scholarly journals Statistical analysis of precipitation time series in Dobrudja region

MAUSAM ◽  
2021 ◽  
Vol 63 (4) ◽  
pp. 553-564
Author(s):  
CARMEN MAFTEI ◽  
ALINA BARBULESCU
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Data Base ◽  
Monthly Precipitation ◽  
Precipitation Series ◽  
Precipitation Time Series ◽  
Southeastern Part ◽  
Temporal Characteristics ◽  
Break Points

Temporal characteristics of precipitation evolution in Dobrudja, a region situated in the Southeastern part of Romania, are analyzed in this article, using a data base of ten monthly series, collected in the period January 1965-December 2005. This paper describes different methods to detect the break points existence in order to detect changes in evolution of the monthly precipitation series. The study indicates a constant trend of precipitation before 2000 and an increasing one after 2000, in concordance with the predictions for this region.

scholarly journals Development of the homogenized monthly precipitation series during 1910–2014 and its changes in Hunan Province, China

2017 ◽  
Vol 8 (4) ◽  
pp. 791-801 ◽  
Author(s):  
Jiadong Peng ◽  
Yufang Liao ◽  
Yuanhua Jiang ◽  
Jianming Zhang ◽  
Xingren Qi
Keyword(s):  
Time Series ◽  
Abrupt Change ◽  
Linear Trend ◽  
Periodic Oscillation ◽  
Hunan Province ◽  
Monthly Precipitation ◽  
Precipitation Series ◽  
Precipitation Time Series ◽  
Significant Linear Trend ◽  
Complete Precipitation

Abstract Based on the statistical method and the historical evolution of meteorological stations, the precipitation time series for each station in Hunan Province of China during 1910–2014 are tested for their homogeneity and then adjusted. The missing data caused by war and other reasons at the eight meteorological stations which had records before 1950 is filled by interpolation using adjacent observations, and complete precipitation time series since the establishment of stations are constructed. After that, according to the representative analysis of each station in different time periods, the precipitation series of Hunan Province during 1910–2014 are built and changes are analyzed. The results indicate that the annual precipitation has no significant linear trend but has obvious inter-decadal fluctuation during 1910–2014 and a periodic oscillation of 20 years is the most significant. Precipitation in winter (DJF) and summer (JJA) shows a slight wetter trend, and a slight dryer trend in spring (MAM) and autumn (SON). Abrupt change test suggests that annual and seasonal precipitations except for MAM and SON have abrupt ascending changes in the recent 100 years.

scholarly journals A multiscale time-space approach to analyze and categorize the precipitation fluctuation based on the wavelet transform and information theory concept

2018 ◽  
Vol 49 (3) ◽  
pp. 724-743 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Vahid Nourani ◽  
Farhad Alizadeh
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Multiple Scales ◽  
Multiscale Entropy ◽  
Discrete Wavelet ◽  
Monthly Precipitation ◽  
Precipitation Time Series ◽  
Rain Gauges ◽  
Time Space ◽  
Precipitation Variation

AbstractThe present study proposed a time-space framework using discrete wavelet transform-based multiscale entropy (DWE) approach to analyze and spatially categorize the precipitation variation in Iran. To this end, historical monthly precipitation time series during 1960–2010 from 31 rain gauges were used in this study. First, wavelet-based de-noising approach was applied to diminish the effect of noise in precipitation time series which may affect the entropy values. Next, Daubechies (db) mother wavelets (db5–db10) were used to decompose the precipitation time series. Subsequently, entropy concept was applied to the sub-series to measure the uncertainty and disorderliness at multiple scales. According to the pattern of entropy across scales, each cluster was assigned an entropy signature that provided an estimation of the entropy pattern of precipitation in each cluster. Spatial categorization of rain gauges was performed using DWE values as input data to k-means and self-organizing map (SOM) clustering techniques. According to evaluation criteria, it was proved that k-means with clustering number equal to 5 with Silhouette coefficient=0.33, Davis–Bouldin=1.18 and Dunn index=1.52 performed better in determining homogenous areas. Finally, investigating spatial structure of precipitation variation revealed that the DWE had a decreasing and increasing relationship with longitude and latitude, respectively, in Iran.

scholarly journals Spatio-Temporal Trends of Monthly and Annual Precipitation in Aguascalientes, Mexico

