Evaluation of Satellite Precipitation Products for Hydrological Modeling in the Brazilian Cerrado Biome

Amorim, Jhones da Silva; Viola, Marcelo Ribeiro; Junqueira, Rubens; Oliveira, Vinícius Augusto de; Mello, Carlos Rogério de

doi:10.3390/w12092571

Cited by 38 publications

(16 citation statements)

References 67 publications

(90 reference statements)

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“…The parameters used in calibration (Table 1) were selected based on previous studies conducted in the Cerrado biome, such as Oliveira et al (2019), Rodrigues et al (2020) and Amorim et al (2020), and based on Latin Hypercube One-factor-At-a-Time sensitivity analysis (LH-OAT) method (Van Griensven et al, 2006). LH-OAT was applied using the SUFI2 algorithm in the SWAT-CUP software.…”

Section: Soil and Water Assessment Tool (Swat)mentioning

confidence: 99%

Hydrological modeling in a basin of the Brazilian Cerrado biome

Rodrigues¹,

Andrade²,

Viola³

et al. 2021

Rev. ambiente água

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The Brazilian Cerrado biome (BCB) is among 25 biodiversity hotspots identified worldwide, and covers the recharge area of important aquifers and rivers in South America. The increase in deforestation has been threatening water availability in this region. In order to assist in the water-resource management of the BCB, this study models the daily streamflow in a basin of the Cerrado, using two approaches: a process-based model (Soil and Water Assessment Tool - SWAT) and the data-driven model (Artificial Neural Network - ANN). The performance of the models was evaluated by the Nash-Sutcliffe coefficient (NSE), coefficient of determination (R2) and flow-duration-curves (FDC). The results indicate that SWAT (NSE > 0.61; R2 > 0.68) and ANN (NSE > 0.91; R2 > 0.79) models are suitable tools in daily streamflow modeling of the studied basin, with the ANN model being the most accurate. Based on FDC, the ANN model was also better than the SWAT model for all frequencies evaluated. Thus, the ANN model is a promising new approach for daily streamflow modelling in this region. Moreover, the results of this study can help water-resource managers in planning and implementing appropriate water allocation and conservation measures in the Brazilian Cerrado biome.

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Section: Soil and Water Assessment Tool (Swat)mentioning

confidence: 99%

Hydrological modeling in a basin of the Brazilian Cerrado biome

Rodrigues¹,

Andrade²,

Viola³

et al. 2021

Rev. ambiente água

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“…Despite its importance, understanding the spatiotemporal dynamics of soil moisture is an arduous task, mainly due to its high nonlinearity (Xiadong et al, 2016). To overcome this difficulty, various physically based models have been used to model soil moisture, such as the Sacramento Soil Moisture Accounting (SAC‐SMA) Model (Peck, 1976), Soil and Water Assessment Tool (SWAT) (Amorin, Viola, Junqueira, Oliveira, & Mello, 2020; Arnold, Srinivasan, Muttiah, & Williams, 1998; Srinivasan, Ranabarayanan, Arnold, & Bednarz, 1998), Variable Infiltration Capacity Model (VIC) (Liang, Lettenmaier, Wood, & Burges, 1994), Lavras Simulation of Hydrology (LASH) (Andrade et al, 2020), and Decision Support System for Agrotechnology Transfer (DSSAT) (Ritchie & Otter, 1985). A common problem associated with the application of these models is that they usually require various physical parameters (e.g., hydraulic conductivity, preferential pathways, porosity and air entry pressure) that can be difficult to obtain due to the complexity of the soil–vegetation–atmosphere continuum (Gill, Asefa, Kemblowski, & McKee, 2006; Meng & Quiring, 2008) and the deficiency of monitoring systems in developing countries such as Brazil.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal modelling of soil moisture in an Atlantic forest through machine learning algorithms

