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This article presents an analysis of monthly precipitation totals based on data from the Global Precipitation Climatology Centre and monthly mean temperatures from the National Oceanic and Atmospheric Administration for 377 catchments located worldwide. The data sequences, spanning 110 years from 1901 to 2010, are analysed. These long-term precipitation and temperature sequences are used to assess the variability in climate characteristics, referred to here as polarisation. This article discusses the measures of polarisation used in the natural sciences. This study adopts two measures to evaluate the phenomenon of polarisation. The first measure is defined based on a stationary time series, calculated as the ratio of the amplitude of values to the standard deviation. The second measure is proposed as the difference in trends of these values. Based on the analysis of monthly precipitation data in the studied catchments, polarisation components are confirmed in 25% of the cases, while in the remaining 75%, they are not. For temperature data, polarisation is confirmed in 12.2% of the cases and not in the remaining 88.8%. The trend analysis employs Mann–Kendall tests at a 5% significance level. The Pettitt test is used to determine the point of trend change for precipitation and temperature data. This article underscores the complex relationship between climate polarisation and sustainable development, reaffirming that sustainable development cannot be pursued in isolation from the challenges posed by climate change. It emphasises the importance of integrating environmental, social, and economic strategies to adapt to extreme climatic events and mitigate their effects. This research is supported by detailed graphical analyses, with the results presented in tabular form. [ABSTRACT FROM AUTHOR]

The socioeconomic sector increasingly relies on accessible and cost-effective tools for predicting climatic conditions. This study employs a straightforward decision tree classifier model to identify similar monthly ENSO (El Niño Southern Oscillation) conditions from December 2000 to November 2023, using historically monthly ENSO Indices data from December 1950 to November 2000 as a reference. The latter is to construct monthly precipitation hindcasts for Mexico spanning from December 2000 to November 2023 through historically high-resolution monthly precipitation rasters. The model's performance is evaluated at a global and local scale across seasonal periods (winter, spring, summer, and fall). Assessment using global Hansen–Kuiper Skill Score and Heidkee Skill Score metrics indicates skillful performance across all seasons (>0.3) nationwide. However, local metrics reveal a higher spatial percent of corrects (>0.40) in winter and spring, corresponding to dry seasons, while a lower percent of corrects (<0.40) are observed in more extensive areas during summer and fall, indicative of rainy seasons, due to increased variability in precipitation. The choice of averaging method influences the degree of underestimations and overestimations, impacting the model's variability. Spearman correlations highlight regions with significant model performance, revealing potential misinterpretations of high hit rates during winter and spring. Notably, during the fall, the model demonstrates spatial skill across most of Mexico, while in the spring, it performs well in the southern and northeastern regions and, in the summer, in the northwestern areas. Integration of accurate forecasts of ENSO Indices to predict precipitation months ahead is crucial for the operational efficacy of this model, given its heavy reliance on anticipating ENSO behavior. Overall, the empirical method exhibits great promise and potential for application in other developing countries directly impacted by the El Niño phenomenon, owing to its low resource costs. [ABSTRACT FROM AUTHOR]

This study delves into the application of Shannon entropy to analyze the long-term variability in climate data, specifically focusing on precipitation and temperature. By employing data from 1901 to 2010 across 377 catchments worldwide, we investigated the dynamics of climate variables using the generalized extreme value (GEV) distribution and Shannon entropy measures. The methodology hinged on the robust bootstrap technique to accommodate the inherent uncertainties in climatic data, enhancing the reliability of our entropy estimates. Our analysis revealed significant trends in entropy values, suggesting variations in the unpredictability and complexity of climate behavior over the past century. These trends were critically assessed using non-parametric tests to discern the underlying patterns and potential shifts in climate extremes. The results underscore the profound implications of entropy trends in understanding climate variability and aiding the prediction of future climatic conditions. This research not only confirms the utility of Shannon entropy in climatological studies but also highlights its potential in enhancing our understanding of complex and chaotic climate systems. The study's findings are vital for developing adaptive strategies in response to the evolving nature of climate extremes, thus contributing to more informed decision-making in environmental management and policy formulation. [ABSTRACT FROM AUTHOR]

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Seasonal precipitation forecasting is vital for weather-sensitive sectors. Global Circulation Models (GCM) routinely produce ensemble Seasonal Climate Forecasts (SCFs) but suffer from issues like low forecast resolution and skills. To address these issues in this study, we introduce a post-processing method, Quantile Ensemble Bayesian Model Averaging (QEBMA). It utilises quantiles from a GCM ensemble forecast to create a pseudo-ensemble forecast. Through their reasonable linear relationships with observations, each pseudo-member connects a hurdle distribution with a point mass at zero for dry months and a gamma distribution for wet months. These distributions are mixed to construct a forecast probability distribution with their weights, proportional to the quantiles' historical forecast performance. QEBMA is applied to three GCMs, including GloSea5 from the United Kingdom, ECMWF from Europe and ACCESS-S1 from Australia, for monthly precipitation forecasts in 32 locations across four climate zones in Australia. Leave-one-month-out cross-validation results illustrate that QEBMA enhances forecast skills compared to raw GCMs and other post-processing techniques, including quantile mapping and Extended Copula Post-Processing (ECPP), for forecast lead time of 0 to 2 months, based on five metrics. The skill improvements achieved by QEBMA are often statistically significant, particularly when compared to raw GCM forecasts across the 32 study locations. Among these post-processing models, only QEBMA consistently outperforms the SCF benchmark climatology, offering a promising alternative for improving seasonal precipitation forecasts. [ABSTRACT FROM AUTHOR]

The present study was conducted to investigate NPP changes and its relationship with drought. The monthly time series of NPP for the country's rangelands was prepared from the sum of net photosynthesis production (PSN) of the eight-day MODIS sensor (MOD17A2H) with a spatial scale of 500 m for 2000 to 2022.The monthly rainfall data of 165 synoptic stations were obtained from the National Meteorological Organization to calculate the Standard Precipitation Index (SPI) and monitor droughts in this period. Box plots of precipitation and NPP were prepared and showed that the highest values of NPP with an average of more than 50 gC.m-2month-1 occur during spring and early summer in humid areas. Results showed that NPP is increasing in Iran's rangeland ecosystems, and on average, the NPP of all rangeland ecosystems in the country is about 112.6 ± 13 gC.m-2.year-1 ,in other words, about 76 million tons of carbon per year. It is absorbed by the plants in the rangeland ecosystems of the country. The trend of changes in the monthly series of NPP was not significant due to the influence of seasonal characteristics, but the annual changes of NPP showed a significant trend of increase in all regions.

