Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods.

Sabzehee, F; Amiri-Simkooei, A R; Iran-Pour, S; Vishwakarma, B D; Kerachian, R

Sabzehee, F; Amiri-Simkooei, A R; Iran-Pour, S; Vishwakarma, B D; Kerachian, R.

Afiliación

Sabzehee F; Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan 81746-73441, Iran.
Amiri-Simkooei AR; Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan 81746-73441, Iran; Department of Geoscience and Remote Sensing, Delft University of Technology, 2600 AA, Delft, the Netherlands. Electronic address: a.amirisimkooei@tudelft.nl.
Iran-Pour S; Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan 81746-73441, Iran.
Vishwakarma BD; Interdisciplinary Centre for Water Research, Indian Institute of Science, Bangalore, 560012, India; Centre for Earth Sciences, Indian Institute of Science, Bangalore, 560012, India; School of Geographical Sciences, University of Bristol, Bristol, BS8 1RL, UK.
Kerachian R; School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.

J Environ Manage ; 330: 117180, 2023 Mar 15.

Article en En | MEDLINE | ID: mdl-36603260

Asunto(s)

Ecosistema; Agua Subterránea; Monitoreo del Ambiente/métodos; Lagos; Agua

Palabras clave

Downscaling methods; GRACE; Groundwater storage; Machine learning; Mann-kendall test; Urmia catchment

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Agua Subterránea / Ecosistema Tipo de estudio: Prognostic_studies Idioma: En Revista: J Environ Manage Año: 2023 Tipo del documento: Article País de afiliación: Irán

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