scholarly journals Análise de Eventos Extremos na Região Amazônica (Analysis of Extreme Events in the Amazon Region)

2013 ◽  
Vol 6 (5) ◽  
pp. 1356 ◽  
Author(s):  
Thalyta Soares dos Santos ◽  
Ana Carla Dos Santos Gomes ◽  
Maytê Duarte Leal Coutinho ◽  
Allan Rodrigues Silva ◽  
Aline Anderson de Castro
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Maximum Likelihood Method ◽  
Global Climate ◽  
Standardized Precipitation Index ◽  
Amazon Region ◽  
Likelihood Method ◽  
Monthly Precipitation ◽  
The Standardized Precipitation Index ◽  
Porto Velho

A frequência de eventos severos e extremos de seca e chuva na Amazônia foi analisada utilizando o Índice de Precipitação Normalizada (SPI) nas escalas de 6 (sazonal estação seca/chuvosa) e 12 meses (interanual). A frequência de eventos secos e chuvosos é importante para a climatologia da região, que é considerada um regulador climático global. Para isso foram selecionadas as séries climatológicas, de 1925 a 2000, de seis localidades da região Amazônica: Belém, Cuiabá, Iauretê, Manaus, Porto Velho, Taguatinga. Os SPIs, 6 e 12, que quantificam excesso ou déficit de chuva, nestas duas escalas de tempo, foram calculados a partir dos ajustes de distribuição gama, pelo método da máxima verossimilhança às médias móveis de 6 e 12 meses das precipitações mensais. Esses foram computados a partir da normalização das probabilidades gama, pelos seus respectivos desvios padrões. As séries temporais dos SPIs 6 e 12, mostram longos períodos de oscilação entre eventos secos e chuvosos. A frequência decenal de ambos SPIs indica variações entre as décadas mais chuvosas e secas nos municípios estudados. As décadas mais chuvosas e secas são periódicas para as duas escalas de tempo analisadas em todas as estações, exceto Iauretê. A B S T R A C T The frequency of severe and extremes events of drought and rainfall in the Amazon was analyzed using the Standardized Precipitation Index (SPI) in the scales of six months (dry/wet seasons) and 12 months (inter-annual). This is important for the climatology of the region, which is considered a global climate regulator. With this objective, the climatological series from 1925 to 2000 were selected for six locations in the Amazon region: Belém, Cuiabá, Iauretê, Manaus, Porto Velho and Taguatinga. With the aim of quantify the excess or deficit of rainfall in the selected time scales, the SPIs 6 and 12 were calculated using the fit of the gamma distribution by the maximum likelihood method for the moving averages 6 and 12 months of monthly precipitation. These were computed from the normalization of gamma probabilities by its standard deviation. The time series of SPIs 6 and 12, show long periods of oscillation between dry and wet events. The frequency of both SPIs indicates variations between wet and dry decades in the cities studied. Wetter and drier decades were shown to be periodic for the two time scales considered in all locations, except for Iauretê. Key-Words: SPI, Amazon, Drought, Rain

scholarly journals Classificação de Eventos Extremos de Precipitação em Múltiplas Escalas de Tempo em Belém-PA: Utilizando o Índice de Precipitação Normalizada (Classification of Extreme Precipitation Events in Multiple Time Scales in Belém, PA: Using the Standardized ...)

2014 ◽  
Vol 7 (4) ◽  
pp. 628
Author(s):  
Sérgio Rodrigo Quadros dos Santos ◽  
Celia Campos Braga ◽  
Ana Paula Paes dos Santos ◽  
Thamiris Luiza De Oliveira Brandão Campos ◽  
José Ivaldo Barbosa de Brito
Keyword(s):  
Time Scales ◽  
Extreme Event ◽  
Standardized Precipitation Index ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Monthly Precipitation ◽  
Extreme Precipitation Events ◽  
The Standardized Precipitation Index ◽  
The City

