Self-organizing map of soil properties in the context of hydrological modeling

Rivas-Tabares, David; Miguel, Ángel de; Willaarts, Bárbara; Tarquis, A. M.

doi:10.1016/j.apm.2020.06.044

Cited by 14 publications

(6 citation statements)

References 49 publications

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“…Para la calibración de la producción de escurrimientos se estableció un conjunto de parámetros (Cuadro 1) que están asociados a procesos como: producción de escurrimientos, flujo de agua subterránea, definición de las HRU y tipo de suelo (Rivas-Tabares et al, 2020) realizaron múltiples simulaciones hasta la obtención de un ajuste satisfactorio entre los datos de gasto observado y simulado a nivel mensual durante el periodo de 1972 a 1976. Para la actualización de los parámetros se realizaron de tres formas: reemplazándolos por un valor dado; adicionando al valor del parámetro existente; o relativo cuando el valor de un parámetro existente se multiplica por un valor (Arnold et al, 2012).…”

Section: Calibración Y Validaciónunclassified

“…La escala de definición de las HRU, afecta directamente al flujo de agua a través de los suelos; por lo tanto, la distribución espacial del tipo de suelo, obtenida mediante la técnica de DSM, puede ser empleada para mejor representación de las unidades del tipo de suelo existentes en una cuenca, ya que el uso de estos algoritmos mejora significativamente el nivel de precisión del modelo SWAT para la producción de escurrimientos en cuencas semiáridas (Kateb et al, 2019;Rivas-Tabares et al, 2020).…”

Section: Cobertura Vegetal Biomasaunclassified

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Modelación hidrológica con el modelo SWAT empleando diferentes distribuciones espaciales del tipo suelo en la cuenca del Río Mixteco

Colín-García¹,

Palacios-Vélez²,

Fernández-Reynoso³

et al. 2023

Terra Latinoam

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La cuenca del río Mixteco se ubica en el estado de Oaxaca, México y comprende una superficie total de 6 559.20 km2; esta presenta fuerte degradación de sus recursos naturales, derivado principalmente de las acciones antrópicas asociadas a una mala administración de sus recursos naturales. El objetivo de este trabajo fue evaluar el comportamiento del escurrimiento superficial mediante el modelo SWAT (Soil and Water Assessment Tool) en la cuenca del río Mixteco utilizando tanto la distribución espacial de suelo del INEGI (Instituto Nacional de Estadística y Geografía) como la obtenida a través de la técnica de DSM (Mapeo Digital de Suelos). La modelación con INEGI (ModINEGI) correspondió al mapa edafológico de la Serie II escala 1:250,000 y la modelación con DSM (ModDSM) se obtuvo a partir de covariables ambientales. Los indicadores de evaluación del desempeño del modelo SWAT durante la fase de calibración fueron R2 = 0.88 y NSE = 0.87 para ModDSM y R2 = 0.85 y NSE = 0.85 para ModINEGI; sin embargo, durante el periodo de validación, los resultados de ModDSM (R2 = 0.83 y NSE = 0.81) fueron mejores que los de ModINEGI (R2 = 0.83 y NSE = 0.61). Por lo tanto, los resultados indican que la variación espacial de los tipos de suelo obtenida a través de la técnica de DSM, en combinación con la cobertura vegetal y la distribución de las pendientes a nivel cuenca, permiten al modelo SWAT simular de forma adecuada el comportamiento del escurrimiento superficial.