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 437
Author(s):  
Osías Ruiz-Alvarez ◽  
Vijay P. Singh ◽  
Juan Enciso-Medina ◽  
Ronald Ernesto Ontiveros-Capurata ◽  
Arturo Corrales-Suastegui
Keyword(s):  
Time Series ◽  
Annual Precipitation ◽  
Irrigated Agriculture ◽  
Monthly Precipitation ◽  
Precipitation Trend ◽  
Precipitation Time Series ◽  
June July August ◽  
Precipitation Decrease ◽  
Weather Stations ◽  
June July

The objective of this research was to analyze the temporal patterns of monthly and annual precipitation at 36 weather stations of Aguascalientes, Mexico. The precipitation trend was determined by the Mann–Kendall method and the rate of change with the Theil–Sen estimator. In total, 468 time series were analyzed, 432 out of them were monthly, and 36 were annual. Out of the total monthly precipitation time series, 42 series showed a statistically significant trend (p ≤ 0.05), from which 8/34 showed a statistically significant negative/positive trend. The statistically significant negative trends of monthly precipitation occurred in January, April, October, and December. These trends denoted more significant irrigation water use, higher water extractions from the aquifers in autumn–winter, more significant drought occurrence, low forest productivity, higher wildfire risk, and greater frost risk. The statistically significant positive trends occurred in May, June, July, August, and September; to a certain extent, these would contribute to the hydrology, agriculture, and ecosystem but also could provoke problems due to water excess. In some months, the annual precipitation variability and El Niño-Southern Oscillation (ENSO) were statistically correlated, so it could be established that in Aguascalientes, this phenomenon is one of the causes of the yearly precipitation variation. Out of the total annual precipitation time series, only nine series were statistically significant positive; eight out of them originated by the augments of monthly precipitation. Thirteen weather stations showed statistically significant trends in the total precipitation of the growing season (May, June, July, August, and September); these stations are located in regions of irrigated agriculture. The precipitation decrease in dry months can be mitigated using shorter cycle varieties with lower water consumption, irrigation methods with high efficiency, and repairing irrigation infrastructure. The precipitation increase in humid months can be used to store water and use it during the dry season, and its adverse effects can be palliated with the use of varieties resistant to root diseases and lodging. The results of this work will be beneficial in the management of agriculture, hydrology, and water resources of Aguascalientes and in neighboring arid regions affected by climate change.

scholarly journals A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 177 ◽  
Author(s):  
Guohui Li ◽  
Xiao Ma ◽  
Hong Yang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Hybrid Model ◽  
Prediction Accuracy ◽  
Monthly Precipitation ◽  
Variational Mode Decomposition ◽  
Precipitation Time Series ◽  
Mode Decomposition ◽  
Learning Machine

The matter of success in forecasting precipitation is of great significance to flood control and drought relief, and water resources planning and management. For the nonlinear problem in forecasting precipitation time series, a hybrid prediction model based on variational mode decomposition (VMD) coupled with extreme learning machine (ELM) is proposed to reduce the difficulty in modeling monthly precipitation forecasting and improve the prediction accuracy. The monthly precipitation data in the past 60 years from Yan’an City and Huashan Mountain, Shaanxi Province, are used as cases to test this new hybrid model. First, the nonstationary monthly precipitation time series are decomposed into several relatively stable intrinsic mode functions (IMFs) by using VMD. Then, an ELM prediction model is established for each IMF. Next, the predicted values of these components are accumulated to obtain the final prediction results. Finally, three predictive indicators are adopted to measure the prediction accuracy of the proposed hybrid model, back propagation (BP) neural network, Elman neural network (Elman), ELM, and EMD-ELM models: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The experimental simulation results show that the proposed hybrid model has higher prediction accuracy and can be used to predict the monthly precipitation time series.

HHT ANALYSIS OF THE GLOBAL AVERAGE MONTHLY PRECIPITATION DATA

2012 ◽  
Vol 04 (03) ◽  
pp. 1250018 ◽  
Author(s):  
SAMUEL S. P. SHEN ◽  
DAVID NEW ◽  
THOMAS M. SMITH ◽  
PHILLIP A. ARKIN
Keyword(s):  
Time Series ◽  
Annual Cycle ◽  
Climate Models ◽  
Southern Oscillation ◽  
Frequency Variation ◽  
Frequency Change ◽  
Monthly Precipitation ◽  
Precipitation Time Series ◽  
Time Frequency ◽  
The Mean

