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Projected climate change and its impacts on glaciers and water resources in the headwaters of the Tarim River, NW China/Kyrgyzstan


Glacierised river catchments are highly sensitive to climate change, while large populations may depend on their water resources. The irrigation agriculture and the communities along the Tarim River, NW China, strongly depend on the discharge from the glacierised catchments surrounding the Taklamakan Desert. While recent increasing discharge has been beneficial for the agricultural sector, future runoff under climate change is uncertain. We assess three climate change scenarios by forcing two glacio-hydrological models with output of eight general circulation models. The models have different glaciological modelling approaches but were both calibrated to discharge and glacier mass balance observations. Projected changes in climate, glacier cover and river discharge are examined over the twenty-first century and generally point to warmer and wetter conditions. The model ensemble projects median temperature and precipitation increases of + 1.9–5.3 °C and + 9–24%, respectively, until the end of the century compared to the 1971–2000 reference period. Glacier area is projected to shrink by 15–73% (model medians, range over scenarios), depending on the catchment. River discharge is projected to first increase by about 20% in the Aksu River catchments with subsequent decreases of up to 20%. In contrast, discharge in the drier Hotan and Yarkant catchments is projected to increase by 15–60% towards the end of the century. The large uncertainties mainly relate to the climate model ensemble and the limited observations to constrain the glacio-hydrological models. Sustainable water resource management will be key to avert the risks associated with the projected changes and their uncertainties.


The Tarim River in the Xinjiang Uighur Autonomous Region of NW China is the country’s largest endorheic river basin and is home to approx. 10 Million people. Runoff is generated in the glacierised Tien Shan (Aksu River), Pamir, Karakoram (Yarkant R.) and Kunlun Mountains (Hotan R.) encircling the Taklamakan Desert and is vital to the river oases that live from irrigation agriculture. A decline in upstream runoff may have severe consequences for the population, agriculture and ecosystems downstream, threatening the very existence of the river as it has done in the past (Thevs 2011). As most mountainous regions, the highly glacierised and largely uninhabited headwaters of the Tarim River are vulnerable to climate change, as higher temperatures endanger the glaciers as natural reservoirs for downstream communities (Barnett et al. 2005; Immerzeel et al. 2020; Pritchard 2019). At the same time, competition for river water has dramatically increased over the past 30 years with the rapid expansion of irrigated agriculture for the production of cotton, fruit trees and subsistence farming. While an increase in headwater discharge has been observed (Liu et al. 2006; Krysanova et al. 2015), downstream discharge has declined in line with a four-fold increase in irrigation area over the past 40 years (Thevs 2011).

The continental climate produces scarce precipitation mainly in summer when it coincides with the glacier melt peak (Aizen et al. 1995). Meteorological measurements are sparse and mainly in valleys or at the fringes of the Taklamakan Desert (Tao et al. 2011; Krysanova et al. 2015), which introduces an underestimation in catchment-wide precipitation interpolations. Comparing various datasets and using glacio-hydrological modelling, Wortmann et al. (2018) show that the best available dataset (APHRODITE, Yatagai et al. 2012) has to be corrected by factors of 1.2–4.3 at the catchment average (more at higher elevations) to be consistent with discharge records and glacier mass balances estimates. Similar findings are reported for other areas in the Tarim basin and its surroundings (Duethmann et al. 2013; Immerzeel et al. 2015; Tong et al. 2014).

Several studies about the Tarim River basin investigate the observed trends in discharge and its anticipated climatic (Xu et al. 2004; Chen et al. 2006; Liu et al. 2006; Yaning et al. 2009; Li et al. 2020), as well as water management drivers (Song et al. 2002; Liu and Chen 2006; Tang et al. 2007; Hao et al. 2008; Wu et al. 2010). A small number of contributions have addressed changes of glaciers or glacier runoff under projected climate change, but only at a global or regional scale (Kraaijenbrink et al. 2017; Huss and Hock 2018; Rounce et al. 2020) or in parts of the Tarim Basin. Duethmann et al. (2016) examine three climate change scenarios in the Aksu headwaters and project glacier area to decrease by 32–90% over the twenty-first century with an initial increase and an eventual decrease in discharge. Zhang et al. (2012) assess glacier runoff, mass balance and area over the first half of the twenty-first century using a simple degree-day glacier melt model and three delta-change scenarios. However, no systematic assessment of future changes under common climate change scenarios, considering the extensive glacier cover and their heterogenous response to climate change in the headwaters, has been conducted to date. This is largely because of a complex hydrology and the data scarcity or restrictions on the limited data availability.

Assessments of glacier area and mass balance in the Tarim headwaters are crucial for adequate hydrological modelling. Advances have been made; for example, different glacier inventories exist covering large parts (2nd Chinese glacier inventory, Guo et al. 2015) or the whole basin (the Randolph Glacier Inventory consisting of different sources, RGI consortium 2017). Investigations of observed glaciological changes at the catchment scale have been conducted for the Aksu headwaters by Pieczonka and Bolch (2015), using multitemporal satellite images and digital terrain models. Although the existing mass balance assessments for the Yarkant and Hotan catchments only cover the period after 2000 (Brun et al. 2017; Shean et al. 2020; Hugonnet et al. 2021) or not the entire catchment area (Holzer et al. 2015; Zhou et al. 2018; Bhattacharya et al. 2021), they still provide evidence for the calibration of glacio-hydrological models.

This study aims to provide a first systematic climate change impact assessment for the twenty-first century of the Tarim River headwaters simulating glacier and hydrologic changes based on two glacio-hydrological models (SWIM-G and WASA). The use of an ensemble of global circulation model (GCM) results, as well as two hydrological models, is meant to expose the inherent uncertainty associated with both sources. Huang et al. (2018) use the projected discharge and consider the water management in the river oases downstream under the same climate change scenarios evaluated here.

Study region: the Tarim River headwaters

The Tarim River is one of the largest endorheic rivers in the world with a topographical catchment size of about 800,000 km2 and a mainstream length of 600–800 km depending on discharge and water abstractions (Tao et al. 2011). The river is fed by three large tributaries with their confluence at the northern edge of the Taklamakan Desert: The Aksu River originating in the central Tien Shan to the north, the Hotan River originating in the Kunlun Shan to the south and the Yarkant River originating in the eastern Pamir and the Karakoram (see Fig. 1). The desert climate in the lower parts of these rivers and the Tarim produces virtually no river runoff (except for rare extreme rain events) with annual potential evapotranspiration exceeding precipitation by factors of 30–50 (Zhao et al. 2012). The vast majority of river discharge is generated in the mountainous and glacierised headwaters. This study focuses on the five catchments of the gauging stations that are situated at the boundary of the Taklamakan Desert, i.e. before significant river abstractions and transmission losses occur (Table 1 and Fig. 1).

Fig. 1
figure 1

© American Meteorological Society. Used with permission

Map of the five considered catchments (their names marked by a white shade) that supply the vast majority of discharge to the Tarim River. Wortmann et al. 2018

Table 1 Catchment statistics for all considered stations with station elevation, annual and June–August mean discharge (Q, 1961–1989, with some gaps), glacier cover of 2008 (Pieczonka and Bolch, 2015; Duethmann et al., 2016), mean annual precipitation (P, 1971–2000) of the APHRODITE dataset (Yatagai et al. 2012), corrected median values of precipitation Ps by Wortmann et al. (2018), used in SWIM-G, and corrected values of precipitation Pw estimated based on observed discharge and glacio-hydrological modelling with WASA

The climate of the study region is highly continental with a strong seasonality governed by the Westerlies, with parts of the Hotan and Yarkant catchment also influenced by the Indian monsoon (Aizen et al. 1995; Maussion et al. 2014). As a consequence, river regimes exhibit a strong peak in summer when snow and glacier melt coincide with the precipitation peak. Mean summer (winter) temperatures fall between 3 and 20℃ (− 19 to − 5℃) with about 75% of precipitation falling between the months of April and September (in the 1971–2000 reference period).

