IWMI Research Report Glacier Systems and Seasonal Snow Cover in Six 149 Major Asian River Basins: Water Storage Properties under Changing Climate Oxana S. Savoskul and Vladimir Smakhtin RESEARCH PROGRAM ON Water, Land and Ecosystems Research Reports The publications in this series cover a wide range of subjects—from computer modeling to experience with water user associations—and vary in content from directly applicable research to more basic studies, on which applied work ultimately depends. Some research reports are narrowly focused, analytical and detailed empirical studies; others are wide-ranging and synthetic overviews of generic problems. Although most of the reports are published by IWMI staff and their collaborators, we welcome contributions from others. Each report is reviewed internally by IWMI staff, and by external reviewers. The reports are published and distributed both in hard copy and electronically (www.iwmi.org) and where possible all data and analyses will be available as separate downloadable files. Reports may be copied freely and cited with due acknowledgment. About IWMI IWMI’s mission is to improve the management of land and water resources for food, livelihoods and the environment. In serving this mission, IWMI concentrates on the integration of policies, technologies and management systems to achieve workable solutions to real problems—practical, relevant results in the field of irrigation and water and land resources. IWMI Research Report 149 Glacier Systems and Seasonal Snow Cover in Six Major Asian River Basins: Water Storage Properties under Changing Climate Oxana S. Savoskul and Vladimir Smakhtin International Water Management Institute (IWMI) P O Box 2075, Colombo, Sri Lanka i The authors: Oxana S. Savoskul is a Research Associate at the International Water Management Institute (IWMI) in Colombo, Sri Lanka; and Vladimir Smakhtin is Theme Leader - Water Availability and Access at IWMI in Colombo, Sri Lanka. Savoskul, O. S.; Smakhtin, V. 2013. Glacier systems and seasonal snow cover in six major Asian river basins: water storage properties under changing climate. Colombo, Sri Lanka: International Water Management Institute (IWMI). 69p. (IWMI Research Report 149). doi:10.5337/2013.203 / glaciers / monitoring / seasonality / snow cover / river basins / climate change / impact assessment / remote sensing / water resources / water availability / water storage / hydrological cycle / mountains / surveys / models / institutions / Asia / ISSN 1026-0862 ISBN 978-92-9090-766-4 Copyright © 2013, by IWMI. All rights reserved. IWMI encourages the use of its material provided that the organization is acknowledged and kept informed in all such instances. Front cover photograph taken in 2010 at the headwaters of the Syr Darya River shows part of the Barkrak Sredniy glacier system (photo credit: Maxim Petrov, Institute of Geology, Tashkent, Uzbekistan). Please send inquiries and comments to IWMI-Publications@cgiar.org A free copy of this publication can be downloaded at www.iwmi.org/Publications/IWMI_Research_Reports/index.aspx Acknowledgments The authors gratefully acknowledge the help of Prasanna Sambandamurthy, Yamuna Udumalagala and Giriraj Amarnath (all of IWMI, Colombo), as well as Samjwal Ji (International Centre for Integrated Mountain Development [ICIMOD], Nepal), Kara Gergely (National Snow and Ice Data Center (NSIDC), USA), Lynn Lay (Goldthwait Polar Library, The Ohio State University, USA) and Elena Shevnina (Saint Petersburg State University, Russia) during the search for published literature and in primary data acquisition, or both. Online resources of NSIDC, World Glacier Monitoring Service (WGMS) and ICIMOD were extensively used, and Graham Cogley (Trent University, Canada) is thanked, in particular, for sharing the World Glacier Inventory - Extended Format (WGI-XF). The assistance of Ameer Rajah and Salman Siddiqui (both of IWMI, Colombo) was essential in downloading and processing data on seasonal snow. The authors would also like to thank Victor Zagorodnov (Byrd Polar Research Center, The Ohio State University, USA), Tobias Bolch (University of Zurich, Switzerland), Andrey Yakovlev (Tashkent State University, Uzbekistan), Felix Pertziger (URS New Zealand, New Zealand), Kenji Matsuura (University of Delaware, USA), Vladimir Aizen (University of Idaho, USA) and Michael Zemp (WGMS, Switzerland) for many fruitful discussions and valuable advice. Constructive comments and criticism of two anonymous reviewers and Peter McCornick (IWMI, Colombo) are gratefully acknowledged. The authors are also indebted to the late Mark Dyurgerov (University of Colorado Boulder, USA) for sharing his ideas and general insights years before this study materialized. Donors This research study was funded by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) is a strategic partnership of the CGIAR and the Earth System Science Partnership (ESSP). CGIAR is a global research partnership for a food secure future. The views expressed in this document cannot be taken to reflect the official opinions of CGIAR or ESSP. Contents Summary vii Introduction 1 Terms and Definitions 3 Water Storage Properties of Glacier Systems in the Study Basins 5 Changes in Glacier Systems between 1961-1990 and 2001-2010 21 Climate Change (CC) Impact on Glacier Systems 31 Seasonal Snow Cover 43 Conclusions 46 References 49 v v Summary The current status, recent and potential future estimated annual rates of areal reduction and changes of glacier systems and seasonal snow ice loss derived from data published by the cover in the Indus, Ganges, Brahmaputra, Amu World Glacier Monitoring Service (WGMS) and a Darya, Syr Darya and Mekong river basins are compilation of sources based on remote sensing for the first time systematically analyzed at the extending from 1960s to 2000s. It is shown basin scale. The baseline (1961-1990) status that the total glacier area reduction in the study of each basin’s glacier system is evaluated basins in this period is within 14-28% range, using a comprehensive meta-database for the and ice volume loss is within 11-40% range. 48,607 glaciers, which represents a new data Changes in maximum seasonal snow cover product in its own right compiled specifically area and maximum seasonal water storage for this study. The data gaps in existing glacier capacity between the periods 1961-1990 and inventories are identified and filled with expert 2001-2010 are assessed using the monthly estimates. The overlaps in glacier inventories data from terrestrial water budget data archive are examined to avoid double counting of some of Delaware University, USA. The reduction data. Uncertainty of estimates of glacier system of maximum seasonal snow cover area was parameters is critically assessed and shown to up to 5-15%, while the maximum seasonal be possibly as high as +(50-70)% for ice volume water storage capacity decreased by 9-27% estimates, and around +20% for the glacier in almost all study basins apart from Mekong. area and glacier numbers. The spatial pattern Glacier sensitivity to climate change (CC) and structure of glacier systems are analyzed is examined in terms of the critical warming using size class frequency distributions of a signal, which would be required for the complete number of individual glaciers, ice-covered area, disappearance of glaciers in a basin under ice volume, and where available – the data on the assumption of 3% changes in precipitation variability of equilibrium line altitude, maximum per degree of global air temperature rise. It is and minimum elevations and elevation intervals. shown that Syr Darya and Mekong basins are The Indus Basin is shown to have the largest likely to become almost glacier-free under the and most diverse glacier system in terms of all projected warming temperature of 4-5 oC by parameters examined, while the glacier system the end of the twenty-first century. In Indus, in the Mekong Basin is the lowest in terms of Ganges, Brahmaputra and Amu Darya river size and diversity. It is illustrated that structural basins, glaciers belonging to the large and diversity of a glacier system determines how it medium size classes are expected to survive the responds to climate change. Recent changes warming of 4-5 oC, with total basin ice reserves in glacier systems are characterized using reduced to 20-50% of the baseline 1961-1990. vii Glacier Systems and Seasonal Snow Cover in Six Major Asian River Basins: Water Storage Properties under Changing Climate Oxana S. Savoskul and Vladimir Smakhtin Introduction Glaciers and seasonal snow are important water Shan and Pamir (Tsarev et al. 1986; Kotlyakov resources in basins of the major rivers originating 1997; Dyurgerov and Meier 2005; Yao et al. from the Asian mountains – namely Indus, 2007, 2012; Bajracharya et al. 2008; Dyurgerov Ganges, Brahmaputra, Mekong, Amu Darya and 2010; Cogley 2011; Bolch et al. 2012) or for Syr Darya. These basins (Figure 1) collectively the territories confined by national boundaries host around 1/6 of the world’s population primarily (Kravtsova and Tsarev 1997; Mool et al. 2001a, represented by poor rural men and women, whose 2001b, 2004, 2005; Karma et al. 2003; Mool and livelihoods and food security directly depend on Bajracharya 2003; Bhagat et al. 2004; Sah et al. basin water resources and their possible changes. 2005; Alford et al. 2009; Alford and Armstrong Impacts of CC on glaciers and snow in High 2010; Ersi et al. 2010). A single summative Asia have recently attracted significant attention study carried out at the basin scale in the Hindu (Barry 2005, 2006; WWF 2005, 2009; Rees and Kush-Himalayan (HKH) region (Bajracharya and Collins 2006; IPCC 2007, 2010; Zhang et al. Shrestha 2011) fills the knowledge gap only 2007; Braun et al. 2009; Eriksson et al. 2009; partially, since this study covers only four basins UNEP 2009; Gosain et al. 2010; Malone 2010; fully and presents a snapshot of basins’ glacier Kääb et al. 2012; Cogley 2012; Yao et al. 2012; status only in 2005+3, i.e., does not provide Committee on Himalaya Glaciers, Hydrology, a reference line for analyzing glacier systems’ Climate Change, and Implications for Water evolution prior to 2000s. For the Aral Sea region, Security 2012). However, a lot of controversy there are detailed basin-scale assessments remains in the current scientific knowledge of the of glacier status (Lebedeva and Larin 1991; subject (Bolch et al. 2012; Viviroli et al. 2011). Lebedeva 1997; Dyurgerov e t a l . 1995; One major gap is the absence of the Shetinnikov 1998), but those were published in assessments of past and current water storage- Russian and, as such, remain largely unknown related properties of glaciers and seasonal snow to the wider research community. Therefore, at the scale of major river basins. Existing large- the first objective of this study is to compile scale assessments, which were carried out in and analyze all available primary and principal High Asia typically report total figures on the secondary data on glaciers and seasonal snow number of glaciers, glacier-covered area, total cover for the study basins during the past 50 ice volume and seasonal snow properties either years, focusing on the 30-year baseline interval for large mountain systems such as Himalaya, of 1961-1990 and the first decade of the new Karakoram, Tibetan Plateau, Hindukush, Tien millennium 2001-2010. 1 FIGURE 1. Map showing the boundaries of the study basins (red line), state borders (light yellow line) and snow- covered high-elevation belts where glaciers are located. At this resolution, individual glaciers cannot be distinguished. Another major gap in the current knowledge et al. 2010; Armstrong 2010; The Economist of the subject is poor understanding of the large- 2010; Malone 2010; Shekhar et al. 2010; Qiu scale processes of glacier and snow evolution 2008, 2010; Singh et al. 2011). Yet, actual under CC (Viviroli et al. 2011; Bolch et al. 2012; hard-core research on this subject has been Cogley 2012). Numerous publications focusing on relatively limited. For example, lessons learned this topic vary from research papers to reviews from monitoring of recent changes in glacier commissioned by nongovernmental organizations systems and structural analysis of their spatial (NGOs), development agencies and mass media organization received very little attention (Xie et (Singh and Bengtsson 2003; Hagg and Braun al. 2006). Snow evolution, however, is understood 2006; Ren et al. 2006; Xu et al. 2007; Jowit 2008; somewhat better (Barnett et al. 2005; Immerzeel Alford et al. 2009; Hasnain 2009; Raina 2009; et al. 2009), but most basin-scale studies either Schmidt and Nüsser 2009; Absar 2010; Archer lack a long-term perspective or do not clearly 2 distinguish between seasonal snow and glaciers. to CC, which might serve as guidelines for The most detailed study in the HKH region future modeling of various aspects of regional (Gurung et al. 2011, p. vii) reported that, “Decadal water cycle and water availability in the study (2000-2010) snow cover area figures for the HKH basins. region were insufficient to yield a statistically Each of the three principal lines of enquiry significant trend…” The second aim of this report (fixed-date snapshots, monitoring of recent is, therefore, to critically review and synthesize all changes, and assessment of CC impact on current scientific knowledge on the recent (last glaciers and snow) has its own section in this 50 years) changes in seasonal snow and glaciers report. Each section describes relevant data types into a coherent and physically plausible picture and methods, followed by summaries of the data of the evolution of seasonal snow and glaciers available for the study basins. Particular attention under CC. is paid to discussion of the data and methods’ The prev ious studies focused on the accuracy and reliability. Wherever possible, assessment of future changes of glaciers and error margins of the most common methods snow under CC impact lack either basin-wide are evaluated. The climatological background of scope or the consistency in the scenarios glacier and snow existence has been broached used (Ye et al. 2003; Singh and Bengtsson in this report only where it was needed for the 2003, 2004; Rees and Collins 2006). The third clarification of terms and methods. objective of this report is to critically overview This report is the first in a series of two. currently available CC-impact assessments and A subsequent second report (Savoskul and to infer the most likely future changes in glacier Smakhtin 2013) deals with the hydrological role of systems and seasonal snow cover in response snow and glaciers in the study basins. Terms and Definitions The recent growth in the number of publications fraction. Part of the atmospheric precipitation on glaciers and snow in Asia is associated with accumulated in a glacier annually is released growing confusion with regards to the context of into hydrosphere during the warmer part of the some commonly accepted terms. To overcome same year, i.e., with a delay of a few months, this issue, the terms used in this report and major the other part of annual accumulation melts with glaciological concepts standing behind these a delay of several decades and even centuries terms are explained below. (Jansson et al. 2003; Kaser et al. 2005, 2010). ‘Glacier’ is defined here as a natural ice The mechanism of water release relies on the body made out of snow consolidated under its ice flow from higher elevations, where it is cold own weight and large enough to flow under enough for the dominance of multi-annual ice force of gravity, either in a spreading pattern accumulation, to lower elevations, where the air from an elevated center towards the edges temperature is high enough for ‘ablation’, i.e., (dome-shaped ice sheets and ice caps) or ice loss, to prevail over accumulation. Under a downward (mountain glaciers) (Hambrey and stable climate, these processes are in balance Alean 2004; Benn and Evans 2006; Zemp et al. and glaciers are in a steady state, which 2009a). Glaciers have a high inter-annual water means that they retain their mass and volume storage capacity, of which their intra-annual or with minor fluctuations due to inter-annual seasonal water storage capacity makes only a variability in precipitation and air temperature. 