Monitoring changes of snow cover, lake and vegetation phenology in Nam Co Lake Basin (Tibetan Plateau) using remote SENSING (2000–2009)

Journal of Great Lakes Research 39 (2013) 224–233

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Monitoring changes of snow cover, lake and vegetation phenology in Nam Co Lake Basin (Tibetan Plateau) using remote SENSING (2000–2009) Bing Zhang a,⁎, Yanhong Wu a, Liping Lei a, Junsheng Li a, Lingling Liu a, Dongmei Chen b, Junbo Wang c a b c

Key Laboratory of Digital Earth Science, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100094, PR China Department of Geography, Queen's University, Kingston, ON K7L3N6, Canada Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, PR China

a r t i c l e

i n f o

Article history: Received 24 May 2012 Accepted 1 February 2013 Available online 20 April 2013 Communicated by Barry Lesht Keywords: Tibetan Plateau Nam Co Lake Snow cover Water storage Lake level Vegetation phenology

a b s t r a c t The changes of environmental factors such as snow cover, vegetation and hydrologic regime of lakes can reflect ecosystem responses to changing climate. A series of satellite imagery-based environmental data archives including variations in snow cover, vegetation phenology and lake level were mapped in the Nam Co Lake Basin for the period 2000–2009. Results of the synthesis indicate that throughout this period, the average annual snow cover was 19.87% of the total basin, and there is an obvious relation between the elevation and a clear decreasing southeast–northwest trend in snow-cover persistence. Snow mainly happens from October to May. The multi-year mean water storage of Nam Co Lake is 86.40 × 109 m3, with a lake level increase of approximately 2.06 m during the study period. Vegetation phenology showed obvious variation with advanced start of season (SOS) and slightly extended duration of season (DOS). The mean DOS for the Nam Co Lake Basin was 154 days from 2000 to 2009. Affected by air temperature, the SOS dates coincided with snowmelt. The seasonal-variability of climate factors was also studied. The satellite-derived continuous and multiple datasets offer the advantage of monitoring the temporal and spatial trends of each of these metrics and mapping extensive, remote in mountainous areas with no in-situ data such as represented by the Tibetan Plateau. © 2013 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Introduction The impact of global climate change is expected to result in greater variation in land-surface conditions, including snow cover, glacier, ecosystem, and the hydrologic regime of lakes and vegetation phenology in highland regions (Bradley et al., 2006; Immerzeel et al., 2009). The Tibetan Plateau (TP) has an average elevation of more than 4000 m asl, and is the highest and largest highland in the world, often called “the Third Pole” of the earth. Because heating on the TP directly acts on the atmosphere of the middle troposphere, the thermodynamic processes over the Tibetan Plateau not only strongly influence the Asian monsoon and precipitation, but also Northern Hemispheric atmospheric circulations and the East Asian monsoon precipitation (Zhou et al., 2009). You et al. (2010) reported that the linear rates of temperature increase during 1955–2004 were about 0.25 °C/decade for the annual mean. Liu and Chen (2000) also proposed that the warming amplitude in the TP exceeded that for the same latitudinal zone of the Northern Hemisphere as a whole. Snow cover, vegetation and water directly affect the values of the solar albedo and change the heating conditions of the land surface and the atmosphere and their interannual and inter-decadal variability features ⁎ Corresponding author. Tel.: +86 10 82178002. E-mail address: [email protected] (B. Zhang).

which significantly influence the Northern Hemisphere's atmospheric circulation and the East Asian monsoon precipitation (Zhao et al., 2007). Consequently, it is important to study the variation features of snow cover, vegetation and water over the TP despite its remote location. Recent investigations show the TP is experiencing rapid changes, such as shrinking of glaciers and a decrease of snow cover, destabilizing cryospheric environments and an increase of environmental hazards (Li et al., 2008; Liu et al., 2009; Quincey et al, 2007; Yang et al., 2010; Zhang, 2007). Remote sensing data and its environmental products have advantages in monitoring environmental changes such as snow cover, vegetation phenology and lake area variation (Dankers and de Jong, 2004; Hall et al., 2002; Harris, 1994; Li et al., 2007b). Several studies have analyzed climate and environmental change in TP using in situ observation and remote sensing data. The seasonal variations in snow cover over the Nyainqentanglha Mountain were estimated using MODIS and ASAR data, and the results show that the snow cover occupies about 28.9%, 69.9%, 11.6% and 14.7% of the whole region in April, May, July and September 2003 respectively, with the snow depth reaching a maximum in May and a minimum in July (Wen et al., 2006). Immerzeel et al. (2009) investigated the seasonal evolution of snow cover from 2000 to 2008 over the Himalayan river basins and reported that the majority of the TP is snow covered less than 20% of the total winter and spring period with different river basins showing