The mountain ranges surrounding the Taklamakan Desert comprise steep, high-altitude terrain that has given rise to an extensive glacier cover accounting for significant proportions of the catchment areas considered here (Fig. 1, Table 1). The two Aksu headwaters comprise a total glacier area of 3410 ± 118 km2 (2008, Pieczonka and Bolch 2015; Osmonov et al. 2013). A unique glaciological feature of the Aksu headwaters is the ice-dammed Merzbacher Lake that sends near-annually recurring subglacial outburst floods (jökulhlaups) downstream with consequences for communities and modelling efforts (Glazirin 2010; Wortmann et al. 2014). The two catchments of the Hotan River span the north-western edge of the Tibetan Plateau and have a glacier cover of some 5880 km2 (Shangguan et al. 2007). The Yarkant headwater has a total glacier cover of about 5600 km2 at extremely high and steep altitudes (Dyurgerov 2010).

Observed signs of a changing climate in the headwaters have been widely discussed. An increasing trend in river discharge over the past 50 years of the Aksu River, the most important tributary to the Tarim River, was reported to be as much as 30% between 1957 and 2004 relative to mean discharge (Wang et al. 2008; Krysanova et al. 2015). This was attributable to increasing trends in air temperature and precipitation, with a higher contribution of air temperature (leading to higher ice melt) in the Xiehela catchment, as found by a modelling and data-based study (Duethmann et al. 2015). An increasing trend could, however, not be confirmed for the discharge of the Hotan and Yarkant catchments (Tao et al. 2011). The observed climate in Xinjiang has experienced a trend towards warmer and wetter conditions since the 1970s (Shi et al. 2006). Statistically significant increasing trends were found for temperature, precipitation and vapour pressure over nearly the entire Tarim catchment (at 1% significance level, Tao et al. 2011). Due to the poor observation density in the Hotan and Yarkant headwaters, however, those results must be interpreted with caution. Long-term, high-altitude observations for these catchments do not exist, rendering the hydrological observations the only long-term glimpse of these catchments’ water balance and even those are interrupted.

Glaciological studies in the Aksu catchment have received most attention, especially over the last decade. Pieczonka and Bolch (2015) have found a heterogeneous glacier mass and area loss over the catchment, but the average mass balance of 0.35 ± 0.34 m weq. a−1 (1970–2008) is comparable to global values. Other studies have confirmed and contributed to this assessment (Farinotti et al. 2015; Pieczonka et al. 2013; Osmonov et al. 2013). Investigations in the Karakoram based on declassified satellite imagery from the 1970s indicate stable conditions or only slight mass loss: Bolch et al. (2017) found a mass balance of − 0.01 ± 0.09 m weq. a−1 (1973–1999) for the Hunza catchment and Zhou et al. (2017) found a mass balance of − 0.04 ± 0.05 m weq. a−1 (1973–2000) for the central Karakoram. However, both studies focus on the part draining south into the Indus. Studies of mass changes since 2000 show a positive or stable balance in the Tarim part of the Karakoram, the Kunlun Mountains and eastern Pamir, e.g. + 0.04 ± 0.05 m weq. a−1 (2000–2018, Shean et al. 2020), + 0.05 ± 0.07 m weq. a−1 (2003–2008, Kääb et al. 2015) and + 0.14 ± 0.08 (2000–2016, Brun et al. 2017), respectively.

Data and methods

This climate change impact assessment considers future hydrological and glaciological changes by assessing multiple scenarios and climate model results by means of two glacio-hydrological models. For this study, the SWIM-G (Soil and Water Integrated Model – Glacier Dynamics; Wortmann et al. 2019) and WASA (Model of Water Availability in Semi-Arid Environments; Güntner 2002) models were as follows: (1) implemented in the five headwater catchments, (2) calibrated and validated to discharge and glacier observations, and then (3) driven by results of eight GCMs. What has become a common practice of climate impact assessment in many river catchments of the world (Fowler et al. 2007; Beniston 2003; Teutschbein and Seibert 2012) is a task marred with difficulties in the complex hydrology of the Tarim River; the stark contrast between desert and mountain climate, severe data scarcity, heterogenous past glacier changes (Shean et al. 2020), substantial river abstractions downstream (Tao et al. 2011) and regular large glacial lake outbursts (Wortmann et al. 2014) to name but a few. The validated models were then run with climate scenarios over the reference period and the twenty-first century. The reference period is defined as 1971–2000 and the near (2011–40), medium (2041–70) and far (2071–2100) future periods are investigated.

Input data

To overcome the precarious data availability, mostly preprocessed (homogenised, synthesised) datasets are used in this study (Table 2). They offer best-practice interpolations of the sparse observations (homogenised also beyond national boundaries), while also circumventing restrictive data sharing policies of China. Precipitation is the most crucial driving variable and the renown APHRODITE dataset (Yatagai et al. 2012), a gridded precipitation interpolation of the densest gauge network in Asia, was used. Although it is the best data available, the station density is still extremely poor, e.g. the Hotan and Yarkant catchments are nearly devoid of any observations with the closest stations located at the edge of the Taklamakan Desert or at significantly lower-laying locations (Tao et al. 2011; Wortmann et al. 2018) leading to a negative bias. The necessary precipitation correction used is described in Duethmann et al. (2013, WASA) and Wortmann et al. (2018, SWIM-G).

Table 2 Input data used to drive and calibrate/validate the models. Topography and glaciers are shown in Fig. 1. Climate variables are temperature T (mean, min., max.), precipitation P, radiation and relative humidity

Other driving data include temperature (daily mean, minimum, maximum), radiation and relative humidity, which are provided by the WATCH Forcing Data (v2) (Weedon et al. 2011). Although it suffers from the same or worse station sparsity as the APHRODITE data, the climate variables are significantly more stable over space and time (Krysanova et al. 2015). The variability of temperature with elevation (lapse rate) is also more stable—especially in dry climates—and is therefore parameterised in the snow and glacier melt components of the models.

Daily discharge observations for model calibration and validation were available for the five outlet gauges in the period 1961–1988 (with some missing years, see Table 2). High-resolution ice thickness simulations (GlabTob, Duethmann et al. 2015; Linsbauer et al. 2012) and regional glacier mass balances for the period ~ 1975–1999 (Pieczonka and Bolch 2015) were used for model initialisation and calibration with variations between the two glacio-hydrological models (see next section).

Glacio-hydrological modelling


The glacio-hydrological model SWIM-G is used to simulate both the glacier and catchment hydrology in a tightly integrated approach (Wortmann et al. 2019; 2018). It was developed from the widely applied and tested semi-distributed ecohydrological model SWIM (Krysanova et al. 1998) that was recently extended by a glacier dynamics module and was specifically developed for long-term climate change impact assessments for medium to large river basins. Krysanova et al. (2015) provide an overview of the hydrological processes considered. The glacier dynamics extension includes all important glacier processes at the glacier scale, including ice flow, avalanching, sublimation and debris evolution (see Annex 1.1 for a brief description and Wortmann et al. 2019). It was developed to overcome the conflict of scale between individual glaciers and the catchment hydrology when modelling larger, highly glacierised, typically data-scarce catchments. It relies on computational units that disaggregate complex terrain into parts of similar elevation, aspect and hydrological subbasin as opposed to gridded (fully distributed) or empirical approaches (e.g. Immerzeel et al. 2011; Huss et al. 2010). They are conceptually similar and computationally equal to the hydrological response units (HRUs) implemented in SWIM, enabling a tight integration between glaciological and hydrological modelling over large catchments.

