Determinación de los rangos de humedad

The soil erosion risk potential in the Xiangxi catchment was estimated using the Revised Universal Soil Loss Equation (RUSLE; RENARD ET AL., 1997). It is a soil erosion prediction technology and presents an advanced version of the empirical Universal Soil Loss Equation (USLE).

Based on long-term series of measurements and studies by ZINGG (1940) and MUSGRAVE

(1947), the USLE was originally developed by WISCHMEIER and SMITH (1965) to predict the average annual rate of soil erosion at the field scale on gently undulating land. The USLE was designed for conditions in the Middle West of the U.S., based on investigations of standardized unit plots on clean- tilled continuous bare fallow and slopes inclining with 9% and a slope length of 22.13 m (WISCHMEIER and SMITH, 1965). By integrating statistical analyses and relationships from more than 11,000 plot-years of research data from 47 locations in 24 states (GILLEY and FLANAGAN, 2007), the grey-box model structure of the USLE expresses the long-term annual average conditions under different cropping and management systems in the U.S. for a given set of rainfall, soil, and topographic settings (MORGAN, 2011). Though, this standardized, empirical model has been widely applied in soil science and environmental planning, critiques on the restriction of the parameter calibration and on its applicability to the conditions for which it was developed (e.g., EL-SWAIFY ET AL., 1982; MCCOOL ET AL., 1987; RENARD ET AL., 1991) led to subsequent revisions by the U.S. Department of Agriculture (USDA; RENARD ET AL., 1997).

Major regionally and locally specific improvements of the basic model structure aimed at the strength of the prediction of soil loss on a wider range of field conditions above the field scale or standardized 'Wischmeier plot' (TOY and RENARD, 1998). A runoff factor was added to the driving force of flow detachment processes that has been primarily described with the modification of the rainfall energy by the slope length and slope angle (JETTEN and MANETA, 2011). RENARD ET AL. (1997) further define the criteria for the identification of an erosive single storm event, e.g., a threshold value of >12.7 mm to separate erosive from nonerosive rainfall as suggested by WISCHMEIER and SMITH (1978) and BROWN and FOSTER (1987). Further improvements, for instance, included the modification of the calculation of the soil erodibility and the crop and management factors to account for their inter-annual variability and dynamic by implementing more field- and time-specific sub-factors, such as the surface cover and roughness sub-factors, and the soil moisture sub-factor (RENARD ET AL., 1997; TOY and RENARD, 1998; MORGAN, 2011). The calculations of the slope steepness and slope angle were also reconstituted to improve their accuracy and to extend the model applicability to steeper hillslope gradients in complex areas that was primarily considered as one of the most stressed arguments on the inability of the USLE (e.g., MCCOOL ET AL., 1987; EL-

Following these improvements, the RUSLE represents a much more 'fit-to-purpose' model (GOVERS, 2011). It can be applied as land use independent to a much higher variety of field settings including disturbed and undisturbed lands (e.g., agricultural sites, forests, mining and constructions sites), and newly or established reclaimed land (TOY and RENARD, 1998). Thus, it is a widely used tool on the assessment and inventory of soil erosion to assist public development and to improve soil conservation and environmental planning by governments and private consultants (RENARD ET AL., 1997; USDA-ARS, 2010).

As its predecessor, the RUSLE involves six major factors that affect upland soil erosion in terms of sheet erosion by raindrop impact and overland flow, and rill erosion (TOY and RENARD, 1998). These factors are: rainfall erosivity, soil erodibility, slope length, slope steepness, cropping management techniques, and supporting conservation practices (WISCHMEIER and SMITH, 1978; RENARD et al., 1997). Using a set of mathematical equations in a multiplicatory approach, the RUSLE for agricultural land is written as (Eq. 1):

A = LS × R × K × C × P (Eq. 1) where A is the potential long-term, average annual soil loss (t ha-1 a-1), R is the rainfall erosivity (MJ mm ha-1 h-1 a-1), LS is the combined factor from the terrain-based slope length factor L (dimensionless; -) and the terrain-based slope steepness factor S (dimensionless; -), K is the soil erodibility (t ha h ha-1 MJ-1 mm-1), C is the crop and management factor (dimensionless; -), and P is the support practice factor (dimensionless; -).

The RUSLE can be run by using computer interfaces such as RUSLE2 (USDA-ARS, 2010), but can also be implemented into GIS-based modeling by integrating and manipulating spatial gridded data on the above factors, for instance based on available RS, DEM, and maps. According to MORGAN

(2011), the success and wider range of application of the RUSLE, especially, lies in the fact that DEMs can be converted into LS maps based on direct calculations or on the integration of the drainage network (e.g., flow accumulation, contributing area), and thus soil losses can be predicted on the catchment scale for each grid-cell.

Compared to more detailed process-based models like WEPP (Water Erosion Prediction Project; FLANAGAN and NEARING, 1995), EROSION3D (SCHMIDT ET AL., 1999), and EUROSEM (European Soil Erosion Model; MORGAN ET AL., 1998), the empirical RUSLE is however regarded to have limitations in terms of describing more complex physically-based water erosion processes (e.g., sediment delivery and deposition). Nevertheless, the easy to parameterize model structure, the lower requirements of input data, and less model run time give the RUSLE an advantage in comparison to the more complex models. Thus, it belongs to one of the worldwide most applied soil erosion prediction technologies (TOY and RENARD, 1998), which was already often applied in China and in

the TGA focusing on the evaluation of soil conservation measures, on the soil erosion risk potential, and on soil erosion dynamics (e.g., SHI ET AL., 2004; LIU and LUO, 2005; ZHOU AND WU, 2008; XU ET AL., 2009; LI ET AL., 2010; HUANG ET AL., 2012; PANG ET AL., 2013). Considering all these facts

and against the background of sheet and rill erosion being the dominating water erosion processes (c.f., Section 3.2.4), the complex steep sloping mountainous areas (c.f., Section 3.2.3), and the data scarcity in terms of spatial and temporal resolution (c.f., Section 3.3), the RUSLE was thus considered to fit to the conditions in the Xiangxi catchment.