The internal structure of erosive and non-erosive storm events for interpretation of erosive processes and rainfall simulation

Todisco, Francesca

doi:10.1016/j.jhydrol.2014.11.002

Cited by 33 publications

(24 citation statements)

References 47 publications

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“…The Huff curve was initially developed by Huff [28] for characterizing temporal rainfall distributions in an area and has been widely applied to describe the hyetograph and to predict the runoff in a watershed [28,[38][39][40][41][42][43]. The Huff curve is a dimensionless hyetograph.…”

Section: Huff Curvementioning

confidence: 99%

Improvement to the Huff Curve for Design Storms and Urban Flooding Simulations in Guangzhou, China

Pan

Wang

Liu

et al. 2017

Water

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Abstract:The storm hyetograph is critical in drainage design since it determines the peak flooding volume in a catchment and the corresponding drainage capacity demand for a return period. This study firstly compares the common design storms such as the Chicago, Huff, and Triangular curves employed to represent the storm hyetographs in the metropolitan area of Guangzhou using minute-interval rainfall data during 2008-2012. These common design storms cannot satisfactorily represent the storm hyetographs in sub-tropic areas of Guangzhou. The normalized time of peak rainfall is at 33 ± 5% for all storms in the Tianhe and Panyu districts, and most storms (84%) are in the 1st and 2nd quartiles. The Huff curves are further improved by separately describing the rising and falling limbs instead of classifying all storms into four quartiles. The optimal time intervals are 1-5 min for deriving a practical urban design storm, especially for short-duration and intense storms in Guangzhou. Compared to the 71 observed storm hyetographs, the Improved Huff curves have smaller RMSE and higher NSE values (6.43, 0.66) than those of the original Huff (6.62, 0.63), Triangular (7.38, 0.55), and Chicago (7.57, 0.54) curves. The mean relative difference of peak flooding volume simulated with SWMM using the Improved Huff curve as the input is only 2%, −6%, and 8% of those simulated by observed rainfall at the three catchments, respectively. In contrast, those simulated by the original Huff (−12%, −43%, −16%), Triangular (−22%, −62%, −38%), and Chicago curves (−17%, −19%, −21%) are much smaller and greatly underestimate the peak flooding volume. The Improved Huff curve has great potential in storm water management such as flooding risk mapping and drainage facility design, after further validation.

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Section: Huff Curvementioning

confidence: 99%

Improvement to the Huff Curve for Design Storms and Urban Flooding Simulations in Guangzhou, China

Pan

Wang

Liu

et al. 2017

Water

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“…In a first step, we disaggregated the rainfall record according to a minimum inter-event time of 6 h using the R-package 'hydromad' (Andrews & Guillaume, 2015). This threshold is commonly applied in event-based erosion studies (Wischmeier & Smith, 1981;Xie et al, 2002;Bagarello et al, 2008;Soulis et al, 2009;Taguas et al, 2011) to identify independent rainfall events with similar initial soil moisture conditions that control runoff generation (Bracken et al, 2008;Todisco, 2014). In a second step, we determined the direct runoff associated with each rainfall event.…”

Section: Determination Of Erosive Eventsmentioning

confidence: 99%

Sediment Reallocations due to Erosive Rainfall Events in the Three Gorges Reservoir Area, Central China

et al. 2016

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Soil erosion by water outlines a major threat to the Three Gorges Reservoir Area in China. A detailed assessment of soil conservation measures requires a tool that spatially identifies sediment reallocations due to rainfall-runoff events in catchments. We applied EROSION 3D as a physically based soil erosion and deposition model in a small mountainous catchment. Generally, we aim to provide a methodological frame that facilitates the model parametrization in a data scarce environment and to identify sediment sources and deposits. We used digital soil mapping techniques to generate spatially distributed soil property information for parametrization. For model calibration and validation, we continuously monitored the catchment on rainfall, runoff and sediment yield for a period of 12 months. The model performed well for large events (sediment yield > 1 Mg) with an averaged individual model error of 7.5%, while small events showed an average error of 36.2%. We focused on the large events to evaluate reallocation patterns. Erosion occurred in 11.1% of the study area with an average erosion rate of 49.9 Mg ha À1 . Erosion mainly occurred on crop rotation areas with a spatial proportion of 69.2% for 'corn-rapeseed' and 69.1% for 'potato-cabbage'. Deposition occurred on 11.0%. Forested areas (9.7%), infrastructure (41.0%), cropland (corn-rapeseed: 13.6%, potatocabbage: 11.3%) and grassland (18.4%) were affected by deposition. Because the vast majority of annual sediment yields (80.3%) were associated to a few large erosive events, the modelling approach provides a useful tool to spatially assess soil erosion control and conservation measures.

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“…The total runoff amount and the soil loss per unit area are measured in each plot after an erosive event, defined as an event yielding a measurable soil loss. The Masse database was therefore developed by considering, for each event, the simultaneous measurements of plot runoff, Q e,i , and soil loss, A e,i , and of the rainfall data required to derive the erosivity factor, R e , according to Wischmeier and Smith (1978), with a mean interval time of 6 h (Bagarello and Ferro, 2004;Mannocchi et al, 2008;Todisco, 2014). The study area and the experimental schemes, installations and procedures are already described more in depth in Bagarello et al (2011) and Todisco et al (2012a).…”

Section: The Masse Experimental Station and The Soil Loss Databasementioning

confidence: 99%

“…6 the highest deviations between the observed and estimated values occur in the dry period events. This is likely due to the particular characteristics of summer rainfall events in central Europe (Todisco et al, 2012b;Todisco, 2014). Summer rainfall events are generally isolated and characterized by high intensity associated with low antecedent soil moisture but elevated soil losses.…”

Section: Model Performance In Wet and Dry Periodsmentioning

confidence: 99%

Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

Todisco

Brocca

Termite

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008-2013. The results showed that including soil moisture observations in the event rainfall-runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ∼ 0.35 and a root mean square error (RMSE) of ∼ 2.8 Mg ha −1 . These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

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The internal structure of erosive and non-erosive storm events for interpretation of erosive processes and rainfall simulation

Cited by 33 publications

References 47 publications

Improvement to the Huff Curve for Design Storms and Urban Flooding Simulations in Guangzhou, China

Improvement to the Huff Curve for Design Storms and Urban Flooding Simulations in Guangzhou, China

Sediment Reallocations due to Erosive Rainfall Events in the Three Gorges Reservoir Area, Central China

Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

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