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This paper discusses the response of climate change impact on future streamflow availability in Upper Awash River basin, Ethiopia. The change of climate was built using the CORDEX, RCM daily precipitation, maximum and minimum temperature under RCP4.5 and 8.5 scenarios. The climate model was examined in the historical period 1996–2015 for its ability to capture observed precipitation and temperature. Bias correction was performed on RCM temperature and precipitation to minimize the uncertainties that may occur from climate model projection. After the successful calibration and validation of the HBV hydrological model, streamflow was simulated for the periods of 2021–2040 and 2041–2060 and compared to streamflow of the baseline period 1996–2015 to investigate the changes. The results suggested that overall, following the precipitation increment, streamflow is expected to increase under both RCPs. The average monthly changes of streamflow are expected to increase by 40.1 and 29.4% under RCP4.5 and 16.9 and 18.5% under RCP8.5 scenarios for 2021–2040 and 2041–2060, respectively. Annual streamflow would increase by 28.5 and 23.95% under RCP4.5 and 8.5, respectively. The results of this work can help and inform the water resources planner and designer to frame an appropriate plan and management for the effective use of water in the future. HIGHLIGHTS This study focus on stream-flow response to projected climate change in the Upper Awash sub-basin, Ethiopia.; This paper coupled RCM with RCPs and HBV hydrological model.; Bias correction was performed on RCM temperature and precipitation.; Comparative analysis of the differences between projected and baseline stream-flow were discussed.; Overall projected stream-flow would increase under both RCP scenarios.;

The 2013-2015 drought in the Metropolitan Region of São Paulo exposed the lack of resilience of the regional water supply system, highly dependent on the Cantareira reservoirs. In this paper, inflows to each of the four main Cantareira reservoirs are tested for systematic change. Persistent trends in streamflow, rainfall, temperature and evapotranspiration are first evaluated. Streamflow was also tested for step change. Double-mass curves were employed to assess modification in the precipitation-runoff relationship. Subsequently, we used the climate elasticity method and the ABCD model to quantify the relative contribution of climate and human activities into the detected trends. Only Cachoeira and Atibainha sub-basins showed a significant downward trend in streamflow. The results for step change were also significant, and the year of occurrence coincided with breakpoints in precipitation-runoff relationship. For both Cachoeira and Atibainha, human activities had a more significant impact on streamflow reduction than climate variability. Land use and cover maps suggest that the reduction of pasture/abandoned land parallel to an increase in reforestation/silviculture is behind streamflow reduction. The results highlight the importance of coordinating land-use patterns and water management, as an important contributor beyond any considerations of a changing climate. Implications for better managing regional water resources are discussed. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate two machine learning (ML) algorithms, namely, convolutional neural network (CNN) and long short-term memories (LSTM) deep learning algorithms, to predict the hydrological regime of the 3S River Basin under various climate change scenarios. Climate models CMCC-CMS, HadGEM-AO2, and MIROC5 were used to predict future climate and streamflow for three future periods: near-future (2020–2050), mid-future (2050–2080), and far-future (2080–2100) under two Representative Concentration Pathways (RCPs) 4.5 and 8.5. The future projection shows an increase in mean annual temperature from 0.08 to 4.3 °C by CMCC-CMS, from 0.13 to 4.4 °C by HadGEM-AO2, and −0.07 to 4.2 °C MIROC5 models. Similarly, the annual precipitation is projected to fluctuate from 13.3 to 62.5% by CMCC-CMS, from −12.4 to 26.1% by HadGEM-AO2, and from 6.9 to 49% by the MIROC5 model. The 3S River Basin expects an increasing trend in streamflow in the Srepok and Sesan Rivers, while the Sekong is projected to have reduced streamflow. ML models predicted the increasing flood risk in the Sekong and Sesan catchments with the increase of the Q5 index in the future but a decrease in the Srepok. HIGHLIGHTS Machine learning (ML) models were used to predict the hydrological changes in the 3S River Basin.; The 3S River Basin is expected to be warmer and have fluctuation in rainfall patterns in the future.; The Srepok and Sesan Rivers are expected to have an increasing trend of streamflow while the Sekong is projected to have reduced streamflow.; ML models predicted the increasing flood risk in the Sekong and Sesan catchments.;

The 2013–2015 drought in the Metropolitan Region of São Paulo exposed the lack of resilience of the regional water supply system, highly dependent on the Cantareira reservoirs. In this paper, inflows to each of the four main Cantareira reservoirs are tested for systematic change. Persistent trends in streamflow, rainfall, temperature and evapotranspiration are first evaluated. Streamflow was also tested for step change. Double-mass curves were employed to assess modification in the precipitation–runoff relationship. Subsequently, we used the climate elasticity method and the ABCD model to quantify the relative contribution of climate and human activities into the detected trends. Only Cachoeira and Atibainha sub-basins showed a significant downward trend in streamflow. The results for step change were also significant, and the year of occurrence coincided with breakpoints in precipitation–runoff relationship. For both Cachoeira and Atibainha, human activities had a more significant impact on streamflow reduction than climate variability. Land use and cover maps suggest that the reduction of pasture/abandoned land parallel to an increase in reforestation/silviculture is behind streamflow reduction. The results highlight the importance of coordinating land-use patterns and water management, as an important contributor beyond any considerations of a changing climate. Implications for better managing regional water resources are discussed.