Publikationsserver der Universitätsbibliothek Marburg

Titel: Satellite-based monitoring of pasture degradation on the Tibetan Plateau: A multi-scale approach
Autor: Lehnert, Lukas
Weitere Beteiligte: Bendix, Jörg (Prof. Dr.)
Veröffentlicht: 2015
URI: https://archiv.ub.uni-marburg.de/diss/z2015/0406
URN: urn:nbn:de:hebis:04-z2015-04061
DOI: https://doi.org/10.17192/z2015.0406
DDC: Geografie, Reisen
Titel(trans.): Satellitenbasiertes Monitoring von Weidedegradation auf dem Tibetischen Plateau: Ein Multiskalenansatz
Publikationsdatum: 2016-06-30
Lizenz: https://rightsstatements.org/vocab/InC-NC/1.0/

Dokument

Schlagwörter:
Tibetan Plateau, Maschinelles Lernen, degradation monitoring, Support-Vektor-Maschine, Degradation, Überweidung, Hochland von Tibet, Fernerkundung, vegetation climate interaction, MODIS, remote sensing, Klimaänderung

Summary:
The Tibetan Plateau has been entitled Third-Pole-Environment'' because of its outstanding importance for the global climate and the hydrological system of East and Southeast Asia. Its climatological and hydrological influences are strongly affected by the local vegetation which is supposed to be subject to ongoing degradation. The degradation of the Tibetan pastures was investigated on the local scale by numerous studies. However, because methods and scales substantially differed among the previous studies, the overall pattern of degradation on the Tibetan Plateau is hitherto unknown. Consequently, the aims of this thesis are to monitor recent changes in the grassland degradation on the Tibetan Plateau and to detect the underlying driving forces of the observed changes. Therefore, a comprehensive remote sensing based approach is developed. The new approach consists of three parts and incorporates different spatial and temporal scales: (i) the development and testing of an indicator system for pasture degradation on the local scale, (ii) the development of a MODIS-based product usable for degradation monitoring from the local to the plateau scale, and (iii) the application of the new product to delineate recent changes in the degradation status of the pastures on the Tibetan Plateau. The first part of the new approach comprised the test of the suitability of a new two-indicator system and its transferability to spaceborne data. The indicators were land-cover fractions (e.g.,~green vegetation, bare soil) derived from linear spectral unmixing and chlorophyll content. The latter was incorporated as a proxy for nutrient and water availability. It was estimated combining hyperspectral vegetation indices as predictors in partial least squares regression. The indicator system was established and tested on the local scale using a transect design and textit{in situ} measured data. The promising results revealed clear spatial patterns attributed to degradation, indicating that the combination of vegetation cover and chlorophyll content is a suitable indicator system for the detection of pasture degradation on local scales on the Tibetan Plateau. To delineate patterns of degradation changes on the plateau scale, the green plant coverage of the Tibetan pastures was derived in the second part. Therefore, an upscaling approach was developed. It is based on satellite data from high spatial resolution sensors on the local scale (WorldView-type) via medium resolution data (Landsat) to low resolution data on the plateau scale (MODIS). The different spatial resolutions involved in the methodology were incorporated to enable the cross-validation of the estimations in the new product against field observations (over 600 plots across the entire Tibetan Plateau). Four methods (linear spectral unmixing, spectral angle mapper, partial least squares regression, and support vector machine regression) were tested on their predictive performance for the estimation of plant cover and the method with the highest accuracy (support vector machine regression) was applied to 14 years of MODIS data to generate a new vegetation coverage product. In the third part, the changes in vegetation cover between the years 2000 and 2013 and their driving forces were investigated by comparing the trends in the new vegetation coverage product against climate variables (precipitation from tropical rainfall measuring mission and 2 m air temperature from ERA-Interim reanalysis data) on the entire Tibetan Plateau. Large areas in southern Qinghai were identified where vegetation cover increased as a result of positive precipitation trends. Thus, degradation did not proceed in these regions. Contrasting with this, large areas in the central and western parts of the Tibetan Autonomous Region were subject to an ongoing degradation. This degradation can be attributed to the coincidence of rising temperatures and anthropogenic induced increases in livestock numbers as a consequence of local land-use change. In those areas, the ongoing degradation influenced local precipitation patterns because sensible heat fluxes were accelerated above degraded pastures. In combination with advected moist air masses at higher atmospheric levels, the accelerated heat fluxes led to an intensification of local convective rainfall. The ongoing degradation detected by the new remote sensing approach in this thesis is alarming. The affected regions encompass the river systems of the Indus and Brahmaputra Rivers, where the ongoing degradation negatively affects the water storage capacities of the soils and enhances erosion. In combination with the feed-back mechanisms between plant coverage and the changed precipitation on the Tibetan Plateau, the reduced water storage capacity will exacerbate runoff extremes in the middle and lower reaches of those important river systems.

