Storm Erosion Index Research Articles

CART MODEL FOR PREDICTING DUNE EROSION BASED ON STORM INTENSITY AND BEACH MORPHOLOGY

Hurricane Sandy (2012) resulted in historic losses to the beach and dune system in many parts of New Jersey, leaving inland structures susceptible to wave and surge-induced damages. To defend against future threats, the state, in conjunction with the US Army Corps of Engineers, has pursued a statewide system of engineered beaches comprised of a dune paired with a beach nourishment. Currently in the state there is much debate over the degree of protection these projects currently offer, and how this may change in the future due to climate change. Process-based models can be used to try to answer these questions; however, as the number of test cases increase the computational costs may become prohibitive. Here, a data-driven modeling approach is used to assess the variability in the vulnerability of the beach-dune system in New Jersey to a range of storms. Inputs to the model include storm intensity as characterized by the Storm Erosion Index (SEI) (Miller and Livermont 2008) and parameterized pre-storm morphology. The resulting classification tree ensemble has been shown to accurately predict dune volume loss (as a percent) due to historical storms (Lemke and Miller, 2021). Here, the model is applied to further investigate how changes to beach-dune system and sea level rise affect the probability of dune impact.

Role of Storm Erosion Potential and Beach Morphology in Controlling Dune Erosion

Coastal erosion is controlled by two sets of factors, one related to storm intensity and the other related to a location’s vulnerability. This study investigated the role of each set in controlling dune erosion based on data compiled for eighteen historical events in New Jersey. Here, storm intensity was characterized by the Storm Erosion Index (SEI) and Peak Erosion Intensity (PEI), factors used to describe a storm’s cumulative erosion potential and maximum erosive power, respectively. In this study, a direct relationship between these parameters, beach morphology characteristics, and expected dune response was established through a classification tree ensemble. Of the seven input parameters, PEI was the most important, indicating that peak storm conditions with time scales on the order of hours were the most critical in predicting dune impacts. Results suggested that PEI, alone, was successful in distinguishing between storms most likely to result in no impacts (PEI < 69) and those likely to result in some (PEI > 102), regardless of beach condition. For intensities in between, where no consistent behavior was observed, beach conditions must be considered. Because of the propensity for beach conditions to change over short spatial scales, it is important to predict impacts on a local scale. This study established a model with the computational effectiveness to provide such predictions.

Factors controlling longshore variations of beach changes induced by Tropical Storm Eta (2020) along Pinellas County beaches, west-central Florida

Tropical Storm Eta impacted the coast of west-central Florida from 11 November to 12 November 2020 and generated high waves over elevated water levels for over 20 hours. A total of 148 beach and nearshore profiles, spaced about 300 m (984 ft) apart, were surveyed one to two weeks before and one to eight days after the storm to examine the beach changes along four barrier islands, including Sand Key, Treasure Island, Long Key, and Mullet Key. The high storm waves superimposed on elevated water level reached the toe of dunes or seawalls and caused dune erosion and overwash at various places. Throughout most of the coast, the dune, dry beach, and nearshore area was eroded and most of the sediment was deposited on the seaward slope of the nearshore bar, resulting in a roughly conserved sand volume above closure depth. The longshore variation of beach-profile volume loss demonstrates an overall southward decreasing trend, mainly due to a southward decreasing nearshore wave height as controlled by offshore bathymetry and shoreline configurations. The Storm Erosion Index (SEI) developed by Miller and Livermont (2008) captured the longshore variation of beach-profile volume loss reasonably well. The longshore variation of breaking wave height is the dominant factor controlling the longshore changes of SEI and beach erosion. Temporal variation of water level also played a significant role, while beach berm elevation was a minor factor. Although wider beaches tended to experience more volume loss from TS Eta due to the availability of sediment, they were effective in protecting the back beach and dune area from erosion. On the other hand, smaller profile-volume loss from narrow beach did not necessarily relate to less dune/ structure damage. The opposite is often true. Accurate evaluation of a storm’s severity in terms of erosion potential would benefit beach management especially under the circumstance of increasing storm activities due to climate change.

