Applying the QREI30 index within the USLE modelling environment

The USLE/RUSLE model was designed to predict long‐term (~20 years) average annual soil loss by accounting for the effects of climate, soil, topography and crops. The USLE/RUSLE model operates mathematically in two steps. The first step involves the prediction of soil loss from the ‘unit’ plot, a bare fallow area 22.1 m long on a 9% slope gradient with cultivation up and down the slope. Appropriate values of the factors accounting for slope length, gradient, crops and crop management and soil conservation practice are then used to adjust that soil loss to predict soil loss from areas that have conditions that are different from the unit plot. Replacing EI₃₀, the USLE/RUSLE event erosivity index, by the product of the runoff ratio (Q_R) and EI₃₀, can enhance the capacity of the model to predict short‐term soil loss from the unit plot if appropriate data on runoff is available. Replacing the EI₃₀ index by another index has consequences on other factors in the model. The USLE/RUSLE soil erodibility factor cannot be used when the erosivity factor is based on Q_REI₃₀. Also, the USLE/RUSLE factors for slope length, slope gradient crops and crop management, and soil conservation practice cannot be used when runoff from other than the unit plot is used to calculate Q_R. Here, equations are provided to convert the USLE/RUSLE factors to values suitable for use when the erosivity factor is based on the Q_REI₃₀ index under these circumstances. At some geographic locations, non linear relationships exist between soil loss from bare fallow areas and the Q_REI₃₀ index. The effect of this on the slope length factor associated with the Q_REI₃₀ index is demonstrated using data from runoff and soil loss plots located at the Sparacia site, Sicily. Copyright © 2012 John Wiley & Sons, Ltd.     

Runoff dependent erosivity and slope length factors suitable for modelling annual erosion using the Universal Soil Loss Equation

Despite revisions and refinements, the Revised Universal Soil Loss Equation (RUSLE), which is the revised version of the Universal Soil Loss Equation (USLE), over predicts small annual soil losses and under predicts large annual soil losses. To some large extent, this results from the equation over estimating small event soil losses and under estimating large event soil losses. Replacing the USLE/RUSLE event erosivity index (EI₃₀) by the product of EI₃₀ and the runoff ratio (Q_R) significantly reduces the errors in estimating event erosion when runoff is measured, but the USLE‐M, the USLE variant that uses the Q_REI₃₀ index, requires crop and support practice factors that differ from those used in the RUSLE. The theory which enables the Q_REI₃₀ index to be used in association with the RUSLE crop and support practice factors is presented. In addition, the USLE/RUSLE approach was developed for conditions where runoff is produced uniformly over a hill slope. A runoff dependent slope length factor that takes account of runoff variations over a hill slope is presented and demonstrated for the situation where runoff from a low runoff producing area passes onto an area where runoff is produced more readily. Copyright © 2007 John Wiley & Sons, Ltd.     

Modelling event soil losses using the QREI30 index within RUSLE2

Recently, a USDA Curve Number‐based method for obtaining estimates of event runoff has been developed for use in enhancing the capacity of Revised Universal Soil Loss Equation (RUSLE2) to deal with runoff‐driven phenomena. However, RUSLE2 still uses the EI₃₀ index as the basis for determining the erosivity of the rainfall for sets of runoff producing storms at a location even though the product of the runoff ratio (Q_R) and EI₃₀ index is better at prediction event erosion when runoff is known or predicted well. This paper reports the results of applying the Q_REI₃₀ index using data available from tables within RUSLE2 to predict storm event soil losses from bare fallow areas and areas with continuous corn at Holly Springs, MS, and Morris, MN. In RUSLE2, all rainfall during a calendar year is considered to detach soil material that is flushed from the area if and when runoff occurs. However, the Q_REI₃₀ index is calculated using the EI₃₀ value for the amount of rain in the storm that produces runoff. Consequently, changes were made to the timing of events during the calendar year in order to meet the criteria for using the Q_REI₃₀ index. As a general rule, the peak event soil loss produced using the Q_REI₃₀ index were higher than produced by RUSLE2, and the peak event soil loss for the bare fallow occurred later than for the continuous corn. The results of the work reported here show that the Q_REI₃₀ index may be used to model event erosion produced by a set of storms within RUSLE2 provided that the appropriate mathematical rules upon which the USLE was developed are adhered to. Copyright © 2013 John Wiley & Sons, Ltd.     

