Evaporation is one of the terms of water budget equations and contributes to describe the water cycle. With the current trend of rising air and water temperatures all over the world, evaporation is also expected to increase and contribute to the depletion of usable water. Controlling and monitoring evaporation rates from water surfaces therefore becomes of primary importance for evaluating the availability of this vital and increasingly scarce resource. One of the most efficient ways to monitor along time with recurrent and comparable observations of the same target is the use of Earth Observation (EO) products. That's why we come to propose an EO-based model for the retrieval of Evaporation fluxes generated from Lake Surfaces (EO-LSEv). The model exploits Lake Surface Water Temperatures (LSWT) derived from satellite acquisitions and combines them with meteorological variables (i.e. air temperature, wind speed, relative humidity of air) following the bulk transfer theory (Dalton's law). The EO-LSEv model outputs are instantaneous (i.e. at satellite overpass) and daily evaporation maps, which in turn can be used to calculate water volume lost by the water bodies due to evaporation. The EO-LSEv model was first tested on a well-known study site (i.e. Lake Garda, Italy) where the ESA CCI Lakes dataset is available for a long period of time. Here we propose an application of the EO-LSEv model performed within the context of the HYdro-POwer-Suite (HYPOS) H2020 Project. HYPOS intends to support hydropower industries in their planning, monitoring and assessment tasks with an easy and cost-efficient access to data and through a Decision Support Tool (DST). The implementation of the EO-LSEv model within the DST will allow the estimation of the volume of water evaporated from hydropower reservoirs. Such an information can drive dam managers to adjust water withdrawal according both to people needs and water losses due to evaporation. In this way, the management of water can be done in a more efficient way maximizing productivity and minimizing water waste. In addition, evaporated water volume, combined with plant energy production data, can be used for the estimation of the "Blue Water Footprint" (BWF) of the system. The BWF gives an estimate of the efficiency of a hydropower plant. Since the BWF can be calculated at different temporal scales, this data can theoretically also be used by water managers to follow the monthly variations of the BWF of the plant and be more aware of water consumption. We present the estimation of the evaporation rates and BWF for two study sites: the Banja reservoir in Albania and the Enguri reservoir in Georgia (EU) for the period 2019-2020. The results allow assessing the temporal variability of evaporated water volumes along the two years, the differences between the evaporation volumes of the two reservoirs, and the differences of the BWF between the two hydropower plants.

Application of an EO-based model for the study of evaporation from water bodies

Erica Matta;Marina Amadori;Mariano Bresciani;Claudia Giardino;
2022

Abstract

Evaporation is one of the terms of water budget equations and contributes to describe the water cycle. With the current trend of rising air and water temperatures all over the world, evaporation is also expected to increase and contribute to the depletion of usable water. Controlling and monitoring evaporation rates from water surfaces therefore becomes of primary importance for evaluating the availability of this vital and increasingly scarce resource. One of the most efficient ways to monitor along time with recurrent and comparable observations of the same target is the use of Earth Observation (EO) products. That's why we come to propose an EO-based model for the retrieval of Evaporation fluxes generated from Lake Surfaces (EO-LSEv). The model exploits Lake Surface Water Temperatures (LSWT) derived from satellite acquisitions and combines them with meteorological variables (i.e. air temperature, wind speed, relative humidity of air) following the bulk transfer theory (Dalton's law). The EO-LSEv model outputs are instantaneous (i.e. at satellite overpass) and daily evaporation maps, which in turn can be used to calculate water volume lost by the water bodies due to evaporation. The EO-LSEv model was first tested on a well-known study site (i.e. Lake Garda, Italy) where the ESA CCI Lakes dataset is available for a long period of time. Here we propose an application of the EO-LSEv model performed within the context of the HYdro-POwer-Suite (HYPOS) H2020 Project. HYPOS intends to support hydropower industries in their planning, monitoring and assessment tasks with an easy and cost-efficient access to data and through a Decision Support Tool (DST). The implementation of the EO-LSEv model within the DST will allow the estimation of the volume of water evaporated from hydropower reservoirs. Such an information can drive dam managers to adjust water withdrawal according both to people needs and water losses due to evaporation. In this way, the management of water can be done in a more efficient way maximizing productivity and minimizing water waste. In addition, evaporated water volume, combined with plant energy production data, can be used for the estimation of the "Blue Water Footprint" (BWF) of the system. The BWF gives an estimate of the efficiency of a hydropower plant. Since the BWF can be calculated at different temporal scales, this data can theoretically also be used by water managers to follow the monthly variations of the BWF of the plant and be more aware of water consumption. We present the estimation of the evaporation rates and BWF for two study sites: the Banja reservoir in Albania and the Enguri reservoir in Georgia (EU) for the period 2019-2020. The results allow assessing the temporal variability of evaporated water volumes along the two years, the differences between the evaporation volumes of the two reservoirs, and the differences of the BWF between the two hydropower plants.
2022
Lake Evaporation
Earth Observation
HYPOS Project
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/432469
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