ABSTRACT

Evaluation of future temperature and precipitation is essential for managing water supplies, reducing the impact of natural disasters, and expanding agricultural opportunities. In the present study, recently released Coupled Model Intercomparison Project Phase 6 (CMIP6) and CMIP5 were analyzed concerning the Mahanadi Reservoir Project (MRP), Complex, Chhattisgarh. The Statistical Downscaling Model (SDSM) projected climatic variables of shared socioeconomic pathways and representative concentration pathways (SSPs-RCPs) such as SSPs 245, SSPs 585, and RCP 4.5, and RCP 8.5, respectively, for two different timescales (2023–2060, 2061–2099). In recognition of subsequent timescales, the chosen GCM CCCmaCanESM2 was found to be the most efficient among CMIP5, whereas MPI-ESM1-2-HR was used for CMIP6. Higher temperatures and less precipitation are predicted in high-emission scenarios (SSP5-8.5 and RCPs 8.5) compared to mid-emission scenarios (SSP2-4.5 and RCPs 4.5). Therefore, the findings of this study could be utilized to forecast the hydrological cycle and analyze the sustainability of the environment. Furthermore, this study will be relevant for future water resource management and adaptation efforts.

1 INTRODUCTION

The current trend of global warming and its intensity are unprecedented compared to the pre-industrial era (Osman et al. 2021). Climate change is expected to lead to longer and more severe extreme weather events, which could have devastating impacts on people (Mora et al. 2018), organizations (Calel et al. 2020), and ecosystems (Cheng et al. 2013; Peng et al. 2022). To mitigate the worst effects of global warming, it is crucial to limit temperature increases to below 1.5°C (Hoegh-Guldberg et al. 2019; Smith et al. 2019). Thus, analyzing potential future changes in climatic variables like temperature and precipitation is vital for stakeholders and decision-makers to manage regional hazards, prevent significant impacts, and develop effective adaptation strategies. Given the limitations of past volatility studies and established patterns, climate projections play a key role in decision-support modeling (Wheater 2006). The Global Climate Models (GCMs), provided through the Coupled Model Intercomparison Project (CMIP) of the World Climate Research Programme (WCRP), are among the most advanced tools for simulating Earth's atmosphere, oceans, land, and glaciers. These models are frequently used to project climate change on a continental scale under various emission scenarios (Wheater et al. 2006). The IPCC's Fourth and Fifth Assessment Reports (AR4 and AR5) have presented and assessed results from CMIP3 and CMIP5 (Themeßl et al. 2012; Meehl et al. 2007). Even so, many researchers have pointed out that GCMs based on CMIP3 and CMIP5 have problems, such as big mistakes and a lack of accuracy because we don't fully understand how the atmosphere affects climate (Taylor et al. 2012; Orlowsky and Seneviratne 2012; Su et al. 2021; Benedict et al. 2019; Moss et al. 2010; Xuan et al. 2017).

Climate change poses a major threat to global water resources by affecting water availability, quality, and distribution. This impact is particularly concerning for extensive reservoir systems. As global temperatures continue to rise, it becomes crucial to assess how climate change will alter regional hydrological cycles and water management practices (Osman et al. 2021; Mora et al. 2018).

General Circulation Models (GCMs), part of the Coupled Model Intercomparison Project (CMIP), are essential for projecting future climate scenarios. The fifth phase, CMIP5, and the more recent CMIP6, offer various emission scenarios to forecast potential changes in temperature, precipitation, and other climate factors (Calel et al. 2020; Cheng et al. 2013). These models provide valuable insights into long-term climate trends and their possible effects on different sectors.

Despite advancements, GCMs still fall short of expert accuracy standards when simulating regional climate variables (Tang et al. 2016). Downscaling techniques address this issue by generating localized weather forecasts from global models. Statistical downscaling is the most common method for bridging the gap between global and regional scales, offering a practical approach to localizing global data (Schoof 2013; Wood et al. 2004). Compared to other methods, such as dynamical downscaling, statistical downscaling is essential for providing detailed data (Schoof 2013). The Statistical Downscaling Model (SDSM) is particularly effective in correcting GCM errors (Wilby et al. 2002) and has demonstrated success in predicting future climate changes (Wilby and Dawson 2013). While SDSM can utilize various GCMs, studies often focus on a few, such as CanESM2 and HadCM3. For instance, Gebrechorkos et al. (2019) applied SDSM with CanESM2 to project climate changes in East Africa, and Emami and Koch (2019) used SDSM to assess water supply impacts in Iran.

