Monthly Standardized Precipitation Index Research Articles

Identifikasi Kekeringan Meteorologi menggunakan SPI 3 Bulanan dan 6 Bulanan di DAS Brantas Hulu

Drought is a state of water availability that is far below the need for water for survival, agriculture, economic activity, and the environment. Drought in the Brantas Hulu watershed has been recorded as hampering the function of the Sutami Reservoir. This study used the Standardized Precipitation Index (SPI) method of 3 monthly and 6 monthly to determine the distribution of drought in the form of duration, severity, and maximum intensity of the Brantas Hulu watershed. This study also analyzed the relationship between the 3-monthly and 6-monthly SPI index to the sutami reservoir inflow discharge in the Brantas Hulu watershed using the Pearson Correlation method including correlations without lag, lag 1, and lag 2. The data used are monthly rainfall data for 30 years for the period 1991 - 2020 from 10 Rain Posts in Malang City, Batu City, and Malang Regency as well as monthly inflow debit data for 10 years for the period 2011 - 2020 from Perum Jasa Tirta 1. In general, the distribution of the maximum drought duration occurs for 15 – 17 months, while the SPI of 6 months occurs for 25 – 29 months. The maximum drought severity occurs for 5 - 6 months, while the 6-month SPI occurs for 9 – 11 months. The maximum drought intensity of SPI is 3 months and 6 months is categorized as Dry. Otherwise, the correlation of 3 monthly SPI and sutami reservoir inflow discharge without lag and lag 1 is worth 0,2 to 0,3, while for lag 2 it is worth 0,1 to 0,2. The correlation value of the 6 -month SPI index and the inflow discharge of Sutami Reservoir without lag is worth 0,3 to 0,4, while the correlation of lag 1 and lag 2 is worth 0,2 to 0,3.

Artificial neural network models for prediction of standardized precipitation index in central Mexico

Some of the effects of climate change may be related to a change in patterns of rainfall intensity or rainfall scarcity. So, humanity is facing environmental challenges due to an increase in the occurrence of droughts. Forecasting of droughts based on cumulative influence of rainfall could be greatly beneficial for mitigating adverse consequences on water-sensitive sectors such as agriculture. Then, predictive models of drought indices could help in assessing water scarcity situations, droughts identification and their severity characterization. In this paper, we tested the feasibility of the Artificial Neural Network as a data-driven model for predicting the monthly Standardized Precipitation Index in 4 regions (Semi-desert, Highlands, Canyons and Mountains) of north-central México using predictive variable data from 1965 to 2004 as training data and simulated data for the period 2005-2014. The best model was found using the Hyperbolic Tangent as activation function and the Adaptive Moment Estimation (Adam) algorithm as optimization method. The best model was set to the following architecture: 26-12-1 network with 4 weights and 365 trainable parameters. Based on analysis of scatter plot between predicted and observed Standardized precipitation Index values for the test dataset, the Coefficient of Determination was between 0.84 and 0.88. In terms of quantitative statistics averaged over the test set, Artificial Network Model performed very well in predicting Standardized Precipitation Index at the four analyzed regions. This was verified by all-region average value of performance statistics Mean Absolute Error (0.0805), Mean Square Error (0.0144) and the Coefficient of Determination (0.8671). In a nutshell we summarize that the Artificial Network models developed and tested in this study had good prediction skills of the monthly Standardized Precipitation Index for stations and its drought-related properties in the study region.

