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Item type:Publication, Access status: without files , Seasonal Variation of Evaporation Suppression Efficiency in Water Reservoirs Covered with Floating Elements(2024-12-12); ; ; ; Since the dawn of civilization water reservoirs have been used to support irrigation and livestock in many arid regions of the world [1]. Evaporative losses diminish reservoir storage efficiency; hence, different means such as floating covers have been proposed to reduce losses. While laboratory and field studies have evaluated effects of cover properties (e.g., geometry, thermal, and radiative characteristics) on Evaporation Suppression Efficiency (ESE) [2-4], the influence of seasonal climate variability on ESE remains understudied. We conducted long-term field measurements in two identical water reservoirs (area: 25 m2, depth: 2 m) in a dry region to investigate how seasonal variations in atmospheric conditions affect ESE. We compared the evaporation rate and water temperature profile in a reservoir covered with Styrofoam discs (D: 50 cm, H: 5 cm) with those in an uncovered reservoir. For 90% surface coverage, measurements indicate that ESE varied from nearly 80% in summer to about 60% in winter. Temperature variations and radiative energy storage within the reservoirs show that the opaque and thermal insulating characteristics of the Styrofoam discs and their effects on wind-induced mixing lead to thermal stratification and formation of a seasonal thermocline in the covered reservoir. These affect the exchange of accumulated summer heat with overlying air resulting in a slower reduction in evaporation rate compared to the uncovered reservoir in cold seasons. The study sheds new light on thermal regimes and ESE in partially covered multiuse reservoirs under different climatic conditions.Publicationtype: Conference Poster27 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Utilizing Satellite Data and Machine Learning Algorithms to Predict Water Level Fluctuations in the Bani River in Africa(2024-12); ; ; We carried out a research focusing on forecasting water levels in the Bani River in Africa, a significant tributary of the Niger River that is vital for agriculture in Mali, Côte d'Ivoire, and Burkina Faso. Our region confronts issues due to political instability that hampers data collection. To address these challenges, we employed satellite data and machine learning methods. We evaluated two satellite rainfall products, CHIRPS and PERSIANN-CDR, to identify which delivers the most precise information for our area. Subsequently, we implemented machine learning models to predict water level variations, improving our management strategies and resilience against environmental changes. This method not only enhances the precision of water level forecasts but also aids in better resource management in politically unstable areas.Publicationtype: Conference Poster19