The model was calibrated and validated in the period 1961–1987 as in situ observations were available, using a multi-objective Pareto optimisation with three objective functions (calibration ensemble median ranges in parentheses): (1) to the observed discharge at the five outlet gauge stations using a combined Nash–Sutcliffe efficiency (0.73–0.94) and the root mean square error of mean annual discharge as objective functions (13–21%), (2) to observed glacier area using the hypsometry matching metric X2 as objective function (8–15%) and (3) to mass balances using an error probability function (0.001–0.023). The calibration also includes the initialisation of the glacier cover by running the glacier dynamics driven by the climate of 1960–1975 repeatedly over 300 years to yield a glacier cover and thickness adapted to both the driving data and the model structure. The model initialisation and calibration is described in more detail in Wortmann et al. (2018) and the median performance values are given in Table A1 (Annex 1.2). For the Hotan and Yarkant catchments, long-term mass balance measurements were not available and the most plausible scenario was chosen instead. The model calibration yielded several ‘non-dominated’ parameter sets, all of which are considered equally valid. The parameter set that offers the best trade-off between all objective functions was chosen for the scenario runs presented in the results (see description and Figure A2 in Annex 1.3). For an uncertainty assessment, the scenarios were also run with the best/worst parameter sets and an analysis of variance (ANOVA, Gottschalk 2006) was conducted, comparing the variance between climate model ensemble, scenario and parameter set (Figure A8 in Annex 2).


WASA is a semi-distributed, process-based hydrological model. It was first applied to semi-arid catchments in Brazil (Güntner 2002; Güntner and Bronstert 2004) and Spain (Francke et al. 2008; Mueller et al. 2010) and more recently to snow and glacier dominated high mountain catchments in Central Asia (Duethmann et al. 2013; 2014; 2015). Detailed model descriptions including equations can be found in Güntner and Bronstert (2004) and Duethmann et al. (2015) with a brief description provided in Annex 1.4.

The present study uses a daily time step and the spatial structure is organized based on catchments, subcatchments and 200 m elevation zones. For calculating glacier area and volume changes, glaciers are considered individually and a finer discretization of 50 m elevation bands is used. The initial glacier ice thickness distribution of each glacier was derived from a spatially distributed ice-thickness model (GlabTop2, Linsbauer et al. 2012, Frey et al 2014). Based on the simulated mass balance of the glacier, thickness changes in each elevation band are calculated using predefined functions derived from observed glacier thickness changes of the Ak-Shirak massif in the period 1977–1999 (Surazakov and Aizen 2006). For the Hotan and Yarkant catchments, the model was calibrated to daily discharge data over a 10-year calibration period from 1979 to 1988. The period 1972–1978 was used for model evaluation. Additional 2-year periods were used for model initialization. Glacier mass balances for this region were not available for the period 1979–1988. It was assumed that glacier mass balances for the Hotan and Yarkant basins during the calibration period were close to balance, and the mass loss was constrained to 0 ± 0.1 m w.eq. a−1 (Bolch et al. 2017; Shean et al. 2020). For the Aksu catchment, the model was calibrated in a multi-objective way using daily discharge variations, interannual variations of seasonal discharge, discharge trends, correlation to the observed annual glacier mass balance series and cumulative glacier mass change based on geodetic estimates (Duethmann et al. 2015). The calibration period was defined from 1976–1999 and the periods 1957–1975 and 2000–2004 were used for model evaluation. From the set of Pareto optimal solutions, one solution was selected for further evaluations in this study. For details of the WASA model calibration in the Aksu catchment, please refer to Duethmann et al. (2015). Daily calibration (validation) Nash–Sutcliffe efficiency ranged between 0.60 and 0.85 (0.71–0.84). All performance results of the calibration are provided in Table A2 (Annex 1.4).

Climate change scenarios

We use three well-established scenarios from the IPCC Representative Concentration Pathways (RCP; IPCC 2014): (a) RCP 2.6 (atmospheric greenhouse gas concentration peaking around 2040 at 490 ppm CO2-eq and eventual decline), (b) RCP 4.5 (stabilisation at 650 ppm CO2-eq at the end of the twenty-first century) and (c) RCP 8.5 (continuous rise above 1370 ppm CO2-eq after 2100). The results of eight GCMs from the coupled model intercomparison project phase 5 (CMIP5) were used (Table A3 in Annex 1.6, Taylor et al. 2012). The models were selected to cover the full range of precipitation and temperature change signals from the CMIP5 ensemble over the Tarim headwater catchments, i.e. the moderate and strong cases of wetter-warmer, wetter-colder, drier-warmer, drier-colder signals. The GCMs have spatial resolutions ranging from 1.5–3° and all data was provided at a daily temporal resolution.

GCM results were bias-adjusted relative to the baseline period 1971–2000 to the original driving data that the glacio-hydrological models were calibrated to (Wang et al. 2013, Duethmann et al. 2016). A non-parametric quantile mapping approach with trend preservation was chosen, as was previously used by Hempel et al. (2013). Despite concerns of the validity of using a bias-adjustment for climate change impact assessments (Ehret et al. 2012), it was considered necessary in the Tarim headwaters because of considerable deviations in precipitation between calibration and scenario driving data as well as the great sensitivity of the glacier cover to even slight differences in climatic conditions. The application was paramount to ensure plausible reference conditions for the ensemble assessment.


Changes in temperature and precipitation

Increases in both temperature and precipitation are projected under most climate scenarios, with significant changes in all headwater catchments relative to the reference period 1971–2000 (Fig. 2 for annual and period changes, Figures A4-A5 in the Annex for monthly regimes of all periods and catchments). All ensemble median values in the three projection periods increase across all regions. Only ensemble minimum signals indicate possible negative changes in precipitation, especially in the Aksu. Strong increases in (median) temperature and precipitation from the reference period to the near future are projected (often stronger than changes in subsequent periods).

Fig. 2
figure 2

Climate change scenarios for the Tarim headwaters for the twenty-first century, including the reference period (1971–2000). Ensemble maximum, median and minimum values are shown as 10-year running means and signals averaged over the near (2011–2040), medium (2041–2070) and far (2071–2100) future. Absolute values are given in the left vertical axis and changes relative to the reference period average along the right axis. Note that these are bias-adjusted GCM results without the static precipitation bias correction applied by the glacio-hydrological models. Monthly regimes for all periods, scenarios and catchments are provided in Figure A4-A5 in the Annex

Temperature increases are similar across all regions. In the near future, they range between 0.5 and 2.4℃ with only small differences between the scenarios. Those differences become more striking in the two later periods, when ensemble median changes increase with RCP scenario. In the medium-term future, they peak for the RCP 2.6 scenario at about 2.5℃ and slightly decrease thereafter, while for the higher RCP scenarios, they continue to increase up to 7℃ (ensemble max.) in the far future for RCP 8.5. The ensemble variability generally increases with time, leading to ranges of about 2–4℃ in the far future. As expected, snowfall is more confined to the winter months, as time and emission scenario progresses (Figure A6 for monthly snow fractions in the Annex).

Changes in precipitation are less uniform across regions and projection periods and exhibit an even greater ensemble variability, a pattern common in other regions (Thompson et al. 2013; Vetter et al. 2013). Relative changes in the near future are modest and similar across the regions and scenarios with median values of about 6–8%. Scenario differences become more pronounced in the two later periods, as ensemble median changes vary with RCP scenario (with the exception of the medium-term in the Aksu catchment). Changes in the Aksu catchment range from decreases of up to 25% to increases of the same magnitude, but the majority of models indicate increases in precipitation with median changes of 11–18%. These increases are similar in the Hotan and Yarkant catchments, but the spread is large and mostly positive (− 15 to + 54%).

Changes in glacier area and volume

A receding trend over the twenty-first century is evident in all catchments for the ensemble medians of both models and it is strengthening with RCP scenario (Fig. 3, period mean changes in Table A4 in the Annex). For the ensemble medians, glaciers are projected to shrink by up to 35% in the near future, 2–64% in the medium future and 8–89% in the far future compared to the reference period across the three scenarios. and large ranges are due to differences between two impact models and RCP scenarios. In the Xiehela and Shaliguilanke (Aksu) catchments, area shrinkage for the high-end RCP 8.5 scenario steadily rises to 55–80% and 80–95% by the end of the century, respectively, compared to 2010. Both lower scenarios show lower levels of 32–49% and 51–74%, respectively (Fig. 3).

