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Climate change and ongoing human activity have been threatening Ethiopia's Lake Ziway's water balance. However, few studies have been conducted to investigate the combined effects of climate change and water withdrawal on the lake's water balance using climate change and water withdrawal for irrigation. We used high-resolution multiple climate models and Representative Concentration Pathways (RCP) scenarios to assess the impact of climatic variables for two future periods: 2021–2050 and 2051–2080. Rainfall and temperature data biases were corrected using power transformation and variance scaling methods, respectively. The Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model was employed to simulate surface inflow into Lake Ziway from the Meki and Katar sub-catchments. The FAO CROPWAT model was used to estimate the irrigation water demand of major crops grown in the study area. The results indicate that future temperatures and wet season runoff levels are expected to rise. Under the worst climate change scenario, climate change and water withdrawal from the lake for agriculture may cause the lake level to drop by 25 cm per year, resulting in a 10 km2 surface area and 101 Mm3 volume reductions. Therefore, implementing preventive measures, proper planning and careful monitoring of lake water use is advised. HIGHLIGHTS Integration of multiple climate model, RCP scenarios, water abstraction, HBV hydrological and water balance model were used to evaluate Lake Ziway's water balance for impact analysis.; Bias-corrected climate model output resulted in improved rainfall and streamflow simulation.; Our findings indicate that the combined effect of climate change and water withdrawal affected both lake level and volume.;

This study aims to evaluate water availability under changing climate scenarios in the Woybo catchment, Ethiopia. The bias-corrected outputs of multiple climate models’ ensemble mean were employed for the 2050 and 2080s against the reference period (1976–2005) under representative concentration pathways (RCPs) for both RCP4.5 and RCP8.5 scenarios. A semi-distributed physically based Hydrologic Engineering Center of Hydrologic Modeling System (HEC-HMS) was used to perform rainfall–runoff simulation. The projected rainfall and temperatures of the watershed will increase in the far future. The predictions from ensemble means of multiple climate models indicated that rainfall of the watershed will likely increase by 25% in the 2050s and 19% in the 2080s under RCP4.5 and RCP8.5, respectively. The discharge projection for the ensemble mean of all climate models shows an increment up to 20 and 19% under RCP 4.5 and RCP8.5, respectively, in the 2050s, whereas it will decline up to 15 and 28% in 2080s, under RCP4.5 and RCP8.5, respectively. This research plays a great role to reduce the impacts of changing climate for sustainable water resources management. HIGHLIGHT The output of this research plays a great role to reduce the impacts of changing climate for sustainable water resources management.; The ensemble mean of five climate models was employed in this study to analyze the impact on water availability.; A multiple climate model’s ensemble reduces uncertainty regarding the relative strengths and weaknesses of a single climate model.;

This research work aimed to project and analyze climatic variability using GCMs and RCMs in the Hathmati River Basin, which happens to be one of the most important tributaries of Western India. The analysis included a baseline period from 1980 to 2014 and a future scenario (2050s) under a representative concentration pathway (RCP4.5). Following the evaluation of bias correction methods, distribution mapping and power transformation were used for temperature and precipitation projection, respectively. The 2050 s RCP4.5 simulations showed an increase in precipitation to 1,015.54 mm from 936.91 mm giving around a 8.45% increase in average precipitation. For temperature, the maximum temperature was around a 7.05% increase taking the average temperature to 34.21 from 33.97. The minimum temperature was 20.24 from 20.41 showing a positive change of around 8.4%. The future precipitation and temperature change projected might worsen the water stress and increase the probability of the occurrence of severe events, and hence mitigation strategies and management options to reduce this negative impact should be encouraged. HIGHLIGHTS Work is on one of the most important tributaries of Western India.; Includes latest technology and method.; GCMs are used.; RCP4.5 projections are used for forecasting.; BIAS correction is incorporated.;

This study analyzed rainfall projections in Egypt throughout the current century in the context of climate change. Three common bias-correction methods were applied to adjust regional climate model (RCM) simulations of precipitation regarding the observed daily rainfall data of 15 locations in Egypt. The results revealed that the empirical quantile mapping was the most appropriate method to correct the projection of rainfall indices. The projected changes in rainfall showed significant increases at most locations for all future scenarios. The western part of the country will be the most affected by climate change, anticipating a significant increase in precipitation, in contrast to the regions of the Nile Valley, Delta, and the Northern Coast, which may witness a decrease in precipitation and an increase in meteorological drought. Under the worst-case scenario, the rate of increase in rainfall indices over the country is expected to decline during the last decade of the century. These findings will provide a better understanding of the future changes in precipitation that may have critical impacts on the development in Egypt. HIGHLIGHTS The research introduces insights into rainfall projections in Egypt throughout the current century in the context of climate change.; The western part of the country will be the most affected by climate change, anticipating a significant increase in precipitation.; The regions of the Nile Valley, Delta, and the Northern Coast may witness a decrease in precipitation.;

