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    Evaluating the impact of dam construction on extreme shrinkage of Urmia Lake using spatial data
    (Springer, 2023) Baris, Mesut
    In this study, the extreme shrinkage of Urmia Lake is investigated, aiming to assess the impact of anthropogenic factors, particularly the over-construction of dams and natural anomalies associated with climate change. Historically available multispectral spatial data obtained from Landsat missions 4-5 TM and Landsat 8 OLI were utilized which totally covers a period of 36 years (1967-2020). Additionally, this data was employed to identify the locations of constructed water reservoirs and determine their construction timelines by analyzing the normalized difference vegetation index (NDVI). To examine the temporal patterns of annual precipitation in the lake basin, we obtained time series data from historical precipitation records, which were then converted into rasterized format. Our findings indicate that approximately 22% of the lake basin has been designated for feeding dam reservoirs. The impact of precipitation anomalies on the lake's water level was found to be relatively less significant when compared to the increased storage capacity of the dams. Furthermore, the construction of dams prior to 2000 contributed to enhancing the lake's stability during periods of drought. However, the substantial increase in the total storage capacity of dams after 2000 has significantly accelerated the shrinkage process. As a result, it was concluded that any effective rescue plan should prioritize ignoring a considerable portion of the reservoirs' storage capacity by releasing stored water, thereby allowing the lake to attain a stable condition.
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    A review on models, products and techniques for evapotranspiration measurement, estimation, and validation
    (Wiley, 2024) Baris, Mesut; Tombul, Mustafa
    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.