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B. Zhang et al. / Journal of Great Lakes Research 39 (2013) 224–233

various variation patterns. Li et al. (2007c) modeled the seasonal dynamics of gross primary production of three alpine grassland ecosystems in the TP using MODIS images and the satellite-based Vegetation Photosynthesis Model (VPM), and obtained good estimation results. However, there has been very little long-term series analysis of vegetation phenology monitoring. Present analyses of lake variations mainly focus on the monitoring of lake area. Primary results have indicated that the fluctuation of inland lakes depends on the variation of sources of water supply (Lu et al., 2005; Morrill, 2004; Shao et al., 2007; Yang et al., 2003; Zhao et al., 2006). Analyses related to water storage and lake level mainly exist in three areas with hydrological observation stations, namely Qinghai Lake, Yamdrok Lake and Zabuye Salt Lake (Bian et al., 2009; Li et al., 2005; Qi and Zheng, 2006). The lake level and water storage analyses in other vast areas of the TP have less data and are still not well known. Land surface ecosystem responses to changing climate are expected to occur over different temporal scales. Remote sensing data can potentially capture much of this variation in seasonality provided that the data record is sufficiently long, and are especially valuable in data-sparse areas such as TP. In view of the significance of previous results, comprehensive regional studies on this topic are still surprisingly lacking. Therefore, more work is clearly required for monitoring temporal trends of environmental factors and mapping the change in vast regions of the TP. Changes of lakes in TP have been investigated, especially Nam Co Lake (Guan et al., 1984; Wang et al., 2010; Zhang et al., 2011a). The hydrological and ecological change in the Nam Co basin is typical of highland lakes. Li et al. (2007a) studied the major ion composition of precipitation and its sources in Nam Co region. Wu and Zhu (2008) concluded that from 1970 to 2000 the Nam Co Lake surface area increased at a rate of 1.27 km2/a, while its catchment glacier area decreased at a rate of 0.86 km2/a. Wang et al. (2009) investigated the bathymetry and water quality of the lake and classified it as a high-altitude lake with a depth of more than 90 m. Zhu et al. (2010) roughly estimated the lake's water balance. Zhang et al. (2011a) estimated the water storage of Nam Co Lake and analyzed the intra-annual and inter-annual fluctuation trend. Besides, Lv et al. (2009) observed the phenological features of ten domain plants of the lake's alpine meadow during 2007 and 2008 and concluded that the general features were characteristics of short growing seasons. Kropacek et al. (2010) found that the seasonal distribution of snow cover in Nam Co Basin is far from being spatially homogenous. Long-time series and seasonal monitoring studies of the lake's ecosystem are lacking, as are comprehensive analyses of environmental change. In this study we focus on temporal pattern detection and synthetic analysis of snow cover, vegetation phenology and water storage changes in the Nam Co Lake basin. We used Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, and vegetation index products for temporal pattern detection in the Nam Co basin. Simultaneously, we chose LANDSAT TM/ETM and HJ-1 satellite images for calculating water storage in the lake. We also estimated the variations in the lake's water level for the first time, using the underwater DEM data measured on the lake. Finally, we conducted a comprehensive analysis of the environmental factors in the Nam Co basin, so as to provide the basic data and analytical support for further understanding the response of land surface environment to global climate change.

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and south of the lake, with all of the biggest rivers in the southwest originating from the Nyainqentanglha range, thus providing the main water supply to the lake. Nam Co Lake lies in the Nam Co basin, and is the largest lake in Tibet as well as the world largest lake at the highest elevation. It is located at 90°16′~91°03′E, 30°30′~30°55′N (Fig. 1). It belongs to Damxung County of Lhasa City and Baingoin County of Nagqu Prefecture of the Tibet Autonomous Region. Its elevation is 4718 m, and its water area is 1920 km 2 measured in 1979 (Guan et al., 1984), and the maximum depth is over 90 m (according to the data measured from 2005 to 2007). Because of its special geographical position, Nam Co Lake is scarcely influenced by human activity. There was no meteorological observation station or hydrological station in the basin before 2005. After 2005, the Institute of Tibetan Plateau Research established the Nam Co Lake Multi-Layer Comprehensive Observation and Research Station (Nam Co Lake Station) of the Chinese Academy of Sciences. The station gradually began monitoring some regular parameters. Researchers conducted three comprehensive investigations from 2005 to 2007 and obtained the water-depth data on Nam Co Lake (Wang et al., 2009). Datasets and methodology Snow extent The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are provided as a sequence of products beginning with a swath product, and progressing, through spatial and temporal transformations, to an 8-day global-gridded product. In this study we use MOD10A2 products for snow cover analysis for the period March 2000 to December 2009. The MOD10A2 data are the 8-day composite snow cover observations at 500 m resolution. The MODIS snow cover mapping algorithm uses satellite reflectance data collected in MODIS bands 4 (0.545–0.565 μm) and 6 (1.628–1.652 μm) to calculate the normalized difference snow index (NDSI; Eq. (1)) (Hall et al., 1995; Salomonsona and Appel, 2004). NDSI ¼ ðband4−band6Þ=ðband4 þ band6Þ:

ð1Þ

The quality of MODIS snow data has been evaluated (Hall and Riggs, 2007; Klein and Barnett, 2003). The overall accuracy of the MODIS snow products is ~93%, with variability due to land cover type and snow condition (Hall and Riggs, 2007). Lake area, volume and lake level Lake area The images and data resources utilized for calculation of lake area and water volume for this research are 32 CCD images from Landsat TM/ETM, CBERS and HJ-1A/1B covering the research area (Table 1). A modified normalized difference water index (MNDWI), which based on McFeeters' (1996) normalized difference water index (NDWI), has been used in this paper to extract the lake's water area. The lake water mapping algorithm uses MIR (TM band 5) instead of NIR (TM band 4) to calculate the MNDWI (Xu, 2005).

Study area

MNDWI ¼ ðGREEN−MIRÞ=ðGREEN þ MIRÞ:

ð2Þ

The Nam Co basin is located in 89°21′~91°23′E, 29°56′~31°7′N, has an area of 10610 km 2, and the replenishment coefficient is 5.53. It is north of Gangdise-Nyainqentanglha Mountain and is a closed basin, with the main water supply from precipitation and melting glacier water (Guan et al., 1984). There were altogether more than 60 river inflows in the investigated seasons, most of them distributed west

Otherwise, according to the characteristics of the sensor of CBERS and HJ-1A/1B, bands 4, 3 and 2 (red, green, blue) were adopted to estimate the lake surface area according to the band threshold value and spectral relationships of each ground object and were supplemented by the visual interpretation method to adjust the result.

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B. Zhang et al. / Journal of Great Lakes Research 39 (2013) 224–233

Fig. 1. a) Map shows the location of observation stations in the Nam Co lake region. The dark gray line represents the Nam Co lake catchment while light gray lines represent rivers, the double circle symbol represent meteorological stations, and the fork circle symbol represent the hydrological stations. b) Map gives the position of Nam Co on the Tibetan Plateau.

Lake volume The water storage (W) for a lake can be written as W = f (S, H), where S is the lake surface area and H is the water depth of the lake underwater landform. The lake underwater landform data (H) were obtained through measurements taken in the field. Based on the lake bathymetric survey data measured from 2005 to 2007 with the accuracy of 0.01 m and 305,721 measuring points (Wang et al., 2009), we drew the isobathic map, and then established the underwater landform digital elevation model (DEM) (Fig. 2-b) with grid units of 30 m ∗ 30 m by the extended module (3D Analyst) in ArcMap. Lake water storage data were gained by the “Area and Volume” module of 3D analysis in ArcGIS, utilizing the calculation of excavation and filling based on the underwater landform DEM and the lake surface area data (Fig. 2-c), which were obtained through the actual measurements in the field and remote sensing images respectively. Lake level The lake level-area curve of Nam Co was acquired based on the digital elevation model (DEM) using mathematical functions from the Grid Module in Arc Info. Moreover, the water level of the lake is estimated

following the relationship between water level (H) and lake area (S) of the Nam Co Lake: 2

3

H ¼ 0:0046S þ 0:00005S −0:00000002S :

ð3Þ

The expression is significant at the level of 1%, where 0 b H b 100 m, and 0 b S b 2012.62 km2. A new lake level series of Nam Co Lake was obtained based on the water level-area curve and the surface area extracted by the images, as listed in Table 1 (Fig. 2-d). Vegetation dynamics The MODIS vegetation index product contains two vegetation indices: a normalized difference vegetation index (NDVI) and an enhanced vegetation index (EVI) (Huete et al., 2002). Vegetation phenology was derived from 250 m, 16-day NDVI data. The compositing process uses a constrained view-angle, maximum value approach, where the maximum vegetation index value over the 16-day period is retained, provided it meets a filtering requirement based on quality, cloud cover, and viewing geometry (van Leeuwen et al., 1999).

