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Land surface modeling informed by earth observation data: toward understanding blue–green–white water fluxes in High Mountain Asia.

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Title: Land surface modeling informed by earth observation data: toward understanding blue–green–white water fluxes in High Mountain Asia.
Authors: Buri, Pascal1 (AUTHOR) pascal.buri@wsl.ch, Fatichi, Simone2 (AUTHOR), Shaw, Thomas E.1,3 (AUTHOR), Fyffe, Catriona L.3 (AUTHOR), Miles, Evan S.1 (AUTHOR), McCarthy, Michael J.1 (AUTHOR), Kneib, Marin4,5 (AUTHOR), Ren, Shaoting6 (AUTHOR), Jouberton, Achille1,7 (AUTHOR), Fugger, Stefan1,7 (AUTHOR), Jia, Li8 (AUTHOR), Zhang, Jing9 (AUTHOR), Shen, Cong8 (AUTHOR), Zheng, Chaolei8 (AUTHOR), Menenti, Massimo8,10 (AUTHOR), Pellicciotti, Francesca1,3 (AUTHOR)
Source: Geo-Spatial Information Science. Jun2024, Vol. 27 Issue 3, p703-727. 25p.
Subjects: Hydrologic cycle, Remote sensing, Runoff, Fresh water, Biosphere
Geographic Terms: Himalaya Mountains
Abstract: Mountains are important suppliers of freshwater to downstream areas, affecting large populations in particular in High Mountain Asia (HMA). Yet, the propagation of water from HMA headwaters to downstream areas is not fully understood, as interactions in the mountain water cycle between the cryo-, hydro- and biosphere remain elusive. We review the definition of blue and green water fluxes as liquid water that contributes to runoff at the outlet of the selected domain (blue) and water lost to the atmosphere through vapor fluxes, that is evaporation from water, ground, and interception plus transpiration (green) and propose to add the term white water to account for the (often neglected) evaporation and sublimation from snow and ice. We provide an assessment of models that can simulate the cryo-hydro-biosphere continuum and the interactions between spheres in high mountain catchments, going beyond disciplinary separations. Land surface models are uniquely able to account for such complexity, since they solve the coupled fluxes of water, energy, and carbon between the land surface and atmosphere. Due to the mechanistic nature of such models, specific variables can be compared systematically to independent remote sensing observations – providing vital insights into model accuracy and enabling the understanding of the complex watersheds of HMA. We discuss recent developments in spaceborne earth observation products that have the potential to support catchment modeling in high mountain regions. We then present a pilot study application of the mechanistic land surface model Tethys & Chloris to a glacierized watershed in the Nepalese Himalayas and discuss the use of high-resolution earth observation data to constrain the meteorological forcing uncertainty and validate model results. We use these insights to highlight the remaining challenges and future opportunities that remote sensing data presents for land surface modeling in HMA. [ABSTRACT FROM AUTHOR]
Copyright of Geo-Spatial Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Land surface modeling informed by earth observation data: toward understanding blue–green–white water fluxes in High Mountain Asia.
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  Data: <searchLink fieldCode="AR" term="%22Buri%2C+Pascal%22">Buri, Pascal</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pascal.buri@wsl.ch</i><br /><searchLink fieldCode="AR" term="%22Fatichi%2C+Simone%22">Fatichi, Simone</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shaw%2C+Thomas+E%2E%22">Shaw, Thomas E.</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fyffe%2C+Catriona+L%2E%22">Fyffe, Catriona L.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Miles%2C+Evan+S%2E%22">Miles, Evan S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22McCarthy%2C+Michael+J%2E%22">McCarthy, Michael J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kneib%2C+Marin%22">Kneib, Marin</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Shaoting%22">Ren, Shaoting</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jouberton%2C+Achille%22">Jouberton, Achille</searchLink><relatesTo>1,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fugger%2C+Stefan%22">Fugger, Stefan</searchLink><relatesTo>1,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jia%2C+Li%22">Jia, Li</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jing%22">Zhang, Jing</searchLink><relatesTo>9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shen%2C+Cong%22">Shen, Cong</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Chaolei%22">Zheng, Chaolei</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Menenti%2C+Massimo%22">Menenti, Massimo</searchLink><relatesTo>8,10</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pellicciotti%2C+Francesca%22">Pellicciotti, Francesca</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="DE" term="%22Hydrologic+cycle%22">Hydrologic cycle</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Runoff%22">Runoff</searchLink><br /><searchLink fieldCode="DE" term="%22Fresh+water%22">Fresh water</searchLink><br /><searchLink fieldCode="DE" term="%22Biosphere%22">Biosphere</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Himalaya+Mountains%22">Himalaya Mountains</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Mountains are important suppliers of freshwater to downstream areas, affecting large populations in particular in High Mountain Asia (HMA). Yet, the propagation of water from HMA headwaters to downstream areas is not fully understood, as interactions in the mountain water cycle between the cryo-, hydro- and biosphere remain elusive. We review the definition of blue and green water fluxes as liquid water that contributes to runoff at the outlet of the selected domain (blue) and water lost to the atmosphere through vapor fluxes, that is evaporation from water, ground, and interception plus transpiration (green) and propose to add the term white water to account for the (often neglected) evaporation and sublimation from snow and ice. We provide an assessment of models that can simulate the cryo-hydro-biosphere continuum and the interactions between spheres in high mountain catchments, going beyond disciplinary separations. Land surface models are uniquely able to account for such complexity, since they solve the coupled fluxes of water, energy, and carbon between the land surface and atmosphere. Due to the mechanistic nature of such models, specific variables can be compared systematically to independent remote sensing observations – providing vital insights into model accuracy and enabling the understanding of the complex watersheds of HMA. We discuss recent developments in spaceborne earth observation products that have the potential to support catchment modeling in high mountain regions. We then present a pilot study application of the mechanistic land surface model Tethys & Chloris to a glacierized watershed in the Nepalese Himalayas and discuss the use of high-resolution earth observation data to constrain the meteorological forcing uncertainty and validate model results. We use these insights to highlight the remaining challenges and future opportunities that remote sensing data presents for land surface modeling in HMA. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Geo-Spatial Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=178418927
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/10095020.2024.2330546
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      – Code: eng
        Text: English
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        PageCount: 25
        StartPage: 703
    Subjects:
      – SubjectFull: Hydrologic cycle
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Runoff
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      – SubjectFull: Fresh water
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      – SubjectFull: Biosphere
        Type: general
      – SubjectFull: Himalaya Mountains
        Type: general
    Titles:
      – TitleFull: Land surface modeling informed by earth observation data: toward understanding blue–green–white water fluxes in High Mountain Asia.
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