The relationship between air temperature fluctuation and Glacial Lake Outburst Floods in Tibet, China

Quaternary International 321 (2014) 78e87

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The relationship between air temperature fluctuation and Glacial Lake Outburst Floods in Tibet, China Jing-Jing Liu a, b, c, Zun-Lan Cheng a, Peng-Cheng Su a, c, * a

Institute of Mountain Hazards and Environment & Key Laboratory of Mountain Hazards and Surface Process, Chinese Academy of Sciences & Ministry of Water Conservancy, Chengdu 610041, China b State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China c Graduate University of Chinese Academy of Sciences, Beijing 100039, China

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 17 December 2013

In recent years, disasters caused by Glacial Lake Outburst Floods (GLOFs) have taken place more frequently in Tibet than previously and have been the cause of considerable losses. Temperature fluctuations are believed a major influencing factor of GLOFs. However, if or how much climatic change influences GLOFs is a question that remains to be answered. In this paper, using 24 GLOFs related to 19 glacial lakes and temperature data from 14 meteorological stations, we explored the relationships between temperature variations on different temporal scales (annual, monthly, and daily) and GLOFs. There were three active periods for GLOFs in the 1960s, 1980s and 2000s in Tibet, when 16 of 24 GLOFs took place. All the studied GLOFs occurred in the ablation months (from May and September, and especially in July and August) and on ablation days with a monthly average temperature and daily average temperature that were both greater than 0
C. Based on the analysis of monthly and daily temperature, GLOFs depend on the coupled influence of ablation temperature and accumulation temperature; even the accumulation temperature may be important. Based on this, temperature increments were defined, representing the change from accumulation to ablation temperature with a significant impact on GLOFs. There is an altitude effect on GLOFs, in that lakes at lower altitude generally burst earlier, whereas those at a higher altitude burst later. The monthly and daily increments increase according to a similar powerlaw form with elevation. Ó 2013 Elsevier Ltd and INQUA. All rights reserved.

1. Introduction Glaciers are of considerable interest because of their high sensitivity to global climatic change (Thompson et al., 1989, 1993; Ames, 1998; Li and Kang, 2006). They grow and shrink in length and area in response to climate fluctuations (Haeberli et al., 2000). Since the mid-19th century, warming has exacerbated the retreat of glaciers in many mountain regions such as the Himalayas, Alps, Rocky Mountains, Cascade Range and the southern Andes, as well as isolated tropical summits such as Mount Kilimanjaro in Africa, where some of the proportionally largest glacial losses are occurring (Bishop et al., 1998; Dyurgerov and Meier, 2000; IPCC, 2001; Berthier et al., 2007; Paul et al., 2007; Bolch et al., 2008,

* Corresponding author. Institute of Mountain Hazards and Environment, CAS, #.9, Block 4, Renminnanlu Road, Chengdu, Sichuan 610041, China. E-mail address: [email protected] (P.-C. Su). 1040-6182/$ e see front matter Ó 2013 Elsevier Ltd and INQUA. All rights reserved. http://dx.doi.org/10.1016/j.quaint.2013.11.023

2012). Temperature rise, especially the increasing warming with altitude, is remarkable in southwestern China, especially in the Tibetan Plateau (Liu and Chen, 2000; Ye et al., 2009; Li et al., 2011), which is geologically young and fragile and is vulnerable even to minor changes in the climatic system (Lama and Devkota, 2009). Liu and Chen (2000) reported a significant warming on the Tibetan Plateau since the 1950s (0.16 C
per decade), and especially during winter (0.32 C
per decade). More than half of the glaciers on this plateau were found to be retreating, according to recent satellite observations (Zhang and Yao, 1998; Liu and Kang, 1999; Shi and Liu, 2000; Shi et al., 2000; Shi, 2001; He et al., 2002a, 2002b; Pu et al., 2004; Yao et al., 2004). At the same time, temperature rise has caused existing glacial lakes to increase in size and has created new hazardous lakes (Liu and Sharma, 1988; Ding and Liu, 1992; Zhang, 1992). The shrinkage of glaciers and the increase in size of glacial lakes include changes in the severity and frequency of Glacial Lake Outburst Floods (GLOFs), as glaciers respond to climate change (Reynolds, 1992; Clague and Evans, 1993; Walder and Driedger, 1995; Rai, 2005).