However, many downscaling studies have not thoroughly evaluated the suitability of GCMs for specific research areas. Comprehensive evaluation of GCMs is necessary to enhance stakeholder confidence in their application for hydrological or agricultural management Perez et al. (2014). For example, Wilby and Harris (2006) developed a probabilistic approach to address discrepancies among CMIP3 GCMs for the Thames River in the UK, and Aloysius et al. (2016) assessed 25 CMIP5 GCMs for performance and uncertainty in Central Africa. Similarly, Wang et al. (2019) evaluated 23 CMIP5 GCMs for predictions in Northwest China. Kreienkamp et al. (2020) analyzed various subsets of CMIP6's GCMs as they advance, using statistical-empirical downscaling to refine projections for Germany.

The primary objectives of this study were: (1) to identify the most appropriate GCMs (CMIP5/CMIP6) for the Mahanadi Reservoir Project Complex to minimize uncertainties, and (2) to project potential future changes in climatic variables (i.e., temperature and precipitation) for the Mahanadi Reservoir Project. This involved examining mid-century projections (2023–2060, 2040s) and late-century projections (2061–2099, 2070s) under various SSPs and RCP scenarios from the GCMs and comparing these projections to the baseline period of 1961–2005.

Therefore, assessing these scenarios is vital for developing effective water resource management and adaptation strategies. Precise projections enable stakeholders to project changes in water availability, manage flood risks, and plan for long-term sustainability. As climate science advances, integrating the latest GCMs outputs with advanced downscaling methods will be crucial for tackling climate change challenges and reinforcing the resilience of essential water infrastructure (Meehl et al. 2007; Taylor et al. 2012).

2 OVERVIEW OF THE STUDY AREA

The Mahanadi Reservoir Project Complex in Chhattisgarh encompasses the prominent Ravishankar Sagar Reservoir (Figure 1), which plays a vital role in the region's water management and agricultural planning. Situated on the Mahanadi River, the Ravishankar Sagar Reservoir, also known as the Gangrel Dam, plays a crucial role in maintaining water resource equilibrium throughout the state (Verma et al. 2021). The Ravishankar Sagar Reservoir, with a storage capacity of around 0.95 billion cubic meters (BCM), plays a crucial role in controlling river flow and maintaining a consistent water supply throughout the year. The reservoir effectively regulates this fluctuation by accumulating surplus water during the monsoon season and discharging it during seasons of low precipitation, thereby reducing the likelihood of both floods and droughts. The reservoir enables widespread irrigation initiatives throughout the region. An intricate system of interconnecting canals transfers the water, providing irrigation to approximately 104,000 hectares of land. This irrigation system greatly enhances agricultural output and aids local farmers by ensuring a consistent water supply for their crops. The Ravishankar Sagar Reservoir plays a crucial role in the Mahanadi Reservoir Project Complex by improving water security and agricultural efficiency in Chhattisgarh (Panda et al. 2013; Verma et al. 2022a; Verma et al. 2022b; Verma et al. 2022c; Verma et al. 2022d; Verma et al. 2023a; Verma et al. 2023b). It achieves this through its large storage capacity, efficient yearly rainfall management, and wide network of canals.

Figure 1 Study area.

Figure 2 shows the multipurpose Ravishankar Sagar Reservoir within the Mahanadi River Basin, located in the Dhamtari district of Chhattisgarh, India. This reservoir fulfils various roles, including supplying water for agricultural, municipal, and industrial purposes. Additionally, the Murumsilli and Dhudhawa reservoirs, which are key components of the Mahanadi River Basin, contribute water to the Ravishankar Sagar reservoir.

Figure 2 Schematic diagram of Mahanadi Reservoir Project Complex, Chhattisgarh.

2.1 Data availability

We sourced daily gridded observed rainfall data spanning 45 years (1961–2005) from the Indian Meteorological Department (IMD), Pune, India. We applied the Standard Normal Homogeneity Test (SNHT) to assess the consistency of the observed data across time series segments, with a p-value of 0.05, indicating compatibility between stations. Therefore, data was excluded from stations like Keskal and Mahanadi Sarangpal in the study due to their p-values being less than 0.05. Additionally, South Asian regional climate data for the CORDEX-CMIP5 experiment was obtained from https://esgf-data.dkrz.de/search/cordex-dkrz/ (Verma et al. 2022a). The WCRP (https://esgf-node.llnl.gov/projects/cmip6/) provides access to data released by the Intergovernmental Panel on Climate Change (IPCC) in its 6th assessment report for the CMIP6 shared socio-economic pathways (SSPs).

