Erosion rate predictions from PESERA and RUSLE at a Mediterranean site before and after a wildfire: Comparison & implications

Abstract

Mediterranean ecosystems are commonly vulnerable to wildfires. Accelerated erosion processes due to wildfires in those environments constitute a major restrictive factor in their sustainability. This study aims at evaluating the use of the Revised Universal Soil Loss Equation (RUSLE) and the Pan-European Soil Erosion Risk Assessment (PESERA) models in predicting the changes in spatial variability of soil erosion following a wildfire event. A site in Greece on which a wildfire occurred in the summer of 2007 is used as a case study. Soil erosion rates for the site before and after the fire outbreak were estimated by the two models. Inputs for both models included climatic, land-use, soil type, topography, Earth Observation (EO) as well as management and other ancillary data. Both models showed a substantial increase of soil erosion rates in the affected area as a result of the fire, particularly towards the steeper slopes and on areas of high burning severity. Yet, there were noticeable differences in the predictions between the 2 models in terms of absolute estimates of soil erosion rates before and after the fire event. Mean pre-fire erosion rates from RUSLE were~2.5 times higher than those from PESERA. RUSLE predicted considerably higher mean erosion in comparison to PESERA for the post-fire conditions, yet of much less spatial variability. Average post-fire soil loss value, compared to pre-fire, was about nine and six times greater when using the RUSLE and the PESERA model respectively. RUSLE predictions were most sensitive to topographic and rainfall erosivity factors. PESERA showed high sensitivity to the vegetation coverage as well as to the soil characteristics inserted as crusting and erodibility variables. To our knowledge, this is the first study performing an intercomparison of soil erosion rate predictions between these two models, particularly so in the context of the influence of wildfire events. This study provides a key contribution towards our ability to better understand the effect of fire on soil erosion in the Mediterranean and elsewhere, as well as the ability of those widely used models as efficient tools to be used for this purpose. The latter is of key significance and practical value for research and policy decision making purposes alike, where information on spatiotemporal estimates of soil erosion rates may be required.

Integrated Use of GCM, RS, and GIS for the Assessment of Hillslope and Gully Erosion in the Mushi River Sub-Catchment, Northeast China

“…The USLE/RUSLE model is considered adaptable for estimation of soil erosion by water in the black soil region of Northeast China, where long slope length and gentle slope are the main topographical features [9,13,75,76]. However, RUSLE may provide extremely high soil erosion predictions [31].…”

“…Forest land plays a predominant role in the control of soil loss compared with farmland. Although with increasing slope gravity-caused soil erosion increased and very steep slopes did not hold much soil [31], the dominant position of forest land gradually increased such that the proportion even exceeded 80% at 20-25 degrees (Figure 8), and forest land reduced the soil loss. When the slope was greater than 15 degrees, the gully intensity decreased significantly as the slope increased.…”

“…Among these models, the revised universal soil loss equation (RUSLE) [23] is a revised version of the universal soil loss equation (USLE) model [22]. RUSLE proved to be the most commonly applied model to estimate soil loss [30][31][32]. Recently, many studies have applied remote sensing (RS) and Geographic Information System (GIS) methodologies to assess soil erosion [29,31,[33][34][35].…”

“…RUSLE proved to be the most commonly applied model to estimate soil loss [30][31][32]. Recently, many studies have applied remote sensing (RS) and Geographic Information System (GIS) methodologies to assess soil erosion [29,31,[33][34][35]. In particular, satellite images with very high spatial resolution have offered possibilities for landscape feature mapping and quantitative evaluation of farm practices in soil erosion assessment [36,37].…”

Soil erosion evolution and spatial correlation analysis in a typical karst geomorphology using RUSLE with GIS

“…Many factors affect the evolution of soil erosion in karst areas (Karamesouti et al, 2016;Krklec et al, 2016;Y. B. Li et al, 2016;Wang et al, 2016;Wu et al, 2016) because of the complicated natural conditions (Bai et al, 2013a, b;Tian et al, 2016).…”