Estimating rainfall erosivity from daily precipitation records: A comparison among methods using data from the Ebro Basin (NE Spain)
Introduction
Rainfall erosivity is of paramount importance among natural factors affecting soil erosion, and unlike some other natural factors, such as relief or soil characteristics, is not amenable to human modification. It thus represents a natural environmental constraint that limits and conditions land use and management. In the context of climate change the effect of altered rainfall characteristics on soil erosion is one of the main concerns of soil conservation studies.
It is well known that several very intense rainfall events are responsible for the largest proportion of soil erosion and sediment delivery. Hence, estimating rainfall erosivity is central to assessment of soil erosion risk. Numerous studies using natural and simulated rainfall have investigated the role of drop size distribution on the detachment of soil particles. The measurements involved are difficult to perform, and reported data are consequently very limited both spatially and temporally. In addition, measurements of natural rainfall properties, for comparison with simulated rain, are scarce (Dunkerley, 2008). This has encouraged studies relating more conventional rainfall indices, such as the maximum intensity during a period of time, to overall rainfall energy or directly to soil detachment rates. Examples of such indices of rainfall erosivity are the USLE R factor, which summarizes all the erosive events quantified by the EI30 index occurred along the year (Wischmeier, 1959, Wischmeier and Smith, 1978, Brown and Foster, 1987), the modified Fournier index for Morocco (Arnoldus, 1977), the KE > 25 index for southern Africa (Hudson, 1971), and the AIm index for Nigeria (Lal, 1976). Among these the most extensively used is the USLE/RUSLE R factor, which is calculated from the EI30 index (Wischmeier, 1959, Wischmeier and Smith, 1978, Brown and Foster, 1987, Renard et al., 1997). At many sites worldwide the R factor has been shown to be highly correlated with soil loss (Van der Knijff et al., 2000, Diodato, 2004, Shi et al., 2004, Hoyos et al., 2005, Curse et al., 2006, Onori et al., 2006, Domínguez-Romero et al., 2007).
One of the main disadvantages in seeking to employ the RUSLE R factor is the need for a relatively continuous rainfall data series, with a time resolution of at least 15 min (pluviograph data). Information of this nature is rarely available with good spatial and temporal coverage. Other attempts to predict rainfall erosivity from mean annual rainfall and/or mean monthly rainfall have provided results that are quite coarse, but these have been extensively cited in the scientific literature (Banasik and Górski, 1994, Renard and Freimund, 1994, Yu and Rosewell, 1996c, Ferro et al., 1999). Renard and Freimund (1994) provided a succinct summary of methods for estimating the R factor in various parts of the world, and also developed a new set of relationships for calculating the R factor using mean annual rainfall data and the modified Fournier index.
Daily weather records with good spatial and temporal coverage that adequately represent rainfall characteristics are usually available for most locations. Because of the high temporal and spatial variability of rainfall erosivity, accurate records based on long data series are required. Attempts to accurately predict rainfall erosivity from daily rainfall records or storm events (Richardson et al., 1983, Bagarello and D’Asaro, 1994, Petkovsek and Mikos, 2004), or from monthly rainfall (Yu and Rosewell, 1996a, Yu and Rosewell, 1996b, Yu and Rosewell, 1996c, Yu et al., 2001), have been based largely on exponential relationships.
As the origin of rainfall erosivity is linked to climate dynamics, there is a need to apply climate analysis methodologies to the study of the erosivity factor. However, long series of rainfall erosivity data are required if consistent results are to be obtained. Daily rainfall erosivity models bridge the gap between climate change scenarios based on general and regional circulation models, and the implications of these scenarios for some land degradation processes (Yu and Rosewell, 1996b). In addition, a daily rainfall erosivity model would have potential application in many erosion constructs, as the daily model would provide robust predictions of rainfall erosivity.
The aim of this study was to review existing methodologies for predicting the R factor, and to compare estimates obtained using these methodologies with R factor values calculated by the RUSLE procedure. The study was conducted using data from a dense network of observatories distributed in a climatically complex region (the Ebro Basin, NE Spain), and covers the period 1997–2006. The methodology described has the potential to be applied to longer daily rainfall data bases, which could improve estimates of the spatial coverage of rainfall erosivity in the Ebro Basin with respect to both long-term average erosivity and seasonal distribution thereof. The proposed methodology can be applied in many parts of the world where short time series of high-resolution rainfall data coexist with long series at a daily resolution.
Section snippets
Study area
The study area covers northeastern Spain (Fig. 1), encompassing an area of about 85,000 km2 that corresponds to the Ebro Basin. The Ebro valley is an inner depression surrounded by high mountain ranges. It is limited in the north by the Cantabrian Range and the Pyrenees, with maximum elevations above 3000 m a.s.l. The Iberian Range closes the Ebro valley to the south, with maximum elevations in the range 2000–2300 m a.s.l. The Ebro valley is closed to the east by the Catalan Prelittoral Range,
Spatial distribution of rainfall erosivity over the study area
A detailed spatial distribution of rainfall erosivity in the study area, as estimated using the RUSLE R factor, is shown in Fig. 2. Overall, the spatial distribution of the R factor in the study area could be explained by the proximity to—or isolation from—the major water masses of the Cantabrian and the Mediterranean Seas. The relief, with mountain ranges to the north, south, and east of the region, modifies this general pattern by increasing rainfall in those areas. Another effect of the
Discussion and conclusions
Estimation of rainfall erosivity is of great importance for soil erosion assessment, and has important implications for agriculture and land planning. Rainfall erosivity is an indicator of precipitation aggressiveness, and depends both on the rainfall energy (raindrop size distribution and kinetic energy) and the intensity of the storm event. Rainfall in Mediterranean climates is characterized by great temporal variability and high, brief, intensity (storms). This latter characteristic
Acknowledgements
We thank the Ebro River Hydrographical Confederation (Confederación Hidrográfica del Ebro; CHE) for providing the data used in this study. The research of M.A. was supported by a JAE-Predoc Research Grant from the Spanish National Research Council (Consejo Superior de Investigaciones Científicas; CSIC).
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