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While such GLOFs may immediately endanger lives, infrastructure and power supplies, flash floods and, in particular, large-scale floods pose an ever greater challenge and risk (Walder and Costa, 1996; Rudoy, 2002; Liu et al., 2013). There have been about 20 GLOFs in Tibet since the 1950s (Liu et al., 2008a). Although infrequent, largely unpredictable and localized impacts can be devastating, as shown by the GLOF that occurred in July, 2009, during which two flooding events took place from Zhemaico Lake and Cilaco Lake, killing two people, destroying 53 km of highway, 20 bridges, and three culverts. At present, this type of disaster is attracting increasing attention from researchers (Chen et al., 2010). Related to glaciers and glacial lakes, the GLOFs are also influenced by climate change. Lu and Li (1989) found that humidity and dry heat are most likely to cause outbursts in Tibet, while Li and You (1992) argued that the major factors that drove the outburst of the Guangxieco Lake, also in Tibet, were sustained high temperatures and abnormal rainfall. Chen et al. (2010) found that, due to the rise in warming, frequency of GLOFs in the Yarkant region of Karakoram almost doubled from 0.4 times annually in 1959e1986 to 0.7 times annually in 1997e2006. It is generally believed that climate fluctuations play a role in stimulating GLOFs (Lu et al., 1999). However, the consequences of change for glaciological hazards and water resources are complex, and large gaps remain in our understanding and ability to model the causeeeffect relationships between GLOFs and climate changes. We considered air temperature to be the most important climate factor for GLOFs, as it plays a prominent role as it is related to radiation balance, turbulent heat exchange, and solid/ liquid precipitation ratio (Ohmura, 2001). With the increase of frequency of GLOFs in Tibet, especially in southern Tibet (Liu et al., 2008b), it is necessary to attempt to study the possible relationships between temperature and the triggers of GLOFs. In this paper, the annual, monthly, and daily air temperatures for Tibet were acquired during 1961e2010, based on meteorological data from 14 stations. The 16 GLOFs that occurred in Tibet from 1964 to 2009 were also studied, based on field surveys and historical records. The relationship of GLOFs to temperature variation at different temporal scales was discussed. This should lead to a better understanding of the frequency, intensity, and duration of fluctuations in temperature and the behavior of glaciers.

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2. GLOFs in Tibet since the 1930s 2.1. Background The Tibet Plateau was formed by a collision between the Indian and Asian plates. This collision is still proceeding and produces the undergoing uplift of the area. The region is one of the most dynamic, fragile, and complex mountain systems in the world. Climatically, it is cold and dry in the northwest and warm and humid in the southeast. The weather is characterized by a thin atmosphere, abundant sunlight, a low temperature with small annual variance and a large daily variance, and low precipitation with uneven distribution (CSECAS, 1986). In summer, the climate is affected by four kinds of air currents: tropospheric tropical easterlies, subtropical westerlies, southwest monsoon from the Indian Ocean, and stratospheric easterlies. In the inner plateau, low thermal centers are often formed, resulting in cloudy and showery weather. In the southern plateau, under the impact of the northward movement of westerlies and the monsoon from southeast, the warm and wet air current from the Indian Ocean is blocked off by the Himalayas. However, the small-scale air currents from north or south can enter deeply into the inner plateau, which favors the formation of warm and rainy climate in the southern hillside of the Himalayas and the inner Tibet (Liu et al., 2008b).The Tibetan Plateau is divided into four distinctive subregions by their different climate environments (CSECAS, 1986): the internal plateau and northern non-monsoon region, the southeastern Tibet, the highland monsoon region, and the western edge region, as shown in Fig. 1. Approximately 36,800 glaciers are distributed in the Tibetan Plateau, occupying an area of about 49,873.44 km2 and having a total ice volume of 4561 km3(Shi, 2008). They account for 79.5% of the total number of glaciers, 84% of the total glaciated area and 81.6% of the total ice volume in China (Shi et al., 2000). ‘Temperate’ glaciers are distributed in humid-maritime regions, such as subregions B and D in Figure.1. Equilibrium lines are at (relatively) low altitudes with warm temperatures and long melting seasons because of the large amount of ablation required to eliminate the thick snow layers (Haeberli et al., 2002). Continental glaciers occur

Fig. 1. Distribution of outburst lakes and nearby meteorological stations in Tibet, China (A. Internal plateau and northern non-monsoon region; B. Southeastern Tibet; C. Highland monsoon region, except region B; D. Western edge of the Tibetan plateau) In this Figure, the serial numbers of the outburst lakes (C) and meteorological stations ( ) list in Tables 1 and 2, respectively.

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in the dry-continental conditions, such as subregions A and C in Figure.1: their equilibrium lines may be at (relatively) high elevations with cold temperatures and short melting seasons. In such regions, glaciers lying far above the tree line can contain polythermal as well as cold ice well below melting temperature, may also have a low mass turnover, and are often surrounded by permafrost (Shumskii, 1964).

in the 1960s. Some cases, from case-1 of Taco lake outburst to case7 of Longdaco lake outburst, were therefore not quantitatively analyzed because of a lack of weather information. After data quality control, the 14 weather stations (Fig.1, Table 2) with data available from 1964 to 2009 located at altitudes between 2737 m and 4490 m, were selected. Detailed information about the weather stations and data sources obtained from the nearby stations of 13 lakes is listed in Tables 2 and 3 respectively.

2.2. GLOFs since the 1930s Glacial Lake Outburst Floods occur for many reasons, such as landslides and avalanches into the lake, heavy precipitation, rises in water level and strong winds (Dahms, 2006). The historic cases we studied in Tibet, however, all resulted from ice avalanche and piping (Liu et al., 2013). The advance and retreat of glaciers, the formation of end-moraine dams, changes in lake volume and the occurrence of ice avalanche comprise a series of factors closely related to GLOFs. At the same time, all these factors are also related to temperature fluctuations. GLOFs are sporadic events, but that there is a certain relationship with temperature. More than twenty GLOFs have taken place in Tibet since the 1930s. For our study, we reviewed information from 24 GLOFs in 19 lakes. Fig. 1 shows the locations of the 19 lakes and the nearby 14 meteorological stations. Table 1 lists the 24 cases, with detailed information inferred from the literature (Li and You, 1992; Lu et al., 1999; Ives et al., 2010; Liu et al., 2008a, 2013), topographic maps, and interviews with local inhabitants.

