Modelagem espacial da recarga das águas subterrâneas sob diferentes usos e coberturas da terra
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Data
2019-09-02
Autores
Silva, César de Oliveira Ferreira
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Editor
Universidade Estadual Paulista (Unesp)
Resumo
Quantificar e monitorar os fluxos de energia e água no solo em grandes áreas com suficiente acurácia para subsidiar o gerenciamento de recursos hídricos na agricultura é um desafio que ultrapassa a abordagem tradicional, que é baseada em medições pontuais dos parâmetros de interesse. O sensoriamento remoto, conjuntamente com dados agrometeorológicos, é um instrumento aplicável à geração de informações essenciais ao gerenciamento racional da água. Pode-se fazer um acompanhamento dos impactos das mudanças climáticas e daqueles causados pelas atividades da agropecuária intensiva sobre as condições ambientais e do consumo hídrico incremental das culturas. A presente pesquisa realizou a associação do uso de imagens de satélites e dados climáticos em análises espaciais e temporais dos componentes dos balanços de energia e de água em usos da terra variados para modelar a evolução espaço-temporal dos níveis freáticos de 39 poços localizados em Águas de Santa Bárbara entre os anos de 2014 e 2018, durante o período mais ativo do ENSO (Oscilação Sul-El Niño-Sul) (2016/2017) e seus efeitos posteriores na vegetação (até o início de 2018). O modelo SAFER (Simple Algorithm for Evapotranspiration Retrieving) foi utilizado para obter a evapotranspiração real por meio de uma implementação construída em ambiente R, que foi publicada oficialmente como o pacote “agriwater” no repositório CRAN. O escoamento superficial foi modelado através do Método Racional. Os níveis freáticos foram modelados em escala diária por meio de uma adaptação do método Water Table Flutuation (WTF). A recarga potencial de água subterrânea para cada uso da terra (pastagem, cultura de cana-de-açúcar, silvicultura e floresta) variou entre 15 e 50% da precipitação. A silvicultura apresentou taxas de evapotranspiração mais altas que as culturas florestais e de cana-de-açúcar. Os níveis de água subterrânea medidos em 46 poços de monitoramento foram analisados para obter dados suficientes para criar os hidrogramas necessários para a validação. 34 poços rasos (que atingiram profundidades menores que 3 metros) obtiveram os melhores resultados (R²> 0,92), nos quais o termo da raiz do erro médio quadrático absoluto (RMSE) foi inferior a 20% do nível freático, indicando que resposta mais rápida à evapotranspiração real com sensoriamento remoto.
Quantifying and monitoring soil energy and water flows in large areas with sufficient accuracy to subsidize water management in agriculture is a challenge that surpasses the traditional approach, which is based on in-situ measurements of parameters of interest. Remote sensing in conjunction with agrometeorological data is an instrument applicable to large-scale water management. The impacts of climate change and those caused by intensive farming activities on environmental conditions and incremental water consumption of crops can be monitored. This research describes a combination of large-scale spatially remote sensed actual evapotranspiration and geographical information systems of surface runoff to estimate groundwater recharge potential, by water balance, to model the spatial-temporal evolution of used groundwater levels measured in 39 wells located in Águas de Santa Barbara, between 2014 and 2018, during recent ENSO (El Niño-Southern Oscillation) most active period (2016/2017) and verify its posteriori effects on vegetation (until early 2018). The Simple Algorithm for Evapotranspiration Retrieving (SAFER) model was used to obtain current evapotranspiration through an implementation built in the R environment, which was officially published as the “agriwater” package in the CRAN repository. The Rational Method for runoff modeling were applied. The groundwater levels were modeled on a daily scale using an adaptation of the Water Table Fluctuation (WTF) method. The average annual groundwater recharge potential for each of the land uses (pasture, sugarcane crop, silviculture and forest) varied between 15 and 50% of the rainfall. Silviculture showed higher evapotranspiration rates than forest and sugarcane crops. Groundwater levels measured at 46 monitoring wells were analyzed to obtain enough data to create the hydrographs required for the validation. 34 shallow wells (which reached depths smaller than 3 meters) had the best results (R² > 0.92), where the root mean squared absolute error (RMSE) term appeared to be less than 20% of the mean groundwater level indicating that it has a faster response to remote sensed actual evapotranspiration.
Quantifying and monitoring soil energy and water flows in large areas with sufficient accuracy to subsidize water management in agriculture is a challenge that surpasses the traditional approach, which is based on in-situ measurements of parameters of interest. Remote sensing in conjunction with agrometeorological data is an instrument applicable to large-scale water management. The impacts of climate change and those caused by intensive farming activities on environmental conditions and incremental water consumption of crops can be monitored. This research describes a combination of large-scale spatially remote sensed actual evapotranspiration and geographical information systems of surface runoff to estimate groundwater recharge potential, by water balance, to model the spatial-temporal evolution of used groundwater levels measured in 39 wells located in Águas de Santa Barbara, between 2014 and 2018, during recent ENSO (El Niño-Southern Oscillation) most active period (2016/2017) and verify its posteriori effects on vegetation (until early 2018). The Simple Algorithm for Evapotranspiration Retrieving (SAFER) model was used to obtain current evapotranspiration through an implementation built in the R environment, which was officially published as the “agriwater” package in the CRAN repository. The Rational Method for runoff modeling were applied. The groundwater levels were modeled on a daily scale using an adaptation of the Water Table Fluctuation (WTF) method. The average annual groundwater recharge potential for each of the land uses (pasture, sugarcane crop, silviculture and forest) varied between 15 and 50% of the rainfall. Silviculture showed higher evapotranspiration rates than forest and sugarcane crops. Groundwater levels measured at 46 monitoring wells were analyzed to obtain enough data to create the hydrographs required for the validation. 34 shallow wells (which reached depths smaller than 3 meters) had the best results (R² > 0.92), where the root mean squared absolute error (RMSE) term appeared to be less than 20% of the mean groundwater level indicating that it has a faster response to remote sensed actual evapotranspiration.
Descrição
Palavras-chave
Águas subterrâneas. Nível freático. Evapotranspiração. Recarga de aquífero.