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In this study, 60-year monthly rainfall data of Bangladesh were analysed to detect trends. Modified Mann–Kendall, Spearman’s rho tests and Sen’s slope estimators were applied to find the long-term annual, dry season and monthly trends. Sequential Mann–Kendall analysis was applied to detect the potential trend turning points. Spatial variations of the trends were examined using inverse distance weighting (IDW) interpolation. AutoRegressive integrated moving average (ARIMA) model was used for the country mean rainfall and for other two stations data which depicted the highest and the lowest trend in the Mann–Kendall and Spearman’s rho tests. Results showed that there is no significant trend in annual rainfall pattern except increasing trends for Cox’s Bazar, Khulna, Satkhira and decreasing trend for Srimagal areas. For the dry season, only Bogra area represented significant decreasing trend. Long-term monthly trends demonstrated a mixed pattern; both negative and positive changes were found from February to September. Comilla area showed a significant decreasing trend for consecutive 3 months while Rangpur and Khulna stations confirmed the significant rising trends for three different months in month-wise trends analysis. Rangpur station data gave a maximum increasing trend in April whereas a maximum decreasing trend was found in August for Comilla station. ARIMA models predict +3.26, +8.6 and −2.30 mm rainfall per year for the country, Cox’s Bazar and Srimangal areas, respectively. However, all the test results and predictions revealed a good agreement among them in the study.

Accurate and in-depth rainfall studies are crucial for understanding and assessing precipitation events’ patterns, intensities, and impacts, enabling effective planning and management of water resources, agriculture, and disaster preparedness. Despite many rainfall studies in Bangladesh at the national and regional scales, study on the spatiotemporal rainfall variability is still rare at the local scale. The current study aims to apply Mann–Kendall (MK), Modified Mann–Kendall (MMK), and Innovative Trend Analysis (ITA) techniques to assess the long-term annual and seasonal rainfall trends and variability over the southeast region of Bangladesh. Monthly rainfall data from ten Bangladesh Meteorological Department climate stations between 1981 and 2022 was used for the analysis on annual and four seasonal scales. The precipitation concentration index results showed significant variations in annual rainfall across the study area, whereas seasonal PCIs were consistent with moderate rainfall. According to standardized rainfall anomaly findings, each station experienced at least one severe to extremely severe drought episode during the 42-year study period. Homogeneity tests revealed significant breakpoints in some rainfall datasets, while 78% were declared homogeneous. MK, MMK, and ITA techniques revealed similar increasing and decreasing trend patterns throughout the study area. Annual rainfall showed an upward trend in the coastal part and a downward trend in the northern part of the study area, with monsoon rainfall exhibiting a similar trend pattern. The ITA technique outperformed the MK and MMK techniques in detecting trends, identifying significant increasing and decreasing trends in 76% (38 out of 50) of the observations, while the MK and MMK techniques detected trends in only 8% and 44% of the total observations, respectively. The outcome of the current study is expected to be helpful for the sustainable planning and management of water resources in the southeast region of Bangladesh.

The focus of this paper was to investigate the spatial and temporal variability of dry and wet events using the standard precipitation and evapotranspiration index (SPEI) in the Tana River Basin (TRB) in Kenya. The SPEI is a new drought index which incorporates the effect of evapotranspiration on drought analysis thus making it possible to identify changes in water demand in the context of global warming. The SPEI was computed at 6- and 12-month timescales using a 54-year long monthly rainfall data from the Global Precipitation and Climate Center (GPCC) and temperature data from the Climate Research Unit (CRU) both recorded between 1960 and 2013. Both datasets have a spatial resolution of 0.5° by 0.5° and were extracted for every grid point in the basin. The SPEI was used to assess the temporal and spatial evolution of dry and wet events as well as determine their duration, severity, and intensity. The evolution of significant historical dry and wet events and the frequency of occurrence were clearly identified. The index showed that the period between 1960 and 1980 was dominated by dry events while wet events were dominant in the period between 1990 and 2000. The SPEI6 had the longest duration of dry events of 30 months and severity of 44.67 which was observed at grid 5while the highest intensity was 2.18 observed at grid 31. Grid 19 had the longest duration (52 months) and highest severity (88.08) of dry events for SPEI12 and the intensity was highest (1.94) in grid 31. The longest duration (23) and highest severity (40.03) of wet events for SPEI6 were recorded in grid 39. The highest intensity of wet events for SPEI6 was 1.91 at grid 23 and 1.81 at grid 37 for SPEI12. The principal component analysis (PCA) was applied to the SPEI time series in order to assess the spatial pattern of variability of the dry and wet events in the basin. The PCA showed that there were two leading components which explained over 80% of the spatial variation of dry and wet events in the basin. Further, the continuous wavelet transform (CWT) was applied to the PCA scores in order to capture the time-frequency dynamics. The wavelet transform of the SPEI6 and SPEI12 identified significant periodicities of 1 to 2 years across the spectrum.