3.2. Data correction The data from weather stations cannot be used directly to represent the lake temperature. All the lakes in study are distant to the stations and located in high-altitude regions with significant differences of elevation. Data correction is necessary before we analyze the temperature variations. 3.2.1. Altitude correction Temperature decreases with altitude (Hulme et al., 1995; Dodson and Marks, 1997; Robeson and Janis, 1998; Li et al., 2003), and each region has its own lapse rate. Li and Xie (2006) drew a contour map of temperature lapse rate for the Tibetan Plateau and its surrounding area (Fig. 2). Based on this, the original temperature data T0 can be corrected for the elevation as follows:

TH ¼ T0  kDH

(1)

Table 1 The 24 GLOFs of 19 lakes in Tibet since the 1930s. Case

Lake name

County

Outburst date

Latitude N

Longitude E

Elevation (m)

Triggering reason

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Taco Qiongbihemaco Sangwangco(1st) Sangwangco(2nd) Guxiang Zhangzangbo Longdaco Jilaico Damenlakeco Ayaco(1st) Ayaco(2nd) Ayaco(3rd) Pogeco(1st) Pogeco(2nd) Zharico Cirenmaco

Nielamu Yadong Kangma

28
17’340 ’ 27
30’560 ’ 28
03’490 ’

86
07’550 ’ 89
05’260 ’ 91
21’560 ’

5245 4660 5150

29
57’550 ’ 28
10’380 ’ 28
31’370 ’ 27
57’490 ’ 29
18’090 ’ 28
20’540 ’

95
27’220 ’ 85
51’250 ’ 85
18’430 ’ 87
48’340 ’ 93
06’220 ’ 86
29’400 ’

4155 5448 5460 5271 5210 5560

Piping Ice avalanche Not known Ice avalanche Not known Piping Ice avalanche Ice avalanche Ice avalanche Ice avalanche

31
57’410 ’

94
09’550 ’

4332

Luozha Nielamu

1935-08-28 1940-07-10 1950 1954-07-16 1953-09-29 1964 1964-08-25 1964-09-21 1964-09-26 1968-08-15 1969-08-17 1970-08-17 1972-07-23 1974-07-06 1981-06-24 1981-07-11

28
54’260 ’ 28
04’450 ’

92
18’430 ’ 86
02’190 ’

5130 4640

17 18

Jingco Guangxieco

Dingjie Bomi

1982-08-27 1988-07-15

28
11’410 ’ 29
27’540 ’

87
38’250 ’ 96
29’580 ’

5353 3816

19 20 21 22 23 24

Zanaco Jialongco(1st) Jialongco(2nd) Degaco Zhemaico Cilaco

Jilong Nielamu

1995-06-06 2002-05-23 2002-06-29 2002-09-18 2009-07-03 2009-07-29

28
39’440 ’ 28
12’540 ’

85
22’190 ’ 85
51’040 ’

4744 4410

Ice avalanche Not known Ice avalanche Piping; Ice avalanche Ice avalanche Piping; Ice avalanche Glacier surge Not known Ice avalanche

28
07’250 ’ 28
00’540 ’ 30
44’030 ’

90
34’010 ’ 92
20’360 ’ 93
58’320 ’

5316 5300 5060

Ice avalanche Ice avalanche Ice avalanche

Bomi Nielamu Jilong Dingjie Gongbujiangda Dingri

Suo

Luozha Cona Bianba

Sangwangco (1st) denotes the first outburst in the Sangwangco Lake and Sangwangco (2nd) is the second outburst, and so forth.

3. Data sources and corrections 3.1. Data sources The annual, monthly and daily average temperatures used in our study were provided by the National Climate Center, China Meteorological Administration (CMA). Against a background of rapid development in meteorological observations, the modern nationwide network of weather stations in Tibet mostly began operating

where TH is the temperature corrected for the elevation, T0 is the recorded original temperature data, DH is the elevation difference between the lake and the station and k is the correction coefficient reflecting the lapse rate (Li and Xie, 2006). 3.2.2. Distance correction After elevation correction, the data (TH) are corrected for distance, and the final temperature data (T) are obtained for the

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Table 2 Information for the 14 weather stations. Number

Weather station

Elevation (m)

Coefficient k

Number

Weather station

Elevation (m)

Coefficient k

(1) (2) (3) (4) (5) (6) (7)

Nielamu Dingri Lazi Jiangzi Langkazi Cona Longzi

3811 4302 4001 4041 4433 4281 3861

0.0053 0.0054 0.0054 0.0054 0.0054 0.0053 0.0054

(8) (9) (10) (11) (12) (13) (14)

Jiacha Linzhi Jiali Biru Bomi Luolong Suo

3261 3001 4490 3941 2737 3640 4024

0.0055 0.0059 0.0059 0.0062 0.006 0.0062 0.0063

Table 3 Data sources for different glacier lake stations. Outburst-lake name (lake)

Data from stations

Outburst-lake name (lake)