Narx Neural Networks Models for Prediction of Standardized Precipitation Index in Central Mexico

Some of the effects of climate change may be related to a change in patterns of rainfall intensity or scarcity. Therefore, humanity is facing environmental challenges due to an increase in the occurrence and intensity of droughts. The forecast of droughts can be of great help when trying to reduce the adverse effects that the scarcity of water brings, particularly in agriculture. When evaluating the conditions of water scarcity, as well as in the identification and characterization of droughts, the use of predictive models of drought indices could be a very useful tool. In this research, the utility of Artificial Neural Networks with exogenous inputs was tested, with the aim of predicting the monthly Standardized Precipitation Index in 4 regions (Semi-desert, Highlands, Canyons and Mountains) of north-central México using predictor data from 1979 to 2014. The best model was found using the scaled conjugate gradient backpropagation algorithm as the optimization method and was set to the following architecture: 6-25-1 network. The correlation coefficient of predicted and observed Standardized Precipitation Index values for the test dataset was between 0.84 and 0.95. As a result, the Artificial Neural Network models performed successfully in predicting Standardized Precipitation Index at the four analyzed regions. The developed and tested Artificial Neural Network models in this research suggest remarkable prediction abilities of the monthly Standardized Precipitation Index in the study region.

EVALUATION OF HOTSPOTS BASED ON CLIMATE DATA IN THE NAGAN RAYA REGENCY, ACEH

Identifying hotspots as a potential for wildfires due to weather/climate factors needs to be studied in more detail to consider the policies taken by relevant agencies in the Nagan Raya Regency, mainly consisting of peatlands. Rainfall observation data in some areas are incomplete, so alternative data are needed for rainfall estimation for those areas, such as satellite data. However, the satellite data does not necessarily match the conditions in the field, so validation is needed. In this study, satellite data were validated with available observational data in the area, so the results can be used as a reference when field data is unavailable. The data used are GSMaP_GNRT6 and observation data from 5 rainfall observation posts: Beutong, Cut Nyak Dien Meteorological Station, Darul Makmur, PT. Socfindo and Pulo Ie for the period 2010-2019. The satellite and the observation data were correlated with the Pearson method to see the relationship between the two data. The difference between each satellite data and observations at the same time and place is calculated using the formulas Mean Error (ME), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Furthermore, a case study of fire incidents and satellite hotspot data at several locations was also observed simultaneously. In addition, the validated rainfall data were also used to calculate the Standardized Precipitation Index (SPI) value. The result shows the validation of rainfall data with GSMaP_GNRT6 satellite data has a moderate correlation with the MAE value ranging from 101.3 to 195.12 from the five rainfall observation posts. The results also show that a 10-day base of rainfall before the occurrence of the wildfires was in a low category (86%). The number of hotspot occurrences was also supported by the negative monthly SPI value, high air temperature, and the type of land in the study area. Keywords: Hotspot, Rainfall, Air Temperature, Wildfires, Peat Lands

Hydrological Extremes and Their Association with ENSO Phases in Ethiopia

In this study, an attempt has been made to determine the occurrence of droughts and floods on monthly basis, by calculating the monthly Standardized Precipitation Index using the available rainfall data during (1975-2005) in Ethiopia.Based on the monthly SPI values computed, the droughts and floods of different intensities; extreme, severe and dry have been determined for all stations. The frequencies of the droughts and floods during the two rainy seasons, Belg (Feb-May) and Kiremt (Jun-Sep) seasons have been determined. For instance, during Belg season, there were 11 extreme (SPI < -2.0) at Nazreth, 10 severe (SPI between -1.99 and -1.50) at Diredawa and 14 moderate (SPI between -1.49 and -1.00) droughts at Kulumsa. The total number of droughts of all intensities over the study period is also highest (22) at Kulumsa and lowest (8) at Mekelle. Moreover, at Kulumsa the numbers of droughts and floods during Belg are more, which shows that the rainfall variability is highest at this station.The association of the hydrological extremes during the two rainy seasons, belg and kiremt with the ENSO phases has also been examined, which forms a basis for the prediction of the occurrence of droughts and floods at individual station using ENSO phases.

