Rainfall Trend Analysis in Uma Oya Basin, Sri Lanka, and Future Water Scarcity Problems in Perspective of Climate Variability

Khaniya, Bhabishya; Jayanayaka, Isuru; Jayasanka, Pradeep; Rathnayake, Upaka

doi:10.1155/2019/3636158

Cited by 30 publications

(20 citation statements)

References 67 publications

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“…Interestingly, similar rainfall trends were detected by Khaniya et al [22] to the Uma Oya catchment, which is near the Samanalawewa catchment. Significantly positive rainfall trends were found in two rain gauging stations (Kirklees estate and Ledgerwatte estate) in the Uma Oya catchment.…”

Section: Figure 3 -Annual Rainfall Variation Over 26 Yearssupporting

confidence: 84%

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Climate Variation and Hydropower Generation in Samanalawewa Hydropower Scheme, Sri Lanka

Dabare¹,

Gunathilake

Miguntanna³

et al. 2020

Engineer

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Climate variation is a challenging scenario on water resources. Therefore, runoffbased hydropower development stations are at an alarming situation across the world and the hydropower industry has significantly been affected. Therefore, it would be interesting to understand the impact of climate change on hydropower development in a country, where a significant energy contribution takes place by the renewable hydropower. However, such studies in Sri Lanka are limited mainly due to data scarcity. Nevertheless, this study was carried out to understand the relationships between the rainfall and the hydropower development in one of the major hydropower developments in Sri Lanka, Samanalawewa hydropower station. Non-parametric statistical trend analyses were carried out to the monthly rainfall over 26 years for the catchment rainfall. As the initial step, the link between rainfall and hydropower development was tested using the Pearson's correlation coefficient. Interestingly, results revealed positive rainfall trends over the catchment. The correlation coefficient suggests that there is an acceptable correlation between the rainfall and the hydropower development. However, non-linear analysis is proposed to achieve more sound conclusions. Initial results revealed that there is no adverse impact to the inflow of the reservoir due to the ongoing climate change.

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Section: Figure 3 -Annual Rainfall Variation Over 26 Yearssupporting

confidence: 84%

“…Sen's slope estimator is usually coupled with Mann-Kendall test to find the quantitative analysis of the trend, if any. More details about the Mann-Kendall test can be found in Khaniya et al[22].…”

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confidence: 99%

Climate Variation and Hydropower Generation in Samanalawewa Hydropower Scheme, Sri Lanka

Dabare¹,

Gunathilake

Miguntanna³

et al. 2020

Engineer

Self Cite

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“…At the same time, climate change and climate variabilities have done many adverse impacts to the water resources not only to Sri Lanka but also all over the world. Some countries, like Ethiopia, Somalia, Djibouti, Eritrea, Kenya, and Slovakia, have experienced arid weather patterns [9,10], while some other countries, like the United Kingdom, Sri Lanka, Lithuania, and Myanmar, have experienced intensified precipitation events [11,12]. e adverse impacts of these extreme climate events have influenced water supply systems [13], sewer systems [14], agriculture [15], hydropower development [16], and transportation [17].…”

