Water balance estimates of ten greatest lakes in China using ICESat and Landsat data

Abstract Lake level and area variations are sensitive to regional climate changes and can be used to indirectly estimate water balances of lakes. In this study, 10 of the largest lakes in China, ∼1000 $ km^{2} $ or larger, are examined to determine changes in lake level and area derived respectively from ICESat and Landsat data recorded between 2003 and 2009. The time series of lake level and area of Selin Co, Nam Co, and Qinghai Lake in the Tibetan Plateau (TP) and Xingkai Lake in northeastern China exhibit an increasing trend, with Selin Co showing the fastest rise in lake level (0.69 m/a), area (32.59 $ km^{2} $/a), and volume (1.25 $ km^{3} $/a) among the 10 examined lakes. Bosten and Hulun lakes in the arid and semiarid region of northern China show a decline in both lake level and area, with Bosten Lake showing the largest decrease in lake level (−0.43 m/a) and Hulun Lake showing the largest area shrinkage (−35.56 $ km^{2} $/a). However, Dongting, Poyang, Taihu, and Hongze lakes in the mid-lower reaches of the Yangtze River basin present seasonal variability without any apparent tendencies. The lake level and area show strong correlations for Selin Co, Nam Co, Qinghai, Poyang, Hulun, and Bosten lakes (R2 >0.80) and for Taihu, Hongze, and Xingkai lakes (∼0.70) and weak correlation for East Dongting Lake (0.37). The lake level changes and water volume changes are in very good agreement for all lakes (R2 > 0.98). Water balances of the 10 lakes are derived on the basis of both lake level and area changes, with Selin Co, Nam Co, Qinghai, and Xingkai lakes showing positive water budgets of 9.08, 4.07, 2.88, and 1.09 $ km^{3} $, respectively. Bosten and Hulun lakes show negative budgets of −3.01 and −4.73 $ km^{3} $, respectively, and the four lakes along the Yangtze River show no obvious variations. Possible explanations for the lake level and area changes in these four lakes are also discussed. This study suggests that satellite remote sensing could serve as a fast and effective tool for estimating lake water balance. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhang, GuoQing [verfasserIn] Xie, HongJie [verfasserIn] Yao, TanDong [verfasserIn] Kang, ShiChang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	water balance lakes ICESat Tibetan Plateau

Übergeordnetes Werk:	Enthalten in: Chinese science bulletin - Beijing, China : Chinese Acad. of Sciences, 1997, 58(2013), 31 vom: 18. Mai, Seite 3815-3829
Übergeordnetes Werk:	volume:58 ; year:2013 ; number:31 ; day:18 ; month:05 ; pages:3815-3829

Links:	Volltext

DOI / URN:	10.1007/s11434-013-5818-y

Katalog-ID:	SPR019483953

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520			\|a Abstract Lake level and area variations are sensitive to regional climate changes and can be used to indirectly estimate water balances of lakes. In this study, 10 of the largest lakes in China, ∼1000 $ km^{2} $ or larger, are examined to determine changes in lake level and area derived respectively from ICESat and Landsat data recorded between 2003 and 2009. The time series of lake level and area of Selin Co, Nam Co, and Qinghai Lake in the Tibetan Plateau (TP) and Xingkai Lake in northeastern China exhibit an increasing trend, with Selin Co showing the fastest rise in lake level (0.69 m/a), area (32.59 $ km^{2} $/a), and volume (1.25 $ km^{3} $/a) among the 10 examined lakes. Bosten and Hulun lakes in the arid and semiarid region of northern China show a decline in both lake level and area, with Bosten Lake showing the largest decrease in lake level (−0.43 m/a) and Hulun Lake showing the largest area shrinkage (−35.56 $ km^{2} $/a). However, Dongting, Poyang, Taihu, and Hongze lakes in the mid-lower reaches of the Yangtze River basin present seasonal variability without any apparent tendencies. The lake level and area show strong correlations for Selin Co, Nam Co, Qinghai, Poyang, Hulun, and Bosten lakes (R2 >0.80) and for Taihu, Hongze, and Xingkai lakes (∼0.70) and weak correlation for East Dongting Lake (0.37). The lake level changes and water volume changes are in very good agreement for all lakes (R2 > 0.98). Water balances of the 10 lakes are derived on the basis of both lake level and area changes, with Selin Co, Nam Co, Qinghai, and Xingkai lakes showing positive water budgets of 9.08, 4.07, 2.88, and 1.09 $ km^{3} $, respectively. Bosten and Hulun lakes show negative budgets of −3.01 and −4.73 $ km^{3} $, respectively, and the four lakes along the Yangtze River show no obvious variations. Possible explanations for the lake level and area changes in these four lakes are also discussed. This study suggests that satellite remote sensing could serve as a fast and effective tool for estimating lake water balance.
