Environ Earth Sci DOI 10.1007/s12665-012-1736-6

ORIGINAL ARTICLE

Lake change and its implication in the vicinity of Mt. Qomolangma (Everest), central high , 1970–2009

Yong Nie • Yili Zhang • Mingjun Ding • Linshan Liu • Zhaofeng Wang

Received: 4 March 2011 / Accepted: 18 May 2012 Ó Springer-Verlag 2012

Abstract High-elevation inland lakes are a sensitive trend (correlation coefficients of 0.68–0.91), with larger indicator of climate change. The extents of lakes in lakes having smaller shrinkage rates, which implies a Mt. Qomolangma region have been extracted using the higher stability (in the order of Peiku [ Langqiang [ object-based image-processing method providing 6–24 Cuochuolong). Lake Peiku, the largest lake, decreased images during 1970–2009. Combined with data from five 10.38 km2 (3.69 % or 0.27 km2 year-1) during 1970– meteorological stations and three periods’ glacier data, the 2009. The changes in Lake Peiku indicate that precipitation inter-annual and intra-annual lake changes and responses to is its main source of supply with glacier melt water a key climate and glacier change have been analyzed. The results supplement. Meanwhile, Lake Como Chamling reduced show that the lakes have shrunk overall, with clear inter- by 13.12 km2 (19.79 %) during 1974–2007, with strong annual and intra-annual fluctuations during 1970–2009. In shrinkage–expansion–shrinkage–expansion fluctuations. general, there appeared a trend of slight shrinkage in the Overall, lakes in the vicinity of Mt. Qomolangma are a 1970s, distinct shrinkage around 1990, general expansion sensitive good indicator to climate change. in 2000 and accelerated decrease after 2000. Lake Peiku and neighboring lakes show a highly consistent change Keywords Lake change Everest Qomolangma Remote sensing Climate change Glacier

Y. Nie Y. Zhang (&) L. Liu Z. Wang Introduction Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, A11 Datun Road, Mountain glaciers and inland lakes are sensitive indicators Anwai, Chaoyang , 100101, of climate change (Shi and Ren 1990; Liu et al. 2009). e-mail: [email protected] Lakes on the are highly sensitive to climate L. Liu change and very vulnerable to human activities (Ding et al. e-mail: [email protected] 2006). The extent and water level of lakes on the Tibetan Z. Wang Plateau reveal the water-balance processes and the coupled e-mail: [email protected] climate–environment relationships at the river basin scale Y. Nie (Liu et al. 2009, 2010; Zhang et al. 2011). Lake change Institute of Mountain Hazards and Environment, directly impacts on surrounding villages and pastures, and Chinese Academy of Sciences, 9-4 South Renmin Road, long-term time series monitoring can promote the interests Chengdu 610041, China e-mail: [email protected] of local people, provide reliable evidence of climate change, and further promote the understanding of water M. Ding resource changes and eco-environmental effects in the Key Laboratory of and Watershed Tibetan Plateau. Recently, advances have been made in the Research, Ministry of Education, Jiangxi Normal University , Nanchang 330022, China field of in situ observations and biogeochemical analyses e-mail: [email protected] of highland lakes, such as major ionic composition of 123 Environ Earth Sci precipitation, heat and water exchanges, bathymetry and and intra-annual fluctuations of lake extent, which can lead water quality, lake core and stable isotope of Lake Nam Co to large uncertainties. To reduce the uncertainty in under- ( et al. 2007; Lin et al. 2008;Mu¨gler et al. 2008; Haginoya standing the lake changes and promote understanding of the et al. 2009; Wang et al. 2009a, submarine topography, processes and mechanisms of lake change on the Tibetan physicochemical features and lake core of Lake Puma Yum Plateau, this paper used multi-source and multi-temporal Co (Murakami et al. 2007; Wang et al. 2009b; Zhu et al. RS data from 1970 to 2009, and adopted object-based 2010), heat and water balance as well as oxygen isotope of interpretation to build a database of 6–24 periods of lake Lake Yamdrok-tso (Gao et al. 2009; Xu et al. 2009), water data in the Mt. Qomolangma region. From this database, the cycle characteristics of Manasarovar Lake (Yao et al. 2009). inter-annual and intra-annual changes were analyzed and Overall, the long-term hydrological and meteorological the coupled relationships between lake, climate and glacier observation data of lakes in the Tibetan Plateau are very change were determined. limited and have high costs due to the remote location of the lakes. However, the characteristics of remote sensing, including real-time observation, large coverage, objectivity, Study area and low cost make it an optimal method for monitoring the dynamics of lake change over multiple periods and large The Mt. Qomolangma (Everest) National Nature Preserve areas in remote regions (Sheng et al. 2008). (QNNP) is the highest in the world and is Remote sensing (RS) images such as MSS, TM, ETM, located on the Chinese side of Mt. Qomolangma, in the ASTER and hyperspectral data have been proven to suitable central high Himalayas (Cidanlunzhu 1997; Nie et al. for lake-change research (Nolan et al. 2002; Matthews et al. 2010). Its location makes it an ideal place to conduct 2008; Jones et al. 2009; Riaza and Mu¨eller 2010). The RS research on water and energy budgets, as well as difference results from the Tibetan Plateau show that total area and in ecosystem structure and function under a changing number of lakes increased from the 1970s to 2000 with an global environment (Zhang et al. 2007, 2012; Nie and increased overall area of 3,316.52 km2 (Wu et al. 2007a, Li 2011). The QNNP covers four administrative counties and evident regional differences in lake area and level (, Gyirong, Nyalam and Dinggye), and a total study changes (Wu et al. 2007a; Zhang et al. 2011). The regional area is 3.6 9 104 km2, including the entire QNNP and differences also have been confirmed by typical lake some surrounding unprotected area (Nie et al. 2010). There changes on the Tibetan Plateau between 1969 and 2001, are only two meteorological stations in the study area where lakes that are mainly supplied by glacier melt water, (in Tingri and Nyalam counties), so to determine climate such as Nam Co, Lake Selin and surrounding lakes change more accurately, data from three additional mete- expanded, but lakes in the source region of the Yellow orological stations (in Latse, Xigaze and counties) River, mainly supplied by precipitation, shrank (Lu et al. near the QNNP have also been used. There are four major 2005). In , both expansion and shrinkage of lakes in river basins in the study area, Pengqu, Gyirong Zangbo, typical regions have been reported. For example, lakes in Poiqu and Yarlung Zangbo rivers, shown in Fig. 1 (Nie the Yamdrok-tso river basin shrank 35 km2 while the gla- et al. 2010; Nie and Li 2011). cier area decreased 1.5 % between 1980 and 2000 (Ye et al. Lake Peiku, the largest lake in the study area, is a typical 2007), and the total area of lakes in the Mapam Yumco river tectonic lake which is controlled by two east–west and basin also reduced from 1974 to 2003 (Ye et al. 2008), but north–south structures with an elevation of 4,590 m and a the maximum expansion rate of Lake Cuona and nearby current area of 270.71 km2 (RS data source October 2009). lakes reached 27.1 % from 1998 to 2005 (Liu et al. 2009), The lake basin is a closed with an area of and Lake Nam Co has expanded by 51.84 km2 (an increase 2,397.40 km2. The ratio of Lake Peiku area to drainage of 2.7 %) from 1970 to 2006 (Liu et al. 2010). Against the area is 11.3 % in 2009. The Daerqiu and Woquma rivers, background of widespread glacier retreated, the shrinkage originating from Mt. Peikukangri in the southwest and the and expansion of lakes in corresponding river basins indi- Barixiong River originating from Mt. Dashishan in the east cates that the process of lake change in river basins supplied flow into Lake Peiku (Fig. 1). Glacier area accounts for by melt water is very complex (Ding et al. 2006; Kang et al. 5.6 % of the whole lake basin. Both precipitation and 2010). Thus, the relationships between lakes, climate and glacier melt water are the water supply for Lake Peiku and glaciers needs to be further explored (Wu and Zhu 2008; Langqiang. Lake Langqiang is located to the southeast of Kang et al. 2010), and research into lake change in the Lake Peiku where they are separated by secondary lacus- vicinity of Mt. Qomolangma has not yet been reported. trine terraces. The small lakes Helin and Cuochuolong The trend of lake change can only be roughly reflected by are located to the west and northwest, respectively, of two or three periods of RS data, so, a detailed understanding Lake Peiku (Cidanlunzhu 1997) and only supplied by of processes and features is very poor due to inter-annual precipitation. 123 Environ Earth Sci

