Geology Reference
In-Depth Information
In most USDA erosion models (e.g. WEPP,
WEPS, RUSLE1), residue production occurs only
during senescence of a crop and is calculated from
the decline in live biomass. This is equivalent to
the assumption that there is no dead biomass pro-
duction during periods of increasing biomass, and
no additional growth after the peak biomass is
reached. This is probably a reasonable and accept-
able assumption for the treatment of annual
crops. However, in perennial vegetation and in
mixed stands where different components mature
at different times, death and growth usually occur
simultaneously. A new type of vegetation descrip-
tion is being developed for RUSLE2 in which resi-
due production is more continuous, based on the
assumption that live biomass has an effective life
span. In the absence of forage harvest or biomass
removal, the daily change in live biomass amount
is calculated as the difference between new
growth and the death of old growth. Live biomass
that is not harvested is added to a dead biomass
pool after its lifespan is reached, thereby provid-
ing the soil the benefits of additional residue
cover. Users input monthly potential growth pat-
terns and shoot and root life-spans, and RUSLE2
calculates corresponding residue production pat-
terns. Growth patterns are altered in response
to management operations involving biomass
removal. Daily changes in residue biomass are
then calculated as the difference between death
and decomposition or residue harvest. RUSLE2's
new routines will simplify the creation of vegeta-
tion descriptions for perennial systems, providing
more realistic estimates of residue creation
throughout the year, and thereby improving run-
off and erosion estimation for pastures, hay fields,
and other systems dominated by perennial
vegetation.
To predict average annual ephemeral gully ero-
sion is challenging because there is no existing
long-term database of ephemeral gully erosion
rates comparable to the plot database underlying
the USLE, which in turn underlies RUSLE.
Ephemeral gully erosion is a process inherently
driven by larger-than-average runoff events (see
Chapter 19). Many process-based models have
developed climate-generators (e.g. CREAMS:
Knisel, 1980) that reproduce the stochasticity of
weather. Applying these long-term weather
records to an ideal runoff and erosion model
would create a distribution of runoff and erosion
events. Taking the monthly means of this popula-
tion of ephemeral gully erosion events would rep-
resent the long-term average values needed to
complement RUSLE2 sheet and rill erosion esti-
mates and to estimate long-term average ephem-
eral gully erosion. The RUSLE2 developers
proposed that modelling the correct storm
amount and sequence of storms could reproduce
the mean values. Toward this end, techniques to
predict a sequence of index storms for any combi-
nation of soil and management anywhere within
the continental US (and elsewhere, with appro-
priate calibration) have been developed, and
require only RUSLE2 climate and profile-level
information. The results approximate the mean
monthly runoff, annual runoff event frequency,
and a gamma distribution function scale parame-
ter that characterizes 30-year stochastic runoff
predictions generated using the AnnAGNPS
(annualized Agricultural Non-Point Source)
model (Bingner & Theurer, 2001).
By taking the largest in a series of runoff events
as a 6-month return period event, and scaling the
magnitudes of the periodic runoff events propor-
tional to the long-term average disaggregated
daily runoff amounts on event days, these param-
eters allow estimation of the date and size of a
series of index runoff events that are proposed as
the basis for an ephemeral gully calculation capa-
bility within RUSLE2. Index event RUSLE2 hills-
lope runoff, sediment yield, and sediment size
distribution will be coupled with a physically-
based ephemeral gully erosion model, possibly
that used in CREAMS, to predict annual average
ephemeral gully erosion.
8.3.7 RUSLE2 examples
RUSLE2 is so flexible that it is very difficult to
decide which capabilities to show in a few exam-
ples, and in which form to display those. In nar-
rowing the possibilities, it was decided to
concentrate on three examples. The first example