Precipitation–temperature relationships over Europe in CORDEX regional climate models

AbstractWe studied spatial and temporal patterns of precipitation–temperature (P–T) relationships through correlations between monthly standardized precipitation index (SPI) and monthly temperature anomalies in individual climatic seasons over Europe. In the observed data (represented by E‐OBS), positive correlations (wet–warm/dry–cold relationships) prevail during winter over most of Europe, while negative values (dry–warm/wet–cold) are dominant in summer. In the next step, an ensemble of seven regional climate models (RCMs) from the CORDEX project driven by the ERA‐Interim reanalysis were examined as to their reproduction of the regional patterns of the P–T correlations. In winter, the RCMs yielded overly strong positive P–T correlations over northern Europe, while the correlations were too weak in the south compared to observed data. During summer, the biases were generally larger; the RCMs were able to capture the overall negative P–T correlations but these tended to be too weak over northern Europe. This deficiency was found to be linked to simulated differences in shortwave radiation (a proxy for cloud cover) between dry and wet months. In western, central, and southeastern Europe, by contrast, most RCMs yielded too strong negative correlations in summer, and overly large decreases of relative humidity during dry months probably contributed to these errors. The results pointed up issues that should be addressed as the reported RCMs' deficiencies may lower credibility of projected compound dry–hot events in climate change scenarios.

Drought assessment for kharif rice using standardized precipitation index (SPI) and vegetation condition index (VCI)

The monthly weather data for 31 years from 1985-2015 was used to analyze the extent of meteorological drought using standardized precipitation index (SPI) over Allahabad, Kanpur and Lucknow. MODIS NDVI data from 2000-2015 was used for monitoring of agricultural drought through NDVI based vegetation condition index (VCI) for all the three districts. The monthly SPI and VCI values from July to October were correlated with productivity index (PI) of kharif rice.Both the indices (SPI and VCI) were positively correlated with PI for all the districts. In Allahabad SPI and VCI during September month showed a significant correlation (0.70**& 0.61*) while in Kanpur VCI during October and SPI of July and August were significantly correlated with PI of kharif Rice. The multiple regression equation developed for predicting kharif rice PI in Allahabad, Kanpur and Lucknow districts explained 69 to 76 per cent variabilityin PI.

Meteorological Drought Analysis Using Standardized Precipitation Index

Drought is a natural hazard which is challenging to quantify in terms of severity, duration, areal extent and impact. The present study was aimed to assess the meteorological drought for Junagadh (Gujarat), India using Standardized Precipitation Index (SPI) and evaluate its correlation with the productivity of Groundnut and Cotton. The SPI was computed for eight durations including monthly (June to August each), 3 monthly (June to August and July to September) and 6 monthly (June to November) time scales for the year1988 to 2018. The results revealed that 54% to 67% of years suffered from drought for SPI-1. Drought years based on SPI-3 and SPI-6 were 48 % to 58%. Among all the eight durations, mild drought was the most dominant drought category. Years 1993, 1999, 2002 and 2012 experienced the most severe droughts for Junagadh. Severe droughts were observed only for SPI-1 (July), SPI-3 and SPI-6. No extreme drought was witnessed in Junagadh. Correlation of groundnut yield with SPI was higher as compared to cotton for all time scales. Kharif groundnut and cotton yield were better correlated with SPI-3 and SPI-6 for Junagadh with significant correlation coefficient ranging from 0.57 to 0.79 for groundnut and 0.46 to 0.56 for cotton. Among monthly SPI, the significantly highest correlation was found for June (0.59) for groundnut and September (0.48) for cotton. The SPI-3 and SPI-6 shown ability to quantify the drought and also shown the potential of yield prediction.

Identifying Associations between Local Drought and Global Sea Surface Temperature

It is clear that droughts have a fundamental impact on many different elements of society. To reduce the drought-related losses, it is necessary to give decision makers visibility into relationships of oceanic-atmospheric parameters that cause drought. The main target of this paper is to show the efficiency of data mining methods (especially association rules mining) for Identifying the associations between local droughts and large scale oceanic-atmospheric climate phenomena such as Sea Surface Temperature (SST). In this paper, association rules mining technique was offered to discover affiliation between drought of Urmia synoptic station (located in Iran) and de-trend SSTs of the Black, Mediterranean and Red Seas. To examine the accuracy of the rules, the confidence measures of the rules were calculated and compared for different considering lag times. The computed measures confirm reliable performance of the association rules mining method to monitor local drought so that the confidence between the monthly Standardized Precipitation Index (SPI) values and the de-trend SST of seas is higher than 87 percent.