Fig. 1 Location of the study area and distribution of the water system

Lake Como Chamling, the second largest lake in the maps at the 1:100,000 scale based on aerial photographs Mt. Qomolangma region, is a saline lake located in the east (acquired time of October) in the Lake Peiku region in of Dinggye County and only supplied by precipitation, and 1970 and Lake Como Chamling in 1974. There are images there are natural pastures and cultivated land around the for 24 time periods at Lake Peiku including continuous RS lake. The four counties in the QNNP primarily consist data during 1999–2009, and six time-period images cov- of agricultural communities, with a total population of ering Lake Como Chamling. approximately 97,000 in 2008 (Tibet Bureau of Statistic The malfunction of the scan lines corrector (SLC-off) in 2009). Landsat-7 ETM? after 31 May 2003 has resulted in the emergence of strips which have damaged the quality of the ETM? data. The image-processing methods for strip Materials and methods removal often include local adaptive regression models or fixed window regression analysis models which are resto- Data sources ration techniques for a single image, which result in some error, but a greatly improved image. The USGS has pro- The RS data used in this study were mainly sourced from vided a mask file for each band to better analyze the ETM? the US Geological Survey (USGS), and partly from the data. The ETM? data with L1T product used here have all Global Land Cover Facility (GLCF). There are 28 RS been processed by an SLC-off repair method, and rectified satellite images and several topographical maps from two with systematic radiance and geometric transformation time periods used to determine the main lake extents from based on ground-control positions, including some images 1970 to 2009 in the Mt. Qomolangma region. The infor- which have been terrain-rectified based on precise ground- mation related to each image is listed in Table 1, including control position and high-quality digital elevation model. 27 images from Landsat satellite (MSS, TM, ETM), one All data have been validated or geo-registered using exactly ASTER image (orbit number: 11004), and topographical rectified reference images and topographic maps to ensure

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Table 1 RS data used to derive lake extents Order Path Row Date Sensor Resolution Order Path Row Date Sensor Resolution source (m) source (m)