In this study, we investigated the spatio-temporal distribution and performance of seasonal precipitation in all districts of Haryana, India. We analysed the gridded precipitation dataset of the India Meteorological Department (IMD) for a period of 120 years (1901–2020) using different statistical methods. We found that Haryana received a mean precipitation of 37.0, 37.7, 468.3, and 24.8 mm during the winter, pre-monsoon, monsoon, and post-monsoon seasons, respectively. During each season, the eastern districts of Haryana received more precipitation than its western counterparts. Sen's slope results obtained after trend-free pre-whitening (TFPW) showed a statistically significant increasing trend of 0.12 mm (p-value; 0.04) during the pre-monsoon period, whereas decreasing but non-significant trends were observed during the winter, monsoon and post-monsoon seasons at the rate of −0.04 mm (p-value; 0.49), −0.26 mm (p-value; 0.52), −0.05 mm (p-value; 0.33) per year, respectively, for the entire Haryana state. The winter precipitation is expected to increase under the Representative Concentration Pathway 4.5 (RCP4.5) scenario, whereas pre-monsoon precipitation is expected to decrease under the RCP8.5 scenario by the end of the 21st century. The monsoon precipitation is expected to decrease under all RCP scenarios, whereas post-monsoon precipitation is expected to gradually increase under the RCP8.5 scenario by the end of the 21st century. HIGHLIGHTS Spatio-temporal distribution, variability, trends, categorization analysis of precipitation for different seasons.; Most stable and variable precipitation was observed during monsoon and post-monsoon season respectively.; Declining trend in precipitation observed during winter, post-monsoon and summer monsoon season while increasing trend was observed during pre-monsoon season.; Future precipitation projections shows increasing/decreasing trend in different seasons under RCP2.6, RCP4.5 and RCP8.5 scenario by the end of 21st century.;

Climate change is one of the current global threats and the topmost challenges. This study aims to investigate the climate change effect on streamflow in the Gelana watershed using the soil and water assessment tool (SWAT) model for three consecutive periods of 2031–2050, 2051–2070, and 2071–2090. Climate variables were downscaled from two regional climate models (RCMs) (RACMO22T and RCA4) from CORDEX-Africa under representative concentration pathway (RCP4.5 and RCP8.5) scenarios. RCMs were evaluated using four statistical indicators and performed very well. Power transformation and distribution mapping methods were used to correct biases of precipitation and temperatures, respectively. The 19 SWAT model-sensitive parameters were transferred from the gauged donor watersheds to the ungauged watershed outlet by using the principal component analysis coupled with the stepwise multiple linear regression. The ensemble mean of RCMs revealed that the maximum and minimum temperatures and potential evapotranspiration were predicted to increase up to 3.48 °C, 4.19 °C, and 17.85%, respectively, in the period of 2071–2090 under the RCP8.5 scenario. These changes translate to possible reductions in the mean annual rainfall and streamflow up to 15.12 and 44.14%, respectively, with a consequent higher decline of surface runoff by 22.23%, groundwater by 42.54%, and water yield by 35.89% in the period of 2051–2070 under the RCP4.5 scenario. The projected rainfall and streamflow are expected to face a higher decline in wet seasons. Detections of trends in hydro-climatic variables were performed by using the Mann–Kendall test. Hence, these projection scenarios should be of interest to river users and water resource managers in the Gelana watershed. HIGHLIGHTS The principal component analysis is used to evaluate and analyze the physical catchment characteristics that contribute to the SWAT model-sensitive parameters.; Climate variables, streamflow, surface runoff, groundwater, and water yield are integrated.; A higher shortage of rainfall in wet seasons than in dry seasons is expected.;