Evaluation of storms through the lens of erosion potential along the New Jersey, USA coast

Coastal erosion is driven by both a storm's erosion potential and by an area's vulnerability. Therefore, the problem of estimating impacts can be approached in two-step. The first includes an assessment of erosion potential based on readily available storm parameters, while the second combines this information with highly localized parameters, describing vulnerability, to more directly predict local impacts. The work presented in this paper focuses on this first step, where a storm erosion potential climatology is developed by analyzing historical storms and is then utilized to identify historic patterns. Specifically, storms which have impacted the New Jersey coast over the past several decades are reevaluated using the Storm Erosion Index (SEI), developed by Miller and Livermont (2008), which considers the three primary storm-related drivers of coastal erosion (wave height, water level, and storm duration). These storms are assessed at thirteen shoreline segments defined along New Jersey's Atlantic coast from a 34 year-period (1980–2013) when concurrent wave and water level data is available. Approximately 130 unique storms are identified with the top three being the December 1992 nor'easter, the November 2009 Veteran's Day Storm, and Hurricane Sandy in October 2012, each having an estimated return period of greater than 15 years. The resulting climatology is found to exhibit several interesting spatial and temporal trends. Both portions of the state as well as months of the year that have historically experienced more storms and/or higher cumulative SEI (over the 34 years) are identified. While analysis of the climatology has also identified periods of reduced storm activity and those of intensified conditions, future monitoring is suggested to assess whether these patterns are persistent or related to climatic variations with cycles longer than can be captured by the thirty years included in this study.

Application of Storm Erosion Index (SEI) to parameterize spatial storm intensity and impacts from Hurricane Michael

Hurricane Michael made landfall as a Category 5 Hurricane on 10 October 2018 between Mexico Beach, Florida and Tyndall Air Force Base in Panama City Beach, Florida causing damages totaling $25 billion (Beven et al. 2019). While damages were caused by both wind and surge, this paper is solely concerned with the surge induced damages which were observed predominantly within 50 km (27 nmi) of the hurricane’s path. Generally, regions to the east of Hurricane Michael’s landfall sustained the most severe damages and appear consistent with the spatial gradient in peak water levels. This gradient was marked; with surge induced damage concentrated in the immediate vicinity of Mexico Beach and attenuating significantly over distances as little as 37 km (20 nmi) to the west in Panama City Beach and east in Port Saint Joe (Kennedy, in review). The gradient in erosion was also pronounced, with Panama City Beach experiencing an average erosion rate of 0.4 m3/m (3.9 cy/ft) while Mexico Beach and Cape San Blas experienced rates approximately three (1.1 m3/m or 11.5 cy/ft) and five (1.7 m3/m or 18.5 cy/ft) times that. For inherent reasons, the pronounced gradient in surge damages and erosion values are of primary interest to coastal researchers and managers. Storm Erosion Index (SEI), developed by Miller and Livermont (2008) combines the three primary drivers of coastal erosion (wave height, total water level, and storm duration) into a physically meaningful form to evaluate storms based on their erosion potential. Here, SEI is applied to explore these spatial variations at seven distinct regions within the Florida Panhandle and are compared to the observed impacts for both erosion and structural damages. These regions include: (west to east) Panama City Beach, Tyndall Air Force Base (AFB), Mexico Beach, Cape San Blas, Port St. Joe, St. Vincent Island, and St. George Island. Empirically, the cumulative SEI relates well with the observed beach erosion; while the Peak Erosion Intensity (PEI) was found to better capture the trends in structural damages. By capturing the spatial variation of the storm intensity, SEI and PEI are therefore proposed as a viable engineering demand parameter with potential applications in community scale fragility curves.

DEVELOPMENT OF A STORM EROSION CLIMATOLOGY FOR THE NEW JERSEY COAST, US

In this study, the Storm Erosion Index (SEI), developed by Miller and Livermont (2008), is used to reevaluate storms that have impacted New Jersey over the past several decades based on their erosion potential. This index considers all three drivers of coastal erosion including wave height, water level, and storm duration and has been shown to more closely correlated to observed erosion than more traditional indices (Miller and Livermont 2008). Here, storms are assessed at thirteen shoreline segments defined along the Atlantic coast of New Jersey. When reevaluated with SEI, the top three storms across all shoreline segments are the December 1992 nor’easter, the Veteran’s Day Storm in November 2009, and Hurricane Sandy in October 2012. In general, the December 1992 nor’easter and Hurricane Sandy are more highly ranked in the northern half of the state with Hurricane Sandy having a maximum return period of 38 years. The Veteran’s Day Storm on the other hand is more highly ranked in the southern half of the state having a maximum return period of 42 years. A closer look at these three storms illustrates the importance of each of the three drivers of coastal erosion in determining erosion potential. A particular emphasis is placed on storm duration which explains why the Veteran’s Day Storm (td = ~90 hours) outranks Hurricane Sandy (td = ~60 hours) in the southern portion of the state. The assessment performed in this study produces a record of historical storms ranked by SEI that future storms can be compared to. This allows for an understanding of the erosion potential of future storms in the context of what has occurred previously.