B. Zhang et al. / Journal of Great Lakes Research 39 (2013) 224–233 Table 1 Remote sensing data and Digital Elevation Model (DEM) data used for lake volume and water level calculations in this paper. SN is satellite identifier. DEM used in calculations is downloaded from: http://asterweb.jpl.nasa.gov/gdem-wist.asp. SN Data type

Acquisition time (year/month/day)

Resolution (m)

1

2007/05/15; 2009/10/17; 2009/12/04

30

2

3 4

Landsat5 TM Landsat7 ETM

2000/03/06; 2000/04/07; 2000/10/16; 2000/11/01; 30 2000/12/19; 2001/02/25; 2001/06/13; 2001/11/04; 2001/12/06; 2002/02/24; 2002/03/28; 2002/05/15; 2002/12/09; 2003/01/10; 2003/04/16 CBERS-CCD 2003/11/07; 2004/09/14; 2004/10/10; 2005/11/30; 19.5 2006/01/21 HJ-CCD 2008/12/13; 2009/01/07; 2009/02/06; 2009/03/14; 30 2009/04/16; 2009/05/19; 2009/06/25; 2009/08/30; 2009/11/08

Although the MODIS NDVI data were well corrected, they were still contaminated with cloud, aerosol, snow cover, etc. The Harmonic Analysis of NDVI Time Series (HANTS) algorithm (Julien and Sobrino, 2010; Roerink et al., 2000) was applied to time series of NDVI images. In HANTS, a curve fitting was applied iteratively; i.e. first, a leastsquares curve is computed based on all data points, and then the observations are compared to the curve. This iteration continued and eventually led to a smooth curve that approached the upper envelope

227

over the data points. For our analysis of MODIS NDVI data, the HANTS parameters were set as follows: Number of frequencies, 2; Fit Error Tolerance, 1000; and Degree of OverDeterminedness, 8. The start of season (SOS) is usually defined as the beginning of measurable photosynthesis in the vegetation canopy, which is always referred to day of year (DOY) identified as having a consistent upward trend in time series NDVI data. The end of Season (EOS) is usually defined as the end of measurable photosynthesis in the vegetation canopy, which is always referred to the DOY identified at the end of a consistent downward trend in time series NDVI data. The duration of season (DOS) is usually defined as the length of photosynthetic activity (the growing season), which is always referred to the days from the DOY of SOS and DOY of EOS. Considering the definition of SOS (Soudani et al., 2008) and the solution of effect of snow melt (Delbart et al., 2005), the dynamic threshold-based approach to extract the phenological metrics from time-series of NDVI data was illustrated as the following formula (Eq. 3). The EOS is defined in a similar way, whereas DOS could be calculated from the number of days from the DOY of SOS to the DOY of EOS. NDVI lim ¼ ðNDVI max −NDVI min Þ•C þ NDVI min

ð4Þ

where NDVIlim is the dynamic threshold, NDVImax is the maximum of NDVI during the DOS, NDVImin is the minimum during the upward and

Fig. 2. a) False color image of Nam Co lake acquired November 8, 2009 with light blue pattern on lake surface being forming ice (b) The DEM-simulated underwater topography from high elevation at lake surface to low elevation (max. depth of 95 m). (c) Extent of surface fluctuation of the Nam Co Lake during 2000 to 2009 with inset A enlarged to show extent of encroachment of higher water levels along shallow eastern shoreline. (d) Mean annual water level of Nam Co reconstructed from map in (c).

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downward phases, and C is coefficient. We calculated the coefficient C based on the in-situ observation data at the phenology observation site of Nam Co Station (Lv et al., 2009). C is 0.23. Climate variables Meteorological data from the Baingoin station (31°23′N, 90°01′E, 4700 a.s.l.) around Nam Co Lake were examined for trends over the study period from 2000 to 2009. The regular monitoring data of the Baingoin station were used, including daily average temperature, lowest temperature, highest temperature, rainfall, sunshine duration, wind speed and water vapor pressure. The location of the station is shown in Fig. 1. Because there are so few records on the direct evaporation of the lake surface, in this paper the Penman–Monteith model recommended by FAO was adopted to estimate the lake evaporation (Allen et al., 1998). The formula is: ET 0 ¼

900 0:408ΔðRn −GÞ þ γ Tþ273 U 2 ðes −ed Þ Δ þ γ ð1 þ 0:34U 2 Þ

ð5Þ

where Δ represents the curve slope of saturated water vapor pressure (kPa°C −1) at temperature T; Rn represents the solar net radiation at the top layer (MJ∙m−2∙d−1); G represents soil-pass heat (MJ∙m−2∙d−1); γ is the dry–wet constant (kPa°C1); T is the average monthly temperature (°C); U2 is the wind speed at the height of 2 m (m/s); es and ed are the saturated water vapor pressure and actual water vapor pressure (kPa) respectively at temperature T. Trend analysis To assess the trend of climatic factors, we have employed the non-parametric Mann–Kendall test (Kendall, 1975; Mann, 1945) to conduct trend analysis. It has been identified as one of the most robust techniques available to uncover and estimate linear trends in environmental data (Hess et al., 2001). As to the series Xt = (x1, x2,…, xn), this method defines the standard normal variate UMK as: S U MK ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffi Var ðsÞ S¼

