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Science of the Total Environment 789 (2021) 147867 Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv Projected changes in temperature, precipitation and potential evapotranspiration across Indus River Basin at 1.5 3.0 C warming levels using CMIP6-GCMs Sanjit Kumar Mondal a,b,1, Hui Tao a,1, Jinlong Huang b, Yanjun Wang b, Buda Su a,b, , Jianqing Zhai b,c, Cheng Jing b, Shanshan Wen b, Shan Jiang b, Ziyan Chen b, Tong Jiang a,b, a State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Institute for Disaster Risk Management, School of Geographical Science, Nanjing University of Information Science & Technology, Nanjing 210044, China c National Climate Center, China Meteorological Administrations, Beijing 100081, China b H I G H L I G H T S G R A P H I C A L A B S T R A C T Temperature and PET will increase largely with continued global warming across IRB. 2.Extra 0.5 C and 1.0 C of SWLs will sharpen temperature by 0.4 and 0.9 C respectively. Both wet events (upper basin) and dry events (lower basin) are anticipated to occur. a r t i c l e i n f o Article history: Received 8 February 2021 Received in revised form 14 May 2021 Accepted 14 May 2021 Available online 20 May 2021 Editor: A P Dimri Keywords: Climate change Global warming Temperature Precipitation a b s t r a c t The projections of mean temperature, precipitation (P), and potential evapotranspiration (PET) re ect the probabilities of long-term changes of hydrologic processes and induced extreme events. In this paper, we investigated the future changes in some pivotal climatic variables (mean temperature, precipitation, and potential evapotranspiration) under 1.5 C, 2.0 C, and 3.0 C speci c warming levels (SWLs) across the Indus River Basin of South Asia. The seven global climate models output under seven different emission scenarios (SSP1 1.9, SSP1 2.6, SSP2 4.5, SSP3 7.0, SSP4 3.4, SSP4 6.0, and SSP5 8.5) from the latest Sixth phase of Coupled Model Intercomparison Project (CMIP6) are used for this purpose. The Penman-Monteith approach is applied to estimate PET, and the water balance equation is for re ecting water surplus/de cit. Results indicate that except for precipitation, the greater increases in temperature and PET are inclined to happen with continued global warming. The highest increase in temperature is accounted for 14.6% (2.4 C), and the enhanced PET is estimated at 5.2% higher than the reference period (1995 2014) under 3.0 C SWL. While the precipitation is projected to increase by the highest 4.8% for 2.0 C warming level. The differences in regional climate for an additional 0.5 C (2.0 1.5 C) and 1.0 C (3.0 2.0 C) of warming, the temperature is projected to increase by 0.4 C and 0.9 C in Corresponding authors at: Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Institute for Disaster Risk Management, School of Geographical Science, Nanjing University of Information Science & Technology, Nanjing 210044, China. E-mail addresses: subd@cma.gov.cn (B. Su), jiangtong@nuist.edu.cn (T. Jiang). 1 First authors (equal contribution): Sanjit Kumar Mondal and Hui Tao. https://doi.org/10.1016/j.scitotenv.2021.147867 0048-9697/ 2021 Elsevier B.V. All rights reserved. S.K. Mondal, H. Tao, J. Huang et al. Evapotranspiration Indus River Basin Science of the Total Environment 789 (2021) 147867 the entire IRB respectively. The highest increase in mean temperature (5.1%) and PET (2.4%) in the IRB are predicted to intensify for an additional 1.0 C than that of 0.5 C of warming, but precipitation is intended to decrease by 0.4%. Spatially, the increase in temperature, precipitation, and PET are dominated towards high elevation in the upper basin (north) under all the SWLs. The increased variability in climatological parameters across IRB depicts an evident occurrence of both wet events (upper basin) as well as dry events (lower basin) with the increase in global average temperature rise. However, these ndings provide an insightful basis for water resource management as well as initiating mitigation and adaptation measures in the IRB related to water surplus ( oods) and water de cit (droughts). 2021 Elsevier B.V. All rights reserved. climate variability which aggravates livelihood and well-being insecurity (Lutz et al., 2014). Eventually, the alternation in its water resources will adversely impact food security and the ecoenvironment in the basin region. Due to increasing water scarcity, almost 13 million ha agricultural land in Pakistan remains barren (ICIMOD, 2010). Kraaijenbrink et al. (2017) stated that continuous warming climate will hasten the loss of snow and ice cover in the basin, causing ash oods and glacial lake outbursts oods. Changes in key climate parameters such as precipitation, temperature, and evapotranspiration have a major in uence on the basin's runoff, water cycle, and water supply chain (Su et al., 2016; Shrestha et al., 2019). In total, how the present and future water availability under changing climate can be unfolded has been the critical aspect in the IRB. Hence, more detailed information on plausible future climatic conditions is pivotal for regional and international-level policymakers to deal with water resource management, planning, and adaptation policy interventions. Global climate models (GCMs) are obligatory tools for anticipating future climate change. Although several studies have carried out in the IRB using GCMs from different previous phases of Coupled Model Intercomparison Project (CMIP) (i.e. CMIP3 and CMIP5), the reliable re ection of future climate status was limited due to GCM's coarse resolution and associated uncertainties. However, the latest sixth phase of CMIP (CMIP6) is now available to contribute IPCC sixth assessment report (AR6). Dynamic improvements are added in the CMIP6 phase to simulate a more reasonable climate, especially a large number of advance GCMs, higher horizontal resolution, better representation of synoptic process, comprehensive scenario development, and up-to-date forcing data consideration (Eyring et al., 2016). In this phase, more realistic scenarios are designed to secure the combination of Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs) (SSP-RCP) (Eyring et al., 2016). To serve the growing research societies, eight indispensable SSP-RCP scenarios (SSP1 1.9, SSP1 2.6, SSP2 4.5, SSP3 7.0, SSP4 3.4, SSP4 6.0, SSP5 8.5, and SSP5 3.4-OS) have been produced within this phase with scenario gap- lling from CMIP5. The scenario-based results from different integrated research will be key in informing climate mitigation and adaptation policy considerations, including processes that are part of the Paris Climate Agreement, 2015 (O'Neill et al., 2016). Therefore, in this research, all the respective scenario combinations are given priority to unveil comprehensive warming thresholds. In light of these issues, the primary objective of this paper is to investigate the changes and trends of projected temperature, precipitation (P), and potential evapotranspiration (PET) in the IRB under 1.5 C, 2.0 C, and 3.0 C SWLs. We also diagnosis how changes matter in the aspect of an additional 0.5 C (2.0 1.5 C) and 1.0 C (3.0 2.0 C) global temperature increase set out based on the Paris agreement targets. In addition, this study demonstrates the re ection of water surplus or de cit across the basin applying the water balance eq. (P-PET). As the projected changes in key climate variables in the basin considering different warming levels have received less attention, this research considers speci c warming levels (SWLs) of 1.5 C, and 2.0 C recognized as mitigation targets in the Paris climate change accord, as well as 3.0 C warming that is closer to what would be evolved by the 21st 1. Introduction Global warming is