APPLICATION OF THE STORM EROSION INDEX (SEI) TO THREE UNIQUE STORMS

During 2012 and 2013, the State of Florida was impacted by three tropical weather systems (Debby, Isaac, and Sandy) that caused significantly more beach erosion than similar, traditionally classified storms. Here, the storms are reclassified using the more recently developed Storm Erosion Index (SEI) which takes into consideration both the storm tide and storm waves, as well as the storm duration. The SEI has been shown previously to accurately represent the impact of coastal storms at a number of other sites (Miller and Livermont, 2008). When reanalyzed with the SEI, Tropical Storm Debby was found to be more significant in terms of beach erosion potential than any other storm in the record (since 1996), ranking as a “Category 5” storm with a return period of 23.4 years. Hurricane Isaac, which followed closely on the heels of Debby, ranked as a “Category 2” storm with an associated return period of 3 years. A sensitivity analysis performed on the results indicated that the wave steepness threshold used to separate erosion and accretion was particularly important during Isaac, as the conditions throughout the storm remained close to the threshold. While Hurricane Sandy is more known for the devastation it caused in the northeast, it also caused significant beach erosion in the State of Florida. The SEI more accurately reflects the significance of the beach erosion experienced during Sandy, and ranks the storm ahead of all of the other storms in the record (since 1994), including Hurricanes Frances, Gordon, and Jeanne which all made landfall near the area considered. Overall, Sandy registered as a “Category 5” storm in terms of beach erosion potential, with a return period of 40.5 years.

Expected impacts of climate change on rainfall erosivity of farmlands in Japan

In this article we have assessed the expected impacts of future climate changes on rainfall erosivity, a rainfall factor associated with soil erosion, of farmlands in Japan. The assessment compared values of R-factors for two future periods with values for a near past period. The R-factor is defined as the mean annual sum of individual storm erosion index values EI30, where E is the total energy for a storm and I30 is the maximum 30-min intensity. The R-values at 1036 meteorological stations for three periods (1981–2000, 2031–2050 and 2081–2100) were calculated with a model that estimates monthly EI30 values based on daily precipitation data. Subsequently, R-values for farmland areas were obtained. The daily precipitation data were provided from the regional climate model RCM20, which simulates climate change based on the SRES A2 scenario. The model to estimate monthly EI30 values performed well with a coefficient of determination in excess of 0.5 for 97% of the 1036 stations when parameters of the model were optimized. Therefore, the rainfall erosivity model can be used to accurately estimate the R-factors. Calculations of relative changes in R-values for the two future periods compared to the near past period indicated that rainfall erosivity will increase in most farmland areas due to climate change. The R-values of farmland areas for the two future periods are expected to increase an average of more than 20% when compared to the near past. Additionally, changes in rainfall erosivity during each of the future periods are expected to vary with different seasonal and spatial patterns. The changes in erosivity appear to be attributed mainly to changes in precipitation intensity as well as amount of precipitation. Therefore, these results suggest the scenario of climate changes occurring in the future up to 2100 will increase soil erosion of farmlands in Japan at an average rate of increase greater than 20% based on just the effects of the rainfall erosivity factor.

COASTAL DUNES VULNERABILITY INDEXES: A NEW PROPOSAL

In the present work it is proposed a new coastal dune vulnerability index based on its exposure (and resistance) to overwashing and erosion under storm events, focusing solely on the short-term events. The methodology is applied and validated against the available data for the Ria Formosa (Algarve, Portugal) coastal beaches. The overwash index is determined as a function of the dune crest height in relation with the maxima water levels for different return periods, and the storm-erosion index is computed as function of the remaining beach/dune volume after the impact of
 the 10-year return period extreme-wave conditions in relation to the pre-storm volume. It is discussed the results of this application, enhancing the necessity of further validation.

Predicting soil loss on moderate slopes using an empirical model for sediment concentration

The objective of this investigation was to estimate event soil loss per unit area from bare plots in central and southern Italy using an empirical model for sediment concentration. The analysis was developed using data collected on bare plots differing in length (11–44 m) and slope (10–26%) at three Italian stations (Masse, Umbria; Caratozzolo, Calabria; Sparacia, Sicily). At first, an analysis was carried out, using the experimental data collected at Sparacia, to establish a relationship between sediment concentration and hydrological variables, such as runoff, rainfall amount and single storm erosion index. Then, an empirical model to estimate plot soil loss as a function of rainfall amount, single storm erosion index, runoff amount, and slope length was developed. Finally, using the complete data set (Masse, Caratozzolo, Sparacia), the model was extended to include soil erodibility and slope steepness effects on plot soil loss.