Testing alternative erosivity indices to predict event soil loss from bare plots in Southern Italy

Methods for predicting unit plot soil loss for the ‘Sparacia’ Sicilian (Southern Italy) site were developed using 316 simultaneous measurements of runoff and soil loss from individual bare plots varying in length from 11 to 44 m. The event unit plot soil loss was directly proportional to an erosivity index equal to (Q_REI₃₀)^1·47, being Q_REI₃₀ the runoff ratio (Q_R) times the single storm erosion index (EI₃₀). The developed relationship represents a modified version of the USLE‐M, and therefore it was named USLE‐MM. By the USLE‐MM, a constant erodibility coefficient was deduced for plots of different lengths, suggesting that in this case the calculated erodibility factor is representative of an intrinsic soil property. Testing the USLE‐M and USLE‐MM schemes for other soils and developing simple procedures for estimating the plot runoff ratio has practical importance to develop a simple method to predict soil loss from bare plots at the erosive event temporal scale. Copyright © 2009 John Wiley & Sons, Ltd.     

Testing assumptions and procedures to empirically predict bare plot soil loss in a Mediterranean environment

Empirical prediction of soil erosion has both scientific and practical importance. This investigation tested USLE and USLE‐based procedures to predict bare plot soil loss at the Sparacia area, in Sicily. Event soil loss per unit area, A_e, did not vary appreciably with plot length, λ, because the decrease in runoff with λ was offset by an increase in sediment concentration. Slope steepness, s, had a positive effective on A_e, and this result was associated with a runoff coefficient that did not vary appreciably with s and a sediment concentration generally increasing with s. Plot steepness did not have a statistically detectable effect on the calculations of the soil erodibility factor of both the USLE, K, and the USLE‐M, K_UM, models, but a soil‐independent relationship between K_UM and K was not found. The erosivity index of the USLE‐MM model performed better than the erosivity index of the Central and Southern Italy model. In conclusion, the importance of an approach allowing soil loss predictions that do not necessarily increase with λ was confirmed together with the usability of already established and largely applied relationships to predict steepness effects. Soil erodibility has to be determined with reference to the specific mathematical scheme and conversion between different schemes seems to need taking into account the soil characteristics. The USLE‐MM shows promise for further developments. The evolutionary concept applied in the development of the USLE should probably be rediscovered to improve development of soil erosion prediction tools. Copyright © 2014 John Wiley & Sons, Ltd.     

Predicting unit plot soil loss in Sicily,south Italy

Predicting soil loss is necessary to establish soil conservation measures. Variability of soil and hydrological parameters complicates mathematical simulation of soil erosion processes. Methods for predicting unit plot soil loss in Sicily were developed by using 5 years of data from replicated plots. At first, the variability of the soil water content, runoff, and unit plot soil loss values collected at fixed dates or after an erosive event was investigated. The applicability of the Universal Soil Loss Equation (USLE) was then tested. Finally, a method to predict event soil loss was developed. Measurement variability decreased as the mean increased above a threshold value but it was low also for low values of the measured variable. The mean soil loss predicted by the USLE was lower than the measured value by 48%. The annual values of the soil erodibility factor varied by seven times whereas the mean monthly values varied between 1% and 244% of the mean annual value. The event unit plot soil loss was directly proportional to an erosivity index equal to , being Q_RR_e the runoff ratio times the single storm erosion index. It was concluded that a relatively low number of replicates of the variable of interest may be collected to estimate the mean for both high and particularly low values of the variable. The USLE with the mean annual soil erodibility factor may be applied to estimate the order of magnitude of the mean soil loss but it is not usable to estimate soil loss at shorter temporal scales. The relationship for estimating the event soil loss is a modified version of the USLE‐M, given that it includes an exponent for the Q_RR_e term. Copyright © 2007 John Wiley & Sons, Ltd.     

