and Sen’s Slope method to compare and determine whether the impacts associated with the study area are consistent with global and regional climate changes which were obtained from the literature review. The trends in intra-annual, seasonal, annual, and decadal scales were computed for the measured values as point climate information. The recent IPCC base period (1961 to 1990) was also used for comparing changes relative to that period. The Streamflow variation and trends were compared with the contributing rainfall computed using Thiessen weights. Observed variations and shifts in the rainfall patterns were compared with the long-term averages and associated magnitudes were further scrutinized with the prevailing hydrological characteristics of the catchment in order to ascertain the consistency of gauged and spatially averaged data. The Linear Regression and Mann-Kendall tests revealed similar results. An increase in temperature in the Kelani basin was observed with a decreasing trend in rainfall and streamflow. These trends were however relatively small with minor increases/decreases. Increase in mean temperature was about 0.018 °C over the 60-year period and the decrease in rainfall and streamflow amounted to values less than 40 mm over the 60-year period. These trends although negligible at present would ultimately distress the catchment moisture condition. Considering the increasing minimum temperature and the decrease in rainfall in both sub-catchments and resulting precipitation elasticity of runoff, the cumulative effects of such events were studied and the behaviour of the parameters and their effect on the watershed wetness was measured. The net loss of water is attributed to the increasing evaporative demand in the sub-catchments due to an increase in the temperature. These results when further examined with composite evaluations of each climate parameter revealed a collective increase in the losses in the recent decades. The cumulative decrease in the rainfall and streamflow in the basin when compared with the long term averages showed escalations in the deficit wet periods of almost 10% higher in the recent decades (1983/84-2013/14). This reveals a rather distressing situation for the available water in the two sub-catchments of the Kelani basin. The loss of water through replenishment of the catchment water storage needs to be measured and monitored for proper water resources management since data on soil moisture within the country are limited. The method adopted in this study was helpful to capture the current situation of water resources in the basin and the moisture status within the sub-catchments. It can be used in other catchments of the country to check the status of available water resources and especially the watershed wetness so that it can be monitored for water security.
item: Thesis-Full-text
Applicability of model parameter transferability of tank model in streamflow simulation in gin river basin
(2020) Boralugoda BPD; Wijesekera NTS
Amidst of the population growth and increased demand due to rising level of living standard, stress on the water resources has been increased rapidly in recent years. Water practitioners, researchers have been stressing on the need of development of water resources in integrated and cohesive manner. Hydrological modelling has become the essential tool for planning and designing of water resources development as it gives the quantity of water available. Many modelers face the problem of developing solutions at ungauged basins. Typically, hydrological models are developed at gauged locations and whenever necessary, modelers tend to use the same model structure with verified parameters. This is a gray area in hydrological society as the model transferability is yet to be convinced. The need of more researches is essential in this regard for increase confidence of use of model parameter transferability. This study developed a lumped conceptual tank model with four tanks for simulating streamflow in Gin Ganga basin at Tawalama and Baddegama and appraise the effectiveness of the model parameter transferability in ungauged basins of Gin basin. Model is developed in MS Excel and multi-start GRG-nonlinear search engine is used as parameter optimizing method while employing Mean Ratio of Absolute Error (MRAE) as the objective function to evaluate goodness of fit of the optimized parameters. Daily precipitation and evaporation data from water years 2008/09 to 2017/18 is used for the modeling. Model was warmed up using five water years to stabilize soil moisture in each calibration and validation. Calibration for each catchment was done using first five years of data and validation was done using remaining portion of data. Thereafter, optimized parameters were transferred under spatiotemporal, spatial and temporal approaches to simulate the flow of each catchment. Then model performance was evaluated in each scenario by comparing goodness of fit, annual water balances, flow hydrographs and flow duration curves for low, high, and medium. The models were calibrated at Baddegama and Tawalama with MRAE value of 0.233 and 0.246 respectively for daily streamflow simulation. Then both models were validated for the two location with MRAE of 0.298 and 0.346 respectively. Better matching in high and medium flow is observed while average annual water balance error varying from 1.7% to 19% on average. All three transferability methods showed adequate results while maintaining accuracy ranges from 59% to 72% in daily streamflow simulation and model predicted average annual and average monthly flow estimations with an accuracy of 81% and 77% respectively under any transferability approach. Among the three approaches spatial transferability is selected as the best since it shows streamflow simulation accuracy over 66% and annual water balance errors varying with 1.7% to 3.4% on average. Further, spatiotemporal transfer method shows accuracy over 56% and temporal transfer has showed accuracy over 69% in daily streamflow simulation. In all modelling effort it was observed that accuracy of monthly flow estimations was over 77% and accuracy of annual water balance was over 81% on average. Finally, the model could be used to predict daily streamflow with an accuracy of 68% and monthly scale flow estimations with an accuracy of 89% by applying either set of optimized parameters, indicating the model suitability for parameter transferability & water management in ungauged catchments in Gin Ganga basin.