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allfields	10.1007/s11434-013-5818-y doi (DE-627)SPR019483953 (SPR)s11434-013-5818-y-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Zhang, GuoQing verfasserin aut Water balance estimates of ten greatest lakes in China using ICESat and Landsat data 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Lake level and area variations are sensitive to regional climate changes and can be used to indirectly estimate water balances of lakes. In this study, 10 of the largest lakes in China, ∼1000 $ km^{2} $ or larger, are examined to determine changes in lake level and area derived respectively from ICESat and Landsat data recorded between 2003 and 2009. The time series of lake level and area of Selin Co, Nam Co, and Qinghai Lake in the Tibetan Plateau (TP) and Xingkai Lake in northeastern China exhibit an increasing trend, with Selin Co showing the fastest rise in lake level (0.69 m/a), area (32.59 $ km^{2} $/a), and volume (1.25 $ km^{3} $/a) among the 10 examined lakes. Bosten and Hulun lakes in the arid and semiarid region of northern China show a decline in both lake level and area, with Bosten Lake showing the largest decrease in lake level (−0.43 m/a) and Hulun Lake showing the largest area shrinkage (−35.56 $ km^{2} $/a). However, Dongting, Poyang, Taihu, and Hongze lakes in the mid-lower reaches of the Yangtze River basin present seasonal variability without any apparent tendencies. The lake level and area show strong correlations for Selin Co, Nam Co, Qinghai, Poyang, Hulun, and Bosten lakes (R2 >0.80) and for Taihu, Hongze, and Xingkai lakes (∼0.70) and weak correlation for East Dongting Lake (0.37). The lake level changes and water volume changes are in very good agreement for all lakes (R2 > 0.98). Water balances of the 10 lakes are derived on the basis of both lake level and area changes, with Selin Co, Nam Co, Qinghai, and Xingkai lakes showing positive water budgets of 9.08, 4.07, 2.88, and 1.09 $ km^{3} $, respectively. Bosten and Hulun lakes show negative budgets of −3.01 and −4.73 $ km^{3} $, respectively, and the four lakes along the Yangtze River show no obvious variations. Possible explanations for the lake level and area changes in these four lakes are also discussed. This study suggests that satellite remote sensing could serve as a fast and effective tool for estimating lake water balance. water balance (dpeaa)DE-He213 lakes (dpeaa)DE-He213 ICESat (dpeaa)DE-He213 Tibetan Plateau (dpeaa)DE-He213 Xie, HongJie verfasserin aut Yao, TanDong verfasserin aut Kang, ShiChang verfasserin aut Enthalten in Chinese science bulletin Beijing, China : Chinese Acad. of Sciences, 1997 58(2013), 31 vom: 18. Mai, Seite 3815-3829 (DE-627)341897809 (DE-600)2069521-4 1861-9541 nnns volume:58 year:2013 number:31 day:18 month:05 pages:3815-3829 https://dx.doi.org/10.1007/s11434-013-5818-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 30.00 ASE AR 58 2013 31 18 05 3815-3829
spelling	10.1007/s11434-013-5818-y doi (DE-627)SPR019483953 (SPR)s11434-013-5818-y-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Zhang, GuoQing verfasserin aut Water balance estimates of ten greatest lakes in China using ICESat and Landsat data 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Lake level and area variations are sensitive to regional climate changes and can be used to indirectly estimate water balances of lakes. In this study, 10 of the largest lakes in China, ∼1000 $ km^{2} $ or larger, are examined to determine changes in lake level and area derived respectively from ICESat and Landsat data recorded between 2003 and 2009. The time series of lake level and area of Selin Co, Nam Co, and Qinghai Lake in the Tibetan Plateau (TP) and Xingkai Lake in northeastern China exhibit an increasing trend, with Selin Co showing the fastest rise in lake level (0.69 m/a), area (32.59 $ km^{2} $/a), and volume (1.25 $ km^{3} $/a) among the 10 examined lakes. Bosten and Hulun lakes in the arid and semiarid region of northern China show a decline in both lake