Dear Prof. Yongqiang ZHANG, Thank You Very Much for Your Valuable Comments

Dear Prof. Yongqiang ZHANG, Thank you very much for your valuable comments. These helpful suggestions will help us improve this manuscript significantly. Following is the point to point reply.

This manuscript comprehensively reviews hydrological modelling conducted in glacierized catchments in Central Asia. It is a very interesting synthesis focusing on the current limits, challenges and directions of hydrological modelling. The authors point it out that it is lack of glacier submodel and points directions for future hydrological modelling in those regions. The manuscript reads well. I recommend the manuscript to be accepted by HESS after a moderate to major revision is conduced. Following are my critical suggestions/comments for improving its quality. 1. Glacierized catchments in Central Asia should be delineated. I cannot find spatial distribution of each catchment. Response: The boundary of each catchment included in our study was added in the revised Figure 1 based on the DEM derived flow direction. Glacier information was obtained from RGI (Randolph Glacier Inventory).

Figure 1: Map of Central Asian headwaters with main river basins or hydrological regions, namely the Tarim River Basin, the watersheds in the northern slope of the Tienshan Mountains, the Issyk Lake Basin, the Ili River Basin, the Amu Darya and the Syr Darya Basins. Lake outlines are from Natural Earth (http://www.naturalearthdata.com/). River system is derived based on elevations of SRTM 90 m data. Glacier information was obtained from RGI (Randolph Glacier Inventory).

2. Need a table summarising attributes of the major catchments (i.e. Ili River, the Amu Darya, the Syr Darya, Tarim River, etc), including climate, soil, vegetation and glacier ratio, etc. Response: Thank you for your advice. We added Table 1 summarizing the attributes of the major catchments. Table 1: Summary of climatic and underlying conditions of the basins. The topography is based on SRTM data, glacier data is from RGI (Randolph glacier inventory), and climate is based on the world map of the Koppen-Geiger climate classification. Vegetation is from the land use data from Xinjiang institute of Ecology and Geography. Catchments in Syr Issyk Lake Amu Darya Catchment Tarim River Basin northern TS Ili River Basin Darya Basin Basin China Basin Surrounded by the Western Western Northern Western Western Location Tienshan Mountains and Tienshan Tienshan and Tienshan Tienshan Tienshan the Kunlun Mountains Valley Pamir Topography Basin area (km2) 868,811 126,463 102,396 429,183 674,848 442,476 Percentage of elevation > 28.00% 13.80% 14.50% 4.60% 20.50% 9.50% 3000m (%) Glaciation area (km2) 15789 1795 994 2170 9080 1850 Climate Arid cold; Arid cold; Arid cold; Arid Dominant climate Arid cold Arid cold continental continental snow cold Vegetation Forest percent (%) 0.7 10.4 6.4 4.1 10.9 2.5 Pasture percent (%) 16.7 14.5 31.2 28.6 19.4 17.3 Percent of water, snow, ice (%) 5.4 3.9 7.8 5.3 5.3 2.8

3. Table 1 should be more explicit. Please separate catchments to hydrological model. This table needs to match the table I recommend in point 2. Reviewers can then easily find catchments where how many hydrological modeling studies have been carried out in literature. Response: Thank you for your valuable suggestions. We separated the models for each catchment (including each sub-catchment) in Table 2. Table 2: Summary of hydrological modeling in glacierized central Asian catchments Catchments Models Major conclusions Innovations and Limitations References Tarim River Modified Improved the original two-parameter monthly two-parameter water balance model by incorporating the semi- distributed topographic indexes and could get comparable water balance results with the TOPMODEL model and model Xin‘anjiang model. (Peng and TOPMODEL In the Aksu River, runoff was more closely Less input data are required; Xu, 2010; related to precipitation, while in the Hotan Lack of glacier and snowmelt model Chen et al., River, it was more closely related to processes. 2006) temperature. Runoffs of the Aksu, Yarkand and Hotan river Xin‘anjiang model exhibited increasing tendencies in 2010 and 2020 under different scenarios generated from

the reference years. Tarim River Basin River Tarim Projection Pursuit If temperature rises 0.5-2.0 °C, runoff will (Wu et al., Regression (PPR) increase with temperature for the Aksu, Lack of physical basis. 2003) model Yarkand and Hotan River. For the Tarim River, runoff will decrease (Liu et al., VIC slightly in 2020-2025 based on HadCM3 under Lack of glacier module. 2010) A2 and B2 when not considering glacier melt. Modified degree-day model Glacier runoff increases linearly with including potential temperature over these ranges whether or not clear sky direct Considered the effect of solar the debris layer is taken into consideration. The (Zhang et solar radiation radiation and quantified the glacier runoff is less sensitive to temperature al., 2007b) coupled with a debris effect. change in the debris covered area than the