Location Suitability for Small Reservoirs at the Bodri-Kuto River Basin Based on Spatial Monthly SPI

Despite efforts to develop and conserve water resources, almost every year during the dry season, some areas in Central Java province in Indonesia still experience a lack of water, especially in rural villages. These areas require water supply via water trucks and/or portable pumps to obtain water from rivers and groundwater. The Central Java government committed to implementing a program involving the construction of 1000 small reservoirs by 2020 to overcome water shortages. However, the technically ideal sites are mostly privately owned, which requires lengthy and costly land acquisition. To avoid the uncertainty of land acquisition, some small reservoirs were placed on state-owned land, which did not require land acquisition. The consideration of putting more emphasis on state-owned land rather than technically ideal sites for the construction of small reservoirs raise the issue on the location suitability of those reservoirs. In this study, we evaluated the suitability of the location of small reservoirs in the Bodri-Kuto river basin using the monthly standardized precipitation index (SPI). We used rainfall records of 25 stations in the river basin from 2000 to 2016 and analyzed yearly and monthly rainfall data. The yearly analysis shows that the dry conditions (SPI < −0.5) from 2005 to 2009 affected more than half of the rainfall stations (>50%), whereas the rainfall stations that experienced more dry years included Kedung Wungu, Babadan, Bojong, Ketapang, Sekopek, and Podowaras (more than 9 out of 17 years). The monthly SPI shows that during July, August, and September, all the rainfall stations experience moderately dry or worse conditions (SPI < −0.50). Using 25 rainfall stations, we determined the spatial spread of dry conditions using monthly SPI values from July, August, and September. Overlay of the spatial spread of dry conditions with the location of small reservoirs can be used to evaluate the suitability of small reservoir locations. We found that 1 (3%) location is very suitable, 7 (21%) locations are suitable, 24 (73%) locations are moderately suitable, and 1 (3%) location is less suitable. The findings indicate that the spatial distribution of SPI can be used as an additional criterion for evaluating the suitability of small reservoirs’ locations should technically ideal locations be unavailable.

Wavelet-Based Detection of Time-Frequency Changes for Monthly Rainfall and SPI Series in Taiwan

This study aims to assess time-frequency changes and trends of both monthly rainfall and SPI (standardized precipitation index) series in Taiwan using wavelet transform and Mann-Kendall test. The monthly data at Taipei, Sunmoon Lake, Kaohsiung, and Dawu stations with recorded length of 118, 73, 83, and 75 years, respectively, are used. The results of MK test reveal that insignificant positive monotonic trends for both rainfall and SPI series at west-side Taiwan (Taipei, Sunmoon Lake, and Kaohsiung stations) are observed, while both series have significant negative trends at Dawu station located at southeastern Taiwan. Wavelet analyses on the rainfall and SPI series indicate similar variation of wavelet power spectrum over time except the noticeable one-year periodicity in rainfall series and less power spectrum in SPI series. It is worth to note that the sub-decadal 36- and 96-month periodicities are common, but insignificant, at four stations for both series, although different data lengths are used. Combined with MK-test and wavelet-analysis results reveal that slightly less severe and less frequent droughts occur at Taipei and Sunmoon Lake stations, while drought frequencies probably remain unchanged at Kaohsiung and Dawu stations with slightly less severe and more severe droughts respectively occur at these two stations.