Zusammenfassung:
Das Tibetische Plateau wird häufig aufgrund seiner herausragenden Bedeutung für das globale Klima sowie die Hydrologie in Ost- und Südostasien als Dritter Pol bezeichnet. Die klimatischen und hydrologischen Einflüsse des Plateaus werden unter anderem von der Vegetation gesteuert. Bereits durchgeführte Studien haben Hinweise geliefert, dass die Vegetationsdecke in den letzten Jahren zunehmende Degradation erfährt. Der genaue Status und Einfluss dieser Degradation konnten bisher jedoch noch nicht umfassend quantifiziert werden, da den bisherigen Studien unterschiedliche Methoden und Skalen zugrunde lagen. Dies führt dazu, dass der gegenwärtige Zustand der plateauweiten Vegetation unbekannt ist. Daher sind das Monitoring der Graslanddegradation und die Untersuchung der zugrundeliegenden Treiber die zentralen Gegenstände dieser Untersuchung. Hierfür wurde ein neuer fernerkundungsbasierter Ansatz entwickelt, der aus drei Teilen besteht und verschiedene räumliche und zeitliche Skalen umfasst. Der erste Teil beinhaltet die Entwicklung eines Indikatorsystems für Weidedegradation auf der lokalen Skala. Im zweiten Teil wird ein MODIS-basiertes Produkt entwickelt, das zum Monitoring der Degradation von der lokalen zur plateauweiten Skala verwendet werden kann. Im dritten Teil wird dieses neue Produkt mit dem Ziel angewendet, kürzlich stattgefundene Veränderungen der Degradation auf dem Tibetischen Plateau aufzuzeigen. Der erste Teil des neuen Ansatzes umfasst den Test auf die Verwendbarkeit eines neuen Indikatorsystems und dessen Übertragbarkeit auf Satellitendaten. Das System besteht aus zwei Indikatoren: den Anteilen verschiedener Landbedeckungsklassen (z.B. grüne Vegetation, offener Boden), die mittels linearer spektraler Entmischung berechnet werden, und dem Chlorophyllgehalt der Blätter, der mit Hilfe von hyperspektralen Vegetationsindices und Partial Least Squares Regression geschätzt wird. Das Indikatorsystem wurde auf der lokalen Skala basierend auf einem Transektdesign und in situ gemessenen Daten aufgebaut und getestet. Da die Methode gute Ergebnisse erzielte und klare räumliche Muster detektiert wurden, die nur durch Degradationsunterschiede erklärbar waren, wurde das neue Zwei-Indikatorsystem als geeignet eingestuft, um auf der lokalen Skala die Degradation der Weiden des Tibetischen Plateaus abzuschätzen. Im zweiten Teil des neuen Ansatzes wurde der Deckungsgrad der grünen Vegetation der tibetischen Weiden entlang einer Kaskade von Satellitendaten mit absteigender räumlicher Auflösung beginnend mit der lokalen Skala (WorldView-Typ) über die regionale Skala (Landsat-Typ) zur plateauweiten Skala (MODIS) abgeleitet. Das Konzept der aufeinander aufbauenden räumlichen Auflösungen wurde angewendet, da es eine direkte Validierung der Schätzwerte des Deckungsgrades mit Feldmessungen ermöglicht (über 600 Messpunkte, verteilt auf das gesamte Plateau). Zur Ableitung des Deckungsgrades wurden vier Methoden getestet (lineare spektrale Entmischung, Spectral Angle Mapper, Partial Least Squares Regression und Support Vector Machine Regression). Mit Hilfe der Methode mit der größten Genauigkeit (Support Vector Machine Regression) wurde schließlich eine 14 Jahre umfassende Zeitreihe des plateauweiten Deckungsgrades aus den MODIS Daten berechnet. Die Veränderungen des Deckungsgrads zwischen 2000 und 2014 und deren Steuergrößen wurden im dritten Teil der Arbeit analysiert, indem plateauweit die Trends des Deckungsgrades mit den Trends