n −1 X

n X

ð6Þ

  sgn xj −xi

ð7Þ

i¼1 j¼iþ1

8 < 1; x > 0 sgnðxÞ ¼ 0; x ¼ 0 : −1; xb0

ð8Þ

Fig. 3. a) Seasonal and smoothed snow pack area from 2000–2009 based on MODIS MOD10A2 product for the Nam Co Lake Basin. (b) Average seasonal snow pack area from data in (a) for 2000–2009.

persistent in the southeast regions of the Nam Co basin, and the duration mainly concentrates from October to May of the following year, while the snow cover vale happened in late July and early August. The extent of snow cover was greater in spring and autumn than summer and winter, and winter had the least snow during the study period (Fig. 3-b). This figure matches well with the analysis result by Wang et al. (2007).

n X nðn−1Þð2n þ 5Þ− t i iði−1Þð2i þ 5Þ

Var ðSÞ ¼

i¼1

18

ð9Þ

where S denotes the relationship between the number of observation pairs (xi, xj, j > i), and n is the total number of samples. A time series has a clear trend, defined as a level of significance of 5%, if |UMK| > Ua/2 = 1.96. A positive UMK indicates an increasing trend in the time series, while a negative UMK indicates a decreasing trend. Results and discussion Snow extent The seasonal evolution of snow cover in Nam Co Basin from 2000 to 2009 is shown in Fig. 3. The average annual snow cover is 19.87% of the total basin during the study period. Snow cover appears more

Fig. 4. Annual persistent snow cover based on MOD10A2 snow cover time series from March 2000 to December 2009. Dotted line is linear regression of persistent snow cover on year.

B. Zhang et al. / Journal of Great Lakes Research 39 (2013) 224–233

We also investigated whether any seasonal trends could be observed in this dataset. The Pearson correlation coefficient was calculated for each season for the entire basin. A negative trend was identified in summer; no significant trends were identified in any other seasons. In summer a significant negative trend in persistent snow cover area was found in every year with a tendency value of −5.85 km 2/a (r = −0.723, p b 0.05), and the obvious shrinking trend can be found during 2000–2009 in the Nam Co basin (Fig. 4). Meteorological data from the Baingoin station (31°23′N, 90°01′E, 4700 a.s.l.), including air temperature and precipitation data, were

229

used from 2000 to 2009. We processed the data into two new data series by 8 days as one unit for easy comparison. The results show that there is a significant opposite correlation (r = − 0.46, p b 0.0001) between the snow cover and daily mean temperature, while the positive relationship is between snow cover and daily precipitation. Across the Nam Co basin there is a large spatial variation in snow cover due to the large climate and altitudinal differences. Fig. 5 shows the extent and variation of snow cover for the entire spatial domain. Values indicate the percentage of time that a pixel was snow covered from March 2000 to December 2009. There is an obvious relation

Fig. 5. Annual snow cover from 2000 to 2009 based on MOD10A2 snow cover time series from March 2000 to December 2009. The values show the percentage of time that a pixel was snow covered throughout a year during the study period (2000–2009). The decreasing trend over time of persistent snow cover and intra-annual pattern of decreasing frequency of cover from SE (higher elevations along watershed divide) to NW can be observed.

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with the elevation and a clear decreasing southeast–northwest trend in snow cover persistency in all years can be observed. Comparison of the three years (2000, 2005 and 2009) shows an obvious shrinking trend in snow cover persistency regions, and the percentage with persistent snow cover of total basin area for the three years is 0.14%, 0.09% and 0.02% separately. Although the seasonal snow cover area of the whole basin expanded slightly and the permanent snow area shrank from 2000 to 2009, the frequency map of snow cover expressed the fluctuating retreating trend under global warming (Fig. 5). Lake level, water storage and surface area The calculated time series of lake levels show an obvious trend of lake level rise (Fig. 2-d). Linear regression of the series showed that the lake level of Nam Co rose from 4722.84 m to 4724.90 m, with a total rise of 2.06 m, and tendency values of 0.2017 m/a in the period from 2000 to 2009. A greater increase rate can be recognized from 2000 to 2005 than from 2006 to2009. Krause et al. (2010) remarked that the Nam Co lake level increased by 17.5 m as determined by the J2000g model from 1961 to 2010, while the lake level rose by about 2.5 m from 2000 to 2009 (≈31 cm/yr) as determined by remote sensing information. Zhang et al. (2011b) estimated the lake level rise of Nam Co as 0.25 m per year in the period from 2003 to 2009. The results of the calculations in this paper are close to the studies above. The lake water storage increased from 84.35 × 10 9 m 3 to 87.62 × 10 9 m 3 in a span of ten years. The period can be divided into two phases with the respective tendency values listed in Table 2. It shows the greater increases and larger number of significant upward trends for lake-water storage from 2000 to2005, with a tendency value of 0.58 × 109 m3/a, and then went into a second increasing phase of lesser change (Fig. 6). The lake surface area also showed the same fluctuation character with the tendency values of 6.95 km2/a, 5.66 km2/a, 6.97 km2/a and a tendency of 1.86 km 2/a separately during the four phases (Table 2). Viewing from the spatial variation characters of lake surface enlargement, it appears that the east and west flat bank areas of the lake obviously enlarge, while the south and north abrupt slope areas of the lake enlarge weakly (Fig. 2-c). We selected the data from the same month in different years to make a further analysis. Compared with the lake surface area in November of 2000, the areas in November of 2001, 2003, 2005 and 2009 were enlarged by 6.94 km2, 18.26 km 2, 32.20 km 2 and 39.64 km2 respectively.

