Projected Intensified Hydrological Processes in the Three‐River Headwater Region, Qinghai Tibetan Plateau

The Three‐River Headwater Region, also known as China's water tower, is highly sensitive to climate change and has experienced profound hydrological alterations in the last few decades. This study assessed the potential impacts of climate change on all the important hydrological components such as precipitation, evapotranspiration, streamflow, snow‐melt flow, and soil moisture (SM) content in the region. For this, climate data (i.e., temperature, precipitation, relative humidity, and windspeed) of three Global Climate Models (i.e., CanESM5, MPI‐ESM1.2‐HR, and NorESM2‐MM) was downscaled with the Statistical DownScaling Model (SDSM) and their ensemble was forced into a hydrological model to simulate the hydrological processes for 1981–2100. The screening process, which is central to all downscaling techniques, is very subjective in the SDSM. Therefore, we developed a quantitative screening approach by modifying the method applied by Mahmood and Babel (2013, https://doi.org/10.1007/s00704‐012‐0765‐0) for the selection of a set of logical predictors to cope with multi‐collinearity and their ranking. The analyses were performed for the near future period (NFP, 2021–2060) and far future period (FFP, 2061–2100) relative to the baseline period (BLP, 1981–2020). The results showed that the region will be hotter and wetter in the future, with intensive and frequent floods. For example, temperature, precipitation, evapotranspiration, and streamflow will increase by 1.0–1.5 (1–1.9)°C, 9–21 (15–27)%, 6–17 (9–29)%, and 9–46 (22–64)% in the NFP and by 2.0–2.8 (2.7–4.6)°C, 16–40 (43–87)%, 11–31 (24–73)%, and 20–95 (60–198)% in the FFP, respectively, under SSP2‐4.5 (SSP5‐8.5). Similar projections were explored for other hydrological components. Among all, surface flow showed an unprecedented increase (500%–1,000%) in the FFP. Peak flows will be much higher and will shift forward, and snowmelt will start earlier in the future. The results of the present study can be a good source for understanding the hydrological cycle and be used for the planning and management of water resources of the highly elevated and complex region of the Qinghai Tibetan Plateau.