Testing the Universal Soil Loss Equation‐MB equation in plots in Central and South Italy

Planning soil conservation strategies requires predictive techniques at event scale because a large percentage of soil loss over a long‐time period is due to relatively few large storms. Considering runoff is expected to improve soil loss predictions and allows relation of the process‐oriented approach with the empirical one, furthermore, the effects of detachment and transport on soil erosion processes can be distinguished by a runoff component. In this paper, the empirical model USLE‐MB (USLE‐M based), including a rainfall‐runoff erosivity factor in which the event rainfall erosivity index EI₃₀ of the Universal Soil Loss Equation (USLE) multiplies the runoff coefficient Q_R raised to an exponent b₁ > 1 is tested by the measurements carried out for the Masse (10 plots) and Sparacia (22 plots) experimental stations in Italy. For the Masse experimental station, an exponent b₁ > 1 was also estimated by tests carried out by a nozzle‐type rainfall simulator. For each experimental site in fallow conditions, the effect of the sample size of the plot soil loss measurements on the estimate of the b₁ coefficient was also studied by the extraction of a fixed number N of randomly obtained pairs of the normalized soil loss and runoff coefficient. The analysis showed that the variability of b₁ with N is low and that 350 pairs are sufficient to obtain a stable estimate of b₁. A total of 1,262 soil loss data were used to parameterize the model both locally and considering the two sites simultaneously. The b₁ exponent varied between the two sites (1.298–1.520), but using a common exponent (1.386) was possible. Using a common b₁ exponent for the two experimental areas increases the practical interest for the model and allows the estimation of a baseline component of the soil erodibility factor, which is representative of the at‐site soil intrinsic and quasi‐static properties. Development of a single USLE‐MB model appears possible, and sampling other sites is advisable to develop a single USLE‐MB model for general use.     

Comparing theoretically supported rainfall‐runoff erosivity factors at the Sparacia (South Italy) experimental site

Interpreting rainfall‐runoff erosivity by a process‐oriented scheme allows to conjugate the physical approach to soil loss estimate with the empirical one. Including the effect of runoff in the model permits to distinguish between detachment and transport in the soil erosion process. In this paper, at first, a general definition of the rainfall‐runoff erosivity factor REF_e including the power of both event runoff coefficient Q_R and event rainfall erosivity index EI₃₀ of the Universal Soil Loss Equation (USLE) is proposed. The REF_e factor is applicable to all USLE‐based models (USLE, Modified USLE [USLE‐M] and Modified USLE‐M [USLE‐MM]) and it allows to distinguish between purely empirical models (e.g., Modified USLE‐M [USLE‐MM]) and those supported by applying theoretical dimensional analysis and self‐similarity to Wischmeier and Smith scheme. This last model category includes USLE, USLE‐M, and a new model, named USLE‐M based (USLE‐MB), that uses a rainfall‐runoff erosivity factor in which a power of runoff coefficient multiplies EI₃₀. Using the database of Sparacia experimental site, the USLE‐MB is parameterized and a comparison with soil loss data is carried out. The developed analysis shows that USLE‐MB (characterized by a Nash–Sutcliffe Efficiency Index NSEI equal to 0.73 and a root mean square error RMSE = 11.7 Mg ha^?1) has very similar soil loss estimate performances as compared with the USLE‐M (NSEI = 0.72 and RMSE = 12.0 Mg ha^?1). However, the USLE‐MB yields a maximum discrepancy factor between predicted and measured soil loss values (176) that is much lower than that of USLE‐M (291). In conclusion, the USLE‐MB should be preferred in the context of theoretically supported USLE type models.     

A new version of the USLE‐MM for predicting bare plot soil loss at the Sparacia (South Italy) experimental site

Improving empirical prediction of plot soil erosion at the event temporal scale has both scientific and practical importance. In this investigation, 492 runoff and soil loss data from plots of different lengths, λ (11 ≤ λ ≤ 44 m), and steepness, s (14.9 ≤ s ≤ 26.0%), established at the Sparacia experimental station, in Sicily, South Italy, were used to derive a new version of Universal Soil Loss Equation (USLE)‐MM model, by only assuming a value of one for the topographic length, L, and steepness, S, factors for λ = 22 m and s = 9%, respectively. An erosivity index equal to (Q_REI₃₀)^b1, Q_R and EI₃₀ being the runoff coefficient and the event rainfall erosivity index, respectively, with b₁ > 1 was found to be an appropriate choice for the Sparacia area. The specifically developed functions for L and S did not differ appreciably from other, more widely accepted relationships (maximum differences by a factor of 1.22 for L and 1.09 for S). The new version of the USLE‐MM performed particularly well for highly erosive events, because predicted soil loss differed by not more than a factor of 1.19 from the measured soil loss for measured values of more than 100 Mg ha^?1. The choice of the relationships to predict topographic effects on plot soil loss should not represent a point of particular concern in the application of the USLE‐MM in other environments. However, tests of the empirical approach should be carried out in other experimental areas in an attempt to develop analytical tools, usable at the event temporal scale, reasonably simple and of wide validity. Copyright © 2015 John Wiley & Sons, Ltd.     