3 The progressive CC disrupts the balance. Mountain glaciers are highly diverse in size Long-term mass loss or gain leads to glacier and morphology (Figure 2). The area of an areal ‘reduction’ or ‘expansion’, respectively, individual glacier may vary from less than 0.1 km2 and either retreat or advance of its frontal to over 1,000 km2. The morphological type of a part. The time of a glacier’s response to a CC glacier depends mostly on its size and the shape signal, i.e., the period needed for attaining a of underlying terrain. Small glaciers hang on the new steady state varies from a few years to slopes being nested in all suitable hollows, such several decades depending on the glacier’s size as niches, shelves, recessions and large crevices. and climatic conditions (Barry 2006; Benn and The medium-sized glaciers are situated in cirques Evans 2006). and in the upper parts of trough valleys. The large Although glacier accumulation and ablation in glaciers are typically composed of a number of the mountains occur within overlapping elevation glaciers originating from side valleys, which adjoin belts, a glacier can be subdivided into upper a major glacier body located in the main valley. and lower parts termed ‘accumulation area’ and High-elevated table-top summits are crowned with ‘ablation area’, respectively. The former is the part spreading glaciers of ice-cap type with one or more of a glacier where the net annual accumulation outlet flows. In the morphological classification, the is higher than the net annual ablation. The principal types are distinguished, respectively, as latter is the part where ablation prevails over ’hanging’, ‘cirque’, ‘valley’, ‘compound valley’ and accumulation in the annual cycle. The border ‘table-top summit’ glaciers. Glaciers are dispersed between accumulation and ablation areas is across wide areas and are generally aligned termed ‘the equilibrium line’. The equilibrium line with high elevations (Figures 1 and 2). A group altitude (ELA) is defined as an altitude where net of glaciers located within a large territory, i.e., a mass balance, i.e., the difference between mass mountain range or a major river basin, is termed loss and gain, equals zero. ‘glacier system’ (Kotlyakov 1997). FIGURE 2. Left: Google Earth view of scattered small glaciers of hanging (h), cirque (c) and valley (v) type in the Pskem sub-catchment of the Syr Darya Basin, Uzbekistan. Right: An intensively glaciated area in the Indian State of Jammu and Kashmir, Upper Indus Basin, where large compound valley (cv) glaciers and medium-sized simple valley glaciers (v) dominate the landscape. Scale in both images is the same. 4 Seasonal snow in the mountains is another 2009). Nonetheless, since perennial snow and important component of the regional water cycle. permafrost participate in annual accumulation– It has a high water storage capacity due to ablation cycle only marginally, their role in the its large areal extent, significantly exceeding regional water cycle is minor too. Perennial snow that of glaciers. Contrary to glaciers, seasonal and permafrost, however, are not considered in snow accumulates and discharges water mainly this report. within one annual cycle. Snow that falls on a In this report, water storage properties of glacier glacier surface should be considered as part of systems and seasonal snow cover are understood the glacier, since it gets involved in the glacier as properties essential for both water storage and water cycle. The snow that lasts over years release, which depend a lot on the spatial properties on an ice-free terrain forms ‘perennial snow of the systems. Therefore, apart from basin total fields’ or ‘snowpacks’. These are normally of volume of water accumulated in the systems, other small thickness in the order of the first few principal water storage-related characteristics are meters or less. Areal extent and water storage areal extent (and in the case of glacier systems, capacity of perennial snowpacks is insignificant number of glaciers). Elevation-related characteristics compared to glaciers and seasonal snow. Alpine of glacier systems are considered too, since those ‘permafrost’, i.e., a ground being frozen for more are crucial for understanding structure and evolution than two consecutive years, might contain, in of glacier systems, i.e., seeing water storage some instances, considerable amounts of frozen properties of the system in a broader time and water (Bolch and Marchenko 2009; Gorbunov space perspective. Water Storage Properties of Glacier Systems in the Study Basins Compilation of Meta-database of Primary surveyed area. The disadvantage of th is Data for Glacier Systems source i s tha t there a re few da ta gaps i n t h e g l a c i e r i n v e n t o r i e s o f t h e f i r s t Compiling a single data bank of statistically gene ra t i on , wh i ch have to be f i l l ed by rel iable information on individual glaciers expert est imates. Besides, s ince g lac ier in the study basins is a challenging task, inventories are compiled by data of single- because none of the exist ing sources of date snapshots of glacier status at the date readily available statistical data covers the of a survey, glacier statistics for the areas entire glaciated area of the study basins. w i th d i f fe rences in the da tes o f g lac ie r Glacier inventories of the f irst generation surveys or differences in methods of survey (e .g . , ICIMOD 2007; WGMS and NSIDC is not entirely compatible. However, since 2 0 0 9 ) a r e b a s e d o n t o p o g r a p h i c a n d the inventories of the first generation had airborne imagery-based surveys of individual been compiled in the period when changes glaciers conducted mainly between 1960s in glacier systems were relatively moderate, and 1980s. The inventories of this type are in genera l they a re cons idered to be a most appropriate for large-scale studies, suitable source for representing the baseline since they cover virtual ly every region in 1961-90 s ta tus o f g lac iers in H igh As ia the wor ld and contain readi ly accessible (Cogley 2009a, 2012). statistics on the location and morphometric Inventories of the second generation are parameters o f every s ing le g lac ie r in a compiled of the raw remote-sensing imagery 5 obtained shortly before or after 2000, e.g., glaciers in China and countries of the former Global Land Ice Measurements from Space USSR, i.e., Tajikistan, Uzbekistan, Kyrgyzstan, (GLIMS) initiative (Raup et al. 2007; http:// and Kazakhstan. It, however, gives inaccurate www.glims.org/; updated version: http://www. information for glaciers in Nepal and Bhutan glims.org/RGI/randolph.html) and Satell i te and contains incomplete datasets for India, I m a g e A t l a s o f G l a c i e r s o f t h e W o r l d Pakistan and Afghanistan, covering just 89, (SIAGW) (Williams and Ferrigno 2010). The 214 and 263 glaciers, respectively. From the disadvantage of these sources for large- river basin perspective, WGI (WGMS and sca le s tud ies i s tha t a lo t o f ana ly t i ca l NSIDC 2009) has full coverage for the Syr work is required to delineate and measure Darya and Mekong basins, partial coverage for individual glaciers in order to assess the the Brahmaputra, Amu Darya and Indus basins, statistics on glacier parameters and patterns and no information for the Ganges Basin. An of spatial organization of glacier systems extended version of WGI, so-called WGI-XF (Bajracharya and Shrestha 2011). (Cogley 2009a, 2011) was compiled recently In this study, stat ist ical descript ion of and is avai lable on request from Graham g l a c i e r s y s t e m p a r a m e t e r s ( n u m b e r Cogley (Trent University, Canada) and online of glaciers, glacier-covered area and ice (http://people.trentu.ca/~gcogley/glaciology/ volume) is based on i) an analysis of a index.htm). mega-dataset compiled from all the available The second resource complementing WGI up-to-date inventories of the first generation (WGMS and NSIDC 2009) in High Asia is for each study basin (that includes 48,607 the database of the International Centre for glaciers); ii) expert estimates for the existing Integrated Mountain Development (ICIMOD data gaps, e.g., areas not covered by detailed 2007). The ICIMOD (2007) inventory fills almost inventories; and iii) published sources based all the blanks in the WGI (WGMS and NSIDC on the inventories of the second generation 2009), providing data for the entire Ganges (Table 1). Basin, Lower Brahmaputra Basin and major The first primary data source for glaciers parts of the Indus Basin. It covers glaciers in the study basins is the World Glacier in Pakistan, Nepal, Bhutan and three out of Inventory (WGI) (WGMS and NSIDC 2009). the four states in India, where glaciers are The WGI is a searchable internet resource located: i) Sikkim, ii) Himachal Pradesh, and iii) that contains information on: glacier’s name, Uttarakhand (former Uttaranchal), leaving only code (which points to the country, major river Arunachal Pradesh uncovered. The ICIMOD basin, sub-catchment and glacier’s individual (2007) database is structured as a series of number); glacier’s coordinates, maximum and national and sub-national inventories (Mool et minimum elevations, orientation, glacier’s area, al. 2001a, 2001b; Mool and Bajracharya 2003; maximum, mean and minimum length, mean Mool et al. 2005; Bhagat et al. 2004; Sah et al. width, mean ice thickness, ELA; glacier type 2005; Lizong et al. 2005) accessible on request according to several classifications; dates and from ICIMOD (infomenris@icimod.org). Each methods of glacier survey; and accuracy of publication in this series (ICIMOD 2007) is given parameters (http://nsidc.org/data/docs/ presented by a report on geographical settings, noaa/g01130_glacier_inventory/). The WGI data materials and methods used in the survey, (WGMS and NSIDC 2009) are compiled from a general accuracy estimate and statistical national inventories (e.g., Katalog Lednikov summary of the data, supplemented with SSSR [Inventory of glaciers of the USSR] schematic sub-catchment maps and glacier 1982; Kulkarni and Buch 1991), which have inventories per se placed in the appendices of been digitized in 1996 (Haeberli et al. 1989; the reports. Bedford and Haggerty 1996). The WGI (WGMS Compared to WGI (WGMS and NSIDC and NSIDC 2009) is a perfect data source for 2009), the ICIMOD (2007) database is less 6 7 TABLE 1. Description of meta-database on glaciers compiled for this study. Country, Number of Area Volume Inventory- Source, comments years of topographic survey glaciers (km2) (km3) covered part of the glaciated area (%) INDUS BASIN Pakistan, 1934, 1977 5,218 15,060 2,729 100 ICIMOD 2007; Mool et al. 2005. Data set has an overlap with WGI-XF and an error. (214) (1,654) n.s. 11 WGI series PK5Q130-131 (WGMS and NSIDC 2009). Small and biased sample, includes only large glaciers. 5,052* 12,664* 2,039* 100 Authors’ estimate based on identified overlaps and errors; ice volume estimate is done by application of ICIMOD (2007) method. India, 1950-70s Himachal 2,510 4,125 385 100 ICIMOD 2007; Bhagat et al. 2004 (excluding series Subbasin_3, glaciers 1966-68 Pradesh 1-20, and series Subbasin_4). (89) (244) n.s. 2 WGI series IN5Q111 (WGMS and NSIDC 2009). Small sample. Jammu and n.s. 4,000* n.s. 100 Cogley 2009a. Kashmir 3,377* 9,347* n.a. 100 Authors’ estimate, based on Cogley 2009a, 2011. India, 1961-90 5,864 13,458 1,694* 100 Number of glaciers and area: WGI-XF series IN5Q150-IN5Q340 (Cogley 2009a); ice volume: authors’ estimate done by ICIMOD (2007) method. China, 1971-83 2,033 1,451 95 100 WGI series CN5Q142-CN5Q222 (WGMS and NSIDC 2009) Afghanistan, 1972-82 656* 186* 8* 0 Number: (Shroder and Bishop 2010); area: authors’ estimate based on Lebedeva and Larin (1991) and Lebedeva (1997); volume: authors’ estimate based on scaling approach (Bahr et al. 1997). 1959 (263) (130) n.s. 70 WGI series AF5Q132 (WGMS and NSIDC 2009). OVERLAPS and ERRORS 29 1,749 388 Subset of glaciers from disputed territory between India and Pakistan double counted in WGI-XF series 5Q151 (Cogley 2009a, 2011) and ICIMOD glacier Inventory (Pakistan) series Shyk_gr_194 to Shyk_gr_222 (ICIMOD 2007; Mool et al. 2005). 137 647 61 Subset of glaciers from Yergiang (Tarim) basin erroneously listed as belonging to Indus Basin in glacier Inventory for Pasistan, series Hunza_914 to Hunza_1050 (ICIMOD 2007; Mool et al. 2005). INDUS BASIN SUM 13,605* 27,759* 3,839* 99 Authors’ estimate. (Continued) 8 TABLE 1. Description of meta-database on glaciers compiled for this study (Continued). Country, Number of Area Volume Inventory- Source, comments years of topographic survey glaciers (km2) (km3) covered part of the glaciated area (%) GANGES BASIN Nepal, 1950-70s, 1992,1996 3,252 5,323 482 100 ICIMOD 2007; Mool et al. 2001a. 1977 (130) (1,642) n.a. 31 WGI series NP5O120 (WGMS and NSIDC 2009). Small and biased sample, includes only large glaciers. India, Uttarakh and 1960-70s Himachal 1,438 4,060 476 100 ICIMOD 2007; Sah et al. 2005. Pradesh 44 35 2 100 ICIMOD 2007; Bhagat et al. 2004. Series Subbasin_3 (glaciers 1-20), Subbasin_4 India, 1961-90 1,481 4,072 n.s. 100 WGI-XF series IN5O142-IN5O163 (Cogley 2009a). China, 1970s 2,192 3,609 330 100 WGI series CN5O161-CN5O198 (WGMS and NSIDC 2009). The dataset erroneously includes several subsets of glaciers from India from the bordering territories. China, 1961-90 2,027* 3,277* 297* 100 Authors’ estimate based on identified overlaps and errors. China, 1988-92 (1,578) (2,906) n.s. ICIMOD 2007; Lizong et al. 2004. China, 1999-2001 (1,578) (2,864) n.s. ICIMOD 2007; Lizong et al. 2004. China, 2005 (1,841) (2,579) n.s. Bajracharya and Shrestha 2011. OVERLAPS AND ERRORS 113 260 27 WGI series CN5O161 is erroneousely included into Chinese inventory WGMS and NSIDC 2009. It overlaps partially with WGI-XF series IN5O152B (Cogley 2009a) and partially with ICIMOD inventory, Himachal Pradesh, India, series subbasin_3 (ICIMOD 2007). 52 83 6 WGI series CN5O163 is erroneousely included into Chinese inventory WGMS and NSIDC 2009. It overlaps with WGI-XF series IN5O153E (Cogley 2009a). 42 144 15 WGI series CN5O195 is a closed interior basin, is not included in the basin total count. GANGES BASIN SUM 6,719* 12,541* 1,243* 100 Authors’ estimate. (Continued) 9 TABLE 1. Description of meta-database on glaciers compiled for this study (Continued). Country, Number of Area Volume Inventory- Source, comments years of topographic survey glaciers (km2) (km3) covered part of the glaciated area (%) BRAHMAPUTRA BASIN China, 1970s 10,816 14,493 1,292 100 WGI series CN5O204-CN5O291 (WGMS and NSIDC 2009). The data set erroneousely includes several subsets of glaciers from India and Bhutan. 10,453* 13,660* 1,209* Authors’ estimate based on identified overlaps and errors. Bhutan, 1950-70s or 1990s 677 1,316 116 100 ICIMOD 2007; Mool et al. 2001b. 1978 (96) (1,341) WGI series BH5O111 (WGMS and NSIDC 2009) wrong number of glaciers. India Sikkim, 1990s, 285 576 65 100 ICIMOD 2007; Mool and Bajracharya 2003. 2000s 1970s 449 705 n.s. 100 WGI-XF series IN5O201 (Cogley 2009a). Arunachal 417 566 n.s. 100 WGI-XF series IN5O207, 209, 211, 290, 291 (Cogley 2009a). Pradesh ,1960s 500* 70 Kotlyakov 1997. India all glaciers 1960s 866 1,271 162* 100 WGI-XFseries IN5O201-IN5O291 (Cogley 2009a). OVERLAPS AND ERRORS 59 388 52 A subset from ICIMOD glacier inventory, Bhutan, series_Out (ICIMOD 2007; Mool and Bajracharya 2003) is erroneously double counted in WGI CN series CN5O212A, 240, 251 (WGMS and NSIDC 2009). 91 173 13 A subset from WGI-XF series IN5O209 (Cogley 2009a) is erroneously double counted in WGI CN series CN5O220 (WGMS and NSIDC 2009). 112 168 12 A subset from WGI-XF series 211AA, 211AB (Cogley 2009a) is erroneously double counted in WGI CN series CN5O221A (WGMS and NSIDC 2009). 97 104 7 A subset from WGI-XF series IN5O290 (Cogley 2009a) is erroneously double counted in WGI CN series CN5O290 (WGMS and NSIDC 2009). BRAHMAPUTRA SUM 11,996* 16,248* 1,487* 100 Authors estimate. (Continued) 10 TABLE 1. Description of meta-database on glaciers compiled for this study (Continued). Country, Number of Area Volume Inventory- Source, comments years of topographic survey glaciers (km2) (km3) covered part of the glaciated area (%) AMU DARYA BASIN Tajikistan, 1950-60s 7,566 8,460 624 100 WGI series SU5X14301 to SU5X14317 and series SU5X14320 (WGMS and NSIDC 1980 2009; Katalog Lednikov SSSR (inventory of glaciers of the USSR 1982)). 9,805 7,257 468 100 Shetinnikov 1998. Afghanistan, 1972-82 n.s. 3,018 180* 0 Number, area: Lebedeva and Larin (1991); volume: authors. 1970-80s 2005+3 2,493* n.s. n.s. 0 Shroder and Bishop 2010. (347) (341) n.s. 14 Small sample: WGI series AF5X140 (WGMS and NSIDC 2009). 3,277 2,566 163 0 Bajracharya and Shrestha 2011. OVERLAPS AND ERRORS 310 377 24 WGI series SU5X17 is a closed interior basin, it is not included in the basin total count. AMU DARYA SUM 9,749* 11,101* 780* 71 Authors’ estimate. SYR DARYA BASIN Kyrgyzstan, Uzbekistan, 3,429 2,522 133 100 WGI series SU5X141 (WGMS and NSIDC 2009; Katalog Lednikov SSSR Tajikistan, 1950-70s (inventory of glaciers of the USSR) 1982) Series 14 (1) The glaciers of the former Soviet Republics bear common SU identification MEKONG BASIN China, 1980s 380 316 18 100 WGI series CH5L (WGMS and NSIDC 2009). Notes: Data used for the basin assessments as presented in Figure 3 are highlighted in grey. * = Expert estimates; n.a. = non-applicable; n.s. = not stated. Figures in round brackets are statistically non-representative samples. informative in the following respects: i) coding 472 km2, i.e., 15% of the glaciated area of the system for individual glaciers points only to country. The expert estimates for the amount the sub-catchment, but gives no indication of glaciers, glacier areal extent and volume of major river basin; ii) fewer parameters per of ice are presented in Table 1. According to glacier, i.e., only geographic coordinates, area, Lebedeva and Larin (1991), the University of length, ice thickness, ice volume and glacier Kabul had compiled a full glacier inventory of orientation (the data on glacier elevations are Afghanistan in 1984-1988, but the whereabouts available only for Nepal, Bhutan and parts of this document are unknown at present (Irina of India (Uttarakhand state)); i i i ) with the Markovna Lebedeva, Institute of Geography exception of Lizong et al. 2005, lack of clarity Russian Academy of Sciences, Moscow, on the survey dates for individual glaciers Russia, pers. comm., August 2, 2012). The (which may lead to significant discrepancies inventory of Kabul University contained data between measurements of the areal extent of for 1,515 glaciers with a total area of 3,210 glaciers taken from topographic maps produced km2, of which 186 km2 were in the Indus Basin in 1950-1970s and the recent satellite imagery and 3,018 km2 – in the Amu Darya Basin of 1990-2000s, since both are reportedly used (Lebedeva and Larin 1991; Lebedeva 1997). in the catalogues); and iv) lack of accuracy Shroder and Bishop (2010) estimate the total ratings for glacier morphometric parameters, number of glaciers in Afghanistan as 3,149 and except ELA. the total glacier-covered area as 2,700 km2. The combined dataset of glacier inventories The differences between these sources are accessible online, i.e., WGI (WGMS and NSIDC most likely due to the differences in the dates 2009), WGI-XF (Cogley 2009a) and ICIMOD of surveys and the lowest area limit accepted (2007), till recently had two major data gaps in the for the glaciers to be recorded. None of the upper parts of the Indus and Amu Darya basins published sources provides estimates for the (Cogley 2009a). One gap has been the lack of