Introduction
The hydrological cycle is a key component of the Earth's climate system (Wu et al., 2013) and is one among the different cycles operating in nature, such as the carbon cycle, the nitrogen cycle, and other biogeochemical cycles (Jain & Singh, 2017).It is recognized worldwide that water plays a major role in the socio-economic development (e.g., energy production, agriculture, domestic and industrial water supply) of a country and is a critical component of the global as well as the regional environment (Cosgrove & Loucks, 2015;Miao & Ni, 2009).However, the available freshwater resources are under great pressure throughout the world due to rapidly increasing population (Islam & Karim, 2020), extending irrigated agriculture (Fischer et al., 2007), developing industry (Flörke et al., 2013), and economic and technological development (V.Singh et al., 2014), which can lead to a critical water resource shortage (Xuan et al., 2018).Moreover, the hydrological cycles of different regions have been intensified throughout the world due to the integrated consequences of global warming, changing climate, and human interference (Cohen et al., 2014;Madakumbura et al., 2019).Anthropogenic climate change is considered one of the biggest threats to freshwater resources in the twenty-first century (Capon et al., 2021) because it can disturb the stability and availability of water resources at local, regional, and national scales.According to the Intergovernmental Panel on Climate Change (IPCC), the global air temperature has increased by 1.1°C since 1880, at a rate of 0.08°C/10 year.The rate of warming is even more than twice (0.18°C/10 year) since 1980 (IPCC, 2022a;NOAA-NCEI, 2023).The situation might be worse as the global temperature is projected to increase by 2.1-3.5°Cunder SSP2-4.5 and 3.3-5.7°Cunder SSP5-8.5 by the end of the twenty-first century relative to the pre-industrial era (IPCC, 2021).Climate change impacts have been reported across every ecosystem on the planet (Scheffers et al., 2016).For example, climate change has induced substantial damages and even increasingly irreversible losses in the terrestrial, coastal, marine, and freshwater ecosystems.In addition, frequent and intense extreme events due to human-induced climate change have reduced food and water security, causing hindrances in meeting sustainable development (IPCC, 2022b).Among them, freshwater resources (e.g., rivers, lakes, and wetlands) are even highly vulnerable and have the potential to be strongly impacted by climate change threats (IPCC, 2008).For example, renewable surface water and groundwater resources are projected to reduce significantly in most dry-subtropical regions, and the frequency of meteorological droughts (less rainfall) and agricultural droughts (less soil moisture [SM]) is likely to increase in dry regions due to climate change.Moreover, climate change negatively impacts freshwater resources by altering the streamflow regimes and water quality (IPCC, 2014).
However, the impacts of climate change on water resources vary from region to region (J.Chu et al., 2010;Mahmood & Jia, 2019).For example, the Three-River Headwater Region (TRHR), the source of three giant rivers of Asia (i.e., the Yangtze, Yellow, and Lancang), has experienced significant hydrological changes because of climate change in the past few decades (T.Su et al., 2023).It has a very fragile ecosystem and has extremely sensitive water resources to changing climate (L.Zhang et al., 2017).It is located in an arid-semiarid zone of the Qinghai Tibetan Plateau (QTP), which is also known as China's water tower (Z.Wang et al., 2010) and represents an important ecological barrier in China (Jiang & Zhang, 2016).It approximately produces an annual discharge of 40 BCM and contributes a considerable amount of water to the downstream regions (Mahmood et al., 2020), which are home to more than 500 million people (Varis et al., 2014).Therefore, it plays a critical role in food security and eco-environmental protection in China and even Southeast Asia (T.Su et al., 2023).However, many studies such as Bei et al. (2019), Deng et al. (2019), Liang et al. (2013), Lu et al. (2018), Shi et al. (2016), You et al. (2014), Yuan et al. (2018), and Y. Zhang et al. (2020), showed noticeable changes in the climate of the region, especially an increased temperature, ranging from 0.28 to 0.54°C/10 year, and precipitation, ranging from 5 to 20 mm/10 year, since the 1950s and 1960s.The region has even experienced twice the rate of the global average temperature (0.18°C/10 year since 1980) (IPCC, 2022a; T. Su et al., 2023).The changing climate along with profound human interventions, specifically the projects related to the protection and restoration of the ecological environment, have disordered the hydrological processes in the region (T.Su et al., 2023).For example, due to increased temperature, actual evapotranspiration has been increasing at a rate of 4-15 mm/ 10 year since the 1980s (Bei et al., 2019;Yuan et al., 2018), while up to 60 mm/10 year after 2003 (X.Li et al., 2019).In addition, regional warming along with increased precipitation has caused rapid glaciers retreat, permafrost degradation, reduction in the active layer of frozen soil, altered streamflow regimes, increased extreme events (e.g., precipitation storms, heat waves, and flooding), biodiversity loss, and ecosystem disturbance (Mahmood et al., 2020;R. Wang et al., 2023;Q. Zhao et al., 2019).Trend analysis conducted by different studies such as H. Chu et al. (2019), Jiang et al. (2016), and T. Mao et al. (2016) in the TRHR have shown significant increased streamflow in the Headwater of the Yangtze River (HYaR) and Headwater of the Lancang River (HLaR), while decreased streamflow in the headwater of the Yellow River (HYeR).These alterations in streamflow regimes are mainly attributed to climate change, about 75%-90%, instead of human interventions (Jiang et al., 2017;T. Su et al., 2023).
These hydro-climatic situations in the TRHR might get worse in the future, especially, after the mid-twenty-first century and under SSP5-8.5 as different studies such as Hu et al. (2022), Ji et al. (2020), L. Liu et al. (2011), Lu et al. (2018), Lutz et al. (2014), F. Su et al. (2016), T. Wang et al. (2022), Y. Zhang et al. (2015), and Q. Zhao et al. (2019) have already shown some serious alterations in hydrological regimes, using the outputs of Global Climate Models (GCMs).For example, T. Wang et al. (2022) projected temperature, precipitation, evapotranspiration, and runoff using the bias-corrected ensemble mean of 5 GCMs under 3 SSPs (i.e., 1, 2, and 5) for 2021-2070 in the upper Yangtze River (above Yichang) and showed increased runoff above Zhimenda while overall decrease, though significantly increased precipitation in future.Hu et al. (2022) used a similar methodology (biascorrected ensemble of 8 GCMs) under different representative concentration pathways (RCPs) to explore the streamflow responses to climate change in the HYaR and showed a 15%-20% decreased streamflow.They concluded decrease in streamflow was mainly due to highly projected increased evapotranspiration (30%-54%).Similarly, Ji et al. (2020) used bias-corrected outputs of 6 GCMs to investigate the extreme hydrological events in the TRHR under global warming levels of 1.5, 2.0, and 3.0°C and found a significant increase in dry-extremes over the HYeL while wet-extreme over the HYaR.However, according to Immerzeel et al. (2010), the runoff in the upper Yangtze basin (5%) will decrease while will increase in the upper Yellow (9%) under A1B.Similarly, Q. Zhang et al. (2017) also explored increased streamflow in the HYeR, especially under RCP8.5.These contradicting results show that there is still a need to investigate future changes in hydrological regimes under changing climate using recently released the GCM's climate data by the Coupled Model Intercomparison Project Phase 6 (CMIP6) because few studies are reported using recently released SSP scenarios in the TRHR.Moreover, the previous studies concentrated mainly on precipitation, streamflow, and evapotranspiration of the water balance components, though other hydrological components such as surface flow (direct flow), baseflow, and SM contents, snowmelt flow, and terrestrial water storage (TWS) are equally important in such important region, which is composed of lakes, rivers, wetlands, glaciers, permafrost, seasonal frozen soil, and snow-covered mountains (Tong et al., 2014).
The common thing we noticed in the previous studies is applying bias-corrected outputs of GCMs because of its simplicity, and to the best of our knowledge, no studies were found applying a sophisticated statistical downscaling technique such as statistical downscaling model (SDSM), where the simulated climate data is first evaluated against the historical observations before using them for the future projections.The SDSM technique is a combination of multiple linear regression (MLR) and stochastic generator and is the most common and sophisticated method (Huang et al., 2011;Mahmood & Babel, 2013) for downscaling climate variables such as temperature and precipitation (Fan et al., 2021;Yang et al., 2017).Different comparative studies have shown that this technique performed relatively better than other methods such as bias correction, as in Campozano et al. (2016), Hernanz et al. (2022), andW. Liu et al. (2013).However, screening (selection) of large-scale variables (predictors), which is central to all statistical downscaling techniques (Wilby et al., 2002), is subjective, time-consuming, and also requires expertise to select some suitable predictors by considering the effect of multi-collinearity (Hammami et al., 2012;Mahmood & Babel, 2013).In this study, we developed a quantitative screening method, which not only minimizes the collinearity but significantly reduces the number of predictors and also ranks them.This is the modified form of the screening method used by Mahmood and Babel (2013).They used a reduction formula of partial correlation (PC) and correlation coefficient between predictand and predictor (CCPP).The main problem in this formula is it, sometimes, selects the predictors with lower CCPP instead of predictors of high CCPP when the difference between PC and CCPP is minimum without considering the CCPP.To overcome this problem, instead of using a reduction formula, we used a product formula of PC and CCPP, along with P-value and backward regression.This showed significant improvement when compared with the results obtained by the stepwise regression method, which is considered a classical method for the screening of predictors (Hammami et al., 2012).Since a large number of predictors limits the number of GCMs, this method can help in including more GCMs and scenarios in the analysis, which is recommended for impact studies.
The main objective of the study is to analyze all the important hydrological components (i.e., precipitation, streamflow, evapotranspiration, surface flow, baseflow, snow-melt flow, TWS, and soil moisture content [SMC]) under moderate and high emission scenarios (SSP2-4.5 and SSP5-8.5) to understand their responses to changing climate in the TRHR.For the accomplishment of this study, the SDSM was used to downscale climate data of three GCMs, and the hydrological modeling system (HEC-HMS) was applied to simulate the hydrological components for the period of 1981-2100.The analyses were performed on monthly and annual basis to explore the potential impacts of climate change on the hydrological components in the near future (2021-2060) and the far future (2061-2100) relative to the baseline period (BLP) .On the whole, the results showed a significant increase in most hydrological components under both SSPs, which can be a good source for understanding the hydrological processes and be used for the planning and management of water resources of the highly elevated and complex region of the QTP.