time, the median end-of-season metric varied from 25 September to 12 October (16 days) (Table 4). Because there is a wide range of end-ofseason dates for the Nam Co Lake basin, the median length of growing season varied by 33 days (140 to 173 days) over the ten years, with the mean value of 162 days during 2006 to 2008. Li et al. (2004) and Lv et al. (2009) calculated the DOS based on the start day when the temperature reaches 3 °C and the end day when it reaches 5 °C, and deemed that the mean DOS for the Nam Co Lake basin was 159 day from 2006 to 2008. The range of median total NDVI values for the Nam Co Lake basin is 67 to 88. SOS has shown an advance tendency (r = −0.49, p = 0.04) by 1.2 day/a, and the DOS extended by 1.29 day/a (r = 0.37, p = 0.30), while the EOS showed a slight retardation tendency. It is generally acknowledged that meadows start to grow when the daily average temperature reaches 3 °C, while grass-growth development and dry matter accumulation accelerate when the temperature reaches 5 °C, and 3 °C is deemed as the critical value of low temperature for an alpine meadow to grow (Zhou, 2001). Table 5 shows the most correlated factors with DOS that are ≥5 °C accumulated temperature (r = 0.66), ≥3 °C accumulated temperature (r = 0.64) and ≥0 °C accumulated temperature (r = 0.59). Li et al. (2004) consider that the initial day of 3 °C, 5 °C and 10 °C are in accord with the start date of vegetation green-up, blossom and fruit yield. Relationships between environmental factors and climate change Climate change During the period 2000–2009 in the Nam Co basin, a general pattern of warming temperature on the lake is evident. Regional annual

Vegetation dynamics For the low temperature at the Nam Co Station (Table 3), DOS of vegetation is about 5 months from late April and early May when vegetation turns green, to late September or mid-October when vegetation dries, which is shorter than that in the low altitude areas. The start date of the growing season varies from 22 April to 13 May (21 days in difference) in the Nam Co Lake basin (Table 4), which is similar to the dates in the Lime Hills (Reed et al., 2009). At the same Table 2 Changes in area (km2) and water volume (108 m3) on the Nam Co Lake during the period 2000–2009. Year/month Lake area (km2)

Water Variation Variation Variation Variation rate percentage volume rate percentage (109 m3) (109 m3/a) (%) (km2/a) (%)

2000/11 2001/11 2003/11 2005/11 2009/11

6.95 5.66 6.97 1.86

1980.41 1987.36 1998.67 2012.62 2020.06

Fig. 6. Inter-annual variation in annual means of lake volume and surface area of Nam Co Lake between 2000 and 2009.

0.35 0.57 0.70 0.37

84.48 85.07 86.02 87.20 87.82

0.59 0.48 0.59 0.15

0.70 1.12 1.37 0.71

Table 3 Climate parameters of the phenology sites at Nam Co Station in Nam Co Basin. Data in boldface are from Lv et al. (2009) which were calculated from the observation data of Nam Co Lake Station with the average values during 2006 to 2008. And the other climate data were acquired from the Baingoin Station, with the average values during 2000 to 2009. Characteristic values

Nam Co Station

Meadow type Location Elevation (m) Annual mean temperature (°C) Average temperature of the coldest month (°C) Average temperature of the hottest month (°C) Annual precipitation (mm) ≥0 °C accumulated temperature (°C) ≥0 °C day (day) ≥3 °C accumulated temperature (°C) ≥3 °C day (day) Mean wind speed (m/s)

High cold Alpine Kobresia 30°46.44′N, 90°59.31′E 4730 −0.6 −16.7 14.6 382.68 1136.5 174 1144.3 149 3.6

B. Zhang et al. / Journal of Great Lakes Research 39 (2013) 224–233

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Table 4 Median vegetation phenology values calculated from NDVI data acquired from the meadow area of Nam Co Lake Basin over the period 2000–2009. Start and end dates for growing season are given as day/month, duration of growing season is in days.