Data from stations

Jilaico Damenlakeco Ayaco Pogeco

Dingri, Lazi,Jiangzi, Jiacha, Jiali, Linzhi Dingri, Nielamu Biru, Suo

Guangxieco Zanaco Jialongco Degaco

Zharico Cirenmaco Jingco

Langkazi, Luolong, Cona Dingri, Nielamu Dingri, Jiangzi, Lazi

Zhemaico Cilaco

Bomi, Linzhi Dingri, Nielamu Nielamu, Dingri Jiangzi,Langkazi, Cona Longzi, Cona Biru, Jiali, Luolong

analysis. The inverse distance-weighted interpolation was chosen as the distance correction; this has been found to be a good method for correcting distance errors of nearby stations in previous studies (Nalder and Wein, 1998; Price et al., 2000):

Pn T ¼

1 t ¼ 1 Dt

Pn

TH

1 t ¼ 1 Dt

(2)

where T is the final corrected temperature, n is the number of nearby stations and Dt is the horizontal distance between the lake and the nearby station. This value of T can be taken as the local temperature of the lake. In the following sections, we describe how we considered temperature fluctuations on annual, monthly, and daily scales, in both the outburst year and the preceding year, because the geomorphic and glacial conditions remain almost the same in the two successive years and the effect of a change in temperature may be prominent. We can then determine the reasons why the outburst happened in that specific year.

3.2.3. Five-day moving average for mean daily temperature A moving average is commonly used with time series data in meteorology in order to smooth out short-term fluctuations and highlight longer-term trends for mean daily air temperatures (MDAT) (Hewitson and Crane, 1996; Wei, 2000). At present, the five-day moving average method is usually used to yield smooth curves of climatological data and determine the start day and end day. For the case of a glacial lake, 0
C is primarily crucial, which determines the ice ablation and accumulation. So we need to find the days with temperature steadily beyond 0
C. The five-day moving average was used to smooth the fluctuation of MDAT. For the recorded daily temperature series {T1, T2, ., TN}, the average temperature over successive five days was considered, as follows

T1* ¼ ðT1 þ T2 þ ::: þ T5 Þ=5:::::: Ti* ¼ ðTi þ Tiþ1 þ ::: þ Tiþ5 Þ=5 When a day with (Ti* ) greater than 0
C and days after the day * .T * ) all greater than 0
C, the period (ni) is the longest with (Tiþ1 n period with (Ti* .Tn* ) over critical temperature. The first day of this period is chosen as the start day (Ds). When a day with (Ti* ) less than 0
C and days before the day with * (Tiþ1 .Tn* ) all greater than 0
C, the day is chosen as the end day (DE). However, in the outburst year, calculations days are all before outburst days with (Ti* ) greater than 0
C, so the outburst days are end days (DE) in following calculations. The calculated number of days (N) represent the number of continuous ablation temperature days, from the start day (Ds) to end day (DE) in the same year. The calculated number of days (n) represent the number of continuous accumulation temperature days, from the end day (DE) in the previous year to start day (Ds) in the year. For example, moving averages based on the data from the Cilaco Lake smooth the fluctuations created by each individual datum, so that we can better see trends over the desired intervals and can determine the first day as 22nd May at 0.45
C more than 0
C, as shown in Fig. 3. The start day (Ds) is 22nd May and the end day (DE) is 29 July 2009. Thus N ¼ 69 for calculation of ablation temperature. Similarly, N ¼ 228, for the accumulation temperature, from 6 October 2008 to 21 May 2009. 4. Impact of air temperature 4.1. Variations in mean annual air temperature

Fig. 2. Distribution of vertical temperature lapse rate in Tibet and surrounding areas (the numbers in pictures are the coefficients of modifier formulas,
C m1, 104) (Li and Xie, 2006).

The sum of accumulation and ablation over any period is the mass budget. In simple terms, the glacial mass balance is directly linked with annual atmospheric conditions. GLOFs respond to variations in mean annual air temperature (MAAT). Table 1 showed that the GLOFs had a greater frequency in 1960e 1975 and 2000e2010. Especially in 1960e1975, there were nine GLOFs, and in 1990e2000 only one case. Fig. 4 indicates the 17

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Fig. 3. Daily temperature fluctuations and five-day moving average for Cilaco Lake in 2009.

GLOFs of the outburst years in 1960e2010. The annual temperature experienced a significant increase in the 1960s and early 1970s, and at the end of the first decade of the 21st century. These major fluctuations have led to greater frequency of GLOFs. Three typical cases were chosen for detailed analysis: Jilaico Lake, representing the earliest occurrence; Ayaco Lake, suffering successive outbursts in three years; and Zharico Lake, occurring in a low-temperature trough (Fig. 4). The region of Jilaico Lake had experienced wet and cold weather in the years before the outburst, especially in 1962 and 1963. The Jilaico Lake outburst occurred in 1964, when the MAAT and its increment achieved a maximum of 3.1
C and 0.9 C
, respectively, following which the climate began to warm. The Ayaco Lake outbursts occurred successively in 1968, 1969, and 1970. Before the outbursts, the years 1965 and 1966 constituted a cold period and the average annual temperature in 1966 was the lowest (about 6.4
C). The annual mean temperature then increased to 2.5
C in 1968 and 1.6
C in 1969, which was the maximum until 2002. The two outbursts both occurred in the peak of MAAT fluctuations before a pre-cold period. Out of the 17 cases, ten outbursts occurred at the maximum of MAAT fluctuations, such as in these two cases, especially when the temperature increased by 0.5 C
. The Zharico Lake outburst occurred in 1981, when there was a MAAT lower than in the preceding years. The year 1981 had a MAAT of 5.5
C, which had decreased by 0.9 C
compared to the previous year. The outburst year was in a trough of the annual temperature fluctuation, and the lake experienced a period of high temperatures before the outburst. Out of the 17 cases, six outbursts were similar to this case. 4.2. Variations in mean monthly air temperature On an annual scale, water that originates from either a glaciated catchment or icefall occurrence has a pronounced seasonality. All the identified GLOFs took place between May and September, particularly in July and August, the hottest months of the year (Fig. 5). All outburst months were also ablation months with a mean monthly air temperature (MMAT) greater than 0
C. Although the outburst lakes have different geographic and climate conditions, they all show an overall trend of a frozen accumulation period from about October or November in the previous year and an ablation period from about April or May in the following year. A strong dependence of GLOFs on variations in MMAT is displayed.

