Geology Reference
In-Depth Information
in surface waters. Remote sensing has long been
used to assess vegetative cover at regional to glo-
bal scales. There is increasing use of libraries of
time series of images to assess time variations in
cover (Lu
et al
., 2003). These time series permit
assessment of spatial and temporal variations of
cover inputs to models and the evaluation of
hindcast predictions from crop and pasture mod-
els. Limitations in the classifications of some
forms of cover, specifically bleached dead vegeta-
tion, have been resolved by Guerschman
et al
.
(2009). Remotely sensed interpretations of soil
moisture and soil surface roughness could also be
important mechanisms for informing erosion
models (Rahman
et al
., 2008). At the catchment
scale there are some prospects for the use of
remote sensing in estimating the concentration
of surface water sediment concentrations directly.
A further form of remote sensing with ramifica-
tions for erosion modelling is the development
and availability of rainfall radar data (Steiner
et al
., 1995). These data could be made useful in
providing more spatially and temporally realistic
inputs of rainfall rates than do conventional and
often sparse networks of rain gauges. Where all of
these techniques could have the advantage of pro-
viding a spatial-rich source of data for evaluation
and identifying the initial conditions of models,
near real-time remote sensing combined with
models also opens up the prospects of data-model
fusion where initial conditions for models,
rainfall rate inputs and parameter estimation are
continuously and automatically updated. These
techniques have been extensively developed in
terrestrial and ocean biogeochemical models (e.g.
Barrett
et al
., 2005).
Ultimately, the model builder must combine
lines of evidence to assess and improve models.
To do this well the product should track the ridge
indicated by the solid arrow in Fig. 20.1. The use
of multiple lines of evidence will result in more
robust models that engender more confidence for
use in predictive environments. A case study of
adaptive changes to a model and the consequent
prediction of spatial erosion processes was pro-
vided by Rustomji
et al
. (2008). This study showed
that default parameters from a national assess-
ment could be significantly improved by local
information so as to give a progressive refinement
of sediment source maps used to target manage-
ment actions.
Underlying the discussion above is also the
increasing understanding that 'Stationarity is
Dead' (Milly
et al
., 2008). We live in a rapidly
changing world with respect to both land use and
climate (Chapter 15). As population increases,
stresses on land resources will continue to
increase, often in those areas of the world that are
already severely stressed. When we add to that
trends of increasing rainfall amounts (Karl &
Knight, 1998), rainfall intensities (Groisman
et al
., 2005), and rainfall erosivity (Nearing, 2001),
it becomes evident that our erosion models must
be able to represent a world of changing land use
and a changing climate.
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