Start of growing season End of growing season Duration of growing season Total NDVI

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

01/05 03/10 155 58

13/05 08/10 147 58

08/05 05/10 150 53

11/05 28/09 140 56

13/05 04/10 144 57

01/05 10/10 162 58

22/04 12/10 173 56

25/04 09/10 167 53

03/05 01/10 146 54

02/05 06/10 157 52

mean air temperature increased significantly (p = 0.006) by 0.135 °C/a, and annual mean evaporation increased slightly (p = 0.02) by 8.11 mm/a during the period. Fig. 7 shows the inter-annual variations in the climatic factor series. Meteorological data from the Baingoin station were examined for trends over the entire period from 2000 to 2009. The mean monthly temperatures show strong rising trends for the winter months, increasing by 0.49 °C in January and 0.13 °C in December. These increases contributed greatly to the average rise of 1.34 °C in mean annual temperature (Table 6). No significant temperature trends were found for the months of February to November. The trend analysis produced similar results for the mean monthly maximum temperatures, with strong rising trends in the winter months. This shows that climate change during this period was mainly caused by the increase in winter temperatures. Whereas annual precipitation shows a slight increase during the ten years, in the warm seasons, especially in April and June, the precipitation shows a significant decreasing trend. In the Nam Co Lake basin, more than 90% of precipitation occurs in the warm seasons (from April to October). This implies that the increase during the cold seasons (from November to March of the following year) contributed greatly to the total increase in the annual precipitation while the evaporation showed an increasing trend during 2000 to 2009, with the tendency value of 8.11 mm/a, and implied significant increase in June.

Relationships between environmental factors and climate change We compared graphical depictions of snow and lake volume variation curves and growing season phenology to look for synchronicity among the metrics (Fig. 8). Seasonal analysis from 2000 to 2009 showed significant increases in temperature and precipitation, and a slight increase in evaporation in cold seasons. There was also a slight rising of temperature, a marked decrease of precipitation and an obvious increase of evaporation in the warm seasons. The peaks of snow cover were mainly in October and May, and the minimums were generally in late July and early August. Under the environmental variation trend, the Nam Co Lake sustained expanding area and lake level rise further illustrating the important supply contributions to the lake from glacier-snowmelt water. All these points verified that the land surface environment exhibited sensitive response to global change. Table 7 shows the correlation coefficient between the three factors and the climate factors. We found a significant positive correlation between NDVI and T, as well as NDVI and P. This indicates that the

temperature and precipitation are both limiting factors in vegetation growth, and that precipitation may be more important as it has a higher correlation coefficient of 0.61. Because temperature did not increase during the DOS while precipitation reduced significantly in summer months (Table 6), reduced precipitation may have contributed to the decrease in NDVI under the warming condition during 2000–2009. The persistence of snow cover is mainly controlled by the temperature and shrank significantly during the study period. At the same time, the significant correlation between lake volume and the temperature further confirms the important contribution from the snow and glacier melt water, which may exceed the contribution from precipitation to the lake's volume. Zhu et al. (2010) showed that the increased amount of precipitation accounted for 46.67% of total increased water supply, while the increased stream runoff coming from glacier melt water reached 52.86% of total increased water supply, based on very rough estimation. Further study and data accumulation need to be carried out regarding this matter in the future.

Conclusions This study has shown that remote sensing is useful in the detection of long-term spatial–temporal patterns of environmental change in inaccessible regions, and provides useful and credible information on the response of the land and lake surfaces to global change. We developed a multiple sensor-based dataset to monitor temporal and spatial trends of snow cover, lake volume, water level and vegetation phenology in the Nam Co basin from 2000 to 2009. The major results include the following: The average annual snow cover is 19.87% of the total basin, but the persistent snow cover shrank significantly during the study period. The snow cover variation pattern is due to increasing air temperatures over this period. The lake level of Nam Co rose from 4722.84 m to 4724.90 m, with the water storage increasing from 84.35 × 10 9 m 3 to 87.62 × 10 9 m 3

Table 5 Correlation analysis between the duration of season and the accumulated days in excess of selected temperatures. R is the correlation coefficient and p is the probability of significant relation being change. Accumulated temperature

R p

≥0 °C

≥3 °C

≥5 °C

0.59 0.071

0.64 0.044

0.66 0.037

Fig. 7. Inter-annual variation trend of annual means of air temperature, precipitation and evaporation from 2000 to 2009 (Baingoin station).