Urbanization and industrialization cause an increase in greenhouse gas emissions, which in turn causes changes in the atmosphere. Climate change is causing extreme rainfalls and these rainfalls are getting stronger day after day. Floods are threatening urban areas, and short-duration rainfall and outdated drainages are responsible for urban floods. Intensity–Duration–Frequency (IDF) curves are crucial for both drainage system design and assessment of flood risk. Once IDF curves are determined from historical data, they are assumed to be stationary. However, IDF curves must be non-stationary and time varying based on preparation for extreme events. This study generates future IDF curves with short-duration rainfalls under climate change. To represent future rainfall, an ensemble of four Global Climate Models generated under Representative Concentration Pathways (RCP) 4.5 and 8.5 were used in this study. A new approach to the HYETOS disaggregation model was applied to disaggregate daily future rainfall into sub-hourly using disaggregation parameters of hourly measured rainfalls. Hence, sub-hourly future rainfalls will be obtained capturing historical rainfall patterns instead of random rainfall characteristics. Finally, historical and future IDF curves were compared. The study concludes that increases in short-duration rainfalls will be highly intensified in both the near and distant futures with a high probability. HIGHLIGHTS Climate change impacts on rainfall intensities are assessed by comparing historical IDF curves with future IDF curves considering different GCMs and RCPs.; Short-duration rainfalls under climate change impacts were simulated for the assessment of urban floods.; Future daily rainfall data can be disaggregated into sub-hourly rainfall with a new approach to HYETOS using disaggregation parameters of hourly measured rainfalls.;

The study focused on the Godavari River basin to understand the alteration in the drought phenomenon for future scenarios. The Standardized Precipitation Evapotranspiration Index (SPEI)-3 is calculated from Climate Research Unit 4.03 precipitation, and minimum and maximum temperatures. The drought magnitude and characteristics are determined using SPEI, which considers both precipitation and temperature data as input variables. The Mann–Kendall trend analysis is performed to identify the trend associated with drought characteristics. The basin is divided into six homogeneous regions using K-means clustering algorithm. The reliability ensemble averaging method is used for ensemble averaging of regional climate models (RCMs). The drought frequency analysis is carried out using trivariate copula for reference and future time periods. Variations in the drought characteristics are observed in the future scenarios with respect to the reference period. The drought duration, severity and peak for different climate divisions showed an increasing trend in future time period, especially in the case of RCP8.5 scenarios. The return periods of future droughts based on weighted-average RCMs under the two scenarios showed the possibility of more frequent droughts in the future (2053–2099) than in the past (1971–2017). HIGHLIGHTS The Standardized Precipitation Evapotranspiration Index (SPEI) for historical and future periods is computed.; The SPEI is used to assess drought characteristics in the Godavari River basin.; The river basin is divided into six regions using K-means clustering algorithm.; The trend analysis is carried out using historical and future drought characteristics.; Future drought return periods are computed using trivariate copula.;

Ugandan rivers are being tapped as a resource for the generation of hydropower in addition to other uses. Studies on the reliability of these hydropower plants due to climate and land-use/land cover changes on the hydrology of these rivers are scanty. Therefore, this study aimed to model the impact of the changing climate and land-use/cover on hydropower reliability to aid proper planning and management. The hydropower reliability of Muzizi River catchment was determined from its past (1998–2010) and midcentury (2041–2060) discharge at 30 and 95% exceedance probability under Representative Concentration Pathways (RCPs) of 4.5 and 8.5, respectively. The past and projected hydropower were compared to determine how future climate and land-use changes will impact the discharge and hydropower reliability of Muzizi River catchment. Six LULC scenarios (deforestation, 31–20%; grassland, 19–3%; cropland, 50–77%; water bodies, 0.02–0.01%; settlement, 0.23–0.37%, and Barren land 0.055–0.046% between 2014 and 2060) and three downscaled Regional Climate Model (REMO and RCA4 for precipitation and RACMO22T for temperature from a pool of four CORDEX (Coordinated Regional Climate Downscaling Experiment) Africa RCMs) were examined. A calibrated SWAT simulation model was applied for the midcentury (2041–2060) period, and a potential change in hydropower energy in reference to mean daily flow (designflow ≥ 30% exceedance probability), firm flow (flow ≥ 95% exceedance probability), and mean annual flow was evaluated under the condition of altered runoff under RCP4.5 and RCP8.5 climate change scenarios for an average of REMO and RCA4 RCM. The future land use (2060) was projected using the MOLUSCE (Module for Land Use Change Evaluation) plugin in QGIS using CA-ANN. Three scenarios have been described in this study, including LULC change, climate change, and combined (climate and LULC change). The results suggest that there will be a significant increase in annual hydropower generation capacity (from 386.27 and 488.1 GWh to 867.82 and 862.53 GWh under RCP4.5 and RCP8.5, respectively) for the combined future effect of climate and land-use/cover changes. Energy utilities need to put in place mechanisms to effectively manage, operate, and maintain the hydropower plant amidst climate and land-use change impacts, to ensure reliability at all times. HIGHLIGHTS Innovative approaches for hydrological modeling in data-scarce scenarios.; The possibility to utilize bias-corrected reanalysis and historical discharge data to build a climate model in data-scarce scenarios.; Sheds light on the potential risks of land-use and climate change on hydropower reliability in data scarcity areas.; Informs the need to implement prudent catchment management practices and develop policies.;