Study Area and Data Description
The TRHR, also known as the Sanjiangyuan region, is the source region of three giant rivers of Asia, that is, the transboundary Lancang (Mekong), Yangtze (Chang Jiang), and Yellow (Huang He) Rivers (Z.Wang et al., 2010).The region is located in the QTP, and its boundary stretches between 32.0-36.0°Nand 89.0-103.0°Eas shown in Figure 1.It covers an area of 292,700 km 2 , where the headwaters of the Yangtze River above Zhimenda (HYaR), the Yellow River above Tangnaihai (HYeR), and the Lancang River above Xiangda (HLaR) contribute 54%, 40%, and 6% to the total area, respectively (Mahmood et al., 2020).It is an important nature reserve and a key source of fresh water in China (Jiang & Zhang, 2016).It contains a very complex mountainous geography (Jiang & Zhang, 2015), a very harsh environment, and an arid to semiarid climate (Shen et al., 2018).The TRHR is the world's largest alpine ecosystem and is composed of a high number of wetlands, rivers, lakes, glaciers, and snowcovered mountains (Tong et al., 2014).Due to the highly elevated area (i.e., 2,600-6,600 m AMSL), the water resources are highly vulnerable to climate change and global warming (Shen et al., 2018;L. Zhang et al., 2017).The region receives an annual precipitation of 262-772 mm, and an annual mean temperature varies from 5.6 to 7.8°C.The region yields 40 km 3 of water, of which 22.2, 12.7, and 4.7 km 3 are generated by the Yellow above Tangnaihai, Yangtze above Zhimenda, and Lancang above Xiangda, respectively (Mahmood et al., 2020).
The Hydrology and Water Resources Survey Bureau (HWRSB) of Qinghai province provided daily streamflow data for Xiangda, Zhimenda, Jimai, Maqu, and Tangnaihai hydrometric stations for the period of 1980-2015.Daily station data of precipitation (PREC), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), solar radiation, and wind speed (WS) were collected from the Qinghai Meteorological Bureau (QMB), for the period of 1980-2015, shown in Figure 1 and described in Table S1 in Supporting Information S1.Since the number of climate stations is scarce in the region, the China Meteorological Forcing Data set (CMFD) was also used along with station data.Different evaluation studies such as Q.He et al. (2021) and Y. Li et al. (2022) showed better performance of CMFD as compared to other gridded data sets such as ERA5 and APHRODITE for temperature and precipitation.This is a gridded high-resolution (0.1°) near-surface meteorological data set, which is developed by combining remote sensing products, reanalysis data sets, and observed station data.It covers the whole of China and is available from 1979 to 2018 (J.He et al., 2020).The gridded data was converted into point data by taking the average of all grid points located inside each subbasin (Figure 1).Two kinds of large-scale data sets are required for the SDSM for the generation of future climate data: reanalysis and GCM data.The reanalysis data of 26 large-scale variables (predictors) were obtained from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) project developed by the NCEP and the NCAR and is available for 1948-2017.All predictors are described in Table S2 in for two Shared Socioeconomic Pathways (i.e., SSP2-4.5 and SSP5-8.5)for the historical period  and future period (2015-2100).The SSPs are a set of scenarios developed to explore and understand possible future socioeconomic conditions and their potential impacts on climate change and sustainable development, which were created as part of the fifth assessment report of the IPCC.SSP2-4.5 is a future scenario with a moderate emission of greenhouse gas, which is compatible with socio-economic development.Under this, effective radiative forcing is supposed to reach 4.5 W/m 2 by the end of the twenty-first century.SSP5-8.5 represents the future with the highest greenhouse gas emission, reaching radiative forcing to 8.5 W/m 2 at the end of the twentyfirst century relative to pre-industrial conditions (Z.Li et al., 2022;Riahi et al., 2017).SSP2-4.5 and SSP5-8.5 represent moderately to highly vulnerable societies (Y.Zhao et al., 2022).
The GCMs, used in this study, are the latest version of the Max Planck Institute Earth System Model (MPI-ESM), the Norwegian Earth System Model (NorESM), and the Canadian Earth System Model, which are used in CMIP6.NCEP predictors are available at 2.5°horizontal resolution while GCMs predictors are at 2.8125°.To remove the resolution mismatch, both data sets were regridded to 1.0°spatial resolution using the bilinear interpolation method, which also made an easy selection of grids for the corresponding station.Other spatial and temporal data sets, such as soil, land cover, evapotranspiration, and GRACE, used in the present study are briefly described in Table 1.

Downscaling
To date, many methods and models have been developed to downscale the outputs of GCMs (Mahmood & Babel, 2013) such as bias correction, quantile mapping, automated SDSM (Hessami et al., 2008), the Long Ashton Research Station Weather Generator (Semenov & Barrow, 1997).Among them, SDSM has widely been applied throughout the world for downscaling climate variables (Fan et al., 2021;Yang et al., 2017).Many comparative studies have shown that SDSM performed relatively better than other models and/or comparable with others, as in Campozano et al. (2016), Hernanz et al. (2022), and W. Liu et al. (2013).
SDSM developed by Wilby et al. (2002) is a combination of MLR and the stochastic weather generator (SWG).MLR is applied to establish a statistical relationship between local scale (gauge data) climate variables (e.g., PREC, Tmax, Tmin, WS, and RH) and large scale variables such PREC, sea level pressure, and meridional windspeed of NCEP during the calibration process.SWG uses the calibrated parameters and simulates climate data for the historical as well as future using NCEP and GCM predictors (Mahmood & Babel, 2013;Wilby et al., 2014).Three data sets are required to configure this model: station data, NCEP, and GCM predictors (Wilby et al., 2014).Station data and NCEP predictors are used to calibrate the model, and GCM predictors are forced into the calibrated SDSM to simulate future climate data.There are three main steps in this method: screening predictors, calibration and validation, and data simulation for the future.
The screening is the central and most important process for all statistical downscaling techniques.In SDSM, the predictors are selected based on the correlation matrix, P-value, and explained variance.However, the screening process in SDSM is very tedious, time-consuming, subjective, and also requires expertise to select some suitable predictors (Hammami et al., 2012;Mahmood & Babel, 2013).For example, a large number of predictors in regression can cause multi-collinearity and a small number may not be enough to explain the local variable, resulting in poor results.Mandal et al. (2016) explored that the statistical downscaling methods often suffer from the multi-collinearity of the predictors, especially, in the case of MLR.The ordinary least square estimations of regression coefficients may be unstable due to the collinearity among predictors (P.Singh et al., 2023).To overcome this issue, we used the following method:

Proposed Screening Method
In this study, we used a combination of correlation, PC, p-value, and backward regression, which will automatically deal with collinearity, to provide an optimum set of predictors, and rank them accordingly.This is the modified form of the approach used by Mahmood and Babel (2013).The major steps of the proposed method are outlined in Figure 2, and are described below: 1. Make a correlation matrix between predictand and predictors.Highlight the predictors having high correlations (e.g., >0.78-0.85)and remove the predictors having a low correlation with predictand.For example, the correlation between predictor X and predictor Y is 0.98, and they have correlations of 0.70 and 0.60, respectively, with predictand.So, the predictor Y will be removed for further analysis.Similarly, do it for others.In this study, we used 0.78-0.85as critical correlation (i.e., 0.6-0.7 R-squared) because different studies have suggested using the variance inflation factor less than 2.5-3.5 to reduce the collinearity effect (Johnston et al., 2018).2. After removing highly correlated predictors, we removed statistically insignificant predictors.For this, we used the backward regression analysis using the P-value criterion, at a significance level of 0.05.Other criteria such as the Akaike information criterion, Bayesian information criterion, or R-square can also be used.In this procedure, the null hypothesis was no relationship (no correlation) between predictor and predictand and the alternative hypothesis was a strong relationship (strong correlation) between predictand and predictor.This means that any P-value greater than 0.05 between predictor and predictand shows that there is no effect of a predictor on predictand and any P-value less than 0.05 shows a strong relationship between predictor and predictand.The stepwise backward PC process can also be used to eliminate the insignificant predictors.3.In this step, we used a product of correlation and PC along with P-value (at α = 0.05) to choose optimal predictors and to rank them.For this, first, all the predictors (selected from the previous steps) were arranged in descending order based on the correlations between predictors and predictand (RPP).The predictor at the top was referred to as a supper predictor (SP).Second, we determined the PC of each of the predictors one by one, taking away the effect of SP, along with P-values.Third, we calculated the product of the correlation coefficient and PC (PCP).Finally, the predictor having the highest PCP value was selected as second SP, and predictors having a P-value greater than 0.05 were removed.To get the third SP, the PCs were calculated by taking away the effect of the first SP and second SP.This process was repeated until all predictors were ranked according to their effectiveness.The p-value was used to remove insignificant predictors in each repetition.The product formula along with P-value can be a very effective and simple tool to rank the most effective predictors and remove insignificant predictors.4.This method was compared with the stepwise regression for evaluation, which is considered a classical method for the screening of predictors (Hammami et al., 2012).

Calibration and Validation
Based on the available daily station data, SDSM was calibrated for 1980-2005 and validated for 2006-2015 for each climate variable (i.e., PREC, Tmax, Tmin, WS, and RH) for all stations and CMFD in the TRHR.There are two modules in SDSM for the calibration of the model, that is, annual and monthly.The annual model develops a single regression model for the whole time series while the monthly module generates 12 regression models, one for each month separately (Huang et al., 2011).In this study, the monthly module was used during the calibration process because according to Mahmood and Babel (2013), the monthly module provides better results than the annual module.In SDSM, there are two modules (i.e., conditional and unconditional), which are selected according to the variable.The unconditional module is used for independent or unconditional variables such as temperature and windspeed and the conditional for conditional variables such as PREC (Wilby et al., 2002).Since PREC data often does not follow normality (normal distribution), it requires transformation before using it in the calibration process to get good results (Khan et al., 2006).There are many transformation methods in SDSM such as lambda, log, ln, X 2 , X 1/4 , and X 1/2 .In the present study, we evaluated all transformations and found fourth root and lambda providing high values of coefficient of determination (Table S3 in Supporting Information S1), fourth root transformation has also been used in Huang et al. (2011) and Khan et al. (2006).Figure 3 shows monthly PREC simulated at Wudaoliang station using no-transformation, lambda, and fourth root.With no transformation, the simulated PREC was well overestimated in all months.Nonetheless, the simulated PREC with transformations captured monthly variations better than no transformation.The coefficient of determination (R 2 ) and root mean square error (RMSE) were applied for the evaluation of SDSM.

HEC-HMS's Setup
The Hydrological Modeling System of Hydrologic Engineering Center (HEC-HMS) (William & Fleming, 2010) is a semi-distributed hydrological model and has been used throughout the world for different hydrologic applications such as flood modeling, water resource assessment, climate change impacts assessment, urban flooding, flood warning system, stream restoration, water availability, and streamflow forecasting, as in Mahmood and Jia (2017), Ramly and Tahir (2016), and Zema et al. (2017).There are four main components for the setup of HEC-HMS: the meteorological model (MM), basin model (BM), control specification (CS), and data manager (DM) (Verma et al., 2010).The BM stores the physical characteristics (e.g., areas, lengths, and slopes) of a catchment, which can be extracted from Digital Elevation Model (DEM).The MM calculates the spatial   (William & Fleming, 2010).
For the setup of the model, we included Thiessen polygon, Penman-Monteith, and Temperature Index Module (TIM) in MM and soil moisture accounting (SMA), Clark Unit Hydrograph, Muskingum Channel Routing, Linear Reservoir Baseflow, Dynamic Canopy, and Surface Storage methods in the BM, as shown in Figure 5.
SMA is an advanced continuous and complex method in HEC-HMS to calculate losses and excess rainfall (Bhuiyan et al., 2017).It simulates the movement of water over time through a set of storage zones in the groundwater and soil profile layers (William & Fleming, 2010).The SMA-algorithm represents the watershed with five layers (i.e., canopy storage, surface storage, soil profile storage, groundwater layer 1, and groundwater layer 2), and it requires 13 parameters (e.g., surface depression storage, canopy interception storage, soil storage, and infiltration rate), which are estimated during the calibration process.Thiessen polygon is used to interpolate climate data over the watershed by providing weights to climate stations according to the covering area.The penman-Monteith method is used to measure the evapotranspiration in the basin.TIM estimates the streamflow from snowfall for each subbasin.In the present study, each subbasin was divided into 3-5 elevation bands to increase the accuracy of simulating streamflow from snow-melt because the region has a complex topography with highly varying elevations.The other methods are comprehensively described in Feldman (2000).
There are two kinds of model parameters: physical and process parameters (Kan et al., 2019;Q. Zhang et al., 2008).Physical parameters represent the physical features (e.g., basin area and river length) of a catchment (Q.Zhang et al., 2008), which were obtained by delineating the SRTM-DEM in this study.There was a total of 22 process parameters for all methods, as shown in Figure 5. Generally, these parameters cannot be measured directly from a catchment, and these are estimated indirectly through model calibration (Q.Zhang et al., 2008).The initial values of these parameters are needed to run the model (Kan et al., 2019).However, these initial values must be rational and logical, otherwise, these can cause misleading results.Therefore, a systematic and comprehensive approach, as in Mahmood and Jia (2022), was applied to estimate these values from soil and land cover data sets.This not only reduced the time to calibrate the model but also helped in the acquisition of realistic parameters during calibration.