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Transferability of model parameters for monthly streamflow estimation in ungauged watersheds in Kalu river basin
(2020) Lakmali EN; Wijesekera NTS
Prediction and forecasting of ungauged streamflow have become a challenge to watershed modelers especially when practical water resources planning and management are a major concern. Over the years, researches have experienced and executed various methodologies and approaches to find a way to estimate streamflows at ungauged locations where they found that transformation of model parameters can be used effectively in this regard, which still requires further research. The present study targeted to find the adequacy of parameter transferability options to estimate streamflow at ungagged outlets. Two parameter monthly water balance model which was developed by Xiong and Guo (1999), is used for the modeling of two gauged watersheds at Ellagawa and Rathnapura which are located in Kalu river basin, Sri Lanka. Three model parameter transfer schemes have been tested under the study. They are namely, temporal, spatial, and spatio-temporal. The transferability of model parameters within Kalu River basin showed that the temporal transfer scheme has the highest capability of predicting overall flows with the average MRAE values of 0.34 while it is 0.42 and 0.35 in spatial and spatiotemporal transfer schemes respectively. Spatial and spatiotemporal transfer schemes perform at the same accuracy level for predicting high flows with an average MRAE value of 0.27 while it is 0.30 in temporal transfer scheme. The temporal transfer scheme has the highest capability to predict intermediate flows with an average MRAE value of 0.32 while it is 0.41 and 0.36 in spatial and spatiotemporal schemes respectively. Spatio-temporal transfer scheme performs best for low flows with an average MRAE value of 0.35 while it is 0.43 and 0.52 in temporal and spatial transfer schemes respectively. Further, compared to high and intermediate flows, low flow estimation has the highest MRAE values in all three considered transfer schemes.Results of seasonal flow analysis indicated that spatiotemporal scheme has the highest capability to predict Yala season streamflows with a 13% of average error for Ellagawa watershed and spatial transfer scheme has the highest capability to predict Maha seasonal flow with an average error of 13.29%. Model parameter C is 2.09 for Rathnapura watershed and it is 2.38 for Ellagawa which is having a 13% difference in each other.SC is 1420 for Rathnapura and 1461 for Ellagawa having a 3% difference from each other, indicating that model parameters do not vary across the catchments in Kalu River Basin and they are stable in a spatial domain. Transferability option 3, which used 19 years total data has the high capability to predict streamflows with a high accuracy level by giving MRAE values 0.35, 0.27, 0.36 and 0.35 for overall, high, intermediate and low flows respectively compared to transferability option 2 which used 12 years of common data period giving MRAE values 0.42, 0.27, 0.41 and 0.52 for overall, high, intermediate and low flows respectively indicates longer the data period, the higher the accuracy of streamflow predictions irrespective of the transfer scheme used. Since high and intermediate flow predictions are in higher accuracy level with lower MRAE values compared to that of for the low flows, streamflows predicted by transferred model parameters are sufficient and adequate to design and planning of water resources infrastructure and their management in ungauged watersheds within Kalu river basin.
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Analysis of precipitation trend and streamflow sensitivity to precipitation in Maduru oya river basin with HEC-HMS model simulations
(2020) Kirupacaran S; Rajapakse RLHL
Water resources management in a basin needs an intensive analysis of historical data in terms of different climate elements and streamflow. Several researchers have examined the influences of climate change over several main basins during the past years. However, no studies have been performed in the Maduru Oya basin and associated sub-catchments. Hence, the main objective of this study was to identify rainfall trends and then to analyze the streamflow elasticity to the climate in the Maduru Oya basin. Widely used non-parametric trend tests such as Mann-Kendall (MK) test, Modified Mann-Kendall (MMK) test and Sen’s slope estimator were adopted to perform the trend analysis in annual, seasonal and monthly scales. The results displayed by all three tests were in very good agreement except for very few cases. On an average, a positive trend of annual rainfall was experienced in Maduru Oya basin with 1.05 and 1.103 trends, respectively from MK and MMK tests with the yearly increment of 12.52 mm/year. During cropping seasons, Maha season predominantly exhibited positive trends where Yala season was witnessed mostly with negative trends. Likewise, during rainfall seasons, except for SWM season, remaining FIM, NEM and SIM seasons displayed positive trends. The monthly analysis found out that November and December experienced strong positive trends whereas the highest negative trends were revealed in September. Further, for Padiyathalawa sub-basin located in the upstream of Maduru Oya river basin, analysis of streamflow elasticity to precipitation, defined as the proportional change in mean annual streamflow divided by the proportional change in mean annual rainfall, was performed on historical data. This part of the study was carried out using a non-parametric estimator and a method proposed by finding the slope of the graph plotted between the proportional variation of annual streamflow and proportional variation of annual precipitation. Both results indicated that the variations in rainfall are magnified in streamflow. The non-parametric method and the graphical method revealed that a 1% change in mean annual rainfall would respectively result in 1.12% and 1.92% change in mean annual streamflow. Moreover, in an attempt to incorporate the impacts of climate change in streamflow variability due to variation in climate elements, a HEC-HMS hydrological model was developed, calibrated and verified for this sub-basin. The model performance was good in both calibration and verification periods with MRAE and Nash-Sutcliffe Efficiency values of 0.433 and 0.665 and 0.559 and 0.642 respectively. Hypothetical climate change scenarios were predicted as future climate change scenarios by modifying the input rainfall and evapotranspiration data. The results indicated that the relationship between rainfall and streamflow is stronger than that between evapotranspiration and streamflow as an increase of 10% in rainfall without any change in evapotranspiration results in 20.42% increase in streamflow while the same amount of increase in evapotranspiration with no variation in rainfall results 6.30% decrease in streamflow. In conclusion, the analyses revealed positive trends of rainfall in annual scale for the entire Maduru Oya river basin as well as for Padiyathalawa sub-basin while the streamflow elasticity for the sub-basin using the non-parametric estimator was found out to be 1.12 for the data periods considered.