Monthly Precipitation Distribution: A Comparative Index

Oliver, John E.

doi:10.1111/j.0033-0124.1980.00300.x

Cited by 516 publications

(376 citation statements)

References 8 publications

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“…The 40 indexes considered in order to test the changes in the daily precipitation series are shown in Table I. Since the Precipitation Concentration Index (PCI) represents how precipitation is distributed over the year, it is worth some further explanation (Oliver, 1980). In the study, the PCI is evaluated with the modified expression of De Luis et al (2000).…”

Section: Daily Precipitationmentioning

confidence: 99%

A comprehensive analysis of changes in precipitation regime in Tuscany

Fatichi

Caporali

2009

Intl Journal of Climatology

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ABSTRACT:The analysis of changes in precipitation is nowadays of great interest because rainfall space-time distribution is considered one of the most important indexes in the natural climatic cycle. This study proposes an investigation of precipitation time series recorded in the region of Tuscany (Central Italy) in the period 1916-2003. Forty indexes are defined to evaluate changes in the precipitation patterns and trend detection is performed through statistical tests to assess the significance of temporal and spatial changes in the precipitation regime. The presented analysis does not show strong evidence of nonstationarity. A few indexes in a given number of stations of the analysed territory do show a slight trend, but the significance of those trends is lost once the multiple location testing issue is considered. The complexity of the climate in central Italy, i.e. the presence of numerous feedbacks might in fact distort or remove the consequences of global warming on the precipitation regime.

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Section: Daily Precipitationmentioning

confidence: 99%

A comprehensive analysis of changes in precipitation regime in Tuscany

Fatichi

Caporali

2009

Intl Journal of Climatology

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“…In addition, the precipitation concentration index (PCI) (Oliver, 1980), used to select the EI 30 equations most suitable for a local data set, indicated that the data of the rain gauge stations of the basin show a monthly distribution similar to that observed in Campinas, SP. The correlation between the monthly rainfall data of the Campinas station with those of the other stations used in the study showed a correlation coefficient always above 0.94.…”

Section: Estimation Of Erosivitymentioning

confidence: 95%

Spatial Distribution of Annual and Monthly Rainfall Erosivity in the Jaguarí River Basin

Pontes

Silva

Bispo

et al. 2017

Rev. Bras. Ciênc. Solo

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ABSTRACT:The Jaguarí River Basin forms the main water supply sources for the São Paulo Metropolitan Region and other cities in the state. Since the kinetic energy of rainfall is the driving force of water erosion, the main cause of land and water degradation, we tested the hypothesis of correlation between the erosive potential of rainfall (erosivity) and geographical coordinates and altitude for the purpose of predicting the spatial and temporal distribution of the rainfall erosivity index (EI 30 ) in the basin. An equation was used to estimate the (EI 30 ) in accordance with the average monthly and total annual rainfall at rainfall stations with data available for the study area. In the regression kriging technique, the deterministic part was modeled using multiple linear regression between the dependent variable (EI 30 ) and environmental predictor variables: latitude, longitude, and altitude. From the result of equations and the maps generated, a direct correlation between erosivity and altitude could be observed. Erosivity has a markedly seasonal behavior in accordance with the rainy season from October to March. ), a region that should be prioritized in soil and water conservation efforts. From this validation, good precision and accuracy of the model was observed for the long period of the annual average, which is the main factor used in soil loss prediction models.

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“…In order to estimate the aggressivity (erosivity) of rainfall, the precipitation concentration index (PCI) and the concentration index (CI), designed for measuring seasonality and daily heterogeneity, respectively, were used (Oliver 1980;Martin-Vide 2004). The PCI was calculated on a monthly rainfall basis using the equation proposed by Oliver (1980):…”

Section: Methodsmentioning

confidence: 99%

“…Also, a PCI value of 16.7 will indicate that the total precipitation was concentrated in 1/2 of the period, and a PCI value of 25 will indicate that the total precipitation occurred in 1/3 of the period. Oliver (1980) suggested that PCI values of less than 10 represent a uniform precipitation distribution or low precipitation concentration, while values between 10 and 15 indicate a moderate precipitation concentration. Values between 15 and 20 represent an irregular distribution of rainfall, while values above 20 show a strong irregularity of precipitation distribution.…”

Section: Methodsmentioning

confidence: 99%

Impact of the Darjeeling–Bhutan Himalayan front on rainfall hazard pattern

Prokop

Walanus

2017

Nat Hazards

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Multiscale interaction between monsoonal circulation and the local topography causes the southern front of the Darjeeling-Bhutan Himalaya to receive one of the highest annual rainfalls (3000-6000 mm) and most frequent heavy rains (up to 800 mm day -1 ) along the whole southern Himalayan margin. An examination of the patterns of annual rainfall, rainfall concentration, overland flow generation and slope instability indices in the Darjeeling-Bhutan Himalaya for 1986-2015 indicates that the mountain front disturbs rainfall gradient between the Bay of Bengal and the Tibetan Plateau. The results show that the precipitation concentration indices are lowest at the Himalayan front where the annual rainfall and the number of rainy days are highest. The Himalayan front has the highest predisposition to produce overland flow compared to adjacent foreland and the mountain interior. The average probability of the rainfall initialising the shallow landslides increases from 0.6% for a 1-day rainfall threshold of 144 mm to 6.1% for a 4-day rainfall threshold of 193 mm in the study area. The highest probability (up to 10%) of 2-day and longer lowintensity storms at the mountain front indicate that its area is threatened with particularly larger and deeper landslides. The multivariate regression analysis reveals statistically significant linear relationships of rainfall hazard indices with elevation and the distance to the mountain front in the mountain foreland and Himalaya, respectively. Regionally, the Darjeeling Himalaya reveals lower values of rainfall hazard indices, in comparison to the

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Monthly Precipitation Distribution: A Comparative Index

Cited by 516 publications

References 8 publications

A comprehensive analysis of changes in precipitation regime in Tuscany

A comprehensive analysis of changes in precipitation regime in Tuscany

Spatial Distribution of Annual and Monthly Rainfall Erosivity in the Jaguarí River Basin

Impact of the Darjeeling–Bhutan Himalayan front on rainfall hazard pattern

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