der Klimavariablen verglichen wurden. Dabei wurde festgestellt, dass der Deckungsgrad in großen Gebieten im Süden Qinghais zunahm, was durch positive Trends im Niederschlag erklärt werden kann. Die Degradation schritt in diesen Gebieten folglich nicht fort. Im Gegensatz hierzu waren große Gebiete im zentralen und westlichen Bereich der Autonomen Region Tibet von einem Rückgang im Deckungsgrad betroffen, der auf die Koinzidenz eines Temperaturanstiegs und steigender Viehzahlen als Konsequenz des Landnutzungswandels zurückzuführen ist. In diesen Gebieten hat die fortschreitende Degradation die lokalen Niederschlagsmuster beeinflusst, da es über degradierten Flächen zu einem Anstieg der fühlbaren Wärmeflüsse kommt, die bei advektiv herangeführten feuchten Luftmassen in hohen Atmosphärenschichten zu einer Verstärkung der Konvektion führen. Die fortschreitende Degradation, wie sie von dem neuen Fernerkundungsansatz detektiert wurde, ist alarmierend, da die betroffenen Regionen die Oberläufe der wichtigen Flusssysteme des Brahmaputra und Indus betreffen. In diesen Gebieten sinkt durch die Degradation die Wasserhaltekapazität der Böden und die Erosion wird verstärkt. Durch das Zusammenspiel mit den Rückkopplungen zwischen fortschreitender Degradation und zunehmendem Niederschlag kann somit eine Verstärkung von extremen Hochwasserereignissen in den Unterläufen beider Flüsse verursacht werden.

Bibliographie / References

  1. BALMFORD, A., BRUNER, A., COOPER, P., COSTANZA, R., FARBER, S., GREEN, R.E., JENKINS, M., JEFFERISS, P., JESSAMY, V., MADDEN, J., MUNRO, K., MY- References ERS, N., NAEEM, S., PAAVOLA, J., RAYMENT, M., ROSENDO, S., ROUGHGARDEN, J., TRUMPER, K., & TURNER, R.K. (2002): Economic reasons for conserving wild nature. Science, 297, 5583, 950–953.
  2. VERMOTE, E.F., TANRE, D., DEUZE, J.L., HERMAN, M., & MORCETTE, J.J. (1997): Second simulation of the satellite signal in the solar spectrum, 6S: An overview. IEEE Transactions on Geoscience and Remote Sensing, 35, 3, 675–686.
  3. HANSEN, M., DEFRIES, R., TOWNSHEND, J., MARUFU, L., & SOHLBERG, R. (2002): Development of a MODIS tree cover validation data set for Western Province, Zambia. Remote Sensing of Environment, 83, 1/2, 320–335.
  4. THENKABAIL, P.S., SMITH, R.B., & PAUW, E.D. (2000): Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71, 2, 158 – 182.
  5. References SEAQUIST, J., OLSSON, L., & ARDÖ, J. (2003): A remote sensing-based primary production model for grassland biomes. Ecological Modelling, 169, 1, 131 – 155.
  6. TEILLET, P.M., GUINDON, B., & GOODEONUGH, D.G. (1982): On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing, 8, 84–106.
  7. SUN, J., CHENG, G.W., LI, W.P., SHA, Y.K., & YANG, Y.C. (2013): On the variation of NDVI with the principal climatic elements in the Tibetan Plateau. Remote Sensing, 5, 4, 1894–1911.
  8. SHI, Y., WANG, Y., MA, Y., MA, W., LIANG, C., FLYNN, D.F.B., SCHMID, B., FANG, J., & HE, J.S. (2014): Field-based observations of regional-scale, temporal variation in net primary production in Tibetan alpine grasslands. Biogeosciences, 11, 7, 2003–2016.
  9. GESSNER, U., MACHWITZ, M., CONRAD, C., & DECH, S. (2013): Estimating the fractional cover of growth forms and bare surface in savannas. A multi-resolution approach based on regression tree ensembles. Remote Sensing of Environment, 129, 90–102.
  10. Science plan of the environmental mapping and analysis program (EnMAP). Technical Report, Deutsches GeoForschungsZentrum GFZ Potsdam.