A comprehensive analysis of Universal Soil Loss Equation-based models at the Sparacia experimental area

Improving Universal Soil Loss Equation (USLE)-based models has large interest because simple and reliable analytical tools are necessary in the perspective of a sustainable land management. At first, in this paper, a general definition of the event rainfall- runoff erosivity factor for the USLE-based models, REF_e = (Q_R)^b1(EI₃₀)^b2, in which Q_R is the event runoff coefficient, EI₃₀ is the single-storm erosion index, and b₁ and b₂ are coefficients, was introduced. The rainfall-runoff erosivity factors of the USLE (b₁ = 0 and b₂ = 1), USLE-M (b₁ = b₂ = 1), USLE-MB (b₁ ≠ 1 and b₂ = 1), USLE-MR (b₁ = 1 and b₂ ≠ 1), USLE-MM (b₁ = b₂ ≠ 1), and USLE-M2 (b₁ ≠ b₂ ≠ 1) can be defined using REF_e. Then the different expressions of REF_e were simultaneously tested against a data set of normalized bare plot soil losses, A_eN, collected at the Sparacia (south Italy) site. As expected, the poorest A_eN predictions were obtained with the USLE. The observed tendency of this model to overestimate small A_eN values and underestimate high A_eN values was reduced by introducing in the soil loss prediction model both Q_R and an exponent for the erosivity term. The fitting to the data was poor with the USLE-MR as compared with the USLE-MB and the USLE-MM. Estimating two distinct exponents (USLE-M2) instead of a single exponent (USLE-MB, USLE-MR, and USLE-MM) did not appreciably improve soil loss prediction. The USLE-MB and the USLE-MM were recognized to be the best performing models among the possible alternatives, and they performed similarly with reference to both the complete data set and different sub-data sets, only including small, intermediate, and severe erosion events. In conclusion, including the runoff coefficient in the soil loss prediction model is important to improve the quality of the predictions, but a great importance has to be paid to the mathematical structure of the model.     

An upland soil erosion model derived from the basic principles

Starting from the basic erosion principles, an upland soil erosion model to predict soil loss by overland flow from individual storms on forested hillslopes can be derived in the form where Q_s is total soil loss for a storm event, n is roughness coefficient, x is down slope distance, K_f is soil erodibility factor, S is slope, α is slope exponent and Q is runoff. Values of n and α are to be determined for different environments and are 0·58 and 2·1 for a mixed pine forest ecosystem. A significant correlation (r = 0·933, n = 96) fits between the observed and predicted values using this expression, and the model fitting is good.     

Plot‐scale measurement of soil erosion at the experimental area of Sparacia (southern Italy)

Obtaining good quality soil loss data from plots requires knowledge of the factors that affect natural and measurement data variability and of the erosion processes that occur on plots of different sizes. Data variability was investigated in southern Italy by collecting runoff and soil loss from four universal soil‐loss equation (USLE) plots of 176 m², 20 ‘large’ microplots (0·16 m²) and 40 ‘small’ microplots (0·04 m²). For the four most erosive events (event erosivity index, R_e ≥ 139 MJ mm ha^?1 h^?1), mean soil loss from the USLE plots was significantly correlated with R_e. Variability of soil loss measurements from microplots was five to ten times greater than that of runoff measurements. Doubling the linear size of the microplots reduced mean runoff and soil loss measurements by a factor of 2·6–2·8 and increased data variability. Using sieved soil instead of natural soil increased runoff and soil loss by a factor of 1·3–1·5. Interrill erosion was a minor part (0·1–7·1%) of rill plus interrill erosion. The developed analysis showed that the USLE scheme was usable to predict mean soil loss at plot scale in Mediterranean areas. A microplot of 0·04 m² could be used in practice to obtain field measurements of interrill soil erodibility in areas having steep slopes. Copyright © 2003 John Wiley & Sons, Ltd.     

