David Favis-Mortlock

Modelling Soil Erosion by Water

This volume is the Proceedings of the NATO Advanced Research Workshop ‘Global Change: Modelling Soil Erosion by Water’, which was held on 11–14th September 1995, at the University of Oxford, UK. The meeting was also one of a series organised by the IGBPGCTE1 Soil Erosion Network, which is a component of GCTE’s Land Degradation Task (3.3.2) (Ingram et al., 1996; Valentin, this volume).

Emergence and erosion : a model for rill initiation and development

Soil erosion by overland flow, resulting from infiltration-excess rainfall, generates rill networks on hillslope areas. The way in which these networks emerge and develop suggests that hillslope erosion functions as a self-organizing dynamic system. Based upon this argument, a model for soil erosion (RillGrow 1) has been developed: this operates at the spatial scale of raindrops and microtopography. In this paper the second generation of the model (RillGrow 2) is described and applied to four different soil surfaces. Results suggest that, even at this early stage in its development, RillGrow 2 is capable of replicating the success of the earlier model and in some cases of extending them. The success of both models suggests that this self-organizing view of rill generation may capture some fundamental aspects of the operation of real erosional systems. Copyright

A self-organizing dynamic systems approach to the simulation of rill initiation and development on hillslopes

Abstract Whereas current erosion models are able to make quantitative estimates of rates of soil erosion by water on hillslopes with reasonable success, they are less competent when there is a need to apply a spatial dimension to their estimates. In particular, the initiation and development of rill networks is poorly modelled. Observations from field and laboratory suggest that it is possible to apply an evolutionary analogy to rill growth and development. Microrills, formed both by the runoff resulting from individual raindrops and from the overflow of ponded surface water through knickpoints, can be thought of as “competing”. The most “successful” of these become discontinuous rills, which in their turn also compete, with a subset dominating to form continuous rills. Rill “success” will depend on both the position on the slope and microtopography. However, microtopography will itself be modified by erosional processes such as rill growth. This creates a feedback loop. Is it possible to use this perspective to model the evolution of hillslope rill networks? This study investigates the feasibility of such an approach. A novel model is described which applies simple rules to govern the iterative interaction between microtopography, runoff routing and soil loss. Runoff is conceptualized as consisting of discrete “packets” so that a Lagrangian frame of reference is adopted, in contrast to the more usual Eulerian perspective. Results from several experiments are described, using both measured and synthetic microtopographic surfaces. The model appears able to reproduce many of the larger-scale “emergent” features of erosional systems. Planform rill networks appear realistic, as does rill depth, which increases both downslope and below confluences. Simulated rill spacing conforms with observed relationships to slope angle; the spatial balance between rill and interrill erosion similarly echoes observational evidence, as does total erosion and change in microtopographic roughness.

Nonlinear responses of soil erosion to climate change: a modelling study on the UK South Downs

Abstract A modelling approach is used to estimate some effects of changed climate upon rates of soil erosion on agricultural land on the UK South Downs. Previous studies have concentrated only on estimating shifts in long-term mean erosion rate: these were found to be approximately linear. However such simple shifts mask changes in the underlying distributions of annual erosion. A first series of simulations indicated that, under a wetter climate, erosion rates in wet years will generally increase more than rates in dry years. Under a “best guess” rainfall scenario with a 10% increase in winter rainfall, annual erosion increased by up to 150%. Erosion rates for individual years were shown to change in more complex nonlinear ways however, with decreases as well as increases occurring. These could be explained by the interaction of timing of rainfall with changes in the rate of crop growth. Most earlier work also assumed an equilibrium climate for the simulations, with climatic parameters such as mean monthly rainfall having stabilised at some new value, usually for a 2 × CO2 atmosphere. This however leads to an “initial conditions” problem: how will soil characteristics have changed by the time of CO2 doubling? A decrease in erodibility of about 20% by the time of CO2 doubling was indicated, resulting from changed soil profile properties. However, a second series of runs employed “transient” weather sequences (i.e. with a trend imposed). For these, present-day soil profiles could legitimately be used.

Spatial and temporal variation in two rainfall simulators: implications for spatially explicit rainfall simulation experiments

