A review on models, products and techniques for evapotranspiration measurement, estimation, and validation

BARIŞ M., Tombul M.

Environmental Quality Management, cilt.34, sa.1, 2024 (Scopus) identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 34 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1002/tqem.22250
  • Dergi Adı: Environmental Quality Management
  • Derginin Tarandığı İndeksler: Scopus, ABI/INFORM, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Business Source Elite, Business Source Premier, Environment Index, Geobase, Greenfile, INSPEC
  • Anahtar Kelimeler: estimation methods, evapotranspiration measurement methods, hydrological models, ML approaches, remote sensing approaches, validation techniques
  • İstanbul Gelişim Üniversitesi Adresli: Evet

Özet

In this review study, the major available methods for measurement and estimation of evapotranspiration (ET) are discussed briefly while explaining the latest developments. The best available validation methods are also reviewed and explained. It highlights the importance of accurate ET quantification in managing water resources, evaluating climate change impacts, and supporting crop water requirement management. Measurement methods such as scintillometry, lysimetry, and the eddy covariance (EC) flux method are presented. Additionally, hydrological models are discussed as estimation approaches for actual and potential ET. The paper explores various ET estimation products, particularly those based on remote sensing techniques. Specifically, methods like Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Operational (SSEBop ET), Moderate Resolution Imaging Spectroradiometer (MOD16), Surface Energy Balance Algorithm for Land (SEBAL), Global Land Surface Evaporation: Amsterdam Methodology (GLEAM), Satellite Application Facility on Land Surface Analysis (LSA-SAF), and Global Land Data Assimilation System (GLDAS) are described. The integration of machine learning (ML) with EC and remote sensing is investigated, with a comprehensive discussion of different ML approaches. Validation methods including the EC method, water balance method-derived ET (WBET), and statistical techniques are explained. Overall, this review paper provides a comprehensive overview of ET quantification, covering measurement techniques, estimation approaches, remote sensing methods, and the integration of ML. The insights gained from this review contribute to a profound knowledge of ET dynamics and helps those sectors dealing with drought monitoring, water resource management and climate change assessments.