For the 24 studied GLOFs, the various glacier lakes were found at a considerable range of elevations. Fig. 5 shows that almost all lakes below an elevation of 5000 m burst in May to July, whereas lakes above 5000 m burst in June to September, and especially in July to September. It appears that higher glacier lakes had a delayed response to monthly temperature conditions. The more vulnerable glaciers are those at relatively low elevations, whereas glaciers at relatively high altitudes are less sensitive. Areas of accumulation and ablation are separated by the equilibrium line, at which the balance between gain and loss of mass is exactly 0
C. We take 0
C, the melting point, to be the turning point that determines the glaciers’ accumulation and ablation. A temperature above 0
C is thought to be the effective ablation temperature and a temperature below 0
C is thought to be the effective accumulation temperature. The monthly effective ablation temperature TMA and the monthly effective accumulation temperature TMC are defined as, respectively:

TMA ¼

iX ¼N

i TM

(3)

n TM

(4)

i¼1

TMC ¼

nX ¼N n¼1

where TM is the corrected MMAT, the sum TMA is taken over all the months that have an average temperature greater than 0
C prior to the outburst month of the outburst year and the sum TMC covers all the months with an average temperature below 0
C from the first month with an MMAT below 0
C from the previous fall to the last month with an MMAT below 0
C in the spring of outburst year. For more careful comparison, we also computed these same quantities over the corresponding months in the preceding year, i.e. the monthly ablation temperature T’MA and the monthly accumulation temperature T’MC. The calculated values are listed in Table 4, which only includes 15 cases because data was missing in some months. Comparing the values of TMA and TMC in the outburst year with those in the preceding year, of the 15 GLOFs, 8 cases had a much 0 j:, and 13 cases had a higher ablation temperature, i.e., jTMA j > j:TMA 0 j:. It much higher accumulation temperature, i.e., jTMC j > j:TMC seems that the influence of low-temperature accumulation (TMC) is more significant than high-temperature ablation TMA. The influence of low-temperature accumulation cannot be ignored.

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83

Table 4 The monthly ablation and accumulation temperature in the outburst and preceding year. Glacier-lake

Jilaico Damenlakeco Ayaco(1st) Pogeco(1st) Pogeco(2nd) Zharico Cirenmaco Jingco Guangxieco Zanaco Jialongco(1st) Jialongco(2nd) Degaco Zhemaico Cilaco

Elevation (m)

Outburst year Accumulation months

TMC(
C)

Ablation months

TMA(
C)

Preceding year Accumulation months

TMC(
C)

Ablation months

TMA(
C)

5271 5210 5560 4332 4332 5130 4640 5353 3816 4744 4410 4410 5316 5300 5060

1963.10e1964.5 1963.10e1964.5 1967.10e1968.5 1971.11e1972.4 1973.11e1974.3 1980.10e1981.5 1980.10e1981.4 1981.10e1982.4 1987.11e1988.3 1994.10e1995.4 2001.11e2002.3 2001.11e2002.3 2001.10e2002.5 2008.10e2009.5 2008.10-2009.4

59.29 67.68 82.75 38.94 46.4 68.1 35.16 50.77 14.75 42.03 21.86 21.86 50.8 59.35 55.73

1964.6e1964.9 1964.6e1964.9 1968.6e1968.8 1972.5e1972.7 1974.4e1974.7 1981.6 1981.5e1981.7 1982.5e1982.8 1988.4e1988.7 1995.5e1995.6 2002.4e2002.5 2002.4e2002.6 2002.6e2002.9 2009.6e2009.7 2009.5e2009.7

18.79 10.5 5.34 23.28 19.84 2.3 13.02 21.56 28.78 10.57 4.26 11.4 12.56 9.53 13.77

1962.10e1963.4 1962.10e1963.5 1966.9e1967.5 1970.10e1971.3 1972.10e1973.3 1979.10e1980.5 1979.10e1980.4 1980.10e1981.4 1986.11e1987.3 1993.11e1994.4 2000.11e2001.4 2000.11e2001.4 2000.10e2001.5 2007.10e2008.4 2007.11e2008.4

66.05 65.89 74.35 40.94 37.54 66.20 32.36 50.16 14.74 33.28 19.15 19.15 45.85 55.85 53.67

