Spatiotemporal analysis of drought and rainfall in Pakistan via Standardized Precipitation Index: homogeneous regions, trend, wavelet, and influence of El Niño-southern oscillation

Abstract

The phenomena of drought is common in the world, particularly in Pakistan.Drought in Pakistan has been studied in terms of its spatial and temporal variability, as well as its impact on the El Niño-Southern Oscillation (ENSO) cycle.The objectives of this study are to identify homogeneous rainfall regions and their trend regions, as well as the impact of ENSO cycle. For the analysis, 44 meteorological sites during 1980-2019 are used for monthly rainfall data. The descriptive and exploratory statistics tests (e.g., Pettitt and Mann-Kendall-MK), Sen method, and cluster analysis (CA) are implemented with the annual Standardized Precipitation Index (SPI). The ENSO occurrences were classified based on the Oceanic Nio Index (ONI) for region 3.4. Using the Cophenetic Correlation Coefficient (CCC) and a significance level of 5%, seven approaches were applied to the pluviometric dataset. The CCC > 0.9082 indicates that the Complete approach is the best. According to the CA method, Pakistan has four homogenous rainfall groups (G1, G2, G3, and G4). Descriptive and exploratory statistics yielded lower values for G1 than for the other groups. Pettitt's technique identified the most extreme El Niño years in terms of the drought's spatial and temporal variability. According to the Pettitt test, the wettest months were March, August, September, June, and December. Non-significant increases in Pakistan's annual rainfall were found in the MK test, with exceptions in the southern and northern regions, respectively. No significant rise in Pakistan's rainfall was found using Sen's Sen's approach, especially in the G2, G3, and G4 regions. The severity of the drought in Pakistan is made worse by months, homogeneous groups of rainfall, and El Niño events, all of which require public officials' attention while managing water resources, agriculture, and the country's economy.

Bioclimatic Zoning for Sheep Farming through Geostatistical Modeling in the State of Pernambuco, Brazil

“…The CV was considered low (<24%) for all the years evaluated [ 44 ], indicating a high homogeneity of the data. As the mean and median values were similar, a behavior verified for all years evaluated in the present study ( Table 1 ), the data were assumed as presenting normality, in accordance with Silva et al [ 43 , 58 ].…”

“…Data were obtained from the Climate Engine platform ( , accessed on 12 September 2022), the image processing platform from TerraClimate, as well as the point data georeferencing. Subsequently, the mean air temperature (Tair, °C) was obtained, and then the annual mean temperature and humidity index from 2010 to 2021 was estimated, as well as the fractional behavior of this index in the study region [ 38 , 39 , 40 , 41 , 42 , 43 ], according to Equation (1) established by [ 40 ]: …”

Spatiotemporal monitoring of drought using satellite precipitation products: The case of Borena agro-pastoralists and pastoralists regions, South Ethiopia

“…As satellite technology advances, a wide ranges of satellite precipitation estimates were developed and used over the last decades. For example, Standardized Precipitation Index [SPI, [ [18] , [19] , [20] , [21] , [22] ]], Microwave/Infrared Rain Rate Algorithm [ 7 ], the SPEI [ 23 ], Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset [ 24 ], and the Tropical Rainfall Measuring Mission [TRMM, [ 25 ]] are some of tools used for analyzing rainfall distribution and variability. CHIRPS is one the satellite-based precipitation estimation systems that integrates ground-based observations with long-term infrared remote sensing data [ [26] , [27] , [28] , [29] , [30] ].…”

Análise multivariada do SPI no Estado de São Paulo

“…Secas hidrológicas são percebidas em escalas de tempo maiores (cerca de 12 meses após a configuração da seca meteorológica), e são caracterizadas por baixos níveis de umidade do solo, diminuição da vazão dos rios e das águas subterrâneas. Ainda, existem as secas socioeconômicas, que são associadas à oferta e demanda de bens econômicos e com uma relação complexa entre os demais tipos de seca (HEIM, 2002;YIHDEGO et al, 2019;DE FREITAS et al, 2022;OLIVEIRA et al, 2022). Diversos índices são utilizados na identificação de eventos extremos de precipitação, podendo calcular também sua duração e severidade.…”

“…Além disso, é um índice extensamente usado no estudo de períodos secos em diversas regiões do Brasil, como a Amazônia (Joetzjer et al, 2013;Chaudhari et al, 2019), o Pantanal (Marengo et al, 2021), o Nordeste (Nascimento et al, 2017;Brito et al, 2018) e o Sudeste e Sul do país (Sobral et al, 2018;Terassi et al, 2018;de Paiva Lima et al, 2022). Especificamente para o estado de São Paulo, o SPI e o SPEI (uma adaptação do SPI que considera no cálculo do balanço hídrico a temperatura do ar e o processo de evapotranspiração) vêm sendo utilizados na caracterização de eventos extremos de seca (Blain e Brunini, 2005;Coelho et al, 2016;Oliveira et al, 2022), e no estudo das tendências de ocorrência (Blain, 2012;Pereira et al 2018;Gozzo et al, 2019a) e das forçantes climáticas de grande escala responsáveis por estes eventos (Gozzo et al, 2021;Gozzo et al 2022). Siqueira e Nery (2017), descrevendo a climatologia do índice SPI sobre São Paulo entre 1970 e 2010, obtiveram diferentes valores médios do SPI sobre o Estado: ao sul e leste, a média do índice é superior a 1,0, enquanto no centro-norte, mais seco, a média fica abaixo de 0,9, chegando a 0 no nordeste.…”