This paper uses the Hilbert–Huang transform (HHT) method to make time–frequency diagnostic analyses of four monthly time series of the global precipitation: MERG (1900–2008), REOF (1900–2008), GPCP (1979–2009), and CMAP (1979–2009). All these data are the global land and ocean average of precipitation anomalies with respect to the mean of the entire data period. The MERG and REOF are spectral reconstructions based on historical data. The GPCP and CMAP are based on station gauge data and satellite remote sensing data. We have made the following analysis of the four datasets: (a) extract intrinsic mode functions (IMF) by HHT empirical model decomposition (EMD) sifting, (b) calculate the mean frequency and energy of each IMF, (c) calculate the Fourier spectra to compare with the IMF spectral properties, (d) calculate the Hilbert spectra and display the time–frequency variation of the precipitation time series, and (e) calculate the basic statistics of the four datasets, including mean, standard deviation, skewness, kurtosis and inter-correlation among the datasets. Our analysis results indicate the following: (i) IMFs may contain physical signals of MJO (Madden–Julian oscillation), monsoon, annual cycle, and ENSO (El Nino southern oscillation), (ii) Hilbert spectra appears to be an effective tool to display the time-frequency change of a precipitation time series and can help identify critical characteristics for improving data aggregation method and climate models, (iii) among the four datasets, MERG is the smoothest data and has the smallest variance and hence the smallest IMF energies, while the CMAP has the largest, followed by GPCP and REOF, and (iv) the nonlinear and nonstationary annual cycle is the IMF3 for all the four datasets, which is modulated by ENSO signals.

Homogenization of monthly precipitation time series in Croatia

2014 ◽  
Vol 34 (14) ◽  
pp. 3671-3682 ◽  
Author(s):  
Pavel Zahradníček ◽  
Dubravka Rasol ◽  
Ksenija Cindrić ◽  
Petr Štěpánek
Keyword(s):  
Time Series ◽  
Monthly Precipitation ◽  
Precipitation Time Series

scholarly journals Ensemble Precipitation Estimation Using a Fuzzy Rule-Based Model

2021 ◽  
Vol 5 (1) ◽  
pp. 48
Author(s):  
O. Burak Akgun ◽  
Elcin Kentel
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Climate Models ◽  
Regional Climate ◽  
Fuzzy Rule ◽  
Regional Climate Models ◽  
Subtractive Clustering ◽  
Monthly Precipitation ◽  
Precipitation Time Series ◽  
Rule Based

In this study, a Takagi-Sugeno (TS) fuzzy rule-based (FRB) model is used for ensembling precipitation time series. The TS FRB model takes precipitation predictions of grid-based regional climate models (RCMs) from the EUR11 domain, available from the CORDEX database, as inputs to generate ensembled precipitation time series for two meteorological stations (MSs) in the Mediterranean region of Turkey. For each MS, RCM data that are available at the closest grid to the corresponding MSs are used. To generate the fuzzy rules of the TS FRB model, the subtractive clustering algorithm (SC) is utilized. Together with the TS FRB, the simple ensemble mean approach is also applied, and the performances of these two model results and individual RCM predictions are compared. The results show that ensembled models outperform individual RCMs, for monthly precipitation, for both MSs. On the other hand, although ensemble models capture the general trend in the observations, they underestimate the peak precipitation events.

scholarly journals Negative regional Standardized Precipitation Index trends prevail in the Mexico’s state of Zacatecas.

2019 ◽  
Vol 37 (4) ◽  
pp. 487
Author(s):  
Rafael Magallanes-Quintanar ◽  
Fidel Blanco-Macías ◽  
Erick Carlos Galván-Tejada ◽  
Jorge Isaac Galván-Tejada ◽  
Miguel Márquez-Madrid ◽  
...  
Keyword(s):  
Time Series ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Trend Test ◽  
Spatial And Temporal Variability ◽  
Monthly Precipitation ◽  
Precipitation Time Series ◽  
Groundwater Levels ◽  
Sen’S Slope ◽  
Precipitation Regimes