Fig. 3
figure 3

Simulated glacier area changes relative to the mean area in the reference period (1971–2000) over the reference and scenario periods for the three RCP scenarios and the five catchments (indicated by their outlet station and main Tarim headwater river). Median values are computed over the climate model ensemble. Period mean values including ensemble standard deviations are provided in Table A4 in the Annex

For the SWIM-G projections, median shrinkages are the highest for Shaliguilanke (Aksu) and lowest for Tongguziluoke (Hotan) catchment. Glacier changes in this highly glacierised catchment are similar to those in the Xiehela (Aksu) catchment (both with a catchment glacier cover ≈ 20%), i.e. reaching about − 50% until 2100 under RCP8.5. The long-term changes of the less glacierised Wuluwati (Hotan) and Kaqun (Yarkant) catchments are similar to the Shaliguilanke (Aksu) catchment (7, 12 and 5% glacier cover, respectively), i.e. close to − 75% under RCP8.5. Differences between the lower two scenarios, however, are larger in the Hotan and Yarkant catchments than in the Aksu, and they show a slower recession in the first half of the century.

The median shrinkages of the WASA projections are also the highest for Shaliguilanke and the lowest for Tongguziluoke. Both Wuluwati and Kaqun show lower levels than Xiehela (all periods) but higher than Tongguziluoke (from the mid-century). In the near future period, practically no changes are projected for the Yarkant and Hotan. The changes for the end of the century are also the highest for Shaliguilanke (reaching almost − 100% under RCP8.5), and the lowest for Tongguziluoke (> − 25%). The final changes by the end of the century are similar for Xiehela and Kaqun (reaching approx. − 75% at RCP8.5), and moderate at Wuluwati.

Comparing projections between the two impact models, it is evident that WASA simulates a much stronger decline of glacier areas in the Aksu basin than SWIM-G from the middle of the century. However, SWIM-G generates higher losses than WASA in the Hotan and Yarkant catchments, though by the end of the century both models come to similar results for the Kaqun catchment (Yarkant).

In line with area changes, glacier volume (in water equivalents) is also projected to decrease under all scenarios (Table A4, Figure A6 in the Annex). Projected mass loss is 4–25% in the near, 12–75% in the medium and 18–97% in the far future across all catchments and scenarios for the ensemble median. Losses are consistently the greatest in the Shaliguilanke (Aksu) catchment, lower in the Xiehela (Aksu), Wuluwati (Hotan) and Kaqun (Yarkant) catchments, and they are the lowest in the Tongguziluoke (Hotan) catchment.

Glacier mass balances give an indication of the glacier imbalance, the hydrological impact thereof and they may be better compared to studies of past glacier evolution. Decadal mean annual mass balances are provided in Figure A7 (Annex). The glacier recession described above is associated with negative mass balances that maintain or exceed the negative rates of the past in the first half of the twenty-first century with a recovery for the RCP 2.6 and 4.5 scenarios and an acceleration for the RCP 8.5 scenario by the end of the century. The near future (2011–2040) shows similar mass balances to those observed in the past across the region — about − 0.4 to 0 m weq. a−1 — with little differences between scenarios. The largest negative mass balances for the two lower-end scenarios are projected for the middle of the century, while they continue to grow more negative in the high-end scenario until they reach − 1.2 to − 0.8 m weq. a−1 by the end of the century. A recovery to stable or even positive mass balances is only projected under the RCP 2.6 and 4.5 scenarios in the far future analogues to the temperature projections (Fig. 2).

Changes in river discharge

The discharge projections of the multi-model analysis are summarised in Fig. 4 for each catchment and period. In the two Aksu subcatchments, the increase in mean annual discharge is most pronounced in the near future but recedes in subsequent periods except for the RCP 8.5 scenario simulated by SWIM-G in the Xiehela catchment. The initial increase is 10 to 25% for the ensemble median relative to the 1971–2000 reference period (higher values from WASA compared to SWIM-G) with a slight increase relative to the RCP forcing. In the medium and long-term, the increase in the Aksu catchment reduces progressively and turns to no or negative changes compared to the reference period, especially under the RCP 2.6 scenario in the Xiehela catchment and under the RCP 8.5 scenario in the Shaliguilanke catchment.

Fig. 4
figure 4

Projected future changes in annual mean discharge simulated by SWIM-G and WASA. Changes are relative to the baseline period 1971–2000

The impacts are different in the Hotan and Yarkant catchments. There is a general trend of increasing mean annual discharge apparent at all stations and nearly all time periods. Changes in mean annual discharge in these catchments show a uniformly increasing trend at greater magnitudes as in the Aksu catchment. The initial increase in the near future is around 25 to 40% (ensemble median). Subsequent periods also show increases, especially under RCP 8.5. The RCP 4.5, simulations peak in the medium-term at 28–60% compared to the baseline period, while the RCP 2.6 scenario shows lower or similar increases in the later periods than in the first.

The changes in discharge are predominantly concentrated in the summer months (Fig. 5). April to June discharge predominantly increases in the Aksu catchments for all scenarios (mostly higher increase according to WASA), July–September discharge tends to increase in the near future but decreases in the far future. The Hotan and Yarkant catchments show increases in the summer months (June to September) that progress with period and RCP scenario. Discharge only decreases under the RCP 2.6 and RCP 4.5 scenarios in August–September in the Yarkant in the far future. Winter discharge changes in all catchments are mostly positive but represent only small fractions of the increase in the annual discharge. Changes in discharge according to SWIM-G are mostly lower than WASA in the Aksu catchment but higher in the Hotan and Yarkant.

Fig. 5
figure 5

Absolute regime changes in the three main headwaters of the Tarim River (sum of both headwaters in the Aksu and Hotan). The ensemble median is shown for each scenario and glacio-hydrological model. Rather than relative changes for each month in comparison with the reference period, the absolute monthly changes are shown to account for the highly seasonal flows

The origin of these changes can be traced by investigating the changes in the runoff generating water inputs, i.e. rain, glacier and snow melt (Fig. 6). As would be expected under warmer and wetter climate conditions, the rain is increasing in all catchments under most scenarios and the glacier melt is mostly decreasing. An exception is the highly glacierised Tongguziluoke (Hotan) catchment, where both rain and glacier melt are increasing, indicating an increased redistribution of ice into the ablation zone and explaining the sharp projected increases in discharge. The snowmelt is nearly constant or slightly increasing, except in the Shaliguilanke (Aksu) catchment according to WASA under the RCP 8.5 scenario. The changes in discharge components reveal the peak in glacier melt predominantly in the first half of the twenty-first century for the Hotan and Yarkant catchments (Fig. 6). Under the RCP 8.5 scenario, the peak is shifted further into the second half of the century, which is especially evident in the Tongguziluoke (Hotan) catchment.

Fig. 6
figure 6

Projected glacier melt, snow melt and rain (absolute input values in mm a−1 distributed over the catchment areas). Ten-year running mean values of the ensemble median for both SWIM-G and WASA are shown


Results indicate a marked increase in river discharge, which is in-line with observations over the past decades (Tao et al. 2011; Li et al. 2020). Given the arid climate combined with the vast ice reserves, higher temperatures leading to negative glacier mass balances are able to drastically change river discharge in the short- and medium-term until ‘peak meltwater’ is reached (Sorg et al. 2012; Huss and Hock 2018). The two existing studies estimate the peak of the glacier meltwater to be reached in about 2030 in the low emission scenario and about 2060 or 2070 in the high emission scenario for the whole Tarim basin (Huss and Hock 2018; Rounce et al. 2020). Our study showed that these projected peaks for the whole basin can mainly be attributed to the Asku basin as Hotan and Yarkand show a distinctly different behaviour. The peak water seems to be in sight under all scenarios in the Aksu catchments as both glacio-hydrological models indicate the largest increases in the near future (Fig. 4, 5). In the Hotan and Yarkant catchments, this peak is only evident in the medium to far future mainly under the low and moderate emissions scenarios. Projected significant increases in precipitation are also driving the strong increases in discharge and are able to compensate for melt water losses. However, precipitation is quite variable from year to year while glacier melt is a more reliable water source (Bolch 2017; Pritchard 2019). Moreover, precipitation projections are subject to high uncertainties.