ice volume of glaciers in Afghanistan. The detailed data on glaciers in the part of the Indus ice volume estimate presented here (Table Basin that is located in the Indian State of Jammu 1) is made using glacier area-volume scaling and Kashmir, a tiny sample of which (133 glaciers approach (Chen and Ohmura 1990; Bahr et with a total area of 94 km2) had been catalogued al. 1997). by the Geological Survey of India (Kaul 1999), Analysis of the meta-dataset on glaciers according to Sah et al. (2005). According to a in the study basins (Table 1) revealed a preliminary estimate by Cogley (2009a), the entire number of data overlaps between different glacier-covered area in this state was 4,000 km2. sources or otherwise conflicting information. This data gap has been filled recently by Cogley In the datasets for the Indus Basin, there is (2011): inventorization of individual glaciers in this a double coverage in WGI-XF and ICIMOD study based on Soviet topographic maps produced database for the territory disputed between in the 1970s yielded a figure of 9,345 km2. Data India and Pakistan (ICIMOD 2007) with a total on individual glaciers in the State of Jammu and glacier area overlap of 1,749 km2; and apart Kashmir have been recently included in WGI-XF from that a subset of glaciers with a total (Cogley 2009a, 2011). area of 647 km2 from the Tarim Inland Basin The second da ta gap in the g lac ie r is erroneousely listed in ICIMOD database inventories of the first generation, i.e., covering (ICIMOD 2007) as belonging to the Hunza period 1961-1990, remains unfilled to date: it is Sub-basin of the Indus Basin. In the datasets the absence of detailed statistics on glaciers available for the Ganges Basin, WGI (WGMS that make up in sum 85% of the glaciated and NSIDC 2009) attributes a Chinese code area in Afghanistan. The WGI (WGMS and to two subsets of glaciers located in India NSIDC 2009) only contains data for a small with a total area of 332 km2. There is also sample of 610 glaciers with a total area of a similar confusion regarding 424 glaciers in 11 the Brahmaputra Basin with a total area of Methods of Glacier Surveys and 833 km2, which are located in the northern Associated Uncertainties of Primary Data territories of Bhutan and India bordering China. ICIMOD (2007) provides no information on The principal statistical parameters used to glaciers in Arunachal Pradesh State in its describe large glacier systems are: a) number coverage for India, whereas WGI (WGMS and of glaciers; b) total glacier-covered area; c) NSIDC 2009) contains data on these glaciers total ice volume; d) average maximum and under a misleading country code of China. minimum elevations of glaciers; e) average Large-scale efforts to repeat the glacier vertical extent (i.e., the difference between inventorization, several decades after the first maximum and minimum elevations of glaciers); glacier surveys, were undertaken in the study and f) average ELA. The accuracy of estimates basins several times but the resulting inventory of these parameters varies a lot depending on (providing i.a. morphometric parameters of the reliability of glacier inventories, the methods individual glaciers) was published only in one used to evaluate the morphometric characteristics case/instance (Lizong et al. 2005). This is a of individual glaciers, the differences between the re-inventorized subset of 1,578 glaciers from a dates of glacier surveys for the separate subsets part of the Ganges Basin located in China and used to compile the entire dataset and the human previously surveyed in the 1970s-1980s (WGMS factor in analyzing the data of glacier surveys. and NSIDC 2009). The methods of glacier surveys make possible Another effort of this kind is an unpublished the accounting of every single glacier. The inventory by A. Shetinnikov (Central Asian measurements of individual glacier parameters Inst i tute of Hydrometeorological Studies, taken directly from topographic maps, and Tashkent), which includes 11,358 glaciers in airborne and satellite imagery, such as glacier Pamir and Gissar-Alay mountains, based on the length, area and elevation-related parameters, materials of an aerial survey conducted in 1980. have a high accuracy with error margins of the This territory had been previously covered by WGI first few percent of the parameter value (Zemp data based on a survey conducted in the late et al. 2009a). However, the number of glaciers 1950s to early 1960s (WGMS and NSIDC 2009). and individual glacier parameters typically have Statistical analysis and comparisons between old been changing in the course of past few decades and new inventories for separate sub-catchments under CC impact. Therefore, the accuracy of total of Amu Darya and Syr Darya basins are available and average estimates of those parameters for from Shetinnikov (1998). a glacier system is significantly less, since the An unprecedented large-scale glacier re- datasets for the large territories are compiled from inventorization in the HKH region based on the the surveys conducted on different dates. satellite imagery from 2005+3, was finalized An example of extreme uncertainty of glacier in 2011 under the leadership of ICIMOD estimates derived from different sources is in (Bajracharya and Shrestha 2011). The report the part of the Ganges Basin that is located in contains no data on individual glaciers, but China. The first glacier inventory compiled for provides an extensive statistical data summary on this territory, WGI (WGMS and NSIDC 2009) the various parameters of glacier systems at the lists 2,027 glaciers with a total area of 3,267 sub-catchment level for the entire Brahmaputra, km2 based on the survey conducted during the Ganges, Indus and Mekong basins, with partial period 1970-1980. Second inventory for the coverage for the Amu Darya Basin (Bajracharya same territory (ICIMOD 2007; Lizong et al. 2005) and Shrestha 2011). includes 1,578 glaciers covering an area of 2,906 12 km2 in 1990, which decreased to 2,864 km2 by recent decades. However, in the opinion of the the year 2000 (Lizong et al. 2005). Since no authors of this report, currently available data coordinates are given for individual glaciers in of glacier area monitoring are hardly sufficient the ICIMOD database (Lizong et al. 2005) and its to justify adoption of this measure for the study glacier identification numbers are not compatible basins. with those of the WGI (WGMS and NSIDC The assessments of ice volume and ice 2009), it is impossible to make glacier-by-glacier thickness of glaciers have even lower accuracy comparison of the two datasets. Hence it remains compared to glacier area and number of unclear, whether 450 glaciers disappeared within glaciers, since the methods employed for the a time span of 10-20 years in the area where the evaluation of glacier ice volume in existing following decade (1990-2000) saw no changes at inventories of the first generation (ICIMOD all in the number of glaciers (Lizong et al. 2005), 2007; WGMS-NCIDS 2009; Cogley 2009a) are or the reason for the discrepancy in the number simple empirical models validated with few of glaciers between the two inventories can be dozens of field measurements at best, which attributed to the human factor in conducting certainly do not provide statistically reliable glacier surveys. samples (Fountain et al. 2009; Farinotti et Glacier estimates for the part of the Amu al. 2009; Cogley 2009b, 2012). The modern Darya Basin that is located in Tajikistan have methods which allow assessment of glacier high uncertainty too. In case the data from the volume and thickness from the topographic inventory of Shetinnikov (1998) based on a maps and remote-sens ing imagery w i th glacier survey conducted in 1980, were used relatively good accuracy (Nuth and Kääb 2011; instead of WGI data based on surveys of late Farinotti et al. 2009) had not been developed 1950s (early 1960s), the assessment of the at the time of most glacier surveys used in the number of glaciers in the entire basin would glacier inventories of the first generation. As a go higher by 20%, whereas the area estimate result, the uncertainties of glacier ice volume would have become lower by 20%. However, estimates in glacier inventories remained in the WGI dataset (WGMS and NSIDC 2009) is most cases simply undefined. not fully compatible with that of Shetinnikov In order to assess the uncertainties of (1998), since the former includes only glaciers ice volume estimates in the study basins, a > 0.1 km2 whereas the latter listed all glaciers. cross-comparison of the methods adopted in In case the glaciers < 0.1 km2 were excluded glacier inventories used in this report (ICIMOD from the inventory of Shetinnikov (1998), the 2007; WGMS and NSIDC 2009; Cogley 2009a, resulting estimate of glacier number and area 2011) is carried out (Table 2). In the former in that part of the basin would drop by 8% and USSR, the empirical models of ice volume 2%, respectively, compared to the status of evaluation were calibrated using relatively 1950s (early 1960s).In view of the discrepancy abundant data, i.e., around 60 radio-sounding in the dates of the glacier surveys, size of records of ice thickness (Aizen et al. 2007). a smallest glacier included in an inventory Hence, the assessments of glaciers’ volume in and human factor in data processing, the the Aral Sea region are likely to be relatively best possible accuracy for assessment of accurate. The empirical model used in glacier glacier numbers and area in High Asia for the inventories for the Chinese territory relies on baseline time period 1961-1990 is within +20% the measurements from the Northern Tien Shan (Cogley 2009a, 2009b, 2011; Dyurgerov 2010). (Mi and Xie 2002). Its adoption for the Upper To reduce the uncertainty range of glacier Brahmaputra Basin (WGMS and NSIDC 2009) areal estimates, Dyurgerov (2010) suggested is likely to increase uncertainty of the estimates, assessing the glacier areal extent in a certain and even more so in the Lower Brahmaputra, year by adjusting the baseline glacier area Indus and Ganges basins (Mool et al. 2001a, by the rate of glacier areal reduction in the 2001b; Mool and Bajracharya 2003; Bhagat et 13 al. 2004; Mool et al. 2005), where ice thickness Baseline (1961-1990) Status of the Glacier estimates too are based on that model (Sah et Systems al. 2005, p. 86). In other cases (ICIMOD 2007), ice volume assessments in the HKH region Assessment of the principal parameters of are based on a model developed for the Swiss glacier systems in the study basins (number Alps, which is also unlikely to yield accurate of glaciers, glaciated area and ice volume) for estimates of the glacier ice volume. For the the baseline period 1961-1990 is presented assessment of the ice volume of the HKH in Figure 3. It is based on the analysis of the glaciers in 2005+3 Bajracharya and Shrestha meta-database on individual glaciers described (2011) used the shear stress based model, in Table 1. which gives the most minimalistic estimate Judging from al l pr incipal parameters among all the currently employed ice volume describing baseline status of glacier systems estimation methods. in the study basins (Figure 3), the Indus Basin The calculations presented in Table 2 show stands out as the one with the largest number of that the ice volume estimates by the methods glaciers, largest glacier-covered area and largest employed in existing glacier inventories may differ ice volume. Next to it are the glacier systems in as much as twofold to threefold for glaciers of Brahmaputra and Ganges basins, which together the same size. The differences are particularly have approximately the same ice volume and great for the large glaciers. Since the large glaciated area as Indus Basin alone, but contain glaciers in any glacier system contain the most more glaciers. Glacier system in the Amu Darya substantial portion of the basins’ ice reserves, Basin is closer to the Ganges and Brahmaputra as demonstrated below, the error margins of ice than to the system of relatively modest proportions volume evaluations for glacier systems in the in the adjacent Syr Darya Basin. Glacier system major river basins may be roughly estimated as of the Mekong Basin is the smallest glacier +50-70% or worse. system among all the basins considered. TABLE 2. The differences between ice volume estimates for the glaciers of the same areal size, as based on the methods employed in different glacier inventories. Glacier area (km2) Ice volume estimate (km2) by methods used in WGI ICIMOD (2007) Bajrachariya and Shrestha (2011) WGMS and NSIDC (2009) 0.1 0.001 0.002 0.003 0.3 0.004 0.008 0.010 1 0.027 0.042 0.040 3 0.140 0.188 0.152 10 0.854 0.948 0.677 30 4.437 4.089 2.728 100 27.000 20.051 12.918 300 140.296 84.963 54.440 1,000 853.815 411.342 267.512 14 FIGURE 3. Baseline 1961-1990 parameters of glacier systems (in the Amu Darya and Syr Darya basins, glaciers < 0.1 km2 are not included). 27,759 30,000 25,000 16,247 20,000 13,605 12,541 15,000 11,996 11,101 10,000 6,719 9,749 3,839 5,000 1,439 2,522 0 1,183 3,429 316 INDUS 648 GANGES BRAHMAPUTRA 380 133 18 AMU DARYA SYR DARYA MEKONG Ice volume (km3) Number of glaciers Area (km2) Data sources: As listed in Table 1. Structural Features of Glacier Systems 1995). The frequency distributions presented in Figure 4 and Table 3 display a common pattern The analysis of the systems’ structure and spatial in all glacier systems, suggesting that glacier organization in this report is based on areal systems in all the study basins are structured size frequency distributions of the number of in a similar way. The first feature that all basins glaciers, glaciated area and ice volume. Special have in common is that small glaciers dominate attention is also given here to the elevation-related in numbers, but, in total, their share in the entire parameters: maximum and minimum glacier glacier-covered area is small and they contain elevations, elevation interval and ELA. a tiny fraction of the basin’s ice reserves. For Analysis of frequency distributions of the instance, in the Indus, Ganges and Brahmaputra morphometric glacier parameters across areal basins, 71-75% of glaciers are from areal size size classes which increase in logarithmic order classes of less than 1 km2. Yet, the share of is the most optimal analytical tool for the groups those glaciers in basins’ glaciated area is around composed of individual members, widely ranging 12-17%, whereas the proportion of ice they store in some characteristics (Haeberli and Hoelzle is a modest 3%, 4% and 8%, respectively. 15 Correspondingly, the bulk of the basin’s ice is distribution of the glacier-covered area follows stored in a few glaciers belonging to the largest size the same pattern, but in this case the differences classes. In the Indus Basin, the largest 112 glaciers between smallest and largest size classes are (compound valley type, all larger than 33.3 km2), less pronounced. comprise less than 1% of the entire glacier system, Diversity of a glacier system is reflected by but contain 66% of its ice volume. Remarkably, the number of areal size classes, differences 52% of the total ice reserves in the Indus Basin is between smallest and largest glaciers (Table 4) stored in 25 extremely large Karakorum glaciers and the elevation interval of glacier occurrences varying in size from 100 km2 to over 1,000 km2, (Table 5). The data presented here suggest that which are among the largest mid-latitude glaciers diversity of glacier systems is higher in the basins on earth. The glacier systems in the Ganges and with large numbers of glaciers and total glacier- Brahmaputra basins are structured similarly: 69% covered area (Figure 4; Tables 3, 4 and 5). The and 60% of total ice reserves are stored in the lower a basin ranks in terms of the total number largest three glacier size classes, which constitute of glaciers and their areal extent and volume, the 3.5% and 2.1% of all glaciers in those two basins, fewer glacier size classes it has, and the less respectively, and are dominated by glaciers of the are the differences between the largest and the compound valley type. smallest glaciers. The glacier size class distribution diagrams Average glacier parameters also reflect the of the glacier systems in the Amu Darya and Syr diversity of a glacier systems’ structure (Table Darya basins have the same pattern as those in 4). The difference between average ice volume the Ganges and Brahmaputra basins, with one per glacier in the Indus and Syr Darya basins is essential difference: the number of areal size almost ninefold. An average glacier area too is classes in Amu Darya and Syr Darya glacier smaller in glacier systems of smaller proportions. systems is less than in the HKH region, therefore For instance, in the three largest glacier systems the role of the principal ice reserve storage is (the Indus, Ganges and Brahmaputra basins), played by glaciers of smaller size classes (Table average glacier size is between 1.3-2.1 km2, 3; Figure 4). In the Amu Darya Basin, 74% of whereas in the Syr Darya and Mekong basins it ice is concentrated in medium-sized glaciers drops to 0.7-0.8 km2. with areas between 1 and 33.3 km2. In the Syr Stratification of glacier systems can be Darya Basin, 71% of its ice belongs to small to compared to the stratification of human societies. medium-sized glaciers ranging from 0.33 to 10 Glaciers accumulate ice in a manner much similar km2. Correspondingly, the type of glaciers where to wealth accumulation in the societies with bulk of glacier ice is stored in the Aral Sea region broadly differentiated incomes, where a majority differs from that in the HKH region. In the Amu of the population is ‘impoverished’, ‘middle class’ Darya Basin, simple valley glaciers start playing a is relatively small in numbers and only a few role as the second principal ice reserves storage, individuals possess ‘fortunes’. Spatial organization although still lagging behind the glaciers of the of glacier systems is ‘social’ too. In favorable compound valley type. In the Syr Darya Basin, environments, glaciers form huge ‘urban centers’ simple valley and cirque glaciers are as important with a high ‘population density’, and have ‘low- ice reserves as compound valley glaciers. In the density rural populations’ in areas with limited Mekong Basin, bulk of the ice is stored mainly in opportunities for ice accumulation (Figure 2). cirque glaciers. Furthermore, if a glacier system is compared to Thus, the principal feature of size class a country, the basins’ ice reserves will stand for stratification of any glacier system is that the ice its gross domestic product (GDP) and average volume is distributed among glacier areal size glacier size for GDP value per capita. In this classes unevenly, with small glaciers containing case, too, glacier systems are somewhat human- a small share of it and the bulk of the ice like: the larger the country’s GDP (net value and reserves concentrated in the largest glaciers. The per capita), the more diverse is its stratification in 16 FIGURE 4. Size class frequency distributions of glaciers’ number, glaciated area (histograms) and ice volume (circular diagrams). 