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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

A geostatistical approach for multi-source data fusion to predict water table depth

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Manzione, Rodrigo Lilla [1] ; Castrignano, Annamaria [2]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] UNESP, FCE, Tupa - Brazil
[2] CNR, IRSA, Bari - Italy
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: Science of The Total Environment; v. 696, DEC 15 2019.
Citações Web of Science: 0

Accurate water table depth mapping is important for water management and activity planning. The joint use of exhausted geospatial raster data with sparse field measurements could improve predictions. The aim of this work was to fuse different support data, collected with remote sensors, with point soil field observations to improve water table depth prediction. A method for multi-source data fusion is described in detail, based on multivariate geostatistics and exemplified with a case study in a conservation area of 5700 ha in the state of Sao Paulo, Brazil. TanDEM-X digital surface model with 90 m resolution and SAFER (Simple Algorithm for Evapotranspiration Retrieving) data calculated from Sentinel-2 images with 20 m resolution, were jointly used with water table depth and soil physical variables measured at 56 locations to predict water table depth in two hydrological years (2015-16 and 2016-17). Data were transformed to normal distributions using the Gaussian anamorphosis approach. A Linear Model of Coregionalization (LMC), calculated for all direct and cross-variograms of the eleven variables of study, was regularized at block support for multi-collocated block cokriging predictions. Support change correction was made to reduce punctual variance to block variances. Univariate and multivariate geostatistical interpolation methods were compared through cross validation. The uncertainty associated to the water table depths estimated by multivariate approach was lower than those by the univariate approach. Moreover, multivariate predictions incorporated the influences induced by local relief, vegetation and soil properties. Confidence interval maps, presented as uncertainty measure, reveal areas with higher and lower precision of groundwater level prediction that could be effectively used as support in land use management. (C) 2019 Elsevier B.V. All rights reserved. (AU)

Processo FAPESP: 16/09737-4 - Modelagem da variabilidade espaço-temporal em águas subterrâneas a partir de dados de monitoramento de níveis freáticos utilizando funções de covariância
Beneficiário:Rodrigo Lilla Manzione
Linha de fomento: Auxílio à Pesquisa - Regular