Does the long-term precipitation variations and dry-wet conditions exist in the arid areas? A case study from China

The detailed precipitation characteristics is required for the investigation of regional hydrological processes, precipitation related disasters, and water resources management. Quantifying and understanding the precipitation change in arid area are crucial for prediction of its future and avoiding disasters. In this study, it was investigated the precipitation concentration, and its variability along with the linkage of global large-scale circulation anomalies in Xinjiang between 1960 and 2016. Using meteorological data from 54 weather stations coupling with atmospheric circulation indices, several precipitation indices were used as standard measures for determining the basic characteristics of floods and droughts. Results showed that the characteristics of the precipitation concentration feature were identified by two precipitation indices: precipitation concentration degree (PCD) and precipitation concentration period (PCP). The PCD value has decreased in recent years, which illustrates the characteristics of drought. The values of the precipitation concentration index (PCI) that indicated extreme precipitation ranged from 9 to 20 in 57 years and exhibited a significant decreasing trend in 1960–2016. However, an increasing precipitation condition can be observed through the monthly standardized precipitation index (SPI), which indicates that drought–flood abrupt alternation will be more frequent and the abrupt change should be noticed in recent years. It is found to be suitable to use the long-cycle drought–flood abrupt alternation index (LDFAI) to investigate the characteristics of drought–flood abrupt alternation during summers. Further investigations on the linkage between drought–flood abrupt alternation and atmospheric circulation indices showed that the time delay of LDFAI after an atmospheric anomaly will be 1–4 months. The results of this study will help mitigate and regulate the precipitation related problems in arid area, as well as in other regions.

Multi-stage committee based extreme learning machine model incorporating the influence of climate parameters and seasonality on drought forecasting

Drought forewarning is an important decisive task since drought is perceived a recurrent feature of climate variability and climate change leading to catastrophic consequences for agriculture, ecosystem sustainability, and food and water scarcity. This study designs and evaluates a soft-computing drought modelling framework in context of Pakistan, a drought-stricken nation, by means of a committee extreme learning machine (Comm-ELM) model in respect to a committee particle swarm optimization-adaptive neuro fuzzy inference system (Comm-PSO-ANFIS) and committee multiple linear regression (Comm-MLR) model applied to forecast monthly standardized precipitation index (SPI). The proposed Comm-ELM model incorporates historical monthly rainfall, temperature, humidity, Southern Oscillation Index (SOI) at monthly lag (t − 1) and the respective month (i.e., periodicity factor) as the explanatory variable for the drought’s behaviour defined by SPI. The model accuracy is assessed by root mean squared error, mean absolute error, correlation coefficient, Willmott’s index, Nash-Sutcliffe efficiency and Legates McCabe’s index in the independent test dataset. With the incorporation of periodicity as an input factor, the performance of the Comm-ELM model for Islamabad, Multan and Dera Ismail Khan (D. I. Khan) as the test stations, was remarkably improved in respect to the Comm-PSO-ANFIS and Comm-MLR model. Other than the superiority of Comm-ELM over the alternative models tested for monthly SPI forecasting, we also highlight the importance of the periodicity cycle as a pertinent predictor variable in a drought forecasting model. The results ascertain that the model accuracy scales with geographic factors, due to the complexity of drought phenomenon and its relationship with the different inputs and data attributes that can affect the overall evolution of a drought event. The findings of this study has important implications for agricultural decision-making where future knowledge of drought can be used to develop climate risk mitigation strategies for better crop management.

Application of Archimedean Copulas to the Analysis of Drought Decadal Variation in China

Based on daily precipitation data collected from 1171 stations in China during 1961-2015, the monthly standardized precipitation index was derived and used to extract two major drought characteristics which are drought duration and severity. Next, a bivariate joint model was established based on the marginal distributions of the two variables and Archimedean copula functions. The joint probability and return period were calculated to analyze the drought characteristics and decadal variation. According to the fit analysis, the Gumbel-Hougaard copula provided the best fit to the observed data. Based on four drought duration classifications and four severity classifications, the drought events were divided into 16 drought types according to the different combinations of duration and severity classifications, and the probability and return period were analyzed for different drought types. The results showed that the occurring probability of six common drought types (0 < D ≤ 1 and 0.5 < S ≤ 1, 1 < D ≤ 3 and 0.5 < S ≤ 1, 1 < D ≤ 3 and 1 < S ≤ 1.5, 1 < D ≤ 3 and 1.5 < S ≤ 2, 1 < D ≤ 3 and 2 < S, and 3 < D ≤ 6 and 2 < S) accounted for 76% of the total probability of all types. Moreover, due to their greater variation, two drought types were particularly notable, i.e., the drought types where D ≥ 6 and S ≥ 2. Analyzing the joint probability in different decades indicated that the location of the drought center had a distinctive stage feature, which cycled from north to northeast to southwest during 1961-2015. However, southwest, north, and northeast China had a higher drought risk. In addition, the drought situation in southwest China should be noted because the joint probability values, return period, and the analysis of trends in the drought duration and severity all indicated a considerable risk in recent years.