50 INDUS 40 30 20% 20% 16% 20 12% 14% 10 10% 6% 0% 2% 0 50 A B C D E F G H I GANGES 40 30 31% 31% 20 7% 10 17% 10% 1% 0 3% A B C D E F G H I 50 BRAHMAPUTRA 40 30 25% 22% 20 20% 11% 10 14% 6% 2% 0 A B C D E F G H I 50 AMU DARYA 40 30 33% 20 22% 10% 10 18% 9% 4% 4% 0 A B C D E F G H I 50 SYR DARYA 40 30 25% 27% 11% 20 10% 10 19% 8% 0 A B C D E F G H 50 MEKONG 40 30 33% 20 23% 27% 3% 10 14% 0 A B C D E F G H I Number of glaciers A: < 0.3 km2 33%D: 3 - 10 km2 G: 100 - 300 km2 Area (km2) B: 0.3 - 1 km2 E: 10- 30 km2 H: 300 - 1,000 km2 C: 1 - 3 km2 F: 30- 100 km2 I: > 1,000 km2 Data sources: Authors’ estimate based on data from WGI (WGMS and NSIDC 2009), WGI-XF (Cogley 2009a) and ICIMOD (2007). 17 TABLE 3. Distribution of the number of glaciers, glacier-covered area and ice volume across glacier size classes in the study basins. Size class Number of Share in Area Share in Ice volume Share in (km2) glaciers basin total (km2) basin total (km3) basin total (%) (%) (%) INDUS BASIN (without parts of Afghanistan and India, 97% of glaciated area covered) < 0.33 5,358 41 869 3 18 0.5 0.33 to 1.0 4,009 30 2,377 9 83 2 1.0 to 3.3 2,533 19 4,572 17 244 6 3.3 to 10 912 7 4,977 18 393 10 10 to 33 286 2 4,748 17 549 14 33 to 100 87 0.6 3,579 13 595 16 100 to 333 19 0.14 3,149 11 760 20 333 to 1,000 5 0.04 2,300 8 752 20 > 1,000 1 0.01 1,056 4 442 12 SUM 13,210 100 27,627 100 3,836 100 GANGES BASIN < 0.33 2,769 41 437 3 9 0.7 0.33 to 1.0 1,973 29 1,183 9 41 3 1.0 to 3.3 1,243 18 2,261 18 122 10 3.3 to 10 492 7 2,725 22 216 17 10 to 33 198 3 3,314 26 383 31 33 to 100 42 1 2,254 18 379 31 100 to 333 2 0.03 366 3 92 7 SUM 6,719 100 12,541 100 1,243 100 BRAHMAPUTRA BASIN < 0.33 5,689 47 909 6 29 2 0.33 to 1.0 3,332 28 2,019 12 88 6 1.0 to 3.3 2,034 17 3,692 23 214 14 3.3 to 10 690 6 3,683 23 295 20 10 to 33 211 2 3,349 21 377 25 33 to 100 36 0.3 1,915 12 322 22 100 to 333 4 0.03 680 4 162 11 SUM 11,996 100 16,248 100 1,487 100 AMU DARYA BASIN (without parts of Afghanistan, 79% of glaciated area covered) < 0.33 3,742 47 701 8 23 4 0.33 to 1.0 2,241 28 1,299 15 50 9 1.0 to 3.3 1,432 18 2,434 28 115 18 3.3 to 10 386 5 1,993 23 143 22 10 to 33 98 1 1,602 18 215 33 33 to 100 13 0.2 616 7 65 10 100 to 333 1 0.01 156 2 23 4 SUM 7,913 100 8,801 100 641 100 (Continued) 18 TABLE 3. Distribution of the number of glaciers, glacier-covered area and ice volume across glacier size classes in the study basins (Continued). Size class Number of Share in Area Share in Ice volume Share in (km2) glaciers basin total (km2) basin total (km3) basin total (%) (%) (%) SYR DARYA BASIN < 0.33 1,750 51 320 13 10 8 0.33 to 1.0 1,139 33 676 27 26 19 1.0 to 3.3 428 13 759 30 36 27 3.3 to 10 101 3 513 20 33 25 10 to 33 9 0.3 136 5 15 11 33 to 100 2 0.1 119 5 13 10 SUM 3,429 100 2,522 100 133 100 MEKONG BASIN < 0.33 166 44 30 9 0.5 3 0.33 to 1.0 142 37 77 24 2.5 14 1.0 to 3.3 53 14 90 28 4.7 27 3.3 to 10 16 4 80 25 6.1 33 10 to 33 3 1 39 12 4.1 23 SUM 380 100 316 100 17.9 100 Data sources: WGI (WGMS and NSIDC 2009) and ICIMOD (2007). Note: To make visual comparison easier, each glacier areal size class is highlightened in its own color. The color palette corresponds to the one used in Figure 4 and Table 8. TABLE 4. Average and largest glacier size and volume per basin as indicators of glacier system diversity. Basin Average glacier Average glacier Number of size Area of the Ice volume of the area (km2) volume (km3) classes in a basin largest glacier largest glacier (km2) (km3) INDUS 2.1 0.31 9 1,056 442 GANGES 1.9 0.18 7 263 71 BRAHMAPUTRA 1.4 0.13 7 207 52 AMU DARYA 1.3 0.09 7 156 23 SYR DARYA 0.7 0.04 6 70 8 MEKONG 0.8 0.05 5 16 1.7 Data sources: WGI (WGMS and NSIDC 2009) and ICIMOD (2007). terms of differences between ‘poor’ and ‘wealthy’ underlying geometry, steep slopes in the vicinity, social classes. For instance, in glacier-‘poor’ etc.), which determines the features of the Syr Darya and Mekong basins, the wealthiest microclimate with an implication that favorable members are still poor compared to glacier-‘rich’ conditions for glacier existense occur at different Amu Darya and basins of the HKH region. elevations (Kulkarni and Buch 1991; Ohmura et The absolute altitudes of glacier occurence al. 1992; Karma et al. 2003). Spatial organization in all basins vary widely, ranging from 2,500 m of glacier systems reflects climatic heterogenity. In to 8,000 m. This is due to the fact that mountain favorable environments, e.g., around high peaks glaciers are products of interplay between regional and where precipitation is abundant, glaciers climate and local topography (e.g., aspect, form hubs with a ratio of glaciated to ice-free 19 terrain of over 1:1. At lower elevations and in systems and the highest in the headwaters, areas with limited precipitation, glaciers are, in which typical ly receive less precipi tat ion general, smaller and scattered loosely across (Kayastha and Harrison 2008). At the mesoscale, mainly ice-free terrain (Figure 2). However, a ELA of south-facing glaciers is higher compared cross-comparison of variability of elevation-related to the north-facing glaciers (Dyurgerov et al. parameters of glacier systems in different basins 1995; Mool et al. 2001a). (Table 5) conforms with the tendency discussed in An important feature of glacier systems, respect to size-frequency distributions - the more which has far-reaching implications for CC glaciers and ice reserves there are in a basin, the impact studies, is that the vertical extent of an more diverse the glacier system is. In this case, in individual glacier, i.e., the difference between terms of increase in the vertical extent of glacier its maximum and minimum elevations, contrary occurence. to elevations of glacier occurrence, shows The scope of diversity of glacier elevations little dependency on local microclimate and within one glacier system can be illustrated by has a high correlation (0.73) with the glacier data on ELA variability (Table 5). In the HKH area (Figure 5). According to the analysis of region, the highest ELA in a basin is located up the glacier elevation variability carried out for to 5,000 m higher than the lowest ELA. In the Aral this study, average altitude intervals of all the Sea region, this difference is around 2,500-4,000 size classes vary from basin to basin within a m. The standard deviation of ELA from basin- range of just +10-15% of the size class average averaged value typically makes 400-500 m in the value of the entire dataset. Little dependency three HKH basins and in the Amu Darya Basin, of vertical glacier extent on the microclimate dropping to a still impressively high 200-300 m in implies that CC is likely to affect all glaciers Syr Darya and Mekong basins. in a basin in a similar way irrespective of the The spat ia l pattern of ELA var iabi l i ty absolute altitudes of their location. In other demonstrates dependency on several factors, words, glaciers at higher altitudes are as likely to the most important being: i) location of a sub- be affected by CC as those at low altitudes, and catchment with respect to the precipitation- the magnitute of the CC impact is determined bearing winds; and ii) glacier exposure. Under not by glacier location but by its vertical extent, the influence of these factors, regional ELAs and which correlates with the glacier areal size class. other elevation parameters are the lowest in the The implication is that glacier sensitivity to CC is well-nourished periphery of the large mountain determined mainly by its areal size. TABLE 5. Variability of ELA in the study basins (The Indus Basin is not included, because the glacier inventories for this basin did not contain information on the elevation-related glacier parameters). Basin Highest ELA Lowest ELA Basin-average ELA Standard Deviation (m) (m) (m) (m) GANGES 8,110 2,760 5,470 450 BRAHMAPUTRA 7,000 2,270 5,420 433 AMU DARYA 6,710 2,810 4,430 530 SYR DARYA 5,380 2,870 4,030 300 MEKONG 5,600 4,090 5,310 180 Data sources: WGI (WGMS and NSIDC 2009); ICIMOD 2007. 20 FIGURE 5. The relationship between (a) ELA and glacier area, and (b) elevation range and glacier area in the Mekong Basin. a) Coefficient of correlation = 0.21 b) Coefficient of correlation = 0.73 5,800 4,500 5,600 4,000 5,400 3,500 5,200 3,000 5,000 2,500 4,800 2,000 4,600 1,500 4,400 1,000 4,200 500 4,000 0 0 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 16 Glacier area (km2) Glacier area (km2) Data source: WGI (WGMS and NSIDC 2009). Note: Elevation range of a glacier is the difference between altitude of the highest and lowest points. Changes in Glacier Systems between 1961-1990 and 2001-2010 Data and Methods of Glacier Monitoring year intervals (IAHS (ICSI)-UNESCO 1967, and Associated Uncertainties 1973, 1977, 1985; IAHS (ICSI)-UNEP-UNESCO 1988, 1993, 1998; IUGG (CCS)-UNEP-UNESCO As early as in the 1950s, several countries 2005, 2008). Publication of another regular started national programs of glacier monitoring, edition, a glacier-mass-balance bulletin (GMBB) which initially included only observations of was initiated in 1991. The GMBB is issued glacier front fluctuations, but were expanded a biannually (WGMS 1991, 1993, 1994, 1996, decade later to include records of glacier area 1999; 2001, 2003, 2005, 2007, 2009, 2011) and volume changes. In 1986, with the support and is accessible online (http://www.geo.uzh.ch/ of the United Nations Environment Programme microsite/wgms/gmbb.html). These two publication (UNEP), the World Glacier Monitoring Service series represent one of the principal sources (WGMS) was established as a coordinating of primary information on glacier changes in body for the collection and dissemination of the past few decades. In addition to the efforts glacier data for several hundreds of mountain coordinated by the WGMS, rapid development of glaciers worldwide. Data of national monitoring remote sensing (RS) techniques in the past few programs have been published in roughly five- decades revolutionized glacier monitoring (Bishop 21 ELA (m) Elevation range (m) et al. 2000; Bhambri and Bolch 2009). As a result, 2009). The major challenges for RS methods the second principal source of information on the are related either to image quality, e.g., a need recent glacier changes include numerous research to fill the voids left by cloud covers and deep publications which, in many instances, surpass the mountain crest shadows, or to data interpretation first source in terms of quantity and quality. bias involved in both automated and manual The records of glacier changes can be delineation of glaciers from RS products (Bhambri subdivided into three major categories: i) data on and Bolch 2009). Particularly problematic is frontal variations of glaciers; ii) changes of areal the identification of small-sized glaciers and extent; and iii) glacier mass-balance measurements. differentiation between debris-covered glacier Frontal variations are derived by mapping or tongues and ice-cored glacial landforms. photographing the position of the glacier termini. Nonetheless, the application of RS techniques It is the earliest, easiest to obtain and the most makes a real breakthrough in terms of increasing common type of records for in-situ measurements. statist ical rel iabil i ty of data by increasing Changes in glacier length and the annual rate of the sample size, and thereby enhancing the glacier termini retreat or advance can be calculated credibility of the large-scale regional assessments from a series of subsequent records. However, the (Racoviteanu et al. 2009). use of that type of data for glacio-climatological Glacier mass-balance, i.e., a figure indicating studies is limited, because the CC forcing signal annual or long-term average gain or loss of does not impact glacier length directly, but is glacier ice in water equivalent (w.e.), may be mediated through changes in glacier mass balance calculated from the geodetic measurements of and, therefore, glacier length is affected by CC glacier volume change and density of ice and signal with delays of varying duration. There are snow (Bamber and Rivera 2007; Cogley 2009b). modeling attempts to link glacier length changes Indirectly, ice volume changes can be assessed with glacier volume changes (Oerlemans 2005; either by modeling glacier volume at subsequent Lüthi et al. 2010), but since all the models require dates based on known changes in areal extent detailed topographic information for calibration, the (Kulkarni et al. 2004) or by glacier water budget value of the glacier length records in regional studies calculations (Dyurgerov 2010). is limited to providing evidence on the dominant The direct measurement of glacier mass- patterns of glacier behavior in a certain time interval balance is one of the most accurate methods in (Bolch et al. 2012). glacier assessments (Kaser et al. 2002, 2006). Its The data on the changes of the elevation error margin is in order of +5-10%, the inaccuracy of glacier front position and its ELA (e.g., Fujita of snow density estimates being the major source et al. 1997; Dobhal et al. 2008; Bajracharya et of errors. Because of the high reliability of this al. 2011) are much more relevant for glacio- method, a number of regional assessments of climatological studies, since changes of vertical glacier mass loss had been already carried out glacier extent are tightly and directly controlled by based on the mass-balance monitoring data (Meier climate, contrary to glacier length. In addition, ELA and Dyurgerov, 2002; Meier et al. 2003; Dyurgerov responds to CC without a delay. and Meier 2005; Oerlemans 2005; Kaser et al. The data on changes in glacier areal extent 2006; Ohmura 2006; Raper and Braithwaite 2006; were relatively rare before the year 2000, when Oerlemans et al. 2007). they were assessed from aerial photographs, The question of representativeness of the topographic maps and in-situ recorded positions mass-balance measurements of individual glaciers of glacier termini (Table 6). The recent advances for the the large-scale glacier systems, however, in this field are closely related with the rapid is widely debated (Kaser et al. 2006; Paul and development in RS- and GIS-based modeling Haeberli 2008; Fountain et al. 2009; Cogley 2009b). techniques (Kääb 2005; Kulkarni et al. 2005; The major problem is the extreme scarcity of direct 2007; Bolch 2007; Konovalov and Desinov measurments of the glacier mass balance, related 2007; Raup et al. 2007; Bhambri and Bolch to the fact that the methods of direct mass balance 22 measurements are both time- and labor-consuming balance record from a single glacier is, in fact, a (Kaser et al. 2002). Therefore, out of the mass- random measurement. Its extrapolation to wider balance observations available for a little over 300 areas is likely to result in a considerable, but hard glaciers on the planet, only 30 records extend over to determine degree of uncertainty from regional 30 years (Zemp et al. 2008, 2009b). In addition, assessments. In the opinion of the authors of existing long-term records most likely constitute a this report, single-glacier mass-balance records biased sample, since the early measurements were may not be representative even for its immediate carried out mostly on small glaciers (Dyurgerov and surrounding, let alone the large region. Meier 2005; Dyurgerov 2010). An alternative way of assessing glacier The main argument in favor of using single- mass-balance is the planimetric or geodetic glacier records for the regional assessment of method, based on the mapping of the glacier mass loss of large glacier systems is the good surface at regular intervals, is (Bishop et al. correlation of the long-term variations of the 2000; Bamber and Rivera 2007; Cogley 2009b). specific mass-balance of the glaciers scattered This method allows deducing glacier volume hundreds and thousands of kilometers apart changes much easier than in the case of direct (Meier and Dyurgerov 2002; Meier et al. 2003; field measurements. A breakthrough in planimetric Dyurgerov and Meier 2005; Dyurgerov 2010). measurements was achieved in the past decade However, careful consideration of the mass-balance through the application of RS techniques, records suggests that the cumulative mass-balance particularly the use of DEMs of high resolution curves have much weaker correlation, if any, derived from oblique satellite images (Bouillon et even between glaciers located in the vicinity of al. 2006; Berthier et al. 2007; Berthier and Toutin each other (Fountain et al. 2009). For instance, 2008; Bolch et al. 2008, 2011; Kääb et al. 2012). records from a Tuyuksu group of nine glaciers in Another novel RS technique based on satellite- the headwaters of Malaya Alamaatinka Valley in gravimetry data (Jacob et al. 2012) offers new Northern Tien Shan (WGMS 1991, 1993, 1994, possibilities for large-scale glacier mass-balance 1996, 1999; 2001, 2003, 2005, 2007, 2009, 2011) studies, but with a very coarse spatial resolution provide strong evidence that there are significant and high uncertainty regarding the interpretation of differences in the local patterns of glacier mass- the data obtained by this method for the mountain balance variability (Figure 6). regions in general. As in assessments of glacier The unique character of the Tuyuksu dataset areal changes, the application of RS techniques is that it is obtained from glaciers located in a for the mass-balance assessments allows the single sub-catchment, unlike all other records in sample size to be increased substantially, High Asia. The long-term observations indicate but at the expense of the method’s accuracy. that out of nine glaciers, eight were losing mass The uncertainties of the method are related to over a period of 25 years (1965-1990), whereas uncertainties of the mapping techniques and one glacier – Partizan (43oN, 71oE) – gained inaccuracies in assessments of snow and ice annually an average of 280 mm w.e. (Figure density. However, despite a certain need for fine- 6). There is strong correlation between annual tuning, the new RS techniques which have been specific mass-balance curves for all glaciers, developed and tested in several locations in High (e.g., coefficient of correlation between records Asia have high potential for yielding a lot of new from Tsentralniy Tuyuksu glacier and Partizan data in the near future (Bhambri and Bolch 2009; glacier is 0.89). Nonetheless, the differences Kääb et al. 2012; Jacob et al. 2012). of the total value of the annual specific mass- balance between some glaciers in that group are up to 2,000 mm (Figure 6a). In other words, Lessons Learned from Glacier Monitoring the variability of mass-balance at the local scale (Figure 6b) is as high as its variability accross the The data on changes in glacier length are not globe (Figure 6c). This strongly implies that mass- considered in this study, because of their low 23 relevance for water storage properties of glaciers. was leaving Karakorum and Hindu Kush, the The monitoring records for several dozens of major mountain systems of the basin where two- glaciers in High Asia are available from regular thirds of all glaciers in the basin are concentrated, publications (IAHS (ICSI)-UNESCO 1967, 1973, not covered. 