Faculty Publications
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Item Trend and variability of hydrometeorological variables of Tikur Wuha watershed in Ethiopia(Springer, 2020) Ketema, A.; Dwarakish, G.S.The study assessed monthly, seasonal, and annual variability and trend of hydrometeorological variables for 1978–2017 of Tikur Wuha watershed in Ethiopia. The Mann-Kendall trend test and Sen’s slope estimator were employed for the trend and size of the trend, respectively. Besides, the coefficient of variation has been computed for variability analysis. The areal average annual rainfall exhibited an insignificant declining trend with a magnitude of 20.8 mm/decade at a watershed scale. The watershed has been suffering from irregular and erratic rainfall during the dry season. Temperature exhibited a statistically significant rising trend with minimum temperature rises faster than that of the maximum temperature. The streamflow of the Tikur Wuha River was found to be increasing at the rate of 21.16 MCM/decade. The increasing trend of streamflow without the corresponding increase of rainfall in the watershed needs further investigation. © 2020, Saudi Society for Geosciences.Item Hydro-meteorological impact assessment of climate change on Tikur Wuha watershed in Ethiopia(Springer Science and Business Media Deutschland GmbH, 2021) Ketema, A.; Dwarakish, G.S.This study focused on examining the potential effects of climate change on hydro-meteorological variables at the Tikur Wuha watershed (TWW). The weighted average of the validated Coordinated Regional Climate Downscaling Experiment (CORDEX) data of the five Regional Climate Models (RCMs) from multiple General Circulation Models (GCMs) was used to simulate the potential impacts of climate change on streamflow using Soil and Water Assessment Tools (SWAT) model in TWW. The result revealed that the Bega, Kiremt, and annual rainfall increased in both mid and end century for all scenarios. In contrast, the Belg rainfall decreased for all cases except for RCP8.5 at the end century. The rainfall increased more in the end century than mid-century. The increase in rainfall is higher in the Bega compared to Belg and Kiremt season. No significant change in variability of precipitation is observed in the study area. Both the average minimum and maximum temperature increased for all scenarios and time horizons. The annual average streamflow in TWW increased in all cases except a slight reduction in the RCP4.5 scenario in mid-century. Climate change affects the streamflow in the study watershed by increasing the wet season flow and reducing the dry season flow. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Comparison of the Multiple Imputation Approaches for Imputing Rainfall Data: A Humid Tropical River Basin Case Study(Springer Nature, 2025) Kumar, G.P.; Dwarakish, G.S.Accurate rainfall data is crucial for agriculture, hydrology, and climate research as it guides water management, crop planning, and disaster preparedness. Missing data affects reliability, requiring effective imputation. The purpose of this study is to address the critical challenge of imputing missing daily rainfall data, which is especially important given rainfall’s nonlinear distribution and variability in missingness patterns. This research aims to develop and evaluate advanced imputation algorithms to improve data completeness and integrity in humid tropical regions. This study evaluates ten imputation algorithms: K-nearest neighbors (KNN), classification and regression trees (CART), predictive mean matching (pmm), random forest (rf), mean method, and Bayesian methods (norm. boot, lasso. norm, norm, norm. nob, midastouch) for addressing missing daily rainfall data. Using 37 years of data from thirteen stations in the Kali River Basin, the methods leverage descriptive statistics to enhance accuracy in humid tropical regions. The proposed algorithms incorporate descriptive statistics of the rainfall time series and are evaluated using 37 years of daily data from thirteen selected rainfall stations in the Kali River Basin, a humid tropical region. Model performance was assessed at four missingness levels (1%, 5%, 10%, and 20%) and evaluated with accuracy metrics root mean square error (RMSE), mean absolute error (MAE), index of agreement (d), and RMSE-observations standard deviation ratio (RSR). Among the evaluated methods, KNN consistently demonstrated superior performance across all levels of missingness (RMSE = 13.22 to 15.42; MAE = 4.68 to 6.08; d = 0.87 to 0.90; RSR = 0.57 to 0.61), followed closely by CART (RMSE = 16.48 to 20.77; MAE = 6.20 to 8.31; d = 0.81 to 0.86; RSR = 0.71 to 0.79). Overall, KNN, CART, pmm, and rf emerged as reliable methods for imputing missing rainfall data of varying lengths, contributing to more accurate weather forecasting and climate change analyses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.