determined to augment climate change and associated risks over the world. According to the IPCC, it is estimated to raise the global mean temperature by 0.2 0.1 C per decade and has already warmed by 1 C (IPCC, 2013). Recently, climate change has been a hard reality that revokes the biophysical environment and perturbs socioeconomic advancement globally. However, precipitation, mean temperature, and evapotranspiration are recognized as the pivotal determining features of the overall climate in a region. The environmental water cycle of an area is controlled by precipitation, surface runoff, and evapotranspiration, where evapotranspiration is the key element of the hydrological cycle (Miao et al., 2020). Evolving evidence signi es that warming-induced climate has been affecting the global hydrological cycle by altering the patterns of precipitation, temperature, surface runoff, evapotranspiration, and increasing atmospheric water vapor content (Huntington, 2010; Rawlins et al., 2010). Such changes usually lead to further intensi cation of different climate extreme events (i.e. droughts, oods, heatwaves, cold waves, extreme precipitation events, etc.) in terms of duration, frequency, and intensity. Accompanying temperature rise, different climate-related dangerous events have escalated signi cantly around the globe (WMO, 2010). Hence, a detailed investigation of changes in global and regional climate patterns could assist policymakers to design national- and international-scale adaptation and mitigation strategies. Global-scale surface temperature rise will likely have an adverse impact on human society and natural systems (IPCC, 2013). To abate the climate risks and severe impacts, the Paris Agreement in 2015 proposed an ambitious goal to hold global mean temperature rise well below 2.0 C above pre-industrial level, and to pursue actions to lock temperature increase to 1.5 C by the end of the 21st century (UNFCCC, 2015). Considering these two speci c warming targets (1.5 C and 2.0 C), though various studies have been carried out focusing on the shift in climates of different regions, there is still no clear overview of how a 1.5 C or 2.0 C world might evolve; as well as how these could discern to worlds with substantially higher warming level. Schleussner et al. (2016) reported a notable discrepancy under 1.5 C or 2.0 C in terms of climate change impacts on precipitation, coral, agriculture, and sea-level rise. It is estimated that the prospective range of global temperature rise is 2.0 C - 4.9 C by the end of the 21st century, and the chance of achieving the Paris Agreement target keeping global temperature rise well below 2.0 C is only 5% (Raftery et al., 2017). However, regardless of the socioeconomic and political achievability of the Paris Agreement goals (Sanderson et al., 2017), there is a scienti c knowledge gap related to the implications of different warming targets. Therefore, it is crucial to diagnose the characteristics of global and regional level climate change under different warming levels. The Indus River Basin (IRB) is one of the most crucial river basins in the world in the aspect of human dependence. This basin supports the livelihood and well-being of nearly 268 million people, which is directly and indirectly in uenced by the water generated from the snow and glacier melt in the Upper Indus Basin (UIB). The basin is vulnerable to 2 S.K. Mondal, H. Tao, J. Huang et al. Science of the Total Environment 789 (2021) 147867 Hasson et al., 2017; Qamar et al., 2019). Climatologically, the basin is featured by dominating arid and semi-arid climatic regions. In general, the temperature in the basin varies from 2 to 49 C. While the mean annual amount ranges 90 500 mm (downstream) and it is >1000 mm in the upstream. The mean evaporation varies from 1650 to 2040 mm (FAO, 2011; Ali, 2013). The Indian monsoon (summer season), and westerlies circulation systems mainly in uence the overall climate of the Indus River Basin (Azmat et al., 2017). The basin's climate is complex, where mountainous climate types and monsoon dynamics are dominant (Lutz et al., 2016b). However, the water resources in the basin show the complex interaction of precipitation, evapotranspiration, temperature, runoff, and snow and ice melt. Faster growing population, climate change, and growing demand for water enhancing the water resource stress in the IRB (ICIMOD, 2010). The small changes in temperature and precipitation could alter the hydrological system, resulting in adverse effects on the millions of inhabitants (Lutz et al., 2014, 2016a). The increasing rate of warming and precipitation in the basin demonstrate varying features in terms of seasonal and spatial aspects. The winter season's temperature shows a strong increase, while it shows decreasing pattern in the summer season. Further, excessive rain in the monsoon season leads to oods, whereas the decrease in winter and spring is likely to deplete the water resource availability (Shrestha et al., 2019). Such variations could adversely in uence the water resources of both upper and lower basin areas. The alteration in the basin's water resources due to climate change and other factors will severely impact the food security and environment in the area (Archer et al., 2010). century if current emission levels sustain. The warming thresholds are derived from seven CMIP6-GCMs outputs under seven SSP-RCP scenarios. We tested the hypothesis of this study that key climatic parameters (surface mean temperature, precipitation, and potential evapotranspiration) under global warming would be increased signi cantly in the entire basin, and resulting in dry events (droughts) are expected to be prominent. In addition, the new-state-of-the-art CMIP6-GCMs outputs will provide a more realistic and reliable overview of expected changes. Hopefully, this study presents the rst analysis of the changes in projected climate parameters across IRB under different warming levels considering all the scenarios from the latest CMIP6. However, this study intends to propose insight into prospective changes in IRB's hydroclimate to ensure water resource management practices and formulate strategies to combat these changes. 2. Data and methods 2.1. Research domain The study domain in this research covers the Indus River Basin of South Asia, which is located at 22 38 N, 65 87 E (Fig.1). The transboundary basin encompasses a total land area of 1.12 million km2 that is shared by four adjacent countries namely Pakistan (47%), India (39%), China (8%), and Afghanistan (6%) (FAO, 2011). In terms of grid points, the total basin includes 432 grid points (for 0.5 0.5 resolution), where the upper Indus River Basin (mountainous northern region) encompasses 140 grid points (32.4% of the total grids), and the southern plains of IRB incorporate 292 grids (67.6% of the total grids). The basin plays a crucial role in the socio-economic advancement of these adjoining countries. The water, food, and energy securities in the basin area are greatly interlinked with sustainable water supplies, which are vulnerable to future climate change (Lutz et al., 2016a; 2.2. Datasets Climate projections demarcate likelihoods of long-term changes to the statistics of future climatic variables (i.e. temperature, precipitation, Fig. 1. Location of the Indus River Basin with elevations. 