1977, 1985; IAHS (ICSI)-UNEP-UNESCO 1988, In the Aral Sea region, the Amu Darya 1993, 1998; IUGG (CCS)-UNEP-UNESCO Basin has 100% coverage, although the data 2005, 2008) and on request from the WGMS (Shetinnikov 1998; WGMS and NSIDC 2009) for (www.wgms.ch). Overviews of the glacier length 75% of the glaciated area that is located within changes in the HKH and the Aral Sea regions former USSR boundaries refer to the interval can be found in Ageta et al. (2001), Aizen et from late 1950s (early 1960s) to year 1980. The al. (2006), WWF (2005), Zemp et al. (2009a), remainder of the territory is covered by data Armstrong (2010), Raina (2009), UNEP (2009), from Bajracharya and Shrestha (2011) extending Malone (2010), Dyurgerov (2010), Braun et al. till 2005+3. The Syr Darya Basin coverage is (2009), Miller et al. (2012) and Bolch et al. (2012). around 60% with most data extending into the The summary of the data on glacier areal 2000s (e.g., Aizen et al. 2006; Kutuzov and changes in the study basins is given in Table 6. Shahgedanova 2009; Narama et al. 2009). One of the principal sources of information on the Comparison of the data from Bajracharya areal extent of glacier systems in 2005+3 is the and Shrestha (2011) for 2005+3 (Table 6) and recent ICIMOD report (Bajracharya and Shrestha data on glacier status in the baseline period 2011), where it is derived from a repetetive glacier 1961-1990, based on glacier inventories of the survey based on satellite imagery. The data of first generation (Table 1), shows that the glacier Bajracharya and Shrestha (2011) cover all three area has reduced by 28% and 24% in the major basins in the HKH region, the Mekong Ganges and Indus basins, respectively, and only Basin and part of the Amu Darya Basin that is by 14% in the Brahmaputra Basin, suggesting located in Afghanistan. However, the findings annual glacier reduction rate of approximately of Bajracharya and Shrestha (2011) in some 0.8-0.9%/year (the Ganges and Indus basins) respects are contradictory to a number of previous and 0.5%/year (the Brahmaputra Basin). These studies in the region. figures are significantly higher than those derived Prior to publication of the ICIMOD report from previously published evidence for this (Bajracharya and Shrestha 2011), in the Ganges region (Table 6), particularly for the upper part glacier system, 34% of glaciated area had been of the Indus Basin located in Karokoram, where, covered by a variety of studies on glacier changes according to Hewitt (2005), Mayer et al. (2006) in the past decades (Mool et al. 2004; Lizong et and Copland et al. (2011), the large glaciers, al. 2005; Bajracharya and Mool 2006; Chen et sampled randomly, were either advancing al. 2007; Bolch et al. 2008; Salerno et al. 2008; recently or remained stable since the beginning Bhambri et al. 2011). In the Brahmaputra and of the twentieth century and evidently gained Indus basins, only 13% and 9% of glaciated area mass (Gardelle et al. 2012). has been covered, respectively (the Brahmaputra Other records of glacier changes in the Basin: Karma et al. 2003; Liu et al. 2005; Ye et al. HKH region derived from small samples (Table 2006, 2007; Frauenfelder and Kääb 2009; Bolch 6) confirm that glaciers are in a general state et al. 2010; the Indus Basin: Kulkarni et al. 2007; of retreat from 1960s onwards, with the rates Ye et al. 2008). The spatial scatter of sampled of areal reduction varying in extremely wide areas in the Brahmaputra Basin is definitely better intervals between different study sites, i.e., in than in the Indus Basin, since it does not leave the Indian part of the Indus Basin: 0.23-0.53%/ any essential gaps. The records available for the year; in the Ganges: 0.07-1.30%/year; and in the Indus Basin were from the Western Himalaya, Brahmaputra: 0.01-1.10%/year. with one exception – a study carried out in the High spatial and temporal variability of Chinese part of the basin (Ye et al. 2008), which the rates of areal reduction is also recorded 24 25 FIGURE 6. Local variations of glacier mass-balance: long term (1965-1990) measurements from the Tuyuksu group of glaciers in Malaya Almaatinka Valley, Northern Tien Shan. a) Tuyuksu group of glaciers: specific mass-balance at ELA time b) Tuyuksu group of glaciers: cumulative mass-balance time series c) Cumulative mass-balances of selected glacier systems compiled series from individual time series 1,000 8,000 15,000 6,000 10,000 500 4,000 5,000 2,000 0 0 0 -500 -2,000 -5,000 -4,000 -1,000 -10,000 -6,000 -8,000 -15,000 -1,500 -10,000 -20,000 -12,000 -2,000 -25,000 -14,000 -2,500 -16,000 -30,000 Partizan Ts Tuyuksu Partizan Ts Tuyuksu Alps Scandinavia Altai Himalaya Tibet Tien Shan St Elias Olympic Igly Tuyuksu Kosmodemianskaya Igly Tuyuksu Kosmodemianskaya Andes Patagonia Mametova Mayakovkiy Mametova Mayakovkiy Molodezhniy Ordjonikidze Molodezhniy Ordjonikidze Visiachie Visiachie Data sources: a), b): WGMS 1991, 1993, 1994, 1996, 1999; 2001, 2003, 2005, 2007, 2009, 2011; c): adopted from Dyurgerov and Meier 2005. 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 for the basins of the Aral Sea region: Amu 1989; Srivastava and Swaroop 1989; Dobhal et Darya 0.2-1.4%/year, and Syr Darya 0.07-0.7%/ al. 1995; Shanker 2001; Srivastava et al. 2001; year (Shetinnikov 1998). There is an evidence Wagnon et al. 2007; Pithan 2011; Azam et al. suggesting the reduction of glacier-covered area 2012), in the Ganges Basin (Gautam and Mukherjee in some catchments of the Amu Darya Basin in 1989; Srivastava and Swaroop 1989; Fujita et al. Afghanistan by more than 50% during twentieth 1997, 1998, 2001a, 2001b; Kadota et al. 1997; century (Yablokov 2006), whereas in the Karakul Tangborn and Rana 2000; Dobhal et al. 2004, Catchment in Central Pamir (Tajikistan) glacier 2008; Nakawo 2009) and in the Brahmaputra Basin area increased by 4% between the late 1950s and (Sharma 1999; Aizen et al. 2002;Yao et al. 2007; year 1980 (Shetinnikov 1998). Comparison of data Yang et al. 2008; Frauenfelder and Kääb 2009). on glacier status in the Wakhan Corridor Valley in Studies conducted to date in the major basins of Afghanistan in the 1960s and 1970s (Lebedeva the HKH region cover from just 1-6% (Ganges, and Larin 1991) and in 2005+3 (Bajracharya and Brahmaputra) to 17% (Indus) of basins’ glaciated Shrestha 2011) indicates an areal increase by area. 10%, which is within the range of the assessment In the Aral Sea region, the situation is accuracy in this area. On average, in the past 50 somewhat better. In addition to a number of years, total glacier-covered area in the Amu Darya mass-balance records from the four benchmark and Syr Darya basins reduced by 15% and 22%, glaciers (Dyurgerov et al. 1995), there are data respectively, i.e., at the annual rates of 0.50%/ of assessments at the sub-catchment level based year and 0.73%/year. During the same period, on the data of areal reduction of glaciers in the the glacier area in the Mekong Basin reduced by Gassar Alay and Pamir mountains between the 26%, at an average rate of 0.86%/year. late 1950s and 1980 (Shetinnikov 1998). Thus, The basins’ coverage by sources listed in the Amu Darya and Syr Darya basins have 75% Table 6 appears reliable enough to suggest high and 35% coverage, respectively. Both basins, spatial variability of glacier areal reduction. The however, lag behind the HKH basins in another basin average annual rates of areal reduction aspect: since most monitoring programs here between 1961-1990 and 2001-2010 have been, were terminated in the 1990s, only few studies in general, two to three times lower in the extend beyond this date (Surazakov and Aizen Brahmaputra and Amu Darya basins than the 2006; Aizen et al. 2007). areal reduction rates in the Ganges, Indus, Syr Similar to the data on glacier areal reduction, Darya and Mekong basins. the data on mass-balance changes show Data on the volumetric changes of glaciers in consistency in two respects: apart from the data the study basins (Table 7) are much more rare and for Karakoram glaciers, all the available records sporadic compared to the data on areal changes. for other basins indicate that glaciers were losing The records of glacier mass-balance monitoring of mass and that the rates of mass-loss had high a short duration are available only for five glaciers spatial and temporal variability (Kääb et al. 2012; in the HKH region located in the Ganges Basin Jacob et al. 2012). This evidence is in good (Zemp et al. 2009a). The mass balance evaluations agreement with more consistent and spatially derived from RS evidence (Berthier et al. 2007; representative data on areal reduction of glaciers, Kulkarni et al. 2007; Bolch et al. 2011; Gardelle et which suggest that spatial and temporal patterns al. 2012; Kääb et al. 2012) provide a reasonably of glacier reduction are far from uniform too. good coverage for the Indus and Ganges basins, Glacier ice volume for the three HKH basins, and Lower Brahmaputra Basin, but their duration the Mekong Basin and the Afghan part of the is limited only to the past decade. The major part Amu Darya Basin in 2005+3 is evaluated by of currently available long-term records is from the Bajracharya and Shrestha (2011). However, the case studies carried out on single glaciers by field method employed for this assessment gives much measurements of mass-balance, e.g., in the Indus lower estimates with up to threefold disparities Basin (Raina et al. 1977; Singh and Sangewar (Table 2) compared to the methods used in 26 27 TABLE 6. Overview of published data on the areal changes of glaciers in the study basins in the past 50 years. Location Number of Glaciated area first Glaciated area Areal reduction Source Sample size (% of (sub-catchment) Period glaciers measured (km2) last measured (%/year) glaciated area of the (km2) major basin) INDUS BASIN Entire basin 1960-80s-2005+3 All 27,759 21,193 -0.78 Authors’s estimate; 100 Bajracharya and Shrestha 2011 Chenab Basin 1962-2001/4 359 1,441 1,110 -0.53 Kulkarni et al. 2007 5.7 Parbati Basin 1962-2001/4 88 488 379 -0.55 Kulkarni et al. 2005, 2007 1.9 (Beas Basin) Baspa Basin 1962-2001/4 19 173 140 -0.48 Kulkarni et al. 2007 0.7 (Satluj Basin) Mapam Yumco 1974-2003 n.s. 108 100 -0.23 Ye et al. 2008 0.4 (Satluj Basin) Tibet GANGES BASIN Entire basin 1960s-2005+3 All 12,541 9,012 -0.93 Authors’ estimate, Bajracharya 100 and Shrestha 2011 Chinese territory 1990-2000 1,578 2,906 2,864 -0.14 Lizong et al. 2005 22.3 Pumqu Basin 1970-2001 999 1,462 1,330 -0.30 Jin et al. 2005 11.2 Tamor Basin 1970-2000 261 474 n.s. -0.20 Bajracharya and Mool 2006 3.6 Dudh Koshi Basin Late 1950s 29 404 385 -0.12 Salerno et al. 2008 3.1 early 1990s (Glaciers < 1 km2 not included) Rongxer Basin 1970-2001 200 334 324 -0.10 Lizong et al. 2005 2.6 Garhwal Himalaya 1968-2006 69 324 306 -0.14 Bhambri et al. 2011 2.5 Saraswati/Alakhanda 75 Garhwal Himalaya 1968-2006 29 275 266 -0.08 Bhambri et al. 2011 2.1 Bhagirathi Poiqu Basin 1970-2001 153 237 232 -0.07 Lizong et al. 2005 1.8 1988-2000 153 229 n.s. -0.42 Mool et al. 2004 1.8 1986-2001 153 229 183 -1.30 Chen et al. 2007 1.8 Khumbu Himal 1962-2005 5 92 87 -0.12 Bolch et al. 2008 0.7 (Continued) 28 TABLE 6. Overview of published data on the areal changes of glaciers in the study basins in the past 50 years (Continued). Location Number of Glaciated area first Glaciated area Areal reduction Source Sample size (% of (sub-catchment) Period glaciers measured (km2) last measured (%/year) glaciated area of the (km2) major basin) BRAHMAPUTRA BASIN Entire basin 1961-90-2005+3 All 16,248 14,020 -0.48 Bajracharya and Shrestha 2011 100 Southeast Tibet 1980-2001 88 798 796 -0.01 Liu et al. 2005 4.9 Gangrigabu Range Tibet, northwest from 1970-2000 476 535 430 -0.70 Frauenfelder and Kääb 2009 3.3 Lhasa Nyaingentanglha Range, 1976-2001 521 505 475 -0.23+0.12 Bolch et al. 2010 3.1 southeast slope Northwest Himalaya 1980-2000 197 449 373 -0.80 Frauenfelder and Kääb 2009 2.7 North from Mount Everest Southern Tibet Yamzhog 1980-2000 n.s. 218 215 -0.07 Ye et al. 2007 1.3 Yumco Bhutan Himalaya 1963-1993 66 147 135 -0.27 Karma et al. 2003 0.9 1993-2003 -0.90 1980-2000 n.s. n.s. n.s. -1.10+0.2 Frauenfelder and Kääb 2009 Naimona’nyi region, 1976-2003 53 84 79 -0.31 Ye et al. 2006 0.5 Western Himalaya AMU DARYA BASIN Pamir 1960-1980 7,071 7,780 7,240 -0.35 Shetinnikov 1998 Sampled area extends beyond Amu Darya Basin Gissar-Alay Range 1960-1980 4,287 2,347 2,040 -0.65 Shetinnikov 1998 Central Pamir 1966-1980 n.s. 1,272 1,239 -0.25 Desinov and 10 Muksu Catchment 1980-2000 1,239 1,199 -0.13 Konovalov 2007; 1966-2000 1,272 1,199 -0.2 Konovalov and Desinov 2007 Central Pamir 1959-1980 n.s. 732 699 -0.23 Desinov and 5 Seldara Catchment 1959-2000 699 689 -0.07 Konovalov 2007; 1959-2000 732 689 -0.15 Konovalov and Desinov 2007 Within Tajikistan 1960-1980 7,781 8,802 7,257 -0.88 Shetinnikov 1998 70 Within Afghanistan 1970s-2005+3 All 3,018 2,566 -0.50 Lebedeva and Larin 1991; 30 Lebedeva 1997; Bajracharya and Shrestha 2011 (Continued) 29 TABLE 6. Overview of published data on the areal changes of glaciers in the study basins in the past 50 years (Continued). Location Number of Glaciated area first Glaciated area Areal reduction Source Sample size (% of (sub-catchment) Period glaciers measured (km2) last measured (%/year) glaciated area of the (km2) major basin) SYR DARYA BASIN Tributaries from 1961-1980 1,207 660 574 -0.65 Shetinnikov 1998 26 Gissar Alay Range Pskem, West Tien Shan 1968-2000 525 220 177 -0.61 Narama et al. 2009 8.7 2000-2007 177 169 -0.67 Naryn, 1943-1977 178 429 405 -0.12 Aizen et al. 2006 8.4 Ak-Shiyrak (Only 213 km2 of the sample 1977-2003 405 370 -0.33 belongs to Syr Darya Basin) 1943-2001 n.s. n.s. n.s. -0.45 Khromova et al. 2003 Southeast Fergana 1968-2000 306 190 173 -0.29 Narama et al. 2009 7.5 2000-2007 173 172 -0.07 Naryn 1990-2003 109 120 105 -0.30 Kutuzov and Shahgedanova 2009 4.8 Terskey Alatau 1968-2000 192 114 100 -0.37 Narama et al. 2009 4.5 At-Bashy 2000-2007 100 96 -0.60 the inventories of the first generation for the of the total glacier-covered area of the basin glacier status in 1961-1990 (ICIMOD 2007; (Gardelle et al. 2012). For the remaining part of WGMS and NSIDC 2009). This implies that the basin, there is a strong indication that the the largest currently available datasets are dominating tendency in the past 50 years has unsuitable for a reliable assessment of ice volume been the considerable mass loss with the rates changes between 1961-1990 and 2001-2010. varying from 200 mm/year in Hindu Kush up to More methodologically consistent data on glacier 550 mm/year in Western Himalaya (Kääb et al. mass-loss in the HKH region (Table 7) cover from 2012). The satellite-gravimetry based data too 1 to 15% of basins’ glacier-covered areas, with suggest an overall mass loss in the HKH region the largest samples being representative only for in the 2000s (Jacob et al. 2012). the past decade. The value of the reviewed mass- The assessments available for the Aral Sea balance data, therefore, is in providing a robust region are statistically more reliable, with 75% of assessment of actual ranges of mass-loss rates glacier-covered area sampled in the Amu Darya in the past 50 years. Basin and 35% in the Syr Darya Basin (Table 7). The analysis of data presented in Table 7 These data indicate that, in the period 1961-1990 suggests that glaciers in richly nourished areas of the average mass-loss rate in the region was the HKH region, particularly south-facing valleys between 300 and 700 mm/year marked by high of the Ganges and Indus basins in Central and spatial variability too. Western Himalaya, tend to lose mass more The quality and