Climate change impacts are among the many challenges facing management of large cities. This study assesses the important climate variables under climate change impacts in Tehran, Iran, for 2021–2040. Eight Coupled Model Intercomparison Project, Phase 5 (CMIP5) models under the scenarios of Representative Concentration Pathway 2.6 (RCP2.6), RCP4.5, and RCP8.5 were used, and seven climate variables were projected utilizing the Fuzzy DownScaling Model (FDSM) and the Statistical DownScaling Model (SDSM). The FDSM and SDSM results underline the high performance of both models and the important capability of the FDSM, showing the increasing trend of annual changes in mean temperature (Tmean) and maximum temperature (Tmax), precipitation, and the mean wind speed (Wmean). The maximum increase of annual average in Tmean and Tmax and the Wmean among all scenarios will be in the order of 1.29 °C, 1.57 °C, and 0.8 m/s (for RCP8.5), and also the maximum increases of annual average precipitation will be 10 mm (for RCP2.6). Furthermore, the monthly long-term averages of Tmean and Tmax in all three scenarios show significant increases in summer. For precipitation, relative stability in summer, and increases in winter and early spring are predicted, but the changes in minimum temperature, relative humidity, and sunshine hours indicate relative stability. HIGHLIGHTS Projection of seven Tehran climate variables by eight CMIP5 models under RCP2.6, RCP4.5 and RCP8.5 in 2021-2040.; Downscaling by both SDSM and FDSM demonstrate there is no single model better than all other models, for all climate variables.; The performance of FDSM in downscaling of the temperature was shown to be the best model for Tehran.; FDSM in this research proved to be innovative and superior to other downscaling models which use the Sugeno system.; Predicted increases in annual temperature, precipitation, mean wind speed and relative stability in changes of relative humidity and sunshine for Tehran in 2021–2040 are provided.;

This research assesses the streamflow response of Werie River to climate change. Baseline (1980–2009) climate data of precipitation, maximum and minimum temperature were analyzed using delta based statistical downscaling approach in R software packages to predict future 90-year (2010–2099) periods under two emission scenarios of Representative Concentration Pathways (RCP) 4.5 and RCP 8.5, indicating medium and extremely high emission scenarios respectively. Generated future climate variables indicate Werie will experience a significant increase in precipitation, and maximum and minimum air temperature for both RCPs. Further, Water and Energy Transfer between Soil, Plants, and Atmosphere (WetSpa) was applied to assess the water balance of Werie River. The WetSpa model reproduced the streamflow well with performance statistics values of R2 = 0.84 and 0.85, Nash–Sutcliffe efficiency = 0.72 and 0.72, and model bias = –0.14 and –0.15 for the calibration data set of 1999–2010 and validation data of 2011–2014, respectively. Finally, by taking the downscaled future climate variables as input, WetSpa future prediction shows that there will be an increase in the Werie catchment mean annual streamflow up to 29.6% for RCP 4.5 and 35.6% for RCP 8.5 compared to the baseline period. HIGHLIGHTS In this study integrating AgMIP climate downscaling script in R-program with hydrological WetSpa model for prediction of catchment wide streamflow response to climate change have been proven to give acceptable results.; Future climate change predictions for Werie catchment were analyzed using R software packages considering two emission scenarios of Representative Concentration Pathways known as RCP 4.5 and RCP 8.5.; Future climate change assessment in Werie catchment showed a significant increase of precipitation, minimum and maximum temperatures.; Application of a fully distributed hydrological WetSpa; Water and Energy Transfer between Soil, Plants, and Atmosphere, model in Werie catchment revealed that the model performs well to reproduce the observed streamflow values, hence can be used in similar climatic and hydrological conditions.; In the 21st century streamflow in Warie catchment in response to climate change is expected to experience an increase during the rainy summer (June to September) season and a decrease in the dry (October to May) season.;

