Prediction of the van Genuchten model soil hydraulic parameters for the 5-m soil profile in China’s Loess Plateau

Bai, Xiaoyong; Shao, Mingan; Jia, Xiaoxu; Zhao, Chunlei

doi:10.1016/j.catena.2021.105889

Cited by 14 publications

(5 citation statements)

References 59 publications

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“…The corresponding surface Ks ( K 0 ) derived from the Ks‐depth curves were 11, 18, 29, 46, 74, and 118 mm/h, respectively. These values were consistent with the near‐surface measured values from other studies, generally covering a range from 10 to 120 mm/h (Bai et al, 2022; Ren et al, 2016; Wang et al, 2013; Zhu et al, 2022). For convenience, the K 0 values will be used to represent the vegetation coverage in the following discussion.…”

Section: Methodssupporting

confidence: 91%

Revegetation impacts on runoff generation processes in a typical loess catchment

Pan,

Ran,

2024

Hydrological Processes

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Since 1999, large‐scale ecosystem restoration has been implemented in the Loess Plateau, effectively increasing regional vegetation coverage. Vegetation restoration has significantly elevated the saturated hydraulic conductivity (Ks) of the near‐surface soil layers and increased the vertical heterogeneity of the Ks profile. Many studies have examined the change of runoff due to revegetation, yet the impacts of Ks profile on the soil moisture distribution and runoff generation processes were less explored. In this study, numerical simulations were conducted to investigate how changes in the Ks profile caused by vegetation restoration influenced the hydrological responses at event scale. The numerical simulation results show that the increase of surface Ks caused by vegetation restoration can effectively reduce runoff at event scale. Moreover, the enhancement of vertical heterogeneity of Ks profiles can significantly change the vertical profile of soil water content, prompting more water to percolate into the deep soil layer. When rainfall exceeds a threshold, the accumulation of soil water above the relatively less permeable layer can cause short‐term saturation in shallow soil layers, resulting in a transient perched water table. As a result, after the vegetation restoration in the Loess Plateau, though Horton overland flow is still the main runoff generation mechanism, there is a possibility of the emergence of Dunne overland flow under the high vegetation coverage (e.g., NDVI larger than 0.5). This emergence of new runoff generation mechanism, saturation excess runoff, in the Loess Plateau due to the vegetation restoration could provide scientific guidance for water and sediment movement, soil and water conservation practices, and desertification control in the Loess Plateau.

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Section: Methodssupporting

confidence: 91%

Revegetation impacts on runoff generation processes in a typical loess catchment

Pan,

Ran,

2024

Hydrological Processes

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“…Barros et al (2013) developed PTFs to estimate van Genuchten parameters in northeastern Brazil, finding R² values equal to 0.12 and 0.21 for parameters α and n, respectively, using soil texture data, bulk density, and organic matter. Other authors, such as Bai et al (2022) and Baumann et al (2022), also found difficulties in estimating the α and n parameters considering different types of soils and other regions of the world.…”

Section: Resultsmentioning

confidence: 99%

Pedotransfer functions for estimating the van Genuchten model parameters in the Cerrado biome

Veloso

Filho

Veloso

et al. 2023

Rev. bras. eng. agríc. ambient.

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The Cerrado biome has presented challenges in reconciling its agricultural expansion with water availability. In this sense, water resources planning and management are fundamental for the economic, social, and environmental development of the Cerrado biome, which has been hampered by the lack of data, especially those referring to irrigation strategies, such as, for example, the water retention curve. The water retention curve is essential to understand water dynamics in the soil; however, obtaining it can be laborious, opening an opportunity for Pedotransfer Functions (PTFs). The current study aimed to develop and evaluate PTFs to estimate the fit parameters of the van Genuchten model for the Cerrado biome. Multiple Linear Regression (MLR) and four machine learning (ML) algorithms were used to develop the PTFs. The ML algorithms were the Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), Support Vector Regression (SVR), and K Nearest Neighbors (KNN). Two combinations of soil data were evaluated, and the predictor variables used in each set were different. Using the RF and SVR models, the best estimates were obtained concerning the parameter θs (saturated water content). As for θr (residual water content), the models showed a moderate predictive capacity. For the other parameters, the models did not perform satisfactorily for α and n (fit parameters).

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“…The considerable costs of in situ sampling has led to a growing interest in the establishment and use of PTFs 9 , 24 . However, most PTFs are developed for specific regions, and their applicability to areas with different soil and climatic conditions is limited, necessitating re-calibration based on field measurements 12 . The challenges associated with obtaining soil parameters not only impede methodological endeavours, such as up-scaling based on PTFs, but also impose limitations on ecosystem process studies that rely on soil parameters 25 .…”

Section: Background and Summarymentioning

confidence: 99%

“…Historically, severe soil erosion in this region has led to substantial loss of soil carbon to the ocean via the Yellow River, profoundly impacting the land carbon budget 42 – 44 . Over the past two decades, China has been one of the leading contributors to the land greening observed around the globe 45 , with the LP taking a prominent role through the “Grain for Green” program for ecological restoration 12 , 46 . Given the significance of the YRB for global carbon cycling, climate change, food security, and ecological stability, the investigation of soil parameters in this region does not only hold the value for regional environmental and agronomic studies, but also provide some valuable supplementation to the current global pool of soil hydraulic datasets.…”

Section: Background and Summarymentioning

confidence: 99%

Dataset of soil hydraulic parameters in the Yellow River Basin based on in situ deep sampling

Tong,

Wang,

Zhou

et al. 2024

Sci Data

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Soil hydraulic parameters are vital for precisely characterizing soil hydrological processes, which are critical indicators for regulating climate change effects on terrestrial ecosystems and governing feedbacks between water, energy, and carbon–nitrogen cycles. Although many studies have integrated comprehensive soil datasets, data quality and cost challenges result in data completeness deficiencies, especially for deep soil information. These gaps not only impede methodological endeavours but also constrain soil parameter-based ecosystem process studies spanning from local profiles to global earth system models. We established a soil dataset across the entire Yellow River Basin (YRB) (795,000 km2) using high-density in situ field sampling. This observation-based dataset contains records of soil texture (2924), bulk density (2798), saturated hydraulic conductivity (2782), and water retention curve parameters (1035) down to a maximum depth of 5 m. This dataset, which extends the recorded data range for deep soil hydraulic parameters, is valuable as a direct data resource for environmental, agronomical and hydrological studies in the YRB and regions with similar pedological and geological backgrounds around the world.

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Prediction of the van Genuchten model soil hydraulic parameters for the 5-m soil profile in China’s Loess Plateau

Cited by 14 publications

References 59 publications

Revegetation impacts on runoff generation processes in a typical loess catchment

Revegetation impacts on runoff generation processes in a typical loess catchment

Pedotransfer functions for estimating the van Genuchten model parameters in the Cerrado biome

Dataset of soil hydraulic parameters in the Yellow River Basin based on in situ deep sampling

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