Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Menezes, Michele Duarte de; Silva, Sérgio Henrique Godinho; Mello, Carlos Rogério de; Owens, Phillip R.; Curi, Nilton

doi:10.1590/0103-9016-2013-0416

Cited by 34 publications

(22 citation statements)

References 25 publications

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“…The entire watershed grid contained 7516 (30 x 30m) grid cells, an area of about 6.76 km 2 , which agrees with published values of the Lavrinha watershed (LW) area (MENEZES et al, 2014). The LW, with elevations between 1137 and 1733 m (digital elevation model-DEM with 30 m resolution), is located in the Grande River Basin, in the Mantiqueira Range, which is the most important headwater region of Southeastern Brazil (Figure 1).…”

Section: Study Site Descriptionsupporting

confidence: 82%

Hydrologic Impacts Due to the Changes in Riparian Buffer in a Headwater Watershed

Alvarenga

Mello

Colombo

et al. 2017

CERNE

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ABSTRACT:In recent years, concerns regarding the impacts of deforestation of riparian vegetation on water resources have created social and political tensions in Brazil. This research analyzed simulated hydrologic components of a 6.76 km 2 headwater watershed with different widths of riparian vegetation. Lavrinha Watershed (LW) hydrological responses were simulated using the Distributed Hydrology Soil Vegetation Model (DHSVM), which was forced using meteorological data from one station (2005)(2006)(2007)(2008)(2009)(2010). Land cover scenarios where the percent land cover of Atlantic Forest was increased from the control resulted in changes in hydrologic components in the watershed due to increased evapotranspiration and rainfall interception and reduced runoff and overland flow. The base flow/runoff relationship has increased, suggesting that riparian vegetation plays an important role in groundwater recharge. Modeling of hydrologic components linked to riparian buffer scenarios, such as the process used in this study, can be a useful tool for decision-making strategies regarding watershed management. IMPACTOS HIDROLÓGICOS DEVIDO ÀS ALTERAÇÕES NA LARGURARIPÁRIA EM UMA BACIA HIDROGRÁFICA DE CABECEIRA RESUMO: Nos últimos anos, as preocupações quanto aos impactos do desmatamento da vegetação ripária nos recursos hídricos criaram tensões sociais e políticas no Brasil. Esta pesquisa analisou os componentes hidrológicos simulados de uma bacia hidrográfica de cabeceira de 6,76 km 2 com diferentes larguras de vegetação ripária. As respostas hidrológicas da Bacia Hidrográfica Lavrinha (BHL) foram simuladas utilizando o Distributed Hydrology Soil Vegetation Model (DHSVM) que foi forçado usando dados meteorológicos de uma estação (2005)(2006)(2007)(2008)(2009)(2010). Os cenários de cobertura do solo em que o percentual da Mata Atlântica foi aumentado do controle resultaram em mudanças nas componentes hidrológicas na bacia hidrográfica devido ao incremento da evapotranspiração e intercepção da precipitação e redução do escoamento total e escoamento superficial. A relação escoamento base/escoamento total aumentou, sugerindo que a vegetação ripária desempenha um papel importante na recarga de água subterrânea. A modelagem dos componentes hidrológicos, associada aos cenários de larguras ripárias, como o processo utilizado para este estudo, pode ser uma ferramenta útil para as estratégias de tomada de decisão referente à gestão de bacias hidrográficas.

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Section: Study Site Descriptionsupporting

confidence: 82%

Hydrologic Impacts Due to the Changes in Riparian Buffer in a Headwater Watershed

Alvarenga

Mello

Colombo

et al. 2017

CERNE

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“…slope, topographic wetness index, curvatures, etc. ), to spatialize soils information (Adhikari et al, 2014;Giasson et al, 2015;Menezes et al, 2014;Silva et al, 2016a;TaghizadehMehrjardi et al, 2015). However, when working in smaller areas, mainly in developing countries, it is common to face difficulties in obtaining data with high spatial resolution, which tends to make the use of these variables unfeasible.…”