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Table 6 Multiple-years mean value of monthly temperatures, precipitation, and evaporation (T , P , E ) as recorded at Baingoin station and the slope β in upward trends of over years of observation. Significance is indicated as ** and * indicating the significance levels, α, at 1% and 5% respectively by Mann–Kendall test; NT means no trend.

T T (°C) P P (mm) E E (mm)

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Year

0.49* −8.87 NT 28 NT 50.13

NT −7.79 NT 14.30 NT 52.81

NT −4.58 NT 26.90 NT 74.78

NT −0.36 −6.80* 59.90 NT 93.02

NT 3.18 NT 350 NT 109.24

NT 7.79 −73.27* 621.80 1.75* 123.95

NT 9.43 NT 953.30 NT 124.12

NT 8.63 NT 842.90 NT 111.05

NT 6.38 NT 506.30 NT 99.35

NT 0.40 NT 78.10 NT 77.62

NT −5.19 NT 28.10 NT 56.29

0.13* −7.61 NT 12.3 NT 49.95

1.34* 0.12 NT 352.19 8.11* 1022.32

Fig. 8. Upper panel, lake volume (bars), snow cover as proportion of basin and vegetation index (NDVI) from 2000 to 2009. Lower panel, temperature and precipitation at Baingoin meteorological station for same time period.

from 2000 to 2009. The expanding trend of the lake surface can be obviously monitored. The contribution from the snow and glacier meltwater seems to exceed that from the precipitation in the enlargement of Nam Co Lake. Nam Co Lake station provides phenological observation in the High Altitude Area with the low air temperature and the shorter DOS. The vegetation generally turns green in late April and becomes brown in late September and mid October. The DOS is about 5 months. It is shown that SOS advanced by 1.2 day/a (r = −0.49, p = 0.04), while the DOS extends by 1.29 day/a (r = 0.37, p = 0.30). Climate change is the driving factor in environmental changes in the Nam Co basin, especially the warming trend in winter. Comparison of the time series (2000–2009) of all seasonal metrics shows that the start of the growing season coincided well with snowmelt at late April and early May when the air temperature rises above 0 °C.

Table 7 Correlation coefficient between the factors Temperature (T) and Precipitation (P) from 2000 to 2009 and the response metrics of the mean summer NDVI vegetation index, persistent snow cover and lake volume. The significance of the correlation coefficient is indicated by the asterisks; ***, ** and * indicating α at 1%, 5% and 10% respectively, NT means no trend.

NDVI Persistent snow cover per year Lake volume

T

P

0.58* −0.87*** 0.64**

0.61* 0.35NT 0.33NT

Acknowledgments This study is supported by the Chinese National Basic Research Program (grant no. 2009CB723902), the National Natural Sciences Foundation of China (grant nos. 40901102; 40901174). The authors thank the Climate Data Center, National Meteorological Information Center, China Meteorological Administration, for providing the meteorological data for this study. We would like to thank all the members who participated in the field investigations of Nam Co Lake during 2005–2008. Special thanks are given to the reviewers for their constructive comments and thoughtful suggestions. References Allen, R.G., et al., 1998. Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. (http://www.fao.org/docrep/ X0490E/x0490e00.htm). Bian, D., Du, J., Hu, J., Li, C., Li, L., 2009. Response of lake water level variation to the climate change within Yamdrok Lake Basin in Tibet from 1975 to 2006. J. Glaciol. Geocryol. 31, 404–409 (in Chinese). Bradley, S.B., Vuille, M., Diaz, H.F., Vergara, W., 2006. Threats to water supplies in the tropical Andes. Science 312, 1755–1756. http://dx.doi.org/10.1126/science.1128087. Dankers, R., de Jong, S.M., 2004. Monitoring snow cover dynamics in Northern Fennoscandia with SPOT VEGETATION images. Int. J. Remote. Sens. 25, 2933–2949 (doi: 1874/95016). Delbart, N., Kergoat, L., Le Toan, T., Lhermitte, J., Picard, G., 2005. Determination of phenological dates in boreal regions using normalized difference water index. Remote. Sens. Environ. 97 (1), 26–38. http://dx.doi.org/10.1016/j.rse.2005.03.011. Guan, Z.H., et al., 1984. Rivers and Lakes in Tibet. Science Press, Beijing 176–182 (in Chinese). Hall, D.K., Riggs, G.A., 2007. Accuracy assessment of the MODIS snow products. Hydrol. Process. 21 (12), 1534–1547. http://dx.doi.org/10.1002/hyp.6715.

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