The use of two glacio-hydrological models with different representations and processes of glaciers allows a more robust understanding of model uncertainties. Both models generally agree on the direction of glacial and discharge changes with the exception of discharge in the Aksu catchment in the far future, where ensemble ranges point in both directions but the medians disagree. Disagreements in change magnitude are most evident in the glacier recession in the Aksu (WASA greater reductions) and the Hotan catchments (SWIM-G greater reduction) with WASA indicating a stronger increase in discharge in the short- and medium-term in the Xiehela catchment and SWIM-G predicting slightly stronger median increases in the Hotan catchments. Ensemble ranges of discharge changes (Fig. 4) are generally greater for WASA with some exceptions in the Tongguziluoke catchment.

While generally in agreement with previous studies (Fang et al. 2018; Liu et al. 2011; Wang et al. 2021; Pieczonka and Bolch 2015), water balance components also show some strong differences between the models, as WASA generally shows higher values of rain and snow melt and SWIM-G larger values of glacier melt in the Hotan and Yarkant catchments. The differences between the glacio-hydrological models is predominantly caused by different assumptions in the precipitation correction, a key calibration term and uncertainty in these catchments (cf. P in Table 1), as well as by differences in the calculation of evapotranspiration. SWIM-G simulates lower rates of rain and snowmelt and in the Hotan and Yarkant catchments in the first half of the twenty-first century also higher rates of glacier melt, since WASA assumes a stronger precipitation correction. While the uncertainties between the two models with respect to precipitation correction appear large, these are actually small when compared to the precipitation estimates based on observation products or to simulated precipitation by GCMs. Thus, glacio-hydrological modelling in combination with observed discharge and glacier mass balances or glacier area distribution can be a suitable alternative to derive precipitation estimates. An ensemble of glacio-hydrological models is useful to assess the uncertainties of such precipitation estimates.

The differences in flow components may explain some of the differences in the changes in annual discharge. Higher contribution of rain makes WASA more susceptible to the higher ensemble variability in precipitation leading to greater uncertainty ranges. Higher shares of glacier melt in SWIM-G in the Tongguziluoke catchment lead to greater sensitivity to temperature increases, producing greater increases in discharge. Differences in glacial changes are likely attributable to the different representations of ice in the models. For example, the glacier recession in WASA is steeper in the first half of the twenty-first century in the Aksu catchments leading to a pronounced glacier melt peak, while SWIM-G produces more gradual changes. This may be caused by the different approaches for calculating glacier area and volume changes, and processes that are considered by the SWIM-G model but not by the WASA model, such as glacier dynamics, sublimation and debris accumulation (Wortmann et al. 2019).

Model parameter uncertainty was quantified using the calibration and climate change scenario ensemble via an analysis of variance (for SWIM-G in Figure A5 in the Annex and for WASA in Duethmann et al. (2016) Fig. 4). For discharge, the variance related to different parameter sets amounts to less than 20% of the total, while the differences related to the climate model ensemble makes up more than 50%. Reductions in the different sources of uncertainties will ultimately only come from better observations to further constrain parameter ranges and improve modelled process representations (Bolch et al. 2012). For example, high elevation precipitation measurements could help constrain the precipitation correction and help improve estimates of elevation-dependent warming (MRIEDWWG 2015) or stable isotope measurements could reveal the share of runoff from rain, snow and glacier melt (Kumar et al. 2011; Ohlanders et al. 2013; He et al. 2019). Lastly, improvements in the GCM projections would likely have the largest impact on uncertainties in the glacier and discharge projections presented here, as the use of a GCM ensemble has shown. This study may serve as a justification for future investments in the improvement of these broader deficiencies.


This study represents the first systematic and comprehensive climate change impact assessment of the Tarim River headwater catchments for the twenty-first century, using state-of-the-art GCM climate projections and scenarios as well as two large-scale, glacio-hydrological models. Together, changes in these catchments will largely determine the water resources of the Tarim River and the communities living in and around the Taklamakan Desert. The assessment is the product of significant research efforts to simulate the complex water resources of these remote and largely ungauged mountain ranges. It required the construction of two large-scale glacio-hydrological models (Wortmann et al. 2019; Duethmann et al. 2016) and the correction of precipitation datasets as well as the subsequent model calibration under data-scarce conditions (Wortmann et al. 2018; Duethmann et al. 2013). These efforts have enabled a robust impact assessment based not only on a single impact model but two that can expose the model-inherent uncertainties.

In line with regional and global trends, results indicate a warmer and wetter climate with substantial consequences for the high-mountain glacier cover and the meltwater-driven rivers. Depending on the low, medium or high emission scenarios, temperatures are projected to rise by about 1.9 °C, 3.2 °C or 5.3 °C in the ensemble mean by the end of this century (2071–2100) compared to the 1971–2000 reference period, while precipitation may intensify by about 9%, 14% or 24%, respectively. The two calibrated and specifically adapted glacio-hydrological models, SWIM-G and WASA, project these climatic changes to result in a shrinkage of the glacier cover by around 45%, 52% or 73% (median of both models and catchments) in the Aksu headwaters and by about 21%, 28% or 40% in the Hotan and Yarkant headwaters for the low, medium or high emissions scenarios, respectively. Similarly, the models project river discharge to mainly increase in the Hotan and Yarkant catchments by the end of the century (by about 15%, 30% or 60%), while in the Aksu headwater, it is projected to first increase (by about 20% regardless of scenario) to return to or even decrease below reference discharge by the end of the century. Discharge increases are the greatest in the spring and summer months (April–September) and decreases in the Aksu catchment mainly occur in August–September due the diminished glacier meltwater.

Despite the shrinking glacier cover, precipitation increases drive the projected changes in river discharge and at least partially compensate the losses in meltwater. The receding trend in the Aksu headwaters is important, however, as they currently dominate the discharge of the Tarim River. With the projected increases in the Hotan and Yarkant headwaters, this could change, depending on the agricultural water abstractions in the river oases lining these tributaries (Huang et al. 2018). Future research should focus on reducing the large uncertainties that remain in the simulation of glacio-hydrological changes in this region that are mainly driven by the climate ensemble, the glacio-hydrological models and the limited observations available for constraining the models during the reference period.

Availability of code and data

Upon reasonable request.


  • Aizen VB, Aizen EM, Melack JM (1995) Climate, snow cover, glaciers, and runoff in the Tien Shan, Central Asia. J Am Water Resour Assoc 31:1113–1129.

  • Barnett TP, Adam JC, Lettenmaier DP (2005) Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438:303–309.

  • Beniston M (2003) Climatic Change in Mountain Regions: A Review of Possible Impacts, in: Diaz, H.F. (Ed.), Climate Variability and Change in High Elevation Regions: Past, Present & Future, Advances in Global Change Research. Springer Netherlands, pp. 5–31

  • Beniston (2006) Mountain weather and climate: a general overview and a focus on climatic change in the alps. Hydrobiologia 562(1):3–16

    Article  Google Scholar 

  • Bhattacharya A, Bolch T, Mukherjee K, King O, Menounos B, Kapitsa V, Neckel N, Yang W, Yao T (2021) High Mountain Asian glacier response to climate revealed by multi-temporal satellite observations since the 1960s. Nat Commun 12:4133.

  • Bolch (2017) Asian glaciers are a reliable water source. Nature 545:161–162

    Article  Google Scholar 

  • Bolch S, Liu et al (2019) Status and Change of the Cryosphere in the Extended Hindu Kush Himalaya Region. In: Wester M, Mukherji S (eds) The Hindu Kush Himalaya Assessment: Mountains, Climate Change, Sustainability and People. Springer International Publishing, Cham, pp 209–255

    Chapter  Google Scholar 

  • Bolch T, Kulkarni A, Kääb A, Huggel C, Paul F, Cogley JG, Frey H, Kargel JS, Fujita K, Scheel M, Bajracharya S, Stoffel M (2012) The State and Fate of Himalayan Glaciers. Science 336:310–314.

