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PART IITERRESTRIAL SEDIMENT AND

HEAT FLUX APPLICATIONS

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Spatial Modelling of the Terrestrial Environment. Edited by R. Kelly, N. Drake, S. Barr.C© 2004 John Wiley & Sons, Ltd. ISBN: 0-470-84348-9.

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Editorial: Terrestrial Sedimentand Heat Fluxes

Nick Drake

Remote sensing has been used to either parameterize or validate a wide diversity of terrestrialenvironmental models. In this Part we consider the integration of remote sensing withmodels of river sediment and soil erosion and fire heat fluxes. These applications illustratesome of the diversity of modelling approaches that can be employed, the different ways bywhich models can be linked to remote sensing, the variety of scales at which they can beapplied and the scaling problems that can be encountered when applying models at coarsescales.

Beginning with soil erosion, natural soil erosion rates are generally low, however, anthro-pogenic practices tend to increase erosion through factors such as overgrazing, agriculturalintensification and the implementation of poor agricultural practices. Accelerated soil ero-sion leads to higher nutrient losses, a reduction in soil depth and thus over time a lower soilwater holding capacity. These factors eventually lead to reduced biomass yields and canultimately result in desertification. Erosion also has important off-site effects. For example,accelerated water erosion rates lead to increased sedimentation rates elsewhere that canadversely affect the ecology and biodiversity of aquatic systems. There is, therefore, a needto model soil erosion in order to predict the consequences of these actions.

Models of soil erosion by water were first developed in the 1940s by analysing the resultsof erosion plot studies (Zingg, 1940). This research led to the development of the universalsoil loss equation (USLE) (Wischmeier and Smith, 1958), an empirical model that provedextremely popular with both managers and researchers and dominated the field for sometime, particularly in America where it was developed. However, the problems of applyingthe model outside America, and the fact that it can only be used to estimate average annualerosion led to the development of alternatives. By the 1980s it was recognized that physically

Spatial Modelling of the Terrestrial Environment. Edited by R. Kelly, N. Drake, S. Barr.C© 2004 John Wiley & Sons, Ltd. ISBN: 0-470-84348-9.

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based modes have the potential to overcome the limitations of empirical models and sincethen numerous physically based models have been developed and tested. However, thediverse array of factors that affect erosion means that the models are becoming increasinglycomplex and have thus, in turn, become more demanding on the number of parameters thatthey require. Furthermore, there is a lack of understanding of some of the key processes, suchas soil crusting and rill initiation, that affect erosion dynamics and these processes are notusually parameterized in models. If they are, the equations used to represent them are derivedfrom field or laboratory experiments, and are usually somewhat empirical. Consequently,many physically based models have empirical elements and, over time, a spectrum ofmodels with varying amounts of theoretical rigour and empiricism has emerged.

Wind erosion modelling started with Bagnold (1941) who developed a theoretical modelto explain the saltation of particles induced by wind. This model forms the basis of manyof the physically based models used today. The development of empirical models suchas the wind erosion equation (WEQ) (Woodruff and Siddoway, 1965) was attractive forresource managers as they could be readily implemented to provide an estimate of on-sitesoil loss. However, as was the case with water erosion models, now much effort is beingspent developing physically based process models in order to overcome the limitations oftheir empirical counterparts. Thus, the physically based wind erosion prediction system(WEPS) has recently been developed as a replacement for the WEQ.

Early wind and water erosion modelling efforts were applied at the plot scale although ithad long been recognized that the majority of eroded soil appeared to come from a limitednumber of sources and that spatial models were required to understand erosion at the re-gional scale. Remote sensing was an attractive solution in this regard but there was a largegulf between the accuracy of information that was required and what remote sensing couldsupply. Remote sensing was first used to study erosion by employing aerial photographsto interpret and map erosional and depositional landforms. Initially aerial photos simplyenabled terrain mapping to be conducted over larger areas than was possible with localizedfield techniques. The next logical step was to apply these methods to monitor landformchanges (for example, interpreting changes in coastal landforms in order to monitor coastalerosion). This spatial monitoring provided new insights. For example, Gay (1962) mon-itored the movement of Barchan Dunes by interpreting aerial photographs acquired indifferent years. He found that not only was the rate of movement proportional to the duneheight, as Bagnold’s (1941) model predicts, but the movement was also proportional to thedune width, and noted that this means that all Berhcan dunes, regardless of size, sweepequal areas at equal rates.

In the 1970s the advent of satellite remote sensing enabled the development of newapplications. Sensors such as the coastal zone colour scanner were developed with wave-lengths on them that can penetrate into the water column and allow monitoring of erodedsediments in near surface waters. Though this development provided new insights into nearsurface sediment transport and dispersal, it supplied little information on hillslope and riverchannel erosion. This was because the fine scale of hillslope processes is unresolved by thecoarse spatial resolution of the sensors. Additionally, water erosion in these regions tendsto occur when it is raining and, therefore, cloud obscures the view from optical sensors.

Developments in image processing methods and the ever-increasing diversity of sensorsin space meant that by the 1980s it had become possible to derive many of the parametersthat control erosion from remotely sensed imagery. For example, remote sensing provides

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numerous ways to map topography (e.g. photogrammetry, interferometry and LiDAR) andthus derive maps of slope and other topographic factors that control water erosion, whilevegetation indices can be used to estimate vegetation cover, the most important parametercontrolling resistance to erosion. Initially these parameters were combined in qualitativeways using GIS overlays to produce maps of erosion risk. However, they were soon usedto implement spatial models of erosion.

