Assessment of Trends in Extreme Precipitation Events: A Case Study in Piedmont (North-West Italy)

Saidi, Helmi; Ciampittiello, Marzia; Dresti, Claudia; Ghiglieri, Giorgio

doi:10.1007/s11269-014-0826-5

Cited by 35 publications

(18 citation statements)

References 29 publications

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“…The regional Kendall test, which was adapted from the seasonal Kendall test, is used to assess whether a general trend occurs across one region (Helsel and Frans 2006). This method was widely applied for regional trend tests for nitrate concentrations in groundwater (Helsel and Frans 2006;Lopez et al 2015), nutrients in streams and rivers (Sprague and Lorenz 2009), snowmelt and streamflow time (Clow 2010;Harpold et al 2012), wet deposition of nutrient (Du et al 2014), climatic factors (Todd et al 2012;Saidi et al 2015), flow discharges (Rood et al 2016), and many other environmental phenomena. The regional Kendall test is a rank-based non-parametric method, which is robust against non-normality and censoring (Hess et al 2001), like a more general form of the Mann-Kendall method (Mann 1945;Kendall 1975).…”

Section: Introductionmentioning

confidence: 99%

Groundwater Level Analysis Using Regional Kendall Test for Trend with Spatial Autocorrelation

Fang

Sun²,

Jia

et al. 2018

Groundwater

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Assessment of historical evolution of groundwater levels is essential for understanding the anthropogenic impact on groundwater exploitation and developing response policies. In this study, regional groundwater level trend was addressed based on the regional Kendall test with correlated spatial data. With a limited number of data at one location, an exponential relation was proposed to be used to approximate covariances of a variable as a function of distances between locations. The effectiveness of the method was demonstrated using synthetic data experiments. The regional Kendall method was applied to assess evolution of groundwater levels and their annual decline rates in Beijing, Tianjin, and Hebei in China based on county-level data in 1959, 1984, 2005, and 2013. Results indicated that a continuing declining regional trend was shown in groundwater levels, revealing generally higher groundwater recharge rates than withdrawal rates in the study region. The annual groundwater decline rates presented a firstly increasing then decreasing regional trend, which is consistent with the environmental Kuznets curve. The earlier accelerating groundwater decline rate was attributed to supply-driven water resources management, whereas the reversed trend in accelerating groundwater decline rate in the latter period was due to many measures implemented to relieve local water stresses.

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Section: Introductionmentioning

confidence: 99%

Groundwater Level Analysis Using Regional Kendall Test for Trend with Spatial Autocorrelation

Fang

Sun²,

Jia

et al. 2018

Groundwater

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“…The water supply from the watershed has instead shown no significant trend although there are evidences of a change in the seasonal distribution, frequency and intensity of the rainfall events (Saidi et al, 2015).…”

Section: Trend Of Time Seriesmentioning

confidence: 99%

A non-deterministic approach to forecasting the trophic evolution of lakes

Bertoni

Morabito

et al. 2016

J Limnol

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Limnologists One of these approaches attempting to model the relationship between a set of inputs and known outputs, is based on Genetic Algorithms (GA) and Genetic Programming (GP). This stochastic optimization tool is based on the process of evolution in natural systems and was inspired by a direct analogy to sexual reproduction and Charles Darwin's principle of natural selection. GP is an evolutionary algorithm that uses selection and recombination operators to generate a population of equations. Thanks to a 25-year long time-series of regular limnological data, the deep, large, oligotrophic Lake Maggiore (Northern Italy) is the ideal case study to test the predictive ability of GP. Testing of GP on the multi-year data series of this lake has allowed us to verify the forecasting efficacy of the models emerging from GP application. In addition, this non-deterministic approach leads to the discovery of non-obvious relationships between variables and enabled the formulation of new stochastic models.

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“…The zero-inflated model with GPD is called as ZIGP model and this model can be represented by Equation (6). P(X = 0) = ω + (1 − ω) exp(−λ), x = 0 (6) P(X = j) = (1 − ω) × 1 x!…”

Section: Zero-inflated Generalized Poisson (Zigp) Modelmentioning

confidence: 99%

Applicability of Zero-Inflated Models to Fit the Torrential Rainfall Count Data with Extra Zeros in South Korea

Lee¹,

Kim²

2017

Water

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Several natural disasters occur because of torrential rainfalls. The change in global climate most likely increases the occurrences of such downpours. Hence, it is necessary to investigate the characteristics of the torrential rainfall events in order to introduce effective measures for mitigating disasters such as urban floods and landslides. However, one of the major problems is evaluating the number of torrential rainfall events from a statistical viewpoint. If the number of torrential rainfall occurrences during a month is considered as count data, their frequency distribution could be identified using a probability distribution. Generally, the number of torrential rainfall occurrences has been analyzed using the Poisson distribution (POI) or the Generalized Poisson Distribution (GPD). However, it was reported that POI and GPD often overestimated or underestimated the observed count data when additional or fewer zeros were included. Hence, in this study, a zero-inflated model concept was applied to solve this problem existing in the conventional models. Zero-Inflated Poisson (ZIP) model, Zero-Inflated Generalized Poisson (ZIGP) model, and the Bayesian ZIGP model have often been applied to fit the count data having additional or fewer zeros. However, the applications of these models in water resource management have been very limited despite their efficiency and accuracy. The five models, namely, POI, GPD, ZIP, ZIGP, and Bayesian ZIGP, were applied to the torrential rainfall data having additional zeros obtained from two rain gauges in South Korea, and their applicability was examined in this study. In particular, the informative prior distributions evaluated via the empirical Bayes method using ten rain gauges were developed in the Bayesian ZIGP model. Finally, it was suggested to avoid using the POI and GPD models to fit the frequency of torrential rainfall data. In addition, it was concluded that the Bayesian ZIGP model used in this study provided the most accurate results for the count data having additional zeros. Moreover, it was recommended that the ZIP model could be an alternative from a practical viewpoint, as the Bayesian approach used in this study was considerably complex.

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Assessment of Trends in Extreme Precipitation Events: A Case Study in Piedmont (North-West Italy)

Cited by 35 publications

References 29 publications

Groundwater Level Analysis Using Regional Kendall Test for Trend with Spatial Autocorrelation

Groundwater Level Analysis Using Regional Kendall Test for Trend with Spatial Autocorrelation

A non-deterministic approach to forecasting the trophic evolution of lakes

Applicability of Zero-Inflated Models to Fit the Torrential Rainfall Count Data with Extra Zeros in South Korea

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