Spatio-temporal variability of dryness/wetness in the middle and lower reaches of the Yangtze River Basin and correlation with large-scale climatic factors

The middle and lower reaches of the Yangtze River Basin (MLYR) are greatly affected by frequent drought/flooding events and abrupt alternations of these events in China. The purpose of this study is to analyze the spatial and temporal variability of dryness/wetness based on the data obtained from 75 meteorological stations in the MLYR for the period 1960–2015 and investigate the correlations between dryness/wetness and atmospheric circulation factors. The empirical orthogonal function method was applied in this study based on the monthly Standardized Precipitation Index at a 12-month time scale. The first leading pattern captured the same characteristics of dryness/wetness over the entire MLYR area and accounted for 40.87% of the total variance. Both the second and third leading patterns manifested as regional features of variability over the entire MLYR. The cross-wavelet transform method was applied to explore the potential relationship between the three leading patterns and the large-scale climate factors, and finally the relationships between drought/wetness events and climate factors were also analyzed. Our results indicated that the main patterns of dryness/wetness were primarily associated with the Nino 3.4, Indian Ocean Dipole, Southern Oscillation Index and Northern Oscillation Index, with the first pattern exhibiting noticeable periods and remarkable changes in phase with the indices.

Trends and variability of meteorological drought over the districts of India using standardized precipitation index

Meteorological drought during the southwest monsoon season and for the northeast monsoon season over five meteorological subdivisions of India for the period 1901–2015 has been examined using district and all India standardized precipitation index (SPI). Whenever all India southwest monsoon rainfall was less than −10% or below normal, for those years all India SPI was found as −1 or less. Composite analysis of SPI for the below normal years, viz., less than −15% and −20% of normal rainfall years indicate that during those years more than 30% of country’s area was under drought condition, whenever all India southwest monsoon rainfall was –15% or less than normal. Trend analysis of monthly SPI for the monsoon months identified the districts experiencing significant increase in drought occurrences. Significant positive correlation has been found with the meteorological drought over most of the districts of central, northern and peninsular India, while negative correlation was seen over the districts of eastern India with NINO 3.4 SST. For the first time, meteorological drought analysis over districts and its association with equatorial pacific SST and probability analysis has been done for the northeast monsoon over the affected regions of south peninsular India. Temporal correlation of all India southwest monsoon SPI and south peninsular India northeast monsoon SPI has been done with the global SST to identify the teleconnection of drought in India with global parameters.

Application of a hybrid association rules/decision tree model for drought monitoring

The previous researches have shown that the incorporation of the oceanic-atmospheric climate phenomena such as Sea Surface Temperature (SST) into hydro-climatic models could provide important predictive information about hydro-climatic variability. In this paper, the hybrid application of two data mining techniques (decision tree and association rules) was offered to discover affiliation between drought of Tabriz and Kermanshah synoptic stations (located in Iran) and de-trend SSTs of the Black, Mediterranean and Red Seas. Two major steps of the proposed model were the classification of de-trend SST data and selecting the most effective groups and extracting hidden information involved in the data. The techniques of decision tree which can identify the good traits from a data set for the classification purpose were used for classification and selecting the most effective groups and association rules were employed to extract the hidden predictive information from the large observed data. To examine the accuracy of the rules, confidence and Heidke Skill Score (HSS) measures were calculated and compared for different considering lag times. The computed measures confirm reliable performance of the proposed hybrid data mining method to forecast drought and the results show a relative correlation between the Mediterranean, Black and Red Sea de-trend SSTs and drought of Tabriz and Kermanshah synoptic stations so that the confidence between the monthly Standardized Precipitation Index (SPI) values and the de-trend SST of seas is higher than 70 and 80% respectively for Tabriz and Kermanshah synoptic stations.

