Using the seasonal and temporal Precipitation concentration index for characterizing the monthly rainfall distribution in Spain

Michiels, P.; Gabriëls, Donald; Hartmann, Roger

doi:10.1016/0341-8162(92)90016-5

Cited by 100 publications

(55 citation statements)

References 3 publications

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“…The annual PCIs calculated for individual years are small compared to the seasonal concentration index, because the monthly rainfall of individual years deviates from the longterm mean monthly rainfall. Higher values for temporal concentration of rainfall for individual years can also be due to seasonal concentration of the rainfall, or to a random deviation from the long-term mean monthly rainfall (Michiels et al 1992). Further analysis at 30-year normals (1940-1969 and 1970-1999) also reveals the same pattern, which further implies that the distribution of rainfall in the sub-basins has not changed with time (Table 2).…”

Section: Rainfall Characteristicsmentioning

confidence: 82%

“…It is therefore important to investigate rainfall concentration needs using a statistically derived index (PCI) that quantifies the relative distribution of the rainfall patterns. Based on the approach described by Michiels et al (1992), two different calculation procedures were used for deriving the PCI. In the first procedure, the mean monthly rainfall data set was estimated by averaging the monthly rainfall data over a number of years; afterwards the PCI was calculated (here referred to as seasonal concentration).…”

Section: Rainfall Characteristicsmentioning

confidence: 99%

“…In the second procedure, the PCI was calculated from rainfall totals of individual years (here referred to as The nature of rainfall in the main drainage sub-basins of Ugandatemporal concentration). The PCI permits grouping of data sets according to the derived value: PCI < 10 suggests a uniform distribution, a value from 11 to 15 denotes a moderate seasonal distribution and a value from 16 to 20 denotes a seasonal distribution (Michiels et al 1992). A PCI > 20 represents strong seasonal effects, with increasing values indicating increasing monthly rainfall concentration.…”

Section: Rainfall Characteristicsmentioning

confidence: 99%

See 2 more Smart Citations

The nature of rainfall in the main drainage sub-basins of Uganda

Nsubuga

Botai

Olwoch

et al. 2014

Hydrological Sciences Journal

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A study of rainfall trends and temporal variations within seven sub-basins of Uganda spanning from 1940 to 2009 has been made. Rainfall climatologies are constructed from observational data, using 36 station records which reflect hydroclimatic conditions. Long-term changes in rainfall characteristics were determined by non-parametric tests (Mann-Kendall and Sen's T tests), coefficient of variation (CV), precipitation concentration index and drought severity index. Magnitude of change was estimated by applying Sen's estimator of slope. Decadal variability of rainfall with marked seasonal cycles is evident. Temporal variability of drought patterns is detected. Variations in annual rainfall are low with no significant trends observed in the main drainage sub-basins. Significant trends occur in October, November, December and January. A noticeable decrease in the annual total rainfall was observed mostly in northwestern and southwestern sub-basins. Rainfall trend in the second normal of June-July-August (JJA) was decreasing in all the main drainage sub-basins.Key words trend analysis; Mann-Kendall; Sen's T test; precipitation concentration index; drought severity index; drainage sub-basins; Uganda La nature des précipitations dans les principaux sous-bassins de l'Ouganda Résumé Une étude des tendances et des variations temporelles des précipitations dans sept sous-bassins de l'Ouganda à été réalisée sur lapériode allant de 1940 à 2009. La climatologie des précipitations a été construite à partir des données observées dans 36 stations représentatives des conditions hydroclimatiques. Les changements à long terme des caractéristiques des précipitations, coefficient de variation, indice de concentration des précipitations et indice de gravité de la sécheresse, ont été déterminés par des tests non paramétriques (test de Mann-Kendall et test T de Sen). L'amplitude du changement a été estimée en appliquant l'estimateur de pente de Sen. La variabilité décennale des précipitations aux cycles saisonniers marqués est évidente. La variabilité temporelle des phénomènes de sécheresse a été détectée. Les variations des précipitations annuelles sont faibles sans tendances significatives observées dans les principaux sous-bassins. Des tendances significatives ont été observées en octobre, novembre, décembre et janvier. Une diminution notable des précipitations totales annuelles a été observée principalement dans les sous bassins du Nord-ouest et du Sud-ouest. Une tendance à la décroissance des précipitations en juin-juillet-août (JJA) a été observée dans tous les sous-bassins principaux.

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Section: Rainfall Characteristicsmentioning

confidence: 82%

Section: Rainfall Characteristicsmentioning

confidence: 99%

Section: Rainfall Characteristicsmentioning

confidence: 99%

See 1 more Smart Citation

The nature of rainfall in the main drainage sub-basins of Uganda

Nsubuga

Botai

Olwoch

et al. 2014

Hydrological Sciences Journal

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“…Since the precipitation concentration index, like other statistical indices, does not reveal the actual month or season of maximum rainfall, the examination of the station data set is necessary to identify the period (season) of the year with maximum rainfall, if there is any (Michiels et al 1992). Mean monthly rainfall over a period of 35 years for the stations in the two zones is shown in Fig.…”

Section: Pci and Coefficient Of Variation Of Pci Seriesmentioning

confidence: 99%

Analysis of mid-twentieth century rainfall trends and variability over southwestern Uganda

Nsubuga

Olwoch

Rautenbach

et al. 2013

Theor Appl Climatol

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A methodology has been applied to investigate the spatial variability and trends existent in a mid-twentieth century climatic time series (for the period recorded by 58 climatic stations in the Albert-Victoria water management area in Uganda. Data were subjected to quality checks before further processing. In the present work, temporal trends were analyzed using Mann-Kendall and linear regression methods. Heterogeneity of monthly rainfall was investigated using the precipitation concentration index (PCI). Results revealed that 53 % of stations have positive trends where 25 % are statistically significant and 45 % of stations have negative trends with 23 % being statistically significant. Very strong trends at 99 % significance level were revealed at 12 stations.

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“…This characteristic is common to all the rainfall stations used in this study, which justifies the use of only one equation to estimate the erosivity index (EI 30 ) in accordance with the average monthly rainfall (Michiels et al, 1992).…”

Section: Resultsmentioning

confidence: 79%

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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Using the seasonal and temporal Precipitation concentration index for characterizing the monthly rainfall distribution in Spain

Cited by 100 publications

References 3 publications

The nature of rainfall in the main drainage sub-basins of Uganda

The nature of rainfall in the main drainage sub-basins of Uganda

Analysis of mid-twentieth century rainfall trends and variability over southwestern Uganda

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

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