3 METHODOLOGY

Figure 3 represents the overall methodology of the present study which mainly consists of three segments:

1. Data acquisition and data preparation,
2. Downscaling of climate data in the context of the study area and selection of appropriate earth system climate (ESMs) models, and
3. Hydrological modeling and streamflow prediction and finally assess the climate change impact (Verma et al. 2023b).

Figure 3 Methodology flowchart.

3.1 Selection of efficient GCM models

General Circulation Models (GCMs) were extensively utilized to reconstruct past climates and project future climatic conditions (Maraun et al. 2010). Using GCMs for regional climates, on the other hand, creates uncertainty because of variations in resolution (fine vs. coarse), climatic response mechanisms (such as aerosols and land-ocean-atmospheric interactions), and spatial and temporal scales (Jain et al. 2019). Therefore, it is essential to carefully evaluate each GCM to minimize these uncertainties before applying them in practice. The standard approach for assessing the accuracy of GCM simulations involves comparing them with reanalysis or observed climate data. Researchers frequently employ various indicators to evaluate climate models. One effective tool is the Taylor diagram, which graphically depicts the correlation coefficient (CC), root-mean-square error (RMSE), and standard deviations (STD) to compare model simulations with observed data (Taylor 2001). If a GCM demonstrates a high correlation and minimal error with observed data, it is considered suitable for a regional climate system.

3.2 Statistical DownScaling Model (SDSM)

Wilby et al. (2002) introduced the Statistical DownScaling Model (SDSM) to analyze the impacts of climate change at a regional level. Many climate studies have utilized SDSM 4.2, a Visual Basic-based tool (Wilby and Dawson 2013). SDSM employs multiple linear regressions to establish statistical relationships between large-scale and regional-scale environmental variables, such as temperature and precipitation. It incorporates interactions between regional forces and local meteorological conditions to simulate precipitation patterns, initially transformed by the fourth root. For instance, the frequency of wet days influences local precipitation, and factors like moisture levels and atmospheric pressure predict broader precipitation patterns. A daily precipitation threshold of 1.0 mm was chosen for this study, following the common practice of statistical downscaling (Peng et al. 2023).

3.3 Hydrological modeling (streamflow prediction)

Developed in the early 1990s by the Agricultural Research Service of the United States Department of Agriculture, the Soil and Water Assessment Tool (SWAT) is a quasi-distributed, physically based hydrological model (Arnold et al. 2012). It has gained widespread use for streamflow modeling in both small and large watersheds. SWAT was employed for this study to forecast monthly streamflow across the MRP Complex. The model segments the watershed into smaller basins with similar land use, soil, and slope characteristics, known as Hydrologic Response Units (HRUs) (Verma et al. 2023b). For a comprehensive overview of the SWAT modeling process and the evaluation of objective functions, refer to Verma et al. (2023b).

The hydrological modeling method using the Soil and Water Assessment Tool (SWAT) entails the simulation of several watershed processes to forecast streamflow and water quality. The SWAT model partitions a watershed into smaller, controllable sections or HRUs. The HRUs were delineated based on comparable land use, soil characteristics, and slope conditions. SWAT is a model that replicates several hydrological processes, such as precipitation, evapotranspiration, runoff, and infiltration, to calculate streamflow and other hydrological results. The model's capacity to accommodate both small and big watersheds renders it highly adaptable for diverse uses. In SWAT, the process of calibration and validation entails a meticulous comparison of model predictions with observed data to determine the model's precision. SWAT provides excellent insights into water resource management and environmental implications in a specific area by incorporating comprehensive watershed characteristics and hydrological processes.

4 RESULTS AND DISCUSSION

4.1 Evaluation of GCMs

Precipitation and temperature data from 16 CMIP5 and 13 CMIP6 general circulation models (GCMs) were utilized. While many studies evaluate only the top 3–10 performing GCMs for multi-model ensembles (MMEs), the literature lacks standardized methods for determining the optimal number of GCMs for MME analysis. A single GCM cannot adequately capture the uncertainty in future climate projections. Consequently, the top three GCMs were selected from CMIP5 and CMIP6 based on their performance.

We used the Statistical DownScaling Model (SDSM) to downscale and adjust GCM temperature and precipitation projections for the baseline period (1961–2005). The accuracy of the downscaled models was assessed using RMSE and correlation coefficient (CC) comparisons with raw data. Figure 4 (a-d) illustrates the Taylor diagram (TD) for evaluating different indices. Notable differences were observed between the CMIP6 and CMIP5 models. In precipitation, CMIP6 had standard deviations of 122 mm and an RMSE of 165.66 mm, whereas CMIP5 had standard deviations of 114 mm and the same RMSE. The correlation coefficients were 0.78 for CMIP6 and 0.76 for CMIP5. CMIP6 simulations were slightly more accurate in capturing precipitation amplitudes and reducing observational errors.