quantity of available data on rapidly, i.e., at a rate of about 600-900 mm/ areal reduction and ice loss is by far insufficient year than glaciers in Eastern Himalaya, where for drawing reliable statements on the patterns of the corresponding figures are 400-700 mm/year. temporal variability of glacier reduction. According The lowest annual mass-loss rates of around to some sources, glacier reduction rates increased 100-300 mm/year among available records are drastically in the 2000s (Ageta et al. 2001; Ye et from poorly nourished cold areas in the Upper al. 2006; Miller et al. 2012). However, this is in Brahmaputra and Upper Indus basins, with some disagreement with the recent evidence on glacier studies suggesting glaciers gaining mass in the mass-loss rates in the HKH region, which have Karakoram part of Upper Indus Basin. been in the range of 30 mm/year to 550 mm/ So-called ‘Karakoram anomaly’ has recently year in 2003-2010 (Kääb et al. 2012), i.e., at the attracted a good deal of attention, with a number level they were before 2000 (Table 7). The lack of studies (Hewitt 2005; Gardelle et al. 2012; of long-term time-series records, in particular, Cogley 2012) suggesting mass gain for glaciers hampers further analysis. For example, there in this part of the Upper Indus Basin. The CC are only seven records of extremely high rates factors that have contributed to this phenomenon of mass loss in the HKH region exceeding 1,000 have been identified by Fowler and Archer mm/year, and those are from glaciers with areas (2005) as i) an increase in winter precipitation between 0.6 km2 and 131 km2, of which three are leading to a higher accumulation, and ii) a observations made prior to the year 2000 and the decrease in summer air temperature leading to other four – after 2000 (Table 7). The sample can lower ablation. However, the largest sampled hardly be considered statistically reliable because area in the Indus Basin, where mass gain has the records are based either on single-date or been observed in the 2000s, makes only 9% single-glacier observations, or both. 30 Climate Change (CC) Impact on Glacier Systems Methods Used in CC Impact Assessments receive CC signal instantly through a vertical shift of the ELA. How much the ELA shifts Application of currently available glaciological upwards or downwards does not depend on methods for the large-scale modeling of glaciers the size of the glacier or its absolute altitude, under CC-impact faces the usual trade-off but only on the magnitude of the CC itself. between being physically accurate on the one Correspondingly, glacier sensitivity to CC is hand, and representative of large-scale areas determined by its vertical extent: under the on the other (Lamadrid and MacClune 2010; same CC signal, the glaciers stretching across a Bolch et al. 2012). Complex models designed large range of altitudes are likely to experience to simulate the behavior of a single glacier, smaller areal reduction and lose less mass e.g., mass-energy fluxes and ice flow dynamics, compared to those with a small vertical extent. depend on detailed knowledge of local topography Complexi ty of ELA model ing depends and, correspondingly, require a thorough ground- on the type of climate scenario. Under the truth verification (Kadota et al. 1997; Oerlemans simplest scenario, which assumes no changes 2001; Pithan 2011). This makes their application in precipitation and considers temperature in large-scale assessments unfeasible given the change as the only driving factor, ELA is diversity of glaciers and heterogenity of glacier expected to shift upwards approximately by environments (Radić and Hock 2011). 150-160 m for each degree of temperature rise Spatial parameters of glacier systems accompanied by global average 3%/oC increase under future CC scenarios may be assessed by in precipitation, depending on the adiabatic projecting the current trends of glacier changes, lapse rate adopted for the assessment (Benn assuming that the rates of changes in a given and Lehmkuhl 2000; Oerlemans 2001; Zemp et region remain constant or increase with time al. 2006). (Kadota et al. 1997; Vilesov and Uvarov 2001; For more complex scenarios, which include Dyurgerov 2010; Cogley 2011). This approach, precipitation change in addition to temperature however, is rather simplistic and is not frequently change (Figure 7), ELA modeling requires used. A more sound approach to quantifying assessment of future glacier mass budget future changes of glacier systems is based on components (Ananicheva and Davidovich 1997; simulations of ELA changes under CC forcing. Lebedeva 1997; Savoskul and Glazirin 2001; The ELA’s key role in glacio-climatological Xie et al. 2006; Rivera et al. 2007). Mass budget modeling is well justi f ied. First, ELA is a approach relies on the established relations of transmitter of CC signals. Second, its response precipitation and air temperature at ELA (Krenke to CC is easy to simulate. Third, the relationship 1982; Ohmura et al. 1992; Braithwaite et al. between ELA and other glacier parameters 2006). It is discussed in detail by Savoskul and too are easy to simulate (Barry 2006). With Smakhtin (2013). Glacier mass budget approach type-specific patterns of glacier geometry and is better suited for small scale assessments glacier area-altitude distribution known, ELA’s because variability of mass budget items in the vertical shift may be firmly linked with changes in mountains is extremely high. glaciers elevation interval and areal extent, and A large-scale assessment of CC impact subsequently - with changes in ice volume and on glaciers can be done in a semi-distributed glacier runoff (e.g., Savoskul and Glazirin 2001; mode, which represents a reasonable level of Aizen et al. 2007; Alford et al. 2009; Glazirin spatial resolution in dealing with the arbitrary 2009). scenarios as well as General Circulation Model The mechanism of CC impact on individual (GCM)-derived scenarios with a coarse resolution glaciers may be described as follows. Glaciers (Savoskul 2001). More complexity is required from 31 32 TABLE 7. Overview of published data on mass-balance of glaciers in the selected basins in the past 50 years. Location Period Specific mass Method Number of Glaciated area at Sample size (% of Source balance glaciers the beginning of glaciated area of (mm w.e./year) observations (km2) the major basin) INDUS BASIN Karakoram 1999-2008 +110+220 RS n.s. (5,615) 9 Gardelle et al. 2012 (approximately half to two- thirds of the sample belongs to the Indus Basin) Karakoram 2003-2009 -30+40 RS n.s. n.s. n.s. Kääb et al. 2012 Hindu Kush 2003-2009 -200+60 RS n.s. n.s. n.s. Kääb et al. 2012 Jammu and Kashmir State, 2003-2009 -550+80 RS n.s. n.s. n.s. Kääb et al. 2012 Western Himalaya Himachal Pradesh State, 2003-2009 -320+60 RS n.s. n.s. n.s. Kääb et al. 2012 Western Himalaya Siachen Glacier, Nubra 1986-91 +358 to -1,084 MB 1 987 3 Bhutiyani 1999 Valley, Karakorum average -514 Spiti-Lahaul Valley (Sutluj 2000-2004 -700 to -850 RS n.s. 915 3 Berthier et al. 2007 Basin) Western Himalaya Baltoro Glacier, Karakorum n.s. 0-250 n.s. 1 524 2 Mayer et al. 2006 Chenab Basin 1962-2001/4 -940 RS 359 n.s. 1.5-2.0 Kulkarni et al. 2007 Parbati Basin (Beas Basin) 1962-2001/4 -790 RS 88 n.s. 1.0 Kulkarni et al. 2005, 2007 Baspa Basin (Sutluj Basin) 1962-2001/4 -640 RS 19 n.s. 0.5 Kulkarni et al. 2004, 2007 2001/2 -900 n.s. 2002/3 -780 n.s. Bara Shigri Glacier 2000-2004 -1,000 to -1,300 MB 1 131 0.5 Berthier et al. 2007 Parbati Glacier 2001 -860 RS 1 11 0.04 Kulkarni et al. 2007 Chhota Shigri Glacier 2000-2004 -1,000 to -1,100 MB n.s. 11 0.06 Berthier et al. 2007 Himachal, 2002-2006 -975 MB 1 9 0.03 Wagnon et al. 2007 -1,400 to +100 2002-2010 -670+400 MB 1 9 0.03 Azam et al. 2012 (Continued) 33 TABLE 7. Overview of published data on mass-balance of glaciers in the selected basins in the past 50 years (Continued). Location Period Specific mass Method Number of Glaciated area at Sample size (% of Source balance glaciers the beginning of glaciated area of (mm w.e./year) observations (km2) the major basin) 1987-88 -154 MB 1 16 0.03 Dobhal et al. 1995 -115 or 190 Gara Glacier, Himachal 1974-83 -324 MB 1 5.2 0.02 Raina et al. 1977 Shaune Garang Glacier, 1984-89 -407 MB 1 4.9 0.02 Singh and Sangewar 1989 Himachal Gor Garang Glacier, Himachal 1976-85 -572 MB 1 2.0 0.01 Shanker 2001 Neh Nar Glacier, Kashmir 1976-84 -535 MB 1 1.2 0.005 Srivastava and Swaroop 1989; Dobhal et al. 2008 Ruling Glacier, Ladak 1980-81 -105 MB 1 0. 0.005 Srivastava et al. 2001 GANGES BASIN Uttarakhand, West Nepal, 2003-2009 -320+60 RS n.s. n.s. n.s. Kääb et al. 2012 Central Himalaya Everest area 1970-2007 -320+80 RS 10 62 0.5 Bolch et al. 2011 2002-2007 -790+520 Gangotri Glacier 1999-2004 -1,050 RS 1 126 1 Berthier et al. 2007 Glacier AX010 1978-1991 -530 MB 1 0.6 0.005 Kadota et al. 1997 Shorong Himal Nepal 1978-1999 -720 MB Nakawo 2009 1991-1996 -1,140 MB Kadota et al. 1997 1996-1999 -800 MB Fujita et al. 2001a Rikha Samba Group 1974-1994 -550 MB 8 23 0.2 Nakawo 2009 Nepal, Hidden Valley Rikha Samba Glacier 1974-1994 -630 MB 1 5.7 0.05 Fujita et al. 1997 Hidden Valley, Nepal 1998-1999 -750 MB Fujita et al. 2001b Yala Glacier, 1982-1996 -390 MB 1 2.5 0.02 Nakawo 2009 Central Nepal 1982-1994 -310 MB Fujita et al. 1998 1994-1996 -1,050 MB Fujita et al. 1998 Dokriani Glacier, 1962-1995 -150 MB 1 9.3 0.1 Dobhal et al. 2004 Uttaranchal 1992-2000 -320 MB Dobhal et al. 2008 Langtang Glacier 1987-1997 -110 MB 1 68 0.5 Tangborn and Rana 2000 Lirung Glacier 1987-1997 MB 1 7.2 0.05 Tangborn and Rana 2000 (Continued) 34 TABLE 7. Overview of published data on mass-balance of glaciers in the selected basins in the past 50 years (Continued). Location Period Specific mass Method Number of Glaciated area at Sample size (% of Source balance glaciers the beginning of glaciated area of (mm w.e./year) observations (km2) the major basin) Dunagiri Glacier, 1984-90 -1,038 MB 1 6.9 0.05 Srivastava and Swaroop Uttaranchal 1989 Tipra Bank Glacier, 1981-88 -241 MB 1 20 0.2 Gautam and Mukherjee Uttaranchal 1989 BRAHMAPUTRA BASIN East Nepal, Bhutan, 2003-2009 -300+90 RS n.s. n.s. n.s. Kääb et al. 2012 Eastern Himalaya Northwestern Himalaya 1980-2000 -260 to -590 RS 197 449 2.7 Frauenfelder and Kääb 2009 Northwest from Lhasa 1970-2000 -160 to -250 RS 576 535 3.3 Upper Brahmaputra 1970/80-2000 -300 RS n.s. n.s. n.a. Xixibangma Glacier 1991 -34 WB 1 Aizen et al. 2002 Changme Khangme 1979-1986 -298 MB 1 4.5 0.03 Sharma 1999 Glacier Sikkim Gurenhekou Glacier, UB 2006 -700 MB 1 n.s. Yao et al. 2007 Glacier No 4, Parlang Zangbo 2006-2007 -700 MB 1 13 0.1 Yang et al. 2008 Catchment, Southeast Tibet Glacier No 10, Parlang Zangbo 2006-2007 -1000 MB 1 5.1 0.03 Yang et al. 2008 Catchment, Southeast Tibet Glacier No 94, Parlang Zangbo 2006-2007 -760 MB 1 3.1 0.03 Yang et al. 2008 Catchment, Southeast Tibet Glacier No 12, Parlang Zangbo 2006-2007 -1,600 MB 1 0.95 0.01 Yang et al. 2008 Catchment, Southeast Tibet AMU DARYA BASIN Pamir 1961-1980 -650 AC 7,071 7,780 70 Shetinnikov 1998 Gissar-Alay Range 1961-1980 -450 AC 4,287 2,347 5 Shetinnikov 1998 Abramov Glacier 1968-98 -457 MB 1 26 0.05 WGMS 1991, 1993, 1994, Pamir Alay 1996 (Continued) 35 TABLE 7. Overview of published data on mass-balance of glaciers in the selected basins in the past 50 years (Continued). Location Period Specific mass Method Number of Glaciated area at Sample size (% of Source balance glaciers the beginning of glaciated area of (mm w.e./year) observations (km2) the major basin) SYR DARYA BASIN Tributaries from Gissar- 1961-1980 -450 AC n.s 660 26.2 Shetinnikov 1998 Alay Range Ak-Shiyrak 1943-1977 -240 AC n.s. 238 8.4 Aizen et al. 2007; 1977-2003 -580 RS Surazakov and Aizen 2006 1977-1999 -660 1943-1977 -256 WB Dyurgerov et al. 1995 Grigoriev Glacier, Terskey 1987-1988 -291 MB 1 9.5 0.04 Dyurgerov et al. 1995; Alatau Range WGMS 1991 Sary-Tor Glacier, Ak-Shiyrak 1985-1989 -125 MB 1 3.6 0.01 Dyurgerov et al. 1995; WGMS 1991 Notes: Methods used for the mass-balance: MB – direct mass-balance measurements; RS – planimetric measurements based on remote-sensing; WB – water budgeting; AC – models based on monitored areal changes. FIGURE 7. A schematic representation of the full glacier mass budget approach for the determination of future ELA position under the assumption of steady-state glaciers. Left: current and future mass budget components under a scenario of mean air temperature increase by +3 oC and no changes in precipitation. Right: the same under precipitation rise by 20% from its current value. Under the same warming signal, upward ELA shift from its current position is expected to be greater by 20-25% under a scenario with no changes of precipitation (right graph) compared to the scenario with increased precipitation (left graph). The mass budget curves shown here are representative for glaciers in temperate climate. 5,000 Current 5,000 Current accumulation accumulation (mm) (mm) 4,750 4,750 Future Current ablation Current ablation ELA (mm) Future (mm) 4,500 4,500 Current specific ELA Current specific mass-balance mass-balance Current 4,250 (mm) Current 4,250 (mm) ELA Future ELA Future 4,000 accumulation 4,000 accumulation (mm) (mm) 3,750 Future ablation 3,750 Future ablation (mm) (mm) 3,500 Future specific 3,500 Future specific -4,000 -2,000 0 2,000 mass-balance -4,000 -2,000 0 2,000 mass-balance Mass-balance items (mm) (mm) Mass-balance items (mm) (mm) a model to accommodate downscaled regionalized (1982) is calculated based on ELA and maximum scenarios based on GCM-outputs, which could and minimum glacier elevations, Hmax and Hmin, as be ideally met by fully distributed models, already shown in Equation (1): available for the thoroughly studied mountain regions such as the Alps (Klok et al. 2001; THAR = (Hmax – ELA) / (Hmax – Hmin) (1) Machguth et al. 2006, 2012; Kotlarski et al. 2010a, 2010b; Paul and Kotlarski 2010; Paul and The THAR shows an average value around Linsbauer 2012). In this case, the CC-scenario 0.5-0.55 and variability in the range of +20% is represented as a fine-grid set of change fields depending on climate and glacier morphological outlining future changes of baseline climatological type. Assuming that THAR and Hmax will remain variables, e.g., air temperature and precipitation. the same under CC, the future Hmin can be Correspondingly, future ELA can be represented calculated from simulated ELA positions. The as a change field depicting its future deviation corresponding glacier extent can be derived from from the baseline value. Glacier datasets in point the topographic maps, DEMs and glacier area- format derived from glacier inventories of first altitude distributions. generation are ideally suited for this purpose. The AAR introduced by Meier and Post (1962) Two principal approaches used to evaluate is a relation of the accumulation area and total changes in glacier areal extent based on the glacier area. The AAR of a steady-state glacier estimates of the future ELA are: i) toe-to-headwall is around 0.6-0.7 in humid climate and 0.5-0.6 altitude ratio1 (THAR); and ii) accumulation-area in continental climate, and its variability within ratio (AAR) (Meierding 1982; Meier and Post +20% range depends on the same factors as 1962). The THAR ratio introduced by Meierding THAR variability. Future accumulation area of 1 Expression “toe to head” is used to denote the difference between minimum and maximum glacier elevations. 