  11. MIEHE, G., KAISER, K., CO, S., XINQUAN, Z., & JIANQUAN, L. (2008a): Geo- ecological transect studies in northeast Tibet (Qinghai, China) reveal human-made mid-holocene environmental changes in the upper Yellow River catchment changing forest to grassland. Erdkunde, 62, 3, 187–199.
  12. CUI, X., GRAF, H.F., LANGMANN, B., CHEN, W., & HUANG, R. (2006): Climate impacts of anthropogenic land use changes on the Tibetan Plateau. Global and Planetary Change, 54, 1-2, 33 – 56.
  13. LIU, J. & DIAMOND, J. (2005): China's environment in a globalizing world. Nature, 435, 7046, 1179–1186.
  14. WANG, D., MORTON, D., MASEK, J., WU, A., NAGOL, J., XIONG, X., LEVY, R., VERMOTE, E., & WOLFE, R. (2012): Impact of sensor degradation on the MODIS NDVI time series. Remote Sensing of Environment, 119, 55–61.
  15. SOMERS, B., ASNER, G.P., TITS, L., & COPPIN, P. (2011): Endmember variability in spectral mixture analysis: A review. Remote Sensing of Environment, 115, 7, 1603–1616.
  16. 5 Climate variability rather than overstocking causes degradation of Tibetan pastures PEOPLES'S REPUBLIC OF CHINA (2000-2013): Tibet Statistical Yearbook. Tibet Statistics Bureau, China Statistical Press, Beijing.
  17. LEHNERT, L.W., MEYER, H., MEYER, N., REUDENBACH, C., & BENDIX, J. (2014): A hyperspectral indicator system for rangeland degradation on the Tibetan Plateau: A case study towards spaceborne monitoring. Ecological Indicators, 39, 54 – 64.
  18. GAO, Q.Z., WAN, Y.F., XU, H.M., LI, Y., JIANGCUN, W.Z., & BORJIGIDAI, A. (2010): Alpine grassland degradation index and its response to recent climate variabil- ity in northern Tibet, China. Quaternary International, 226, 143–150.
  19. DORJI, T., TOTLAND, Ø., & MOE, S.R. (2013): Are droppings, distance from pastoralist camps, and pika burrows good proxies for local grazing pressure? Rangeland Ecology and Management, 66, 1, 26–33.
  20. SUN, D., LI, Y., & WANG, Q. (2009): A unified model for remotely estimating chlorophyll a in Lake Taihu, China, based on SVM and in situ hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 47, 8, 2957–2965.
  21. PIAO, S., WANG, X., CIAIS, P., ZHU, B., WANG, T., & LIU, J. (2011): Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006. Global Change Biology, 17, 10, 3228–3239.
  22. SOUDANI, K., FRANCOIS, C., LE MAIRE, G., LE DANTEC, V., & DUFRENE, E. (2006): Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment, 102, 1-2, 161–175.
  23. YOU, Q.Y., XUE, X., PENG, F., XU, M.H., DUAN, H.C., & DONG, S.Y. (2014): Comparison of ecosystem characteristics between degraded and intact alpine meadow in the Qinghai-Tibetan Plateau, China. Ecological Engineering, 71, 133–143.
  24. TIAN, L., ZHANG, Y., & ZHU, J. (2014): Decreased surface albedo driven by denser vegetation on the Tibetan Plateau. Environmental Research Letters, 9, 10, 1–11.
  25. 5 Climate variability rather than overstocking causes degradation of Tibetan pastures FAN, B., GUO, L., LI, N., CHEN, J., LIN, H., ZHANG, X., SHEN, M., RAO, Y., WANG, C., & MA, L. (2014): Earlier vegetation green-up has reduced spring dust storms. Scientific Reports, 4, 1–6.
  26. References BAI, Y., HAN, X., WU, J., CHEN, Z., & LI, L. (2004): Ecosystem stability and compensatory effects in the Inner Mongolia grassland. Nature, 431, 7005, 181–184.
  27. WU, G.L., DU, G.Z., LIU, Z.H., & THIRGOOD, S. (2009): Effect of fencing and grazing on a Kobresia-dominated meadow in the Qinghai-Tibetan Plateau. Plant and Soil, 319, 1-2, 115–126.