O Índice de Precipitação Normalizada (SPI) é utilizado para quantificar o déficit e/ou excesso de precipitação nas múltiplas escalas de tempo. Ele tem se mostrado bastante útil no monitoramento da precipitação, principalmente pela sua flexibilidade, simplicidade de cálculo e interpretação. Desta forma este estudo tem como objetivo quantificar os eventos extremos secos e chuvosos na cidade de Belém-PA nas escalas de tempo de 3, 6 e 12 meses por meio do SPI. Para isto, utilizaram-se dados mensais de precipitação provenientes da estação meteorológicas de superfície do INMET no período de 1980-2011. Os resultados mostraram que a escala de tempo do SPI é inversamente proporcional à frequência dos eventos de chuva e seca. Os SPIs 3,6 e 12 mostraram mais eventos secos do que chuvosos para a cidade e a maioria dos eventos de chuva e seca estavam associados, principalmente, ao fenômeno ENOS. ABSTRACT The Standardized Precipitation Index (SPI) is used to quantify the deficit/ excess rainfall at multiple time scales. It has been very useful in monitoring of precipitation, mainly because of its flexibility, ease of calculation and interpretation. Thus this study aims to quantify the extreme wet and dry events in the city of Belém-PA in time scales of 3, 6 and 12 months by SPI. For this, we used monthly precipitation data from meteorological station at the INMET in the period 1980-2011. The results show that the timescale of the SPI is inversely proportional to the frequency of rain and dry events. The SPIs 3.6 and 12 showed driest events that rainy events to the city and most of the rainfall and drought events were associated, mainly, with the ENSO phenomenon. Key Words: Belem; SPI; Extreme Event.   

scholarly journals The sensitivity of vegetation in the lower Tigris basin landscapes to regional and global climate variability

2021 ◽  
Vol 30 (1) ◽  
pp. 159-170
Author(s):  
Ali Alhumaima ◽  
Sanjar Abdullaev
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Southern Oscillation ◽  
Global Climate ◽  
Standardized Precipitation Index ◽  
Monthly Precipitation ◽  
Anthropogenic Landscapes ◽  
Precipitation And Temperature

This study investigates the lower Tigris basin’s the normalized difference vegetation index (NDVI) sensitivity in 2000–2016 to regional climate variability reflected by the monthly precipitation and temperature time series of seven global datasets as well as to four global circulation indices. To examine the effect of climate variability on the different ecosystems, the study area has been classified into 10 smaller natural and anthropogenic landscapes based on landforms and land cover patterns. The preliminary analysis showed that the maximum biological productivity reflected by the NDVI of March and April has the highest correlation (0.5–0.8) to the same cumulative amounts of October–March period total precipitation and January–March period mean temperatures according to all datasets. In addition, this article showed there is a correlation between landscapes’ NDVI and global modulation represented by the September–February state of El Nińo-Southern Oscillation (ENSO) (0.55–0.70) and December state of the dipole mode index (DMI) (0.35–0.72). The significant differences in the original precipitation and temperature levels according to the different datasets have urged the use of normalized time series: z-score of temperatures and analogous six-months the standardized precipitation index (SPI). However, the multiple correlation analysis showed that using ERA-

scholarly journals Modeling a Drought Index Using a Nonparametric Approach

2017 ◽  
Vol 19 (1) ◽  
pp. 58-68 ◽  
Keyword(s):  
Time Scales ◽  
Gamma Distribution ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Kernel Density ◽  
Density Estimator ◽  
Monthly Precipitation ◽  
Nonparametric Approach ◽  
The Standardized Precipitation Index ◽  
Drought Classes