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Section: Calibración Y Validaciónunclassified

Section: Cobertura Vegetal Biomasaunclassified

Modelación hidrológica con el modelo SWAT empleando diferentes distribuciones espaciales del tipo suelo en la cuenca del Río Mixteco

Colín-García¹,

Palacios-Vélez²,

Fernández-Reynoso³

et al. 2023

Terra Latinoam

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“…Of great relevance are those services related to water provision and regulation where soil physical properties play a fundamental role, given that they represent the interface where the atmosphere meets the Earth and modulates fluxes of matter and energy within the continuum that ranges from substrate underlying soils to the top of the atmosphere 4 . Indeed, soil physical properties and their soil hydraulic parameters may be relevant in ecosystem and hydrological modeling since many processes implemented in such tools directly depend on those properties 5 , 6 . Since soil texture and bulk density determine the capacity of soil to store and infiltrate water, both properties are linked to runoff control 7 , 8 , CO 2 efflux 9 , and organic matter mineralization 10 – 12 , among other natural processes.…”

Section: Background and Summarymentioning

confidence: 99%

CLSoilMaps: A national soil gridded database of physical and hydraulic soil properties for Chile

Dinamarca,

Galleguillos,

Seguel

et al. 2023

Sci Data

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Spatially explicit soil information is crucial for comprehending and managing many of Earth´s processes related to carbon, water, and other biogeochemical cycles. We introduced a gridded database of soil physical properties and hydraulic parameters at 100 meters spatial resolution. It covers the continental area of Chile and binational basins shared with Argentina for six standardized depths following the specifications of the GlobalSoilMap project. We generated soil maps based on digital soil mapping techniques based on more than 4000 observations, including unpublished data from remote areas. These maps were used as input for the pedotransfer function Rosetta V3 to obtain predictions of soil hydraulic properties, such as field capacity, permanent wilting point, total available water capacity, and other parameters of the water retention curve. The trained models outperformed several other DSM studies applied at the national and regional scale for soil physical properties (nRMSE ranging from 6.93% to 15.7%) and delivered acceptable predictions (nRMSE ranging from 10.4% to 15.6%) for soil hydraulic properties, making them suitable for countless environmental studies.

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“…The digital soil map used in this work is based in a previous study [30] and is employed to discriminate soils of agroclimatic zones. This map uses the self-organizing map (SOM) algorithm [31] to facilitate the clustering of similar soil properties and provide a spatial arrangement of these in clusters.…”

Section: Site Description and Zones Selectionmentioning

confidence: 99%

“…Location of the two agroclimatic zones as case study in the midlands of Eresma-Adaja (EA) watershed. Selected areas with similar edaphoclimatic conditions, denoted as SOM5 and SOM15 that overlies the subbasins 50 and 24 respectively, adapted from [30].…”

Section: Site Description and Zones Selectionmentioning

confidence: 99%

Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

et al. 2021

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Vegetation indices time series analysis is increasingly improved for characterizing agricultural land processes. However, this is challenging because of the multeity of factors affecting vegetation growth. In semiarid regions the rainfall, the soil properties and climate are strongly correlated with crop growth. These relationships are commonly analyzed using the normalized difference vegetation index (NDVI). NDVI series from two sites, belonging to different agroclimatic zones, were examined, decomposing them into the overall average pattern, residuals, and anomalies series. All of them were studied by applying the concept of the generalized Hurst exponent. This is derived from the generalized structure function, which characterizes the series’ scaling properties. The cycle pattern of NDVI series from both zones presented differences that could be explained by the differences in the climatic precipitation pattern and soil characteristics. The significant differences found in the soil reflectance bands confirm the differences in both sites. The scaling properties of NDVI original series were confirmed with Hurst exponents higher than 0.5 showing a persistent structure. The opposite was found when analyzing the residual and the anomaly series with a stronger anti-persistent character. These findings reveal the influences of soil–climate interactions in the dynamic of NDVI series of rainfed cereals in the semiarid.

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Self-organizing map of soil properties in the context of hydrological modeling

Cited by 14 publications

References 49 publications

Modelación hidrológica con el modelo SWAT empleando diferentes distribuciones espaciales del tipo suelo en la cuenca del Río Mixteco

Modelación hidrológica con el modelo SWAT empleando diferentes distribuciones espaciales del tipo suelo en la cuenca del Río Mixteco

CLSoilMaps: A national soil gridded database of physical and hydraulic soil properties for Chile

Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

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