Extreme rainfall, vulnerability and risk: a continental-scale assessment for South America

Vörösmarty, Charles J; Guenni, Lelys Bravo de; Wollheim, W. M.; Pellerin, Brian A.; Bjerklie, David M.; Cardoso, Manoel; D’Almeida, Cassiano; Green, Pamela; Colon, Lilybeth

doi:10.1098/rsta.2012.0408

Cited by 53 publications

(33 citation statements)

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“…Rainfall is one of main causes of natural hazards in hydrological systems (e.g. Vörösmarty et al, 2013;Wilhelmi and Morrs, 2013;Kyselý et al, 2013) and is inseparably related to flood risk (e.g. Hlavcova et al, 2005;Blöschl, 2008;Borga et al, 2011;Winsemius et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland

Gaál¹,

Molnár²,

Szolgay³

2014

Preprint

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Abstract. This paper presents a method to identify intense warm season storms of convective character based on intensity thresholds and lightning, and analyzes their statistical properties. Long records of precipitation and lightning data at 4 stations and 10 min resolution in different climatological regions in Switzerland are used. Our premise is that thunderstorms associated with lightning generate bursts of high rainfall intensity. We divided all storms into those accompanied by lightning and those without lightning and found the threshold I* that separates intense events based on peak 10 min intensity Ip ≥ I* for a chosen misclassification rate α. The performance and robustness of the selection method was tested by investigating the inter-annual variability of I* and its relation to the frequency of lightning strikes. The probability distributions of the main storm properties (rainfall depth R, event duration D, average storm intensity Ia and peak 10 min intensity Ip) for the intense storm subsets show that the event average and peak intensities are significantly different between the stations, and highest in Lugano in southern Switzerland. Non-parametric correlations between the main storm properties were estimated for the subsets of intense storms and all storms including stratiform rain. The differences in the correlations between storm subsets are greater than those between stations, which indicates that care must be exercised not to mix events when they are sampled for multivariate analysis, e.g. copula fitting to rainfall data.

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Section: Introductionmentioning

confidence: 99%

Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland

Gaál¹,

Molnár²,

Szolgay³

2014

Preprint

View full text Add to dashboard Cite

show abstract

“…Vörösmarty et al, 2013;Wilhelmi and Morrs, 2013;Kyselý et al, 2013) and is inseparably related to flood risk (e.g. Hlavcova et al, 2005;Blöschl, 2008;Borga et al, 2011;Winsemius et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland

Gaál

Molnár

Szolgay

2014

Hydrol. Earth Syst. Sci.

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Abstract. This paper presents a method to identify intense warm season storms with convective character based on intensity thresholds and the presence of lightning, and analyzes their statistical properties. Long records of precipitation and lightning data at 4 stations and 10 min resolution in different climatological regions in Switzerland are used. Our premise is that thunderstorms associated with lightning generate bursts of high rainfall intensity. We divided all recorded storms into those accompanied by lightning and those without lightning and found the threshold I * that separates intense events based on peak 10 min intensity I p ≥ I * for a chosen misclassification rate α. The performance and robustness of the selection method was tested by investigating the inter-annual variability of I * and its relation to the frequency of lightning strikes. The probability distributions of the main storm properties (rainfall depth R, event duration D, average storm intensity I a and peak 10 min intensity I p ) for the intense storm subsets show that the event average and peak intensities are significantly different between the stations. Nonparametric correlations between the main storm properties were estimated for intense storms and all storms including stratiform rain. The differences in the correlations between storm subsets are greater than those between stations, which indicates that care must be exercised not to mix events of different origin when they are sampled for multivariate analysis, for example, copula fitting to rainfall data.

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“…At less direct risk are an additional 140 million people living within 25 km of these deltas, who, together with 3.5 billion people in upstream catchment basins, produce additional human impacts. We defined risk (R), or expected loss, to a delta population as a product of hazard (H), exposure (E), and vulnerability (V): R = HEV (17,22,23). Hazard is the probability of a damage-producing event, defined as fluvial or coastal flooding.…”

mentioning

confidence: 99%

Profiling risk and sustainability in coastal deltas of the world

Tessler

Vörösmarty

Grossberg

et al. 2015

Science

550

344

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Peer reviewed eScholarship.orgPowered by the California Digital Library University of California largest absolute correlations with the label. However, he or she verifies the correlations (with the label) on the holdout set and uses only those variables whose correlation agrees in sign with the correlation on the training set and for which both correlations are larger than some threshold in absolute value. The analyst then creates a simple linear threshold classifier on the selected variables using only the signs of the correlations of the selected variables. A final test evaluates the classification accuracy of the classifier on the holdout set. Full details of the analyst's algorithm can be found in section 3 of (17). In our first experiment, each attribute is drawn independently from the normal distribution N(0,1), and we choose the class label y ∈ f−1; 1g uniformly at random so that there is no correlation between the data point and its label. We chose n = 10,000 and d = 10,000 and varied the number of selected variables k. In this scenario no classifier can achieve true accuracy better than 50%. Nevertheless, reusing a standard holdout results in reported accuracy of >63 ± 0.4% for k = 500 on both the training set and the holdout set. The average and standard deviation of results obtained from 100 independent executions of the experiment are plotted in Fig. 1A, which also includes the accuracy of the classifier on another fresh data set of size n drawn from the same distribution. We then executed the same algorithm with our reusable holdout. The algorithm Thresholdout was invoked with T = 0.04 and t = 0.01, which explains why the accuracy of the classifier reported by Thresholdout is off by up to 0.04 whenever the accuracy on the holdout set is within 0.04 of the accuracy on the training set. Thresholdout prevents the algorithm from overfitting to the holdout set and gives a valid estimate of classifier accuracy. In Fig. 1B, we plot the accuracy of the classifier as reported by Thresholdout. In addition, in fig. S2 we include a plot of the actual accuracy of the produced classifier on the holdout set.In our second experiment, the class labels are correlated with some of the variables. As before, the label is randomly chosen from {-1,1} and each of the attributes is drawn from N(0,1), aside from 20 attributes drawn from N(y·0.06,1), where y is the class label. We execute the same algorithm on this data with both the standard holdout and Thresholdout and plot the results in Fig. 2. Our experiment shows that when using the reusable holdout, the algorithm still finds a good classifier while preventing overfitting.Overfitting to the standard holdout set arises in our experiment because the analyst reuses the holdout after using it to measure the correlation of single attributes. We first note that neither cross-validation nor bootstrap resolve this issue. If we used either of these methods to validate the correlations, overfitting would still arise as a result of using the same data for training and validation (...

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Extreme rainfall, vulnerability and risk: a continental-scale assessment for South America

Cited by 53 publications

References 48 publications

Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland

Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland

Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland

Profiling risk and sustainability in coastal deltas of the world

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