1963.5e1963.9 1963.6e1963.9 1967.6e1967.8 1971.4e1971.7 1973.4e1973.7 1980.6 1980.5e1980.7 1981.5e1981.8 1987.4e1987.7 1994.5e1994.6 2001.5 2001.5-2001.6 2001.6e2001.9 2008.5e2008.7 2008.5e2008.7

18.58 11.32 5.4 19.34 21.44 2.4 13.02 20.41 27.82 8.43 3.99 11.41 13.95 8.15 11.27

In order to unite the separate influences of ablation and accumulation, the parameters (DTO, DTP) were taken to represent the increments or fluctuations between ablation temperature and accumulation temperature in the outburst year and the preceding year, defined as follows:

DTO ¼ TMA  TMC

(5)

0 0 DTP ¼ TMA  TMC

(6)

Large DTO and DTP indicate a dramatic change in temperature from cold to hot weather. In Table 5, 14 cases out of the 15 GLOFs show DTO greater than DTP, showing that the outburst years had more fluctuations or increments than years with no outbursts in the same period.

Based on the data corrected by the five-day moving average, we can consider the cumulative ablation temperature over the successive days until the outburst day, which is defined as ðNÞ

TDA ¼

ðnÞ

Glacier-lake

Jilaico Damenlakeco Ayaco(1st) Pogeco(1st) Pogeco(2nd) Zharico Cirenmaco Jingco

Outburst Year

Preceding Year

DTO(
C)

DTP(
C)

78.07 78.18 88.10 62.22 66.24 70.40 48.17 72.34

84.64 77.21 79.75 60.28 58.98 68.60 45.37 70.58

Glacier-lake

Guangxieco Zanaco Jialongco(1st) Jialongco(2nd) Degaco Zhemaico Cilaco

Outburst Year

Preceding Year

DTO(
C)

DTP(
C)

43.54 52.6 26.12 33.26 63.36 68.88 69.51

42.56 41.71 23.14 30.56 59.80 64.00 64.94

Furthermore, the elevation effect is also significant. As shown in Fig. 6, these increments generally increase with elevation. Except for three points at low elevation (below 4500 m, in the cases of Guangxieco and Pogeco Lakes), DTO increases with elevation in a power-law form (DTO ¼ 4E-15H4.3804). The more elevated glacier lakes require more monthly temperature increments or fluctuations. This also explains why lakes at a higher altitude outburst later. These analyses suggest that GLOFs respond more significantly to monthly variations in temperature (i.e. DTO) than they do to annual variations.

4.3. Variations in mean daily air temperature Using the five-day moving average method, all days on which outbursts occurred had a mean daily air temperature (MDAT) of more than 0
C. The outburst day is the end day for averaging.

TDk

(7)

k¼1

where N represents the number of continuous ablation temperaðNÞ ture days, TDk is the corrected daily average temperature, and TDA is the cumulative daily temperature for all the days that reach ablation temperature and represents the sum of the heat over a period. TDA is the final calculated value before the outburst day. Similarly, we also define the accumulation daily temperature TDC as

TDC ¼ Table 5 DTO and DTP in the outburst year and the preceding year for 15 GLOFs.

kX ¼N

iX ¼n

TDi

(8)

i¼1

where n represents the number of continuous accumulation temperature days, TDi is the corrected daily average temperature and ðnÞ TDC is the cumulative negative temperature for n days. TDC is the final calculated value before the start day in the year. Table 6 indicates 9 cases having a greater TDA and 13 cases having a greater jTDC j in comparison with the case of non-outburst. This is consistent with the previous analysis of monthly average temperatures, that is, there is a more obvious influence of accumulation temperature on GLOFs than there is for ablation temperature. Analogous to the monthly case, we defined the difference between TDA and TDC as

TF ¼ TDA  TDC

(9)

All the results, including, are listed for the 15 outbursts in Table 6. A total of 93.3% of the outbursts had daily temperature increments in the outburst year greater than in years with no outburst, except the GLOF of Jilaico Lake. We also found an elevation effect. The outburst lakes at higher altitudes had a greater number of daily temperature increments. Some lakes located above 5000 m had daily temperature increments of about 2 C
or more, while the lakes at lower elevations of about 4000 m had fewer daily temperature increments. In addition, TF also increased with elevation according to a power-law form (TF ¼ 7E-15H 4.7012), which accorded with the analysis of monthly temperature. Monthly and daily temperature increments can be used as a typical index for warning of potential GLOF occurrences.

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Table 6 T,T,V DA and TF in the outburst year and the preceding year for the 15 GLOFs. Glacier-lake

Elevation (m)

Jilaico Damenlakeco Ayaco(1st) Pogeco(1st) Pogeco(2nd) Zharico Cirenmaco Jingco Guangxieco Zanaco Jialongco(1st) Jialongco(2nd) Degaco Zhemaico Cilaco

5271 5210 5560 4332 4332 5130 4640 5353 3816 4744 4410 4410 5316 5300 5060

Outburst year

Preceding year

T (
C)

T (
C)

V DA

TF

T (
C)

T (
C)

V DA

TF

552.52 326.35 159.05 631.85 424.27 44.60 311.70 624.12 707.79 165.76 87.55 319.52 377.64 156.24 413.11