An Entropy-Based Investigation into Bivariate Drought Analysis in China

Because of the high correlation between random variables of drought duration and severity, their joint distribution is difficult to obtain by traditional mathematical methods. However, the copula method has proved to be a useful tool for analyzing the frequency of drought duration and severity. Most studies have used different marginal distribution functions to fit the drought duration and severity distributions. This requires a great deal of contrast analysis, and sometimes two or more distributions fit the data well. Based on entropy theory, however, a unified probability distribution function is derived which reduces complex contrast analysis and improves the filtering distribution function. Based on monthly precipitation data at 162 stations in China for 1961–2015, the monthly standardized precipitation index was calculated and used to extract drought duration and severity. Then the entropy distribution was used to fit the distributions of drought duration and severity, and to establish the correspondence between them. The probabilities of the interval and return periods were then determined using the copula method. An analysis of the discrepancy between the conventional and entropy-based methods indicated that the entropy distribution showed a better fit than conventional methods for drought duration distribution, although no obvious difference was found in drought severity distribution. The entropy-based results were more consistent with the empirical data, whereas conventional methods showed apparent deviation in some drought types. Hence, the entropy-based method is proposed as an alternative method of deriving the marginal distributions of drought duration and severity, and for analyzing the interval probability and return period in China.

Trend analysis of precipitation and drought in the Aegean region, Turkey

ABSTRACTTrend analysis of precipitation and drought will play a significant role in the future development and sustainable management of water resources. It is the main purpose of this paper to analyse meteorological droughts and trends. The variability of the standardized precipitation index (SPI) was investigated at 1, 3, 6, 9, 12 and 24 monthly intervals. The trends of 1, 3, 6, 9, 12 and 24 monthly SPI results were analysed by applying linear regression and the Mann–Kendall and Spearman's rho tests at the 5% significance level. The linear slopes of the trends were calculated with a technique proposed by Sen. Meteorological data from eight meteorological stations in the Aegean region of Turkey were used for the period 1960–2013.The monthly precipitation trend decreases in December, January, February and March in all regions according to the linear regression analysis results. Annual precipitation decreased at five stations. In drought analysis by the SPI, in a short time period (such as 3 months) drought is more frequent but shorter, and as the period increases the duration of drought also increases but frequency decreases. Winter droughts are beginning to occur in the class of ‘severe’ in recent years. In this study carried out for different time periods according to SPI values in all stations, the highest ranges are in the normal and mild drought degrees. In addition, severe and extreme droughts are also seen intensely frequently. Trend analysis of precipitation and drought is compared. The trend analyses of precipitation are in parallel with the results of drought analyses.

Recent changes of extreme dryness/wetness pattern and its possible impact on rice productivity in Jiangsu Province, southeast China

Based on the data of monthly precipitation and rice productivity at 61 meteorological stations in Jiangsu during 1966–2010, the possible impact of dryness/wetness variability on rice productivity was explored in this paper. Principal component analysis was applied to the climate-induced rice productivity index (CRPI) series and divided Jiangsu into six sub-regions—south, northwest, east, middle, southwest and northernmost. Mann–Kendall trend test for monthly Standardized Precipitation Index (SPI) series in each sub-regions shows that most of Jiangsu was dominated by wetter tendency in August and drier tendency in September. CRPI had stronger negative correlation with monthly SPI series in the majority of sub-regions. Especially, the SPI in October, March, July and August had significantly negative linear relationship with CRPI in south Jiangsu, northwest Jiangsu, middle Jiangsu and southwest Jiangsu, respectively, which indicated the obvious unfavourable impact of extreme wetness episodes on rice productivity.