Figure 4 (a–d) Taylor diagrams of precipitation and temperature variables for CMIP5 and CMIP6, respectively.

For temperature, CMIP6 had a higher RMSE of 4.14°C compared to CMIP5's 3.84°C with correlation coefficients of 0.90 and 0.75, respectively. The standard deviation was 1.82°C for CMIP6 and 3.83°C for CMIP5. After bias correction and downscaling, the RMSE and CC for monthly precipitation were 165.66 mm and 0.78 for CMIP6, compared to 148.33 mm and 0.76 for CMIP5. Temperature simulations showed a smaller increase compared to precipitation, with CMIP6 exhibiting a slight improvement in RMSE and CC over CMIP5. Overall, CMIP6 models performed better in simulating temperatures, while CMIP5 models showed slightly better results for precipitation. Statistical downscaling methods have effectively replicated precipitation and temperature records for the Mahanadi Reservoir Project Complex in Chhattisgarh.

4.2 Annual streamflow variability

The study observed how the predictions from different GCMs changed by using the SWAT model to simulate future streamflow in the baseline, RCP 4.5, and RCP 8.5 scenarios in CMIP5 and the SSP 2-4.5 and SSP 5-8.5 scenarios in CMIP6. Figure 5 (a-d) shows that annual streamflow projections for these scenarios exhibit similar trends across the basin. For CMIP5 models, the projected annual streamflow ranges from 165.77 to 1,657.50 m³ under the mid-RCP 4.5 scenario, and from 166 to 1,657.50 m³ for other scenarios. For CMIP6 models, annual streamflow ranges from 316.90 to 2,351.13 m³ under the mid-scenario, and from 263.70 to 3,327.05 m³ under the high scenario. Figure 5 (a-d) indicates significant discrepancies between GCM ensembles. Notably, the CCCma-CanESM2 (RCP 8.5) simulation shows the highest streamflow projections, ranging from 251.90 to 1,705.29 m³. Similarly, the CMIP6 MPI-ESM1-2HR (SSPs 5-8.5) model produces a wide range of simulated streamflow, from 263.70 to 3,327.05 m³, surpassing other GCMs in variability. The differences between the RCP 8.5 and SSP 5-8.5 simulations may be due to uncertainty caused by models that aren't calibrated properly and models with different features across the GCMs (Bağçaci et al. 2021; Lutz et al. 2016).

Figure 5 (a–d) Annual streamflow changes between the corresponding GCMs (CMIP5/CMIP6).

4.3 Climatic variables assessment concerning observed datasets

Table 1 shows the changes in GCMs (CMIP5 and CMIP6) climatic variables for catchment on a mean annual basis in the context of a multi-model mean ensemble concerning observed datasets. According to Table 1, there is a 20 to 47% rise in mean annual precipitation under SSPs 5-8.5 (CMIP6) by the end of the 21st century, whereas a 10 to 30% rise is observed in RCPs 8.5 (CMIP5) in comparison to the observed dataset. In contrast, a 2.2°C temperature has an increase in SSPs 5-8.5 by the end of the 21st century, whereas a 2–3°C temperature decreases for RCPs 8.5. Therefore, the climatic variables significantly increase in the case of CMIP6 in comparison to the observed dataset, whereas for CMIP5, the same decreases in comparison to the observed dataset. In the present analysis, we have used mid-emission (RCPs 4.5/SSPs 2-4.5) and high-emission (RCP 8.5/SSPs 5-8.5) scenarios to examine the potential climate change impact on the study area. Hence, the study reveals that there is still research scope to quantify which emission scenario is best suited for the study area. For validation purposes, a similar pattern was also carried out by Mishra et al. (2020).

Table 1 Changes in climatic variables for catchment on a mean annual basis w. r. t. observed datasets.

Table 1 presents projected changes in precipitation (PCP) and temperature (TEMP) based on the CMIP5 and CMIP6 models in comparison to historical measurements from 1961 to 2005. In CMIP5, precipitation exhibits minimal changes under the RCP 4.5/SSP 2-4.5 scenarios. In the near future (NF), there is a slight increase of 1.09%, while in the far future (FF), there is a slight drop of -0.93%. The temperature in NF is expected to decline by 10.41%, while in FF it is anticipated to decrease by 9.05%. However, the CMIP6 estimates for the same scenarios indicate a significant 6.76% increase in precipitation and a 2.55% increase in temperature in NF. According to the forecast, there is a planned increase of 20.17% in precipitation and 4.97% in temperature for FF.