Rainfall simulators are widely used yet there is little evidence in the literature to show that their spatial and temporal variability has been adequately taken into account. For experiments that are concerned only with some aggregate or mean effect of simulated rain then such variations may be unimportant. However, where rainfall simulation is being used to study (and perhaps model) small-scale processes that are themselves spatially variable (such as rill initiation) then knowledge of the simulators inherent variability is vital. A first aim of this paper is therefore to examine this variability, and to appraise methodologies by which it may be quantified. A second aim is to evaluate the implications for spatially explicit rainfall simulation experiments. Two simulators were used, a portable drip-screen simulator and a laboratory-based full-cone nozzle simulator. Neither produced a spatially uniform distribution of rainfall depth: both produced distributional patterns that were fairly consistent despite varying intensities and run times. Small-scale, apparently random variations were superimposed on these more deterministic patterns. However, despite this marked spatial variability, calculation of uniformity coefficients (1−SD/mean) resulted in high values. Thus it appears that the uniformity coefficient gives little real indication of the spatial uniformity of simulated rainfall, despite its established usage in the literature. Additionally, spatial distributions of raindrop size –and hence kinetic energy –were calculated for the full-cone nozzle simulator. These show that zones of high rainfall amount do not necessarily relate to zones of high energy reaching the surface. The presence of such variability raises a number of issues for spatially explicit rainfall simulation experiments. While there has been little work on the spatial variability of natural rainfall at field scale and smaller, it appears that the spatial heterogeneity of simulated rainfall depths observed in this study does not differ greatly from that of natural rain. But since a major attraction of rainfall simulation experiments is additional control over rainfalls many variables, the spatial non-uniformity of depth observed in this study is unwelcome. The existence of an apparently deterministic component to this non-uniformity nonetheless suggests that it can, at least in principle, be corrected by calibration. Less easily handled is the discrepancy between spatial distributions of rainfall depth and energy, since this will certainly affect rainfall simulation experiments that are, for example, concerned with erosion processes due to raindrop impact. Copyright

Validation of Field-Scale Soil Erosion Models Using Common Datasets

As a first step toward evaluating the suitability of erosion models for global change studies, common datasets (representing 73 site-years of data from seven sites in three countries) were prepared for use with six field-scale erosion models. Five of these are continuous-simulation types (GLEAMS, EPIC, CSEP, MEDRUSH and WEPP); the other is event-based (EUROSEM). Each dataset was split into a ‘training set’ and a ‘testing set’. Measured values for runoff and erosion from the testing set were withheld from the modellers.

Automated Digital Photogrammetry: A Valuable Tool for Small-scale Geomorphological Research for the Non-photogrammetrist?

Digital photogrammetry provides a tool with which to automatically generate digital elevation models (DEMs). The necessary equipment is now both readily available and affordable: thus there is considerable potential for this technique to be widely adopted in geomorphological studies. But is it possible for geomorphologists without a background in photogrammety to use it successfully? As part of a larger study into rill initiation by overland flow, a non-metric digital camera and ERDAS IMAGINE OrthoMAX software were used to generate small-scale DEMs of soil surface microtopography. This paper reports on the procedure used, highlights potential pitfalls, and comments on the quality of the resultant DEMs. Whilst acquisition of high-quality images using a digital camera is relatively straightforward, problems were subsequently encountered due to the small size of the internal imager and the need for camera calibration. Potential stumbling blocks in the use of the software lay in the setting-up of ground control points and the use of tie-points and check-points, as well as several software glitches not identified in the current manual. Nonetheless, once these problems were overcome the technique proved to be a simple, effective and fast tool for generating high quality microtopographical DEMs. This methodology shows great promise for future geomorphological studies that require these kinds of surface data.

The Limits of Erosion Modeling

Modeling soil erosion by water is only about sixty years old as a scientific activity, but has played a vital role both in advancing our understanding of erosional processes, and in applications to the problem of prediction and design of conservation strategies. Yet despite some ambitious claims, current soil erosion models are still inadequate in many respects (e.g., De Roo, 1993; Favis-Mortlock, 1994; 1998c; Jetten et al., 1999; Parsons and Wainwright, 2000). Very few models have been ‘validated’ in any scientifically acceptable sense. They may work reasonably well for specific circumstances, or with calibration. Outside of this domain results are disappointing and often are not easy to explain. This chapter discusses some of the weaknesses associated with present-day models for soil erosion by water, and considers the constraints (and opportunities) which these shortcomings might present for the next generation of models.

Evaluation of Field-Scale Erosion Models on the UK South Downs

This study evaluates three field-scale erosion models — GLEAMS, EPIC and WEPP — against high-quality measured erosion data for a hillslope site in the UK South Downs, collected during the period 1982–88. Calibrated and uncalibrated runs were carried out; however the values used for calibration were constrained so that they remained within an ‘acceptable’ range, and were consistent between models.

The Influence of Global Greenhouse-Gas Emissions on Future Rates of Soil Erosion: a Case Study from Brazil Using WEPP-CO2

Since the mid-198os, increases in the global concentrations of greenhouse gases have been paralleled by rising international concern over their potential to affect climate. Concentrations of these gases (most importantly, carbon dioxide, methane, nitrous oxide, tropospheric ozone, chlorofluorocarbons and water vapour) have been observed to increase dramatically during the last 1oo years or so. This rise results from anthropogenic activity. Emissions of the naturally-occurring gases have increased due to modifications of natural cycles by growing human populations, while some new gases (e.g. chlorofluorocarbons) have been added. Atmospheric concentrations of carbon dioxide, for example, have risen by about 26% since the Industrial Revolution (Fig. 1.1): this results both from increased burning of fossil fuels, and from deforestation (Houghton et al. 1990).