36 Elevation (m) Elevation (m) a single glacier or a number of glaciers can be state of retreat instead of providing data for a new estimated from the topographic maps or glacier equilibrium, which are needed to calibrate steady- area-altitude distribution curves as a part of state models. In addition, long-term monitoring current accumulation area, which will remain above records are really rare and short-term records simulated ELA. The corresponding total area of a available are not sufficient to capture the long-term reduced glacier can be calculated assuming that trends of changes, since both CC signal and glacier future AAR will not change under CC. response to it are heavily ‘contaminated’ by the Both methods are optimally suited for large- noise produced by their short-term variations (Zemp scale modeling. The associated uncertainties et al. 2008, 2009b; Dyurgerov 2010). derived from variabil ity of AAR and THAR from one glacier to another and the quality of topographic and hypsometric data can be Review of Published CC Impact minimized by calibrating the model separately for Assessments on Glaciers in High Asia different morphological types of glaciers (Benn and Lehmkuhl 2000). The topic of retreating glaciers in High Asia The methods described above work under the became a highly politicized issue recently and, assumption of a steady-state future glacier status. as such, got a lot of attention from developing There is, however, one serious methodological agencies, international donors, governments, difficulty affecting overall certainty of large-scale NGOs, mass-media, etc. Al l those efforts simulations of glacier changes made under this (WWF 2005, 2009; Jowit 2008; Hasnain 2009; assumption. In view of the high pace of the current UNEP 2009; ADB 2009; Raina 2009; Absar CC, the uncertainties are already inherent in the 2010; Malone 2010), however, did not yield calibration of the models. A simple conceptual much in terms of clarifying the issue, since scheme explains this. Under CC forcing, ELA many publications share a high degree of instantly moves upwards from its former steady- incompetence with regards to basic knowledge state position, thus enlarging the ablation area of glacier science. Many sources consistently and reducing the accumulation area. The former mix up and misuse the terms, misread and sheds off some mass by releasing more water into exaggerate the numbers, and thereby lead to streamflow until it reaches the size corresponding general confusion. to the reduced accumulation area. The steady-state An example can be quoted from an otherwise models describe the results of this process as if it highly competent source (Cruz et al. 2007, p. occurred simultaneously with the upward shift of the 493). “Glaciers in the Himalaya are receding ELA. In reality, the glaciers need years to arrive to faster than in any other part of the world, and a new equilibrium; the larger the glacier, the more if the present rate continues, the likelihood time it takes. The process may take decades and of them disappearing by the year 2035 and even hundreds of years, depending on glacier size perhaps sooner is very high if the earth keeps and the rate of glacier mass-exchange. Meanwhile, warming at the current rate... Its total area the climate changes further and sends new signals will likely shrink from the present 500,000 to to the glaciers, some of which have not yet fully 100,000 km2 by the year 2035...” The issue responded to the previous signals. The observations stirred a hot debate (e.g., The Economist 2010; of small glaciers retreating faster compared to the Schiermeier 2010; Qiu 2010; Cogley et al. 2010) large glaciers, where the pattern of retreat is not so and IPCC (2010) admitted a number of serious much of an areal reduction but an overall thinning errors in the relevant section, with one source of ice (Fujita et al. 1997, 1998), may simply be offering an explanation (http://www.msnbc.msn. explained by the fact that to melt down the sheer com/id/34958926/ns/us_news-environment/) ice volume of large glaciers takes more time. that a typing error caused the confusion, and Thus, the data on glacier monitoring, in general, that ‘2035’ was supposed to stand for ‘2350’. give a somewhat distorted image of a continuous However, regardless of the date of future change, 37 ‘shrinkage’ of glacier area in the HKH region to national boundary of Nepal. The model was 100,000 km2 still means a double-fold expansion run for the year 2100 under the assumption of from baseline 1961-1990 glacier-covered area of a temperature rise of 3 oC with no changes in approximately 50,000 km2. precipitation. The assessment is based on semi- Professional studies that are focused distributed glacier budget modeling based on specifically on modeling future status of entire glacier area-altitude distributions in the selected glacier systems in the study basins are rare and sub-catchments. The modeling results suggest hardly compatible because of the methodological that ELA rise will be 450 m, 41-67% of the differences, particularly in constructing CC currently glaciated area will remain ice-covered scenarios. For instance, Cogley (2011) makes a and the glacier volume will decrease by 60% prognosis of glacier status in the year 2035 for (Alford et al. 2009; Alford and Armstrong 2010). the entire HKH region without subdividing it into In the Aral Sea region, the situation is separate basins. This assessment is based on slightly better than in the HKH region. The CC- assumptions of constant and accelerated rates impact assessment for the glaciers of the entire of i) decline in glacier numbers, and ii) glacier Tien Shan Mountains by Aizen et al. (2007, mass-loss. The author concludes, “If mass loss p. 1) suggests that “an increase in mean air were to remain constant at the average rate for temperature of 4 oC and precipitation of 1.1 times 1975–2008, from 3,000 to 13,000 more glaciers the current level could increase ELA by 570 m might disappear by 2035. If mass loss were to during the 21st century. Under these conditions, continue to accelerate as inferred for 1985–2008, the number of glaciers, glacier covered area, only a few thousand to a few hundred glaciers glacier volume and glacier runoff are predicted might remain in 2035. Total area and total to be 94%, 69%, 75%, and 75% of current mass would each decrease by about one-half values. The maximum glacier runoff may reach (constant-rate assumption) or three-quarters as much as 1.25 times current levels while the (constant-trend assumption)…” (Cogley 2011, p. minimum will likely equal zero.” The assessment 69). The evidence provided by repetitive glacier of ELA change is carried out in this study by inventorization (Table 1), however, contradicts the modeling of glacier mass budget (Aizen et this statement, showing an increase in number of al. 2007). Glacier area changes are simulated glaciers due to disintegration of large compound based on the assumption that glacier area-altitude valley glaciers into a number of simple valley and distributions of a large glacier system at any stage cirque glaciers. of its evolution can be approximated by a normal Xie et al. (2006, p 313) model of CC-impact distribution, the parameters of which are described on glacier systems in China suggests that by the by the vertical glacier extent in the system and end of the twenty-first century, “the glacier area of the average of the ELA. Simulated ELA is used to China will on average be reduced by 14%, 40% determine minimum elevation of glaciers and total and 60% under the climatic scenarios of 0.01, glacier-covered area under the adopted climate 0.03 and 0.05 oC /year, respectively.” The model scenario. of Xie et al. (2006) utilizes glacier mass budget CC-impact assessment on glacier systems approach, however, since it is run for a number in the Syr Darya Basin and adjacent areas of of glacier systems within national boundaries Tien Shan Mountains run under a set of similar of such a large country as China, the modeling scenarios by Savoskul (2001), suggests that outputs are hardly compatible with models run at CC-impact on glaciers will be more significant a smaller scale because of the local differences in compared to that modeled by Aizen et al. (2007). glacier sensitivity to CC. The study used the regionalized scenarios based Another large-scale assessment conducted on the outputs from two GCMs for 2070-2099, in the HKH region is that of Alford et al. (2009), downscaled to 1 x 1 km2 (Savoskul 2001). The who evaluated the glacier area changes in nine first scenario derived from HadCM2 outputs sub-catchments of the Ganges Basin within the suggests warming of 3-4 oC accompanied by a 38 precipitation increase up to 25-30% relative to the multiplying the baseline glacier vertical interval baseline (1961-1990) value. Under the second (Hmax – Hmin)1961-1990 by the factor of THAR. scenario, based on ECHAM4, air temperature will increase by 5-6 oC and precipitation only dELAmax = (Hmax – Hmin)1961-1990 * THAR (2) by 5-10%. Simulated ELA rise will be 350 m (HadCM2-based scenario) and 650 m (ECHAM4- Consequently, the threshold value of the based scenario). Under a more moderate first temperature increase from baseline value (dTmax) scenario, 23% of the glaciated area remains ice- can be estimated from dELAmax through the covered. Under the second scenario, only 4% of application of adiabatic lapse rate (ALR) as: the currently glaciated area will retain glaciers (Figure 8). dTmax = dELAmax* ALR (3) In the Amu Darya Basin, future glacier extent and water availability has been assessed only This approach allows glacier areal size for the Pyanj catchment, which contains just 3% classes to be categorized according to their of glaciated area of the entire basin (Hagg et al. sensitivity to CC, based on size class average 2011, 2013). The modeling was conducted under elevation interval. Sensitivity of a glacier size two arbitrary scenarios for the year 2050, which class to CC is defined in this report as size class suggests a temperature rise of 2.2 oC and 3.1 oC, average dTmax with a reference to its baseline with no changes in precipitation. The results show value in the period 1961-1990. As discussed a reduction of the glacier extent by 36% and 45% above, glacier elevation interval shows a variability for the two arbitrary scenarios, respectively. of +10-15% from basin to basin, and glacier areal Results of other medium-scale assessments size can, therefore, be taken as an approximation available from (Ananicheva and Davidovich of its sensitivity to CC irrespective of the location 1997; Lebedeva 1997; Glazirin 2009), do not of the glacier. Correspondingly, the average value change the general state of current research: of the highest dTmax for the largest glacier size previous studies are too scarce and, as such, do class in a glacier system quantifies the critical not allow quantification of the potential changes warming signal, which would be required for the of the glacier extent in the study basins in a complete disappearance of all glaciers in a basin, methodologically coherent manner, i.e., following and as such, it determines the entire basin glacier standard protocol for creating regional climate system sensitivity to CC in the study basins. scenarios (Lamadrid and MacClune 2010). Assessment of glacier size class and glacier system sensitivity to CC (Tables 8 and 9) has been carried under assumptions of i) increase in Glacier Sensitivity to CC precipitation from baseline value in 1961-1990 by 3% for each degree of air temperature increase Instead of evaluat ing changes in glacier from its baseline value (dT); ii) uniform ALR of 0.65 systems under a range of warming scenarios, oC/100 m of elevation; and iii) THAR of 0.55. Under quantification of CC impact on glaciers in this these conditions, an upward ELA shift (dELA) is report is approached through evaluation of expected to be 155 m for each degree of dT. a warming scenario which would cause total The accuracy of the assessment of dELA is disappearance of al l glaciers in a basin. within +15-20% range. The inaccuracies arise Conceptually, a glacier will disappear if its from the following factors: i) most regional climate ELA will move upwards beyond the highest models predict slight precipitation changes from glacier elevation (Hmax), because this means the -1% to +5% of its current value for each degree disappearance of the accumulation area that of dT (Singh et al. 2011); ii) actual variability sustains the glacier. The threshold value of an of ALR is within +15% (this report); and iii) ELA upward shift (dELAmax) that is expected THAR variability is around +10-15% (Meierding to eliminate a glacier can be estimated by 1982). Analysis of the worst-case scenario 39 FIGURE 8. Baseline (1961-1990; grey background) status of glacier system (top); and simulated changes in the Syr Darya Basin under regionalized scenarios for the period 2070-2099 (orange background), based on outputs of HadCM2 (middle) and ECHAM4 (bottom). Each dot represents a glacier and indicates its areal size. In the areas of intensive glacierization the dots are overlapping (Adopted from Savoskul 2001). 40 of the future precipitation change (Figure 7) (Cruz et al. 2007), only Syr Darya and Mekong indicates that a precipitation increase by 7%/oC basins are likely to become almost ice-free. In of air temperature rise is likely to offset dELA by the other four basins, only glaciers from the only 7-8%, which means that even in the worst largest and medium size classes are likely to case the accuracy of dELA remains within +15- survive a temperature rise by 4-5 oC from its 20% range. Therefore, the uncertainty of this baseline 1961-1990 value (Table 8). Simple assessment is enveloped by the uncertainty range projection of the current glacier mass-loss rates of currently available GCMs in projecting regional and ice volume loss suggests that, by the end of changes in future precipitation and air temperature the twenty-first century, 25 to 50% of baseline (Cruz et al. 2007). 1961-1990 ice reserves will remain in the Indus, Accord ing to our assessment , an a i r Brahmaputra, Ganges and Amu Darya basins temperature rise by 13-15 oC from the baseline (Table 9). 1961-1990 value would be required to make the Indus Basin ice-free. In the Ganges and Brahmaputra basins, an air temperature rise of Conceptual Model of Glacier Evolution 10-12 oC would be needed to melt all glaciers under CC Impact down. In the Amu Darya Basin, a warming of 6-8 oC would eliminate all glaciers. Under an A basin’s deglaciation is a complex process, air temperature increase by 4-5 oC, the most which can be conceptually modeled based on likely global air temperature rise scenario for the structural analysis of the glacier systems the end of the twenty-first century (2070-2099) conducted in this study (Tables 4 and 5). TABLE 8. Glacier sensitivity to CC according to size class. Glacier size class (km2) Vertical extent (m) dTmax ( oC) CC-sensitivity 0.1-0.3 270-350 1 High 0.3-1.0 500-600 2-3 High 1-3 750-900 3-4 High 3-10 1,000-1,350 4-5 Medium 10-30 1,500-2,000 6-8 Medium 30-100 2,200-2,500 8-10 Low 100-300 3,100-3,600 10-12 Low >300 4,000-4,500 13-15 Low Note: The color palette corresponds to the one used in Figure 4 and Table 3 TABLE 9. Sensitivity of glacier systems to CC and likely changes by the end of the twenty-first century. Basin Elevation interval of Critical value of dELA Critical value of air Ice volume, under air the largest size classes relative to 1961-1990 temperature departure temperature rise by 4-5 (m) (m) from its reference value oC (% of basin total in in 1961-1990 (oC) 1961-1990) INDUS 4,500 2,300 13-15 40-50 GANGES 3,500 1,800 10-12 25-35 BRAHMAPUTRA 3,100 1,500 10-12 35-45 AMU DARYA 2,250 1,200 6-8 20-25 SYR DARYA 1,400 700 4-5 4-8 MEKONG 1,000 500 3 0-5 41 The entire sequence of the glacier size class km2 under a HadCM2 based scenario (Savoskul distributions in the study basins (Figure 4), starting 2001). This process also involves changes in from the Indus Basin and ending up with the the glacier type: shift from simple valley glaciers Mekong Basin, can be perceived as a scenario of to the cirque type, disintegration of compound glacier system evolution under CC-impact, which valley glaciers into a number of separate glaciers unfolds in the following way. The glaciers currently of various types, etc. As a result, the larger size belonging to the smallest classes will disappear classes will become less numerous and gradually first. However, this class will be simultaneously disappear one by one with the progresseion of “refilled” by the glaciers, which currently belong deglaciation. Thus, glacier system diversity will to larger size classes, but will be reduced in decrease under CC-impact. The general pattern size under temperature increase. For instance, of glacier system stratification, however, is not the simulation results from the Syr Darya Basin likely to be affected. The smallest glaciers will still (Figure 9) suggest that in the western fringes dominate in numbers, whereas the larger ones will of the basin glaciers from the current areal size still contain the bulk of the basin’s ice. class 1-3 km2 will move to size class of 0.3-1.0 FIGURE 9. a) Maximum annual snow extent in the baseline 1961-1990 period (30-year average for January); b) Maximum annual snow extent in 2000-2008 (9-year average for January); c) Maximum annual snow accumulation in the baseline period 1961 (30-year average for March); and d) Maximum annual snow accumulation in 2000-2008 (9-year average for March). a ) b) c) d) Data source: University of Delaware Terrestrial Water Budget Archive (http://climate.geog.udel. edu/~climate/; Willmott and Matsuura 2006). 42 Seasonal Snow Cover The attention paid in this report to glaciers and Recent Changes in Seasonal Snow Cover seasonal snow is disproportional due to the and Likely CC Impact following reasons: i) significantly more complicated nature of the glacier systems, marked by large Based on this study analysis of long-term average spatial heterogeneity and pronounced diversity of data, maximum seasonal snow cover in all study individual glaciers in many respects; ii) availability basins has been observed in February, January of primary data sources: in order to arrive at (Ganges) or March (Mekong). However, maximum certain conclusions on glaciers on a basin-scale accumulation in terms of total snow volume (w.e.) level, a vast number of partially overlapping occurs in March in all the basins apart from the datasets had to be compared and analyzed, Syr Darya Basin, where it occurs in February. whereas data sources for seasonal snow Table 10 presents the average data on maximum parameters are relatively uniform, straightforward average snow cover extent and water storage and easy to access and process; and i i i ) capacity and their changes from baseline (1961- availability of published research: the papers 1990) to current (2000-2009) time intervals. The focused on glaciers in the study region by far basin monthly snow water storage capacity is outnumber those concerned with seasonal snow assessed from the monthly data on snow cover cover. depth (Figure 9). The accuracy of the estimates of the maximum seasonal water storage capacity is +40%. Data Source In the baseline period (1961-1990), seasonal snow cover in the Ganges and Mekong basins did Data for the assessment of the changes in not extend over large areas; it covered a mere 6% seasonal snow extent and depth are acquired of the basin’s area. In the Indus and Brahmaputra from the data archive of simulated terrestrial basins, maximum seasonal snow extended over water budgets available from the University of 27-28% of the basin area. In the Amu Darya and Delaware web portal on climatology (Willmott Syr Darya basins, stable seasonal snow cover was and Matsuura 2006). The data archive provides forming over the major part of basin area, i.e., Amu full coverage for the reference period 1961-1990 Darya: 66% and Syr Darya: 90%. The maximum and partial coverage for the period 2001-2010 seasonal water storage capacity of snow in the at a spatial grid of 0.5 x 0.5 degrees and a baseline period (1961-1990) was the largest in the temporal resolution of one month. The unique Indus, Amu Darya, Syr Darya and Brahmaputra character of the database (http://climate.geog. basins, making 49 km3, 34 km3, 24 km3 and 17 udel.edu/~climate/; Willmott and Matsuura 2006) km3, respectively. In the Ganges and Mekong is that it offers data on snow depth. Its only basins, it was 9 km3 and 2 km3. Ratio