3 S.K. Mondal, H. Tao, J. Huang et al. Science of the Total Environment 789 (2021) 147867 Change (WATCH) is a program of the European Union. To allow direct comparison of model output, WATCH Forcing Data-ERA Interim (WFDEI) was produced. The ISIMIP makes available these outputs under the name of EWEMBI (EartH2Observe, WFDEI, and ERA-Interim data Merged and Bias-corrected for ISI-MIP). The WATCH Forcing Data were driven from ERA-Interim, whose capability in terms of strength, and inadequacy is explored by some previous studies (Azmat et al., 2016, 2018). They reported having improved ef ciency in capturing the overall climate. These reanalysis climate parameters are further used to downscaling, bias-correction, and evaluating the GCMs results. All the datasets are available (directly or after transformation) at 0.5 0.5 resolution. In this study, the period 1995 2014 is selected as the reference period to estimate relative changes under 1.5 C, 2.0 C, and 3.0 C warming levels. In general, as results under SWLs in this paper are computed over 20-year windows of the projection period (2015 2100), it is reasonable to compare these results to the closest 20 years of the historical period (securing same time length;1995 2014) to explore relative changes (Su et al., 2020; Mondal et al., 2021). Moreover, the period 1995 2014 is expected to be considered as the baseline period in the upcoming IPCC sixth assessment report (AR6), since it's largely consistent with AR5 base period (1986 2005) to some extent (Tokarska et al., 2020). Therefore, the historical simulation period 1995 2014 is selected as the reference period in this paper to depict a better relative and statistical analysis in line with IPCC forthcoming report. evapotranspiration, etc.). This research looked at both historical (1850 2014) and future (2015 2100) simulations of climate change in the IRB that were inferred from seven CMIP6-GCMs. In this analysis, monthly climate variables including precipitation, mean surface temperature, maximum temperature, minimum temperature, wind speed, shortwave solar radiation, and near-surface relative humidity under seven SSPs-RCPs forcing scenarios (SSP1 1.9, SSP1 2.6, SSP2 4.5, SSP3 7.0, SSP4 3.4, SSP4 6.0, and SSP5 8.5) were downloaded from CMIP6 archive. We selected seven emission scenarios, where SSP1 2.6, SSP2 4.5, SSP4 6.0, and SSP5 8.5 are the updated version of RCPs scenarios, and SSP1 1.9, SSP3 7.0, and SSP4 3.4 are the new scenario pathways to ll the scenario gap in CMIP5. To date, only for seven models, all these variables have been made available under seven selected combinations of SSP-RCP scenarios. The details of the available seven models are presented in Table 1. Scenarios are the heart of future climate projection, which have been the primary basis for calculating different warming thresholds. The newly designed gap scenario combinations (SSP1-1.9, SSP4-3.4, and SSP3-7.0) de ne the pathways not considered by the CMIP5 RCPs scenarios. The updated CMIP5 RCPs scenarios (SSP1-2.6, SSP2-4.5, SSP4-6.0, and SSP5-8.5) were developed based on SSPs and the new Integrated Assessment Model (IAMs), with up-to-date forcing. The gap scenario SSP1-1.9 bounds the global warming level to below 1.5 C regarding the aim of the Paris Agreement. Whereas, SSP4-3.4 is the indication of an intermediate mitigation pathway that ts between RCP2.6 and RCP4.5. Finally, SSP3-7.0 represents the medium-to-high end future forcing and warming (O'Neill et al., 2016). In addition, unlike RCPs scenarios, these three new gap scenarios could reveal more reasonable outcomes (Hausfather and Peters, 2020). One of the main focuses of designing ScenarioMIP is to help to address the dimensions of the Paris Agreement goals achievement (O'Neill et al., 2016). Though the increase in the number of examined models provides a more trustworthy result (Pierce et al., 2009; Mondal et al., 2021, Su et al., 2020; Zhai et al., 2020), it is also imperative to explore future changes under all the newly designed SSP-RCP based scenarios (Eyring et al., 2016; O'Neill et al., 2016). It is further believed that the use of more diverse scenarios could increase the con dence in warming levels identi cation. However, to reduce the uncertainty, the GCM outputs were downscaled to a common resolution of 0.5 0.5 using the spatial disaggregation (SD) method and bias-corrected adopting the equidistant cumulative distribution functions (EDCDF) method. To correct bias in the future CMIP6 data, historical model simulation and historically observed data were used in this study. Description of bias correction process in this study are shown in the supplementary material (see section-S1). More details on the downscaling (SD) and bias-correction (EDCDF) methods are described in the studies by Su et al. (2018); Su et al. (2016). For this research, we downloaded gridded observed monthly precipitation (pr), mean temperature, maximum temperature (tasmax), minimum temperature (tasmin), wind speed (sfcWind), shortwave solar radiation (rsds), and surface relative humidity (hurs) from the InterSectoral Impact Model Intercomparison Project (ISIMIP) website. The WFDEI meteorological forcing data set is based on the WATCH Forcing Data (WFD) and ERA-Interim (EI) reanalysis data. The Water and Global 2.3. Potential Evapotranspiration (PET) calculation There are many methods to calculate PET but Thornthwaite (1948) and Penman-Monteith (Allen et al., 1998) are two widely used equations (Su et al., 2018; Zhou et al., 2020; Zhai et al., 2020). Since the Thornthwaite method is solely based on temperature, PET tends to be underestimated in the arid and semi-arid regions (Jensen et al., 1990). Due to the improved physical calculation process, the PenmanMonteith is widely accepted as the most accurate method to calculate PET (Gao et al., 2017). The superiority of Penman-Monteith over Thornthwaite in the calculation of different drought indices has been con rmed by several studies (Chen and Sun, 2015; Gao et al., 2017). Further, the Penman-Monteith approach comprehensively perceives the impact of both thermos-dynamic factors (temperature, wind speed, solar radiation, and relative humidity), which makes the results more consistent with true reference crop evapotranspiration (Dai, 2011; Shef eld et al., 2012; Trenberth et al., 2014; Zhou et al., 2020). Considering the superiority of the Penman-Monteith method is adopted in this study. The Penman-Monteith equation to estimate PET, which is elaborated by Allen et al. (1998), takes the form: PET 900 0:408 Rn G T 273 U 2 ea ed 1 0:34u2 1 where is the slope of the saturation vapor pressure curve; T is the mean daily air temperature; ea is the saturation vapor pressure; Rn is the net radiation at the surface; ed is the actual atmospheric water vapor Table 1 Summary of the seven CMIP6-GCMs. Model name Modeling center Original resolution Downscaled common (lon lat) resolution ( ) CanESM5 Canadian Centre for Climate Modeling and Analysis, Canada CNRM-ESM2-1 Centre National de Recherches M t orologiques- Centre Europ en de Recherche et de Formation Avanc e en Calcul Scienti que (CNRM-CERFACS), France FGOALS-g3 Chinese Academy of Sciences (CAS), China GISS-E2-1-G Goddard Institute for Space Studies (NASA-GISS), USA IPSL-CM6A-LR Institut Pierre-Simon Laplace, France MIROC6 AORI-UT-JAMSTEC-NIES, Japan MRI-ESM2-0 Meteorological Research Institute Earth System, Japan 4 ~2.8 2.8 1.4 1.4 2.3 2.0 2.0 2.5 2.5 1.2676 1.4063 1.40 ~1.125 1.12 0.5 0.5 S.K. Mondal, H. Tao, J. Huang et al. Science of the Total Environment 789 (2021) 147867 pressure; G is the all-wave ground heat ux; is the psychometric constant; and u2 is the daily average wind speed at 2 m height. In this study, the required parametric values to estimate P-M-based PET is extracted by using the equations delineated by Zotarelli and Dukes (2010). the years in which global average temperature rises would reach 1.5 C, 2.0 C, and 3.0 C thresholds relative to the pre-industrial period under the seven SSP-RCP scenarios from seven selected CMIP6 GCMs (Table- S1). The SWLs are de ned as the 20-yr windows positioned on the years when global warming levels are exceeded (Table- S1). All the targeted results under SWLs are computed rst by the average of changes in the respective warming periods for each GCM and SSP-RCP scenario and afterward obtained from the mean of multi-model ensemble with all the selected SSP-RCP scenarios. Notably, some models, as well as some scenarios, don't have a particular threshold warming target which indicates that those models or scenarios don't consider particular warming levels in their projection period (2015 2100). For example, the CanESM5 model doesn't consider the 1.5 C warming threshold in their projection period (2015 2100) but this threshold exists in their simulation period under all SSP-RCP scenarios. 