Rainfall Erosivity: An Overview of Methodologies and Applications

Yin, Shuiqing; Nearing, M. A.; Borrelli, Pasquale; Xue, Xiaochan

doi:10.2136/vzj2017.06.0131

Cited by 67 publications

(32 citation statements)

References 88 publications

(111 reference statements)

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“…A comparison between the results from the energy equation that was used by Panagos and his associates [8] and the one used in the study, showed a systematic underestimation of about 14% percent in the calculations of EI 30 in the previous study. These results are similar to the ones found for Italy, also a Mediterranean country, and from various places worldwide [39], about the variation among RUSLE and RUSLE2.…”

Section: Annual and Monthly Erosivity Density Calculationssupporting

confidence: 90%

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Robustness Spatiotemporal Clustering and Trend Detection of Rainfall Erosivity Density in Greece

Vantas

Sidiropoulos

Λουκάς

2019

Water

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Soil erosion is affected by rainfall, among other factors, and it is likely to increase in the future due to climate change impacts, resulting in higher rainfall intensities. This paper evaluates the impact of the missing values ratio on the computation of the rainfall erosivity factor, R, and erosivity density, ED. The paper also investigates the temporal trends and defines regions of Greece with a similar monthly distribution of ED using an unsupervised method. Preprocessed and free from noise and errors rainfall data from 108 stations across Greece were extracted from the Greek National Bank of Hydrological and Meteorological Information. The rainfall data were analyzed and erosive rainfalls were identified, their return period was determined using intensity–duration–frequency curves and R and ED values were computed. The impact of missing data in the computation of annual values of R and ED was investigated using a Monte Carlo simulation. The findings indicated that missing rainfall data resulted in a linear underestimation of R, while ED is more robust. The trends in ED timeseries were evaluated using the Kendall’s Tau test and their autocorrelation and partial autocorrelation were computed for a small subset of stations using criteria based on the quality of data. Furthermore, cluster analysis was applied to a larger subset of stations to define regions of Greece with similar monthly distribution of ED. The findings of this study indicate that: (a) ED should be preferred for the assessment of erosivity in Greece over the direct computation of R, (b) ED timeseries are found to be stationary for the majority of the selected stations, in contrast to reported precipitation trends for the same time period, (c) Greece is divided into three clusters/areas of stations with distinct monthly distributions of ED.

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Section: Annual and Monthly Erosivity Density Calculationssupporting

confidence: 90%

“…The quantity e r was calculated for each time sub-interval, r, applying the kinetic energy equation used in RUSLE2 [38,39]:…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Robustness Spatiotemporal Clustering and Trend Detection of Rainfall Erosivity Density in Greece

Vantas

Sidiropoulos

Λουκάς

2019

Water

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“…(5) should not be used for calculations. The rules that apply in order to single out the storms causing erosion and to divide rainfalls of large duration in RUSLE2 [10,11] are: 1. A rainfall event is divided into two parts, if its cumulative depth for duration of 6 hours at a certain location is less than 1.27 mm.…”

Section: Computation Of the R Factormentioning

confidence: 99%

Rainfall Erosivity and Its Estimation: Conventional and Machine Learning Methods

Vantas¹,

Sidiropoulos²,

Evangelides³

2019

Soil Erosion - Rainfall Erosivity and Risk Assessment

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Rainfall erosivity concerns the ability of rainfall to cause erosion on the surface of the earth. The difficulty in modeling the distribution, the size, and the terminal velocity of raindrops in relation to the detachment of soil particles led to the use of more tractable rainfall indices. Thus, in the universal soil loss equation (USLE), the coefficient of rainfall erosivity, R, was introduced. This coefficient is based on the product of the rainfall kinetic energy of a storm and its maximum 30-minute intensity. An important problem in the application of USLE and its revisions in various parts of the world concerns the computation of R, which requires pluviograph records with a length of at least 20 years. For this reason, empirical equations have been developed that are based on coarser rainfall data, such as daily, monthly, or yearly, which are available on larger spatial and temporal extents. However, the lack of denser data is dealt more effectively by means of machine learning methods. Computational systems for this purpose were recently developed based on feed-forward neural networks, yielding significantly better results.

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“…For example, b 1 > 1 indicates that run‐off is both a detaching and a transport agent (Bagarello et al, ), and a b 2 exponent equal to 1 is consistent with Wischmeier and Smith (), which established a linear relationship between soil loss and the rainfall erosivity factor EI 30 . These authors also stated that EI 30 summarizes the combined potential effect of raindrop splash and run‐off scour on soil erosion (Yin, Nearing, Borrelli, & Xue, ). Consequently, the USLE‐MR ( b 1 = 1 and b 2 ≠ 1) is a merely empirical choice whereas the USLE‐MB ( b 1 ≠ 1 and b 2 = 1) is conceptually justified.…”

Section: Introductionmentioning

confidence: 99%

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

Bagarello

Ferro

Pampalone

2020

Hydrological Processes

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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, REFe = (QR)b1(EI30)b2, in which QR 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 REFe. Then the different expressions of REFe were simultaneously tested against a data set of normalized bare plot soil losses, AeN, collected at the Sparacia (south Italy) site. As expected, the poorest AeN predictions were obtained with the USLE. The observed tendency of this model to overestimate small AeN values and underestimate high AeN values was reduced by introducing in the soil loss prediction model both QR 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.

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Rainfall Erosivity: An Overview of Methodologies and Applications

Cited by 67 publications

References 88 publications

Robustness Spatiotemporal Clustering and Trend Detection of Rainfall Erosivity Density in Greece

Robustness Spatiotemporal Clustering and Trend Detection of Rainfall Erosivity Density in Greece

Rainfall Erosivity and Its Estimation: Conventional and Machine Learning Methods

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

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