The socioeconomic sector increasingly relies on accessible and cost-effective tools for predicting climatic conditions. This study employs a straightforward decision tree classifier model to identify similar monthly ENSO (El Niño Southern Oscillation) conditions from December 2000 to November 2023, using historically monthly ENSO Indices data from December 1950 to November 2000 as a reference. The latter is to construct monthly precipitation hindcasts for Mexico spanning from December 2000 to November 2023 through historically high-resolution monthly precipitation rasters. The model’s performance is evaluated at a global and local scale across seasonal periods (winter, spring, summer, and fall). Assessment using global Hansen–Kuiper Skill Score and Heidkee Skill Score metrics indicates skillful performance across all seasons (>0.3) nationwide. However, local metrics reveal a higher spatial percent of corrects (>0.40) in winter and spring, corresponding to dry seasons, while a lower percent of corrects (

Basin-based water management strategy is one of the necessary instruments for the protection and sustainable use of water resources against climate change. In this paper, the monthly precipitation distributions of the 25 major basins in Türkiye were produced, and amounts and volumes were computed and analyzed. Only annual modeling and assessments of precipitation may hide months with precipitation shortages. Empirical Bayesian Kriging (EBK), Ordinary Kriging (OK), and Inverse Distance Weighting (IDW) were implemented in interpolation. EBK outperformed in all months and calculations were based on the EBK. The month with the highest precipitation potential in Türkiye is December (77.9 mm, 60.77 billion m3), and the month with the lowest precipitation potential is August (13.8 mm, 10.76 billion m3). In the basins, the monthly precipitation amounts range between 2.7 mm and 185.2 mm, and the volumes range between 0.02 billion m3 and 13.24 billion m3. The basins with the highest precipitation depth were determined as the East Black Sea, Antalya, Asi, and Ceyhan, and the lowest as the Small Menderes, Konya, and Tigris-Euphrates in different months. The monthly precipitation patterns and potentials of the basins vary widely. In May, June, July, August, and September, when water, particularly agricultural irrigation, is required the most, the 20 basins, except for the 5 located in Northern Türkiye precipitation shortage was determined.

This study delves into the application of Shannon entropy to analyze the long-term variability in climate data, specifically focusing on precipitation and temperature. By employing data from 1901 to 2010 across 377 catchments worldwide, we investigated the dynamics of climate variables using the generalized extreme value (GEV) distribution and Shannon entropy measures. The methodology hinged on the robust bootstrap technique to accommodate the inherent uncertainties in climatic data, enhancing the reliability of our entropy estimates. Our analysis revealed significant trends in entropy values, suggesting variations in the unpredictability and complexity of climate behavior over the past century. These trends were critically assessed using non-parametric tests to discern the underlying patterns and potential shifts in climate extremes. The results underscore the profound implications of entropy trends in understanding climate variability and aiding the prediction of future climatic conditions. This research not only confirms the utility of Shannon entropy in climatological studies but also highlights its potential in enhancing our understanding of complex and chaotic climate systems. The study’s findings are vital for developing adaptive strategies in response to the evolving nature of climate extremes, thus contributing to more informed decision-making in environmental management and policy formulation.

The present study was conducted to investigate NPP changes and its relationship with drought. The monthly time series of NPP for the country's rangelands was prepared from the sum of net photosynthesis production (PSN) of the eight-day MODIS sensor (MOD17A2H) with a spatial scale of 500 m for 2000 to 2022. The monthly rainfall data of 165 synoptic stations were obtained from the National Meteorological Organization to calculate the Standard Precipitation Index (SPI) and monitor droughts in this period. Box plots of precipitation and NPP were prepared and showed that the highest values of NPP with an average of more than 50 gC.m-2month-1 occur during spring and early summer in humid areas. Results showed that NPP is increasing in Iran's rangeland ecosystems, and on average, the NPP of all rangeland ecosystems in the country is about 112.6 ± 13 gC.m-2.year-1, in other words, about 76 million tons of carbon per year. It is absorbed by the plants in the rangeland ecosystems of the country. The trend of changes in the monthly series of NPP was not significant due to the influence of seasonal characteristics, but the annual changes of NPP showed a significant trend of increase in all regions. [ABSTRACT FROM AUTHOR]

Accurate forecasting of monthly precipitation is of great significance for national production, disaster prevention and mitigation, and water resources allocation management. However, it is difficult for individual models to accurately accomplish the task of predicting precipitation, and there is also the problem of insufficient accuracy of peak and trough prediction. Therefore, to solve this problem, this paper will provide a CEEMDAN-SVM-LSTM model that combines a fully adaptive noise ensemble empirical modal decomposition (CEEMDAN),a support vector machine (SVM) and the long short-term memory (LSTM) neural network. The CEEMDAN algorithm is first used to decompose the precipitation time series data into different modal components, the SVM model is then applied to the first modal component and the LSTM is applied to the remaining modal components. The precipitation data of Lanzhou city is taken as an example and brought into the model for testing and comparing with the performance of single LSTM model, differential integrated moving average autoregressive model (ARIMA), back propagation (BP) neural network model,support vector machine(SVM), extreme gradient boosting(XGBOOST), CEEMDAN-LSTM model and CEEMDAN-SVM model. After the experimental verification, the CEEMDAN-SVM-LSTM model effectively improves the fit between the observed and predicted values, overcomes the problem of low accuracy of peak and trough prediction, and significantly outperforms other models. [ABSTRACT FROM AUTHOR]

Studies of the spatial and temporal variability of precipitation concentration are necessary. This variability is a significant climate element and also a critical socioeconomic factor. This study aims to contribute to the knowledge of rainfall in Argentina using records of monthly precipitation for 64 stations (period 1991–2021) to calculate the monthly precipitation concentration index (PCI). Precipitation in Argentina, given its vast territory, presents a great variability with a wide range of rainfall regimes; the range of PCI values is 10.6–27.3. Values of PCI range from uniform distribution (less than 10) to strong irregularity of precipitation distribution (greater than 20). The north-west of the country shows a high concentration and the south has a low concentration. Analysis shows that the majority of the stations have positive trends in PCI (although not significant), and this increase in concentration of precipitation could become a risk depending on the evolution of the associated rain.

This study analyzes the spatiotemporal variability of precipitation at the scale of the Moulouya watershed in eastern Morocco, which is very vulnerable to the increasing water shortage. For this purpose, we opted for wavelet transformation, a method based on the spectral analysis of data which allows for periodic components of a rainfall time series to change with time. The results obtained from this work show spectral power across five frequency ranges of variability: 1 to 2 years, 2 to 4 years, 4 to 8 years, 8 to 16 years, and 16 to 32 years. The duration of significant power at these frequencies is generally not homogeneous and varies from station to station. The most widespread frequency over the entire study area was found in the 4- to 8-year range. This mode of variability can last up to 27 consecutive years. In most of the basin, this mode of variability was observed around the period between 1990 and 2010. Oscillations at 8 to 16 years in frequency appear in only five series and over different time periods. The 16- to 32-year mode of variability appears in 15 stations and extends over the period from 1983 to 2008. At this level, signal strength is very weak compared to other higher-frequency modes of variability. On the other hand, the mode of variability at the 1- to 2-year frequency range appeared to be continuous in some stations and intermittent in others. This allowed us to regionalize our study basin into two homogeneous clusters that only differ in variability and rainfall regime. [ABSTRACT FROM AUTHOR]