Tailan River Tailan linear reservoir debris free area. model

Kaidu River Aksu River inclusing Kumalike and Toxkan River Xin‘anjiangmodel potentialclear sky SRM including HBVmodel model rainfall NAM model; revised AR(p) and precipitation The temperature WASAmodel SWIMmodel VIC dynamicsmodel Modifiedsystem SWAT temperature. effectiveactive radiationand the direct solar model MIKE - 3Lmodel

- runoff

- SHE

pecks thein Kaidu River. spring muchbetter than the high Simulationsof low RCM, bias correctionand SWAT model. compared 1986to rates Runoff lapse temperature accountfor 64.0% of model uncertainty. and Precipitation futurethe basedon HadCM3. in increase to projected is streamflow Spring Kaiduthe River. floodare 50 mmand 30 mm respectively for rainfallsfor primary and secondary warning When the base runoff 100is m remote or developing in regions. data input model hydrological necessary of lack the overcome conventional hydrological to the comparable is simulating data sensing Remote processes. in station data and data based sensing remote Compared streamflowin the AksuRiver The AR(p) model capableis to predict the — glaciergeometry. streamflowincrease compared to constant changeslead ato reduction of 14 ForKumalikethe River,glacier geometry forKumalikethe Riverand the Toxkan Glaciermeltcontributes to35 increasingriver discharge downstream. reducingirrigation water consumption and effi and showedthat the improvementof irrigation Different irrigation scenarioswere developed Alar. alongthe river reaches betweenXidaqiao and aboutand 30 % additional water is consumed resources consumed until gaugethe Xidaqiao, About 18%of the observedupstream inflowdischarges. localthe irrigationinformation and the dischargeat the downstreamgauge well using The model capableis reproduceto the monthly 2.8% (about discharge annual and summer will in water area glacier >30% by River, reduce Kumalike the precipitation For by attributed increase. were increases 44.9% and 1970 during 13.6% increased have runoff the ToxkanRiver,the and River Kumalike the For 50.9%for the the for43.8%, 27. Glacier melt, snowmelt andrainfall accounted runoff Kumalikethe in River. Precipitation ahas weak relationship with

ciencythewas most effective measure for KumalikeRiver while 23.0%, 26.

– - 19.4% in the 2050s).

peak flowpeak are better than the summer increasesunder RCP4.5 and RCP8.5

- 2007, and 94.5% and 100% of the of 100% and 94.5%and 2007, Toxkan River. 7

and 28.5%of the dischargefor

aa R dt cud partly could data RS data. - incomingheadwater 2005 basedon a cascade of eutn i dcesd melt decreased in resulting - flow normaland

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glacier areaand surface glacierin geometry (e.g., The model consideredchanges periods. thein snow and glacier melt Model uncertaintiesare largest SWIMthe model. transmissionlosses moduleof irrigationmodule and a river River.Inclusion of an modeling toolin the Aksu outburstfloods using a Investigated glacierthe lake stillremains. precipitation in alpineregion accuratelyestimating areal glacier couplinga degree obviouslyimproved through The model performancewas streamflow. precipitation induced peak capture the snowmelt/ The model could no Joined the snowmeltmodule. watermovement in soil. forlayer each to describ processand soil temperature robust bymodified snowmelt The modifiedmodel was meteorologicalinputs. resultedfrom the Quantified uncertainty Kaiduthe River. in hydrologicalmodeling in precipitation performedgood The lowin modeling precision. Limitedobservations resulted overestimatedthe flow.base streamflowwhile Itunderestimated the peak mountainousthe regions. meteorologicalcondition in observationto verify the Missingglacier melt;Lack of meteorologicaldata. Needingless hydrologicaland mass balanceand discharge. calibration basedon glacier Itused a mu elevation). APHRODITE - meltscheme, but