According to the RCP 8.5/SSP 5-8.5 scenarios, the CMIP5 expects a 1.60% decrease in precipitation in NF and a 2.39% minor increase in temperature. Within the FF context, the amount of precipitation remains nearly the same, with a slight increase of 0.0041%, while the temperature experiences a significant jump of 7.99%. In CMIP6, the estimates indicate that precipitation will increase by 7.64% in NF and 47.98% in FF. The temperature is expected to increase by 2.39% in NF and by 7.99% in FF. The disparities in projected climatic changes between CMIP5 and CMIP6 models highlight the significant variations, which have a direct impact on future water resources and temperature trends.

5 Limitations of the Present Study

This study has several limitations. First, the study's reliance on a limited selection of GCMs from CMIP5 and CMIP6 might not encompass the full spectrum of climate variability and uncertainties. Second, despite adjustments, inherent biases in GCM projections could impact the accuracy of future climate forecasts. Third, the quality of input data and calibration assumptions have an impact on the SWAT model's effectiveness. Moreover, the focus on temperature and precipitation may neglect other important climate factors such as humidity and wind speed. Finally, the use of specific emission scenarios introduces uncertainties regarding their long-term applicability. These limitations suggest a need for cautious interpretation and further validation of the findings through additional research.

6 Future Scope of the Present Study

Incorporating both CMIP5 and CMIP6 models enhances the accuracy of climate projections, offering a deeper understanding of the complex interactions between climate variables and the reservoir system. This increased precision is vital for anticipating potential challenges such as changes in water availability, heightened flood risks, and ecological impacts. Advanced modeling techniques not only improve prediction accuracy but also facilitate the evaluation of uncertainties related to climate change projections. This information is crucial for policymakers and stakeholders, enabling informed decision-making. Continuous monitoring and assessment of these models are necessary for timely updates and refinements, ensuring that the Climate Change Impact Assessment remains relevant as scientific knowledge and technology evolve. This approach ultimately supports the development of more effective and sustainable water resources management strategies for the Mahanadi Reservoir Project Complex in response to climate change.

7 How Present Work Will Benefit the Chhattisgarh State

The findings of this study can significantly benefit Chhattisgarh State by providing actionable insights into climate change adaptation and mitigation strategies. Here are some potential benefits:

The study aims to unveil the impact of climate change on the Mahanadi Reservoir Project Complex by employing advanced models such as CMIP5 and CMIP6. These models help in simulating future climate scenarios, enabling a comprehensive assessment of potential changes in temperature and precipitation climatic variables.

Informed Decision-Making: Policymakers and authorities in Chhattisgarh State will have access to scientifically grounded information about the potential impacts of climate change on the Mahanadi Reservoir Project Complex.

Risk Reduction: Understanding the suitability of different emission scenarios helps in identifying high-risk areas and potential challenges.

Sustainable Development: The research outcomes can contribute to the development of sustainable practices and policies.

International Collaboration: Sharing the study findings with the global scientific community and participating in international discussions on climate change can position Chhattisgarh State as a proactive and responsible entity.

8 Conclusions

To the best of the author's knowledge, few studies have assessed the impact of climate change on the hydroclimate of the Mahanadi Reservoir Project Complex. Water availability is a critical issue in Dhamtari's water resources management. This study addresses this concern by examining climate change effects in the Dhamtari region using two Shared Socioeconomic Pathways (SSPs) for CMIP6 (SSP 2-4.5 and SSP 5-8.5) and two Representative Concentration Pathways (RCPs) for CMIP5 (RCP 4.5 and RCP 8.5). A SWAT model was used to simulate streamflow in the region, adjusting meteorological data from 13 CMIP6 and 16 CMIP5 Earth System Models for systematic biases and calibrating with local observational data to ensure accurate simulations.

The study compared future climate conditions for two time periods (2023–2060 and 2061–2099) under different scenarios to assess changes in hydroclimate variables like precipitation (PCP) and temperature (TEMP). The results indicate that the CCCma-CanESM2 model (CMIP5) and the MPI-ESM1-2-HR model (CMIP6) were the most effective for hydrological projections in the near and far future, respectively. By the end of the 21st century, SSP 5-8.5 predicts a temperature increase of 2.2°C, and a 20 to 47% rise in mean annual precipitation. CMIP5 models project an increase in streamflow of 30.78–35.22% under RCP 4.5 to RCP 8.5 scenarios, while CMIP6 models predict a rise of 28.29–58.24%. These findings underscore the importance of strategic water resources management and enhanced agricultural planning to address the long-term impacts of hydroclimate changes in Dhamtari.

