Using hydropedological characteristics to improve modelling accuracy in Afromontane catchments

Harrison, Rowena; Tol, Johan van; Toucher, Michele

doi:10.1016/j.ejrh.2021.100986

Cited by 6 publications

(9 citation statements)

References 52 publications

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“…These results agree with Harrison et al. (2022), where the calibration of the lateral lag time coefficient parameter within the SWAT+ model was required to improve the simulation lateral flow for each hydrological soil type within a mountainous research catchment in South Africa.…”

Section: Resultssupporting

confidence: 90%

“…These results suggest that additional calibration of model parameters would be required to reflect the hydrological responses of different soils more adequately for different catchments. These results agree with Harrison et al (2022), where the calibration of the lateral lag time coefficient parameter within the SWAT+ model was required to improve the simulation lateral flow for each hydrological soil type within a mountainous research catchment in South Africa.…”

Section: Hydrological Processessupporting

confidence: 84%

“…These results also indicated that accurate streamflow simulations do not necessarily mean accurate internal hydrological processes. Harrison et al (2022) used hydropedological insight and measured hydraulic properties to substantially improve lateral flow simulations using hydropedological insight in a mountainous catchment of South Africa. However, others maintain that the statistically small modeling improvements do not necessarily justify the cost and time to gather the improved soil information, or that improved soil information does not necessarily lead to more accurate hydrological modeling (Chen et al, 2016;Geza & McCray, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Examining the value of hydropedological information on hydrological modeling at different scales in the Sabie catchment, South Africa

Smit,

van Zijl,

Riddell

et al. 2023

Vadose Zone Journal

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Detailed soil information is increasingly sought after for watershed‐scale hydrological modeling to better understand the soil–water interactions at a landscape level. In South Africa, 8% of the surface area is responsible for 50% of the mean annual runoff. Thus, understanding the soil–water dynamics in these catchments remains imperative to future water resource management. In this study, the value of hydropedological information is tested by comparing a detailed hydropedological map based on infield soil information to the best readily available soil information at five different catchment sizes (48, 56, 174, 674, and 2421 km2) using the soil and water assessment tool (SWAT)+ model in the Sabie catchment, South Africa. The aim was to determine the value of hydropedological information at different scales as well as illustrate the value of hydropedology as soft data to improve hydrological process representation. Improved hydropedological information significantly improved long‐term streamflow simulations at all catchment sizes, except for the largest catchment (2421 km2). It is assumed that the resulting improved streamflow simulations are a direct result of the improved hydrological process representation achieved by the hydropedological information. Here, we argue that hydropedological information should form an important soft data tool to better understand and simulate different hydrological processes.

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Section: Resultssupporting

confidence: 90%

Section: Hydrological Processessupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

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Examining the value of hydropedological information on hydrological modeling at different scales in the Sabie catchment, South Africa

Smit,

van Zijl,

Riddell

et al. 2023

Vadose Zone Journal

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“…The Cathedral Peak research catchments (Figure 1) are long‐term monitoring sites where the South African Environmental Observation Network (SAEON) continuously monitors various hydro‐meteorological variables. These catchments (naturally grasslands) are situated in the northern part of uKhahlamba‐Drakensberg in KwaZulu‐Natal, South Africa, and fall under the uKhahlamba‐Drakensberg Park World Heritage Site (R. L2; Harrison et al., 2022). Although these catchments comprise 15 distinct hydrological units (viz., I–XV), only catchments VI and IX were utilized for this study due to the present instrumentation.…”

Section: Study Areamentioning

confidence: 99%

“…The catchment characteristics include a mean annual precipitation (MAP) of 1340 mm, a total area of 0.667 km 2 , an altitude ranging between 1845–2073 meters above sea level (m.a.s.l.) and an north–northwest aspect (Harrison et al., 2022; 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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Model calibration using hydropedological insights to improve the simulation of internal hydrological processes using SWAT+

Smit,

van Zijl,

Riddell

et al. 2024

Hydrological Processes

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Soils affect the distribution of hydrological processes by partitioning precipitation into different components of the water balance. Therefore, understanding soil‐water dynamics at a catchment scale remains imperative to future water resource management. In this study, the value of hydropedological insights was examined to calibrate a processes‐based model. Soil morphology was used as soft data to assist in the calibration of the Soil Water Assessment Tool (SWAT+) model at five different catchment scales (48, 56, 174, 674, and 2421 km2) in the Sabie River catchment, South Africa. The aim of this study was to calibrate the SWAT+ model to accurately simulate long‐term monthly streamflow predictions as well as to reflect internal soil hydrological processes using a procedure focusing on hydropedology as a calibration tool in a multigauge system. Results indicated that calibration improved streamflow predictions where R2 improved by 2%–8%. Nash‐Sutcliffe Efficiency (NSE) improved from negative correlations to values exceeding 0.5 at four of the five catchment scales compared to the uncalibrated model. Results confirm that soil mapping units can be calibrated individually within SWAT+ to improve the representation of hydrological processes. Particularly, the spatial linkage between hydropedology and hydrological processes, which is captured within the soil map of the catchment, can be adequately reflected within the model simulations after calibration. This research will lead to an improved understanding of hydropedology as soft data to improve hydrological modelling accuracy.

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Using hydropedological characteristics to improve modelling accuracy in Afromontane catchments

Cited by 6 publications

References 52 publications

Examining the value of hydropedological information on hydrological modeling at different scales in the Sabie catchment, South Africa

Examining the value of hydropedological information on hydrological modeling at different scales in the Sabie catchment, South Africa

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

Model calibration using hydropedological insights to improve the simulation of internal hydrological processes using SWAT+

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