A. Laguna

The description of soil erosion through a kinematic wave model

Kinematic wave models offer a powerful tool for interpreting erosion experiments and for forecasting soil and water losses under different management systems. One important problem is the identification of the model parameters. To estimate the influence of error in the estimation of parameters a sensitivity analysis was performed. A previous survey of the soil erosion literature provided data to establish the expected ranges for each parameter. Of the three main parameters, the interrill and rill erosion coefficients, and the rill water depth coefficient, the last is the most important since sediment concentration is most sensitive to variations in this parameter, although the range of variation in the model is affected by the reference values of the other parameters. A simulated rainfall experiment in rectangular plots of 5 × 15 m2 on a 20% slope provided data which were fitted to the model. The agreement between the model and the data was reasonably good. Sediment yield was related linearly to total runoff volume in individual simulation events.

An educational computer tool for simulating long‐term soil erosion on agricultural landscapes

Due to its economic and environmental impacts, soil erosion has been a major concern to farmers, engineers and policy makers in recent years. Water and tilling are two of the main agents responsible for this phenomenon and considerable efforts have been made to model them in previous work but not with educational purposes. A computer tool for facilitating any users simulation of long‐term landscape evolution in a plot due to the combined action of water and tillage erosion is presented here. It integrates a graphic user interface with two well‐verified erosion models, each one independently devoted to reproduce the effects of water and tilling. This computer tool permits to the student the consideration of the erosivity index and the presence of a crop in the plot, when simulating water erosion, as well as the planning of a different type of tilling each year. Each kind of tilling corresponds to a different combination of tillage tools with their own date, tillage depth and tillage direction. A handy ASCII (XYZ) file is generated containing the long‐term soil erosion spatial pattern as result. From this information, the student can derive other results that will help to understand soil erosion. An example is presented here with the aim of showing how to use this computer tool to simulate this phenomenon on an agricultural landscape with a complex topography.

A description of water and sediment flow in the presence of obstacles with a two‐dimensional, lattice BGK‐cellular automata model

[1] The spatial distribution of water and sediment flow in the presence of obstacles on the soil surface is described with a coupled two-dimensional, lattice BGK (the Bhatnagar, Groos, and Krook version of the lattice Boltzmann equation) cellular automata model. The model reproduces the main water flow characteristics of the Bunte and Poesen [1993] experiment well, as well as the mean runoff velocity. The model also approximates the main features of local scouring and sediment yield of the experiment. Although the results obtained in two dimensions are encouraging, the consideration of the erosive action of the vortexes on a plane perpendicular to the bed would improve the characterization of the scouring patterns in the stagnation points developed upstream from the obstacles. INDEX TERMS: 1815 Hydrology: Erosion and sedimentation; 1860 Hydrology: Runoff and streamflow; 1869 Hydrology: Stochastic processes; KEYWORDS: flow erosion, obstacles, lattice BGK model, cellular automata model ��

Multifractal analysis of passive tracer transport in simulated skimming and wake interference flows

Multifractal turbulence formalism has been shown to be suitable for analyzing high resolution time series in the passive tracer transport when skimming and wake interference flows are developed in a water flume. A study of the empirical statistical moment scaling function for several scale ratios reveals the multifractal nature of these flow regimes. The parameter of the universal multifractal model related to the sparseness of the mean of the process is influenced by the strength of the shear layer developed over the roughness elements in the simulated flow regimes.