As the earth atmosphere warms, it is unclear how the precipitation will change or how these changes will impact regional rainfall. For the study of spatial and temporal variability of rainfall, several indexes have been developed. The Standardized Precipitation Index (SPI) that only involves recorded rainfall data has been used as a tool for climatic zone classif ication and a drought indicator. Then, the aims of the present study were: 1) to cluster monthly precipitation time series into groups that represent regions under the basis of similar precipitation regimes, 2) to compute regional SPI’s using all the members (time series) of each cluster, and 3) to estimate trends of the regional SPI’s. The cluster analysis approach was used to identify four groups of monthly precipitation time series that represent regions of similar precipitation regimes. Afterwards, regional SPI’s were estimated using all the members of each cluster. Finally, four regional SPI trends were estimated by means of the Mann-Kendall trend test and Sen’s slope estimator. Estimated decreasing SPI trends imply prevail of negative values at the end of the study period (1964-2014), which indicate less than median precipitation in the entire Zacatecas state territory. For instance, SPI at 12-month time scale Sen’s slope values were -0.17 and -0.18 for the wet and dry seasons, respectively in the Semi-desert region. Thus, the evidenced trends may be having influence on the availability of surface water, groundwater levels and aquifers recharge in the near future. So, it is imperative to adjust inhabitants’ activities according to design planned climate change adaptation strategies.

scholarly journals A wavelet-SARIMA-ANN hybrid model for precipitation forecasting

2016 ◽  
Vol 28 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Maryam Shafaei ◽  
Jan Adamowski ◽  
Ahmad Fakheri-Fard ◽  
Yagob Dinpashoh ◽  
Kazimierz Adamowski
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Moving Average ◽  
Low Frequency ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Monthly Precipitation ◽  
Ann Model ◽  
Precipitation Time Series ◽  
Precipitation Forecasting

Abstract Given its importance in water resources management, particularly in terms of minimizing flood or drought hazards, precipitation forecasting has seen a wide variety of approaches tested. As monthly precipitation time series have nonlinear features and multiple time scales, wavelet, seasonal auto regressive integrated moving average (SARIMA) and hybrid artificial neural network (ANN) methods were tested for their ability to accurately predict monthly precipitation. A 40-year (1970–2009) precipitation time series from Iran’s Nahavand meteorological station (34°12’N lat., 48°22’E long.) was decomposed into one low frequency subseries and several high frequency sub-series by wavelet transform. The low frequency sub-series were predicted with a SARIMA model, while high frequency subseries were predicted with an ANN. Finally, the predicted subseries were reconstructed to predict the precipitation of future single months. Comparing model-generated values with observed data, the wavelet-SARIMA-ANN model was seen to outperform wavelet-ANN and wavelet-SARIMA models in terms of precipitation forecasting accuracy.

scholarly journals Stochastic modelling of spatially and temporally consistent daily precipitation time-series over complex topography

2014 ◽  
Vol 11 (7) ◽  
pp. 8737-8777 ◽  
Author(s):  
D. E. Keller ◽  
A. M. Fischer ◽  
C. Frei ◽  
M. A. Liniger ◽  
C. Appenzeller ◽  
...  
Keyword(s):  
Time Series ◽  
Daily Precipitation ◽  
Stochastic Modelling ◽  
Temporal Correlation ◽  
Climate Impact ◽  
Swiss Alps ◽  
Correlation Structure ◽  
Added Value ◽  
Monthly Precipitation ◽  
Precipitation Time Series

Abstract. Many climate impact assessments over topographically complex terrain require high-resolution precipitation time-series that have a spatio-temporal correlation structure consistent with observations. This consistency is essential for spatially distributed modelling of processes with non-linear responses to precipitation input (e.g. soil water and river runoff modelling). In this regard, weather generators (WGs) designed and calibrated for multiple sites are an appealing technique to stochastically simulate time-series that approximate the observed temporal and spatial dependencies. In this study, we present a stochastic multi-site precipitation generator and validate it over the hydrological catchment Thur in the Swiss Alps. The model consists of several Richardson-type WGs that are run with correlated random number streams reflecting the observed correlation structure among all possible station pairs. A first-order two-state Markov process simulates intermittence of daily precipitation, while precipitation amounts are simulated from a mixture model of two exponential distributions. The model is calibrated separately for each month over the time-period 1961–2011. The WG is skilful at individual sites in representing the annual cycle of the precipitation statistics, such as mean wet day frequency and intensity as well as monthly precipitation sums. It reproduces realistically the multi-day statistics such as the frequencies of dry and wet spell lengths and precipitation sums over consecutive wet days. Substantial added value is demonstrated in simulating daily areal precipitation sums in comparison to multiple WGs that lack the spatial dependency in the stochastic process: the multi-site WG is capable to capture about 95% of the observed variability in daily area sums, while the summed time-series from multiple single-site WGs only explains about 13%. Limitation of the WG have been detected in reproducing observed variability from year to year, a component that has not been considered in the WG calibration. Given the obtained performance, the presented stochastic model is expected to be a useful tool to re-sample the observed record and valuable to be used as a statistical downscaling method in a climate change context.

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