Calibration, Validation, and Sensitivity Analysis
In the present study, the split sample approach was applied for the calibration of HEC-HMS, which is a classic method and central to a  2022).In the case of PF, the top four sensitive parameters were maximum infiltration, storage coefficient, soil percolation, and surface storage; and in the case of TFV, these were the percolations and storage coefficients of groundwater layer 1 and groundwater layer 2, as shown in Figure 4.
In addition to the model evaluation with streamflow data, the model was also evaluated for other hydrological components (e.g., baseflow, SMC, and TWS), because our objective was to study all important hydrological components in the region.However, the observed data of these components were not available in the region.Therefore, we obtained reanalysis and remote sensing data for SMC, AET, SWE, and TWS from freely available sources, described in Table 1.

Estimating Baseflow, TWS Changes, and AET
Baseflow is one of the most important parameters of a hydrological cycle, however, difficult to measure in the watershed (Miller et al., 2014), especially located in harsh climates like the TRHR, where it is even very difficult to manage the streamflow gauges.Mostly, baseflow separation techniques are used to separate the baseflow from streamflow, as described in Brodie and Hostetler (2005), Murphy et al. (2009), and Nathan and McMahon (1990).
A frequently used digital filter, the recursive digital filter developed by Nathan and McMahon (1990), was applied in this study, as below: where f k is quick flow at the kth time, α is a filter parameter, and y k is streamflow.An α value of 0.925 for daily flow and 0.995 for hourly flow is recommended by Nathan and McMahon (1990).
TWS is crucial for global as well as regional hydrological cycles and water resources management (Chen et al., 2017).It includes canopies, snow/ice, rivers, lakes, wetlands, soil, and groundwater and is a critical component of the water and energy budget (Pokhrel et al., 2021).However, it is difficult to measure TWS directly (Xu, 2017).Xiong et al. (2022) estimated TWS by summing the SM and SWE simulated by GCMs.In this study, TWS was estimated by summing the canopy storage (S canopy ), surface storage (S surface ), soil storage (S soil ), groundwater storage from layer 1 and layer 2 (S ground1 , S ground2 ), and water stored in snow as SWE (S swe ); and AET, another one of the most important hydrological components, was calculated by the water balance equation, as given below: (2) where P, Q sim , and ∆S represent PREC, simulated flow, and changes in water storage, respectively, in the basin.Mostly ∆S is assumed negligible for analysis over a longer period (≥10 years) (Zhu et al., 2019) because it is difficult to measure.Nonetheless, in the present study, ∆S was estimated from TWS, as below: Five statistical indicators (described below), Nash-Sutcliffe efficiency (E), coefficient of determination (R 2 ), RMSE, normalized root mean square error (NRMSE), percent bias (PBIAS), and graphs were used for the model evaluation.The whole procedure (e.g., input, outputs, hydrologic parameters, calibration, and validation) for the setup of HEC-HMS for the TRHR is presented in Figure 5.After successful calibration and validation of SDSM and HEC-HMS, climate data (i.e., PREC, Tmax, Tmin, RH, and WS) were simulated by SDSM for 1981-2100, which was then used in HEC-HMS to simulate streamflow, surface flow, baseflow, snowmelt water, SMC, AET, and TWS for the period of 1981-2100.The whole data was divided into three equal periods for further analysis: 1981-2020, 2021-2060, and 2061-2100.The period 1981-2020 was used as the BLP to assess the relative changes in hydrological components for the near future period (NFP, 2021-2060) and far future period (FFP, 2061(FFP, -2100)).
where Q obs , Q sim , and σ describe observed streamflow, simulated streamflow, and standard deviation (SD) of observed data, respectively.

Evaluation of Screening Method
The proposed screening method (PSM) was evaluated with the stepwise regression screening method (SRSM), which is considered a classical method.Table 2 shows the predictors selected by the SRSM and PSM for Tmax and PREC at different sites in the TRHR.The SRSM selected 12-18 predictors for each site for Tmax and PREC while the PSM only 4-6 predictors for each variable, removing insignificant predictors, which minimized the effect of multi-collinearity in the regression model.To check the performance of selected predictors, 16 predictors selected by SRSM and four by the PSM (Table 2) out of 26 NCEP predictors for PREC at Ruoergai were used in SDMS to simulate PREC, and the simulations were compared with observations, as shown in Figure 6.This shows that even the number of predictors selected by PSM were much smaller, the simulation results were well comparable with the SRSM.Nonetheless, the correlation coefficient was a little higher in the case of SRSM (i.e., 0.89 by SRSM and 0.87 by this method), which might be due to multi-collinearity.The multi-collinearity produces the variance of the coefficient estimates, and the increased variance makes the coefficient estimates very sensitive to monitor changes in the data, which produces unstable coefficients (P.Singh et al., 2023).

Evaluation of SDSM and GCMs
After the successful screening of NCEP predictors using the PSM, SDSM was calibrated for PREC, Tmax, Tmin, RH, and WS at more than 100 points (Figure 1) for 1981-2005 and validated for 2006-2015.Table 3 shows the maximum, minimum, and mean values of correlation coefficients (R), coefficient of determination (R 2 ), and RMSE calculated at all sites in the TRHR, using monthly time series.On average, R and R 2 values were higher than 0.89 and 0.80 for all variables except WS, which had average values of 0.6 and 0.67, respectively.The values of RMSE for all the variables were also well within the acceptable range (Table 3).SDSM showed the best performance for temperature and relatively bad for WS.It was also observed that validation results were even a little better than the calibration.Additionally, to check the performance of GCMs, SDSM was forced with the GCM predictors to simulate climate variables for the historical period 1981-2015 and compared with the observations using the statistical indicators, as described in Table 4. R and R 2 for the three GCMs ranged between 0.64-0.99 and 0.41-0.98,while varied from 0.88 to 0.99 and 0.77-0.99 for the ensemble of GCMs (GCM-ENSM).RMSE values for PREC, Tmax/Tmin, RH, and WS were less than 25 mm/month, 2°C, 6%, and 0.33 m/s, respectively, for all GCMs and GCM-ENSM.Furthermore, an annual cycle of each variable was plotted for all GCMs and GCM-ENSM against the observations for a deep investigation of simulated results, as shown in Figure 7.In the case of PREC, NorESM well captured the peaks months, while other models followed well the low PREC months.Nonetheless, Tmax and Tmin were well simulated by all the GCMs.Table 4 and Figure 7 showed that all variables simulated by forcing GCM-ENSM data performed better than individual GCM.Therefore, GCM-ENSM was applied for further analysis in the TRHR, as an ensemble mean provides more dependable and robust estimations than an individual model (Tebaldi & Knutti, 2007).