Sediment delivery from hillslopes and the Universal Soil Loss Equation: some perceptions and misconceptions

The Universal Soil Loss Equation (USLE) or the revised USLE (RUSLE) are often used together with sediment delivery ratios in order to predict sediment delivery from hillslopes. In using sediment delivery ratios for this purpose, it is assumed that the sediment delivery ratio for a given hillslope does not vary with the amount of erosion occurring in the upslope area. This assumption is false. There is a perception that hillslope erosion is calculated on the basis that hillslopes are, in effect, simply divided into 22·1 m long segments. This perception fails to recognize the fact the inclusion of the 22·1 m length in the calculation has no physical significance but simply produces a value of 1·0 for the slope length factor when slopes have a length equal to that of the unit plot. There is a perception that the slope length factor is inappropriate because not all the dislodged sediment is discharged. This perception fails to recognize that the USLE and the RUSLE actually predict sediment yield from planar surfaces, not the total amount of soil material dislocated and removed some distance by erosion within an area. The application of the USLE/RUSLE to hillslopes also needs to take into account the fact that runoff may not be generated uniformly over that hillslope. This can be achieved by an equation for the slope length factor that takes account of spatial variations in upslope runoff on soil loss from a segment or grid cell. Several alternatives to the USLE event erosivity index have been proposed in order to predict event erosion better than can be achieved using the EI₃₀ index. Most ignore the consequences of changing the event erosivity index on the values for the soil, crop and soil conservation protection factors because there is a misconception that these factors are independent of one another. Copyright © 2007 John Wiley & Sons, Ltd.     

Soil erodibility as impacted by vegetation restoration strategies on the Loess Plateau of China

Near soil surface characteristics change significantly with vegetation restoration, and thus, restoration strategies likely affect soil erodibility. However, few studies have been conducted to quantify the effects of vegetation restoration strategies on soil erodibility in regions experiencing rapid vegetation restoration. This study was conducted to evaluate the effects of vegetation restoration strategies on soil erodibility, reflected by soil cohesion (Coh), penetration resistance (PR), saturated conductivity (K_s), number of drop impacts (NDI), mean weight diameter of soil aggregates (MWD), and soil erodibility K factor on the Loess Plateau. One slope farmland and five 25-year-restored lands covered by old world bluestem, korshinsk peashrub, shrub sophora, sea-buckthorn, and black locust were selected as test sites. The old world bluestem was restored via natural succession, while the other four lands were restored by artificial planting. A comprehensive soil erodibility index (CSEI) was produced by a weighted summation method to quantify the effects of vegetation restoration strategies on soil erodibility completely. The results showed that Coh, K_s, NDI, and MWD of the five restored lands were greater than those of the slope farmland. However, the PR and K of the five restored lands were less than those of the slope farmland. CSEI varied greatly under different restoration strategies, from 1 to 0.214. Compared with the control, these indices decreased on average by 68.2%, 78.6%, 72.7%, 75.8%, and 62.8% for old world bluestem, korshinsk peashrub, shrub sophora, sea-buckthorn, and black locust, respectively. The variation in soil erodibility was significantly influenced by biological crust thickness, bulk density, organic matter content, plant litter density, and root mass density. Shrub-lands via artificial planting, especially korshinsk peashrub, were considered the most effective restoration strategies to reduce soil erodibility on the Loess Plateau. The results are helpful for selecting vegetation restoration strategies and asking their benefits in controlling soil erosion. © 2018 John Wiley & Sons, Ltd.     

