&amp;lt;i&amp;gt;Preface&amp;lt;/i&amp;gt; &amp;quot;Nonlinear and scaling processes in Hydrology and Soil Science&amp;quot;

Tarquis, Ana M.; Lima, João L.M.P. de; Krajewski, Witold F.; Cheng, Qiuming; Gaonac’h, H.

doi:10.5194/npg-18-899-2011

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…The crossover length derived from semivariogram analysis is an index that is commonly used in most recent soil microrelief studies to describe surface microroughness, with the advantage of its quantification being scale independent through the consideration of the spatial correlation between surface elevations (Vázquez et al, 2007;Paz-Ferreiro et al, 2008;Tarquis et al, 2011). The semivariogram is a useful geostatistical tool developed to depict the spatial autocorrelation of data.…”

Section: Soil Surface Roughness Quantificationmentioning

confidence: 99%

Quantifying the changes of soil surface microroughness due to rainfall-induced erosion on a smooth surface

Abban

Papanicolaou

Giannopoulos

et al. 2017

Preprint

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Abstract. This study examines the rainfall induced change in soil microroughness of a bare soil surface in agricultural landscapes. The focus is on the quantification of roughness length under the action of rainfall for initial microroughness length scales of 2 mm or less, defined here as initial smooth surface conditions. These conditions have not been extensively examined in the literature as most studies have focused on initial disturbed surface conditions (bed surface conditions with initial length scales greater than 2 mm and varying between 5–50 mm). Three representative intensities namely 30 mm/h, 60 mm/h and 75 mm/h were applied over a smoothened bed surface at a field plot via a rainfall simulator. Soil surface microroughness measurements were obtained via a surface-profile laser scanner. Two indices were utilized to quantify soil surface microroughness, namely the Random Roughness (RR) index and the crossover length. Findings show a consistent increase in roughness under the action of rainfall for initial microroughness length scales of 2 mm. This contradicts existing literature where a monotonic decay of roughness of soil surfaces with rainfall is recorded for disturbed surfaces. Analysis shows that on an average the RR and the crossover length post run increase by a multiple of 3.15 and 1.9, respectively from their corresponding values apriori the runs.

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Section: Soil Surface Roughness Quantificationmentioning

confidence: 99%

Quantifying the changes of soil surface microroughness due to rainfall-induced erosion on a smooth surface

Abban

Papanicolaou

Giannopoulos

et al. 2017

Preprint

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“…If a linear relationship characterizes the underlying system, well established selection methods exist to obtain a candidate subset of the input variables [e.g., Miller , ]. However, the assumption of linear dependency between the inputs and the output is overly restrictive for most real physical systems [see, e.g., Tarquis et al ., ]. In addition, advances in monitoring systems, from remote sensing techniques to pervasive real and virtual sensor networks [e.g., Hart and Martinez , ; Hill et al ., ], has made available an increasingly larger amount of data at the local and global scale at progressively finer temporal and spatial resolution, thus not only increasing data set dimension from dozens to tens or hundreds of thousand but also adding considerably to data set redundancy.…”

Section: Introductionmentioning

confidence: 99%

Tree‐based iterative input variable selection for hydrological modeling

Galelli

Castelletti

2013

Water Resources Research

114

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[1] Input variable selection is an important issue associated with the development of several hydrological applications. Determining the optimal input vector from a large set of candidates to characterize a preselected output might result in a more accurate, parsimonious, and, possibly, physically interpretable model of the natural process. In the hydrological context, the modeled system often exhibits nonlinear dynamics and multiple interrelated variables. Moreover, the number of candidate inputs can be very large and redundant, especially when the model reproduces the spatial variability of the physical process. The ideal input selection algorithm should therefore provide modeling flexibility, computational efficiency in dealing with high dimension data set, scalability with respect to input dimensionality and minimum redundancy. In this paper, we propose the tree-based iterative input variable selection algorithm, a novel hybrid model-based/model-free approach specifically designed to fulfill these four requirements. The algorithm structure provides robustness against redundancy, while the tree-based nature of the underlying model ensures the other key properties. The approach is first tested on a well-known benchmark case study to validate its accuracy and subsequently applied to a real-world streamflow prediction problem in the upper Ticino River Basin (Switzerland). Results indicate that the algorithm is capable of selecting the most significant and nonredundant inputs in different testing conditions, including the real-world large data set characterized by the presence of several redundant variables. This permits one to identify a compact representation of the observational data set, which is key to improving the model performance and assisting with the interpretation of the underlying physical processes.Citation: Galelli, S., and A. Castelletti (2013), Tree-based iterative input variable selection for hydrological modeling, Water Resour.

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