HEC-HMS's Performance Evaluation
Calibration and validation Table 5 shows the Nash-Sutcliffe efficiency (E), coefficient of determination (R 2 ), NRMSE, and percent bias (PBIAS) calculated from monthly streamflow at five hydrometric stations.The E and R 2 , which are used to quantify how well a model can predict the outcome variable, ranged from 0.70 to 0.93, and the PBIAS values were less than 10% at all gauges both for calibration and validation, except at Jimai having PBIAS of 12% and 21% during validation period-1 and -2, respectively.According to Moriasi et al. (2015), the performance of a hydrological model is considered satisfactory if E > 0.50 and PBIAS ≤±25% and very good if E > 0.75 and PBIAS ≤±10%.NRMSE varied from 0.21 to 0.37, which was a little higher but still within the acceptable range, as it should be less than half of the SD of measured data (J.Singh et al., 2004).Additionally, simulated streamflow at Xiangda, Zhimenda, and Tangnaihai was plotted against the observations to analyze how well the model's simulations capture the monthly variations during each year, which are shown in Figure 8.The graphs showed that the model well captured all the components of observed hydrograph (e.g., baseflow, falling limb, rising limb, and time to peaks) except peaks.For example, at Xiangda, the model overestimated in 2008, 2009, and 2012, while underestimated during 2010, 2011, and 2013.On the whole, the statistical indicators and hydrographs showed that the model was well established to simulate streamflow in the region.

Evaluation of Hydrological Components
For the evaluation of HEC-HMS simulating SMC, TWS, SWE, and AET, the data was obtained from ESA-CCI-SM, the GRACE solution of UT-CSR, Advanced Microwave Scanning Radiometer-Earth Observing System, and TERACLIMATE products, respectively, described in Table 1.However, baseflow was evaluated with that separated by the RDF.Since GRACE-TWS is available in an anomaly format relative to the mean of 2004-2009, we also calculated water storage anomalies (HEC-TWSA) relative to 2004-2009, as in Yuan et al. (2018)).Three statistical indicators (i.e., correlation coefficient [R], PBIAS, and RMSE) were used for the evaluation of these components, which were calculated from monthly time series and are described in Table 6.The R values ranged from 0.66 to 0.96 in all three basins for all the components, except in the HYaR for SWE where the R value was 0.33.The highest correlations were observed in the case of baseflow followed by AET.PBIAS varied between 13% and 20% in all three basins except for SWE in the HLaR and HYeR.On the whole, the best results were observed for baseflow followed by AET, SMC, and TWS.The SWE comparison with remote sensing data was not as good as compared to other components, which might be due to uncertainties exhibited in remote sensing data.It is required to evaluate SWE with other remote sensing products as well as observations.
Under SSP5-5.8, the potential changes for hydro-climatic elements in the NFP and FFP relative to the BLP are described in Table 8. Almost all the changes (positive or negative) were quite similar to that under SSP2-4.5 but the magnitudes were much higher than that under SSP2-4.5.For example, Tmax was predicted to rise by 1.7-2.0°Cand 4.0-4.6°C in the NFP and FFP, respectively, which was almost 1.3-2.0times higher than that under SSP2-4.5.Similarly, PREC was increased by 15.8%-26.8%and 43.0%-86.8% in the NFP and FFP, respectively, which was almost, 1.4-2.2times higher than that under SSP2-4.5.
Although the increased temperature accelerated AET, the increased PREC dominated the region, causing an increase in all the hydrological components.
It was observed that the hydro-climatic changes were mostly maximum in the HYeR followed by HYaR, which was mainly due to higher changes in temperature and PREC in the HYeR.The highest rise was observed in surface flow (direct flow) under both scenarios, indicating more floods in the future.All hydrological components showed higher changes in the FFP as compared to the NFP, except snow-melt flow in the HYaR.

Annual Cycle Changes
Figures 9 and 10 show the annual cycles of hydro-climatic components for the BLP, NFP, and FFP under SSP2-4.5 and SSP5-8.5, respectively.Instead of individual headwaters, these cycles were plotted for the whole TRHR, by taking the weighted mean.On the whole, almost all variables showed profound intensification in the NFP and FFP, under both scenarios.The changes were even much higher in the FFP and under SSP5-8.5.Climate variables, that is, Tmax, Tmin, and RH displayed an increase in all months, while WS showed a decrease, especially under SSP5-8.5.On the other hand, the hydrological components such as PREC, streamflow (runoff), and AET were projected to increase mainly during the melting and rainy season (April-October) relative to the BLP.For example, PREC showed a significant increase from May to October, with peaks shifting toward August and September (Figures 9e and 10e).Consequently, the PREC-dependent hydrological components such as streamflow, baseflow, SMC, and TWS exhibited similar patterns to PREC.Streamflow and baseflow also displayed higher values in the winter and spring seasons, especially under SSP5-8.5 and in the FFP, though PREC did not.This can be mainly due to increased temperature in the FFP, causing early snow/ice melting.This melting effect can also be seen in Figures 9j and 10j, where melting is starting in February instead of April.A profoundly

Evolution of Hydro-Climatic Elements
To analyze the temporal changes in hydro-climatic elements over the whole study period , annual time series plots are shown in Figure 11 along with the rate of change for the whole period, which were estimated by linear regression.On the whole, all elements showed continuous increasing trends under both scenarios, except WS.However, ROCs were higher under SSP5-8.5 than that under SSP2-4.5, even extremely high after 2050.It

Table 7
Projected Hydro-Meteorological Changes in 2021-2060and 2061-2100Relative to 1981-2020, Under SSP2-4.5  was observed that the elements started stabilizing after 2050 under SSP2-4.5, while exaggerated under SSP5-8.5.For example, Tmax (Tmin) was estimated to increase at the rate of 0.29-0.35(0.19-0.24)°C/10 year and 0.5-0.57(0.34-0.46)°C/10 year under SSP2-4.5 and SSP5-8.5, respectively, showing almost double figures under SSP5-8.5 (Figures 11a and 11b).Similarly, the ROCs of PREC under SSP5-8.5 were almost two times higher than that under SSP2-4.5,ranging from 11 to 31 mm/10 year and 33-67 mm/10 year, respectively (Figure 11e).Nonetheless, the ROCs of AET were smaller than PREC, ranging from 4 to 20 mm/10 year under SSP2-4.5 and 10-47 mm/10 year under SSP5-8.5 (Figure 11f).Since the ROCs of PREC dominated the AET, this can be the main reason for an increase in all other hydrological components such as streamflow, surface flow, and SMC.For example, streamflow will increase at the rate of 7-11 mm/10 year and 18-24 mm/10 year under SSP2-4.5 and SSP5-8.5, respectively (Figure 11g).Although all hydrological components showed high ROCs in the last decade  Water (2091-2100) relative to the rest period (1981-2090) under SSP5-8.5,surface flow displayed an unprecedented increase during this period.So, the last decade will face a lot of flooding in the region.It was also noticed that ROCs were higher in the HYeR than the other two basins under both scenarios in the case of almost all hydroclimatic elements except melt-flow.