Predicting physical equations of soil detachment by simulated concentrated flow in Ultisols (subtropical China)

Soil detachment in concentrated flow is due to the dislodging of soil particles from the soil matrix by surface runoff. Both aggregate stability and shear strength of the topsoil reflect the erosion resistance of soil to concentrated runoff, and are important input parameters in predicting soil detachment models. This study was conducted to develop a formula to predict soil detachment rate in concentrated flow by using the aggregate stability index (As), root density (R_d) and saturated soil strength (σ_s) in the subtropical Ultisols region of China. The detachment rates of undisturbed topsoil samples collected from eight cultivated soil plots were measured in a 3.8 m long, 0.2 m wide hydraulic flume under five different flow shear stresses (τ = 4.54, 9.38, 15.01, 17.49 and 22.54 Pa). The results indicated that the stability index (As) was well related with soil detachment rate, particularly for results obtained with high flow shear stress (22.54 Pa), and the stability index (As) has a good linear relationship with concentrated flow erodibility factors (K_c). There was a positive linear relationship between saturated soil strength (σ_s) and critical flow shear stress (τ_c) for different soils. A significant negative exponential relationship between erodibility factors (K_c) and root density (R_d) was detected. This study yielded two prediction equations that allowed comparison of their efficiency in assessing soil detachment rate in concentrated flow. The equation including the root density (R_d) may have a better correlation coefficient (R² = 0.95). It was concluded that the formula based on the stability index (As), saturated soil strength (σ_s) and root density (R_d) has the potential to improve methodology for assessing soil detachment rate in concentrated flow for the subtropical Chinese Ultisols. Copyright © 2012 John Wiley & Sons, Ltd.     

Exploring the effect of different time resolutions to calculate the rainfall erosivity factor R in Calabria,southern Italy

The rainfall erosivity factor R of the Universal Soil Loss Equation is a good indicator of the potential of a storm to erode soil, as it quantifies the raindrop impact effect on the soil based on storm intensity. The R‐factor is defined as the average annual value of rainfall erosion index, EI, calculated by cumulating the EI values obtained for individual storms for at least 22 years. By definition, calculation of EI is based on rainfall measurements at short time intervals over which the intensity is essentially constant, i.e. using so‐called breakpoint data. Because of the scarcity of breakpoint rainfall data, many authors have used different time resolutions (Δt = 5, 10, 15, 30, and 60 min) to deduce EI in different areas of the world. This procedure affects the real value of EI because it is strongly dependent on Δt. In this contribution, after a general overview of similar studies carried out in different countries, the relationship between EI and Δt is explored in Calabria, southern Italy. The use of 17 139 storm events collected from 65 rainfall stations allowed the calculation of EI for different time intervals ranging from 5 to 60 min. The overall results confirm that calculation of EI is dependent on time resolution and a conversion factor able to provide its value for the required Δt is necessary. Based on these results, a parametric equation that gives EI as a function of Δt is proposed, and a regional map of the scale parameter a that represents the conversion factor for converting fixed‐interval values of (EI₃₀)_Δt to values of (EI₃₀)₁₅ is provided in order to calculate R anywhere in the region using rainfall data of 60 min. Copyright © 2015 John Wiley & Sons, Ltd.     

Interrill erosion on cultivated Greek soils: modelling sediment delivery

For interrill erosion, raindrop‐induced detachment and transport of sediment by rainfall‐disturbed sheet flow are the predominant processes, while detachment by sheet flow and transport by raindrop impact are negligible. In general, interrill subprocesses are inter‐actively affected by rainfall, soil and surface properties. The objective of this work was to study the relationships among interrill runoff and sediment loss and some selected para‐meters, for cultivated soils in central Greece, and also the development of a formula for predicting single storm sediment delivery. Runoff and soil loss measurement field experiments have been conducted for a 3·5‐year period, under natural storms. The soils studied were developed on Tertiary calcareous materials and Quaternary alluvial deposits and were textured from sandy loam to clay. The second group of soils showed greater susceptibility to sealing and erosion than the first group. Single storm sediment loss was mainly affected by rain and runoff erosivity, being significantly correlated with rain kinetic energy (r = 0·64***), its maximum 30‐minute intensity (r = 0·64***) and runoff amount (r = 0·56***). Runoff had the greatest correlation with rain kinetic energy (r = 0·64***). A complementary effect on soil loss was detected between rain kinetic energy and its maximum 30‐minute intensity. The same was true for rain kinetic energy and topsoil aggregate instability, on surface seal formation and thus on infiltration characteristics and overland flow rate. Empirical analysis showed that the following formula can be used for the successful prediction of sediment delivery (D_i): D_i = 0·638βEI₃₀tan(θ) (R² = 0·893***), where β is a topsoil aggregate instability index, E the rain kinetic energy, I₃₀ the maximum 30‐minute rain intensity and θ the slope angle. It describes soil erodibility using a topsoil aggregate instability index, which can be determined easily by a simple laboratory technique, and runoff through the product of this index and rain kinetic energy. Copyright © 2006 John Wiley & Sons, Ltd.     