Climate change is one of the major challenges affecting natural ecosystems and various aspects of human life. The effects of global warming on the hydrology and water cycle in nature are very serious, and the quantitative recognition of these effects creates more readiness to deal with its consequences. In the present study, the 2006–2100 period is predicted based on the statistical downscaling model (SDSM). Finally, the effects of climate change on the hydrological conditions in the Darabad watershed are simulated using the Soil and Water Assessment Tool (SWAT) model. The SWAT model calibration is done based on the SUFI-2 algorithm, and the effective and optimal parameter is identified. The results of the study, while confirming the efficiency of both SDSM in climate simulations and SWAT in hydrological simulation, showed that the increase in precipitation and temperature is probably in future climate conditions for the 2010–2040 period. The surface flow and runoff at the watershed area during the observation period (1970–2010) is 0.29 m3/s, but this value for the predicted period with regard to climate change in the RCP 2.6, RCP 4.5, and RCP 8.5 scenarios is equal to 0.43, 0.44, and 0.45 m3/s. The results of research, while highlighting the importance of effects of climate change, make it essential to apply them for proper management in order to adapt to climate change in the future policies of the Darabad watershed management. HIGHLIGHTS Evaluation of climate change effects on surface runoff.; SWAT model, the best simulation daily runoff for watershed.; The model predicted average temperature increase by 2006–2100 of 6 °C by the CanEMS2.; Precipitation in the upcoming period of 2006–2100 shows a 52% increase.; The runoff in the upcoming periods of 2006–2040 under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios is increased by 45, 49, and 50%, respectively.;

The present study aims to assess flood depth, building risk analysis, and the effectiveness of various flood adaptation strategies to attenuate building risk caused by urban floods in climate change scenarios. A framework is proposed where a hydraulic model, Hydrologic Engineering Center's-River Analysis System 2D (HEC-RAS 2D), is applied for 2-dimensional flood modeling to estimate (a) submerged areas, (b) flood depth, and (c) building risk for extreme events corresponding to two representative concentration pathways (RCPs), 6.0 and 8.5. Greater Hyderabad Municipal Corporation (GHMC), India, is chosen for demonstration. Percentages of buildings in GHMC under high, medium, and low risks for RCP 6.0 are 38.19, 9.91, and 51.9% in the respective order, and these are 40.82, 10.55, and 48.63% for RCP 8.5. Six flood proofing (FP) strategies (S1–S6) are proposed for attenuating building risk along with the required capital cost. The capital investment required for FP to achieve the ideal situation of no risk for all buildings (strategy S6) works out to Rs. 3,740 × 107 and Rs. 3,800 × 107 for RCPs 6.0 and 8.5. It is observed that the effect of adaptation strategies is significant. HIGHLIGHTS Generation of flood inundation maps using Hydrologic Engineering Center's-River Analysis System 2D (HEC-RAS 2D) for the urban catchment of GHMC under two representative concentration pathways (RCPs).; Building risk analysis and formulation of adaptation strategies under two RCPs.;

Uncertainties of runoff projections arise from different sources of origin, such as climate scenarios (RCPs), global climate models (GCMs) and statistical downscaling (SD) methods. Assessment of uncertainties related to the mentioned sources was carried out for selected rivers of Lithuania (Minija, Nevėžis and Šventoji). These rivers reflect conditions of different hydrological regions (western, central and southeastern). Using HBV software, hydrological models were created for river runoff projections in the near (2021–2040) and far (2081–2100) future. The runoff projections according to three RCP scenarios, three GCMs and three SD methods were created. In the Western hydrological region represented by the Minija River, the GCMs were the most dominant uncertainty source (41.0–44.5%) in the runoff projections. Meanwhile, uncertainties of runoff projections from central (Nevėžis River) and southeastern (Šventoji River) regions of Lithuania were related to SD methods and the range of uncertainties fluctuates from 39.4% to 60.9%. In western Lithuania, the main source of rivers' supply is precipitation, where projections highly depend on selected GCMs. The rivers from central and southeastern regions are more sensitive to the SD methods, which not always precisely adjust the meteorological variables from a large grid cell of GCM into catchment scale.

Uncertainties of runoff projections arise from different sources of origin, such as climate scenarios (RCPs), global climate models (GCMs) and statistical downscaling (SD) methods. Assessment of uncertainties related to the mentioned sources was carried out for selected rivers of Lithuania (Minija, Nevėžis and Šventoji). These rivers reflect conditions of different hydrological regions (western, central and southeastern). Using HBV software, hydrological models were created for river runoff projections in the near (2021-2040) and far (2081-2100) future. The runoff projections according to three RCP scenarios, three GCMs and three SD methods were created. In the Western hydrological region represented by the Minija River, the GCMs were the most dominant uncertainty source (41.0-44.5%) in the runoff projections. Meanwhile, uncertainties of runoff projections from central (Nevėžis River) and southeastern (Šventoji River) regions of Lithuania were related to SD methods and the range of uncertainties fluctuates from 39.4% to 60.9%. In western Lithuania, the main source of rivers' supply is precipitation, where projections highly depend on selected GCMs. The rivers from central and southeastern regions are more sensitive to the SD methods, which not always precisely adjust the meteorological variables from a large grid cell of GCM into catchment scale. [ABSTRACT FROM AUTHOR]