The Balkan Peninsula is a geographical region under various large-scale climatic influences, one of the most significant being the Mediterranean Sea in the southwest and the continent in the northeast. The novelty of this study is that the border between the zones with prevailing maritime or continental climate conditions is clearly identified by the month with the highest precipitation during the year. We use the gridded data product TS_4.06 of the Climatic Research Unit for monthly precipitation to identify the timing of the maximum rainfall at different locations. The grid boxes with highest precipitation in the cold part of the year (October to March) are considered to be under prevailing Mediterranean influences and, on the contrary, the ones with the highest precipitation are in the warm part of the year (April to September); these are climates with prevailing continental characteristics. In general, this border separates the zones with Cs and Dw types of climates. Its spatial variability at a decadal time-scale is discussed for the period from 1901–2021 and a general weakening of the Mediterranean influence over the Balkan Peninsula is found. However, for the last three decades, from 1991–2021, the tendency is the opposite. A periodicity of ~20 years is identified, suggesting that during the decade of 2020–2030, the Mediterranean influence in this region will dominate. [ABSTRACT FROM AUTHOR]

The lack of a complete and reliable data series often represents the main difficulty in carrying out climate studies. Diverse causes, such as human and instrumental errors, false and incomplete records, and the use of obsolete equipment in some meteorological stations, give rise to inhomogeneities that do not represent climatic reality. This work in the northern part of the Moroccan Middle Atlas used 22 meteorological stations with sometimes-incomplete monthly precipitation data from 1970 to 2019. The homogenization and estimation of the missing data were carried out with the R software package Climatol version 3.1.1. The trends in the series were quantified by the Mann–Kendall nonparametric test. The results obtained show a low root mean square error (RMSE), between the original and homogenized data, of between 0.5 and 38.7 mm per month, with an average of 8.5 mm. Rainfall trends for the months of December through June are generally downward. These negative trends are significantly stronger in the southern and eastern parts of the study area, especially during the month of April (the wettest month). On the other hand, July shows positive trends, with 71% of stations having an increasing precipitation tendency, although only five (or 1/3) of these are statistically significant. From August to November, generally positive trends were also observed. For these months, the percentage of series with a positive and significant trend varied between 55 and 77%. [ABSTRACT FROM AUTHOR]

Drought is ranked second in type of natural phenomena associated with billion dollars weather disaster during the past years. It is estimated that in EU countries the number of people affected by drought was increased by 20% over the last decades. It is widely recognized that the Standardized Precipitation Index (SPI) can effectively provide drought characteristics in time and space. The paper questions the standard approach to estimate the SPI based on the Gamma probability distribution function, assessing the fitting performance of different biparametric distribution laws to monthly precipitation data. We estimate SPI time series, for different scale of temporal aggregation, on an unprecedented dataset consisting of 332 rain gauge stations deployed across Italy with observations recorded between 1951 and 2000. Results show that the Lognormal distribution performs better than the Gamma in fitting the monthly precipitation data at all time scales, affecting drought characteristics estimated from SPI signals. However, drought events detected using the original and the best fitting approaches does not diverge consistently in terms of return period. This suggests that the SPI in its original formulation can be applied for a reliable detection of drought events and for promoting mitigation strategies over the Italian peninsula. [ABSTRACT FROM AUTHOR]

The simulation of precipitation changes can provide references for the prediction and prevention of flood disasters, and has guiding significance for the comprehensive utilization of regional water resources. Precipitation forecasting is difficult due to the randomness and uncertainty of precipitation events. CEEMD can effectively overcome modal aliasing and white noise interference. The WTD process has obvious denoising effects on the original signal. GRU can effectively solve long-term memory and reflection. Based on the advantages of problems such as gradients in propagation, a CEEMD-WTD-GRU precipitation prediction coupling model is constructed. The second decomposition of CEEMD-WTD-GRU can more effectively extract complex time series information. The time series forecasting provided a new method, which effectively improved the accuracy of the forecast and applied it to the forecast of monthly precipitation in Shanghai. The research results show that the average absolute error of the CEEMD-WTD-GRU model is 3.86, the average relative error is 3.30%, and the Nash efficiency coefficient is 0.99. The prediction accuracy is better than the CEEMD-WTD-GRU model without noise reduction, the CEEMD-LSTM model and GRU model, which shows that it has strong nonlinear and complex process learning ability in hydrological factor simulation, and can be used for regional precipitation prediction. HIGHLIGHTS Complementary ensemble empirical modal decomposition (CEEMD) is a relatively novel data preprocessing method that can effectively reduce the non-smoothness of time series.; Wavelet threshold noise (WTD) reduction is an excellent noise reduction technology that can effectively reduce the noise in the signal.; Gated Recurrent Unit (GRU) as a prediction model is more adept at handling long time series.;

The teleconnection modeling of hydro-climatic events is a complex problem with highly uncertain circumstances. In contrast to the classic fuzzy logic methods, by using the Z-number in addition to the constraint of information, and by evaluating the data reliability, it is possible to characterize the degree of ambiguity of data. In this regard, this study investigates the performance of the Z-number-based model (ZBM) in prediction of classified monthly precipitation (MP) events of two synoptic stations in Iran (up to five months in advance). To this end, the sea surface temperature (SST) of adjacent seas was used as a predictor. The suggested model, by using Z-number directly and applying fuzzy Hausdorff distance to determine weights of if-then rules, predicted MP events of both the stations with over 70% confidence. Analysis of the results in the test step showed that the ZBM compared to the traditional fuzzy approach improved the results by 69% for Kermanshah and 112% for Tabriz. Overall, the Z-number concept by assessing events reliability can be used in various sectors of water resources management such as decision-making and drought monitoring. HIGHLIGHTS In this study, the performance of the Z-number-based model in teleconnection modeling is investigated.; In contrast to classic fuzzy logic, Z-numbers consist of both restraint and reliability of data.; The performance of the Z-number-based model and the conventional fuzzy model are compared.; The Z-number-based model can predict classified monthly precipitation based on SST variations.;

Precipitation forecasting is an important guide to the prevention and control of regional droughts and floods, the rational use of water resources and the ecological protection. The precipitation process is extremely complex and is influenced by the intersection of many variables, with significant randomness, uncertainty and non-linearity. Based on the advantages that complementary ensemble empirical modal decomposition (CEEMD) can effectively overcome modal aliasing, white noise interference, and the ability of long short-term memory (LSTM) networks to handle problems such as gradient disappearance. A CEEMD–LSTM coupled long-term and short-term memory network model was developed and adopted for monthly precipitation prediction of Zhengzhou City. The performance shows that the CEEMD–LSTM model has a mean absolute error of 0.056, a root mean square error of 0.153, a mean relative error of 2.73% and a Nash efficiency coefficient of 0.95, which is better than the CEEMD–Back Propagation (BP) neural network model, the LSTM model and the BP model in terms of prediction accuracies. This demonstrates its powerful nonlinear and complex process learning capability in hydrological factor simulation for regional precipitation prediction. HIGHLIGHTS Complementary ensemble empirical modal decomposition (CEEMD) is a relatively novel data preprocessing method that can effectively reduce the non-smoothness of time series.; Long short-term memory network (LSTM) as a prediction model is more adept at handling long time series.; The CEEMD–LSTM coupled has better nonlinear and complex process learning ability in hydrological factor simulation.;

