Deducing the digital evaporation loss model of high dam reservoir

Evaporation is the most negative phenomenon, causing huge losses in the water capacities of reservoirs. For example, evaporation at the High Dam (HD) reservoir (the water resources bank of Egypt that supplies Egypt with about 97% of its freshwater needs) which is characterized by its large water surface area (about 6000 km2 at water level 178 A.M.S.L). This reservoir, due its geographical location, is exposed to a range of climatic factors which increase evaporation rate annually, and consequently, increase the total loss of its capacity (about 10 Billion m3 of the HD reservoir capacity is lost annually due to evaporation phenomenon). Many organizations rely on an evaporation chart that gives the value of evaporation loss according to some climate variables. With respect to the problem of malfunctions of some stations, or in case of lack of data for any reason, it is important to have a digital evaporation loss model that gives the missed evaporation loss in the High Dam reservoir directly, according to two factors; the given month and the average monthly water level. This paper will explain how to get benefit from the available data of hydro-meteorological stations distributed within the High Dam reservoir, to predict the digital evaporation loss surface using the GIS prediction and conversion tools

Developing an Optimized Policy Tree-Based Reservoir Operation Model for High Aswan Dam Reservoir, Nile River

The impacts of climate change on the Nile River and Grand Ethiopian Renaissance Dam (GERD) along with the increased water demand downstream suggest an urgent need for more efficient management of the reservoir system that is well-informed by accurate modeling and optimization of the reservoir operation. This study provides an updated water balance model for Aswan High Dam Reservoir, which was validated using combined heterogeneous sources of information, including in situ gauge data, bias-corrected reanalyzed data, and remote sensing information. To investigate the future challenges, the spatial distribution of the annual/seasonal Aswan High Dam Reservoir surface air temperature trends over the period from 1979 to 2018 was studied. An increase of around 0.48 °C per decade in average annual temperature was detected, a trend that is expected to continue until 2100. Moreover, a set of machine learning models were developed and utilized to bias-correct the reanalyzed inflow and outflow data available for Aswan High Dam Reservoir. Finally, a policy tree optimization model was developed to inform the decision-making process and operation of the reservoir system. Results from the historical test simulations show that including reliable inflow data, accurate estimation of evaporation losses, and including new regulations and added projects, such as the Toshka Project, greatly affect the simulation results and guide managers through how the reservoir system should be operated in the future

Influência do uso e ocupação do solo na evapotranspiração utilizando técnicas de sensoriamento remoto para a bacia do Xingu

Changes in the use and land cover of a watershed have significant impacts on hydrological processes and water balance variables, such as real evapotranspiration (ETr), a component of the hydrological cycle evaluated as one of the most affected by changes in the type of watershed cover. surface. Allied to the fact, the technique of remote sensing has become an excellent tool for assessing environmental degradation, as it allows analyzing the changes caused by anthropic action in temporal and spatial scales in complex environments of hydrographic basins. In this context, focused on the Xingu Hydrographic Basin, and its five sub-basins (Lower Xingu, Middle Xingu, Upper Xingu, Iriri and Nascentes do Xingu), the present work aimed to: evaluate the performance of seven ETr products (FLDAS , MOD16A2, PML_V2, TerraClimate, ERA5-Land, GLEAM_v3.3a and SSEBop) and the upscaling of FLUXCOM, compared to the median of the eight models in the common period from 2003 to 2014; to study the influence of changes in land use and land cover on the ETr, estimated by the product created through the median of the eight models, relating it to the data from MapBiomas, in the available interval of 1985-2020; and to analyze the effects on real evapotranspiration arising before (1993-2015) and after (2016-2020) the filling of the reservoir of the Belo Monte Hydroelectric Power Plant, through the SSEBop BR Evapotranspiration application, which uses images from the Landsat 5, 7 and 8 series TOA reflectance in obtaining the ETr. All datasets described were accessed and processed through the Google Earth Engine platform. For most analyses, the results found suggested that the products MOD16A2 and GLEAM_v3.3a returned data closer to the median of the models, with convergence of evapotranspiration values around 93.5% and 91.7%, respectively; decrease in forest areas (-16.23%), with conversion to pasture areas, in the order of +12.51%, and agricultural areas, reaching +5.5%, with the maximum peak of ET during the season conversion (October-November); and two trends in the average ETr related to the construction of the Belo Monte HPP, with a downward trend of 0.066 mm/d in the period prior to the reservoir (1993-2015), and an upward trend after its filling (2016-2020) equal to 0.040 mm /d.As modificações no uso e cobertura do solo de uma bacia têm impactos significativos nos processos hidrológicos e nas variáveis do balanço hídrico, tais como a evapotranspiração real (ETr), componente do ciclo hidrológico avaliada como uma das mais afetadas pela alteração do tipo de cobertura da superfície. Aliado ao fato, a técnica do sensoriamento remoto vem se tornando uma excelente ferramenta para avaliação da degradação ambiental, pois permite analisar as alterações provocadas pela ação antrópica nas escalas temporal e espacial em ambientes complexos de bacias hidrográficas. Nesse contexto, focado na Bacia Hidrográfica do Xingu, e suas cinco sub-bacias (Baixo Xingu, Médio Xingu, Alto Xingu, Iriri e Nascentes do Xingu), o presente trabalho teve como objetivo: avaliar o desempenho de sete produtos de ETr (FLDAS, MOD16A2, PML_V2, TerraClimate, ERA5-Land, GLEAM_v3.3a e SSEBop) e do upscalling do FLUXCOM, frente à mediana dos oito modelos no período comum de 2003 a 2014; estudar a influência das mudanças no uso e cobertura do solo sobre a ETr, estimada pelo produto criado através da mediana dos oito modelos, relacionando-a aos dados do MapBiomas, no intervalo disponível de 1985-2020; e analisar os efeitos na evapotranspiração real oriundos antes (1993-2015) e após (2016-2020) o enchimento do reservatório da Hidrelétrica de Belo Monte, por meio do aplicativo SSEBop BR Evapotranspiration, que utiliza imagens da série Landsat 5, 7 e 8 TOA reflectance na obtenção da ETr. Todos os conjuntos de dados descritos foram acessados e processados por meio da plataforma Google Earth Engine. Para a maioria das análises, os resultados encontrados sugeriram que os produtos MOD16A2 e GLEAM_v3.3a retornaram dados mais próximos à mediana dos modelos, com convergência de valores de evapotranspiração em torno de 93,5% e 91,7%, respectivamente; decréscimo nas áreas de floresta (-16,23%), com conversão às áreas de pastagens, na ordem de +12,51%, e áreas agrícolas, chegando a +5,5%, sendo o pico máximo da ET durante a estação de conversão (outubro-novembro); e duas tendências na ETr média relacionada à construção da UHE Belo Monte, sendo tendência de decréscimo de 0,066 mm/d no período anterior ao reservatório (1993-2015), e tendência de acréscimo após seu enchimento (2016-2020) igual a 0,040 mm/d