1 – – 1970 T-map – 16 141 40 14 October 2003 ETM 30 2 152 40 15 December 1972 MSS 60 17 141 40 1 November 2004 ETM 30 3 152 40 2 January 1973 MSS 60 18 – – 21 February 2005 ASTER 15 4 151 40 2 December 1976 MSS 60 19 141 40 12 November 2005 TM 30 5 151 40 7 January 1977 MSS 57 20 141 40 14 October 2006 TM 30 6 151 40 20 March 1977 MSS 60 21 141 40 26 November 2007 ETM 30 7 151 40 18 June 1977 MSS 60 22 141 40 28 November 2008 ETM 30 8 151 40 16 September 1977 MSS 60 23 141 40 16 June 2009 TM 30 9 141 40 12 October 1988 TM 28.5 24 141 40 30 October 2009 ETM 30 10 141 40 30 November 1991 TM 30 25 – – 1974 T-map – 11 141 40 24 March 1993 TM 30 26 150 40 19 December 1976 MSS 57 12 141 40 6 December 1999 ETM 30 27 139 40 10 November 1989 TM 28.5 13 141 40 22 November 2000 ETM 28.5 28 139 40 8 November 2000 ETM 28.5 14 141 40 24 October 2001 ETM 30 29 139 40 14 November 2005 TM 30 15 141 40 28 November 2002 ETM 30 30 139 40 11 October 2007 ETM 30 T-map, topographic map; –, no orbit number

Table 2 Meteorological Order Site Latitude Longitude Elevation First Usage Missing stations and observation data (°N) (°E) (m) year period used 1 Nyalam 28°110 85°580 3,810 1966 1970–2009 – 2 Tingri 28°380 87°050 4,300 1959 1971–2009 1968–1970 3 Gyantse 28°550 89°360 4,040 1956 1970–2009 – 4 Xigaze 29°150 88°530 3,836 1955 1970–2009 – 5 Latse 29°050 87°360 4,000 1977 1978–2009 – their reliability in lake-change analysis. The lake extents Methods can be clearly extracted based on their clear borders. Other data used in this study include: (1) five meteoro- Remote-sensing processing method logical stations (Tingri, Nyalam, Xigaze, Latse and Gyantse) with ground observations until 2009 are provided Traditional RS processing methods involve human visual by the Climate Data Center of the National Meteorological interpretation, supervised classification, unsupervised clas- Information Center in the China Meteorological Adminis- sification and other similar methods. Visual interpretation, tration (Table 2). Daily average air temperature, maximum which has high accuracy, has been widely employed to temperature, minimum temperature, average relative extract lake information (Lu et al. 2005; Ye et al. 2007, 2008; humidity, average wind speed, number of sunshine hours, Wu et al. 2007a; Wu and Zhu 2008; Bianduo et al. 2009; Liu and precipitation have been used to calculate daily et al. 2009). However, this method is affected by low effi- evapotranspiration, annual aridity index and other climate ciency, high cost over large areas, mismatch of borders indicators. Of the meteorological stations, site records at (Racoviteanu et al. 2008), and poor locations (Huang et al. Tingri were absent for 1968–1970, and there were no data 2004). The results obtained by automatic pixel-based clas- for the Latse site between 1970 and 1978. The calculations sification methods, such as supervised classification (Bolch of mean precipitation, evapotranspiration and aridity index et al. 2008) or unsupervised classification, are very limited were based on the data from the other four sites in with a strong salt and pepper effect (Blaschke and Hay 2001). Mt. Qomolangma region. (2) Glacier data from 1976 to Recently, object-oriented image processing has been repor- 2006 which have been derived from 23 RS images. (3) ted in theoretical and practical fields (Baatz and Scha¨pe Field data which have been collected by our team since 2000; Blaschke and Hay 2001; Benz et al. 2004; Van-Coillie 2005. (4) Other materials, such as topographical maps at et al. 2007; Rutzinger et al. 2008; Jones et al. 2009; Nie the 1:100,000 scale. and Li 2011), and has gradually become the advanced RS 123 Environ Earth Sci

image-processing technology, and is used widely due to its Rn has been modified by some Chinese scholars using classification accuracy and reduced salt and pepper effect national climate observatory data and field data (Zuo et al. (Im et al. 2008; Castillejo-Gonza´lez et al. 2009). 1963; Yin et al. 2008). In this study, the equation for cal-