Acknowledgements

The authors would like to thank the National Institute of Technology, Raipur for providing lab facilities and software availability throughout the study.

References

1. Aloysius, N.R., J. Sheffield, J.E. Saiers, H. Li, and E.F. Wood. 2016. “Evaluation of historical and future simulations of precipitation and temperature in central Africa from CMIP5 climate models.” Journal of Geophysical Research: Atmospheres 121 (1): 130–152. https://doi.org/10.1002/2015JD023656
2. Arnold, J.G., D.N. Moriasi, P.W. Gassman, K.C. Abbaspour, M.J. White, R. Srinivasan, et al. 2012. “SWAT: Model use, calibration, and validation.” Transactions of the ASABE 55 (4): 1491–1508.
3. Bağçaci, S.Ç., I. Yucel, E. Duzenli, and M.T. Yilmaz. 2021. “Intercomparison of the expected change in the temperature and the precipitation retrieved from CMIP6 and CMIP5 climate projections: A Mediterranean hot spot case, Turkey.” Atmospheric Research 256, 105576. https://doi.org/10.1016/j.atmosres.2021.105576
4. Benedict, I., C.C. Van Heerwaarden, A.H. Weerts, and W. Hazeleger. 2019. “The benefits of spatial resolution increase in global simulations of the hydrological cycle evaluated for the Rhine and Mississippi basins.” Hydrology and Earth System Sciences 23 (3): 1779–1800. https://doi.org/10.5194/hess-23-1779-2019
5. Calel, R., S.C. Chapman, D.A. Stainforth, and N.W. Watkins. 2020. “Temperature variability implies greater economic damages from climate change.” Nature Communications 11 (1): 5028. https://doi.org/10.1038/s41467-020-18797-8
6. Cheng, H., A. Sinha, F.W. Cruz, X. Wang, R.L. Edwards, F.M. d’Horta, C.C. Ribas, et al. 2013. “Climate change patterns in Amazonia and biodiversity.” Nature Communications 4 (1): 1411. https://doi.org/10.1038/ncomms2415
7. Emami, F. and M. Koch. 2019. “Modeling the impact of climate change on water availability in the Zarrine River Basin and inflow to the Boukan Dam, Iran.” Climate 7 (4): 51. https://doi.org/10.3390/cli7040051
8. Gebrechorkos, S.H., S. Hülsmann, and C. Bernhofer. 2019. “Regional climate projections for impact assessment studies in East Africa.” Environmental Research Letters 14 (4): 044031. https://doi.org/10.1088/1748-9326/ab055a
9. Hoegh-Guldberg, O., D. Jacob, M. Taylor, T. Guillén Bolaños, M. Bindi, S. Brown, I.A. Camilloni, et al. 2019. “The human imperative of stabilizing global climate change at 1.5°C.” Science 365 (6459): eaaw6974. https://doi.org/10.1126/science.aaw6974
10. Jain, S., P. Salunke, S.K. Mishra, and S. Sahany. 2019. “Performance of CMIP5 models in the simulation of Indian summer monsoon.” Theoretical and Applied Climatology 137: 1429–1447. https://doi.org/10.1007/s00704-018-2674-3
11. Kreienkamp, F., P. Lorenz, and T. Geiger. 2020. “Statistically downscaled CMIP6 projections show stronger warming for Germany.” Atmosphere 11 (11): 1245. https://doi.org/10.3390/atmos11111245
12. Lutz, A.F., H.W. ter Maat, H. Biemans, A.B. Shrestha, P. Wester, and W.W. Immerzeel. 2016. “Selecting representative climate models for climate change impact studies: An advanced envelope‐based selection approach.” International Journal of Climatology 36 (12): 3988–4005. https://doi.org/10.1002/joc.4608
13. Maraun, D., F. Wetterhall, A.M. Ireson, R.E. Chandler, E.J. Kendon, M. Widmann, S. Brienen, et al. 2010. “Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user.” Reviews of Geophysics 48, 3. https://doi.org/10.1029/2009RG000314
14. Meehl, G.A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J.F. Mitchell, R.J. Stouffer, and K.E. Taylor. 2007. “The WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research.” Bulletin of the American Meteorological Society 88 (9): 1383–1394. https://doi.org/10.1175/BAMS-88-9-1383