1791.45 2074.45 2531.12 1225.58 1502.05 2058.82 1051.74 1536.79 455.35 1290.83 647.90 647.90 1543.53 1817.48 1685.00

0.059 0.048 0.099 0.085 0.083 0.248 0.066 0.079 0.057 0.16 0.406 0.165 0.055 0.122 0.088

2343.97 2400.80 2690.17 1857.43 1926.32 2103.42 1363.44 2160.91 1163.15 1456.59 735.45 967.42 1921.17 1973.72 2098.10

580.77 347.64 138.48 504.53 412.57 47.41 260.87 603.20 671.88 72.31 91.80 349.24 419.39 143.34 333.04

2010.62 1984.48 2273.34 1196.26 1511.01 2010.32 996.15 1523.52 432.00 1031.52 584.47 584.47 1408.89 1712.42 1657.53

0.061 0.054 0.09 0.075 0.072 0.196 0.092 0.061 0.08 0.296 0.164 0.092 0.155 0.11 0.087

2591.39 2332.12 2411.82 1700.79 1923.58 2057.73 1257.02 2126.72 1103.88 1103.83 676.27 933.71 1828.28 1855.76 1990.57

The cumulative daily average temperature, both for ablation and accumulation, was studied. In comparison with the cumulative curves for each lake in the outburst year and the preceding year, the average growth rate (the curve slope) was also found to be important. In the following section, V DA refers to the average growth rate of the cumulative ablation temperature day by day, and is defined as:

 V DA ¼

ð2Þ

ð1Þ

TDA  TDA

surge through the outlet channel and initiate rapid incision of the moraine. Alternatively, the dam may progressively weaken through seepage and enlargement of drainage conduits in the moraine until it is unable to hold back the impounded water (Kattelmann, 2003). GLOFs triggered by ice avalanches are most typical and representative, and the main reason of all of the GLOFs we studied in Tibet. The occurrence of ice avalanches associated with glacier melting,

.   .   .  ð1Þ ð3Þ ð2Þ ð2Þ ðNÞ ðN1Þ ðN1Þ TDA þ TDA  TDA TDA þ ::: þ TDA  TDA TDA N1

where N is the number of continuous ablation temperature days ðNÞ and TDA is the same as defined by Eq. (7). The average growth rates of the cumulative temperature day by day for the 15 events were calculated and are also listed in Table 6. According to these results, only 60% of outbursts have a higher growth rate than in a year with no outburst. The influence of temperature is more noticeable than the average growth rate on a day by day basis. 5. Discussion GLOFs in Tibet have marked seasonality and strong elevation dependence. And GLOFs occurrence depends on the combined influence of ablation temperature and accumulation temperature, with the influence of accumulation temperature being more important. The findings benefit the understanding of the key processes and cause-effect relationships driving glacier and glacier lake response to temperature change. In particular, the relationship between temperature fluctuation and GLOFs provides a warning index for glacial lakes prone to break in the critical period (years, months, days), combined with the recent remote-sensing techniques (Huggel et al., 2002; Jain et al., 2012; Xie et al., 2012) in order to monitor the glacial lakes and assess the hazard of GLOFs. 5.1. Role of air temperature in GLOFs Glacier Lake Outburst Floods (GLOFs) represent in general the highest and most far-reaching glacial risk with the highest potential of disaster and damages (Richard and Gay, 2003). They occur through two main principle mechanisms. Waves generated by rockfall, snow and ice avalanches, glacier calving, or earthquakes can

(10)

retreat and new hazardous glacial lakes born (Kumar and Murugesh Prabhu, 2012), are influenced by climate change. The glacial lakeeclimate change response system is a complex chain of processes. Many non-linear feedback effects contributing to the growth/decay of lakes and the triggering of outburst events have been documented (Kaltenborn et al., 2010). Although GLOFs triggering modes show a great amount of inherent complexity and variation, there are clear overall trends indicating the increase in GLOFs frequencies, such as southern Tibet (Liu et al., 2008b), Karakoram in China (Chen et al., 2010), Hindu Kush Himalayan (HKH) region (Ives et al., 2010) and Sikkim Himalayas (Kumar and Murugesh Prabhu, 2012). 5.2. The importance of accumulation temperature less than 0
C The incidence of GLOF is not only influenced by ablation temperature more than 0
C, but also by accumulation temperature less than 0
C at an early stage, which is even more important. Comparing the trends for MAAT, MMAT and MDAT in the previous and outburst years indicates that the single factor of high temperature was not a direct explanation for outbursts. The combined effects of sustained low-temperature periods in the earlier stages and apparent high-temperature fluctuations in the later stages were what promoted the possibility of GLOF occurrences. This differs from the previous understanding that it is high temperature alone that plays a positive role in GLOF occurrences. This influence could be explained by GLOF processes. The glaciers advanced in the colder periods and the ice snout moved forward or even entered the lake. Afterwards, in the warmer years or months, the glaciers melted and the glacier lakes therefore

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85

Fig. 5. GLOFs at different elevations and months.