Using deep learning to identify meteorological factors has enabled optimal predictions of Thailand’s seasonal pre‐ cipitation two months in advance. A combination of surface temperature and pressure, specific humidity, and wind speed (zonal and meridional components) was tested. Examining each combination of meteorological factor has created optimal input data for seasonal precipitation fore‐ casts. In addition, the hyperparameters of each model were calculated by Bayesian optimization. Predictive model per‐ formance tended to be better when the weight for pressure was higher, while a higher weight for specific humidity reduced predictive performance. Finally, visualization of the positive neuron values in all the coupled layers of the first layer showed that the regions with the highest fre‐ quency of occurrence were the El Niño monitoring areas such as the “Indian Ocean Basin Wide” (IOBW) and “NINO WEST”. [ABSTRACT FROM AUTHOR]

The validation of precipitation estimates is necessary for the selection of the most appropriate dataset, as well as for having confidence in its selection. Traditional validation against gauges or radars is much less effective when the quality of these references (which are considered the 'truth') degrades, such as in areas of poor coverage. In scenarios like this where the 'truth' is unreliable or unknown, triple collocation analysis (TCA) facilitates a relative ranking of independent datasets based on their similarity to each other. TCA has been successfully employed for precipitation error estimation in earlier studies, but a thorough evaluation of its effectiveness over Australia has not been completed before. This study assesses the use of TCA for precipitation verification over Australia using satellite datasets in combination with reanalysis data (ERA5) and rain gauge data (AGCD) on a monthly timescale from 2001 to 2020. Both the additive and multiplicative models for TCA are evaluated. These results are compared against the traditional verification method using gauge data and Multi-Source Weighted-Ensemble Precipitation (MSWEP) as references. AGCD (KGE = 0.861), CMORPH-BLD (0.835), CHIRPS (0.743), and GSMaP (0.708) were respectively found to have the highest KGE when compared to MSWEP. The ranking of the datasets, as well as the relative difference in performance amongst the datasets as derived from TCA, can largely be reconciled with the traditional verification methods, illustrating that TCA is a valid verification method for precipitation over Australia. Additionally, the additive model was less prone to outliers and provided a spatial pattern that was more consistent with the traditional methods. [ABSTRACT FROM AUTHOR]

Monthly precipitation samples were collected at Hirosaki, Aomori Prefecture from January 2018 to December 2020 to measure the ion species and stable hydrogen and oxygen isotope ratios in order to understand the regional properties. The tritium concentration ranged from 0.28 to 1.20 Bq/L, with mean values (±S.D.) of 0.52 ± 0.18, 0.67 ± 0.25 and 0.63 ± 0.21 Bq/L in 2018, 2019 and 2020, respectively. This concentration level was almost the same as for Rokkasho, Aomori Prefecture. The tritium concentration had clear seasonal variation: high in the spring and low in the summer. This trend was thought to arise from seasonal fluctuations in the atmospheric circulation. On the other hand, the pH tended to be low, and the electrical conductivity (EC) tended to be high from the winter to the spring. The ion components, which major ion species contained in sea salt, also tended to be high in the winter, and these components had a strong influence on EC. The d-excess values were high in the winter and low in the summer, and when this trend was considered from the viewpoint of the wind direction data in Hirosaki, these dust components were attributed to the northwest monsoon in the winter to the spring coming from the Asian continent. [ABSTRACT FROM AUTHOR]

This study analyzes the spatiotemporal variability of precipitation at the scale of the Moulouya watershed in eastern Morocco, which is very vulnerable to the increasing water shortage. For this purpose, we opted for wavelet transformation, a method based on the spectral analysis of data which allows for periodic components of a rainfall time series to change with time. The results obtained from this work show spectral power across five frequency ranges of variability: 1 to 2 years, 2 to 4 years, 4 to 8 years, 8 to 16 years, and 16 to 32 years. The duration of significant power at these frequencies is generally not homogeneous and varies from station to station. The most widespread frequency over the entire study area was found in the 4- to 8-year range. This mode of variability can last up to 27 consecutive years. In most of the basin, this mode of variability was observed around the period between 1990 and 2010. Oscillations at 8 to 16 years in frequency appear in only five series and over different time periods. The 16- to 32-year mode of variability appears in 15 stations and extends over the period from 1983 to 2008. At this level, signal strength is very weak compared to other higher-frequency modes of variability. On the other hand, the mode of variability at the 1- to 2-year frequency range appeared to be continuous in some stations and intermittent in others. This allowed us to regionalize our study basin into two homogeneous clusters that only differ in variability and rainfall regime.

The Balkan Peninsula is a geographical region under various large-scale climatic influences, one of the most significant being the Mediterranean Sea in the southwest and the continent in the northeast. The novelty of this study is that the border between the zones with prevailing maritime or continental climate conditions is clearly identified by the month with the highest precipitation during the year. We use the gridded data product TS_4.06 of the Climatic Research Unit for monthly precipitation to identify the timing of the maximum rainfall at different locations. The grid boxes with highest precipitation in the cold part of the year (October to March) are considered to be under prevailing Mediterranean influences and, on the contrary, the ones with the highest precipitation are in the warm part of the year (April to September); these are climates with prevailing continental characteristics. In general, this border separates the zones with Cs and Dw types of climates. Its spatial variability at a decadal time-scale is discussed for the period from 1901–2021 and a general weakening of the Mediterranean influence over the Balkan Peninsula is found. However, for the last three decades, from 1991–2021, the tendency is the opposite. A periodicity of ~20 years is identified, suggesting that during the decade of 2020–2030, the Mediterranean influence in this region will dominate.

The teleconnection modeling of hydro-climatic events is a complex problem with highly uncertain circumstances. In contrast to the classic fuzzy logic methods, by using the Z-number in addition to the constraint of information, and by evaluating the data reliability, it is possible to characterize the degree of ambiguity of data. In this regard, this study investigates the performance of the Z-number-based model (ZBM) in prediction of classified monthly precipitation (MP) events of two synoptic stations in Iran (up to five months in advance). To this end, the sea surface temperature (SST) of adjacent seas was used as a predictor. The suggested model, by using Z-number directly and applying fuzzy Hausdorff distance to determine weights of if-then rules, predicted MP events of both the stations with over 70% confidence. Analysis of the results in the test step showed that the ZBM compared to the traditional fuzzy approach improved the results by 69% for Kermanshah and 112% for Tabriz. Overall, the Z-number concept by assessing events reliability can be used in various sectors of water resources management such as decision-making and drought monitoring. [ABSTRACT FROM AUTHOR]

Precipitation forecasting is of great importance in various aspects of catchment management, drought, and flood warning. Precipitation is regarded as one of the important components of the water cycle and plays a crucial role in measuring the climatic characteristics of each region. The present study aims to forecast monthly precipitation in Semnan city by using artificial neural networks (ANN). For this purpose, we used the minimum and maximum temperature data, mean relative humidity, wind speed, sunshine hours, and monthly precipitation during a statistical period of 18 years (2000-2018). Moreover, an artificial neural network was used as a nonlinear method to simulate precipitation. In this research, all data were normalized due to the different units of inputs and outputs in the forecasting model. Further, seven different scenarios were considered as input for the ANN model. Totally, 70% of the data were used for training while the other 30% were used for testing. The model was evaluated with appropriate statistics such as coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). Scenario 6, which included the inputs of minimum and maximum temperature, mean relative humidity, wind speed, and pressure, provided the best performance compared to other scenarios. The values of , RMSE, and MAE for the superior scenario were 0.8597, 4.0257, and 2.3261, respectively.