level and area, with Bosten Lake showing the largest decrease in lake level (−0.43 m/a) and Hulun Lake showing the largest area shrinkage (−35.56 $ km^{2} $/a). However, Dongting, Poyang, Taihu, and Hongze lakes in the mid-lower reaches of the Yangtze River basin present seasonal variability without any apparent tendencies. The lake level and area show strong correlations for Selin Co, Nam Co, Qinghai, Poyang, Hulun, and Bosten lakes (R2 >0.80) and for Taihu, Hongze, and Xingkai lakes (∼0.70) and weak correlation for East Dongting Lake (0.37). The lake level changes and water volume changes are in very good agreement for all lakes (R2 > 0.98). Water balances of the 10 lakes are derived on the basis of both lake level and area changes, with Selin Co, Nam Co, Qinghai, and Xingkai lakes showing positive water budgets of 9.08, 4.07, 2.88, and 1.09 $ km^{3} $, respectively. Bosten and Hulun lakes show negative budgets of −3.01 and −4.73 $ km^{3} $, respectively, and the four lakes along the Yangtze River show no obvious variations. Possible explanations for the lake level and area changes in these four lakes are also discussed. This study suggests that satellite remote sensing could serve as a fast and effective tool for estimating lake water balance. water balance (dpeaa)DE-He213 lakes (dpeaa)DE-He213 ICESat (dpeaa)DE-He213 Tibetan Plateau (dpeaa)DE-He213 Xie, HongJie verfasserin aut Yao, TanDong verfasserin aut Kang, ShiChang verfasserin aut Enthalten in Chinese science bulletin Beijing, China : Chinese Acad. of Sciences, 1997 58(2013), 31 vom: 18. Mai, Seite 3815-3829 (DE-627)341897809 (DE-600)2069521-4 1861-9541 nnns volume:58 year:2013 number:31 day:18 month:05 pages:3815-3829 https://dx.doi.org/10.1007/s11434-013-5818-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 30.00 ASE AR 58 2013 31 18 05 3815-3829
allfields_unstemmed	10.1007/s11434-013-5818-y doi (DE-627)SPR019483953 (SPR)s11434-013-5818-y-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Zhang, GuoQing verfasserin aut Water balance estimates of ten greatest lakes in China using ICESat and Landsat data 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Lake level and area variations are sensitive to regional climate changes and can be used to indirectly estimate water balances of lakes. In this study, 10 of the largest lakes in China, ∼1000 $ km^{2} $ or larger, are examined to determine changes in lake level and area derived respectively from ICESat and Landsat data recorded between 2003 and 2009. The time series of lake level and area of Selin Co, Nam Co, and Qinghai Lake in the Tibetan Plateau (TP) and Xingkai Lake in northeastern China exhibit an increasing trend, with Selin Co showing the fastest rise in lake level (0.69 m/a), area (32.59 $ km^{2} $/a), and volume (1.25 $ km^{3} $/a) among the 10 examined lakes. Bosten and Hulun lakes in the arid and semiarid region of northern China show a decline in both lake level and area, with Bosten Lake showing the largest decrease in lake level (−0.43 m/a) and Hulun Lake showing the largest area shrinkage (−35.56 $ km^{2} $/a). However, Dongting, Poyang, Taihu, and Hongze lakes in the mid-lower reaches of the Yangtze River basin present seasonal variability without any apparent tendencies. The lake level and area show strong correlations for Selin Co, Nam Co, Qinghai, Poyang, Hulun, and Bosten lakes (R2 >0.80) and for Taihu, Hongze, and Xingkai lakes (∼0.70) and weak correlation for East Dongting Lake (0.37). The lake level changes and water volume changes are in very good agreement for all lakes (R2 > 0.98). Water balances of the 10 lakes are derived on the basis of both lake level and area changes, with Selin Co, Nam Co, Qinghai, and Xingkai lakes showing positive water budgets of 9.08, 4.07, 2.88, and 1.09 $ km^{3} $, respectively. Bosten and Hulun lakes show negative budgets of −3.01 and −4.73 $ km^{3} $, respectively, and the four lakes along the Yangtze River show no obvious variations. Possible explanations for the lake level and area changes in these four lakes are also