scholarly journals Spatial and Temporal Analysis of Drought in Nepal using Standardized Precipitation Index and its Relationship with Climate Indices

1970 ◽  
Vol 7 (1) ◽  
pp. 59-74 ◽  
Author(s):  
M Sigdel ◽  
M Ikeda
Keyword(s):  
Principal Component Analysis ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Component Analysis ◽  
Precipitation Index ◽  
Climate Indices ◽  
Indian Ocean Dipole Mode ◽  
Time Space

Drought over Nepal is studied on the basis of precipitation as a key parameter. Using monthly mean precipitation data for a period of 33 years, Standardized Precipitation Index (SPI) is produced for the drought analysis with the time scale of 3 months (SPI-3) and 12 months (SPI-12) as they are applicable for agriculture and hydrological aspects, respectively. Time-space variability is explored based on Principal Component Analysis (PCA) along with Rotated PCA (RPCA). Four rotated components were explored for both SPI-3 and SPI-12 representing climatic variability with cores over eastern, central and western Nepal separately. Droughts associated with SPI-3 occurred almost evenly over these regions. Droughts associated with SPI-12 were consistent with SPI-3 for summer, since summer precipitation dominates annual precipitation. Connection between SPI and the climate indices such as Southern Oscillation Index (SOI) and Indian Ocean Dipole Mode Index (DMI) was studied, suggesting that one of the causes for summer droughts is El Nino, while the winter droughts could be related with positive DMI. Keywords: Standardized Precipitation Index; Nepal; Principal component analysis; Drought DOI: http://dx.doi.org/10.3126/jhm.v7i1.5617 JHM 2010; 7(1): 59-74

scholarly journals Spatial and temporal variability of precipitation and drought in Portugal

2012 ◽  
Vol 12 (5) ◽  
pp. 1493-1501 ◽  
Author(s):  
D. S. Martins ◽  
T. Raziei ◽  
A. A. Paulo ◽  
L. S. Pereira
Keyword(s):  
Principal Component Analysis ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Component Analysis ◽  
Maximum Temperature ◽  
Drought Indices ◽  
Drought Severity ◽  
Spatial And Temporal Variability ◽  
Monthly Precipitation ◽  
Precipitation Regimes

Abstract. The spatial variability of precipitation and drought are investigated for Portugal using monthly precipitation from 74 stations and minimum and maximum temperature from 27 stations, covering the common period of 1941–2006. Seasonal precipitation and the corresponding percentages in the year, as well as the precipitation concentration index (PCI), was computed for all 74 stations and then used as an input matrix for an R-mode principal component analysis to identify the precipitation patterns. The standardized precipitation index at 3 and 12 month time scales were computed for all stations, whereas the Palmer Drought Severity Index (PDSI) and the modified PDSI for Mediterranean conditions (MedPDSI) were computed for the stations with temperature data. The spatial patterns of drought over Portugal were identified by applying the S-mode principal component analysis coupled with varimax rotation to the drought indices matrices. The result revealed two distinct sub-regions in the country relative to both precipitation regimes and drought variability. The analysis of time variability of the PC scores of all drought indices allowed verifying that there is no linear trend indicating drought aggravation or decrease. In addition, the analysis shows that results for SPI-3, SPI-12, PDSI and MedPDSI are coherent among them.

scholarly journals Climate indices for Baltic States from principal component analysis

2017 ◽  
Author(s):  
Liga Bethere ◽  
Juris Sennikovs ◽  
Uldis Bethers
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Climate Models ◽  
Regional Climate ◽  
Principal Component ◽  
Regional Climate Models ◽  
Component Analysis ◽  
Climate Indices ◽  
Baltic States ◽  
The Baltic

Abstract. We used principal component analysis (PCA) to derive climate indices that describe the main spatial features of the climate in the Baltic States (Estonia, Latvia and Lithuania). Monthly mean temperature and total precipitation values derived from the ensemble of bias-corrected regional climate models (RCM) were used. Principal components were derived for years 1961–1990. The first three components describe 92 % of the variance of the initial data and were chosen as climate indices in further analysis. Spatial patterns of these indices and their correlation with the initial variables were analyzed and it was observed that higher values of each index corresponded to: (1) less distinct seasonality, (2) warmer and (3) wetter climate. The loadings from the chosen principal components were then further used to calculate values of the climate indices for years 2071–2100. Overall increase was found for all three indices with minimal changes in their spatial pattern.

scholarly journals ENSO dynamics in current climate models: an investigation using nonlinear dimensionality reduction

2008 ◽  
Vol 15 (2) ◽  
pp. 339-363 ◽  
Author(s):  
I. Ross ◽  
P. J. Valdes ◽  
S. Wiggins
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Dimensionality Reduction ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Principal Component ◽  
Component Analysis ◽  
Nonlinear Dimensionality Reduction ◽  
Climate Data