2.4. Water surplus (wetness)/de cit (dryness) estimation The water balance was assessed in this study considering the principle of conservation of mass (the water enters an area, whether leave the area or be stored within the area), which is controlled by the spatiotemporal distribution of water supply (p), and demand (PET) and is stored in the soil (Milly, 1994). The adopted water balance equation (P-PET) depicts the net ux of water from the atmosphere to the earth's surface (Swenson and Wahr, 2006). It usually indicates the dryness as water shortage (negative values) and wetness as the water surplus (positive values). Importantly, the widely accepted drought metric Standardized Precipitation Evapotranspiration Index (SPEI) is constructed based on the simple climatic water balance as the difference between P and PET. The P-PET based results signify the ux of water between the atmosphere and the earth's surface. So, it offers important information regarding the interaction of the atmosphere with the land surface. It is easy to calculate and provides a simple measure of the water surplus or de cit. The P-PET considers key hydro-climatic variables (PET and P) that can directly in uence oods, droughts, and water resources (Zhang et al., 2019). This simple water balance equation can re ect reasonable changes in surface water signal under a warming climate (Byrne and O'Gorman, 2015). It usually explores long-term water balance over land, which can easily be applied over any climatic region (Padr n et al., 2017). However, Several previous studies already applied this method to demonstrate extreme weather events (dryness and wetness) over the South Asian region (Nath et al., 2017; Aadhar and Mishra, 2019). In this paper, we applied thus the water balance equation to demonstrate the water surplus or de cit over the IRB climatic zone. Following is the adopted water balance equation, which usually ts the log-logistic distribution. D P PET 3. Results 3.1. Projected changes in temperature The changes in annual mean temperature for the reference period, and 1.5 C, 2.0 C, and 3.0 C warming levels are presented in Fig. 2. In the reference period (1995 2014), the areal average annual mean temperature across the IRB was recorded by16.3 C In terms of absolute change, it can be seen that the temperature is inclined to increase respective to SWLs. The temperature in the IRB is predicted to increase by 1.1 C, 1.4 C, and 2.4 C corresponding to SWLs of 1.5 C, 2.0 C, and 3.0 C respectively. In the relative aspect, it is estimated to increase by 6.6%, 8.7%, and 14.6% compared to the reference period, accordingly. However, as the global warming level becomes higher, there is a larger increase in projected temperature across IRB, eventually, the highest temperature rise is accounted for 2.4 C (14.6%) to occur under SWL (3.0 C). To examine the spatial distributions of temperature, Fig. 3 demonstrates the spatial features of temperature distribution across the IRB region for the reference period (1995 2014), and changes under three SWLs (1.5 C, 2.0 C, and 3.0 C) relative to1995 2014. In the reference period, the annual mean temperature was highest in the southeast (lower basin), and lowest in the northeast (upper basin) part of the basin (Fig. 3a). The highest mean temperature (>25 C) was distributed encompassing 35.6% of the total grids in the basin, whereas the lowest annual mean temperature ( 5 C) was recorded across 13.4% of the total grids. Besides, temperature with 16 25 C was mainly in the middle of the basin covering 27.7% of the total grids. However, the annual mean temperature in the historical period is found to increase with the decrease in altitude. In the aspect of different warming levels, geographic features of projected temperature rise in the IRB are varying among 1.5 C, 2.0 C, and 3.0 C SWLs. A signi cant increase can be found over the entire basin under all the SWLs (Fig. 8b-d). With regard to 1995 2014, the annual maximum mean temperature is predicted to increase largely in the northwest of the upper basin, but the minimum mean temperature tends to increase in the northeast for all the SWLs (Fig. 3b-d). For global warming level of 1.5 C (Fig. 3b), the projected temperature is inclined to increase by >10% (highest) in the northwest mountainous area of the upper basin, which is 18.7% of the entire basin. While the lowest increase in the range 0 5% is distributed across more than half of the basin area (52.8%), especially in the lower basin area (south-central). At 2.0 C global warming threshold, 22.2% of the whole basin area will experience projected temperature to increase by the highest >10% compared to the reference period, particularly over the similar northwest mountain terrain. Besides, about 45% of the grid points exhibit an annual mean temperature increase by 6% 10% in the central part of the basin, whereas temperature is estimated to increase by 5% in the southern part with 20% of the grids. Under the 3.0 C warming threshold, predicted temperature will escalate by >10% across the northwest upper basin and extended towards the western region, 2 Here, water surplus/de cit is re ected by the difference (D) between precipitation (P) and potential evapotranspiration (PET). 2.5. Determination of different warming thresholds In this study, we applied a time sampling approach to estimate SWLs. This is the most commonly used method to investigate the changes in climate patterns and associated impacts at different warming levels (i.e. 1.5 C 4.0 C) (Su et al., 2018; Naumann et al., 2018; Wen et al., 2019; Miao et al., 2020; Jiang et al., 2020; Jian Ting et al., 2021). This is the technique to use available climate model outputs to examine the increments of global mean temperature increase is to determine that every bit of warming is reached, and explore regional climate changes which happen at that time (Kaplan and New, 2006; Schewe et al., 2014; Swain and Hayhoe, 2015). Importantly, because of having multiple advantages compared to others, it has been a broadly accepted and used method (James et al., 2017). Easy computation process and it considers the direct comparison of the increments of global mean temperature increase which does not undertake linear relationship between global temperature and local change. Likewise, it represents model variability as temperature sensitivity to GHGs is excluded, reducing the range of projections for some temperature-related variables. More details on the time sampling approach are described in the study by James et al. (2017). However, in this study, the timing of the SWLs by 1.5 C, 2.0, and 3.0 C above preindustrial level are estimated using 20-yr running-average global mean surface temperature for all individual models (7 GCMs) and scenarios (7 SSP-RCP scenarios). The years 1850 1890 are selected as pre-industrial period, to calculate 5 S.K. Mondal, H. Tao, J. Huang et al. Science of the Total Environment 789 (2021) 147867 Fig. 2. Projected changes in mean surface temperature in the IRB region for the reference period, as well as 1.5 C, 2.0 C, and 3.0 C SWLs. The blue dot line shows a linear trend, the bar with circle represents the mean