Though these developments in remote sensing have enabled the estimation of numerousparameters of importance to soil erosion modelling, there is a gap between what the modelsrequire and what it is possible to obtain. As a result, in nearly all cases, some parametersneed to be measured in the field and interpolated in a GIS for effective implementation ofthe model. It is often costly and time-consuming to frequently monitor such parametersin detail over large areas. Thus, there is a need for parsimonious models that utilize anyparameters available from remote sensing.

The first chapter in this Part (chapter 6) is by Lane et al. and demonstrates how errorcan be managed in the application of digital photogrammetry to the quantification of rivertopography of large, braided, gravel-bed rivers. The chapter reviews the traditional treat-ment of error in digital elevation models (DEM), and then considers how the error can beidentified, explained and corrected in this study in the context of a specific example. Thisis an important topic because many land erosion models rely on the parameterization ofmass transfer processes using a DEM. The second two chapters of this Part illustrate theimplementation of water and wind erosion models at coarse scales. In Chapter 7, Okinand Gilette outline the need for regional modelling of sand and dust emission, transportand deposition. They then implement a regional scale wind erosion model developed fromBagnold’s equations and compare the results to ground measured observations in the Jor-nada Basin, New Mexico. They find that the model under-estimates wind erosion and dustflux throughout much of the basin because the parameterization using soil and landusemaps failed to capture the surface variability in the landscape. They conclude that there isa need to develop remote techniques to map the fine scale heterogeneity in the landscapethat exerts a large control on wind erosion and dust flux.

In Chapter 8 Drake et al. implement a water erosion model for the catchment of LakeTanganyika with the aim of quantifying the source areas within the catchment, the transferof sediment to the lake and the sediment dispersal within the catchment. The model isapplied using remote sensing to estimate spatial and temporal variations in vegetationcover, rainfall and the near surface sediment distribution within the lake. The erosion modelresults have been shown to be highly sensitive to scale, with increasingly coarser spatialresolutions causing a gradual reduction in predicted erosion rates. Scaling techniques havebeen employed in an effort to overcome these problems. It is concluded that validationof the model is needed but that this is problematic when applying coarse resolution dataover large areas. These two chapters thus highlight the numerous problems that can beencountered when applying models to coarse resolution imagery but also provide methodsthat point towards solutions to certain aspects of the problem.

The final process that is considered in this section is fire. Fire affects humans, atmo-sphere, vegetation and soils, and successful fire modelling has the potential to further ourunderstanding, prediction and management of this phenomenon. The first model of fire con-sidered its spread and was developed by Fons (1946). Since then a large number of modelshave been developed to consider not only diffusion, but also fire properties and physical

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characteristics (e.g. surface, crown and ground fires). Similarly with approaches to ero-sion modelling, both empirical and physically based models have been developed, withthe semi-empirical fire spread model of Rothermel (1972) attaining the most widespreaduse. More recently the secondary effects of fires on the atmosphere, hydrology, ecologyand geomorphology of affected regions have started to be modelled, as has the economicimpact. Many of these models are empirical (or contain significantly empirical elements)because our understanding of the fine detail of the way fire affects atmosphere, vegetationand soil systems is often poorly understood. Further research is needed into both processunderstanding and model development.

The development of GIS brought the first attempts to spatially predict fire growth, how-ever, it was soon realized that the spatial data needed to parameterize such models were notavailable and would be extremely hard to collect. Developments in remote sensing havealleviated this problem to a certain extent and methods are being developed that allow firedetection, assessment of burnt areas, fuel load and fuel moisture content. This informationhas, in some cases, been integrated with models. For example, burnt area estimates derivedfrom remote sensing have been used to provide important inputs into models that predictthe quantity of carbon emitted by fires and the quantity of soil eroded after these events.Though such models provide a significant advance, it has been shown that factors suchas errors in estimated fuel load can introduce a large amount of uncertainty. Therefore,new methods to accurately derive such parameters are needed. In the final chapter in thisPart (Chapter 9), Wooster et al. introduce a new remote sensing method that holds greatpotential in directly measuring the amount of biomass combusted by the passage of a fire.They introduce a method that models the total amount of energy emitted by the fire, andshow both empirically and theoretically that this can be related to the amount of biomasscombusted and gases emitted. The method holds great promise in overcoming some of thecurrent limitations to the implementation of the fire models outlined above over large areas.

References

Bagnold, R.A., 1941, The Physics of Blown Sand and Desert Dunes (New York: Methuen).Fons, W.L., 1946, Analysis of fire spread in light forest fuels, Journal of Agricultural Research, 72,

93–121.Gay, S.P., 1962, Origen distribucion y movimiento de las arenas eolicas en el area de Yauca a palpa,

Boletın de la Sociedad del Peru, 27, 37–58.Rothermel, R.C., 1972, A Mathematical Model for Predicting Fire Spread in Wildland Fuels, USDA

Forest Service Research Paper INT-115.Wischmeier, W.H. and Smith, D.D., 1958, Rainfall energy and its relationship to soil loss, Transactions

of the American Geophysical Union, 39, 285–291.Woodruff, N.P. and Siddoway, F.H., 1965, A wind erosion equation, Proceedings of the Soil Science

Society of America, 29, 602–608.Zingg, A.W., 1940, Degree and length of slope as it affects soil loss runoff, Agricultural Engineering,

21, 59–64.