The lack of a complete and reliable data series often represents the main difficulty in carrying out climate studies. Diverse causes, such as human and instrumental errors, false and incomplete records, and the use of obsolete equipment in some meteorological stations, give rise to inhomogeneities that do not represent climatic reality. This work in the northern part of the Moroccan Middle Atlas used 22 meteorological stations with sometimes-incomplete monthly precipitation data from 1970 to 2019. The homogenization and estimation of the missing data were carried out with the R software package Climatol version 3.1.1. The trends in the series were quantified by the Mann–Kendall nonparametric test. The results obtained show a low root mean square error (RMSE), between the original and homogenized data, of between 0.5 and 38.7 mm per month, with an average of 8.5 mm. Rainfall trends for the months of December through June are generally downward. These negative trends are significantly stronger in the southern and eastern parts of the study area, especially during the month of April (the wettest month). On the other hand, July shows positive trends, with 71% of stations having an increasing precipitation tendency, although only five (or 1/3) of these are statistically significant. From August to November, generally positive trends were also observed. For these months, the percentage of series with a positive and significant trend varied between 55 and 77%.

Drought is ranked second in type of natural phenomena associated with billion dollars weather disaster during the past years. It is estimated that in EU countries the number of people affected by drought was increased by 20% over the last decades. It is widely recognized that the Standardized Precipitation Index (SPI) can effectively provide drought characteristics in time and space. The paper questions the standard approach to estimate the SPI based on the Gamma probability distribution function, assessing the fitting performance of different biparametric distribution laws to monthly precipitation data. We estimate SPI time series, for different scale of temporal aggregation, on an unprecedented dataset consisting of 332 rain gauge stations deployed across Italy with observations recorded between 1951 and 2000. Results show that the Lognormal distribution performs better than the Gamma in fitting the monthly precipitation data at all time scales, affecting drought characteristics estimated from SPI signals. However, drought events detected using the original and the best fitting approaches does not diverge consistently in terms of return period. This suggests that the SPI in its original formulation can be applied for a reliable detection of drought events and for promoting mitigation strategies over the Italian peninsula.

The aim of this study is to get a better understanding of the variation of tritium concentration for monthly precipitation collected at Okinawa Island, the southwestern part of Japan. Tritium concentrations of monthly precipitation in Okinawa from June 2014 to December 2019 ranged from 0.05 to 0.27 Bq L-1. Seasonal patterns of tritium concentration are determined with higher in spring and lower in autumn. The results of δD and δ18O indicate that the tritium concentration in winter is affected by the dilution effect of isotopes over the East China Sea. The stable isotope ratios and major ion components of tritium concentrations in monthly precipitation on Okinawa Island suggest that tritium concentrations are strongly influenced by the surrounding ocean throughout the year due to the dynamics of air masses. This study makes a significant contribution to the understanding of the behavior of tritium concentrations in monthly precipitation under the special environment of subtropical island regions. In order to investigate the source of high tritium concentration in monthly precipitation in the future, it is necessary to understand the supply of tritium from the stratosphere to the troposphere due to vertical convection of the atmosphere. [ABSTRACT FROM AUTHOR]

In the present study, we used 27 precipitation average monthly data from synoptic, climatologic, rain-guage and evaporative stations located in Zayandeh-Rud river basin for the period of 1970-2014. Before interpolating, the missing data in the time series of each station was reconstructed by the normal ratio method. Also, for the data quality control, the Dickey-Fuller and Shapiro-Wilk tests were used to check the data stationarity and normality. Then, these data were interpolated by six interpolation methods including Inverse Distance Weighting, Natural Neighbor, Tension Spline, Regularized Spline, Ordinary Kriging and Universal Kriging; then each method was evaluated using the cross-validation technique with MAE, MBE and RMSE indices. The results showed that among the spatial interpolation methods, Natural Neighbor method with MAE of 0.24 had the best performance for interpolating precipitation among all of the methods. Also, among Ordinary Kriging, Universal Kriging, Spline and Inverse Distance Weighting methods, respectively, Exponential Kriging with MAE 0.54, Quadratic Drift Kriging with MAE of 0.5, Tension Spline with the MAE of 0.54 and Inverse Distance Weighting with the power of 4 with MAE of 0.57 had the least error compared to other IDW methods.

The validation of precipitation estimates is necessary for the selection of the most appropriate dataset, as well as for having confidence in its selection. Traditional validation against gauges or radars is much less effective when the quality of these references (which are considered the ‘truth’) degrades, such as in areas of poor coverage. In scenarios like this where the ‘truth’ is unreliable or unknown, triple collocation analysis (TCA) facilitates a relative ranking of independent datasets based on their similarity to each other. TCA has been successfully employed for precipitation error estimation in earlier studies, but a thorough evaluation of its effectiveness over Australia has not been completed before. This study assesses the use of TCA for precipitation verification over Australia using satellite datasets in combination with reanalysis data (ERA5) and rain gauge data (AGCD) on a monthly timescale from 2001 to 2020. Both the additive and multiplicative models for TCA are evaluated. These results are compared against the traditional verification method using gauge data and Multi-Source Weighted-Ensemble Precipitation (MSWEP) as references. AGCD (KGE = 0.861), CMORPH-BLD (0.835), CHIRPS (0.743), and GSMaP (0.708) were respectively found to have the highest KGE when compared to MSWEP. The ranking of the datasets, as well as the relative difference in performance amongst the datasets as derived from TCA, can largely be reconciled with the traditional verification methods, illustrating that TCA is a valid verification method for precipitation over Australia. Additionally, the additive model was less prone to outliers and provided a spatial pattern that was more consistent with the traditional methods.

Monthly precipitation samples were collected at Hirosaki, Aomori Prefecture from January 2018 to December 2020 to measure the ion species and stable hydrogen and oxygen isotope ratios in order to understand the regional properties. The tritium concentration ranged from 0.28 to 1.20 Bq/L, with mean values (±S.D.) of 0.52 ± 0.18, 0.67 ± 0.25 and 0.63 ± 0.21 Bq/L in 2018, 2019 and 2020, respectively. This concentration level was almost the same as for Rokkasho, Aomori Prefecture. The tritium concentration had clear seasonal variation: high in the spring and low in the summer. This trend was thought to arise from seasonal fluctuations in the atmospheric circulation. On the other hand, the pH tended to be low, and the electrical conductivity (EC) tended to be high from the winter to the spring. The ion components, which major ion species contained in sea salt, also tended to be high in the winter, and these components had a strong influence on EC. The d-excess values were high in the winter and low in the summer, and when this trend was considered from the viewpoint of the wind direction data in Hirosaki, these dust components were attributed to the northwest monsoon in the winter to the spring coming from the Asian continent.