Abstract. Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These linear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. Here, we apply Isomap, one such technique, to the study of El Niño/Southern Oscillation variability in tropical Pacific sea surface temperatures, comparing observational data with simulations from a number of current coupled atmosphere-ocean general circulation models. We use Isomap to examine El Niño variability in the different datasets and assess the suitability of the Isomap approach for climate data analysis. We conclude that, for the application presented here, analysis using Isomap does not provide additional information beyond that already provided by principal component analysis.

scholarly journals A Non-Stationary and Probabilistic Approach for Drought Characterization Using Trivariate and Pairwise Copula Construction (PCC) Model

2021 ◽  
Author(s):  
Soumyashree Dixit ◽  
K V Jayakumar
Keyword(s):  
Drought Index ◽  
Southern Oscillation ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Drought Indices ◽  
Climate Indices ◽  
Copula Model ◽  
Reconnaissance Drought Index ◽  
Drought Characteristics ◽  
Recurrence Intervals

Abstract Under the variable climatic conditions, the conventional Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) are inadequate for predicting extreme drought characteristics. So in the present study, two indices namely, Non-stationary Standardized Precipitation Index (NSPI) and Non-stationary Reconnaissance Drought Index (NRDI) are developed by fitting non-stationary gamma (for precipitation series) and lognormal (for initial values,δ0) distributions. The Generalized Additive Model in Location, Scale and Shape (GAMLSS) framework, with time varying location parameters considering the external covariates, is used to fit the non-stationary distributions. This includes various large scale climate indices namely Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), Sea Surface Temperature (SST), and Indian Ocean Dipole (IOD) as external covariates for the non-stationary drought assessment. The performances of stationary and non-stationary models are compared based on the Akaika Information Criterion (AIC). Additionally, the drought characteristics are evaluated using Run theory analysis for both stationary and non-stationary drought indices. The study also concentrated on the trivariate copula as well as the Pairwise Copula Construction (PCC) models to estimate the drought recurrence intervals. The comparison of two copula models revealed that the PCC model performed better than the trivariate Student’s t copula model. The recurrence intervals arrived at for the drought events are different for trivariate copula model and PCC model. The area taken for the study is the Upper and Lower sub basins of the Godavari River basin. This study shows that non-stationary drought indices will be helpful in the accurate estimate of the drought characteristics under the changing climatic scenario.

scholarly journals Climate indices for the Baltic states from principal component analysis

2017 ◽  
Vol 8 (4) ◽  
pp. 951-962 ◽  
Author(s):  
Liga Bethere ◽  
Juris Sennikovs ◽  
Uldis Bethers
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Climate Models ◽  
Regional Climate ◽  
Principal Component ◽  
Component Analysis ◽  
Climate Indices ◽  
Baltic States ◽  
The Baltic ◽  
The Impact

Abstract. We used principal component analysis (PCA) to derive climate indices that describe the main spatial features of the climate in the Baltic states (Estonia, Latvia, and Lithuania). Monthly mean temperature and total precipitation values derived from the ensemble of bias-corrected regional climate models (RCMs) were used. Principal components were derived for the years 1961–1990. The first three components describe 92 % of the variance in the initial data and were chosen as climate indices in further analysis. Spatial patterns of these indices and their correlation with the initial variables were analyzed, and it was detected (based on correlation coefficient between principal components and initial variables) that higher values in each index corresponded to locations with (1) less distinct seasonality, (2) warmer climate, and (3) wetter climate. In addition, for the pattern of the first index, the impact of the Baltic Sea (distance to coast) was apparent; for the second, latitude and elevation were apparent, and for the third elevation was apparent. The loadings from the chosen principal components were further used to calculate the values of the climate indices for the years 2071–2100. An overall increase was found for all three indices with minimal changes in their spatial pattern.

scholarly journals Principal Component Analysis for Extremes and Application to U.S. Precipitation

2020 ◽  
Vol 33 (15) ◽  
pp. 6441-6451 ◽  
Author(s):  
Yujing Jiang ◽  
Daniel Cooley ◽  
Michael F. Wehner
Keyword(s):  
Principal Component Analysis ◽  
Correlation Matrix ◽  
Southern Oscillation ◽  
Principal Component ◽  
Component Analysis ◽  
Extreme Value Analysis ◽  
Positive Trend ◽  
Value Analysis ◽  
Extremal Behavior ◽  
Basis Vectors

AbstractWe propose a method for analyzing extremal behavior through the lens of a most efficient basis of vectors. The method is analogous to principal component analysis, but is based on methods from extreme value analysis. Specifically, rather than decomposing a covariance or correlation matrix, we obtain our basis vectors by performing an eigendecomposition of a matrix that describes pairwise extremal dependence. We apply the method to precipitation observations over the contiguous United States. We find that the time series of large coefficients associated with the leading eigenvector shows very strong evidence of a positive trend, and there is evidence that large coefficients of other eigenvectors have relationships with El Niño–Southern Oscillation.

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