Predicting Soil Infiltration and Horizon Thickness for a Large-Scale Water Balance Model in an Arid Environment

Saito, Tadaomi; Yasuda, Hiroshi; Suganuma, Hideki; Inosako, Koji; Abe, Yukuo; Kojima, Toshinori

doi:10.3390/w8030096

Cited by 14 publications

(13 citation statements)

References 49 publications

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“…The spatial distribution of AL soil thickness has high heterogeneity as a function of parent material, topography, climate, vegetation cover, and intense human activities [ 8 , 17 , 22 – 25 ]. Among them, topography plays a crucial role in the soil attributes variability [ 11 , 25 , 26 – 28 ], especially at a small watershed area under relatively uniform parent material, vegetation cover, and climate conditions (e.g. precipitation and temperature) [ 5 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting active-layer soil thickness using topographic variables at a small watershed scale

Tan

et al. 2017

PLoS ONE

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Knowledge about the spatial distribution of active-layer (AL) soil thickness is indispensable for ecological modeling, precision agriculture, and land resource management. However, it is difficult to obtain the details on AL soil thickness by using conventional soil survey method. In this research, the objective is to investigate the possibility and accuracy of mapping the spatial distribution of AL soil thickness through random forest (RF) model by using terrain variables at a small watershed scale. A total of 1113 soil samples collected from the slope fields were randomly divided into calibration (770 soil samples) and validation (343 soil samples) sets. Seven terrain variables including elevation, aspect, relative slope position, valley depth, flow path length, slope height, and topographic wetness index were derived from a digital elevation map (30 m). The RF model was compared with multiple linear regression (MLR), geographically weighted regression (GWR) and support vector machines (SVM) approaches based on the validation set. Model performance was evaluated by precision criteria of mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). Comparative results showed that RF outperformed MLR, GWR and SVM models. The RF gave better values of ME (0.39 cm), MAE (7.09 cm), and RMSE (10.85 cm) and higher R2 (62%). The sensitivity analysis demonstrated that the DEM had less uncertainty than the AL soil thickness. The outcome of the RF model indicated that elevation, flow path length and valley depth were the most important factors affecting the AL soil thickness variability across the watershed. These results demonstrated the RF model is a promising method for predicting spatial distribution of AL soil thickness using terrain parameters.

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Section: Introductionmentioning

confidence: 99%

Predicting active-layer soil thickness using topographic variables at a small watershed scale

Tan

et al. 2017

PLoS ONE

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“…0-10, 10-20, 20-30, and so on) to efficiently access the impact of root distribution on soil organic matter and soil macropore and aggregate stability throughout the soil profile. This approach is based on studies which report that infiltration process is greatly influenced by the existence of soil layers with low permeability that can appear in the deeper of soil profile (Chaplot et al 2011;Mahapatra et al 2020;Saito et al 2016), and are not necessarily correlated to the topsoil characteristics.…”

Section: Soil Sampling and Preparationmentioning

confidence: 99%

Can cocoa agroforestry restore degraded soil structure following conversion from forest to agricultural use?

et al. 2020

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Alternating degradation and restoration phases of soil quality, as is common in crop-fallow systems, can be avoided if the restorative elements of trees and forests can be integrated into productive agroforestry systems. However, evidence for the hypothesis of ‘internal restoration’ in agroforestry is patchy and the effectiveness may depend on local context. We investigated to what extent cocoa (Theobroma cacao, L.) agroforestry can recover soil structure and infiltration in comparison to monoculture systems across the Konaweha Watershed, Southeast Sulawesi. We compared soil organic carbon, fine root length and weight, soil aggregate stability, macroporosity and infiltration from three soil layers at five land use systems: i.e. degraded forests, 9–14 years old of complex-cocoa agroforestry, simple-cocoa agroforestry, monoculture cocoa and 1–4 years old annual food crops, all with three replications. In general, roots were concentrated in the upper 40 cm of soil depth, contained of 70% and 86% of total fine root length and weight. Compared to simple agroforestry and cocoa monoculture, complex agroforestry had greater root length and weight in the topsoil, even though it attained only half the values found in degraded forests. Higher root density was positively correlated to soil organic carbon. In upper soil layers, complex agroforestry had slightly higher soil aggregate stability compared to other agricultural systems. However, no significant difference was found in deeper layers. Complex agroforestry had higher soil macroporosity than other agricultural systems, but not sufficient to mimic forests. Degraded forests had two times faster steady-state soil infiltration than agricultural systems tested (13.2 cm h−1 and 6 cm h−1, respectively), relevant during peak rainfall events. Compared to other agricultural systems, complex agroforestry improves soil structure of degraded soil resulting from forest conversion. However, a considerable gap remains with forest soil conditions.

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“…First, several experimental and theoretical studies show that infiltration capacity is not under the direct physiological control of plants. 26 Therefore, the complex coupling between vegetation and infiltration is unlikely to be linear in nature. The linear regression model used to describe the infiltration capacity as a function of vegetation coverage reveals, indeed, that a linear fit is just an oversimplification.…”

Section: Introductionmentioning

confidence: 99%

“…First, several experimental and theoretical studies show that infiltration capacity is not under the direct physiological control of plants 26 . Therefore, the complex coupling between vegetation and infiltration is unlikely to be linear in nature.…”

Section: Introductionmentioning

confidence: 99%

Turing vegetation patterns in a generalized hyperbolic Klausmeier model

Consolo

Currò

Valenti

2020

Math Methods in App Sciences

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The formation of Turing vegetation patterns in flat arid environments is investigated in the framework of a generalized version of the hyperbolic Klausmeier model. The extensions here considered involve, on the one hand, the strength of the rate at which rainfall water enters into the soil and, on the other hand, the functional dependence of the inertial times on vegetation biomass and soil water. The study aims at elucidating how the inclusion of these generalized quantities affects the onset of bifurcation of supercritical Turing patterns as well as the transient dynamics observed from an uniformly vegetated state towards a patterned state. To achieve these goals, linear and multiple-scales weakly nonlinear stability analysis are addressed, this latter being inspected in both large and small spatial domains. Analytical results are then corroborated by numerical simulations, which also serve to describe more deeply the spatio-temporal evolution of the emerging patterns as well as to characterize the different timescales involved in vegetation dynamics.

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Predicting Soil Infiltration and Horizon Thickness for a Large-Scale Water Balance Model in an Arid Environment

Cited by 14 publications

References 49 publications

Predicting active-layer soil thickness using topographic variables at a small watershed scale

Predicting active-layer soil thickness using topographic variables at a small watershed scale

Can cocoa agroforestry restore degraded soil structure following conversion from forest to agricultural use?

Turing vegetation patterns in a generalized hyperbolic Klausmeier model

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