Pairing remote sensing and clustering in landscape hydrology for large-scale changes identification. Applications to the subarctic watershed of the George River (Nunavik, Canada)
International audience For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we...
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ftunigrenoble:oai:HAL:hal-04165224v1 2024-04-14T08:00:12+00:00 Pairing remote sensing and clustering in landscape hydrology for large-scale changes identification. Applications to the subarctic watershed of the George River (Nunavik, Canada) Sicaud, Eliot Fortier, Daniel Dedieu, Jean-Pierre Franssen, Jan Institut des Géosciences de l’Environnement (IGE) Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA) 2023-07 https://hal.science/hal-04165224 https://hal.science/hal-04165224/document https://hal.science/hal-04165224/file/hess-2023-101.pdf https://doi.org/10.5194/hess-2023-101 en eng HAL CCSD European Geosciences Union info:eu-repo/semantics/altIdentifier/doi/10.5194/hess-2023-101 hal-04165224 https://hal.science/hal-04165224 https://hal.science/hal-04165224/document https://hal.science/hal-04165224/file/hess-2023-101.pdf doi:10.5194/hess-2023-101 info:eu-repo/semantics/OpenAccess ISSN: 1027-5606 EISSN: 1607-7938 Hydrology and Earth System Sciences https://hal.science/hal-04165224 Hydrology and Earth System Sciences, 2023, ⟨10.5194/hess-2023-101⟩ [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology [SDE.IE]Environmental Sciences/Environmental Engineering [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing info:eu-repo/semantics/article Journal articles 2023 ftunigrenoble https://doi.org/10.5194/hess-2023-101 2024-03-21T16:10:47Z International audience For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised Geographic Object-Based Image Analysis (GeOBIA) paired with the Fuzzy C-Means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985–2019 time-period, each spanning five years. Our study site is the George River watershed (GRW), a 42,000 km2 watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015–2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985–2014 to investigate past changes in hydrological regime. The seven-classification time series showed a homogenization of subwatershed types associated to increases in vegetation productivity and in water content in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evapotranspiration and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (∼ 0.16 km3 y−1) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using ... Article in Journal/Newspaper Active layer thickness permafrost Subarctic Nunavik Université Grenoble Alpes: HAL Canada Nunavik |
institution |
Open Polar |
collection |
Université Grenoble Alpes: HAL |
op_collection_id |
ftunigrenoble |
language |
English |
topic |
[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology [SDE.IE]Environmental Sciences/Environmental Engineering [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
spellingShingle |
[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology [SDE.IE]Environmental Sciences/Environmental Engineering [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Sicaud, Eliot Fortier, Daniel Dedieu, Jean-Pierre Franssen, Jan Pairing remote sensing and clustering in landscape hydrology for large-scale changes identification. Applications to the subarctic watershed of the George River (Nunavik, Canada) |
topic_facet |
[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology [SDE.IE]Environmental Sciences/Environmental Engineering [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
description |
International audience For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised Geographic Object-Based Image Analysis (GeOBIA) paired with the Fuzzy C-Means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985–2019 time-period, each spanning five years. Our study site is the George River watershed (GRW), a 42,000 km2 watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015–2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985–2014 to investigate past changes in hydrological regime. The seven-classification time series showed a homogenization of subwatershed types associated to increases in vegetation productivity and in water content in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evapotranspiration and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (∼ 0.16 km3 y−1) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using ... |
author2 |
Institut des Géosciences de l’Environnement (IGE) Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA) |
format |
Article in Journal/Newspaper |
author |
Sicaud, Eliot Fortier, Daniel Dedieu, Jean-Pierre Franssen, Jan |
author_facet |
Sicaud, Eliot Fortier, Daniel Dedieu, Jean-Pierre Franssen, Jan |
author_sort |
Sicaud, Eliot |
title |
Pairing remote sensing and clustering in landscape hydrology for large-scale changes identification. Applications to the subarctic watershed of the George River (Nunavik, Canada) |
title_short |
Pairing remote sensing and clustering in landscape hydrology for large-scale changes identification. Applications to the subarctic watershed of the George River (Nunavik, Canada) |
title_full |
Pairing remote sensing and clustering in landscape hydrology for large-scale changes identification. Applications to the subarctic watershed of the George River (Nunavik, Canada) |
title_fullStr |
Pairing remote sensing and clustering in landscape hydrology for large-scale changes identification. Applications to the subarctic watershed of the George River (Nunavik, Canada) |
title_full_unstemmed |
Pairing remote sensing and clustering in landscape hydrology for large-scale changes identification. Applications to the subarctic watershed of the George River (Nunavik, Canada) |
title_sort |
pairing remote sensing and clustering in landscape hydrology for large-scale changes identification. applications to the subarctic watershed of the george river (nunavik, canada) |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04165224 https://hal.science/hal-04165224/document https://hal.science/hal-04165224/file/hess-2023-101.pdf https://doi.org/10.5194/hess-2023-101 |
geographic |
Canada Nunavik |
geographic_facet |
Canada Nunavik |
genre |
Active layer thickness permafrost Subarctic Nunavik |
genre_facet |
Active layer thickness permafrost Subarctic Nunavik |
op_source |
ISSN: 1027-5606 EISSN: 1607-7938 Hydrology and Earth System Sciences https://hal.science/hal-04165224 Hydrology and Earth System Sciences, 2023, ⟨10.5194/hess-2023-101⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/hess-2023-101 hal-04165224 https://hal.science/hal-04165224 https://hal.science/hal-04165224/document https://hal.science/hal-04165224/file/hess-2023-101.pdf doi:10.5194/hess-2023-101 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/hess-2023-101 |
_version_ |
1796316777004662784 |