Monthly precipitation samples from Singapore were collected between 2013 and 2019 for stable isotope analysis to further our understanding of the drivers of tropical precipitation isotopes, in particular, 17O‐excess. δ18O ranges from –11.34‰ to –2.34‰, with a low correlation to rainfall (r = –0.31, p = 0.014), suggesting a weak amount effect. d‐excess is relatively consistent and has an average value of 10.89‰ ± 3.45‰. Compared to high‐latitude regions, 17O‐excess in our samples generally falls in a narrower range from 2 to 47 per meg with an average of 21 ± 11 per meg. Moreover, 17O‐excess shows strong periodic variability; spectral analysis reveals 3‐month, 6‐month, and 2.7‐year periodicities, likely corresponding to intraseasonal oscillations, monsoons, and the El Niño–Southern Oscillation (ENSO), respectively. In contrast, d‐excess shows no clear periodicities. Although spectral analysis only identifies 6‐month periodicity in the δ18O time series, δ18O tracks the Nino3.4 sea surface temperature variability; the average δ18O value (–5.2‰) is higher during El Niño years than ENSO neutral years (–7.6‰). Therefore, regional convection associated with monsoons and ENSO has different impacts on δ18O, d‐excess, and 17O‐excess. 17O‐excess and d‐excess are anticorrelated and do not relate to the relative humidity in moisture source regions. Extremely low humidity and drought conditions in moisture source regions would be required to account for high 17O‐excess. Processes during transport and precipitation likely modify these two parameters, especially 17O‐excess, which no longer record humidity conditions of moisture source regions. Our findings will be useful for further modeling studies to identify physical processes during convection that alter d‐excess and 17O‐excess. Key Points: Monthly tropical precipitation in Singapore exhibits a weak amount effectRegional convection has different impacts on precipitation δ18O, d‐excess, and 17O‐excessProcesses during transport and precipitation have modified 17O‐excess, which no longer records the relative humidity in moisture source regions [ABSTRACT FROM AUTHOR]

Sample entropy can be used to investigate the complexity of precipitation series. However, the randomness of similar tolerance selection may lead to inaccurate results. To solve this problem, the distinction degree theory was introduced to optimize the similar tolerance of sample entropy, and a specific reference frame for the optimization process was provided. The optimized sample entropy was used to study the spatial differences in precipitation complexity and the possible causes from the perspective of the monthly precipitation (MP) and extreme daily precipitation (EDP) in Heilongjiang Province, China. The results demonstrated that the MP and EDP in Heilongjiang Province both had unsteady complex fluctuation characteristics and the optimal similar tolerances of the sample entropy were 0.24 and 0.13 times the standard deviation, respectively. The complexity of the EDP was higher than that of the MP, and their fluctuation ranges were 1.641–2.268 and 0.433–0.870, respectively. The complexity of the MP in the study area could be spatially represented as a basic pattern, which gradually decreased from east to west. The spatial distribution pattern of the EDP complexity indicated that the northern part was higher than the southern part. Altitude was the common influencing factor of the MP and EDP complexities, and it had a greater impact on the spatial pattern of the EDP complexity. Potential factors influencing the spatial difference in MP complexity also included changes in precipitation intensity, water area and residential and industrial land areas. Except for altitude, industrial economic development level may also affect the complexity of the EDP. The results may improve the application of sample entropy in climate system complexity measurements and could provide guiding significance for revealing the spatial variation of precipitation complexity. [ABSTRACT FROM AUTHOR]

The Urmia Lake basin is one of the most important basins in Iran, facing many problems due to poor water management and rainfall reduction. Under current circumstances, it becomes critical to have an advanced understanding of rainfall patterns in the basin, setting the motivation of this study. In this research, the mean monthly meteorological data of six synoptic stations of Urmia Lake basin were used (including relative humidity, temperature, minimum–maximum temperature and pressure) and large-scale atmospheric circulation indices (Southern Oscillation Index, North Atlantic Oscillation, Western Mediterranean Oscillation, Mediterranean Oscillation-Gibraltar/Israel and Mediterranean Oscillation-Algiers/Cairo) and sea surface temperatures of the Mediterranean, Black, Caspian, Red seas and Persian Gulf in the period 1988–2016. Various combinations of these variables used as input to the M5 tree and random forest models were selected by Relief algorithm for each month in three scenarios including atmospheric circulation indices, meteorological variables and combination of both. After the implementation of two models with three different scenarios, the evaluation criteria including correlation coefficient (R), mean absolute error and root-mean-square error were calculated and the Taylor diagram for each model was plotted. Our results showed that the M5 tree model performed superior in January, February, March, April, June, September, November and December, while the random forest model did in the remaining months. In addition, the indications of this study showed that the combination of atmospheric circulation indices and meteorological variables used as input to the models mostly constituted improved results. [ABSTRACT FROM AUTHOR]

Precipitation is one of the most important components of the hydrologic cycle as it is required for multi-objective applications including flood estimation, drought monitoring, watersheds management, hydrology, agriculture, etc. Therefore, its estimation and modeling via a suitable method is a challenging task for hydrologists. The present study seeks to model monthly precipitation at two stations located in Iran. Two artificial intelligence (AI)-based models consisting of multivariate adaptive regression splines (MARS) and k-nearest neighbors (KNN) were used as the modeling techniques. In doing so, nine single-input scenarios under limited climatic data are implemented using minimum, maximum, and mean air temperatures, dew point temperature, station pressure, vapor pressure, relative humidity, wind speed, and antecedent precipitation data. The attained results illustrate that the performance of single MARS and KNN is relatively poor when modeling the monthly precipitation. Additionally, this study develops hybrid models to enhance the precipitation modeling through combining the MARS and KNN models with three diverse types of the time series (TS) models, namely autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA). The most important justification for integrating the models applied is that the AI and TS-based models are respectively capable of modeling the non-linear and linear terms of the hydrological variables such as precipitation. It is therefore necessary to be considered both of the aforementioned terms in the modeling procedure. A performance comparison of the single and hybrid models denotes the higher accuracy of hybrid models than the single ones. However, the hybrid models generated by combining the KNN and the TS models used are the best-performing models. [ABSTRACT FROM AUTHOR]

This paper proposes an interpolation model for monthly rainfall in large areas of complex orography. It has been implemented in the Iberian Peninsula (continental territories of Spain and Portugal), Balearic and Canary Islands covering a territory of almost 600.000 km2. To do this a data set that comprises a total number of 11,822 monthly precipitation series has been created (11,042 provided by the Spanish Meteorological Agency and 780 provided by the National Water Resources Information System of the Portuguese Water Institute). The data set covers the period from October 1940 until September 2005. The interpolation model has been based on the assumption of two different components on monthly precipitation. The first component reflects local and seasonal characteristics and 24 different mean monthly precipitation maps (12) and SDs maps (12) compose it. It considers the varying influence of physiographic variables such as altitude and orientation. The second precipitation component reflects the synoptic pattern that dominated each month of the series and it is composed by series of anomalies of monthly precipitation (780). Anomalies have been interpolated by means of ordinary kriging once local spatial continuity was assumed. Gridded maps of each variable have been developed at 200 m resolution following a hybrid methodology that implements two different interpolation techniques. The first technique applies a regression analysis to derive maps depending on altitude and orientation; the second one is a weighting technique to consider the non‐linearity of the precipitation/altitude dependence. Cross validation has been applied to estimate the goodness of both techniques. Results show an average annual precipitation of 655 mm/year. Although this figure is only 4% less than the estimate of MAGRAMA (2004), regional and local differences are highlighted when the spatial distribution is considered. The model constitutes a comprehensive implementation considering the availability of historical records and the need of avoiding slow calculations in large territories. [ABSTRACT FROM AUTHOR]