Assessing changes in rainfall erosivity in Sicily during the twentieth century

Changes in rainfall erosivity are an expected consequence of climate change. Long‐term series of the single storm erosion index, EI, may be analysed to detect trends in rainfall erosivity. An indirect approach has to be applied for estimating EI, given that long series of rainfall intensities are seldom available. In this paper, a method for estimating EI from the corresponding rainfall amount, h_e, was developed for Sicily. This method was then applied at 17 Sicilian locations, representative of different climatic zones of the region, to generate a long series (i.e. from 1916 to 1999 in most cases) of EI values. Linear and step (step located at 1970) trends in annual and seasonal erosivity were detected by both classical approaches (Mann–Kendall test, Wilcoxon‐Mann‐Whitney rank‐sum test) and a new empirical approach (quantile approach, QA), based on the determination of the erosivity values corresponding to selected probability levels. A power relationship between EI and h_e with a space‐ and time‐variable scale factor and a time‐variable process parameter yielded the most accurate predictions of EI. However, a simpler model, using a time‐variable scale factor and a constant process parameter, yielded reasonably accurate EI estimates. Annual erosivity did not increase in Sicily during the twentieth century. At the most, it decreased at a few locations (three of the 17 considered locations). Significant trends were observed more frequently for winter erosivity (six locations) than for summer erosivity (two locations), suggesting that the erosive storms of winter determined the occasional occurrence of a negative trend in annual erosivity. In general, the QA compared reasonably well with more classical approaches. The QA appears promising since step trends for different return periods may be detected but efforts are needed to statistically formalize the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.     

Geomorphic controls on the physical and hydrologic properties of soils in a valley floor

The distribution of soil hydraulic and physical properties strongly influences runoff processes in landscapes. Although much work has been done to quantify and predict the properties of hillslope soils, far less is known about the distribution of soil properties in valley floors. A technique that links the estimation and distribution of soil hydraulic properties in valleys, with easily identified geomorphic features, was developed along a 2 km length of a valley at Brooks Creek in New South Wales, Australia. Soil physical and hydraulic property data were collected across a set of floodplain and fan features within the valley and analysed statistically to determine if soil properties varied significantly between geomorphic features and stratigraphic layers. The results show that the depth‐averaged saturated hydraulic conductivity, K_s, of the soil varies significantly with landform: fan units have K_g values that are twice that of floodplains and colluvial toeslope deposits have K_s values four times higher than floodplains. Given the notorious variability of K_s values in space, the strong statistical separation of soil properties by landform, backed up by strong separation of soil particle size by landform, suggests a way forward in understanding the distribution of soil properties in valleys and their influence on catchment hydrology. Copyright © 2000 John Wiley & Sons, Ltd.     

Impact of forest degradation on streamflow regime and runoff response to rainfall in the Garhwal Himalaya,Northwest India

Baseflows have declined for decades in the Lesser Himalaya but the causes are still debated. This paper compares variations in streamflow response over three years for two similar headwater catchments in northwest India with largely undisturbed (Arnigad) and highly degraded (Bansigad) oak forest. Hydrograph analysis suggested no catchment leakage, thereby allowing meaningful comparisons. The mean annual runoff coefficient for Arnigad was 54% (range 44–61%) against 62% (53–69%) at Bansigad. Despite greater total runoff Q_t (by 250 mm year^–1), baseflow at Bansigad ceased by March, but was perennial at Arnigad (making up 90% of Q_t vs. 51% at Bansigad). Arnigad storm flows, Q_s, were modest (8–11% of Q_t) and occurred mostly during monsoons (78–98%), while Q_s at Bansigad was 49% of Q_t and occurred also during post-monsoon seasons. Our results underscore the importance of maintaining soil water retention capacity after forest removal to maintain baseflow levels.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR D. Gerten