Faculty Publications
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Item Uncertainties in predicting impacts of climate change on hydrology in basin scale: a review(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2020) Jose, D.M.; Dwarakish, G.S.The sensitivity of the hydrological system to climate change and the role of hydrological systems in the environment have motivated researchers to study the impacts of climate change on hydrology. Modelling the hydrological impacts of climate change is generally done in various stages and has uncertainty associated with each of them. These include scenario uncertainty in climate scenario selection, model uncertainty in climate simulation by global climatic models (GCMs), uncertainties while downscaling GCMs, biases in downscaled data, erroneous input to the hydrological model, and uncertainty in the structure and parameterisation of the hydrological model. The present paper aims at reviewing the uncertainties involved at each stage of climate change impact assessment of hydrology. In the near future, climate scenario uncertainties would be smaller than those associated with the choice of GCMs. Multi-model ensemble approach takes better account of uncertainties involved with GCMs. Moreover, considering a range of possible climate scenarios is recommended than using a single best or average case climate scenario. GCMs shall be downscaled by statistical or dynamical methods (regional climatic models (RCMs)) before using them for regional studies. Bias correction methods can considerably improve the RCM simulations. Evaluation of model performance is recommended for regional-scale studies for the preparation of adaptation strategies. Taking into account the uncertainties associated with climate impact studies can help formulate effective adaptation strategies. © 2020, Saudi Society for Geosciences.Item Hydrological effects of land use /land cover changes on stream flow at Gilgel Abay River Basin, Upper Blue Nile, Ethiopia(CAFET INNOVA Technical Society 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2016) Mulu, A.; Dwarakish, G.S.Water is the most important resource for the survival of living things and it is the most essential resource associated with land use/ land cover (LU/LC) changes. Therefore, it is very important to make evaluations of the expected impact on the hydrology and water resources due to expected changes. The main objective of this study is to assess the hydrological effect of land use/ land cover changes on stream flow at GilgelAbay river basin using Precipitation Runoff Modeling System (PRMS) model. System inputs are daily time-series values of precipitation, minimum and maximum air temperature, and parameter files which are generated from GIS Weasel. To identify effect of changes in LU/LC, vegetation type and vegetation density on stream flow, LU/LC, vegetation type and vegetation density data from 1990-2000 and 2001-2010 years were considered. This different period LU/LC, vegetation type and vegetation density with soil data and DEM were given to GIS Weasel to generate different parameters for PRMS model. These generated parameters together with time series data (daily minimum and maximum air temperature, daily precipitation and daily stream flow) feed to PRMS model to simulate stream flow for the years 1993-2000 and 2001-2008. From the time series data, climate changes (daily maximum and minimum temperature and daily precipitations) were kept the same as baseline period (1993-2000). The stream flow of 2001-2008 compared with baseline period (1993-2000) and the effect of LU/LC, vegetation type and vegetation density was identified using calibrated and simulated PRMS model. Hence, as LU/LC, vegetation type and vegetation density changed from 1993-2000 period to 2001-2010 period, stream flow increased from 7.8% (128.4 Mm3) to 25.3% (432 Mm3) and ET decreased from 4.2% (75 Mm3) to 20% (524 Mm3) from baseline period. For the whole simulation periods (2001-2008) stream flow increased by 10.9% (784 Mm3), but ET decreased 6.7% (43 Mm3) related to baseline periods. © 2016 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.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 Time-series analysis of erosion issues on a human-intervened coast– A case study of the south-west coast of India(Elsevier Ltd, 2023) Parvathy, M.M.; Balu, R.; Dwarakish, G.S.Coastal erosion has long been identified as a cause of concern for the state of Kerala, situated in the Indian subcontinent, affecting the life and livelihood of millions residing in the coastal belt. The increased human interference supplemented by changes in the climatic pattern in recent years has modified the coastal scenario of the state altogether. The present study attempts to evaluate the effect of anthropogenic influences in modifying the coastal scenario to review the efficiency of the coastal management policy adopted by the state over the years. For this purpose, the shorelines extracted from the available multi-temporal satellite images are analysed using DSAS software to calculate the shoreline change rate prior to 2000 (1973-98) and post-2000 (2002-21) using the linear rate of regression method. The study seeks to key out critically eroding areas, subsequently exploring the possible conducive reasons for the changed coastal scenario. The results indicate a reduction of 34.5% in the share of eroding length, with a visible shift in a substantial portion of coastal stretch from the mild erosion category to the stable category. Despite the state's continuous efforts to curb the issue, the long-term shoreline change over the past 49 years (1973–2021) reveals erosion to be dominant in nearly 39.12% of the coastal length, with the share of eroding length in the southern, central and northern regions as 33.8%, 38.67% and 44.04%, respectively. The results point towards the dominance of human interventions accompanied by climate change impacts as the primary reason for transforming the coast, necessitating the need to modify the state's current coastal management policy. The research emphasises the need for a comprehensive coastal management plan for the state to take heed of the changing climatic scenario. © 2023 Elsevier LtdItem Machine learning-based ensemble of Global climate models and trend analysis for projecting extreme precipitation indices under future climate scenarios(Springer Science and Business Media Deutschland GmbH, 2025) Kumar, G.P.; Dwarakish, G.S.Monitoring changes in climatic extremes is vital, as they influence current and future climate while significantly impacting ecosystems and society. This study examines trends in extreme precipitation indices over an Indian tropical river basin, analyzing and ranking 28 Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) based on their performance against India Meteorological Department (IMD) data. The top five performing GCMs were selected to construct multi-model ensembles (MMEs) using Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), Multiple Linear Regression (MLR), and the Arithmetic Mean. Statistical metrics reveal that the application of an RF model for ensembling performs better than other models. The analysis focused on six IMD-convention indices and eight indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). Future projections were examined for three timeframes: near future (2025–2050), mid-future (2051–2075), and far future (2076–2100) for SSP245 and SSP585 scenarios. Statistical trend analysis, the Mann-Kendall test, Sen’s Slope estimator, and Innovative Trend Analysis (ITA), were applied to the MME to assess variability and detect changes in extreme precipitation trends. Compared to SSP245, in the SSP585 scenario, Total Precipitation (PRCPTOT) shows a significant decreasing trend in the near future, mid-future, and far future and Moderate Rain (MR) shows a decreasing trend in the near future and far future of monsoon season. The findings reveal significant future trends in extreme precipitation, impacting Sustainable Development Goals (SDGs) achievement and providing crucial insights for sustainable water resource management and policy planning in the Kali River basin. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.