<p>Alternatively, to other studies that used parametric distributions (e.g. Gamma) in the estimation of the Standardized Precipitation Index (SPI), this study aims to apply a nonparametric method based on Kernel Density Estimator (KDE) for calculating the SPI. Results of the proposed method were compared with the ones from the most widely used parametric distribution, using a long dataset of monthly precipitation of four meteorological stations in Iran (including Bushehr, Mashhad, Tehran and Esfahan) over a period of 107 water years (1895-2002). The capability of KDE-based SPI was compared with the Gamma-based SPI at four-time scales of 3, 6, 9 and 12 months. The frequencies of the drought classes of SPI were calculated and compared with corresponding expected frequencies. The results revealed that the KDE is more consistent with the expected values of the SPI drought/wet classes frequencies (especially in the extreme classes) at all stations as well as at the four-time scales, compared to the Gamma distribution. The greatest deviation from the expected frequencies for KDE and Gamma distribution were about 10% and 150%, respectively. This study proposes a new analytical approach in modeling SPI that provides more accurate results pertaining frequency of occurrences of extreme drought events. The output of the study can be used in many fields (e.g. tourism, agriculture, insurance, etc.) that are influenced by severe droughts.</p>

Drought and Flood Distribution Variation Based on SPI in Nanjing, China

2013 ◽  
Vol 295-298 ◽  
pp. 2116-2120
Author(s):  
Jian Fen Liu ◽  
Xing Nan Zhang ◽  
Hui Min Wang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Precipitation Data ◽  
Temporal Scales ◽  
Drought And Flood ◽  
Three Stages ◽  
The Standardized Precipitation Index

Many drought and flood indices have been developed, the Standardized Precipitation Index (SPI) is one which has various temporal scales together to form an overall judgment of drought and flood and can be applied easily to different locations to identify and monitor drought and flood. Take Nanjing, China in the study as an example to analysis drought and flood variation by computing SPI values of four time scales including 3-months, 6-months, 12-months and 24-months, applying precipitation data from 1946-2000 of the study area. The results demonstrated SPI can be appropriate to analyze drought and flood variation of Nanjing, while the precipitation data were divided into three stages(1946-1963,1964-1981,1982-2000), the frequencies of various drought and flood classes from various time scales are different, particularly 12-months and 24-months. The time series is longer, the frequencies are more reliable and the differences more little.

scholarly journals Hydrological response to different time scales of climatological drought: an evaluation of the Standardized Precipitation Index in a mountainous Mediterranean basin

2005 ◽  
Vol 9 (5) ◽  
pp. 523-533 ◽  
Author(s):  
S. M. Vicente-Serrano ◽  
J. I. López-Moreno
Keyword(s):  
Time Scales ◽  
Hydrological Cycle ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Drought Indices ◽  
Long Time ◽  
The Standardized Precipitation Index ◽  
Hydrological Droughts ◽  
Different Time Scales

Abstract. At present, the Standardized Precipitation Index (SPI) is the most widely used drought index to provide good estimations about the intensity, magnitude and spatial extent of droughts. The main advantage of the SPI in comparison with other indices is the fact that the SPI enables both determination of drought conditions at different time scales and monitoring of different drought types. It is widely accepted that SPI time scales affect different sub-systems in the hydrological cycle due to the fact that the response of the different water usable sources to precipitation shortages can be very different. The long time scales of SPI are related to hydrological droughts (river flows and reservoir storages). Nevertheless, few analyses empirically verify these statements or the usefulness of the SPI time scales to monitor drought. In this paper, the SPI at different time scales is compared with surface hydrological variables in a big closed basin located in the central Spanish Pyrenees. We provide evidence about the way in which the longer (>12 months) SPI time scales may not be useful for drought quantification in this area. In general, the surface flows respond to short SPI time scales whereas the reservoir storages respond to longer time scales (7–10 months). Nevertheless, important seasonal differences can be identified in the SPI-usable water sources relationships. This suggests that it is necessary to test the drought indices and time scales in relation to their usefulness for monitoring different drought types under different environmental conditions and water demand situations.