  • Bolch T, Pieczonka T, Mukherjee K, Shea J (2017) Brief communication: Glaciers in the Hunza catchment (Karakoram) have been nearly in balance since the 1970s. The Cryosphere 11:531–539.

  • Brun F, Berthier E, Wagnon P, Kääb A, Treichler D (2017) A spatially resolved estimate of High Mountain Asia glacier mass balances from 2000 to 2016. Nature Geoscience 10:668–673.

  • Chen Y, Takeuchi K, Xu C, Chen Y, Xu Z (2006) Regional climate change and its effects on river runoff in the Tarim Basin, China. Hydrological Processes 20:2207–2216.

  • Duethmann D, Zimmer J, Gafurov A, Güntner A, Kriegel D, Merz B, Vorogushyn S (2013) Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling. Hydrol Earth Syst Sci 17:2415–2434.

  • Duethmann D, Peters J, Blume T, Vorogushyn S, Güntner A (2014) The value of satellite-derived snow cover images for calibrating a hydrological model in snow-dominated catchments in Central Asia. Water Resour Res 50:2002–2021.

  • Duethmann D, Bolch T, Farinotti D, Kriegel D, Vorogushyn S, Merz B, Pieczonka T, Jiang T, Su B, Güntner A (2015) Attribution of streamflow trends in snow- and glacier melt dominated catchments of the Tarim River, Central Asia. Water Resour Res 4727–4750.

  • Duethmann, Menz, Jiang, Vorogushyn 2016. Projections for headwater catchments of the Tarim River reveal glacier retreat and decreasing surface water availability but uncertainties are large. Environ Res Lett 11(5)

  • Dyurgerov (2010) Reanalysis of glacier changes: from the IGY to the IPY, 1960–2008, number 108 in data of glaciological studies. Glaciological Association, Geographical Institute of the Russian Academy of Sciences, Moscow

    Google Scholar 

  • Ehret U, Zehe E, Wulfmeyer V, Warrach-Sagi K, Liebert J (2012) HESS Opinions “Should we apply bias correction to global and regional climate model data?” Hydrology and Earth System Sciences; Katlenburg-Lindau 16:3391.

  • Fang G, Yang J, Chen Y, Li Z, Ji H, De Maeyer P (2018) How Hydrologic Processes Differ Spatially in a Large Basin: Multisite and Multiobjective Modeling in the Tarim River Basin. J Geophys Res Atmos 123:7098–7113.

  • FAO, IIASA, ISRIC, ISSCAS, JRC (2011) The Harmonized World Soil Database (Database No. 1.2). FAO and IIASA, Rome

  • Farinotti D, Longuevergne L, Moholdt G, Duethmann D, Mölg T, Bolch T, Vorogushyn S, Güntner A (2015) Substantial glacier mass loss in the Tien Shan over the past 50 years. Nature Geosci 8:716–722.

  • Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27:1547–1578.

  • Francke T, Güntner A, Mamede G, Müller EN, Bronstert A (2008) Automated catena-based discretization of landscapes for the derivation of hydrological modelling units. Int J Geogr Inf Sci 22(2):111-132

  • Frey H, Machguth H, Huss M, Huggel C, Bajracharya S, Bolch T, Kulkarni A, Linsbauer A, Salzmann N, Stoffel M (2014) Estimating the volume of glaciers in the Himalayan-Karakoram region using different methods. Cryosphere 8:2313–2333

    Article  Google Scholar 

  • Friedl MA, McIver DK, Hodges JCF, Zhang XY, Muchoney D, Strahler AH, Woodcock CE, Gopal S, Schneider A, Cooper A, Baccini A, Gao F, Schaaf C (2002) Global land cover mapping from MODIS: algorithms and early results. Remote Sens Environ 83:287–302.

  • Glazirin (2010) A century of investigations on outbursts of the ice-dammed Lake Merzbacher (Central Tien Shan). Austrian J Earth Sci 103(2):171–179

    Google Scholar 

  • Gottschalk L (2006) Methods of Analyzing Variability, in: Encyclopedia of Hydrological Sciences. John Wiley & Sons, Ltd

  • Güntner A (2002) Large-scale hydrological modelling in the semi-arid North-East of Brazil, Potsdam Institute for Climate Impact Research, Potsdam, Germany, PIK-Report 77:128.

  • Güntner A, Bronstert A (2004) Representation of landscape variability and lateral redistribution processes for large-scale hydrological modelling in semi-arid areas. J Hydrol 297(1-4):136–161

  • Guo W, Liu S, Xu J, Wu L, Shangguan D, Yao X, Wei J, Bao W, Yu P, Liu Q, Jiang Z (2015) The second Chinese glacier inventory: data, methods and results. Journal of Glaciology 61:357–372.

    Article  Google Scholar 

  • Hao X, Chen Y, Xu C, Li W (2008) Impacts of Climate Change and Human Activities on the Surface Runoff in the Tarim River Basin over the Last Fifty Years. Water Resour Manage 22:1159–1171.

    Article  Google Scholar 

  • He Z, Unger-Shayesteh K, Vorogushyn S, Weise SM, Kalashnikova O, Gafurov A, Duethmann D, Barandun M, Merz B (2019) Constraining hydrological model parameters using water isotopic compositions in a glacierized basin, Central Asia. J Hydrol 571:332–348.

    Article  Google Scholar 

  • Hempel S, Frieler K, Warszawski L, Schewe J, Piontek F (2013) A trend-preserving bias correction; the ISI-MIP approach. Earth Syst Dyn 4:219–236.

    Article  Google Scholar 

  • Holzer N, Vijay S, Yao T, Xu B, Buchroithner M, Bolch T (2015) Four decades of glacier variations at Muztagh Ata (eastern Pamir): a multi-sensor study including Hexagon KH-9 and Pléiades data. The Cryosphere 9:2071–2088.

    Article  Google Scholar 

  • Huang S, Krysanova V, Österle H, Hattermann FF (2010) Simulation of spatiotemporal dynamics of water fluxes in Germany under climate change. Hydrol. Process. 24:3289–3306.

    Article  Google Scholar 

  • Huang X, Xie H, Liang T, Yi D (2011) Estimating vertical error of SRTM and map-based DEMs using ICESat altimetry data in the eastern Tibetan Plateau. International Journal of Remote Sensing 32:5177–5196.

    Article  Google Scholar 

  • Huang S, Hattermann FF, Krysanova V, Bronstert A (2013) Projections of climate change impacts on river flood conditions in Germany by combining three different RCMs with a regional eco-hydrological model. Clim Chang 116:631–663.

    Article  Google Scholar 

  • Huang S, Wortmann M, Duethmann D, Menz C, Shi F, Zhao C, Su B, Krysanova V (2018) Adaptation strategies of agriculture and water management to climate change in the Upper Tarim River basin, NW China. Agric Water Manag 203:207–224.

    Article  Google Scholar 

  • Hugonnet R, McNabb R, Berthier E, Menounos B, Nuth C, Girod L, Farinotti D, Huss M, Dussaillant I, Brun F, Kääb A (2021) Accelerated global glacier mass loss in the early twenty-first century. Nature 592:726–731.

    Article  Google Scholar 

  • Huss M, Hock R (2018) Global-scale hydrological response to future glacier mass loss. Nat Clim Chang 8:135–140.

    Article  Google Scholar 

  • Huss M, Jouvet G, Farinotti D, Bauder A (2010) Future high-mountain hydrology: a new parameterization of glacier retreat. Hydrol Earth Syst Sci 14:815–829.