  28. HU, Z., YU, G., FU, Y., SUN, X., LI, Y., SHI, P., WANGW, Y., & ZHENG, Z. (2008): Effects of vegetation control on ecosystem water use efficiency within and among four grassland ecosystems in China. Global Change Biology, 14, 7, 1609–1619.
  29. GAO, L., HAO, L., & CHEN, X.W. (2014): Evaluation of ERA-interim monthly temper- ature data over the Tibetan Plateau. Journal of Mountain Science, 11, 5, 1154–1168.
  30. WANG, A.H. & ZENG, X.B. (2012): Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau. Journal of Geophysical Research – Atmospheres, 117, D05 102.
  31. MIEHE, G., MIEHE, S., KAISER, K., REUDENBACH, C., BEHRENDES, L., DUO, L., & SCHLÜTZ, F. (2009): How old is pastoralism in Tibet? An ecological approach to the making of a Tibetan landscape. Palaeogeography, Palaeoclimatology, Palaeoecology, 276, 130–147.
  32. MIEHE, G., MIEHE, S., BÖHNER, J., KAISER, K., HENSEN, I., MADSEN, D., LIU, J.Q., & OPGENOORTH, L. (2014): How old is the human footprint in the world's largest alpine ecosystem? A review of multiproxy records from the Tibetan Plateau from the ecologists' viewpoint. Quaternary Science Reviews, 86, 190–209.
  33. TAN, M., LI, X., & XIN, L. (2014): Intensity of dust storms in China from 1980 to 2007: A new definition. Atmospheric Environment, 85, 0, 215–222.
  34. FANG, J., PIAO, S., TANG, Z., PENG, C., & JI, W. (2001): Interannual variability in net primary production and precipitation. Science, 293, 5536, 1723–1723.
  35. GÖTTLICHER, D., OBREGON, A., HOMEIER, J., ROLLENBECK, R., NAUSS, T., & BENDIX, J. (2009): Land-cover classification in the Andes of southern Ecuador using Landsat ETM plus data as a basis for SVAT modelling. International Journal of Remote Sensing, 30, 8, 1867–1886.
  36. FAO (2005): Livestock sector brief, China. Food and Agriculture Organization of the United Nations -Livestock Information, Sector Analysis and Policy Branch (AGAL), Rome.
  37. SOHN, Y.S. & MCCOY, R.M. (1997): Mapping desert shrub rangeland using spectral un- mixing and modeling spectral mixtures with TM data. Photogrammetric Engineering and Remote Sensing, 63, 6, 707–716.
  38. MÖLG, T., MAUSSION, F., & SCHERER, D. (2014): Mid-latitude westerlies as a driver of glacier variability in monsoonal High Asia. Nature Climate Change, 4, 1, 68–73.
  39. FRIEDL, M.A., SULLA-MENASHE, D., TAN, B., SCHNEIDER, A., RAMANKUTTY, N., SIBLEY, A., & HUANG, X. (2010): MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114, 168–182.
  40. GUTMAN, G.G. (1999): On the use of long-term global data of land reflectances and vegetation indices derived from the advanced very high resolution radiometer. Journal of Geophysical Research – Atmospheres, 104, D6, 6241–6255.
  41. BAI, Y., WU, J., PAN, Q., HUANG, J., WANG, Q., LI, F., BUYANTUYEV, A., & HAN, X. (2007): Positive linear relationship between productivity and diversity: Evidence from the Eurasian Steppe. Journal of Applied Ecology, 44, 5, 1023–1034.
  42. FU, C. (2003): Potential impacts of human-induced land cover change on East Asia monsoon. Global Planetary Change, 37, 219–229.
  43. MAUSSION, F., SCHERER, D., MÖLG, T., COLLIER, E., CURIO, J., & FINKELNBURG, R. (2014): Precipitation seasonality and variability over the Tibetan Plateau as resolved by the high asia reanalysis. Journal of Climate, 27, 5, 1910–1927.
  44. HARRIS, R.B. (2010): Rangeland degradation on the Qinghai-Tibetan Plateau: A review of the evidence of its magnitude and causes. Journal of Arid Environments, 74, 1, 1–12.
  45. References vegetation cover are urgently required to simulate processes and interactions between the vegetation, atmosphere and pedosphere on the Tibetan Plateau. This will help to close the knowledge gaps still remaining regarding the influence of the Tibetan Plateau on global climate change.