Yamada (1998). At the same time, the strong elevation dependence of delay in GLOFs occurrences shows that higher glacier lakes outburst relatively later. Both the effect of season and elevation can be explained partly by ablation temperature TMA, TDA and accumulation temperature TMC, TDC. TMC and TDC are important in accelerating the growth and advance of glaciers, causing the glacial tongue to move near or even into the lake in the cold months. In summer, TMA and TDA caused by high temperatures and rapid warming leads to strong ablation of the glacier and rising water levels in the lake, or even ice avalanches into the lake. That is the reason that all outbursts happened in the month and day with high average temperature. The elevation dependence is related to temperature lapse rate. Lower lakes receive more heat than higher lakes in the same months, and the higher lakes require more days to accumulate enough heat for ablation to start. In general, the most vulnerable glaciers are at relatively low elevations, whereas glaciers at high altitudes are more robust (Kaltenborn et al., 2010). 5.4. Data availability and accessibility In general, the related meteorological stations are far below the lower limit of outburst lakes, and some are affected by progressive

Fig. 4. Variations in mean annual air temperature of 13 lakes between 1960 and 2010 (The dots C indicates the outburst years. The boxed positive numbers mean the increase in MAAT, whereas the boxed negative numbers mean the decrease in MAAT compared with the year immediately preceding that in which the GLOF occurred.).

contained more water. With the increase in temperature, ice avalanches collapsed into the lakes, thus applying instant pressure against the moraine dam, eventually leading to the outburst occurrence. 5.3. GLOFs seasonality and elevation dependence All the identified GLOFs in Tibet took place between May and September with MMAT more than 0
C, particularly in July and August. The outburst days also have an MDAT of more than 0
C. GLOF occurrence has significant seasonality, consistent with

Fig. 6. Elevation effect on mean monthly air temperature increments.

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urbanization (Bolch et al., 2012), as is the situation here. Then we have made data corrections to reduce the uncertainty caused by differences in elevation and distance. Even though, we choose three ways to correct the data including altitude correction by regional lapse rate, distance correction by inverse distance-weighted interpolation, and data screening by five-day moving average, there are still two limitations. The climatic sensitivity of a glacier and glacier lake not only depends on regional climate variability but also on local topographic effects and the distribution of the glacier area with elevation, which can result in two adjacent glaciers featuring different specific mass balance responses (Kuhn et al., 1985). The analytical or numerical method is needed to quantify the related topographic effects as well as to attribute the glacier mass and glacier lake changes to individual meteorological or climate parameters (e.g., Kuhn, 1981; Oerlemans, 2001). In this paper, we ignore the influence of terrain to minimize variables because of the complexity of microtopograph. Future research should include local microtopography data correction. Different glacier-lakes behave in different ways and timescales, so it is important to monitor change over the long-term. Tibetan meteorological stations were established since the 1960s and only cover a part of Tibet in China. Currently, the paucity of data in many areas, the lack of institutional capacity to analyze and short duration of data records limit the validity of conclusions. It is difficult to discuss the qualitative relationship between climate change and glacier behavior in Tibet owing to the limited observation in the weather stations and the complexity of climate change and glacier lake dynamic response. Improved understanding of the effect of climate change in GLOFs will require significant efforts to establish a meteorological station network and develop databases of the GLOFs on reference glaciers. 6. Conclusions 1. On annual temporal scales, 1960e1975 and 2000e2010 were active periods for GLOFs in Tibet. A total of 58.8% of the outbursts happened at the inflection point between a lowtemperature to a high-temperature year. On monthly midtemporal scales, GLOFs displayed a strong dependence on monthly variations. All GLOFs occurred within an ablation month, in which the average temperature was greater than 0
C. On daily small temporal scales, all the outburst days also had an average temperature of more than 0
C. 2. GLOFs occurrence depends on the combined influence of ablation temperature and accumulation temperature, with the influence of accumulation temperature being more important. A major change or increments in the monthly and daily temperature from cold to hot weather can be used as a typical index for the combined influences; 93.3% of outbursts had more temperature increments in an outburst year than in a year with no outbursts. 3. The elevation effect is remarkable for a GLOF in that the lake at lower altitude usually bursts earlier. Almost all outburst lakes below an elevation of 5000 m burst in May to July, whereas lakes above 5000 m burst in June to September, and especially in July to September. This can be explained by temperature increments and the temperature lapse rate. The higher lake needs a greater number of temperature increments and more time to accumulate heat. The monthly increment DTO and daily increment TF increase according to a similar power-law form with elevation. Acknowledgements We would like to thank Prof. Li Yong for his thoughtful revisions of earlier versions of this document and Prof. Xie Hong for his help

in the field survey. We are very grateful to the anonymous reviewers and the associate editors who provided useful comments, which significantly improved the clarity and presentation of the results. This work is supported by the National Natural Science Foundation of China (Grant No.41201010, 41371038), the Directional Projects of IMHE (Grant No. SDS-135-1202-02) and the Open Research Fund of the State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (Grant No.IWHR-SKL201209). References Ames, A., 1998. A documentation of glacier tongue variations and lake development in the Cordillera Blanca, Peru. Zeitschrift fur Gletscherkunde und Glazialgeologie 34 (1), 1e36. Berthier, E., Arnaud, Y., Kumar, R., Ahmad, S., Wagnon, P., Chevallier, P., 2007. Remote sensing estimates of glacier mass balances in the Himachal Pradesh (Western Himalaya, India). Remote Sensing of Environment 108 (3), 327e338. Bishop, M.P., Shroder Jr., J.F., Hickman, B.L., Copland, L., 1998. 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