scholarly journals Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index

2013 ◽  
Vol 17 (6) ◽  
pp. 2359-2373 ◽  
Author(s):  
E. Dutra ◽  
F. Di Giuseppe ◽  
F. Wetterhall ◽  
F. Pappenberger
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Seasonal Forecasting ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Blue Nile ◽  
Forecasting System ◽  
The Standardized Precipitation Index

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.

scholarly journals Computation of the Standardized Precipitation Index (SPI) and Its Use to Assess Drought Occurrences in Cameroon over Recent Decades

2014 ◽  
Vol 53 (10) ◽  
pp. 2310-2324 ◽  
Author(s):  
Guy Merlin Guenang ◽  
F. Mkankam Kamga
Keyword(s):  
Time Scales ◽  
Goodness Of Fit ◽  
Standardized Precipitation Index ◽  
Research Unit ◽  
Distribution Functions ◽  
Precipitation Index ◽  
Severe Drought ◽  
East Anglia ◽  
The Standardized Precipitation Index ◽  
Short Time

AbstractThe standardized precipitation index (SPI) is computed and analyzed using 55 years of precipitation data recorded in 24 observation stations in Cameroon along with University of East Anglia Climate Research Unit (CRU) spatialized data. Four statistical distribution functions (gamma, exponential, Weibull, and lognormal) are first fitted to data accumulated for various time scales, and the appropriate functions are selected on the basis of the Anderson–Darling goodness-of-fit statistic. For short time scales (up to 6 months) and for stations above 10°N, the gamma distribution is the most frequent choice; below this belt, the Weibull distribution predominates. For longer than 6-month time scales, there are no consistent patterns of fitted distributions. After calculating the SPI in the usual way, operational drought thresholds that are based on an objective method are determined at each station. These thresholds are useful in drought-response decision making. From SPI time series, episodes of severe and extreme droughts are identified at many stations during the study period. Moderate/severe drought occurrences are intra-annual in short time scales and interannual for long time scales (greater than 9 months), usually spanning many years. The SPI calculated from CRU gridded precipitation shows similar results, with some discrepancies at longer scales. Thus, the spatialized dataset can be used to extend such studies to a larger region—especially data-scarce areas.

scholarly journals Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

2021 ◽  
Author(s):  
Morteza Lotfirad ◽  
Hassan Esmaeili-Gisavandani ◽  
Arash Adib
Keyword(s):  
Random Forest ◽  
Correlation Coefficient ◽  
Potential Evapotranspiration ◽  
Standardized Precipitation Index ◽  
Monthly Precipitation ◽  
Model Combination ◽  
The North ◽  
Drought Prediction ◽  
Lag Times ◽  
The Standardized Precipitation Index

Abstract The aim of this study is to select the best model (combination of different lag times) for predicting the standardized precipitation index (SPI) and the standardized precipitation and evapotranspiration index (SPEI) in next time. Monthly precipitation and temperature data from 1960 to 2019 were used. In temperate climates, such as the north of Iran, the correlation coefficient of SPI and SPEI was 0.94, 0.95, and 0.81 at the time scales of 3, 12, and 48 months, respectively. Besides, this correlation coefficient was 0.47, 0.35, and 0.44 in arid and hot climates, such as the southwest of Iran because potential evapotranspiration (PET) depends on temperature more than rainfall. Drought was predicted using the random forest (RF) model and applying 1–12 months lag times for next time. By increasing of time scale, the prediction accuracy of SPI and SPEI will improve. The ability of SPEI is more than SPI for drought prediction, because the overall accuracy (OA) of prediction will increase, and the errors (i.e., overestimate (OE) and underestimate (UE)) will reduce. It is recommended for future studies (1) using wavelet analysis for improving accuracy of predictions and (2) using the Penman–Monteith method if ground-based data are available.

scholarly journals Global and regional performances of SPI candidate distribution functions in observations and simulations