Object-based image processing has been used to extract culating Rn, with coefficients that have been calibrated by the primary lake information during 1970–2009, and the surface observations at 81 national meteorological stations detailed workflow and processes are based on Nie et al. from 1971 to 2000 and field experimental observations on (2010). The effects of ETM strips on lake extents have been the TP by Yin et al. (2008): "# modified manually, and several lake extents sometimes n T4 þ T4 were not available, because heavy snow affected the data Rn ¼ 0:77 0:2 þ 0:79 Rso r max;K min;K making extraction impossible. Meanwhile, three time peri- N 2 ffiffiffiffi ods of glacier and glacial lake data have been derived from p n ðÞ0:56 0:25 ea 0:1 þ 0:9 ð2Þ RS the latest Chinese glacial lake inventory. The definition N of a glacial lake in the inventory includes three items: (1) where the variables are defined as follows: n, actual sun- source: lakes that are mainly supplied by melt water from shine duration (hours); N, maximum possible sunshine modern glaciers, either directly or indirectly; lake basins duration (hours); n/N, relative sunshine duration; formed by modern glaciation but supplied mainly by pre- -2 -1 Rso, extra-terrestrial radiation (MJ m day ); r, Stefan– cipitation. (2) Area: the minimum area for identification by Boltzmann constant (4.903910-9 MJK-4 m-2 day-1); TM or ETM is 0.0225 km2, or 25 pixels. (3) Location: the Tmax,K, maximum absolute temperature during the 24-h distance to a modern glacier is\10 km and the elevation of period (K); Tmin,K, the minimum absolute temperature the lakes is more than 3,000 m (Wang et al. 2010). during the 24-h period (K); ea, actual vapor pressure (kPa). The aridity index (AI) is a numerical indicator of the Calculation of climate indicators degree of dryness in the climate at a given location. The boundaries that define various degrees of aridity are: Precipitation and evapotranspiration are the important AI B 0.99 humid zone; 1.0 \ AI \ 1.49 sub-humid zone; indicators for measuring the degree of regional water gain 1.5 \ AI \ 3.99 semi-arid zone, and AI C 4.00 arid zone or loss. Evapotranspiration (ETO, also called potential (Wu et al. 2005). The aridity index can be calculated using evapotranspiration) is an indicator of atmospheric evapo- the following equation (Wu et al. 2007b): rative capability over a hypothetical reference surface ET (Zhang et al. 2009), which is based on local climate con- AI ¼ O ð3Þ ditions including air temperature, vapor pressure, number P of sunshine hours, and wind speed. The standardized where P is the annual average precipitation (mm), and ETO equation for estimating evapotranspiration is shown in is the annual average potential evapotranspiration (mm). Eq. (1). This equation is based on the widely used Penman– Monteith method (Allen et al. 2005; Gavilan et al. 2007; Yin et al. 2008; Zhang et al. 2009, modified by the Results and discussion American Society of Civil Engineers (ASCE) with the latest version (ASCE-PM) published in 2005, and recom- There are two large lake regions in study area, the Lake mended by the United Nations Food and Agriculture Peiku and Lake Como Chamling zones. The average areas Organization (FAO). of Lake Peiku and its surrounding four lakes, Langqiang, 900 Cuochuolong, Kemen and Helin, during 1970–2009 are 0:408DðRn GÞþr U2ðes eaÞ ET ¼ Tþ273 ð1Þ 276.14 km2, 23.95 km2, 13.02 km2, 2.08 km2 and O D c 1 0:34U þ ð þ 2Þ 0.97 km2, respectively (Table 3). The mean areas of Lake

Where the variables are defined as follows: Rn, net radiation Como Chamling and its surrounding lakes Qiangzuo and at the crop surface (MJ m-2 day-1); G, soil heat flux den- Wuming between 1974 and 2007 are 53.81, 10.01 and sity (MJ m-2 day-1); T, mean daily air temperature at 2 m 0.42 km2, respectively (Table 4; Fig. 2). -1 (°C); U2, wind speed at 2 m (m s ); es, saturation vapor pressure (kPa); ea, actual vapor pressure (kPa); D, slope of Lake change the saturation vapor pressure curve at air temperature T (kPa °C-1); and c, psychrometric constant (kPa °C-1). Changes in the Lake Peiku region Due to regional differences in the natural environment in China, this method needed to be validated to gain the Lakes in the Peiku region have shrunk with evident intra- experiential coefficients based on local observed data, annual and inter-annual variation from 1970 to 2009. The especially on the Tibetan Plateau (TP) called the third pole. area of Lake Peiku fell by 10.38 km2 from 1970 to October

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Table 3 Lake extent change in the Lake Peiku region during 1970–2009 Date Peiku Langqiang Cuochuolong Kemen Helin Area Proportion Area Proportion Area Proportion Area Proportion Area Proportion (km2) (%) (km2) (%) (km2) (%) (km2) (%) (km2) (%)

1970 281.09 – 27.40 – 17.47 – 2.19 – 2.34 – 15 December 1972 280.64 -0.16 – – 16.38 -6.25 – – – – 2 January 1973 280.53 -0.04 – – 16.17 -1.75 2.23 1.82 0.40 -82.77 2 December 1976 280.84 0.11 25.37 -7.42 – – 2.34 5.12 0.00 -100.00 7 January 1977 279.06 -0.63 25.32 -0.18 – – 2.23 -4.69 0.17 – 30 March 1977 278.81 -0.19 25.31 -0.04 – – 2.21 -1.15 – – 18 June 1977 280.22 0.50 24.88 -1.68 – – 2.26 2.55 – – 16 September 1977 280.96 0.26 25.35 1.88 – – 2.29 1.33 – – 12 October 1988 279.45 -0.53 26.50 4.75 13.74 -13.36 2.29 -0.25 0.00 – 30 November 1991 277.06 -0.86 25.74 -2.88 – – 2.13 -7.01 0.00 – 24 March 1993 273.90 -1.14 24.85 -3.44 12.62 -8.13 2.08 -2.08 0.00 – 6 December 1999 275.98 0.76 25.46 2.46 13.79 9.25 2.07 -0.45 0.00 – 22 November 2000 276.14 0.06 25.86 1.58 14.86 7.77 2.16 4.40 0.00 – 24 October 2001 276.04 -0.03 25.39 -1.84 14.90 0.27 2.14 -1.20 0.00 – 28 November 2002 275.37 -0.24 23.97 -5.57 14.47 -2.88 2.10 -1.81 0.00 – 14 October 2003 275.24 -0.05 23.05 -3.87 13.81 -4.59 1.96 -6.47 0.00 – 1 November 2004 273.64 -0.58 20.84 -8.12 14.22 2.97 1.89 -3.76 0.00 – 21 February 2005 271.12 -0.92 16.20 -22.29 – – 1.91 0.89 0.00 – 12 November 2005 272.50 0.51 19.66 21.36 13.68 -3.77 1.84 -3.26 0.00 – 14 October 2006 273.44 0.34 21.77 11.30 12.47 -8.86 1.99 7.78 0.00 – 26 November 2007 272.43 -0.37 23.82 9.42 12.02 -3.57 1.86 -6.60 0.00 – 28 November 2008 271.57 -0.32 24.48 2.77 12.00 -3.58 2.00 7.65 0.00 – 16 June 2009 270.22 -0.50 23.33 -4.71 11.33 -5.56 1.94 -3.06 0.00 – 30 October 2009 270.71 0.18 22.37 -4.10 10.39 -8.26 1.74 -10.45 0.00 –