Drought is a complex natural phenomenon, so, precise prediction of drought is an effective mitigation tool for measuring the negative consequences on agriculture, ecosystems, hydrology, and water resources. The purpose of this research was to explore the potential capability of support vector regression (SVR) integrated with two meta-heuristic algorithms i.e., Grey Wolf Optimizer (GWO), and Spotted Hyena Optimizer (SHO), for meteorological drought (MD) prediction by utilizing EDI (effective drought index). For this objective, the two-hybrid SVR–GWO, and SVR–SHO models were constructed at Kumaon and Garhwal regions of Uttarakhand State (India). The EDI was computed in both study regions by using monthly rainfall data series to calibrate and validate the advanced hybrid SVR models. The autocorrelation function (ACF) and partial-ACF (PACF) were utilized to determine the optimal inputs (antecedent EDI) for EDI prediction. The results produced by the hybrid SVR models were compared with the calculated (observed) values by employing the statistical indicators and through graphical inspection. A comparison of results demonstrates that the hybrid SVR–GWO model outperformed to the SVR–SHO models for all study stations located in Kumaon and Garhwal regions. Also, the results highlighted the better suitability, supremacy, and convergence behavior of meta-heuristic algorithms (i.e., GWO and SHO) for meteorological drought prediction in the study regions.

Understanding and quantifying long-term rainfall variability at regional scale is important for a country like India where economic growth is very much dependent on agricultural production which in turn is closely linked to rainfall distribution. Using machine learning techniques viz., cluster analysis (CA) and principal component analysis (PCA), the spatial and temporal rainfall patterns over the meteorological subdivisions in India are examined. Monthly rainfall data of 117 years (1901–2017) from India Meteorological Department over 36 meteorological subdivisions in India is used in this study. Using hierarchical clustering method, six homogeneous rainfall clusters were identified in India. Among the rainfall clusters, Group 1 had 30% dissimilarity with Groups 2, 3, and 4 while Group 5 and Group 6 are highly dissimilar (more than 90% dissimilarity) with the rest of the groups. Rainfall seasons in each group were further classified into dry, wet, and transition periods. The duration of dry period is smaller in group which consists of subdivisions from southern part of the country. The transition period between dry and wet period was found to be smaller for subdivisions in the coastal region. Both CA and PCA showed high rainfall variability in Groups 5 and 6, which comprise subdivisions from north east, Kerala, Konkan, and costal Karnataka and low rainfall variability in Groups 1 and 2 which comprise subdivisions from east, north, and central part of the country. Strong negative trend in annual and Indian summer monsoon rainfall is seen in northeast India and Kerala while positive trend is observed over costal Karnataka and Konkan region. The negative trend in post monsoon rainfall particularly over the peninsular and northeast India indicates weakening of northeast monsoon rainfall in the country.

In this study, an attempt has been made to analyze long-term annual and seasonal rainfall trends along with change point of annual rainfall in West Bengal, India for 102 years (1901 to 2002) using monthly rainfall data of 18 rainfall stations. The Mann-Kendall test is used to identify trend in rainfall time series and Theil-Sen’s slope estimator to assess the magnitude of this trend. Trend-free pre-whitening method is used to eliminate the influence of significant lag-1 correlation from the series. Change in magnitude is derived in terms of percentage change over mean rainfall. Pettitt-Mann-Whitney and standard normal homogeneity test have been used to identify change point of annual rainfall. The results show that significant trend is found at five stations in annual rainfall, six stations in monsoon rainfall, and eight stations in postmonsoon rainfall. Maldah station has recorded highest negative change in magnitude in annual (− 14%) as well as monsoon rainfall (− 20.48%). South 24 Parganas rainfall station exhibits highest positive change in magnitude in annual (+ 13.98%) and monsoon (+ 13.27%) rainfall. Postmonsoon rainfall portrays positive change in magnitude at 16 rainfall stations with highest change in Birbhum station (+ 40.07%). Three most probable change point years of annual rainfall, viz. 1956, 1967, and 1952 have been observed for the rainfall stations situated in northern, southern, and western part in West Bengal. In the post change point period, the number of rainfall stations with decreasing trend has risen in northern and western part whereas it has lessened in southern part.