Section: Introductionmentioning

confidence: 99%

Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks

Karunanayake

Gunathilake

Rathnayake

2020

Applied Computational Intelligence and Soft Computing

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Prediction of water resources for future years takes much attention from the water resources planners and relevant authorities. However, traditional computational models like hydrologic models need many data about the catchment itself. Sometimes these important data on catchments are not available due to many reasons. Therefore, artificial neural networks (ANNs) are useful soft computing tools in predicting real-world scenarios, such as forecasting future water availability from a catchment, in the absence of intensive data, which are required for modeling practices in the context of hydrology. These ANNs are capable of building relationships to nonlinear real-world problems using available data and then to use that built relationship to forecast future needs. Even though Sri Lanka has an extensive usage of water resources for many activities, including drinking water supply, irrigation, hydropower development, navigation, and many other recreational purposes, forecasting studies for water resources are not being carried out. Therefore, there is a significant gap in forecasting water availability and water needs in the context of Sri Lanka. Thus, this paper presents an artificial neural network model to forecast the inflows of one of the most important reservoirs in northern Sri Lanka using the upstream catchment’s rainfall. Future rainfall data are extracted using regional climate models for the years 2021–2050 and the inflows of the reservoir are forecasted using the validated neural network model. Several training algorithms including Levenberg–Marquardt (LM), BFGS quasi-Newton (BFG), scaled conjugate gradient (SCG) have been used to find the best fitting training algorithm to the prediction process of the inflows against the measured inflows. Results revealed that the LM training algorithm outperforms the other tests algorithm in developing the prediction model. In addition, the forecasted results using the projected climate scenarios clearly showcase the benefit of using the forecasting model in solving future water resource management to avoid or to minimize future water scarcity. Therefore, the validated model can effectively be used for proper planning of the proposed drinking water supply scheme to the nearby urban city, Jaffna in northern Sri Lanka.

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“…Climate variability involves changes in precipitation and atmospheric temperature, slower winds, and acidification of the ocean (Khaniya et al, 2019). However, temperature and rainfall are very important factors influencing climate variability and extremes.…”

Section: Introductionmentioning

confidence: 99%

“…Malmgren et al, (2003), Ranatunge et al, (2003), Ampitiyawatta and Guo (2010), and Wickramagamage (2016) have shown rainfall trends in many other important areas in Sri Lanka. Recently, Khaniya et al (2019) analyzed the rainfall trend of Uma Oya catchment and showed significant positive trends in three rainfall gauges; Kirklees Estate, Ledgerwatte Estate and Welimada, only in the first intermediate period (March-April). The effect of delayed southwest monsoon to Sri Lanka was found by Naveendrakumar et al (2018) indicating a significant decrease of wet days in May at 15% of the 32 considered gauge stations within the country.…”

Section: Introductionmentioning

confidence: 99%

Rainfall Trends in the North-Western and Eastern Coastal Lines of Sri Lanka Using Non – Parametric Analysis

2020

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The changes in the global rainfall have raised concerns over the present and possible future use of coastal areas in many Asian countries including Sri Lanka. However, no in-depth analysis of rainfall of these coastal lines of Sri Lanka has been performed yet to understand any changes in the pattern in general. This study was carried out to fulfil the above need, especially focusing on rainfall of North-Western and Eastern coastal lines of Sri Lanka. The rainfall data for a period of 30 years were used in the study and analysis was performed using the tests, Mann Kendall, Sen's slope estimator, and sequential Mann Kendall. Mann-Kendall's test indicated positive trend in annual rainfall of most parts of North-Western coastal line but negative monotonic decrease at 4 stations, namely, Chilaw, Horakele, Lunuwila and Palaviya. In the Eastern coastal line, significant positive trend in annual rainfall was observed at 4 stations, namely, Kantale, Batticaloa, Pottuvil and Mylambaveli. The monthly rainfall analysis of North-Western coastal line revealed significant positive trends at Kottukachchiya and Anamaduwa stations in March and December respectively and in the Eastern costal line, positive trends were observed at Kantalae, Mylambaveli and Navatkiri Aru for February. The stations except Anamaduwa in North-Western coastal line recorded a significant negative trend in rainfall in the month of May. No station showed a significant positive trend during Southwest Monsoon season. During the Northeast Monsoon season, all the nineteen stations showed an increase in rainfall amounts while two locations in North-Western region and six locations in Eastern showed a significant upward trend. With these dynamic rainfall trends, it can be reiterated that it is vital to advocate climate mitigating actions to reduce impacts of changes in rainfall trends.

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Rainfall Trend Analysis in Uma Oya Basin, Sri Lanka, and Future Water Scarcity Problems in Perspective of Climate Variability

Cited by 30 publications

References 67 publications

Climate Variation and Hydropower Generation in Samanalawewa Hydropower Scheme, Sri Lanka

Climate Variation and Hydropower Generation in Samanalawewa Hydropower Scheme, Sri Lanka

Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks

Rainfall Trends in the North-Western and Eastern Coastal Lines of Sri Lanka Using Non – Parametric Analysis

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