Hotter and Wetter Future
Hydroclimatic projections of a region are critical to cope with climate change and long-term water resources planning and management in a better way.These kinds of scientific-based predictions offer a great source of information to help policy-makers, regional communities as well as governments in planning and adapting the projected hydrological changes such as changes in water availability.In this study, a statistical downscaling method along with a hydrological model was applied to assess the hydrological responses to changing climate under two scenarios for the future relative to the BLP.The results showed that all hydrological components such as streamflow, baseflow, surface flow, SMC, and AET responded positively (increase) to the projected climate such as increased temperature, PREC, and humidity in the TRHR.Most previous studies exhibited similar kind of results in and around the study area, increased streamflow (runoff) in the TRHR such as by F.  2022) over the HYeR.The main reason for contradicting results might be due to using different downscaling methods, hydrological models, input data sets, scenarios, and analysis periods for both baseline and future.
It is quite obvious that increased flows (e.g., streamflow, surface flow, and melt-flow) in the region are mainly due to projected increased PREC because it is the primary factor affecting the flow regimes (T.Zhang et al., 2018).IPCC (2021) showed positive changes in PREC not only on the global level but also over China and the TRHR.Over China, they showed a 10%-40% increase under 1.5-4.0°C of global warming scenarios.The PREC projections in the region are also in-line with the previous studies such as F. Su et al. (2016), T. Wang et al. (2022), and Q. Zhao et al. (2019), however, with different increasing magnitudes in the TRHR (ranging from 10% to 40% increase at the end of twenty-first century under different RCPs).In addition, Lu et al. (2018) showed an increase in PREC up to 70% in some parts of the TP in 2041-2060.However, the projected increase in PREC in the TRHR is much higher than the increased global land PREC, which is about 1.5%-8% and 1%-13% under SSP2-4.5 and SSP5-8.5 in 2081-2100.It is also reported that every 1°C increase in global warming will increase PREC by about 7% (IPCC, 2021).This means the TRHR will be wetter and wetter in the twentyfirst century because temperature in the region will increase by 1.0-1.5 (1-1.9)°C and 2.0-2.8(2.7-4.6)°C in 2021-2060 and 2061-2100 relative to 1981-2020 under SSP2-4.5 (SSP5-8.5),respectively, which was highly expected in the region because it is well established that temperature will increase in the future on global as well as region scales.However, the increasing magnitude could vary from region to region.For example, similar increasing values have been explored in the previous studies conducted in and/or around the TRSR such as by Gu et al. (2018) 2019) over TP (1.9-3.5°C,2090-2100, RCP2.6-4.5).Similarly, an increase in global mean temperature of 1-5.7°C has been reported by IPCC (2021) under SSP1-1.9-SSP5-8.5 in 2081-2100 relative to the pre-industrial era.This is mainly due to the present and projected increasing concentration of greenhouse gases in the atmosphere due to burning fossil fuel, deforestation, agricultural activities, etc. (J.Chu et al., 2010).It was also noticed that the studies that estimated a higher increasing percentage of ET than PREC exhibited decreasing streamflow such as Hu et al. (2022).This means the TRHR will be wetter and hotter in the future, with much more rain and snow, and a higher risk of flooding because surface flow was projected to be much higher, even up to a 500%-1,000% increase relative to the BLP.To explore the reason for this unprecedented increase in surface flow, some extreme precipitation indices, that is, P-1 day (maximum annual daily precipitation), P-5 day (maximum annual 5-daily precipitation), P20 (annual count of days when precipitation was ≥20 mm), P90P (90th percentile) and SD, were calculated for the BLP, NFP, and FFP, as in Vu et al. (2019).It was found that all extreme indices showed a profound increase in the FFP relative to the BLP.For example, P-1 day, P-5 day, P20, P90P, and SD increased by 40%, 52%, 1,200%, 38%, and 35%, respectively, in the FFP. Figure 12 shows that the extreme events will increase at a faster rate after 2070 in the FFP.Nonetheless, in the NFP, the precipitation extreme events were even less than that in the BLP.Increases in other hydrological components such as increased baseflow, surface flow, snowmelt flow, SMC, and TWS are mainly due to increased annual PREC and increased extreme events because it is the primary source of input for all these components.Despite some uncertainties, the results of the present study can be more reliable than the previous study because we calibrated and validated the SDSM, with high satisfaction.However, previous studies just corrected the GCM's outputs using bias correction methods.

Limitations and Uncertainties
Although advances in science and technology have improved significantly in predicting hydroclimatic phenomena, uncertainties cannot be removed completely.There are different sources of uncertainties in projecting hydroclimatic variables such as an incomplete understanding of Earth's systems, natural variability in the climate system, and limitations related to climate models, hydrological models, downscaling methods, climate scenarios, and input data sets (Cho, 2023).SDSM assumes that the empirical relationship between predictand and predictors is temporally stationary, which is the main limitation of this method (Mahmood & Babel, 2013).The selection of a set of predictors can be another source of uncertainty in this method.So, the selected predictors must be logical and have minimum collinearity effect in the regression model.The second source of uncertainty is related to the hydrological model, which is the simplified representation of land surface processes (Mahmood & Jia, 2019).In this study, soil types and land use land cover remain static throughout the simulation period.It means that calibrated parameters remain the same throughout the simulation, which represents the soil and land cover characteristics.Although we used ensemble mean of downscaled data of three GCMs to capture the uncertainty, accurate projection of climate variables and hydrological responses is not possible due to the complex Earth's hydro-climatic processes, inherent uncertainties in the GCMs and hydrological modeling processes (Hu et al., 2022).Another limitation of the hydrological model is not dealing with the permafrost variation.Since the region is composed of seasonal to permanent permafrost, its coverage and depth can be decreased due to increasing temperature, which can increase the infiltration and decrease the streamflow.So, it is recommended to consider above mentioned limitations and assumptions in future studies.If we assume that the above-mentioned uncertainties can affect the results by 15%-20%, as assumed in Coe and Foley (2001), the results of the present study are still quite satisfactory and can be a good source for understanding the hydrological cycle of the highly elevated and complex region of the QTP.