Drought is a complex natural disaster that adversely affects human life and the ecosystem. A variety of drought indexes are available for monitoring meteorological drought events. In the present study, Effective Drought Index (EDI) was utilized to quantify the drought and wet conditions with their probability at 13 districts of Uttarakhand State (India). The EDI was calculated for all study locations by using monthly rainfall data. The drought and wet incidents (i.e., moderate, severe, extreme) were categorized by setting a truncation level to the respective EDI value by using the runs theory concept. The results of the EDI examination explore that the probability of occurrence (PO) of normal events (conditions) was found to be 68% at Almora and Dehradun; 72% at Bageshwar; 71% at Chamoli and Pauri Garhwal; 69% at Champawat; Haridwar, Nainital, and Rudraprayag; 77% at Pithoragarh; 74% at Tehri Garhwal; and 44% at Pantnagar. Similarly, the PO of moderate drought and wet (MD and MW) events was found to be relatively more than the severe and extreme drought and wet events, except the Pantnagar station (extreme wet = 0.54). Thus, the outcomes of this study can be utilized to formulate the mitigation strategy in that way to store the surplus water in wet eras and utilized the same during drought for domestic and agricultural drives in the study districts.

This study investigates rainfall and drought characteristics in southeastern Australia (New South Wales and Victoria) using data from 45 rainfall stations. Four homogeneity tests are adopted to determine inhomogeneities in the annual total rainfall (ATR) and monthly rainfall data, namely The Pettitt test, the SNHT, the Buishand range test and the Von Neumann ratio test at significance levels of 1%, 5%, and 10%. Temporal trends in rainfall (ATR, monthly, and seasonal) and droughts are examined using autocorrelated Mann–Kendall (A-MK) trend test at 1%, 5%, and 10% significance levels. We also assess meteorological droughts by using multiple drought indices (3-, 6-, 9-, 12-, 24-, and 36-month Standardized Precipitation Index (SPI) and Effective Drought Index (EDI)). Furthermore, spatial variability of temporal trends in rainfall and drought are investigated through interpolation of Sen’s slope estimator. The results represent an increasing trend in ATR between 1920 and 2019. However, southeast Australia is highly dominated by a significant negative trend in the medium term between 1970 and 2019. Winter is found to be dominated by a significantly negative trend, whereas summer and spring seasons are dominated by a positive trend. April is detected as the driest month according to magnitude of Sen’s slope and the A-MK test result. Positive trends on droughts are observed at inner parts of the study area, whereas a negative trend is detected in the south, southeast, and northeast of the study area based on SPIs and EDI. The findings of this study help to understand changes in rainfall and droughts in southeastern Australia.

Drought is the most frequent natural disaster in Algeria during the last century, with a severity ranging over the territory and causing enormous damages to agriculture and economy, especially in the north-west region of Algeria. The above issue motivated this study, which is aimed to analyse and predict droughts using the Standardized Precipitation Index (SPI). The analysis is based on monthly rainfall data collected during the period from 1960 to 2010 in seven plains located in the north-western Algeria. While a drought forecast with 2 months lead-time is addressed using an artificial neural network (ANN) model. Based on SPI values at different time scales (3-, 6-, 9-, and 12-months), the seven plains of north-western Algeria are severely affected by drought, conversely of the eastern part of the country, wherein drought phenomena are decreased in both duration and severity. The analysis also shows that the drought frequency changes according to the time scale. Moreover, the temporal analysis, without considering the autocorrelation effect on change point and monotonic trends of SPI series, depicts a negative trend with asynchronous in change-point timing. However, this becomes less significant at 3 and 6 months’ time scales if time series are modelled using the corrected and unbiased trend-free-pre-whitening (TFPWcu) approach. As regards the ANN-based drought forecast in the seven plains with 2 months of lead time, the multi-layer perceptron networks architecture with Levenberg–Marquardt calibration algorithm provides satisfactory results with the adjusted coefficient of determination ( R adj 2 ) higher than 0.81 and the root-mean-square-error (RMSE) and the mean absolute error (MAE) less than 0.41 and 0.23, respectively. Therefore, the proposed ANN-based drought forecast model can be conveniently adopted to establish with 2 months ahead adequate irrigation schedules in case of water stress and for optimizing agricultural production.

The main objective of this paper is to analyze the rainfall regimes of Pernambuco, a northeastern Brazilian state, based on the seasonality indices (the individual, SIi, and the general, SI¯) and the replicability index, RI. These indices were derived from monthly rainfall data, recorded between 1953 and 2012, at 125 weather stations. The modified Mann–Kendall test and Sen’s slope estimator were applied to investigate trends in the SIi time series. A regression analysis of SIi¯, SI¯, and RI with the geographical longitude produced a statistically significant linear correlation. The calculated SI¯ index indicates that the mean rainfall regime of the coastal area (Zona da Mata) and the central area (Agreste) of the state can be classified as rather seasonal with a short drier season. The western part (Sertão), in turn, exhibited mean rainfall regime between seasonal and markedly seasonal with a long drier season. Concerning the replicability index, results suggest that Agreste is the region with the least replicable rainfall regime in the state. The analysis of the two 30-year sub-periods shows significant changes in the mean values of SIi indicating the shift in rainfall regime toward extreme seasonality with prolonged dry season in the Sertão region and toward more regular with shorter dry periods in Zona da Mata and Agreste region.