value, and the straight black line indicates GCMs ranges. pronounced over the northeast of the upper basin, and weaker across the middle of the basin. However, it is increasing by some amount (Fig. 5b). The northeast part of the upper basin tends to experience the highest increase in precipitation >15% with an area of 10.2% of the basin. The precipitation amount is projected to increase by 5% over the middle part comprising a major portion (53.7%) of the basin. For the 2.0 C warming world, most of the basins (69.6%) in the lower basin will exhibit precipitation quantity with an increase by 5%. Whereas, the highest amount of rainfall (>15%) will be prominent over the upper basin area (north) covering only 13.4% of the basin. Surprisingly, precipitation is predicted to decline by 5% across lower basin area (south and middle part) with a maximum 39.8% area of the basin under 3.0 C SWLs. While highest increase (>15%) in precipitation will be dominated over the upper basin area encompassing 16.7% of the entire basin. In terms of signi cant increase, precipitation will increase signi cantly across grids with 22.1%, 25.3%, and 24.2% of the total basin under 1.5 C, 2.0 C, and 3.0 C SWLs respectively, which is dominant in the upper basin area (north). Although a major portion of the basin is subjective to experience precipitation with an increase by 0 5% for all the warming levels, the basin area with the highest precipitation increase (>15%) is inclined to escalate from lower warming (1.5 C) to higher warming (3.0 C) scenario, which is represented by 10.2%, 13.4%, and 16.7% accordingly. Notably, at 3.0 C warming, precipitation will decrease by 5% across 39.8% of the basin located in the lower basin area (south and middle part). comprising ~40% of the entire basin area. While temperature is subjective to increase by 6% 10% in the lower basin area (southeast-central), which covers nearly 47% of the basin. However, for all the SWLs, it can be concluded that annual mean temperature will intensify (relative to the reference period) mainly in the northwest region of the upper basin, and minimum mean temperature in the northeast of the IRB. Importantly, the projected increase in mean temperature is statistically signi cant across the entire basin, where areas under the highest temperature increase (>10%) is projected to expand more, as the global warming levels become higher, which are 18.7%, 22.2%, and ~ 40% respective to 1.5 C, 2.0 C, and 3.0 C SWLs. In addition, the response to global warming is prominent towards higher altitudes. 3.2. Projected changes in precipitation Predicted changes in areal averaged annual mean precipitation across the IRB region for the reference period (1995 2014), and three SWLs (1.5 C, 2.0 C, and 3.0 C) are illustrated in Fig. 4. Annually, the estimated mean precipitation in the past 20 years (1995 2014) over the basin was approximately 478.3 mm. Under the 1.5 C warming world, the precipitation amount is projected to be 500.2 mm, which is 4.5% higher than the reference period. With regard to the reference period, it is anticipated to increase by 4.8% and 4.3% under 2.0 C and 3.0 C respectively. Notably, precipitation will increase from 1.5 C to 2.0 C, but reduce under higher SWL of 3.0 C compared to 1.5 C and 2.0 C. The highest increase is estimated at 4.8% for 2.0 C warming. To discern spatial distribution, the geographical characteristics of annual mean precipitation for the reference period (1995 2014), and changes under three SWLs (1.5 C, 2.0 C, and 3.0 C) relative to1995 2014 across the IRB region is demonstrated in Fig. 5. Annual mean precipitation in the reference period was dominated over foothill area (windward hillside) of the upper Indus basin, where precipitation amount (average) recorded >600 mm per year with an area coverage of 27.8% of the basin (Fig. 5a). But the lowest precipitation quantity ( 200 mm) was distributed in the south of the lower basin region, as well as the northern border area of the upper basin encompassing 21.7% of the total grids. The largest portion of the basin (35.4%) experienced precipitation by 400 mm both in the lower and upper basin area. Further, varying patterns (area coverage) in the projected precipitation relative to the reference period are observed under 1.5 C, 2.0 C, and 3.0 C SWLs. Under the 1.5 C, precipitation is projected to be 3.3. Projected changes in potential evapotranspiration Prospective changes in mean potential evapotranspiration across IRB for the reference period (1995 2014) and at the 1.5 C, 2.0 C, and 3.0 C SWLs are demonstrated in Fig. 6. The estimated potential evapotranspiration in the basin was an average of 1059.7 mm in the reference period (1995 2014). A slight escalation in PET is anticipated for all the SWLs compared to the reference period. In terms of relative change, it is projected to increase by 1.8%, 2.8%, and 5.2% under 1.5 C, 2.0 C, and 3.0 C SWLs respectively. However, an increasing trend is obvious in PET across IRB from lower warming (1.5 C) to higher SWL (3.0 C), the largest increase is accounted for 5.2%. The spatial distribution of PET during 1995 2014 in the IRB and changes compared to the reference period under the three SWLs 6 S.K. Mondal, H. Tao, J. Huang et al. Science of the Total Environment 789 (2021) 147867 Fig. 3. Spatial distribution of annual mean temperature over IRB for (a) Reference period (1995 2014), and relative changes under (b-d) three SWLs (1.5 C, 2.0 C, and 3.0 C). The signi cance of the change is scrutinized by applying the two-sample t-test at the 0.05 signi cance scale (black dots). (lower basin area), which includes 37.5% of the total area. While the largest amount of PET (>60 mm) is distributed in the south of the lower basin with 27.7% of the total basin. Further, estimated PET for the 1.5 C warming scenario is mostly projected to increase by 2 4% in (1.5 C, 2.0 C, and 3.0 C) are presented in Fig. 6. In the 20 years of the past (1995 2014), the estimated PET was 20 40 mm (average) in the upper basin, which accounted for 34.7% of the total basin area (Fig. 7a). The region with 41 60 mm PET is in the middle of the basin 7 S.K. Mondal, H. Tao, J. Huang et al. Science of the Total Environment 789 (2021) 147867 Fig. 4. Projected changes in annual mean precipitation in the IRB region for the reference period, as well as 1.5 C, 2.0 C, and 3.0 C SWLs. The blue dot line shows a linear trend, the bar with circle represents the mean value, and the straight black line indicates GCMs ranges. slightly (0.2%) for an additional 0.5 C in the basin, in contrast, it decreases by 0.4% for an extra 1.0 C warming (Fig. c-d). Geographically, precipitation is intended to be dominated on the road to high elevation in the upper basin (north), while it inclines to decline in the lower basin area (South) mostly in the middle part for both additional warming levels (0.5 C, and 1.0 C). Precipitation is predicted to increase signi cantly over 8.6% and 14.5% areas (upper mountainous zone) of the entire basin for an additional 0.5 C, and 1.0 C respectively. Whereas, the signi cant decrease in precipitation tends to occur across 3.2% (a portion of middle and south), and 6.5% areas (a portion of southwest and southeast) of the entire basin for an additional 0.5 C, and 1.0 C warming respectively. Further, potential evapotranspiration is anticipated to increase by 0.9% for an additional 0.5 C than that of 1.5 C global warming threshold, while greater escalation in PET is estimated at 2.4% higher for an extra 1.0 C compared to 2.0 C Paris agreement target (Fig.8e-f). PET will increase across the whole of IRB, which tends to be concentrated in the lower basin area (southwest) for both additional warming levels (0.5 C, and 1.0 C). For extra warming of 1.0 C, the signi cant increase in PET