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et et al., 2014) Wortmann al., 2015; (Huanget et al., 2015) 2013;Zhao (Zhaoet al., al., 2012a) (Wang et al., 2016) (Zhanget 2015c) et al., 2015a;Fang (Fanget al., 2014). et al., al., 2013;Li 2008;Ma et Zhanget al., al., 2007a; (Zhanget 2014) (Fan et 2012a;Liu (Liuet al., al., 2007) et (Ouyang et al., 2015) (Duethmann

al., 2013) etal.,

Catchments in northern slope of TS China Ebinur Lake Catchment Juntangh including Jinghe River, Hotan Tizunafu Urumqi River Manas River Yarkand river u Basin: Kuytun River and River River Bortala River network(BPANN) artificialneural back Analysis(WA) and Integrating Wavelet MIKE 6) 6) THmodel model 5) SRMmodel regression Exponential 4) HBV 3) model model water 2) balance Separation(IHS) Hydrograph 1) model 3) 2) SRMmodel 1) SWATmodel network(BPANN) artificialneural back Analysis(WA) and Integrating Wavelet snow albedo SRM including Degree Model SnowmeltRunoff Distributed (CAR) model Auto Regressive RunoffControlled clustermethod the sequential SWATmodel and

Isotope - -

propagation propagation

-

SHEmodel (DHSVM) - daymodel EasyDHM

within15%. predict 24h snowmeltrunoff with relativeerror coupledThe WRFand DHSVM model could decre Riverand Bortala River will increase, it will Inwarma trend, butan adverse trend in the BortalaRiver. KuytunRiver exhibiteda slightly increasing exhibited different changes Runoffof riversthree Ebinurin 14.3% byclimate change. reduction causedis byhuman activityand ForJinghethe River, 85.7%of the runoff ground temperature. 4) Glacierrunoff criticallyis affected bythe orglacier disap by±13%40 ifglacierthe size keeps unchanged dischargewill increase by66±35% or decrease For 3) glacierizeda catchment (18%), the glacier runoffincreased from 62.8%to 72.1%. was during 10.0% 2) Thecumulative massbalance o by of9% runoff. risen has 1950 Runoff 1) rainfall than runoff snowmelt of simulation Better 2) during discharge. 11% by decreased 1961 area Glacier 1) north slopeof Kunlun Mountains. the for well summer in °C correlates height 0 level the runoff with River, Hotan the For by temperaturelapse rates and snowalbedo. dominated is Runoff Tizinapu River. Itcould well simulate runoffthe of the obvious increase Summer.in 2011 13% increase will runoff Glacier a ofbalancemass to temperature 0.16is mm 0.23×10 a Decreasingrate of glacier mass was 4.39mm variation. 16 ofscale 32 with temperatureand precipitationat thetime Runoffpresented anincreasing trend similar sensing data. remote using that from significantly differs data station using pack snow Simulated - - 1 1

- °C resultingin runoffa increasing trend of yeartime

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8 runoffby SRM.the

m - - humid scenario,runoff the in Jinghe years. But atthe 2, 4,8,and 3

- a

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2006.Sensitivity

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catchment - linear linear

methodwere included i moduleand two 1) Both glacierthe melt hydro characteristicsof the Interpreted the nonlinear Lackof glacier module. sensing data. calibrated usingremote volume scalin consideredand the area glacierThe dynamics are statistic method. hydro characteristicsof the Interpret Lackof glacier moduld modeling. usedin the snowmelt runoff calculated snow depth aredata MODISsnow coverand the physical basis. presentvalues without The CAR basedis on past and runoff.on activitiesand climate change Identified the effectsof human thawing. equationof soil freezing and proposed closeto the balance 6) Anenergy balan MODISon data. snow cover shrinkage based 5) Calculated the curveof forscarcedata regions. basedon ground temperature glacier runoffestimation 4) Thisstudy shed light on scenarios. under differentglacier change 3) Consid ablationand area. Focusing 2) on glacierizedthe watersheds. componentsfor ungauged analyzing hydrological overwhelming potential in 1) TheIHS method has model performance. algorithm addedis validateto 2) Snow covercalculation hydrologicalsimulations. - - climatic process. climatic process using ed the nonlinear

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03 Gan 2013; al., et Luo 2012; al., et Luo 2011; al., et (Yu SWAT: 2011) et(Xu al., 2008) Williams, (Liand 2012b) Zhanget al., 2012a; Zhanget al., 2006; (Xie etal., al., 2014;) 2011;Xu et et(Xu al., 2016b Liuet al., 2009) al., et (Zhao et al., 2014) Yao 2014; (Donget al., 2008;) al., et Mou et al., 2013; Huai 2012; al., et Chen 2015bal., et Sun 2013; al., et Sun 2012; Pang, and (Kong 2014) al., et (Xing EasyDHM: 2013) al., et (Yu SRM: Luo, 2013) and

Issyk Lake Basin

Runoff contribution is varying in a broad range The glacier melt runoff depending on the degree of glacierization in the (Dikich and Degree-day fraction at the catchment particular sub-catchment. All rivers showed a Hagg, 2003) approach outlet can be considerably relative increase in annual river runoff ranging

overestimated.

around around the Lake Issyk Small Small rivers between 3.2 and 36%.

