Scale Issues in Soil Hydrology

Vogel, Hans‐Jörg

doi:10.2136/vzj2019.01.0001

Cited by 39 publications

(23 citation statements)

References 70 publications

(84 reference statements)

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“…These estimates of preferential flow could be used to test or verify hypotheses posited by numerous studies about the relationships of preferential flow to soil structure (Macrae et al., 2019), runoff volumes (Zhang et al., 2019), and P loads (Grant, Macrae, & Ali, 2019). This information could also be used to improve models of subsurface flow and P transport from soil columns (Urbina et al., 2019) to watershed and regional scales (Vogel, 2019).…”

Section: Resultsmentioning

confidence: 99%

Contribution of preferential flow to tile drainage varies spatially and temporally

Pluer

Macrae

Buckley

et al. 2020

Vadose Zone Journal

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Tile drainage of agricultural fields is a conduit for nutrient losses. Preferential flow in the soil can more directly connect surface runoff with tile drainage compared with matrix flow, which may also increase P losses. In this study, water temperature was monitored in surface runoff and tile drainage and electrical conductivity (EC) was measured in tile drainage at two sites in southern Ontario with different soil types (i.e., clay and loam). These data were used to estimate the percentage of preferential flow in tile drainage based on end member mixing. Estimates using temperature were compared with estimates using EC, and both were evaluated across seasons and hydrographs and compared with P concentration and load data. There was strong correlation (r = .83) between estimates of preferential flow using the two methods, but due to variability in surface temperatures, EC provided a less flashy estimate for preferential flow (Durbin-Watson statistics of 0.34 for temperature and 0.09 for EC). Preferential flow accounted for a higher percentage of tile drainage flow in clay soil than loam, but percentages were not significantly different between seasons or timing within events. Phosphorus concentrations and loads were weakly correlated with preferential flow, suggesting that P transport was influenced by other factors as well. Although further work is necessary to calibrate these methods for estimating preferential flow from continuously monitored temperature and EC, this technique can be applied to already collected data to model and test posited explanations of observed phenomena in P, other nutrients, and water transport from tile-drained agricultural land.

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Section: Resultsmentioning

confidence: 99%

Contribution of preferential flow to tile drainage varies spatially and temporally

Pluer

Macrae

Buckley

et al. 2020

Vadose Zone Journal

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“…At the watershed scale, there is a need to identify the spatial variability patterns of soil hydro-physical properties (Fig. 4), especially those ones related to the soil structure (Vogel 2019) which directly affects Ksat. Therefore, the development of pedotransfer functions (PTFs) for estimating the Fig.…”

Section: Discussionmentioning

confidence: 99%

Dominant Control of Macroporosity on Saturated Soil Hydraulic Conductivity at Multiple Scales and Locations Revealed by Wavelet Analyses

Centeno

Timm

et al. 2020

J Soil Sci Plant Nutr

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Knowledge on multi-scale and localized control of saturated soil hydraulic conductivity (Ksat) at the watershed scale is lacking. The objective of this study was to evaluate the multi-scale spatial relationships among Ksat and environmental factors (i.e., soil and topographic attributes and land-use systems) using wavelet coherency and multiple wavelet coherence methods. In the Fragata River Watershed (FRW) in southern Brazil, one hundred points were distributed at equal distances along a 15-km transect. In the 0-20 cm layer, clay and sand fractions, organic carbon content, bulk density, macroporosity, Ksat, and soil water retention curve were determined from soil sampled at each point. The digital elevation model was used for obtaining topographic attributes. A land-use map was developed by use of satellite images. All data sets were analyzed using descriptive statistics, and the relationship among Ksat and the other variables was evaluated through the Spearman correlation coefficient. Wavelet coherency and multiple wavelet coherence were used to examine the correlation among Ksat and each of the explanatory variables and to investigate the scale-specific and localized multivariate relationships among Ksat and predictor variables, respectively. According to the bivariate wavelet coherency and multiple wavelet coherence analyses, macroporosity showed the greatest mean wavelet coherence and percent area of significance coherence with Ksat. The variations of soil macroporosity itself were enough to explain the variations in Ksat in a multiple-scale andlocation domain. Soil macroporosity could be used as a proxy for assessing runoff potential at different land-use systems with different scales in the FRW in southern Brazil.