  46. ELVIDGE, C. D AND, Y.D., WEERACKOON, R.D., & LUNETTA, R.S. (1995): Relative radiometric normalization of Landsat Multispectral Scanner (MSS) data using an auto- matic scattergram-controlled regression. Photogrammetric Engineering and Remote Sensing, 61, 1255–1260.
  47. LEHNERT, L.W., MEYER, H., WANG, Y., MIEHE, G., THIES, B., REUDENBACH, C., & BENDIX, J. (2015): Retrieval of grassland plant coverage on the Tibetan Plateau based on a multi-scale, multi-sensor and multi-method approach. Remote Sensing of Environment, 164, 197–207.
  48. RAO, C.R.N. & CHEN, J. (1999): Revised post-launch calibration of the visible and near- infrared channels of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-14 spacecraft. International Journal of Remote Sensing, 20, 18, 3485–3491.
  49. MOUNTRAKIS, G., IM, J., & OGOLE, C. (2011): Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 3, 247 – 259.
  50. DAVIDSON, E.A. & JANSSENS, I.A. (2006): Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440, 7081, 165–173.
  51. ZHANG, W., AN, S., XU, Z., CUI, J., & XU, Q. (2011): The impact of vegetation and soil on runoff regulation in headwater streams on the east Qinghai-Tibet Plateau, China. Catena, 87, 2, 182–189.
  52. PIAO, S., CIAIS, P., HUANG, Y., SHEN, Z., PENG, S., LI, J., ZHOU, L., LIU, H., MA, Y., DING, Y., FRIEDLINGSTEIN, P., LIU, C., TAN, K., YU, Y., ZHANG, T., & FANG, J. (2010): The impacts of climate change on water resources and agriculture in China. Nature, 467, 7311, 43–51.
  53. HUFFMAN, G.J., BOLVIN, D.T., NELKIN, E.J., WOLFF, D.B., ADLER, R.F., GU, G., HONG, Y., BOWMAN, K.P., & STOCKER, E.F. (2007): The TRMM multisatellite References precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8, 1, 38–55.
  54. DUAN, A., WANG, M., LEI, Y., & CUI, Y. (2012): Trends in summer rainfall over China associated with the Tibetan Plateau sensible heat source during 1980-2008.
  55. ZHAI, P., ZHANG, X., WAN, H., & PAN, X. (2005): Trends in total precipitation and frequency of daily precipitation extremes over China. Journal of Climate, 18, 7, 1096–1108.
  56. HOU, X.Y. (Ed.) (2001): Vegetation Atlas of China. Science Press, Beijing. References MEYER, H., LEHNERT, L.W., WANG, Y., REUDENBACH, C., & BENDIX, J. (2013): Measuring pasture degradation on the Qinghai-Tibet Plateau using hyperspectral dissimilarities and indices. In: Proceedings of the SPIE, volume 8893. Dresden, pp. 88 931F–88 931F–13.
  57. XU, X., LU, C., SHI, X., & GAO, S. (2008): World water tower: An atmospheric perspective. Geophysical Research Letters, 35, 20.
  58. CURIO, J., MAUSSION, F., & SCHERER, D. (2014): A twelve-year high-resolution climatology of atmospheric water transport on the Tibetan Plateau. Earth System Dynamics Discussions, 5, 2, 1159–1196.
  59. FU, R., HU, Y., WRIGHT, J.S., JIANG, J.H., DICKINSON, R.E., CHEN, M., FILIPIAK, M., READ, W.G., WATERS, J.W., & WU, D.L. (2006): Short circuit of water vapor and polluted air to the global stratosphere by convective transport over the Tibetan Plateau. Proceedings of the National Academy of Sciences, 103, 15, 5664–5669.
  60. YU, H., LUEDELING, E., & XU, J. (2010): Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proceedings of the National Academy of Sciences, 107, 51, 22 151–22 156.
  61. LI, Y.K., LIAO, J.J., GUO, H.D., LIU, Z.W., & SHEN, G.Z. (2014): Patterns and potential drivers of dramatic changes in Tibetan lakes, 1972-2010. Plos One, 9, 11, e111 890.
  62. CUI, X. & GRAF, H.F. (2009): Recent land cover changes on the Tibetan Plateau: A review. Climatic Change, 94, 1-2, 47–61.


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