Proportion = (Sprevious - Snext)/Sprevious –, no data or data not considered

Table 4 Change in lake extent of Lake Como Chamling during 1974–2007 Date Como Chamling Wuming Qiangzuo Area Change in Proportion Area Change in Proportion Area Change in Proportion (km2) area (km2) (%) (km2) area (km2) (%) (km2) area (km2) (%)

1974 66.33 – – 0.29 – – 12.14 – – 19 December 1976 53.14 -13.19 -19.89 0.44 0.15 53.10 5.18 -6.96 -57.36 10 November 1989 39.01 -14.13 -26.59 0.47 0.03 7.41 0.33 -4.84 -93.58 8 November 2000 59.21 20.20 51.78 0.44 -0.03 -7.41 19.27 18.94 5701.84 14 November 2005 52.00 -7.21 -12.18 0.47 0.03 7.50 0.00 -19.27 -100.00 11 October 2007 53.21 1.21 2.33 0.41 -0.06 -13.39 13.12 13.12 –

2009, a shrinkage of 3.69 % (maximum shrinkage of this region, the order of both size and stability is Lake 3.87 % in June 2009) and annual mean shrinkage speed of Peiku [ Lake Langqiang [ Lake Cuochuolong. 0.27 km2 year-1. The amounts of shrinkage of lakes The change in lake extents in the Lake Peiku region Langqiang, Cuochuolong, Kemen and Helin are 18.39, 40. presents a consistent trend of slight shrinkage in the 1970s, 53, 20.71, and 100 % from 27.40, 17.47, 2.19, and distinct shrinkage from 1988 to 1993, followed by a gen- 2.34 km2 in 1970 to 22.36, 10.39, 1.74, and 0.00 km2 in eral expansion with a peak in 2000, and rapid decrease 2009, respectively. For the lakes greater than 10 km2 in after 2000 with lakes Peiku and Langqiang reaching a low

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Fig. 2 Lakes distribution in study area in 2005. Lakes Peiku and Cuochuolong shrank rapidly after had a relatively stable area of about 280 km2 in the 1970s, 2006 and lakes Langqiang and Kemen also shrank from gradually shrinking in the 1980s to a low of 273.90 km2 in 2007. Statistical analysis reveals that the lakes in this March 1993. This was followed by expansion around region have strong positive relationships with each other 2000 to an area of about 276 km2, and then accelerated (correlation coefficients of 0.68–0.91**) except between shrinkage after 2000, especially during 2003–2005 and lakes Langqiang and Cuochuolong, implying a strong 2006–2009 when the decrease was approximately linear. consistent change process in lake extent area in this region The minimum area of Lake Peiku was measured in June (Table 5; Fig. 3). 2009 at 270.22 km2 (Fig. 3). Using Lake Peiku as an example, it can be seen that the The intra-annual change in Lake Peiku is 0.49–2.14 km2 change in lake extent is not linear, but fluctuates. The lake (0.18–0.51 %). The lake extent in 1977 was smaller in

Table 5 Correlation statistics Lake Description Peiku Langqiang Cuochuolong Kemen for main lake changes in Lake Peiku region Peiku Pearson correlation 1 0.68** 0.89** 0.91** Sig. (2-tailed) 0.00 0.00 0.00 N 24 22 17 23 Langqiang Pearson correlation 0.68** 1 0.45 0.70** Sig. (2-tailed) 0.00 0.09 0.00 N 22 22 15 22 Cuochuolong Pearson correlation 0.89** 0.45 1 0.69** Sig. (2-tailed) 0.00 0.09 0.00 N 17 15 17 16 Kemen Pearson correlation 0.91** 0.70** 0.69** 1 Sig. (2-tailed) 0.00 0.00 0.00 N 23 22 16 23 ** Significant at the 99 % level 123 Environ Earth Sci

Fig. 3 Extent of lake change in the vicinity of Lake Peiku

March and June than in September, and a similar trend was The change in rate of Lake Peiku is increasing as it seen in 2005 with the lake area in February smaller than in shrinks between 1970 and 2009. To reduce the impact of November, and in 2009, the lake extent in June was less intra-annual fluctuation on the lake-change trend, data from than in October. Due to the poor availability of RS data 16 periods for Lake Peiku were selected to analyze the rate within a resolution of 100 m, the intra-annual data for these of change, including one topographic map, five, seven and 3 years were not sufficient to reflect the detailed processes, three images from October, November and December, but help identify the high water period of Lake Peiku respectively, which represent the lake area after rainy sea- occurred during September–November, and the low water son. The results show that Lake Peiku shrank and expanded season during February (March) to June due to the rainy in the 1970s with a rate of change \0.23 km2 year-1, fell season in July and August. strongly around 1990 with a rate of change of

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Table 6 Extent of change in Order Date Area Change in Proportion Rate Lake Peiku during 1970–2009 (km2) area (km2) (%) (km2 year-1)