15. Mishra, V., U. Bhatia, and D. Tiwari. 2020. “Bias-corrected climate projections for South Asia from coupled model intercomparison project-6.” Scientific Data 7, 338. https://doi.org/10.1038/s41597-020-00681-1
16. Mora, C., D. Spirandelli, E.C. Franklin, J. Lynham, M.B. Kantar, W. Miles, C.Z. Smith, et al. 2018. “Broad threat to humanity from cumulative climate hazards intensified by greenhouse gas emissions.” Nature Climate Change 8, 1062–1071. https://doi.org/10.1038/s41558-018-0315-6
17. Moss, R.H., J.A. Edmonds, K.A. Hibbard, M.R. Manning, S.K. Rose, D.P. Van Vuuren, T.R. Carter, et al. 2010. “The next generation of scenarios for climate change research and assessment.” Nature 463, 747–756. https://doi.org/10.1038/nature08823
18. Orlowsky, B. and S.I. Seneviratne. 2012. “Global changes in extreme events: regional and seasonal dimension.” Climatic Change 110, 669-696. https://doi.org/10.1007/s10584-011-0122-9
19. Osman, M.B., J.E. Tierney, J. Zhu, R. Tardif, G.J. Hakim, J. King, and C.J. Poulsen. 2021. “Globally resolved surface temperatures since the Last Glacial Maximum.” Nature 599, 239–244. https://doi.org/10.1038/s41586-021-03984-4
20. Panda, D.K., A. Kumar, S. Ghosh, and R.K. Mohanty. 2013. “Streamflow trends in the Mahanadi River basin (India): Linkages to tropical climate variability.” Journal of Hydrology 495, 135–149. https://doi.org/10.1016/j.jhydrol.2013.04.054
21. Peng, Q., S.P. Xie, D. Wang, R.X. Huang, G. Chen, Y. Shu, J.R. Shi, and W. Liu. 2022. “Surface warming–induced global acceleration of upper ocean currents.” Science Advances 8 (16): eabj8394. https://doi.org/10.1126/sciadv.abj8394
22. Peng, S., C. Wang, Z. Li, K. Mihara, K. Kuramochi, Y. Toma, and R. Hatano. 2023. “Climate change multi-model projections in CMIP6 scenarios in Central Hokkaido, Japan.” Scientific Reports 13, 230. https://doi.org/10.1038/s41598-022-27357-7
23. Perez, J., M. Menendez, F.J. Mendez, and I.J. Losada. 2014. “Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region.” Climate Dynamics 43, 2663–2680. https://doi.org/10.1007/s00382-014-2078-8
24. Schoof, J.T. 2013. “Statistical downscaling in climatology.” Geography Compass 7 (4): 249–265. https://doi.org/10.1111/gec3.12036
25. Smith, C.J., P.M. Forster, M. Allen, J. Fuglestvedt, R.J. Millar, J. Rogelj, and K. Zickfeld. 2019. “Current fossil fuel infrastructure does not yet commit us to 1.5°C warming.” Nature Communications 10, 101. https://doi.org/10.1038/s41467-018-07999-w
26. Su, B., J. Huang, S.K. Mondal, J. Zhai, Y. Wang, S. Wen, M. Gao, et al. 2021. “Insight from CMIP6 SSP-RCP scenarios for future drought characteristics in China.” Atmospheric Research 250, 105375. https://doi.org/10.1016/j.atmosres.2020.105375
27. Tang, J., X. Niu, S. Wang, H. Gao, X. Wang, and J. Wu. 2016. “Statistical downscaling and dynamical downscaling of regional climate in China: Present climate evaluations and future climate projections.” Journal of Geophysical Research: Atmospheres 121 (5): 2110–2129. https://doi.org/10.1002/2015JD023977
28. Taylor, K.E. 2001. “Summarizing multiple aspects of model performance in a single diagram.” Journal of Geophysical Research: Atmospheres 106 (D7): 7183–7192. https://doi.org/10.1029/2000JD900719
29. Taylor, K.E., R.J. Stouffer, and G.A. Meehl. 2012. “An overview of CMIP5 and the Experiment Design.” Bulletin of the American Meteorological Society 93 (4): 485–498. https://doi.org/10.1175/BAMS-D-11-00094.1
30. Themeßl, M.J., A. Gobiet, and G. Heinrich. 2012. “Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal.” Climatic Change 112, 449–468. https://doi.org/10.1007/s10584-011-0224-4
31. Verma, S., A.D. Prasad, and M.K. Verma. 2021. “Trend analysis and rainfall variability of monthly rainfall in Sheonath River Basin, Chhattisgarh.” In Recent Trends in Civil Engineering: Select Proceedings of ICRTICE 2019, pp. 777–790. Springer, Singapore. https://doi.org/10.1007/978-981-15-5195-6_58