General decrease was expected in glacier

runoff (–26.6% to –1.0%), snow melt (–21.4% Use the glacier dynamics and to +1.1%) and streamflow (–27.7% to assessed the model (Ma et al., SWAT-RSG model –6.6%); Peak streamflow will be put forward performance based on both 2015)

Chu River Chu for one month. streamflow and glacier area.

Ili River Basin For the runoff is sensitive to snow cover area and temperature. (Ma and If temperature increases 4°C, the runoff will SRM is capable to model the

SRM model Cheng, decrease by 9.7% with snow coverage and snowmelt runoff. 2003)

runoff shifting forward. River Gongnaisi Gongnaisi Glaciers retreated about 22% since 1970s, which was considerably higher than the Using two land use data and Tianshan average (4.7%) and China average two Chinese glacier (Xu et al., SWAT model (11.5%), resulting in a decrease of proportion inventories, the model could 2015) of precipitation recharged runoff from 9.8% in well reproduce streamflow. Tekes River Tekes 1966-1975 to 7.8% in 2000-2008.

This method reduced Daily runoff correlated closely with snowmelt, dependence on conventional MS-DTVGM (Cai et al., suggesting a snowmelt module is observation, and was applied model 2014) indispensable. to the Ili River basin where traditional data are scarce. (Kezer and Ili River Ili Water decrease in 1911-1986 in the middle and Matsuyama, Water Balance lower reaches of the Lake Balkhash is due to — 2006; Guo decreased rainfall and reservoirs storage. et al., 2011) Amu Darya and Syr Darya Basin The runoff of the Syr Darya declined considerably over the last 9000 years. For the Amu Darya and Syr Darya Basin, the glacier covered areas have decrease 21% and 22% in 2001-2010 compared to the baseline Simulated long term discharge (Aerts et al., (1960-1990). for the Holocene and future 2006; The runoff of the Syr Darya is not so sensitive period. Savoskul et to future warming compared to the past 9000 The model includes the al., 2003;

years. STREAM calculation of rainwater, Savoskul et For the Amu River Basin, 20-25% of the snowmelt water and glacier al., 2004;

Basin glaciers will retain under a temperature runoff (based on the glacier Savoskul et increment being 4-5°C and precipitation altitude and equilibrium lime al., 2013) increase rate being 3%/°C. altitude). For the Syr Darya, runoff under the A2 and B2 scenarios will increase 3%-8% in 2010-2039, with sharpened spring peak and a slight lowered runoff from late June to August.

For the Amu Darya, glacier melt and snowmelt contribute to 38% and 26.9% of runoff, while for the Syr Darya, the proportions are 10.7% Amu Darya and Syr Darya Darya Syr and Darya Amu and 35.2%. Glacier retreat by 46.4% - 59.5% depending on (Immerzeel AralMountain selected GCM. For the Syr Darya, average Fully simulated the et al., model water supply to the downstream will decrease hydrological processes. 2012a) by 15% for 2021-2030 and 25% for 2041-2050. For the Amu Darya the expected decreases are 13% (2021-2030) and 31% (2041-2050).

Overall good model performances were

in in in

achieved with the maximum discrepancy of It considered geographical, simulated and observed monthly runoff within topographical and 20 mm. hydrometeorological features General enhanced snowmelt during spring and of test sites, and reduced “Oigaing” HBV-ETH and a higher flood risk in summer are predicted (Hagg et al., modeling uncertainties. OEZ model under a doubling atmospheric CO 2007) 2 This procedure requires a lot concentration with greatest runoff increases meteorological and land occurring in August for the highly glaciated surface data and knowledge of catchments and in June for the Nival the modeler.

est sites “Abramov” catchment.