discussed. This study suggests that satellite remote sensing could serve as a fast and effective tool for estimating lake water balance. water balance (dpeaa)DE-He213 lakes (dpeaa)DE-He213 ICESat (dpeaa)DE-He213 Tibetan Plateau (dpeaa)DE-He213 Xie, HongJie verfasserin aut Yao, TanDong verfasserin aut Kang, ShiChang verfasserin aut Enthalten in Chinese science bulletin Beijing, China : Chinese Acad. of Sciences, 1997 58(2013), 31 vom: 18. Mai, Seite 3815-3829 (DE-627)341897809 (DE-600)2069521-4 1861-9541 nnns volume:58 year:2013 number:31 day:18 month:05 pages:3815-3829 https://dx.doi.org/10.1007/s11434-013-5818-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 30.00 ASE AR 58 2013 31 18 05 3815-3829
allfieldsGer	10.1007/s11434-013-5818-y doi (DE-627)SPR019483953 (SPR)s11434-013-5818-y-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Zhang, GuoQing verfasserin aut Water balance estimates of ten greatest lakes in China using ICESat and Landsat data 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Lake level and area variations are sensitive to regional climate changes and can be used to indirectly estimate water balances of lakes. In this study, 10 of the largest lakes in China, ∼1000 $ km^{2} $ or larger, are examined to determine changes in lake level and area derived respectively from ICESat and Landsat data recorded between 2003 and 2009. The time series of lake level and area of Selin Co, Nam Co, and Qinghai Lake in the Tibetan Plateau (TP) and Xingkai Lake in northeastern China exhibit an increasing trend, with Selin Co showing the fastest rise in lake level (0.69 m/a), area (32.59 $ km^{2} $/a), and volume (1.25 $ km^{3} $/a) among the 10 examined lakes. Bosten and Hulun lakes in the arid and semiarid region of northern China show a decline in both lake level and area, with Bosten Lake showing the largest decrease in lake level (−0.43 m/a) and Hulun Lake showing the largest area shrinkage (−35.56 $ km^{2} $/a). However, Dongting, Poyang, Taihu, and Hongze lakes in the mid-lower reaches of the Yangtze River basin present seasonal variability without any apparent tendencies. The lake level and area show strong correlations for Selin Co, Nam Co, Qinghai, Poyang, Hulun, and Bosten lakes (R2 >0.80) and for Taihu, Hongze, and Xingkai lakes (∼0.70) and weak correlation for East Dongting Lake (0.37). The lake level changes and water volume changes are in very good agreement for all lakes (R2 > 0.98). Water balances of the 10 lakes are derived on the basis of both lake level and area changes, with Selin Co, Nam Co, Qinghai, and Xingkai lakes showing positive water budgets of 9.08, 4.07, 2.88, and 1.09 $ km^{3} $, respectively. Bosten and Hulun lakes show negative budgets of −3.01 and −4.73 $ km^{3} $, respectively, and the four lakes along the Yangtze River show no obvious variations. Possible explanations for the lake level and area changes in these four lakes are also discussed. This study suggests that satellite remote sensing could serve as a fast and effective tool for estimating lake water balance. water balance (dpeaa)DE-He213 lakes (dpeaa)DE-He213 ICESat (dpeaa)DE-He213 Tibetan Plateau (dpeaa)DE-He213 Xie, HongJie verfasserin aut Yao, TanDong verfasserin aut Kang, ShiChang verfasserin aut Enthalten in Chinese science bulletin Beijing, China : Chinese Acad. of Sciences, 1997 58(2013), 31 vom: 18. Mai, Seite 3815-3829 (DE-627)341897809 (DE-600)2069521-4 1861-9541 nnns volume:58 year:2013 number:31 day:18 month:05 pages:3815-3829 https://dx.doi.org/10.1007/s11434-013-5818-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 30.00 ASE AR 58 2013 31 18 05 3815-3829