32. Verma, S., A.D. Prasad, and M.K. Verma. 2022a. “Optimizing Multi-reservoir Systems with the Aid of Genetic Algorithm: Mahanadi Reservoir Project Complex, Chhattisgarh.” In: Boonpook, W., Z. Lin, P. Meksangsouy, P. Wetchayont (eds). Applied Geography and Geoinformatics for Sustainable Development. Springer Geography. Springer, Cham. https://doi.org/10.1007/978-3-031-16217-6_3
33. Verma, S., A.D. Prasad, and M.K. Verma. 2022b. “Time Series Modelling and Forecasting of Mean Annual Rainfall Over MRP Complex Region Chhattisgarh Associated with Climate Variability.” In: Reddy, K.R., S. Kalia, S. Tangellapalli, D. Prakash. (eds) Recent Advances in Sustainable Environment. Lecture Notes in Civil Engineering, 285, 51–67. Springer, Singapore. (). https://doi.org/10.1007/978-981-19-5077-3_5
34. Verma, S., A.D. Prasad, and M.K. Verma. 2022c. “Trends of rainfall and temperature over Chhattisgarh during 1901–2010.” In: Advanced Modelling and Innovations in Water Resources Engineering: Select Proceedings of AMIWRE 2021, 3–19. Springer, Singapore. https://doi.org/10.1007/978-981-16-4629-4_1
35. Verma, S.K., R.T. Sahu, H. Singh, A.D. Prasad, and M.K. Verma. 2022d. “A Study of Environmental and Ecological Impacts due to Construction and Operation of Tehri-Polavaram Dam.” IOP Conference Series: Earth and Environmental Science 1032, 012020. https://doi.org/10.1088/1755-1315/1032/1/012020
36. Verma, S., R. Sahu, A. Prasad, and M. Verma. 2023a. “Reservoir operation optimization using ant colony optimization a case study of Mahanadi Reservoir Project Complex, Chhattisgarh-India.” LARHYSS Journal 53, 73–93.
37. Verma, S., M.K. Verma, A.D. Prasad, D.J. Mehta, and M.N. Islam. 2023b. “Modeling of uncertainty in the estimation of hydrograph components in conjunction with the SUFI-2 optimization algorithm by using multiple objective functions.” Modeling Earth Systems and Environment 10, 61–79. https://doi.org/10.1007/s40808-023-01758-7
38. Wang, R., Q. Cheng, L. Liu, C. Yan, and G. Huang. 2019. “Multi-model projections of climate change in different RCP scenarios in an arid inland region, Northwest China.” Water 11 (2): 347. https://doi.org/10.3390/w11020347
39. Wheater, H.S. 2006. “Flood hazard and management: a UK perspective.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 364 (1845): 2135–2145. https://doi.org/10.1098/rsta.2006.1817
40. Wheater, H.S., C. Neal, and D. Peach. 2006. “Hydro-ecological functioning of the Pang and Lambourn catchments, UK: An introduction to the special issue.” Journal of Hydrology 330 (1–2): 1–9. https://doi.org/10.1016/j.jhydrol.2006.04.035
41. Wilby, R.L. and C.W. Dawson. 2013. “The statistical downscaling model: Insights from one decade of application.” International Journal of Climatology 33 (7): 1707–1719. https://doi.org/10.1002/joc.3544
42. Wilby, R.L., C.W. Dawson, and E.M. Barrow. 2002. “SDSM—A decision support tool for the assessment of regional climate change impacts.” Environmental Modelling & Software 17 (2): 145–157. https://doi.org/10.1016/S1364-8152(01)00060-3
43. Wilby, R.L. and I. Harris. 2006. “A framework for assessing uncertainties in climate change impacts: Low‐flow scenarios for the River Thames, UK.” Water Resources Research 42, 2. https://doi.org/10.1029/2005WR004065
44. Wood, A.W., L.R. Leung, V. Sridhar, and D.P. Lettenmaier. 2004. “Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs.” Climatic Change 62, 189–216. https://doi.org/10.1023/B:CLIM.0000013685.99609.9e
45. Xuan, W., C. Ma, L. Kang, H. Gu, S. Pan, and Y.-P. Xu. 2017. “Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province, China.” Theoretical and Applied Climatology 128: 207–222. https://doi.org/10.1007/s00704-015-1704-7