1 1970 281.09 – – – 2 15 December 1972 280.64 -0.45 -0.16 -0.23 3 2 December 1976 280.84 0.20 0.07 0.05 4 12 October 1988 279.45 -1.38 -0.49 -0.12 5 30 November 1991 277.06 -2.40 -0.86 -0.80 6 6 December 1999 275.98 -1.08 -0.39 -0.13 7 22 November 2000 276.14 0.16 0.06 0.16 8 24 October 2001 276.04 -0.10 -0.03 -0.10 9 28 November 2002 275.37 -0.67 -0.24 -0.67 10 14 October 2003 275.24 -0.13 -0.05 -0.13 11 1 November 2004 273.64 -1.61 -0.58 -1.61 12 12 November 2005 272.50 -1.13 -0.41 -1.13 13 14 October 2006 273.44 0.93 0.34 0.93 14 26 November 2007 272.43 -1.01 -0.37 -1.01 15 28 November 2008 271.57 -0.87 -0.32 -0.87 Proportion = (Sprevious - Snext)/ 16 30 October 2009 270.71 -0.86 -0.32 -0.86 Sprevious

0.80 km2 year-1 in 1991; the amount of change was con- 57 times during 1989–2000, disappearing in 2005, and sistently low during 1999–2003 with a rate of change below then, expanding to 13.12 km2 in 2007 (Table 4). 0.2 km2 year-1 (except 2002), and this accelerated to an annual rate of change of more than 0.86 km2 year-1 during Spatial characteristics of lake change 2004–2009 (Table 6). Meanwhile, the maximum amounts of shrinkage of lakes Peiku, Langqiang, Cuochulong and The geological structure is the basis of lake formation, and Kemen are 3.87, 40.88, 40.46, and 20.71 %, respectively, terrain controls the spatial features of lake change during 1970–2009 (except for Lake Langqiang which only (Vaughan et al. 2007). The four lakes of Peiku, Cuochuo- included data from 1970 to 2005). Based on the available long, Langqiang and Como Chamling have different spatial data, Lake Kemen disappeared in December 1976, change characteristics. increased briefly to 0.17 km2 in 1977, and never appeared The shrinking zone of Lake Peiku is mainly located after 1988 on the RS images. At the 40-year scale, lakes in around the gentle sandy shores to the southeast, southwest, Lake Peiku region have all shrunk, especially since 2002. center-east and northeast. When lake level fell, the lake extent did not change much due to the steep rocky shores in Changes in the Lake Como Chamling region the west and east (Fig. 4). The shrinking zone of Lake Cuochuolong is mainly Lakes in the Lake Como Chamling region fluctuated located in the southeast, with some smaller areas in the strongly showing a pattern of shrinkage–expansion– northeast and northwest. In the southeast of the lake, the shrinkage–expansion. Of the lakes, Lake Como Chamling first occurrence of sand islands was in 2005, and these shrank rapidly from 1974 to 1989, with shrinkages of 19.89 expanded rapidly in 2006, as the delta continued to accu- and 26.59 % during 1974–1976 and 1976–1989, respec- mulate sediment resulting in the shrinkage of Lake tively, then expanded by 20.20 km2 (51.78 %) during Cuochuolong (Fig. 4). Decrease in water level and accu- 1989–2000, shrank in 2005, and expanded again in 2007. mulation of sediments are the main driving forces causing The overall extent of Lake Como Chamling reduced by the shrinkage as determined by the RS analysis. 13.12 km2 (or 19.79 %) from 66.33 km2 in 1974 to The west and south coasts of Lake Langqiang shrank 53.21 km2 in 2007. However, lakes Qiangzuo and Wuming distinctly, especially during 2004–2005. Because the lake expanded by areas of 0.12 and 0.98 km2 (41.76 and is deep in the east and shallow in the west, lake levels in the 8.07 %), respectively, during 1974–2009. Lake Wuming south and west fell first in 2004. The shrinkage of the lake remained relatively stable within 0.41–0.47 km2 or a increased in February 2005, and it expanded slightly in change of 7.41–13.39 % during 1976–1989; while Lake November 2005 with some sand islands remaining when Qiangzuo changed significantly with a shrinkage of the lake level rose. The continuous and bare white beach 97.26 % during 1974–1989, then expanding approximately has been formed over the shrinkage zone (Fig. 4).

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Fig. 4 Fluctuations in Lakes Peiku (a), Cuochuolong (b) and Langqiang (c) during 1970–2009 and the Lake Como Chamling (d) during 1970–2007

The fluctuation zone of Lake Como Chamling is mainly irrigation started based on the farmland distribution in the found on the east coast and partly in the southwest and southeast. Due to the rivers which are substantially impacted northwest. The gentle terrain and river inflows make the east by runoff or climate change, the trend of change in Lake coast the main fluctuation zone; the lake area does not change Qiangzuo is similar to Lake Como Chamling with stronger in the south and north when the lake level rises or falls due to fluctuations as the lake almost disappeared in 1989 with only the steep rocky shore. The lowest area of Lake Como a little water area in the deepest zone, and dried up in 2005, Chamling was measured in 1989 during the period of strong yet expanded to its maximum extent in 2007 which flooded shrinkage. Lake Wuming has remained nearly stable, since some grassland in the southeast and northwest (Fig. 4).