allfieldsSound	10.1007/s11434-013-5818-y doi (DE-627)SPR019483953 (SPR)s11434-013-5818-y-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Zhang, GuoQing verfasserin aut Water balance estimates of ten greatest lakes in China using ICESat and Landsat data 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Lake level and area variations are sensitive to regional climate changes and can be used to indirectly estimate water balances of lakes. In this study, 10 of the largest lakes in China, ∼1000 $ km^{2} $ or larger, are examined to determine changes in lake level and area derived respectively from ICESat and Landsat data recorded between 2003 and 2009. The time series of lake level and area of Selin Co, Nam Co, and Qinghai Lake in the Tibetan Plateau (TP) and Xingkai Lake in northeastern China exhibit an increasing trend, with Selin Co showing the fastest rise in lake level (0.69 m/a), area (32.59 $ km^{2} $/a), and volume (1.25 $ km^{3} $/a) among the 10 examined lakes. Bosten and Hulun lakes in the arid and semiarid region of northern China show a decline in both lake level and area, with Bosten Lake showing the largest decrease in lake level (−0.43 m/a) and Hulun Lake showing the largest area shrinkage (−35.56 $ km^{2} $/a). However, Dongting, Poyang, Taihu, and Hongze lakes in the mid-lower reaches of the Yangtze River basin present seasonal variability without any apparent tendencies. The lake level and area show strong correlations for Selin Co, Nam Co, Qinghai, Poyang, Hulun, and Bosten lakes (R2 >0.80) and for Taihu, Hongze, and Xingkai lakes (∼0.70) and weak correlation for East Dongting Lake (0.37). The lake level changes and water volume changes are in very good agreement for all lakes (R2 > 0.98). Water balances of the 10 lakes are derived on the basis of both lake level and area changes, with Selin Co, Nam Co, Qinghai, and Xingkai lakes showing positive water budgets of 9.08, 4.07, 2.88, and 1.09 $ km^{3} $, respectively. Bosten and Hulun lakes show negative budgets of −3.01 and −4.73 $ km^{3} $, respectively, and the four lakes along the Yangtze River show no obvious variations. Possible explanations for the lake level and area changes in these four lakes are also discussed. This study suggests that satellite remote sensing could serve as a fast and effective tool for estimating lake water balance. water balance (dpeaa)DE-He213 lakes (dpeaa)DE-He213 ICESat (dpeaa)DE-He213 Tibetan Plateau (dpeaa)DE-He213 Xie, HongJie verfasserin aut Yao, TanDong verfasserin aut Kang, ShiChang verfasserin aut Enthalten in Chinese science bulletin Beijing, China : Chinese Acad. of Sciences, 1997 58(2013), 31 vom: 18. Mai, Seite 3815-3829 (DE-627)341897809 (DE-600)2069521-4 1861-9541 nnns volume:58 year:2013 number:31 day:18 month:05 pages:3815-3829 https://dx.doi.org/10.1007/s11434-013-5818-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 30.00 ASE AR 58 2013 31 18 05 3815-3829
language	English
source	Enthalten in Chinese science bulletin 58(2013), 31 vom: 18. Mai, Seite 3815-3829 volume:58 year:2013 number:31 day:18 month:05 pages:3815-3829
sourceStr	Enthalten in Chinese science bulletin 58(2013), 31 vom: 18. Mai, Seite 3815-3829 volume:58 year:2013 number:31 day:18 month:05 pages:3815-3829
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author	Zhang, GuoQing
spellingShingle	Zhang, GuoQing ddc 500 bkl 30.00 misc water balance misc lakes misc ICESat misc Tibetan Plateau Water balance estimates of ten greatest lakes in China using ICESat and Landsat data
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topic_title	500 ASE 30.00 bkl Water balance estimates of ten greatest lakes in China using ICESat and Landsat data water balance (dpeaa)DE-He213 lakes (dpeaa)DE-He213 ICESat (dpeaa)DE-He213 Tibetan Plateau (dpeaa)DE-He213
topic	ddc 500 bkl 30.00 misc water balance misc lakes misc ICESat misc Tibetan Plateau
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title	Water balance estimates of ten greatest lakes in China using ICESat and Landsat data
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title_full	Water balance estimates of ten greatest lakes in China using ICESat and Landsat data
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title_sort	water balance estimates of ten greatest lakes in china using icesat and landsat data
title_auth	Water balance estimates of ten greatest lakes in China using ICESat and Landsat data