Oliveira

Rodrigues

Morais

et al. 2021

European J Soil Science

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Understanding the spatiotemporal behaviour of soil moisture in tropical forests is fundamental because it mediates processes such as infiltration, groundwater recharge, runoff and evapotranspiration. This study aims to model the spatiotemporal dynamics of soil moisture in an Atlantic forest remnant (AFR) through four machine learning algorithms, as these dynamics represent an important knowledge gap under tropical conditions. Random forest (RF), support vector machine, average neural network and weighted k‐nearest neighbour were studied. The abilities of the models were evaluated by means of root mean square error, mean absolute error, coefficient of determination (R2) and Nash‐Sutcliffe efficiency (NS) for two calibration approaches: (a) chronological and (b) randomized. The models were further compared with a multilinear regression (MLR). The study period spans from September 2012 to November 2019 and relies on variables representing the weather, geographical location, forest structure, soil physics and morphology. RF was the best algorithm for modelling the spatiotemporal dynamics of the soil moisture with an NS of 0.77 and R2 of 0.51 in the randomized approach. This finding highlights the ability of RF to generalize a dataset with contrasting weather conditions. Kriging maps highlighted the suitability of RF to track the spatial distribution of soil moisture in the AFR. Throughfall (TF), potential evapotranspiration (ETo), longitude (Long), diameter at breast height (DBH) and species diversity (H) were the most important variables controlling soil moisture. MLR performed poorly in modelling the spatiotemporal dynamics of soil moisture due to the highly nonlinear condition of this process. Highlights Modelling soil moisture in an Atlantic forest through machine learning. Machine learning algorithms are powerful tools to address the spatiotemporal dynamics of soil moisture. Climate, position and forest variables drive the spatiotemporal pattern of soil moisture. Random forest is the best algorithm to simulate soil moisture dynamics.

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“…Homepage:https://periodicos.ufpe.br/revistas/rbgfe economia, como por exemplo no setor do agronegócio (Casaroli et al, 2018) que pode ser afetado pelas perdas de solo devido a erosividade. Além disso, a precipitação na região tropical é considerada como uma componente meteorológica de alta variabilidade espaço-temporal (Amorim et al, 2020), por isso em área marcadas por extremos climáticos são mais suscetíveis aos impactos negativos promovido por chuvas intensas. Tadesse et al (2017) comentam o quanto os problemas ambientais estão preocupando a sociedade mundial através da degradação do solo.…”

Section: Introductionunclassified

Vulnerabilidade à erosão hídrica do solo, bacia hidrográfica do rio Araguaia

Gomes

Sousa

Ferreira

et al. 2021

Rev. Bras. Geog. Fis.

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O entendimento dos efeitos climáticos e do uso do solo em conjunto é crucial para análise ambiental e prevenções de desastres naturais. O objetivo deste trabalho é analisar a vulnerabilidade à erosão hídrica do solo na bacia hidrográfica do rio Araguaia. Usou-se dados de precipitação (Agência Nacional das Águas); uso e ocupação da terra (Projeto MapBiomas); declividade (Instituto Nacional de Pesquisas Espaciais) e de solo (Empresa Brasileira de Pesquisas Agropecuária) no cálculo de mapas através do geoprocessamento para obter-se a representação da erosão hídrica do solo. A bacia hidrográfica do rio Araguaia caracteriza-se na maior parte por zonas de transição de estabilidade e vulnerabilidade, porém destaca-se as áreas estáveis nas proximidades da foz, regiões críticas á sudoeste (Rio das Mortes), nos limites (leste) do Alto Araguaia, nas partes sudeste e sudoeste do Baixo Araguaia. Na escala climática, a precipitação não apresenta riscos significativos. No entanto, o intenso uso e ocupação da terra na região foi a forçante que maior agravou a vulnerabilidade, principalmente em áreas (declives e solo) naturalmente instáveis. Assim, algumas cidades precisam estar em alerta para possíveis eventos erosivos. Vulnerability to soil water erosion, Araguaia basin riverA B S T R A C TThe Understanding climate effects and land use together is crucial for environmental analysis and natural disaster prevention. The objective of this work is to analyze the vulnerability to soil water erosion in the hydrographic basin of the Araguaia river. Rainfall data (National Water Agency) was used; land use and occupation (MapBiomas Project); slope (National Institute for Space Research) and soil (Brazilian Agricultural Research Corporation) in the calculation of maps through geoprocessing to obtain the representation of soil water erosion. The hydrographic basin of the Araguaia river is characterized in the most part by transition zones of stability and vulnerability, however the stable areas in the vicinity of the mouth stand out, critical regions to the southwest (Rio das Mortes), on the (eastern) limits of the Alto Araguaia, in the southeast and southwest parts of the Lower Araguaia. On the climate scale, the rainfall does not present significant risks. However, the intense use and occupation of land in the region was the forcing that increased the risk, mainly in naturally unstable areas (slopes and soil). Thus, some cities need to be alert to possible erosive events.

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Evaluation of Satellite Precipitation Products for Hydrological Modeling in the Brazilian Cerrado Biome

Cited by 38 publications

References 67 publications

Hydrological modeling in a basin of the Brazilian Cerrado biome

Hydrological modeling in a basin of the Brazilian Cerrado biome

Spatiotemporal modelling of soil moisture in an Atlantic forest through machine learning algorithms

Vulnerabilidade à erosão hídrica do solo, bacia hidrográfica do rio Araguaia

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