SyrDarya SyrDarya and Darya Amu T are increasingly used to define water components (Sun et al., 2016) and it would be a fortune for fortune a be would it and 2016) al., et (Sun components water define to used increasingly are rep data could sensing remote that indicated (2013) al. et Liu as modeling, hydrological in used be could reanalysis, and sensing remote including data, of types several preprocessing, appropriate After sources data different of Integration 4.2 taken onthe current and future development of hydrological models for the are Tienshan discussions some addition, In missing. are modules several or one when especially effects, emphasizesignificanthe balance mass isotopedata,remote sensing multiple e.g., dataset,of useintegrated publications. present the on based simulation discussed we manuscript, revised the In Response which kind of glacier models should be applied for), which will really benefit thismanuscript. observation;modelimprovingmeltglacier observa the if (i.e. 4 Section in in discussion more have can author that think I simulations. melt glacier improve can hydrologists i high it that in looks modelling It data. hydrological balance mass glacier of lack and data variation glacier of lack includes simulation melt glacier for challenge major that point authors melt, Glacier 3.2 section In simulation. melt glacier improve to how on discussion more Need 5. P Response: is Following reorganisation.hydrological modelling submodels by clear be can 1 Table 4. oint 3above. Syr Panj Naryn River Darya River roduce comparable results with the traditional station data. In recent years, isotope data isotope years, recent In data. station traditional the with results comparable roduce :

Thank Wehave WT ih glacier SWAT with HBV model separateLand NAMmodel with a module .

- ETH Section

s for your comment

updated - ice . epess h sm ie o ―oe parameterization‖. ―model of idea same the expresses 4.3 ce of multiof ce winter/earlyspring runoff regime. spring/earlysummer towards latea forwardby 30 A2 until 2050s,and runoffthe willpeak shift Glaciervolume will lose 31%±4% underSRES about in 2040 peakand decreaselater. reach will runoff melt glacier net and RCP8.5 under 2100 by retain glaciers during small the of 7.3% 8% onlywith recede will Glaciers decreased has 1973 area Glacier being2.2 °C and3.1 °C. respectively,assuming temperature increment glacier extentwill decrease by36% and 45%, Forupperthe Panj catchment, the current T able 1 according to your comment your to according 1 able - 2002. - lvto ad high and elevation major conclusions limit. - objectivevalidationcalibrationand thehandle to

.

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oe hruhy n o t ipoe h gair melt glacier the improve to how on thoroughly more parametrization in icroae no eoe esn osrain to observation sensing remote into incorporate tions fromcurrentthe

First of all, S all, of First

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to balance equifinalitysubmodels;balance toand ection 4.2 stressed the significance of significance the stressed 4.2 ection

just an example. Catchment study study Catchment example. an just ny ie rey calibrated was freely parameters. five model only using robust be to NAM improved The twoglacier inventories. validatedand the modelusing Incorporated parameterization scheme. Applicationof glacier . Please refer to refer Please . a iey xse ise for issues existed widely a s tracer,glaciersingleobserved

glacier dynamics

the

Mountains ― responses to responses equifinality e r to try We al., 2012) et(Siegfried 2015) al., et (Gan 2013) al., et (Hagg - depth depth

.

‖ hydrologists to validate their models, or even calibrate the models (Fekete et al., 2006). The overall idea here is to build and integrate more comprehensive datasets in order to improve hydrological modeling. An example of this approach can be found in Naegeli et al. (2013), who attempted to construct a worldwide dataset of glacier thickness observations compiled entirely from a literature review.

4.3 Multi-objective calibration and validation

A hydrological model should not just ‖mimic‖ observed discharge but also reproduce snow accumulation and melt dynamics or the glacier mass change (e.g. Konz and Seibert, 2010). As discussed previously, hydrological models that are calibrated based on discharge alone may be of high uncertainty and even ―equifinality‖ for different parameters or inputs. This could happen especially when one or several modules are missing. For example, one might overestimate the mountainous precipitation or underestimate the evapotranspiration if the glacier melt module is missing. Therefore, it is suggested to account for each hydrological component as much as possible. We strongly suggest the use of multi-objective functions and multi-metrics to calibrate and evaluate hydrological models. Compared to single objective calibration, which was dependent on the initial starting location, multi-objective calibration provides more insight into parameter sensitivity and helps to understand the conflicting characteristics of these objective functions (Yang et al., 2014). Therefore, use different kinds of data and objective functions could improve a hydrological model and provide more realistic results. For the data-scarce Tienshan Mountains, however, we do not recommend an over-complex or physicalized modelling of each component as lack of validation data which may result in equifinality discussed previously when the climate system stays stable. The more empirical models (enhanced temperature index approaches) could reproduce comparable results with the sophisticated, fully-physical based models (Hock, 2005). What worth mentioning is that, the physically-based glacier models are more advancing when quantify future dynamics of glaciers and glacier/snow redistribution when the climatic and hydrologic systems are not stable (Hock, 2005). The physical models should be further developed and used in glacier modelling as long as there is enough input and validation data

6. Line 270. Semicolon should be after ‗ text‘. Response: We have corrected it.