abstract	Abstract Lake level and area variations are sensitive to regional climate changes and can be used to indirectly estimate water balances of lakes. In this study, 10 of the largest lakes in China, ∼1000 $ km^{2} $ or larger, are examined to determine changes in lake level and area derived respectively from ICESat and Landsat data recorded between 2003 and 2009. The time series of lake level and area of Selin Co, Nam Co, and Qinghai Lake in the Tibetan Plateau (TP) and Xingkai Lake in northeastern China exhibit an increasing trend, with Selin Co showing the fastest rise in lake level (0.69 m/a), area (32.59 $ km^{2} $/a), and volume (1.25 $ km^{3} $/a) among the 10 examined lakes. Bosten and Hulun lakes in the arid and semiarid region of northern China show a decline in both lake level and area, with Bosten Lake showing the largest decrease in lake level (−0.43 m/a) and Hulun Lake showing the largest area shrinkage (−35.56 $ km^{2} $/a). However, Dongting, Poyang, Taihu, and Hongze lakes in the mid-lower reaches of the Yangtze River basin present seasonal variability without any apparent tendencies. The lake level and area show strong correlations for Selin Co, Nam Co, Qinghai, Poyang, Hulun, and Bosten lakes (R2 >0.80) and for Taihu, Hongze, and Xingkai lakes (∼0.70) and weak correlation for East Dongting Lake (0.37). The lake level changes and water volume changes are in very good agreement for all lakes (R2 > 0.98). Water balances of the 10 lakes are derived on the basis of both lake level and area changes, with Selin Co, Nam Co, Qinghai, and Xingkai lakes showing positive water budgets of 9.08, 4.07, 2.88, and 1.09 $ km^{3} $, respectively. Bosten and Hulun lakes show negative budgets of −3.01 and −4.73 $ km^{3} $, respectively, and the four lakes along the Yangtze River show no obvious variations. Possible explanations for the lake level and area changes in these four lakes are also discussed. This study suggests that satellite remote sensing could serve as a fast and effective tool for estimating lake water balance.
abstractGer	Abstract Lake level and area variations are sensitive to regional climate changes and can be used to indirectly estimate water balances of lakes. In this study, 10 of the largest lakes in China, ∼1000 $ km^{2} $ or larger, are examined to determine changes in lake level and area derived respectively from ICESat and Landsat data recorded between 2003 and 2009. The time series of lake level and area of Selin Co, Nam Co, and Qinghai Lake in the Tibetan Plateau (TP) and Xingkai Lake in northeastern China exhibit an increasing trend, with Selin Co showing the fastest rise in lake level (0.69 m/a), area (32.59 $ km^{2} $/a), and volume (1.25 $ km^{3} $/a) among the 10 examined lakes. Bosten and Hulun lakes in the arid and semiarid region of northern China show a decline in both lake level and area, with Bosten Lake showing the largest decrease in lake level (−0.43 m/a) and Hulun Lake showing the largest area shrinkage (−35.56 $ km^{2} $/a). However, Dongting, Poyang, Taihu, and Hongze lakes in the mid-lower reaches of the Yangtze River basin present seasonal variability without any apparent tendencies. The lake level and area show strong correlations for Selin Co, Nam Co, Qinghai, Poyang, Hulun, and Bosten lakes (R2 >0.80) and for Taihu, Hongze, and Xingkai lakes (∼0.70) and weak correlation for East Dongting Lake (0.37). The lake level changes and water volume changes are in very good agreement for all lakes (R2 > 0.98). Water balances of the 10 lakes are derived on the basis of both lake level and area changes, with Selin Co, Nam Co, Qinghai, and Xingkai lakes showing positive water budgets of 9.08, 4.07, 2.88, and 1.09 $ km^{3} $, respectively. Bosten and Hulun lakes show negative budgets of −3.01 and −4.73 $ km^{3} $, respectively, and the four lakes along the Yangtze River show no obvious variations. Possible explanations for the lake level and area changes in these four lakes are also discussed. This study suggests that satellite remote sensing could serve as a fast and effective tool for estimating lake water balance.