Section: Introductionmentioning

confidence: 99%

Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)

et al. 2017

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Determination of soil properties helps in the correct management of soil fertility. The portable X-ray fluorescence spectrometer (pXRF) has been recently adopted to determine total chemical element contents in soils, allowing soil property inferences. However, these studies are still scarce in Brazil and other countries. The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil properties. 120 soil samples were collected at three depths and submitted to laboratory analyses. pXRF was used in the samples and total element contents were determined. From pXRF data, SMLR and RF were used to predict soil laboratory results, reflecting soil properties, and the models were validated. The best method was used to spatialize soil properties. Using SMLR, models had high values of R² (≥0.8), however the highest accuracy was obtained in RF modeling. Exchangeable Ca, Al, Mg, potential and effective cation exchange capacity, soil organic matter, pH, and base saturation had adequate adjustment and accurate predictions with RF. Eight out of the 10 soil properties predicted by RF using pXRF data had CaO as the most important variable helping predictions, followed by P 2 O 5 , Zn and Cr. Maps generated using RF from pXRF data had high accuracy for six soil properties, reaching R 2 up to 0.83. pXRF in association with RF can be used to predict soil properties with high accuracy at low cost and time, besides providing variables aiding digital soil mapping.Index terms: Soil analyses; spatial prediction; proximal sensor. RESUMOA determinação de atributos do solo auxilia no correto manejo da sua fertilidade. O equipamento portátil de fluorescência de raios-X (pXRF) foi recentemente adotado para determinar o teor total de elementos químicos em solos, permitindo inferências sobre atributos do solo. No entanto, esses estudos ainda são escassos no Brasil e em outros países. Os objetivos deste trabalho foram prever atributos do solo a partir de dados do pXRF, comparando-se os métodos de regressão linear múltipla stepwise (SMLR) e de random forest (RF), além de mapear e validar atributos do solo. 120 amostras de solo foram coletadas em três profundidades e submetidas a análises laboratoriais. Utilizou-se o pXRF para leitura das amostras e determinou-se o teor total de elementos. A partir dos dados do pXRF, foram utilizadas SMLR e RF para predizer resultados laboratoriais, que refletem atributos do solo, e os modelos foram validados. O melhor método foi utilizado para espacializar os atributos do solo. Utilizando SMLR, os modelos apresentaram valores elevados de R² (≥0,8), porém maior acurácia foi obtida na modelagem com RF. A capacidade de troca de cátions potencial e efetiva, matéria orgânica do solo, pH, saturação por bases e teores trocáveis de Ca, Al e Mg apresentaram ajustes adequados e predições acuradas com RF. Dos dez atributos do solo preditos por RF a partir de dados do pXRF, sete apr...

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“…The watershed lies at 1,137 to 1,732 m above sea level with a drainage area of about 6.76 km 2 and strongly rolling topography (Menezes et al, 2014;Pinto et al, 2015). The dominant soil class is Inceptsol (Cambissolo Háplico), and the predominant vegetation is Atlantic Forest (ombrophilous forest) (Menezes et al, 2014). …”

Section: Geographical Location Of the Area Studiedmentioning

confidence: 99%

“…The LCW has been a scientific laboratory, with other studies ); and the water table depth (m) (e, f), on December 31, 2008 (Menezes et al, 2009). that involved soils, climate, water table monitoring, water quality and sediment transport, groundwater recharge, and water balance (Junqueira Júnior et al, 2008;Menezes et al, 2009;Ávila et al, 2010;Mello et al, 2012;Oliveira et al, 2014;Menezes et al, 2014;Pinto et al, 2015). This served as a background for conducting DHSVM calibration, allowing comparisons between a soil map and a moisture zone map as inputs for the model.…”

Section: Dhsvm Calibration Based On Soil and Moisture Zone Mapsmentioning

confidence: 99%

Performance of a Distributed Hydrological Model Based on Soil and Moisture Zone Maps

Alvarenga

Mello

Colombo

et al. 2017

Rev. Bras. Ciênc. Solo

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ABSTRACT:The scarcity of field data to develop soil maps through a pedological survey is one of the main limitations to using distributed hydrological models, especially in small and medium-sized watersheds. The aim of this study was to compare a distributed hydrological model prediction to a soil map based on a pedological survey and a moisture zone map obtained using the Height Above the Nearest Drainage -HAND model. The Distributed Hydrology Soil Vegetation Model -DHSVM, which is a physically-based and distributed hydrological model, was applied to a mountainous watershed, located in the region of the Mantiqueira Range in the south of Minas Gerais in southeastern Brazil, and was compared to both maps mentioned above, taking the soil map developed by the pedological survey as a reference. Daily weather and streamflow data-sets were used for DHSVM calibration and validation using the two different maps (soil and moisture zone maps) as inputs. In both simulations, the DHSVM performed well, with outputs indicating a good relationship between topographical and hydrological characteristics from the two types of maps. Thus, the moisture zone map obtained by the HAND model can also be successfully used in distributed hydrological modeling, especially for mountainous regions in southeastern Brazil.

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Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Cited by 34 publications

References 25 publications

Hydrologic Impacts Due to the Changes in Riparian Buffer in a Headwater Watershed

Hydrologic Impacts Due to the Changes in Riparian Buffer in a Headwater Watershed

Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)

Performance of a Distributed Hydrological Model Based on Soil and Moisture Zone Maps

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