Research Publications

Now showing 1 - 3 of 3

Open Access
Trends and Variabilities in Rainfall and Streamflow: A Case Study of the Nilwala River Basin in Sri Lanka
(MDPI, 2023-01) Panditharathne, R; Gunathilake, M. B; Chathuranika, I.M; Rathnayake, U; Babel, M. S; Jha, M. K
Rainfall is one of the dominating climatic parameters that affect water availability. Trend analysis is of paramount significance to understand the behavior of hydrological and climatic variables over a long timescale. The main aim of the present study was to identify trends and analyze existing linkages between rainfall and streamflow in the Nilwala River Basin (NRB) of Southern Sri Lanka. An investigation of the trends, detection of change points and streamflow alteration, and linkage between rainfall and streamflow were carried out using the Mann–Kendall test, Sen’s slope test, Pettitt’s test, indicators of hydrological alteration (IHA), and Pearson’s correlation test. Selected rainfall-related extreme climatic indices, namely, CDD, CWD, PRCPTOT, R25, and Rx5, were calculated using the RClimdex software. Trend analysis of rainfall data and extreme rainfall indices demonstrated few statistically significant trends at the monthly, seasonal, and annual scales, while streamflow data showed non-significant trends, except for December. Pettitt’s test showed that Dampahala had a higher number of statistically significant change points among the six rainfall stations. The Pearson coefficient correlation showed a strong-to–very-strong positive relationship between rainfall and streamflow. Generally, both rainfall and streamflow showed non-significant trend patterns in the NRB, suggesting that rainfall had a higher impact on streamflow patterns in the basin. The historical trends of extreme climatic indices suggested that the NRB did not experience extreme climates. The results of the present study will provide valuable information for water resource planning, flood and disaster mitigation, agricultural operations planning, and hydropower generation in the NRB.
Open Access
Appraisal of Satellite Rainfall Products for Malwathu, Deduru, and Kalu River Basins, Sri Lanka
(MDPI, 2022-10-20) Perera, H; Senaratne, N; Gunathilake, M. B; Mutill, N; Rathnayake, U
Satellite Rainfall Products (SRPs) are now in widespread use around the world as a better alternative for scarce observed rain gauge data. Upon proper analysis of the SRPs and observed rainfall data, SRP data can be used in many hydrological applications. This evaluation is very much necessary since, it had been found that their performances vary with different areas of interest. This research looks at the three prominent river basins; Malwathu, Deduru, and Kalu of Sri Lanka and evaluates six selected SRPs, namely, IMERG, TRMM 3B42, TRMM 3B42-RT, PERSIANN, PERSIANNCCS, PERSIANN-CDR against 15+ years of observed rainfall data with the use of several indices. Four Continuous Evaluation Indices (CEI) such as Root Mean Square Error (RMSE), Percentage Bias (PBIAS), Pearson’s Correlation Coefficient (r), and Nash Sutcliffe Efficiency (NSE) were used to evaluate the accuracy of SRPs and four Categorical Indices (CI) namely, Probability of Detection (POD), Critical Success Index (CSI), False Alarm Ratio (FAR) and Proportion Correct (PC) was used to evaluate the detection and prediction accuracy of the SRPs. Then, the Mann–Kendall Test (MK test) was used to identify trends in the datasets and Theil’s and Sens Slope Estimator to quantify the trends observed. The study of categorical indicators yielded varying findings, with TRMM-3B42 performing well in the dry zone and IMERG doing well in the wet zone and intermediate zone of Sri Lanka. Regarding the CIs in the three basins, overall, IMERG was the most reliable. In general, all three basins had similar POD and PC findings. The SRPs, however, underperformed in the dry zone in terms of CSI and FAR. Similar findings were found in the CEI analysis, as IMERG gave top performance across the board for all four CEIs in the three basins. The three basins’ overall weakest performer was PERSIANN-CCS. The trend analysis revealed that there were very few significant trends in the observed data. Even when significant trends were apparent, the SRP projections seldom captured them. TRMM-3B42 RT had the best trend prediction performance. However, Sen’s slope analysis revealed that while the sense of the trend was properly anticipated, the amplitude of the prediction significantly differed from that of the observed data.
Open Access
Spatiotemporal rainfall variability and trend analysis over Mahaweli Basin, Sri Lanka
(Springer, Cham, 2022-02-07) Rathnayake, U; Pawar, U
The hydrometeorological characteristics of the Mahaweli Basin are infuenced by rainfall distribution. For that reason, it is signifcant to identify spatiotemporal rainfall fuctuations and trends over the Mahaweli Basin. Accordingly, rainfall data from 1990 to 2019 available for the 15 raingauge stations were analyzed for rainfall variability and trends. Serial autocorrelation was checked before applying rainfall time series data to Mann–Kendall (MK) test. The result exhibited no serial autocorrelation in the data. The MK test, Sen’s slope estimator (SSE), and innovative trend analysis (ITA) were applied to recognize rainfall trends. The inverse distance weighting (IDW) interpolation method was applied to show the spatial pattern of rainfall characteristics with the support of ArcGIS 10.1. Some fuctuations were observed in the rainfall over the 30 years with decreasing and increasing trends. Nevertheless, signifcant trends in the annual rainfall were noted for Bandarawela (+15.7 mm), Ledgerwatta (+40.3 mm), Duckwari (−36.3 mm), and Bakamuna (24.3 mm). At the basin scale, no signifcant trends were noted in rainfall of the Mahaweli Basin. The rainfall trend analysis results obtained by ITA have validated the results of the nonparametric test. Therefore, the analysis showed that despite the seasonal variations in rainfall over the Mahaweli Basin, rainfall is regular, and results acquired by MK test, SSE, and ITA methods are reliable.

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