abstract_unstemmed	Abstract Lake level and area variations are sensitive to regional climate changes and can be used to indirectly estimate water balances of lakes. In this study, 10 of the largest lakes in China, ∼1000 $ km^{2} $ or larger, are examined to determine changes in lake level and area derived respectively from ICESat and Landsat data recorded between 2003 and 2009. The time series of lake level and area of Selin Co, Nam Co, and Qinghai Lake in the Tibetan Plateau (TP) and Xingkai Lake in northeastern China exhibit an increasing trend, with Selin Co showing the fastest rise in lake level (0.69 m/a), area (32.59 $ km^{2} $/a), and volume (1.25 $ km^{3} $/a) among the 10 examined lakes. Bosten and Hulun lakes in the arid and semiarid region of northern China show a decline in both lake level and area, with Bosten Lake showing the largest decrease in lake level (−0.43 m/a) and Hulun Lake showing the largest area shrinkage (−35.56 $ km^{2} $/a). However, Dongting, Poyang, Taihu, and Hongze lakes in the mid-lower reaches of the Yangtze River basin present seasonal variability without any apparent tendencies. The lake level and area show strong correlations for Selin Co, Nam Co, Qinghai, Poyang, Hulun, and Bosten lakes (R2 >0.80) and for Taihu, Hongze, and Xingkai lakes (∼0.70) and weak correlation for East Dongting Lake (0.37). The lake level changes and water volume changes are in very good agreement for all lakes (R2 > 0.98). Water balances of the 10 lakes are derived on the basis of both lake level and area changes, with Selin Co, Nam Co, Qinghai, and Xingkai lakes showing positive water budgets of 9.08, 4.07, 2.88, and 1.09 $ km^{3} $, respectively. Bosten and Hulun lakes show negative budgets of −3.01 and −4.73 $ km^{3} $, respectively, and the four lakes along the Yangtze River show no obvious variations. Possible explanations for the lake level and area changes in these four lakes are also discussed. This study suggests that satellite remote sensing could serve as a fast and effective tool for estimating lake water balance.
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container_issue	31
title_short	Water balance estimates of ten greatest lakes in China using ICESat and Landsat data
url	https://dx.doi.org/10.1007/s11434-013-5818-y
remote_bool	true
author2	Xie, HongJie Yao, TanDong Kang, ShiChang
author2Str	Xie, HongJie Yao, TanDong Kang, ShiChang
ppnlink	341897809
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hochschulschrift_bool	false
doi_str	10.1007/s11434-013-5818-y
up_date	2024-07-04T01:48:38.630Z
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In this study, 10 of the largest lakes in China, ∼1000 $ km^{2} $ or larger, are examined to determine changes in lake level and area derived respectively from ICESat and Landsat data recorded between 2003 and 2009. The time series of lake level and area of Selin Co, Nam Co, and Qinghai Lake in the Tibetan Plateau (TP) and Xingkai Lake in northeastern China exhibit an increasing trend, with Selin Co showing the fastest rise in lake level (0.69 m/a), area (32.59 $ km^{2} $/a), and volume (1.25 $ km^{3} $/a) among the 10 examined lakes. Bosten and Hulun lakes in the arid and semiarid region of northern China show a decline in both lake level and area, with Bosten Lake showing the largest decrease in lake level (−0.43 m/a) and Hulun Lake showing the largest area shrinkage (−35.56 $ km^{2} $/a). However, Dongting, Poyang, Taihu, and Hongze lakes in the mid-lower reaches of the Yangtze River basin present seasonal variability without any apparent tendencies. 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Water balance estimates of ten greatest lakes in China using ICESat and Landsat data

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