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“…An advantage of hyPTF is that for field‐scale application soil texture information may be derived directly from soil maps (Hengl et al, 2017; Tóth et al, 2017). Laboratory measurements can be effective in estimating large‐scale effective SHP (Hopmans et al, 2002), but, in spite of the large number of data sets of laboratory measured SHPs included in the construction of hyPTF, concerns linked to scale and effectiveness may certainly prevail (Philip, 1980; Vogel, 2019). We can, nevertheless, expect that if the uncertainties during PTF development are contained (input uncertainty) and reported (output uncertainty), the application of hyPTF to the field scale can capture some problems of scale if adequate approaches (e.g., stochastic modeling; Beyer et al, 2009; Famiglietti & Wood, 1994) are used to represent the soil spatial variability (Hopmans et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Pedotransfer Function for the Brunswick Soil Hydraulic Property Model and Comparison to the van Genuchten‐Mualem Model

Weber

Finkel

Gonçalves

et al. 2020

Water Resources Research

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Modeling soil hydraulic properties requires an effective representation of capillary and noncapillary storage and conductivity. This is made possible by using physically comprehensive yet flexible soil hydraulic property models. Such a model (Brunswick [BW] model) was introduced by Weber et al. (2019, https://doi.org/10.1029/2018WR024584), and it overcomes some core deficiencies present in the widely used van Genuchten-Mualem (VGM) model. We first compared the performance of the BW model to that of the VGM model in its ability to describe water retention and hydraulic conductivity data on a set of measurements of 402 soil samples with textures covering the entire range of classes. Second, we developed a simple transfer function to predict BW parameters based on VGM parameters. Combined with our new function, any existing pedotransfer function for the prediction of the VGM parameters can be extended to predict BW model parameters. Based on information criteria, the smaller variance of the residuals, and a 40% reduction in mean absolute error in the hydraulic conductivity over all samples, the BW model clearly outperforms VGM. This is possible as the BW model explicitly accounts for hydraulic properties of dry soils. With the new pedotransfer function developed in this study, better descriptions of water retention and hydraulic conductivities are possible. We are convinced that this will strengthen the utility of the new model and enable improved field-scale simulations, climate change impact assessments on water, energy and nutrient fluxes, as well as crop productivity in agroecosystems by soil-crop and land-surface modeling. The models and the pedotransfer function are included in an R package spsh (https://cran.r-project.org/package¼spsh). Plain Language Summary Soil hydraulic property models are mathematical functions, which describe the relationship between the soil water pressure head and the state of soil water saturation, on the one hand, and the soil water pressure head and the unsaturated soil hydraulic conductivity, on the other. These types of mathematical functions are flexible by adjustable parameters. With one set of model equations, the hydraulic properties of soils which may have very different properties due to their vast natural variability can be described. The models treated in this work are (i) the van Genuchten-Mualem model, a model with well-known problems, but still frequently applied, and (ii) a relatively new physical comprehensive model, named the Brunswick model. First of all, in a data-based comparison of model performance, we demonstrate that the Brunswick model has systematic advantages. Second, knowledge about these above-mentioned parameters can be determined through other mathematical functions, so-called hydro-pedotransfer functions, which empirically relate these parameters to observed soil properties. The information about these soil properties can be measured in the laboratory and is also recorded in soil maps. We created a new pedotransfer function to facilitate the predictio...

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Scale Issues in Soil Hydrology

Cited by 39 publications

References 70 publications

Contribution of preferential flow to tile drainage varies spatially and temporally

Contribution of preferential flow to tile drainage varies spatially and temporally

Dominant Control of Macroporosity on Saturated Soil Hydraulic Conductivity at Multiple Scales and Locations Revealed by Wavelet Analyses

Pedotransfer Function for the Brunswick Soil Hydraulic Property Model and Comparison to the van Genuchten‐Mualem Model

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