    Article  Google Scholar 

  • Immerzeel WW, Beek LPH van, Konz M, Shrestha AB, Bierkens MFP (2011) Hydrological response to climate change in a glacierized catchment in the Himalayas. Clim Chang 110:721–736.

  • Immerzeel WW, Pellicciotti F, Shrestha AB (2012) Glaciers as a Proxy to Quantify the Spatial Distribution of Precipitation in the Hunza Basin. Mt Res Dev 32:30–38.

    Article  Google Scholar 

  • Immerzeel WW, Lutz AF, Andrade M, Bahl A, Biemans H, Bolch T, Hyde S, Brumby S, Davies BJ, Elmore AC, Emmer A, Feng M, Fernández A, Haritashya U, Kargel JS, Koppes M, Kraaijenbrink PDA, Kulkarni AV, Mayewski PA, Nepal S, Pacheco P, Painter TH, Pellicciotti F, Rajaram H, Rupper S, Sinisalo A, Shrestha AB, Viviroli D, Wada Y, Xiao C, Yao T, Baillie JEM (2020) Importance and vulnerability of the world’s water towers. Nature 577:364–369.

    Article  Google Scholar 

  • Immerzeel WW, Wanders N, Lutz AF, Shea JM, Bierkens MFP (2015) Reconciling high-altitude precipitation in the upper Indus basin with glacier mass balances and runoff. Hydrol. Earth Syst. Sci. 19:4673–4687.

    Article  Google Scholar 

  • IPCC (2007) IPCC Fourth Assessment Report: Climate Change 2007 (AR4)

  • IPCC (2014) Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

  • Jarvis A, Reuter HI, Nelson A, Guevara E (2007) Hole-filled seamless SRTM data, version 4 (DEM). International Centre for Tropical Agriculture (CIAT)

  • Kääb A, Treichler D, Nuth C, Berthier E (2015) Brief Communication: Contending estimates of 2003–2008 glacier mass balance over the Pamir–Karakoram–Himalaya. The Cryosphere 9:557–564.

    Article  Google Scholar 

  • Kraaijenbrink PDA, Bierkens MFP, Lutz AF, Immerzeel WW (2017) Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers. Nature 549:257–260.

    Article  Google Scholar 

  • Krysanova V, Müller-Wohlfeil DI, Becker A (1998) Development and test of a spatially distributed hydrological/water quality model for mesoscale watersheds. Ecol Model 106:261–289.

    Article  Google Scholar 

  • Krysanova V, Hattermann F, Huang S, Hesse C, Vetter T, Liersch S, Koch H, Kundzewicz ZW (2015) Modelling climate and land-use change impacts with SWIM: lessons learnt from multiple applications. Hydrol Sci J 60:606–635.

    Article  Google Scholar 

  • Kumar B (2011) Isotopic Characteristics of Ice, Snow, and Glaciers. In: Singh VP, Singh P, Haritashya UK (eds) Encyclopedia of Snow, Ice and Glaciers, Encyclopedia of Earth Sciences Series. Springer, Netherlands, pp 665–668

    Chapter  Google Scholar 

  • Li Z, Shi X, Tang Q, Zhang Y, Gao H, Pan X, Déry SJ, Zhou P (2020) Partitioning the contributions of glacier melt and precipitation to the 1971–2010 runoff increases in a headwater basin of the Tarim River. J Hydrol 583

  • Linsbauer A, Paul F, Haeberli W (2012) Modeling glacier thickness distribution and bed topography over entire mountain ranges with GlabTop: Application of a fast and robust approach. J Geophys Res 117:F03007.

    Article  Google Scholar 

  • Liu Y, Chen Y (2006) Impact of population growth and land-use change on water resources and ecosystems of the arid Tarim River Basin in Western China. Int J Sustain Dev World Ecol 13:295–305.

    Article  Google Scholar 

  • Liu S, Ding Y, Shangguan D, Zhang Y, Li J, Han H, Wang J, Xie C (2006) Glacier retreat as a result of climate warming and increased precipitation in the Tarim river basin, northwest China. Ann Glaciol 43:91–96.

    Article  Google Scholar 

  • Liu T, Willems P, Pan XL, Bao AM, Chen X, Veroustraete F, Dong QH (2011) Climate change impact on water resource extremes in a headwater region of the Tarim basin in China. Hydrology and Earth System Sciences 15:3511–3527.

    Article  Google Scholar 

  • Maussion F, Scherer D, Mölg T, Collier E, Curio J, Finkelnburg R (2014) Precipitation Seasonality and Variability over the Tibetan Plateau as Resolved by the High Asia Reanalysis. J Climate 27:1910–1927.

    Article  Google Scholar 

  • Mueller E, Güntner A, Francke T, Mamede G (2010) Modelling sediment export, retention and reservoir sedimentation in drylands with the WASA-SED model, Geoscientific Model Development 3(1):275–291

  • MRIEDWWG (2015) Elevation-dependent warming in mountain regions of the world. Nature Clim Change 5:424–430

    Article  Google Scholar 

  • Ohlanders N, Rodriguez M, McPhee J (2013) Stable water isotope variation in a Central Andean watershed dominated by glacier and snowmelt. Hydrol Earth Syst Sci 17:1035–1050.

    Article  Google Scholar 

  • Osmonov A, Bolch T, Xi C, Kurban A, Guo W (2013) Glacier characteristics and changes in the Sary-Jaz River Basin (Central Tien Shan, Kyrgyzstan) – 1990–2010. Remote Sens Lett 4:725–734.

    Article  Google Scholar 

  • Pieczonka T, Bolch T (2015) Region-wide glacier mass budgets and area changes for the Central Tien Shan between ~ 1975 and 1999 using Hexagon KH-9 imagery. Glob Planet Chang 128:1–13.

    Article  Google Scholar 

  • Pieczonka T, Bolch T, Junfeng W, Liu S (2013) Heterogeneous mass loss of glaciers in the Aksu-Tarim Catchment (Central Tien Shan) revealed by 1976 KH-9 Hexagon and 2009 SPOT-5 stereo imagery. Remote Sens Environ 130:233–244.

    Article  Google Scholar 

  • Pritchard (2019) Asia’s shrinking glaciers protect large populations from drought stress. Nature 569:649–654

    Article  Google Scholar 

  • RGI Consortium (2017) Randolph glacier inventory – a dataset of global glacier outlines: Version0: Technical Report, Global Land Ice Measurements from Space, Colorado. Digital Media, USA

    Google Scholar 

  • Rounce DR, Khurana T, Short MB, Hock R, Shean DE, Brinkerhoff DJ (2020) Quantifying parameter uncertainty in a large-scale glacier evolution model using Bayesian inference: application to High Mountain Asia. J Glaciol 1–13.

  • Shangguan D, Shiyin L, Yongjian D, Jing L, Yong Z, Lianfu D, Xing W, Changwei X, Gang L (2007) Glacier changes in the west Kunlun Shan from 1970 to 2001 derived from Landsat TM/ETM+ and Chinese glacier inventory data. Ann Glaciol 46:204–208.

    Article  Google Scholar 

  • Shean DE, Bhushan S, Montesano P, Rounce DR, Arendt A, Osmanoglu B (2020) A Systematic, Regional Assessment of High Mountain Asia Glacier Mass Balance. Front Earth Sci 7.

  • Shi XZ, Yu DS, Warner ED, Pan XZ, Petersen GW, Gong ZG, Weindorf DC (2004) Soil Database of 1:1,000,000 Digital Soil Survey and Reference System of the Chinese Genetic Soil Classification System. Soil Horizons 45:129.

    Article  Google Scholar 

  • Shi Y, Shen Y, Kang E, Li D, Ding Y, Zhang G, Hu R (2006) Recent and Future Climate Change in Northwest China. Clim Chang 80:379–393.

    Article  Google Scholar 

  • Shi Y, Liu C, Kang E (2009) The Glacier Inventory of China. Ann Glaciol 50:1–4.