occurs largely across 40.5% area of the entire basin, especially in the part of the west, southeast (lower basin), and northeast (upper basin) region. But the signi cant increase is negligible (only 0.7% area) for an additional 0.5 C warming. However, it can be sum up that the temperature is projected to increase by 0.4 C (1.7% higher than that of 1.5 C) and 0.9 C (5.1% higher than that of 2.0 C) in the entire IRB for an additional 0.5 C (2.0 1.5 C) and 1.0 C (3.0 2.0 C) of warming respectively. The highest increase in mean temperature (5.1%) and PET (2.4%) in the IRB are predicted to intensify for an additional 1.0 C (3.0 2.0 C) than 0.5 C warming, but precipitation is intended to decrease by 0.4%. Geographically, temperature and precipitation tend to be dominated towards the high altitude region (upper basin), whereas PET will be intensi ed on the road to the low altitude region (lower basin). Notably, for an additional 1.0 C warming, signi cant increases in temperature, precipitation, and PET are projected to happen greatly over ~100% area, 14.5% area, and 40.5% area of the entire basin, respectively. Besides, precipitation is anticipated to decrease over 6.5% area of the basin. the basin (Fig. 7b). The largest increase of 4% under this threshold is demonstrated in the north (upper basin) to middle-west (lower basin) with ~52% area coverage of the basin. Whereas, the southern basin area exhibits the lowest 2% increase securing 46.2% area. Under the 2.0 C SWL (Fig. 7c), PET will increase by 4% over most of the basin area (~70% area coverage). While the highest escalation in PET by 6% is concentrated mainly in the northeast part with 14.5% of the total grids. The lowest increase (2%) is distributed over 15.4% of the basin area in the south. However, the prominent escalation of PET is found under the 3.0 C warming (Fig. 7c). At the 3.0 C warming, the highest increase (>6%) in PET is dominated in the upper basin area (north) and extended towards the west, which represents 37.7% of the total area. Moreover, about half of the basin area (~50%) (mostly in the middle part) will experience an increase in PET by 6%. Notably, PET is anticipated to increase signi cantly across areas with 95.3%, and 99.5% of the total basin under 1.5 C, and 2.0 C, respectively, while it is signi cant across the entire basin at 3.0 C SWL. However, it can be highlighted that the prospective PET tends to be dominated in the upper basin area (north) under all the SWLs, especially under SWL. Besides, area coverage with the highest increase (>6) in PET is projected to expand towards lower to higher SWL, which is the highest 37.7% of the total area under 3.0 C. 3.4. Changes in climate variables for an additional 0.5 C and 1.0 C of warming The differences in mean temperature, precipitation, and PET for an additional 0.5 C (2.0 1.5 C) and 1.0 C (3.0 2.0 C) of warming are depicted in Fig. 8. For an additional 0.5 C (2.0 1.5 C) of warming, the temperature is projected to increase by 0.4 C (average) in the entire IRB which is 1.7% higher than that of the 1.5 C global warming threshold. Spatially, the increase in mean temperature tends to be concentrated from low altitudes (lower basin) to high altitudes (upper basin). A signi cant increase can be found especially in the northeast part with 7.6% area coverage of the basin (Fig. 8a). Whereas, the mean temperature in the IRB will increase by 0.9 C (average) for an additional 1.0 C (3.0 2.0 C) of warming, which is 5.1% higher than the 2.0 C warming target by the Paris agreement (Fig. 8b). Likewise, temperature for an additional 1.0 C warming is inclined to be pronounced towards high altitude area, especially in the upper basin area (north) and extended towards west. A signi cant increase is observed all over the basin. Moreover, on average, precipitation is subjective to increase 3.5. Changes in water surplus/de cit (P-PET) The estimated changes in water availability in terms of water surplus (wetness) and de cit (dryness) using the water balance eq. (P-PET) under 1.5 C, 2.0 C, and 3.0 C SWLs are shown in Fig. 9.In addition, 8 S.K. Mondal, H. Tao, J. Huang et al. Science of the Total Environment 789 (2021) 147867 Fig. 5. Spatial distribution of annual mean precipitation over IRB for the (a) reference period (1995 2014), and relative changes under (b-d) three SWLs (1.5 C, 2.0 C, and 3.0 C). The signi cance of the change is scrutinized by applying the two-sample t-test at the 0.05 signi cance scale (black dots). exhibits a low level of water availability. The change in water surplus for 1.5 C warming is projected to increase signi cantly across 36.8% area of the entire basin, especially north (upper basin) and part of the southwest (lower basin). Whereas, a signi cant increase at 2.0 C warming level is anticipated over 28.7% area of the basin mostly in the the signi cance in changes over IRB is further re ected by applying the two-sample t-test at the 0.05 signi cant level. Under 1.5 C and 2.0 C SWLs (Fig. 9a-b), almost the whole of IRB is subjective to experience wetness (water surplus), where it is intended to be concentrated towards higher elevation (upper basin). But the lower basin (South) 9 S.K. Mondal, H. Tao, J. Huang et al. Science of the Total Environment 789 (2021) 147867 Fig. 6. Projected changes in mean potential evapotranspiration in the IRB region for the reference period, as well as 1.5 C, 2.0 C, and 3.0 C SWLs. The blue dot line shows a linear trend, the bar with circle represents the mean value, and the straight black line indicates GCMs ranges. global SWL. In terms of area coverage, the projected increase in mean temperature is statistically signi cant across the entire basin for all the SWLs. The response of regional temperature rise to global warming is prominent towards higher altitudes, especially in the northwest mountainous region. The ndings of this study corroborate those of some previous results. Gebre and Ludwig (2014) reported that the increase in maximum temperature in the basin varied between 1 and 7 C. Huang et al. (2017) reported that the mean annual temperature in the basin is inclined to increase by 1.2, 1.9, and 2.7 for 2047 2061, and by 0.9, 2.3, and 5.0 during 2081 2100 under RCP2.6, RCP4.5, and RCP8.5. So, it is obvious that the basin is going to incur robust warming under changing climate during the 21st century, as all the phases of CMIP (CMIP3, CMIP5, and CMIP6) project a consistent increase in temperature. But some previous studies reported that the mean temperature in the basin is projected to be higher than that of global-scale warming (Kraaijenbrink et al., 2017; Jian Ting et al., 2021). In this regard, our ndings based on CMIP6 show inconsistency with those previous studies (based on CMIP5). This difference could be due to the use of upgraded GCMs, and a large number of integrated scenarios ensuring three gap scenarios. Climate projections in CMIP6 will differ from CMIP5 not only due to updated version of climate models but also because of driving with SSP-based scenarios produced with updated versions of Integrated Assessment Models (IAMs) and based on updated data on recent emissions trends (O'Neill et al., 2016). Furthermore, the most severe increase in temperature is distributed mostly in the upper Indus basin towards higher emission scenarios (Rajbhandari et al., 2015; Ali et al., 2015; Su et al., 2016; Huang et al., 2017; Jian Ting et al., 2021), which is similar to our ndings. Several pivotal factors such as water vapor changes, snow albedo, latent heat relief, globalscale warming, and aerosol could in uence the higher warming in the upper altitude of the basin (Kraaijenbrink et al., 2017). However, the increase in temperature will probably affect the glaciers and snow melting in the upper basin, resulting in more frequent