Conclusions
In the present study, the potential impacts of climate change were assessed on all the important hydrological components such as precipitation, evapotranspiration, streamflow, and SM content in the TRHR.Climate data (i.e., precipitation, maximum and minimum temperature, RH, and windspeed) of three GCMs was downscaled using SDSM for 1981-2100 and forced into HEC-HMS to simulate the hydrological processes in the region.A PSM was applied to select a set of logical predictors considering, specifically, the multi-collinearity effect.Finally, the hydro-climate changes were assessed for the NFP (2021-2060) and the FFP (2061-2100) relative to the BLP (1981BLP ( -2020)).In addition, the annual cycle and annual evolution of hydrological components were analyzed to explore deep information in the TRHR that leads to the following conclusions.
1.All climatic variables were projected to increase in the TRHR except WS.
a. Temperatures in the region will increase by 1.0-1.5 (1-1.9)°C and 2.0-2.8(2.7-4.6)°C in the NFP and FFP relative to the BLP under SSP2-4.5 (SSP5-8.5),respectively, with maximum temperature at a faster rate than the minimum.b.Precipitation will rise by 9-21 (15-27)% and 16-40 (43-87)% in the NFP and FFP relative to the BLP under SSP2-4.5 (SSP5-8.5),respectively 2. All hydrological components were also projected to increase in the future under both scenarios.However, in the FFP and under SSP5-8.5, the changes were determined to be extremely higher than the BLP and even the NFP.For example, in the NFP and FFP and under SSP2-4.5 (SSP5-8.5)a. AET was estimated to rise by 6-17 (9-29)% and 11-31 (24-73)% b.Streamflow was predicted to increase by 9-46 (22-64)% and 20-95 (60-198)% c.The surface flow showed the highest percentages, especially in the FFP (500%-1,000%), and even higher in the last decade (2091-2100) under SSP5-8.5,indicating intense flooding in the region.3. The changes in the peak months (June-August) were projected to be much higher than other months relative to the BLP, and peaks were found to be shifted forward except for AET and snowmelt.Snow melting will start earlier in February-March instead of April 4. On the whole, the TRHR will be hotter and wetter, with intensive and frequent flood events.
Overall, the research provides valuable insights into the future hydrological dynamics of the TRHR, with implications for environmental sustainability, socio-economic development, and climate change adaptation in the region.The projected future changes in hydrological processes can be applied by policymakers, planners, and stakeholders for proactive adaptation strategies.This could include investments in water infrastructure, land use planning, ecosystem restoration, and community resilience-building initiatives to mitigate potential risks.

Figure 1 .
Figure 1.Location map of the Three-River Headwater Region (TRHR), showing the streamlines of the major rivers and hydro-climatic stations.China Meteorological Forcing Data set (CMFD), Headwater of the Lancang River (HLaR), Headwater of the Yangtze River (HYaR), headwater of the Yellow River (HYeR) refer to CMFD, the Headwater of Lancang, Yangtze, and Yellow Rivers, respectively.

Figure 2 .
Figure 2. Schematic diagram of (a) the proposed method for selection of predictors for statistical downscaling, and (b) the process of projecting hydrological components under shared socioeconomic pathways.PCP is the product of correlation coefficient and partial correlation.

Figure 3 .
Figure 3. Effects of transformations on simulated precipitation at Wudaoliang station in the Three-River Headwater Region.

Figure 4 .
Figure 4. Ranked parameters according to their sensitivity to streamflow.

Figure 5 .
Figure 5. Process developing the hydrological modeling system (HEC-HMS) for the simulation of hydrological components in the Three-River Headwater Region.

Figure 7 .
Figure 7.Comparison of the annual cycle of (a) precipitation, (b) maximum temperature, (c) minimum temperature, (d) wind speed, and (e) relative humidity simulated by Statistical DownScaling Model using inputs from Global Climate Models (i.e., Canadian Earth System Model [CanESM], Norwegian Earth System Model [NorESM], Max Planck Institute Earth System Model) and their ensemble against the observations for the period of 1981-2015 in the Three-River Headwater Region.

Figure 8 .
Figure 8. Evaluation of HEC-HMS by simulating the streamflow at (a) Xiangda, (b) Zhimenda, and (c) Tangnaihai located at the Lancang, Yangtze, and Yellow Rivers, respectively, in the Three-River Headwater Region.

Figure 11 .
Figure 11.Evolution and rate of change of hydro-climatic components for the period of 1981-2100, under SSP2-4.5 and SSP5-8.5 in the Three-River Headwater Region.

Table 1
Characteristics of Temporal and Spatial Data Applied in This Study

Table 2
Selected National Centers for Environmental Prediction Predictors for Maximum Temperature and Precipitation at Different Sites in the Three-River Headwater Region, Using Stepwise Regression and the Proposed Method distribution of climate variables over a catchment; the CS is used to specify a simulation period to run the model; and the DM stores and manages time-series data (e.g., temperature, WS, and PREC) Acheampong et al. (2023)hmood & Jia, 2022)For this, the whole period was divided into three parts1981-2005, 2006 -2010 , and  2011 -2015 .The middle one (2006 -2010) )was used for calibration because of the least missing observations, and the other two were used for validation to make a robust evaluation of the model.The model was calibrated and validated at five available gauges: Xiangda at the Lancang River, Zhimenda at the Yangtze River, Jimai, Maqu, and Tangnaihai at the Yellow River.Before calibration, we performed the sensitivity analysis, as inBelayneh et al. (2020)andMahmood and Jia (2019).Sensitivity analysis explores the important, sensitive, and influential parameters in a watershed and triggers the calibration process, especially in the case of manual calibration(Devak & Dhanya, 2017;Mahmood & Jia, 2022).In the present study, the sensitivity analyses were carried out by changing each parameter by 10% each time (between 40% and +40%) to observe the changes in simulated discharge.Two indicators, Peak Flow (PF) and Total Flow Volume (TFV) were used to observe the effect of each parameter on streamflow, which are mostly used in literature, as inAcheampong et al. (2023),Mahmood et al. (2020), and  Palacios-Cabrera et al. ( Figure 6.Comparison of simulated precipitation by Statistical DownScaling Model using the predictors selected by the stepwise regression and the proposed method, at Rouergai station.hierarchical S canopy + S surface + S soil + S ground1 + S ground2 + S swe

Table 4
Evaluation of Global Climate Models (GCMs) and Ensemble for the Historical Period 1981-2015 in the Three-River Headwater Region MAHMOOD ET AL.

Table 5
Calibration and Validation of HEC-HMS in the Three-River Headwater Region MAHMOOD ET AL.

Table 6
Evaluation of Different Hydrological Components in the Three-River Headwater RegionNote.RRSD, reanalysis and remote sensing data; DRF, Recursive Digital Filter; SMC, Soil moisture content; SWE, Snow water equivalent; TWSC, Terrestrial water storage changes; AET, Actual evapotranspiration.
MAHMOOD ET AL.