In the present study, we used 27 precipitation average monthly data from synoptic, climatologic, rain-guage and evaporative stations located in Zayandeh-Rud river basin for the period of 1970-2014. Before interpolating, the missing data in the time series of each station was reconstructed by the normal ratio method. Also, for the data quality control, the Dickey-Fuller and Shapiro-Wilk tests were used to check the data stationarity and normality. Then, these data were interpolated by six interpolation methods including Inverse Distance Weighting, Natural Neighbor, Tension Spline, Regularized Spline, Ordinary Kriging and Universal Kriging; then each method was evaluated using the crossvalidation technique with MAE, MBE and RMSE indices. The results showed that among the spatial interpolation methods, Natural Neighbor method with MAE of 0.24 had the best performance for interpolating precipitation among all of the methods. Also, among Ordinary Kriging, Universal Kriging, Spline and Inverse Distance Weighting methods, respectively, Exponential Kriging with MAE 0.54, Quadratic Drift Kriging with MAE of 0.5, Tension Spline with the MAE of 0.54 and Inverse Distance Weighting with the power of 4 with MAE of 0.57 had the least error compared to other IDW methods. [ABSTRACT FROM AUTHOR]

Precipitation is regarded as the basic component of the global hydrological cycle. This study develops a recursive approach to long-term prediction of monthly precipitation using genetic programming (GP), taking the Three-River Headwaters Region (TRHR) in China as the study area. The daily precipitation data recorded at 29 meteorological stations during 1961-2014 are collected, among which the data during 1961-2000 are for calibration and the remaining data are for validation. To develop this approach, first, the preliminary estimations of annual precipitation are computed based on a statistical method. Second, the percentage of the monthly precipitation for each month of a year is calculated as the mean monthly precipitation divided by the mean annual precipitation during the study period, and then the preliminary estimation of monthly precipitation for each month of a year is obtained. Third, since GP can be used to improve the prediction results through establishing the relationship of the observations with the preliminary estimations at the past and current times, it is adopted to improve the preliminary estimations. The calibration and validation results reveal that the recursive approach involving GP can provide the more accurate predictions of monthly precipitation. Finally, this approach is used to predict the monthly precipitation over the TRHR till 2050. Overall, the proposed method and the obtained results will enhance our understanding and facilitate future studies regarding the long-term prediction of precipitation in such regions. [ABSTRACT FROM AUTHOR]

The Mediterranean basin is indicated as a hot spot of climate change, which is an area whose climate is especially responsive to variations. The insular environment is one of the most threatened by the current climate change, especially in terms of drought events, with serious consequences for water scarcity and water stress. This issue is even enhanced in small islands, whose ecosystems are among more sensitive to climatic changes and water availability. The stable isotope composition of hydrogen (δ2H) and oxygen (δ18O) in precipitation is globally recognized as a powerful natural tracer in the water cycle and represents the starting point to investigate hydrological processes. The understanding of the prevailing factors that drive the isotopic variability of precipitation in the Mediterranean is therefore essential to unravel the hydrological processes and to ensure proper and sustainable management of potentially vulnerable resources to climate change. Here, we discuss the results of multi-year isotopic monitoring in the period 2014–2021 of monthly precipitation collected on Pianosa Island (Italy), a small island located in the northern Tyrrhenian (western Mediterranean). The lower slope and intercept of the Local Meteoric Water Line of the island compared to the Global Meteoric Water Line indicated warmer and drier climatic conditions, suggesting the existence of sub-cloud evaporation processes of raindrops during precipitation, especially in summer. The mean δ18O of precipitation was lower with respect to other sites placed at higher elevation in this Mediterranean region, due to the lack of summer precipitation which were generally enriched in heavy isotopes. Temperature and amount effects may explain part of the δ18O variability observed at the monthly and seasonal scale. An HYSPLIT-based moisture uptake analysis indicated the area between the western Mediterranean basin, Italy, and the Adriatic Sea as the region that supplied most of the humidity associated with monthly precipitation samples on Pianosa Island. Less moisture was picked from the northwestern areas of Europe, the North Atlantic Ocean, the proximal Atlantic Ocean, the Iberian Peninsula and North Africa. Consistently with the rainout effect, the higher the moisture fraction picked from the more proximal regions, the more positive the δ18O of precipitation occurring on Pianosa Island; conversely, the higher the percentage of moisture sourced from more distal regions, the more negative the δ18O. A multiple linear model was proposed to predict the δ18O of monthly precipitation from temperature, precipitation amount and moisture origin data, which explained 45% of the δ18O variability. The deuterium excess variability on the island was partly controlled by the local climatic variables, whose effect potentially modifies the original d-excess signature imprinted at the moisture source. No relationship was found between the precipitation deuterium excess and moisture sources, suggesting that more attention should be paid when using the deuterium excess as a tracer of moisture origin, especially in the Mediterranean. • Multi-year isotopic monitoring of monthly precipitation on Pianosa Island (Italy) • Sub-cloud evaporation processes of raindrops may occur during precipitation • Temperature, precipitation and moisture origin data explain 45% of the δ18O variability • Opposite-sign correlations between δ18O and moisture picked from proximal or distal regions • No relationship between the precipitation deuterium excess and moisture source [ABSTRACT FROM AUTHOR]