and extreme runoff events. Besides, different temperature-related extreme events (i.e. heat waves) are likely to frequent in the future. Whereas compared to temperature, the mean annual precipitation in this study exhibits highly variable and uncertain features. Overall, precipitation is projected to increase for the entire basin under all the three SWLs compared to the reference period, where the highest increase is estimated at 4.8% for 2.0 C and the lowest at 3.0 C warming. Precipitation will increase signi cantly across grids with 22.1%, 25.3%, and 24.2% of the total basin upper basin area (north). Nonetheless, at a 3.0 C SWL(Fig. 9c), the water surplus (wetness) is predicted to occur in the upper basin region (north), and water de cit (dryness) will be dominated in the lower basin area (South). The wetness is inclined to increase signi cantly over 26.3% of the entire basin, particularly in the north. In contrast, signi cant dryness is intended to escalate over 2.1% of the basin which is distributed in the southwest part. However, it can be underlined that the water surplus (wetness) in the IBR is probable to occur in the upper basin area (north) for all the warming thresholds. As the area quantity with water surplus (36.8%, 28.7%, and 26.3% respectively) is projected to decline with continued SWLs, there is a tendency to happen dryness condition (water de cit) in the lower basin area (South). The area with water surplus is larger under 1.5 C, while the water de cit is greater for the 3.0 C SWL. 4. Conclusion and discussion This paper has explored the changes in pivotal climate indicators using seven up-to-date climate model outputs under seven SSP-RCP scenarios from CMIP6. It is expected that the use of all these seven scenarios can be called a comprehensive scenario pack which will outline different global warming thresholds more logically. The purpose of this research is to investigate the prospective changes in temperature, precipitation, and potential evapotranspiration across IRB under 1.5 C, 2.0 C, and 3.0 C SWLs. The difference in climate change features over IRB for an additional 0.5 C (2.0 1.5 C) and 1.0 C (3.0 2.0 C) warming is further elucidated. Furthermore, probable aspects of future water surplus (wetness) and de cit (dryness) are also diagnosed in the distinct warming scenarios. The changes are depicted in terms of relative difference from the reference period and signi cance in trend. Overall, the projected climate in the IRB under different warming thresholds signi es a trend towards a warmer and wetter climate condition. Relative to the reference period (1995 2014), the intensi cation of mean temperature, precipitation, and potential evapotranspiration are predicted to increase at all the SWLs. The greater increases in temperature and PET are inclined to happen with continued global warming, but precipitation is intended to lowering in the 3.0 C than that of 1.5 C and 2.0 C SWLs. However, the mean annual temperature demonstrates a consistent increase across IRB, as the global warming level continues to become higher, where the highest increase is accounted for 2.4 C (14.6% higher than reference period) under 3.0 C 10 S.K. Mondal, H. Tao, J. Huang et al. Science of the Total Environment 789 (2021) 147867 Fig. 7. Spatial distribution of annual mean potential evapotranspiration over IRB for the (a) reference period (1995 2014), and relative changes under (b-d) three SWLs (1.5 C, 2.0 C, and 3.0 C). The signi cance of the change is scrutinized by applying the two-sample t-test at the 0.05 signi cance scale (black dots). under 1.5 C, 2.0 C, and 3.0 C SWLs respectively, which is dominant in the upper basin area (north).The results indicate a non-uniform change in precipitation over the basin in the future. Such uneven changes in precipitation across the basin are also widely reported by several previous studies. Huang et al. (2017) stated that mean annual precipitation in the IRB will decrease under higher emission scenarios (RCP4.5 and 11 S.K. Mondal, H. Tao, J. Huang et al. Science of the Total Environment 789 (2021) 147867 Fig. 8. Differences in (a-b) temperature (c-d) precipitation, and (e-f) potential evapotranspiration over IRB for an additional 0.5 C (2.0 1.5 C) and 1.0 C (3.0 2.0 C) global warming. The signi cance of the change is scrutinized by applying the two-sample t-test at the 0.05 signi cance scale (black dots). 12 S.K. Mondal, H. Tao, J. Huang et al. Science of the Total Environment 789 (2021) 147867 Fig. 9. Changes in water surplus/de cit (P-PET) over IRB under three SWLs (1.5 C, 2.0 C, and 3.0 C). The positive and negative values indicate water surplus (wetness) and de cit (dryness) respectively. The signi cance of the change is scrutinized by applying the two-sample t-test at the 0.05 signi cance scale (black dots). additional 1.0 C (3.0 2.0 C) than 0.5 C of warming, but precipitation is intended to decrease by 0.4%. Geographically, temperature and precipitation have a tendency to be dominated towards the high altitude region (upper basin), whereas PET will be intensi ed on the road to the low altitude region (lower basin). Similar ndings were reported by Xu et al. (2017) that for an increment of 0.5 of warming the mean temperature intensi cation will vary from 0.5 C to 1.0 C over Asian most regions. Global precipitation can increase by 1% 3% per C of warming increment, with pronounced spatial variability (Liu et al., 2013). While potential evapotranspiration is anticipated to increase by 1.5% 4% per degree of warming (Scheff and Frierson, 2014). The differences in regional climate between 1.5 C and 2.0 C demonstrate an increase in mean temperature in most land and ocean areas, heatwaves, excessive rainfall in some regions, and the likelihood of droughts and precipitation de cit across some climate zones globally (IPCC, 2018; Naumann et al., 2018). The ndings reveal that the IRB region will not only become wetter with continued warming but there is also a probability of drying events escalation. The area affected by wet events is dominated in the upper Indus basin (north), whereas the likelihood of getting the area to dry events pronounced in the plain arid and semi-arid region of the lower basin (south). The increase in PET will overrule under higher warming level (i.e. 3.0 C), resulting in a water shortage for the agricultural area (especially lower basin) in the IRB. In the lower basin, the escalation of PET is higher than the precipitation increase. The region is likely to become drier in the future; the upsurge in P-PET is in uenced by growing evaporative demand and strengthened PET, particularly in the SWL. Zhai et al. (2020) using CMIP6 reported an intense drought condition along a similar region of South Asia. Whereas, the strength of increased PET will be overridden by the mitigating effect of the excessive precipitation in the northern hilly region (upper basin), consequently dominated wetness. A CMIP6-GCM based latest study by Mondal et al. (2021) also found obvious wetness across the similar northern border region of South Asia with a larger escalation in precipitation, surface runoff, and soil moisture abundance. Notably, the upper basin is projected to face an obvious risk of persistent increase in wet events ( oods) with an increase in global warming level, while the risk of intense dry events (droughts) in the lower basin becomes more apparent under higher warming threshold. In relative terms, precipitation increase faster than evapotranspiration in the IRB under 1.5 C and 2.0 C (wet condition), decrease under 3.0 C resulting in dry condition (Table-S2). The spatial pattern of P-PET in this study corroborates the drying trends re ected using standardized precipitation evapotranspiration index (SPEI) by Zhai et al. (2020) and Mondal