of maximum disadvantage is that it does not extend beyond storage capacity of seasonal snow to mean annual 2008. Therefore, the average basin totals for the flow (MAF) is significant only in the Aral Sea region period 2000-2008 are taken here to represent (42-63%) and in the Indus Basin (21%). the first decade of the current millennium (2001- Data on the changes in maximum seasonal 2010). Other principal resources of readily- snow extent (Table 10) indicate that in four basins available data based on RS materials only offer maximum seasonal snow cover extent decreased short-term data on areal extent and no evaluation by 5-15% in the past 50 years. There were no of the water storage properties of seasonal snow significant changes only in the Ganges and cover (e.g., Zhetker and Tsarev 1991; Gurung et Mekong basins. However, the maximum seasonal al. 2011; Butt 2012). snow water storage capacity in all basins apart 43 from Mekong shows much more significant basins apart from the Mekong Basin, where it reduction in the range of 9-27%. was negligible in comparison to mean annual flow. Maximum snow coverage and its water The changes are particularly large in the Indus, storage capacity are indicative of changes in Ganges, Amu Darya and Syr Darya basins, where total winter precipitation. The observed monthly maximum seasonal storage capacity of snow differences between baseline and current time decreased by 21-27% (Table 10). periods (Figure 10), however, reflect the impact of At present, the available large-scale studies an overall increase in air temperature. In the Aral addressing CC impact on snow in the study Sea region, November, February and March are basins are too few (Zhetker and Tsarev 1991; the months with the most pronounced differences Singh and Bengtsson 2003; Gupta et al. 2005; between baseline and current snow cover extent. Barnett et al. 2005; Immerzeel et al. 2009, 2010; In the HKH region, the months with the largest Tahir et al. 2011; Gurung et al. 2011), and do not differences are October, April and May. These provide sufficient data for a comprehensive and findings are indicative of the overall shortening methodologically consistent assessment of future of the duration of stable seasonal snow cover changes. The CC impact on seasonal snow in at lower altitudes, where at present snow cover mountain catchments can be conceptualized as forms later and melts earlier compared to the extrapolation of the tendency already observed, baseline period (1961-1990). This explains why i.e., decrease in areal extent, water storage maximum seasonal water storage capacity of capacity and duration (Barnett et al. 2005; snow has significantly reduced in all the study Bookhagen and Burbank 2010). TABLE 10. Changes in average maximum seasonal snow area and water storage capacity in the past 50 years. Basin Maximum seasonal extent Difference Maximum seasonal water storage Difference (long-term mean for 1961-1990) between capacity between 1961-1990 ( long-term mean for 1961-1990) 1961-1990 and 2001- and 2001- Month Area (km2) Share in 2010 (% of Month Volume, (% of 2010 (% of basin total 1961-1990 (km3 w.e.) MAF) 1961-1990 area (%) value) value) INDUS February 341,191 28 -10 March 49 21 -21 GANGES January 59,134 6 -2 March 9 2 -18 BRAHMAPUTRA February 184,678 27 -5 March 17 2 -9 AMU DARYA February 527,049 66 -7 March 34 42 -24 SYR DARYA February 413,428 90 -15 February 24 63 -27 MEKONG March 50,209 6 3 March 1.9 0.4 5 Data source: Snow extent and duration: University of Delaware Terrestrial Water Budget Archive (http://climate.geog.udel.edu/~climate/; Willmott and Matsuura 2006). MAF: AQUASTAT database of the Food and Agriculture Organization of the United Nations (FAO) (http://www.fao.org/nr/water/aquastat/main/index.stm); United Nations Environment Programme (UNEP) Global Environment Outlook (GEO) data portal (http://geodata.grid.unep.ch/). 44 FIGURE 10. Changes in monthly mean areal extent (km2) of seasonal snow from 1961-1990 to 2001-2010. 600,000 600,000 OCT 1961-1990 FEB 1961-1990 OCT 2000-2008 FEB 2000-2008 500,000 500,000 400,000 400,000 300,000 300,000 200,000 200,000 100,000 100,000 0 0 S S A A A DU GE TR RYA YA NG S S RA NG IN GAN DA AR KO DU GE UT ARY RY APU D E IN U R M GAN AP D D A EKO U HM YR M AM SY HM AM S BRA BRA 600,000 600,000 NOV 1961-1990 MAR 1961-1990 NOV 2000-2008 MAR 2000-2008 500,000 500,000 400,000 400,000 300,000 300,000 200,000 200,000 100,000 100,000 0 0 US ES A S S YA A G RY DU GE RA A YA NG IN D NG TR AR ON IN UT RY GA APU EK GAN AP DA AR M U D R D A M U D EKO M AH AM SY HM AM SYR BR BRA 600,000 600,000 DEC 1961-1990 APR 1961-1990 DEC 2000-2008 APR 2000-2008 500,000 500,000 400,000 400,000 300,000 300,000 200,000 200,000 100,000 100,000 0 0 S S A A G DU GE RA YA NG US ES TR YA A IN UT RY AR IN D NG AR RY ON GAN AP DA U D EKO M GA APU HM M U D R D A EK M AM SYR AH AM SY BRA BR 600,000 600,000 JAN 1961-1990 MAY 1961-1990 JAN 2000-2008 MAY 2000-2008 500,000 500,000 400,000 400,000 300,000 300,000 200,000 200,000 100,000 100,000 0 0 S S A A G DU GE RA RY YA NG US ES A YA RY ON IN UT GAN AP DA AR EKO IN D NG TR AR EK U D M GA APU M HM AM SYR M U D R D A AH AM SY BRA BR Data source: University of Delaware Terrestrial Water Budget Archive (http://climate.geog.udel.edu/~climate/; Willmott and Matsuura 2006). 45 Long-term mean monthly snow covered area (km3) Conclusions 1. For the first time, the baseline (1961-1990) changes may be slightly improved through and current (2001-2010) water storage cross-validating them by records of annual properties of glacier systems and seasonal rates of glacier mass-loss, which have much snow cover have been systematical ly better accuracy (within +10%). However, in analyzed at the basin scale for six major most basins, these data are available only Asian river basins. The assessment of water from very small samples. The best possible storage-related properties of glacier systems accuracy for basin seasonal snow water such as number of glaciers, glacier-covered storage capacity is in +40% range. The CC area and ice volume, relies on the meta- sensitivity of glaciers is estimated with an database for 48,607 glaciers, which has been accuracy of +20%. compiled specifically for this study based on 3. The analysis of areal size class frequency all available glacier inventories and represents distributions of spatial parameters of individual a new data product in its own right. Seasonal glaciers reveals that glacier systems are snow cover is characterized by maximum structured in a broadly similar pattern across seasonal areal extent and maximum seasonal all the study basins. Approximately half to water storage capacity assessed by analysis two-thirds of the total basin ice volume is of monthly mean simulated values derived concentrated in just a few dozens of the from the terrestrial water budget data archive largest glaciers, which in sum constitute the of Delaware University, USA. first few percent of total glacier numbers. The 2. Critical review of the currently available larger the glacier system the more structurally data and methods of assessment of water diverse it is in terms of differences between storage properties of glaciers and snow cover the smallest and the largest glaciers, number suggests the following range of accuracies. of glacier areal size classes and glacier The methods of glacier surveys make possible morphological types, vertical interval of glacier the accounting of every single glacier and occurrence, ELA variability, etc. measurements of glacier area with an 4. The observed and reconstructed changes accuracy of +5%. However, the total number in glacier systems between 1961-1990 and of glaciers per basin and the total glacier 2001-2010 are marked by the overall areal covered area as a 30-year or decadal mean reduction and ice volume loss. At the same should be considered as an approximation time, the number of glaciers in most basins with +20% accuracy, since these parameters (e.g., the Indus, Ganges, Mekong basins may change from year to year and hence and parts of Amu Darya Basin), increased are affected by the date of glacier survey, due to disintegration of large glaciers into the size of the smallest glacier class included a number of smaller ones. The monitored in the inventories and apart from the human changes in glacier systems (areal reduction, factor in data processing. Areal extent of mass loss, changes in glacier numbers) seasonal snow is assessed here with a spatial are far from uniform in temporal and spatial resolution of 0.5 x 0.5 degrees. Estimates of respects, suggesting that spatial and temporal total basin glacier ice volume have a very magnitude of the recent changes are highly low accuracy of +50-70% due to systematic variable due to high regional and local errors in the models employed for ice volume variability of the recent CC signal. evaluation in the currently available glacier inventories, and the extreme scarcity of 5. The assessment of areal reduction of the field measurements of glacier thickness. glaciers between 1961-1990 and 2001-2010 The estimates of total basin ice volume made in this study is based on data obtained 46 from i) repetitive glacier inventories; ii) data of 1961-1990) that is likely to lead to complete glacier monitoring; and iii) published research meltdown of all glaciers is 13-15 oC in the based on remote sensing. In the Indus, Indus Basin, 10-12 oC in the Ganges and Brahmaputra, Ganges and Mekong basins, Brahmapura basins, and 6-8 oC in the Amu such records are available for 100% of the Darya Basin. For comparison, glacier system basin glacier-covered area, but in the basins of in the Mekong Basin is projected to dissapear the Aral Sea region the records are available under a temperature warming of 3 oC, and in only for 60-75% of the glacier-covered area. the Syr Darya Basin, the glacier system will be The estimated mean annual rates of basin reduced to a dozen of glaciers under warming total glacier-covered area reduction vary from of 4-5 oC. 0.4-0.5%/year (the Brahmaputra and Amu 9. Under the most widely accepted scenario of Darya basins) to 0.7-0.9%/year (all the other CC by the end of the twenty-first century, basins). In some catchments of the Indus and i.e., 4-5 oC temperature rise accompanied by Amu Darya basins, however, the observations 3%/oC increase in precipitation, large glacier indicated either no pronounced reduction in systems are likely to retain 40-50% (Indus glacier area or even a slight increase. Basin) to 20-25% (Amu Darya Basin) of their 6. With regards to ice volume changes in the baseline ice reserves. past 50 years, much less data are available. 10. Maximum seasonal snow cover area in the The poorest area coverage is in the Ganges baseline 1961-1990 varied from 6% of total Basin, where just 1% of the glaciated area is basin area (the Ganges and Mekong basins) sampled, and in the Indus and Brahmaputra to 90% (Syr Darya Basin). Maximum seasonal basins it is 15% and 6%, respectively. In the water storage capacity in the same period was Amu Darya and Syr Darya basins, the sampled between 49 km3 (the Indus Basin) and 2 km3 area is 75% and 35% of ice-covered terrain, (the Mekong Basin). In 50 years, maximum respectively. On average all the glacier systems seasonal snow extent decreased in most lose glacier mass with the estimated annual basins apart from the Ganges and Mekong rates of ice volume losses varying from 0.4%/ basins. The maximum seasonal water storage year (Brahmaputra) to 1%/year (Ganges). capacity of seasonal snow has reduced too 7. It is shown that the conceptual model of in most study basins apart from the Mekong glacier system evolution under CC, in the Basin. The CC impact on seasonal snow in long run, will be the decrease of system’s mountain catchments can be conceptualized as diversity in terms of number of areal size extrapolation of the tendency already observed, classes, vertical extent of glacier occurence, i.e., decrease in maximum seasonal areal extent differences between the largest and the and maximum seasonal water storage capacity. smallest glaciers, etc. The principal system 11. Table 11 summarizes the baseline (1961- structure, however, is not likely to change: 1990) and current (2001-2010) states and small glaciers will dominate in numbers, possible future changes of water storage-related whereas the bulk of a basin’s ice will remain properties of glacier systems and seasonal snow in the largest and medium-sized glaciers. cover in the study basins more specifically. 8. It has been found that glacier system sensitivity 12. Water storage capacity of glacier systems to CC depends on the diversity of the glacier in the study basins by far exceeds that of systems' structure. Correspondingly, highly seasonal snow cover, from approximately diverse glacier systems in the Indus, Ganges, 120-fold (in the Indus Basin) to sixfold (in Brahmaputra and Amu Darya basins have low the Mekong Basin). However, this proportion to medium sensitivity to CC change. The air difference has not changed significantly in the temperature rise (relative to baseline period past 50 years. 47 TABLE 11. The summary of current state, recent changes, and possible CC-impacts on glacial systems and seasonal snow cover. Indus Ganges Brahmaputra Amu Darya Syr Darya Mekong BASELINE (1961-1990) Number of glaciers 13,605 6,719 11,996 9,749 3,429 380 Glacier-covered area (km2) 27,759 12,541 16,248 11,101 2,522 316 Glacier-covered area (% of basin area) 2.7 2.7 2.4 1.7 0.6 0.04 Ice volume (km3 w.e.) 3,839 1,243 1,487 648 133 18 Maximum elevation of glacier occurrence (m) 8,500 8,800 8,300 7,300 5,200 6,700 Minimum elevation of glacier occurrence (m) 2,400 3,600 3,700 2,100 2,400 2,700 Area of the largest glacier area (km2) 1,056 263 207 156 70 16 Maximum seasonal snow areal extent (km2) 341,191 59,134 184,678 527,049 413,428 50,209 Maximum seasonal snow extent (% of basin area) 28 6 27 66 90 6 Maximum seasonal snow water storage capacity (km3 w.e.) 49 9 17 34 24 2 CURRENT STATUS (2001-2010) Number of glaciers 18,495 7,963 11,497 n.a. n.a. 482 Glacier-covered area (km2) 21,193 9,012 14,020 8,736 1,967 235 Ice volume (km3 w.e.) 2,696 794 1,303 538 105 10.7 Maximum seasonal snow areal extent (km2) 307,807 57,963 175,155 488,289 349,358 51,465 Maximum seasonal snow extent (% of basin area) 25 6 26 61 76 6 Maximum seasonal water storage capacity of snow (km3 w.e.) 39 7 15 26 18 2 CHANGES BETWEEN 1961-1990 and 2001-2010 Reduction of glacier-covered area relative to 1961-90 (%) -24 -28 -14 -15 -22 -26 Ice volume reduction relative to 1961-90 (%) -30 -33 -11 -17 -21 -40 Changes of maximum seasonal snow areal extent relative to 1961-90 (%) -10 -2 -5 -7 -15 3 Changes of maximum seasonal snow water storage capacity, relative to 1961-1990 (%) -21 -23 -9 -24 -27 0 SENSITIVITY TO CC AND POSSIBLE CC IMPACT Glacier system sensitivity to CC Low Low Low Medium High High Critical air temperature rise required for overall glacier disappearance (oC) 13-15 10-12 10-12 6-8 4-5 3 Number of glaciers likely to survive air temperature rise by 4-5 oC (end of twenty-first century) 268 251 260 118 11 3 Share of basin total ice volume concentrated in the above glaciers (% of basin total for 1961-1990) 85 68 63 47 22 23 Ice volume under air temperature rise by 4-5 oC (% of basin total for 1961-1990) 50 35 45 20 8 7 48 References Absar, M. 2010. 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USSR, Tashkent: Central Asian Hydrometeorological Institute. 134 pp. (in Russian). 61 IWMI Research Reports 149 Glacier Systems and Seasonal Snow Cover in Six Major Asian River Basins: Water Storage Properties under Changing Climate. Oxana S. Savoskul and Vladimir Smakhtin. 2013. 148 Evaluating the Flow Regulating Functions of Natural Ecosystems in the Zambezi River Basin. Matthew McCartney, Xueliang Cai and Vladimir Smakhtin. 2013. 147 Urban Wastewater and Agricultural Reuse Challenges in India. Priyanie Amerasinghe, Rajendra Mohan Bhardwaj, Christopher Scott, Kiran Jella and Fiona Marshall. 2013. 146 The Water Resource Implications of Changing Climate in the Volta River Basin. Matthew McCartney, Gerald Forkuor, Aditya Sood, Barnabas Amisigo, Fred Hattermann and Lal Muthuwatta. 2012. 145 Water Productivity in Context: The Experiences of Taiwan and the Philippines over the Past Half-century. Randolph Barker and Gilbert Levine. 2012. 144 Revisiting Dominant Notions: A Review of Costs, Performance and Institutions of Small Reservoirs in Sub-Saharan Africa. Jean-Philippe Venot, Charlotte de Fraiture and Ernest Nti Acheampong. 2012. 143 Smallholder Shallow Groundwater Irrigation Development in the Upper East Region of Ghana. Regassa E Namara, J.A. Awuni, Boubacar Barry, Mark Giordano, Lesley Hope, Eric S. Owusu and Gerald Forkuor. 2011. 142 The Impact of Water Infrastructure and Climate Change on the Hydrology of the Upper Ganges River Basin. Luna Bharati, Guillaume Lacombe, Pabitra Gurung, Priyantha Jayakody, Chu Thai Hoanh and Vladimir Smakhtin. 2011. 141 Low-cost Options for Reducing Consumer Health Risks from Farm to Fork Where Crops are Irrigated with Polluted Water in West Africa. Philip Amoah, Bernard Keraita, Maxwell Akple, Pay Drechsel, R.C. Abaidoo and F. Konradsen. 2011. 140 An Assessment of Crop Water Productivity in the Indus and Ganges River Basins: Current Status and Scope for Improvement. Xueliang Cai, Bharat R. Sharma, Mir Abdul Matin, Devesh Sharma and Sarath Gunasinghe. 2010. 139 Shallow Groundwater in the Atankwidi Catchment of the White Volta Basin: Current Status and Future Sustainability. Boubacar Barry, Benony Kortatsi, Gerald Forkuor, Murali Krishna Gumma, Regassa Namara, Lisa-Maria Rebelo, Joost van den Berg and Wolfram Laube. 2010. Electronic copies of IWMI's publications are available for free. Visit www.iwmi.org/publications/index.aspx Postal Address P O Box 2075 Colombo Sri Lanka Location 127 Sunil Mawatha Pelawatta Battaramulla Sri Lanka Telephone +94-11-2880000 Fax +94-11-2786854 E-mail iwmi@cgiar.org Website www.iwmi.org ISSN: 1026-0862 ISBN: 978-92-9090-766-4