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Lake responses to climate and glacier change The results suggest that decreases in precipitation and increases in evapotranspiration are the main driving forces Lake Peiku, the largest lake, was selected for the correla- behind lake shrinkage in each period during 1970–2009, tion analysis with climate and glacier change as it had the while reverses in this trend have led to lake expansion maximum number of acquired RS images and was most (Fig. 5). The process of lake fluctuation and climate change highly correlated with change in the surrounding lakes. can be divided into four stages: relatively stable period Meanwhile, the data from the five meteorological stations with little fluctuation during 1970–1977; rapid shrinkage were used in this discussion to represent climate change in period during 1977–1994 which can be divided into three the Mt. Qomolangma region. sub-stages including 1977–1982, 1982–1991 and 1991–1994;

Fig. 5 Changes in Lake Peiku and the climate in the study area

123 Environ Earth Sci expansion period of Lake Peiku following an increase of predicts that the maximum glacier runoff will occur in 224.12 mm in precipitation and a decrease of 116.78 mm in 2030, and fall gradually after that under three scenarios evapotranspiration during 1994–2000, and accelerated (Xie et al. 2006). An increase in the total area of high- shrinking resulting from a drop in precipitation, and elevation lakes and glacial lakes with continuous warming increase in the aridity index and evapotranspiration. Two from 1970 to 2009 would indicate that glacier runoff is clear change points of Lakes occurred in 2000 and 2005, increasing in accordance with the prediction model. In the corresponding to the maximum lake expansion and Lake Peiku basin during 1976–2006 (Fig. 6), glaciers shrinkage periods (the rapid shrinking also occurred at Lake retreated by 14.25 % (22.19 km2), lakes decreased by Langqiang with a loss of 22.29 % in 2005), respectively. 1.64 % (4.60 km2), and glacial lakes expanded by 47.68 % Thus, lakes in the vicinity of Mt. Qomolangma are sensitive (5.30 km2). From 1970 to 2009, the five meteorological to climate change and reflect the climate-change processes. station data show that the precipitation decreased with a Extreme warming in the vicinity of Mt. Qomolangma linear rate of decrease of 0.67 mm year-1, evapotranspi- has resulted in significant glacier retreat (Ren et al. 1998; ration decreased with a linear rate of decrease of 2004; Che et al. 2005; Yang et al. 2006; Yao et al. 2007; 1.62 mm year-1, and the aridity index was increasing Nie et al. 2010). Initially, glacier runoff resulting from slightly overall. Nyalam station, the nearest meteorological glacier retreat will increase based on the functional pre- station to Lake Peiku, has a consistent changing trend in diction model for climate change in glacier systems, which evapotranspiration, precipitation and aridity index with the

Fig. 6 Changes in the glacier, lake and glacial lake at Lake Peiku Basin during 1976–2006