Introduction: In recent years, according to the intelligent models increased as new techniques and tools in hydrological processes such as precipitation forecasting. ANFIS model has good ability in train, construction and classification, and also has the advantage that allows the extraction of fuzzy rules from numerical information or knowledge. Another intelligent technique in recent years has been used in various areas is support vector machine (SVM). In this paper the ability of artificial intelligence methods including support vector machine (SVM) and adaptive neuro fuzzy inference system (ANFIS) were analyzed in monthly precipitation prediction. Materials and Methods: The study area was the city of Gonbad in Golestan Province. The city has a temperate climate in the southern highlands and southern plains, mountains and temperate humid, semi-arid and semi-arid in the north of Gorganroud river. In total, the city's climate is temperate and humid. In the present study, monthly precipitation was modeled in Gonbad using ANFIS and SVM and two different database structures were designed. The first structure: input layer consisted of mean temperature, relative humidity, pressure and wind speed at Gonbad station. The second structure: According to Pearson coefficient, the monthly precipitation data were used from four stations: Arazkoose, Bahalke, Tamar and Aqqala which had a higher correlation with Gonbad station precipitation. In this study precipitation data was used from 1995 to 2012. 80% data were used for model training and the remaining 20% of data for validation. SVM was developed from support vector machines in the 1990s by Vapnik. SVM has been widely recognized as a powerful tool to deal with function fitting problems. An Adaptive Neuro-Fuzzy Inference System (ANFIS) refers, in general, to an adaptive network which performs the function of a fuzzy inference system. The most commonly used fuzzy system in ANFIS architectures is the Sugeno model since it is less computationally exhaustive and more transparent than other models. A consequent membership function (MF) of the Sugeno model could be any arbitrary parameterized function of the crisp inputs, most like lya polynomial. Zero and first order polynomials were used as consequent MF in constant and linear Sugeno models, respectively. In addition, the defuzzification process in Sugeno fuzzy models is a simple weighted average calculation. The fuzzy space was divided via grid partitioning according to the number of antecedent MF, and each fuzzy region was covered with a fuzzy rule. Results Discussion: The statistical results showed that in first structure determination coefficient values for both the training and test was not good performance in precipitation prediction so that ANFIS and SVM had determination coefficient of 0.67 and 0.33 in training phase and 0.45 and 0.40 in test phase. Also the error RMSE values showed that both models had failed to predict precipitation in first structure. The results of second structure in precipitation prediction showed that determination coefficient of ANFIS at training and testing was 0.93 and 0.87 respectively and RMSE was 7.06 and 9.28 respectively. MBE values showed that the ANFIS underestimated at training phase and overestimated at test phase. Determination coefficient of SVM at training and testing was 0.89 and 0.91 respectively and RMSE was 9.28 and 5.59 respectively. SVM underestimated precipitation at train phase and overestimated it at test phase. ANFIS and SVM modeled precipitation using precipitation gauging stations with reasonable accuracy. Determining coefficient in the test phase was almost the same for ANFIS and SVM but the RMSE error of SVM model was about 20% lower than the ANFIS. The coefficient of determination and error values indicated SVM had greater accuracy than ANFIS. ANFIS overestimated precipitation for less than 20 mm but for higher values of uniformly distributed around the 1:1. SVM underestimated precipitation for more than 90 mm precipitation due to the low number of data in the training phase, which made this model, did not train well. When meteorological parameters were introduced as input, minimum determination coefficient and maximum error in the test phase occurred while humidity parameters were removed. By removing any of the parameters of temperature, pressure and wind speed the error values and coefficient of determination in test phase was approximately equal. Conclusion: The potential of the support vector machine (SVM) and neuoro fuzzy inference system (ANFIS) in monthly precipitation pattern were analyzed. In order to model, two data sets were used containing meteorological parameters (temperature, humidity, pressure and wind speed) and the stations precipitation. The results showed that the simulated precipitation using meteorological parameters by ANFIS and SVM had low accuracy. Precipitation forecasting using stations precipitation in the region had good accuracy by ANFIS and SVM. Comparing the results of this study showed the high efficiency of SVM in simulating precipitation. This method can be successfully used in modeling precipitation to increase efficiency of precipitation modelling.

The analysis of various characteristics and trends of precipitation is an essential task to improve the utilization of water resources. Lake Issyk-Kul basin is an upper alpine catchment, which is more susceptible to the effects of climate variability, and identifying rainfall variations has vital importance for water resource planning and management in the lake basin. The well-known approaches linear regression, Şen’s slope, Spearman’s rho, and Mann-Kendall trend tests are applied frequently to try to identify trend variations, especially in rainfall, in most literature around the world. Recently, a newly developed method of Şen-innovative trend analysis (ITA) provides some advantages of visual-graphical illustrations and the identification of trends, which is one of the main focuses in this article. This study obtained the monthly precipitation data (between 1951 and 2012) from three meteorological stations (Balykchy, Cholpon-Ata, and Kyzyl-Suu) surrounding the Lake Issyk-Kul, and investigated the trends of precipitation variability by applying the ITA method. For comparison purposes, the traditional Mann–Kendall trend test also used the same time series. The main results of this study include the following. (1) According to the Mann-Kendall trend test, the precipitation of all months at the Balykchy station showed a positive trend (except in January (Zc = −0.784) and July (Zc = 0.079)). At the Cholpon-Ata and Kyzyl-Suu stations, monthly precipitation (with the same month of multiple years averaged) indicated a decreasing trend in January, June, August, and November. At the monthly scale, significant increasing trends (Zc > Z0.10 = 1.645) were detected in February and October for three stations. (2) The ITA method indicated that the rising trends were seen in 16 out of 36 months at the three stations, while six months showed decreasing patterns for “high” monthly precipitation. According to the “low” monthly precipitations, 14 months had an increasing trend, and four months showed a decreasing trend. Through the application of the ITA method (January, March, and August at Balykchy; December at Cholpon-Ata; and July and December at Kyzyl-Suu), there were some significant increasing trends, but the Mann-Kendall test found no significant trends. The significant trend occupies 19.4% in the Mann-Kendall test and 36.1% in the ITA method, which indicates that the ITA method displays more positive significant trends than Mann–Kendall Zc. (3) Compared with the classical Mann-Kendall trend results, the ITA method has some advantages. This approach allows more detailed interpretations about trend detection, which has benefits for identifying hidden variation trends of precipitation and the graphical illustration of the trend variability of extreme events, such as “high” and “low” values of monthly precipitation. In contrast, these cannot be discovered by applying traditional methods.

Studying the capacity of some plant species to adapt to climate change is essential for ecological research and agricultural policy development. Chinese Torreya (Torreya grandis ‘Merrillii’) has been an important crop tree in subtropical China for over a thousand years. It is necessary to characterize its adaptation to climate change. In this study, the average monthly temperature and precipitation from 1901 to 2017 in the six regions with Chinese Torreya plantations at different provinces were analyzed. The results indicated that the average annual air temperature across these regions had increased by about 2.0 °C, but no general trend in the annual precipitation and incidence of drought was found. The annual air temperature that Chinese Torreya plantations had experienced was 12.96−18.23 °C; the highest and the lowest average monthly air temperatures were 30.1 °C and −0.8 °C, respectively. The lowest and the highest annual precipitation were 874.56 mm and 2501.88 mm, respectively. Chinese Torreya trees endured a severe drought period in the 1920s. The monthly air temperature at Zhuji, which is the central production region, showed a significant correlation with the air temperature in the other five regions. The monthly precipitation in Hunan and Guizhou had no significant correlation with that of Zhuji. Chinese Torreya plantations have been grown in the regions with a similar climate distance index of air temperatures but different precipitation. This tree has a high capacity to adapt to climate change based on the climate dynamics across its range. This approach may provide a way to evaluate climate adaptation in other tree species. These results may provide helpful information for the development of Chinese Torreya plantations.

The present article investigates dry and wet periods in a large area of the Mediterranean basin. First, a stochastic model was applied to a homogeneous database of monthly precipitation values of 46 rain gauges in five regions of southern Italy. In particular, after estimating the model parameters, a set of 104 years of monthly precipitation for each rain gauge was generated by means of a Monte Carlo technique. Then, dry and wet periods were analyzed through the application of the standardized precipitation index (SPI) over 3-month and 6-month timespan (short-term) and 12-month and 24-month period (long-term). As a result of the SPI application on the generated monthly precipitation series, higher occurrence probabilities of dry conditions than wet conditions have been detected, especially when long-term precipitation scales are considered. [ABSTRACT FROM AUTHOR]