2020 ◽  
Author(s):  
Patrick Pieper ◽  
André Düsterhus ◽  
Johanna Baehr
Keyword(s):  
Time Series ◽  
Distribution Function ◽  
Weibull Distribution ◽  
Standardized Precipitation Index ◽  
Distribution Functions ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Data Set ◽  
Exponentiated Weibull Distribution ◽  
Exponentiated Weibull

Abstract. The Standardized Precipitation Index (SPI) is a widely accepted drought index. Its calculation algorithm normalizes the index via a distribution function. Which distribution function to use is still disputed within literature. This study illuminates the long-standing dispute and proposes a solution which ensures the normality of the index for all common accumulation periods in observations and simulations. We compare the normality of SPI time-series derived with the gamma, Weibull, generalized gamma, and the exponentiated Weibull distribution. Our normality comparison evaluates actual against theoretical occurrence probabilities of SPI categories, and the quality of the fit of candidate distribution functions against their complexity with Akaike's Information Criterion. SPI time-series, spanning 1983–2013, are calculated from Global Precipitation Climatology Project's monthly precipitation data-set and seasonal precipitation hindcasts from the Max Planck Institute Earth System Model. We evaluate these SPI time-series over the global land area and for each continent individually during winter and summer. While focusing on an accumulation period of 3-months, we additionally test the drawn conclusions for other common accumulation periods (1-, 6-, 9-, and 12-months). Our results suggest to exercise caution when using the gamma distribution to calculate SPI; especially in simulations or their evaluation. Further, our analysis shows a distinctly improved normality for SPI time-series derived with the exponentiated Weibull distribution relative to other distributions. The use of the exponentiated Weibull distribution maximizes the normality of SPI time-series in observations and simulations both individual as well as concurrent. Its use further maximizes the normality of SPI time-series over each continent and for every investigated accumulation period. We, therefore, advocate to derive SPI with the exponentiated Weibull distribution, irrespective of the heritage of the precipitation data or the length of analyzed accumulation periods.

scholarly journals Regional and Seasonal Precipitation and Drought Trends in Ganga–Brahmaputra Basin

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2218
Author(s):  
Bikram Parajuli ◽  
Xiang Zhang ◽  
Sudip Deuja ◽  
Yingbing Liu
Keyword(s):  
Spatial Pattern ◽  
Time Scales ◽  
Standardized Precipitation Index ◽  
Monthly Precipitation ◽  
Seasonal Precipitation ◽  
Precipitation Data ◽  
Rain Gauges ◽  
Geographical Regions ◽  
Brahmaputra Basin ◽  
Different Time Scales

Satellite-based precipitation products can be a better alternative of rain gauges for hydro-meteorological studies in data-poor regions. This study aimed to evaluate how regional and seasonal precipitation and drought patterns had changed in the Ganga–Brahmaputra Basin between 1983 and 2020 with PERSIANN-CDR precipitation data. The spatial pattern of winter drought, monsoon drought, and Standardized Precipitation Index (SPI) calculated for different time scales were evaluated using principal component analysis. Ganga–Brahmaputra is one of the most populated river basins that flows through different geographical regions. Rain gauges are heterogeneously distributed in the basin due to its complex orography, highlighting the significance of gridded precipitation products over gauge observations for climate studies. Annual and monthly precipitation trends between 1983 and 2020 were evaluated using the original and modified Mann–Kendall trend test, and annual precipitation in the basin was found to be declining at a rate of 5.8 mm/year. An increasing trend was observed in pre-monsoon rainfall, whereas precipitation exhibited a decreasing trend for other months. Results of the Pettitt test showed precipitation time series was inhomogeneous and changepoint occurred around 2000. Decreasing trends of SPI indicated increasing frequency and intensity of drought events. Winter drought showed a clear spatial pattern in the basin; however, SPIs calculated for different time scales and monsoon drought had complex spatial patterns. This study demonstrates the applicability of satellite-based PERSIANN-CDR precipitation data in climate research in the Ganga–Brahmaputra Basin.

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