123 Environ Earth Sci overall climate changes, especially, and the station has a changes over the past 40 years, Lake Helin has been the more significant decreasing precipitation of 2.30 mm most vulnerable and has disappeared. Of the lakes larger year-1. Lake Peiku is a closed lake, continued to shrink. than 10 km2, Lake Cuochuolong is the most vulnerable and Assuming no significant change in underground water, the could dry up in around 50 years if the shrinkage rate and water-balance law implies that melt water is only a key speed of climate change are consistent with those in the additional supplement, and precipitation is the main supply past 40 years. Lake Langqiang is the next most vulnerable, source for Lake Peiku based on dynamic changes of lake, while Lake Peiku is relatively stable. glacier and climate. The quantitative understandings of water balance of lakes are limited due to the lack of hydrological and cli- Uncertainty and implication matic observations in the Lake Peiku basin. The impact of climate change on lakes in the watershed where a supply of Difference in acquisition time or limited RS data with only melt water exists is very complicated (Ding et al. 2006). So 2–3 time periods will result in uncertainties due to the the coupling mechanisms and impact of glacier melt water intra-annual and inter-annual fluctuations of lake area. on lakes and glacial lakes needs be explored in future Distinct intra-annual change in lake areas and poor avail- research, including in situ observation and isotope tracing ability of optical RS data make monthly change monitoring of water samples. Lake Peiku, the largest lake in the impossible. To reduce the impact of seasonal change, RS QNNP, should have a hydrological and climatic station for images acquired at the same or similar time of year should long-term observations to support local sustainable devel- be used to analyze the long-term lake change. In this opment and research into the water cycle and the balance research, 24 RS images have been selected to analyze the of water and heat, and the national construction of 40-year lake change, with a particular focus on continuous ecological security. processes during the past 11 years. However, processes in the 1980s–1990s cannot be detailed due to the unacquirable data. Overall, 2–3 periods of RS data are unsuitable for Conclusion revealing the process of lake change and using these lim- ited data is bound to result in uncertainty. At the 40-year Lakes in the vicinity of Mt. Qomolangma have shrunk with scale, this paper demonstrates that lakes in the vicinity of clear intra-annual and inter-annual fluctuations during Mt. Qomolangma are shrinking. 1970–2009. The 40-year change trend shows a slight The trend of lake change in the study area is clear shrinkage in the 1970s, a distinct shrinkage around 1990, shrinkage from the 1970s to around 1990, expansion from followed by a general expansion in 2000, and accelerated earlier 1990 to 2000, and continuous shrinkage since 2000 shrinkage after 2000. The maximum amounts of shrinkage at (with accelerated shrinking at Lake Peiku, Fig. 5). This lakes Peiku, Cuochuolong and Kemen all occurred in 2009. result provides strong evidence supporting the reliability of The largest lake, Lake Peiku has shrunk 10.38 km2 (3.69 %) lake change in Tibet from 1970s to 2000 based on three with a mean rate of shrinkage of 0.27 km2 year-1 during periods of RS data (Zhang et al. 2008). 1970–2009. Consistent patterns of lake change also occurred Large lakes which are naturally evolving are more stable in lakes around Lake Peiku (correlation coefficient of to indicate climate change than small vulnerable lakes. (1) 0.68–0.91). The larger lake has experienced less shrinking Comparing the two lake regions, lakes in the Lake Peiku and the order of stability is Peiku [ Langqiang [ Cuo- region are more stable than those in the Lake Como chuolong. Lake Helin is the most vulnerable, having dis- Chamling region, and are a good indicator for climate due appeared, followed by Lake Cuochuolong which could to the weak impacts by human activities, and their natural disappear in the next 50 years, and Lake Langqiang. The evolution, large area of more than 250 km2 and synchro- second largest lake in the study area, Lake Como Chamling, nicity to climate change. In comparison, Lake Como has decreased by 13.12 km2 (19.79 %) during 1974–2007. Chamling is not a good indicator due to the strong human Lakes in the Lake Peiku region are more stable than influence which can be seen by the existence of cultivated those in the Lake Como Chamling region where a strong land. Meanwhile, the regulating effect of glacier runoff in fluctuation of shrinkage–expansion–shrinkage–expansion the Lake Peiku region is another reason that they are more was observed. The driving forces of these extreme varia- stable than those in Lake Como Chamling (which is not tions are affected by human activities and can be regulated supplied by melt water). (2) Considering the lake sizes, the by glacier runoff. larger lakes are more stable, and have less intra-annual The relationship between lakes, climate and glaciers at variation, making them more stable for revealing natural the 40-year scale implies that precipitation is the main processes than small lakes which are vulnerable and easily source of water to the lakes, and glacier melt water is a changed by minor climate change. (3) Considering the lake necessary additional supplement for Lake Peiku. Lakes in 123 Environ Earth Sci the vicinity of Mt. Qomolangma are sensitive to climate Castillejo-Gonza´lez IL, Lo´pez-Granados F, Garcı´a-Ferrer A, Jose´ change and indicate that two clear climatic change points Manuel P, Jurado-Expo´sito M, de La Orden MS, Gonza´lez- Audicana M (2009) Object- and pixel-based analysis for recently occurred in 2000 and 2005 based on the syn- mapping crops and their agro-environmental associated mea- chronicity between the lake extents and climate change. sures using QuickBird imagery. Comput Electron Agr 68:207– Differences in acquisition time and limited RS data with 215 only 2–3 time periods could result in large uncertainties in Che T, Li X, Mool PK, Xu JC (2005) Monitoring glaciers and associated glacial lakes on the east slopes of Mt. Xixabangma lake-change analysis. This study used 6–24 RS data images from remote sensing images. J Glaciol Geocryol 27:801–805 over 40 years, proving that lakes in the Mt. Qomolangma Cidanlunzhu (1997) Overview of Qomolangma National Nature region are shrinking. However, a lack of hydrological and Preserve. China Tibetol 21:3–22 climatic observations around the lake restricts the analysis Ding YJ, Liu SY, Ye B, Zhao L (2006) Climatic implications on variations of lakes in the cold and arid regions of China during of the coupled relationship. Further research into the the recent 50 years. J Glaciol Geocryol 28:623–632 mechanisms and processes of the lake–climate–glacier Gao J, Tian LD, Liu YQ, Gong TL (2009) Oxygen isotope variation system, the impact of melt water on lakes/glacial lakes, and in the water cycle of the Yamdrok-tso Lake Basin in southern establishment of a hydrological and climatic observation Tibetan Plateau. Chin Sci Bull 54:2758–2765 Gavilan P, Berengena J, Allen RG (2007) Measuring versus station at Lake Peiku are recommended. estimating net radiation and soil heat flux: impact on Penman– Monteith reference ET estimates in semiarid regions. Agr Water Acknowledgments This study was supported in part by the Manage 89:275–286 National Basic Research Program of China (Grant No. 2010 Haginoya S, Fujii H, Kuwagata T, Xu J, Ishigooka Y, Kang S, Zhang CB951704), Institutional Consolidation for the Coordinated and Y (2009) Air-lake interaction features found in heat and water Integrated Monitoring of Natural Resources towards Sustainable exchanges over Nam Co on the Tibetan Plateau. SOLA Development and Environmental Conservation in the Hindu Kush– 5:172–175 Karakoram–Himalaya Mountain Complex, the National Natural Sci- Huang HP, Wu BF, Li MM, Zhou WF, Wang ZW (2004) Detecting ence Foundation of China (Grant No. 40901057 and 41101082) and urban vegetation efficiently with high resolution remote sensing the Foundation of IMHE for Young Scientists. The authors would like data. J Remote Sens 8:68–74 to thank Prof. Rongfu Huang of Northwest Institute of Plateau Im J, Jensen JR, Tullis JA (2008) Object-based change detection Biology of CAS, Mr. Gama from State Qomolangma Administration using correlation image analysis and image segmentation. Int J in Xigaze, Mr. Zhizhong Wang, Mr. Pubuzhaxi and Mr. Cirenduoji of Remote Sens 29:399–423 , Mr. Yajun of , Mr. Cisang and Jones B, Arp C, Hinkel K, Beck R, Schmutz J, Winston B (2009) Mr. Daqiong of Gyirong County, Mr. Suolang of Dinggye County for Arctic lake physical processes and regimes with implications for their support and enthusiastic help during the field survey; and give winter water availability and management in the National many thanks to Ms. Qinqin Zhang, Ms. Yingying Wu, PhD Xueru, Petroleum Reserve Alaska. 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