    Article  Google Scholar 

  • Song Y, Wang R, Peng Y (2002) Water resources and ecological conditions in the Tarim Basin. Science in China Series D: Earth Sci 45:11–17.

    Article  Google Scholar 

  • Sorg A, Bolch T, Stoffel M, Solomina O, Beniston M (2012) Climate change impacts on glaciers and runoff in Tien Shan (Central Asia). Nature Clim. Change 2:725–731.

    Article  Google Scholar 

  • Sorg A, Bolch T, Stoffel M, Solomina O, Beniston M (2012) Climate change impacts on glaciers and runoff in Tien Shan (Central Asia). Nature Clim Change 2:725–731.

    Article  Google Scholar 

  • Surazakov A, Aizen VB (2006) Estimating Volume Change of Mountain Glaciers Using SRTM and Topographic Data. IEEE Trans. Geosci Remote Sensing 44:2991–2995

  • Tang Q, Hu H, Oki T, Tian F (2007) Water Balance within Intensively Cultivated Alluvial Plain in an Arid Environment. Water Resour Manag 21:1703–1715.

    Article  Google Scholar 

  • Tao H, Gemmer M, Bai Y, Su B, Mao W (2011) Trends of streamflow in the Tarim River Basin during the past 50 years: Human impact or climate change? J Hydrol 400:1–9.

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An Overview of CMIP5 and the Experiment Design. Bull Amer Meteor Soc 93:485–498.

    Article  Google Scholar 

  • Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. J Hydrol 456–457:12–29.

    Article  Google Scholar 

  • Thevs (2011) Water scarcity and allocation in the Tarim Basin: decision structures and adaptations on the local level. J Curr Chin Aff 40(3):113–137

    Article  Google Scholar 

  • Thompson JR, Green AJ, Kingston DG, Gosling SN (2013) Assessment of uncertainty in river flow projections for the Mekong River using multiple GCMs and hydrological models. J Hydrol 486:1–30.

    Article  Google Scholar 

  • Tong K, Su F, Yang D, Zhang L, Hao Z (2014) Tibetan Plateau precipitation as depicted by gauge observations, reanalyses and satellite retrievals. Int. J. Climatol. 34:265–285.

    Article  Google Scholar 

  • Vetter T, Huang S, Yang T, Aich V, Wang X, Gu H, Krysanova V, Hattermann FF (2013) Intercomparison of climate impacts and evaluation of uncertainties from different sources using three regional hydrological models for three river basins on three continents, in: Proceedings of the IMPACTS World Conference

  • Vetter T, Huang S, Aich V, Yang T, Wang X, Krysanova V, Hattermann F (2015) Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents. Earth Syst Dynam 6:17–43.

    Article  Google Scholar 

  • Wang (ed) (2006) Local records of the Aksu River basin. Fangshi Publisher, Beijing, China

    Google Scholar 

  • Wang G, Shen Y, Su H, Wang J, Mao W, Gao Q, Wang S (2008) Runoff Changes in Aksu River Basin during 1956–2006 and Their Impacts on Water Availability for Tarim River. J Glaciol Geocryol 30:562–568

    Google Scholar 

  • Wang D, Menz C, Simon T, Simmer C, Ohlwein C (2013) Regional dynamical downscaling with CCLM over East Asia. Meteorol Atmos Phys 121:39–53.

    Article  Google Scholar 

  • Wang N, Liu W, Wang H, Sun F, Duan W, Li Z, Li Z, Chen Y (2021) Improving streamflow and flood simulations in three headwater catchments of the Tarim River based on a coupled glacier-hydrological model. J Hydrol 603:127048.

  • Weedon GP, Gomes S, Viterbo P, Shuttleworth WJ, Blyth E, Oesterle H, Adam JC, Bellouin N, Boucher O, Best M (2011) Creation of the WATCH Forcing Data and Its Use to Assess Global and Regional Reference Crop Evaporation over Land during the Twentieth Century. J Hydrometeorol 12:823–848.

    Article  Google Scholar 

  • Wortmann M, Krysanova V, Kundzewicz ZW, Su B, Li X (2014) Assessing the influence of the Merzbacher Lake outburst floods on discharge using the hydrological model SWIM in the Aksu headwaters, Kyrgyzstan/NW China. Hydrol Process 28:6337–6350.

    Article  Google Scholar 

  • Wortmann M, Bolch T, Menz C, Tong J, Krysanova V (2018) Comparison and Correction of High-Mountain Precipitation Data Based on Glacio-Hydrological Modeling in the Tarim River Headwaters (High Asia). J Hydrometeor 19:777–801.

    Article  Google Scholar 

  • Wortmann M, Bolch T, Su B, Krysanova V (2019) An efficient representation of glacier dynamics in a semi-distributed hydrological model to bridge glacier and river catchment scales. J Hydrol 573:136–152.

    Article  Google Scholar 

  • Wu Z, Zhang H, Krause C, Cobb N (2010) Climate change and human activities: a case study in Xinjiang, China. Clim Chang 99:457–472.

    Article  Google Scholar 

  • Xu ZX, Chen YN, Li JY (2004) Impact of Climate Change on Water Resources in the Tarim River Basin. J Water Res 18:439–458.

    Article  Google Scholar 

  • Xu H, Zhou B, Song Y (2010) Impacts of climate change on headstream runoff in the Tarim River Basin. Hydrol Res 42:20.

    Article  Google Scholar 

  • Yaning C, Changchun X, Xingming H, Weihong L, Yapeng C, Chenggang Z, Zhaoxia Y (2009) Fifty-year climate change and its effect on annual runoff in the Tarim River Basin, China. Quat Int 208:53–61.

    Article  Google Scholar 

  • Yatagai A, Kamiguchi K, Arakawa O, Hamada A, Yasutomi N, Kitoh A (2012) APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges. Bull Amer Meteor Soc 93:1401–1415.

    Article  Google Scholar 

  • Zhang Y, Liu S, Ding Y (2006) Observed degree-day factors and their spatial variation on glaciers in western China. Ann Glaciol 43:301–306.

    Article  Google Scholar 

  • Zhang X, Yang D, Xiang X, Huang X (2012) Impact of agricultural development on variation in surface runoff in arid regions: a case of the Aksu River Basin. J Arid Land 4:399–410

    Article  Google Scholar 

  • Zhao C, Shi F, et al (2012) The tendency and influencing factors of surface water evaporation in Aksu Oasis in recent 30 years

  • Zhou Y, Li Z, Li J, Zhao R, Ding X (2018) Glacier mass balance in the Qinghai-Tibet Plateau and its surroundings from the mid-1970s to 2000 based on Hexagon KH-9 and SRTM DEMs. Remote Sens Environ 210:96–112.

    Article  Google Scholar 

  • Zhou Y, Li Z, Li J (2017) Slight glacier mass loss in the Karakoram region during the 1970s to 2000 revealed by KH-9 images and SRTM DEM. Journal of Glaciology 63:331–342.

    Article  Google Scholar 

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This study was conducted within the project SuMaRiO (Sustainable Management of River Oases along the Tarim River;, funded by the German Federal Ministry of Education and Research (BMBF grants 01LL0918J, 01LL0918I and 01LL0918B). T. Bolch acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, BO3199/2–1). Open Access funding enabled and organized by Projekt DEAL.

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M.W., D.D. and V.K. designed the study. M.W. performed the SWIM-G simulations and drafted the manuscript. D.D. carried out the WASA simulations and contributed to the draft. C.M. processed and analysed the climate input data. T.B. provided the glacier input and validation data. S.H., J.T. and Z.K. were involved in data acquisition and research design. All authors contributed to the final version of the manuscript.

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Correspondence to Michel Wortmann.

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Wortmann, M., Duethmann, D., Menz, C. et al. Projected climate change and its impacts on glaciers and water resources in the headwaters of the Tarim River, NW China/Kyrgyzstan. Climatic Change 171, 30 (2022).

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  • Tarim River
  • SWIM-G
  • WASA
  • Climate change impact assessment
  • Glacio-hydrological modelling