et al. (2021). It is RCP8.5) but increase under lower emission scenario (RCP2.6). Hasson et al. (2013) also concluded similar inconsistent changes in projected precipitation over the region, which could be due to the selected GCMs do not incorporate irrigation or water diversion in the area. Moreover, in the aspect of seasonal and spatial distribution, anticipated precipitation also shows larger inconsistency (Rajbhandari et al., 2015; Gebre and Ludwing, 2014; Su et al., 2016; Ali et al., 2015). However, some previous studies reported an increase in annual mean precipitation by10 30% in the basin throughout the 21st century, with dominated pattern in the upper basin area (Forsythe et al., 2014; Ali et al., 2015; Wen et al., 2019; Wang et al., 2020), which corroborates our ndings. It can be highlighted that the increase in precipitation under lower warming thresholds (1.5 C to 2.0) in uences the risk of ooding (especially in the upper basin). Whereas droughts are subjective to occur in the lower basin mostly under higher warming level (3.0 C) as lowering the precipitation and increase in PET from lower warming (1.5 C) to higher warming (3.0 C) thresholds. The largest increase is accounted for 5.2% at the higher warming (3.0 C) threshold. Notably, the prospective escalation in PET tends to be dominated in the upper basin area (north) under all the warming thresholds, which is more pronounced under higher warming. PET is anticipated to increase signi cantly across areas with 95.3%, and 99.5% of the total basin under 1.5 C, and 2.0 C, respectively, while it is signi cant across the entire basin at 3.0 C SWL. It is predicted that the rise in global temperature will lead to a change in precipitation and PET which have varying degrees of in uence on a dryness condition. The aggravation of dryness is usually in uenced by the changes in the environmental water budget. The hydrological cycle of a climate region is controlled by precipitation, surface water runoff, and evapotranspiration (Azmat et al., 2016). Dai (2013) reported that due to global warming, drought conditions will likely be aggravated by increasing evaporative demand. The greatest increase in PET usually increases the risk of water shortage and drought persistence. A larger increase in PET is projected to occur across IRB, especially in the upper basin area (Wen et al., 2019; Wang et al., 2020). According to the Paris agreement, if efforts fail to limit global temperature rise to 1.5 C, it must be locked below 2.0 C (0.5 C increment) above the pre-industrial level. Every fraction of global temperature increase matters, there are obvious bene ts to keeping global warming level at 1.5 C rather than 2.0 C (Schleussner et al., 2018). The temperature is projected to increase by 0.4 C (1.7% higher than that of 1.5 C) and 0.9 C (5.1% higher than that of 2.0 C) in the entire IRB for an additional 0.5 C (2.0 1.5 C) and 1.0 C (3.0 2.0 C) of warming respectively. It can be boldly written that the increase in mean temperature (5.1%) and PET (2.4%) in the IRB are predicted to be higher for an 13 S.K. Mondal, H. Tao, J. Huang et al. Science of the Total Environment 789 (2021) 147867 innovative ideas and improved the complete research and manuscript. All authors discussed the results and edited the manuscript. noteworthy that as the area quantity with wetness (36.8%, 28.7%, and 26.3% respectively) is projected to decline with continued global warming levels, there is a tendency to happen dryness condition (water de cit), mostly in the lower basin area (South). The area with water surplus is larger under 1.5 C warming, while the water de cit is greater for the 3.0 C warming scenario. However, the increased variability in climatological parameters across IRB depicts an evident occurrence of both wet events ( oods) as well as dry events (droughts) with the increase in global average temperature rise. The increase in PET and decrease in precipitation in uence the intensi cation of dryness (Huang et al., 2018; Su et al., 2018; Zhai et al., 2017). The dynamics behind the changes in climate variables are quite complex to explore reasonably. In general, the intensi cation of temperature over sea surface leads to produce more water vapor, which can be carried towards South Asian lands by wind eld activity, resulting in heavy precipitation. In this study (using CMIP6-GCMs), overall, the intense wind eld changes are dominant over the Arabian Sea as well as the Bay of Bengal under all the selected SWLs (Fig.S2). The magnitude of wind speed over the Arabian Sea can be the dependent factor of the input and output of the water vapor across IRB. This intensi cation carries more water vapor-laden wind over the IRB. Further, the signi cant increase in surface temperature over the studied domain strengthens the water vapor holding capacity, which is prominent in the northern mountainous region. Increased air temperature across the domain will result in an intensi cation of the water holding capacity of the atmosphere (Suman and Maity, 2020). Moreover, the southwest monsoon generally occurs during June September of each year, which mainly enhances the occurrence of precipitation in the northern part of South Asia. Southwest monsoon reaches on South Asian landmass in two main dimensions; through the Arabian Sea, and the Bay of Bengal. The monsoon wind moves to the northern part of the region holding more water content. Our ndings (Fig.S2), strongly support this southwest monsoon mechanism to in uence precipitation changes in the IRB under different SWLs. This study provides insightful ndings for water resource management as well as initiating mitigation and adaptation measures in the IRB related to water surplus ( oods) and water de cit (droughts). The ndings of our study can be used to improve the water supply and ood forecasting models. In the upper basin region (north), the increased precipitation and warming-induced enhanced glacier and snow melting will intensify ood risks in the future. Further, our ndings urge to design effective water supply management to sustain irrigation systems in the lower basin area (south), as well as socioeconomic advancement in the IRB. Climate change prediction usually contains some unavoidable uncertainties, which need to be recognized and constrained (Jiang et al., 2020; Kundzewicz et al., 2018; Su et al., 2018). This study addresses some uncertainties associated with climate model projections. In this study, unavoidable uncertainties can arise from climate models, climate scenarios, emission level consideration, and PET calculation method. To reduce biases and uncertainty, we used bias-corrected MME mean (Fig. S1). The bias correction and multi-model ensemble result show better representativeness by reducing the spatial vagueness and inconsistency (Pierce et al., 2009; Su et al., 2020). Declaration of competing interest The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to in uence the work reported in this paper. Acknowledgments Our research work was jointly supported by the National Key Research and Development Program of China MOST (2018FY100501) and by the National Science Foundation of China(41671211 and 41661144027). The authors would like to become thankful for the support by the High-level Talent Recruitment Program of the Nanjing University of Information Science and Technology (NUIST) and the Guest Professor Program of the Xinjiang Institute of Ecology and Geography, CAS. All the authors express their gratitude to the World Climate Research Program's working group on coupled modeling for producing and publishing their GCM outputs publicly. The authors are also thankful to the ISI-MIP for making available observed datasets for bias correction. Appendix A. 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