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

AbstractMethods 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 (QREI30)1·47, being QREI30 the runoff ratio (QR) 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.

Predicting unit plot soil loss in Sicily, south Italy

AbstractPredicting 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 $Q_{\rm R}R_{\rm e}^{1{\cdot}609}$, being QRRe 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. Copyright © 2007 John Wiley & Sons, Ltd.

Assessing changes in rainfall erosivity in Sicily during the twentieth century

AbstractChanges 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, he, 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 he 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.

USING CLIGEN TO GENERATE RUSLE CLIMATE INPUTS

CLIGEN is a stochastic weather generator that produces continuous daily variables to drive processbased runoffand erosion prediction models such as WEPP. To test CLIGENs ability to generate precipitationrelated variables, whichare particularly important to runoff and erosion prediction, algorithms were developed to compute the Rfactor, its monthlydistribution, and 10year storm erosion index (EI) needed to apply the Revised Universal Soil Loss Equation (RUSLE).Measured Rfactor and 10year storm EI for 76 sites in the U.S. were used for calibration, and 89 additional sites were usedfor validation. It was found that the generated Rfactor using CLIGEN is highly correlated with the measured Rfactor forthe calibration sites (r2 = 0.96), although the generated Rfactor is systematically larger than the measured Rfactor. Thepredicted Rfactor for validation sites has a model efficiency (Ec) of 0.92 and a root mean squared error of around 600 MJmm ha1 h1 year1, or 24% of the average Rfactor for the 89 sites. In addition, CLIGENgenerated precipitation data canalso be used to predict 10year storm EI (Ec = 0.75) and monthly distribution of rainfall erosivity for a wide range of climateenvironments (average discrepancy = 2.6%). This represents considerable improvement over existing methods to estimateRfactor and 10year storm EI for locations with only monthly precipitation data, although the systematic overestimationof the Rfactor using CLIGENgenerated climate data suggests possible inadequacies in the assumed storm patterns in CLIGEN and WEPP.

Using monthly precipitation data to estimate the R-factor in the revised USLE

The methods used to calculate both the Revised Universal Soil Loss Equation (RUSLE) erosivity factor ( R) and the 10 year frequency storm erosion index value ( EI 10) are presented. As the calculation methods require long-term rainfall intensity data, and such data are not available for all application sites, an approach used to estimate the R-factor is described. Examples illustrating applications of the estimation technique in Africa, Asia, and other parts of the world are summarized. The method, which establishes correlations between measured R-values and more readily available precipitation data, is used to develop relations for estimating R-values in the USA. Correlations based on average monthly precipitation data and the R-factor values for 155 US stations were initially used to develop estimation relations. The 155 stations were segregated based on the annual distribution of monthly precipitation and the correlations improved. Exclusion of 23 stations with both ‘winter-type’ precipitation distributions and modified Fournier index values greater than 100 mm improved the relations for the remaining 132 stations ( r 2 = 0.81). An estimation relation for the EI 10 is also presented. The R-factor and EI 10 estimation relations should facilitate the use of RUSLE for locations with only monthly precipitation data.

Erosividade da chuva: sua distribuição e relação com as perdas de solo em Campinas (SP)

O potencial de erosão de uma chuva, representado pelo produto da energia cinética pela intensidade máxima em 30 minutos, foi calculado para chuvas individuais, para Campinas (SP). Durante um período de 22 anos (de 1954 a 1975), o índice de erosão médio anual computado foi de 6.738 MJ.mm/ha.h.ano, tendo os valores variado de 3.444 a 13.830. Foram estabelecidas as distribuições mensais e estacionais do índice de erosão. Os dados mostraram que 62% do potencial de erosão anual ocorre durante dezembro-fevereiro. A freqüência de distribuição dos valores totais anuais e do valor máximo anual do índice de erosão seguiu o tipo de curva inclinada, típica de dados hidrológicos, mas o logaritmo dos dados apresentou distribuição normal. Foi encontrada alta correlação entre a média mensal do índice de erosão e a média mensal do coeficiente de chuva, o que simplifica o método para calcular o índice de erosão de um local.