Digital Soil Mapping for Hydropedological Purposes of the Cathedral Peak Research Catchments, South Africa

Harrison, Rowena; Tol, Johan van

doi:10.1007/978-3-031-04855-5_10

Cited by 2 publications

(3 citation statements)

References 56 publications

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“…These catchments are situated within the South African summer rainfall region and experience a mean annual precipitation of ∼1200–1600 mm (Bosch, 1979; Harrison & van Tol, 2022), predominantly occurring during October and March (Schulze, 1975). The region's geology is acidic, greatly leached soils (Schulze & George, 1987) with underlying basaltic lavas over Clarens Sandstone (Nänni, 1956; Toucher et al., 2016).…”

Section: Study Areamentioning

confidence: 99%

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

Kibirige,

Gokool,

Mkhize

2023

Vadose Zone Journal

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Soil moisture (SM) is a fundamental constituent of the terrestrial environment and the hydrological cycle. Owing to its significant influence on catchment hydrological responses, it can be utilized as an indicator of floods and droughts to aid early warning systems. This study aimed to develop a field‐scale method to estimate SM using parametric and machine learning‐based methods to compare whether advanced artificial intelligence methods can give similar results as traditional methods. Considering this, monthly observed SM data (from the top 10 cm), environmental covariates, and remotely sensed data from March 2019 to July 2021 for the Cathedral Peak Research Catchments VI and IX in South Africa were obtained. From the 241 observations obtained across 12 sites, 160 (∼66%) were used for model training, while the remaining 81 (∼34%) were used for model testing. Employing 10‐fold cross‐validation, the individual machine learning models (viz., support vector machine [SVM], random forest (RF), k‐nearest neighbor, classification and regression trees [Rpart], and generalized linear model) displayed a satisfactory performance (R2 = 0.52–0.79; root mean square error = 3.79–5.80). In the validation phase, the RF model displayed a superior performance, followed by the SVM. Subsequent SM estimation using the hybrid model produced satisfactory results in training (R2 = 0.90) and testing (R2 = 0.45). The results obtained from this study can aid in predicting SM variations in catchments with limited monitoring. Furthermore, this model can be applied in drought monitoring, forecasting, and informing agricultural management practices.

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Section: Study Areamentioning

confidence: 99%

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

Kibirige,

Gokool,

Mkhize

2023

Vadose Zone Journal

View full text Add to dashboard Cite

show abstract

“…A digital soil mapping exercise was undertaken for the three catchment areas utilising these hydropedological soil classifications [22]. The procedure used for the digital soil maps (DSMs) is detailed in [19] and is briefly described here. The soils of the three catchment areas were mapped and classified as per the South African classification system [23] and then regrouped into hydropedological soil types, namely shallow recharge soils, deep recharge soils, interflow soils, and saturated responsive soils.…”

Section: Hydropedology and Soil Mappingmentioning

confidence: 99%

“…Comparison of the hydropedological soil group maps revealed that each catchment had a different percentage of the various hydropedological soil groups. This is based on the different topographies of the catchments as well as the various soil characteristics of each hydropedological soil group [19]. Table 3 gives an indication of the dominant hydropedological soil groups in CP-III, CP-VI, and CP-IX.…”

Section: Dominant Hydropedological Soil Groups Of the Catchmentsmentioning

confidence: 99%

Hydropedological Characteristics of the Cathedral Peak Research Catchments

2022

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It has long been recognised that the role of soils is critical to the understanding of the way catchments store and release water. This study aimed to gain an understanding of the hydropedological characteristics and flow dynamics of the soils of three mountain catchment areas. Digital soil maps of the hydropedological characteristics of the catchments were interpreted and a conceptual response of these watersheds to precipitation was formed. This conceptual response was then tested with the use of site-specific precipitation and streamflow data. Furthermore, piezometers were installed in soils classified as the interflow hydropedological soil group as well as the saturated responsive hydropedological soil group and water table depth data for the three catchments were analysed. Climatic data indicated that there is a lag time effect in the quantity of precipitation that falls in the catchment and the corresponding rise in streamflow value. This lag time effect coupled with data obtained from the piezometers show that the various hydropedological soil groups play a pivotal role in the flow dynamics. Of importance is the unique influence of different wetland systems on the streamflow dynamics of the catchments. The drying and wetting cycles of individual wetland systems influenced both the baseflow connectivity and the overland flow during wetter periods. They are the key focus in understanding the connectivity between the hydropedological flow paths and the contribution of soil water to the stream networks of the three catchments.

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Digital Soil Mapping for Hydropedological Purposes of the Cathedral Peak Research Catchments, South Africa

Cited by 2 publications

References 56 publications

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

Hydropedological Characteristics of the Cathedral Peak Research Catchments

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