Influence of the rainfall measurement interval on the erosivity determinations in the Mediterranean area

Agnese, Carmelo; Bagarello, Vincenzo; Corrao, Cecilia; D'Agostino, L.; D’Asaro, Francesco

doi:10.1016/j.jhydrol.2006.02.002

Cited by 51 publications

(38 citation statements)

References 12 publications

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“…In Equation (1), the water balance statement equates total rainfall (P) to the sum of the amount of direct runoff (Q), the initial abstraction (I a ), and the amount of infiltration (F). The first assumption is the ratio of the amount of actual infiltration (F) to the amount of S (Equation (2)).…”

Section: Curve Number Methods and Soil Moisture Retentionmentioning

confidence: 99%

“…In this study, 6 h was used as the minimum IETD to classify an independent rainfall event. Other analysts have used 6 h as a proper IETD [1,[36][37][38][39]. Sixteen rainfall events were identified for the study period.…”

Section: Methodology To Derive R-curvementioning

confidence: 99%

“…The state of soil moisture retention (SMR) plays a key role for estimating the antecedent moisture content (AMC) at storm initiation, which has a large effect on the overall runoff response [1][2][3][4][5][6]. SMR is the amount of water a soil column can retain.…”

Section: Introductionmentioning

confidence: 99%

“…SMR is the amount of water a soil column can retain. Continuously estimating SMR, however, is a challenge at sub-daily time resolution due to the recovery of SMR during non-rain intervals of multi-pulse storms [1,7]. The recovery is attributed to soil water drainage by subsurface flow, interflow, and evapotranspiration.…”

Section: Introductionmentioning

confidence: 99%

“…Various terms have been described in the literature focusing on the recovery of infiltration capacity to account for the water balance in soil [1,7,8]. Bauer [7] proposed a modification of the Horton's equation by introducing a soil drainage term into the equation to overcome a lack of the recovery of infiltration capacity during the interval of no rain (INR).…”

Section: Introductionmentioning

confidence: 99%

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Derivation of Soil Moisture Recovery Relation Using Soil Conservation Service (SCS) Curve Number Method

Kim

Johnson

Cifelli

et al. 2018

Water

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Soil moisture retention (SMR) capacity plays a key role in estimating the direct runoff when a multi-pulse storm event occurs. It is very important to know how much SMR will be recovered during the intervals of no rain of a multi-pulse storm. This study developed a new approach for derivation of the SMR recovery curve (R-curve) at sub-daily time-scales using the Curve Number (CN) method. The methodology was applied using complex storm events in the Napa River basin, California. The R-curve is classified into three sections depending on the recovery rate of SMR during the inter-storm interval of no rain (INR), and this study defines the characteristics. The first section of the R-curve (INR 0-21 h with 0.97 mm/h) is described as gradually recovering SMR, since water is being infiltrated and the upper soil layer is not fully saturated. The second section (INR 21-36 h with 2.11 mm/h) is defined as steeply recovering S due to downward drainage (sub-surface/inter flows) and evaporation without infiltration. The third section (INR 36-68 h with 0.34 mm/h) is described as gradually decreasing recovery dependent on evaporation since percolation and drainage have almost stopped.

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Section: Curve Number Methods and Soil Moisture Retentionmentioning

confidence: 99%

Section: Methodology To Derive R-curvementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Derivation of Soil Moisture Recovery Relation Using Soil Conservation Service (SCS) Curve Number Method

Kim

Johnson

Cifelli

et al. 2018

Water

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

Porto

2015

Hydrological Processes

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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.

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Predicting unit plot soil loss in Sicily, south Italy

Bagarello

Piazza

Ferro

et al. 2007

Hydrological Processes

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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 QRe1-609, being QRRc 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 QRRe term

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Influence of the rainfall measurement interval on the erosivity determinations in the Mediterranean area

Cited by 51 publications

References 12 publications

Derivation of Soil Moisture Recovery Relation Using Soil Conservation Service (SCS) Curve Number Method

Derivation of Soil Moisture Recovery Relation Using Soil Conservation Service (SCS) Curve Number Method

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

Predicting unit plot soil loss in Sicily, south Italy

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