Long-Term Analysis and Short-Term Forecasting of Dry Spells by Palmer Drought Severity Index

Lohani, Vinod K.; Loganathan, G. V.; Mostaghimi, Saied

doi:10.2166/nh.1998.0002

Cited by 62 publications

(34 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…same and have ordered categories, resulting from a pairwise comparison of dependent samples, which is the case [39]. In adjusting these models, it is assumed that the n ijk , i, j, k = 1,..., 4 are values taken by independent Poisson distributed variables and the parameter estimatorsλ,λ a i ,λ b j ,λ c k ,β, δ h andm i,j,h , h, j = 1,..., 4, obtained using the maximum likelihood method, are asymptotically normally distributed [50].…”

Section: Discussionmentioning

confidence: 99%

“…The methodologies include regression analysis [33], time series modeling such as ARIMA and seasonal ARIMA [34,35], artificial neural network models (ANN) [36,37] and stochastic and probability models such as Markov chains [38][39][40], log-linear models [31,41] and others [42,43]. Also, hybrid models combining two techniques have been used, for instance wavelet transforms and neural networks [44], stochastic and neural network modeling [45], wavelet and fuzzy logic models [46], adaptive neuro-fuzzy inference [47] and data mining and ANFIS techniques [48].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SPI Drought Class Predictions Driven by the North Atlantic Oscillation Index Using Log-Linear Modeling

Moreira

Pires

Pereira

2016

Water

View full text Add to dashboard Cite

Abstract:This study aims at predicting the Standard Precipitation Index (SPI) drought class transitions in Portugal, considering the influence of the North Atlantic Oscillation (NAO) as one of the main large-scale atmospheric drivers of precipitation and drought fields across the Western European and Mediterranean areas. Log-linear modeling of the drought class transition probabilities on three temporal steps (dimensions) was used in an SPI time series of six-and 12-month time scales (SPI6 and SPI12) obtained from Global Precipitation Climatology Centre (GPCC) precipitation datasets with 1.0 degree of spatial resolution for 10 grid points over Portugal and a length of 112 years . The aim was to model two monthly transitions of SPI drought classes under the influence of the NAO index in its negative and positive phase in order to obtain improvements in the predictions relative to the modeling not including the NAO index. The ratios (odds ratio) between transitional probabilities and their confidence intervals were computed in order to estimate the probability of one drought class transition over another. The prediction results produced by the model with the forcing of NAO were compared with the results produced by the same model without that forcing, using skill scores computed for the entire time series length. Overall results have shown good prediction performance, ranging from 73% to 76% in the percentage of corrects (PC) and 56%-62% in the Heidke skill score (HSS) regarding the SPI6 application and ranging from 82% to 85% in the PC and 72%-76% in the HSS for the SPI12 application. The model with the NAO forcing led to improvements in predictions of about 1%-6% (PC) and 1%-8% (HSS), when applied to SPI6, but regarding SPI12 only seven of the locations presented slight improvements of about 0.4%-1.8% (PC) and 0.7%-3% (HSS).

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SPI Drought Class Predictions Driven by the North Atlantic Oscillation Index Using Log-Linear Modeling

Moreira

Pires

Pereira

2016

Water

View full text Add to dashboard Cite

show abstract

“…Markov chains [48] are commonly used to assess drought occurrence probability, and to evaluate and predict the time of occurrence of a drought event [49][50][51][52]. In this study, this method was used to examine changes in drought severity at different time scales, and to predict the occurrence probability for each degree of severity.…”

Section: Markov Chains Evaluation Methodsmentioning

confidence: 99%

SDI and Markov Chains for Regional Drought Characteristics

et al. 2015

View full text Add to dashboard Cite

Abstract:In recent years, global climate change has altered precipitation patterns, causing uneven spatial and temporal distribution of precipitation that gradually induces precipitation polarization phenomena. Taiwan is located in the subtropical climate zone, with distinct wet and dry seasons, which makes the polarization phenomenon more obvious; this has also led to a large difference between river flows during the wet and dry seasons, which is significantly influenced by precipitation, resulting in hydrological drought. Therefore, to effectively address the growing issue of water shortages, it is necessary to explore and assess the drought characteristics of river systems. In this study, the drought characteristics of northern Taiwan were studied using the streamflow drought index (SDI) and Markov chains. Analysis results showed that the year 2002 was a turning point for drought severity in both the Lanyang River and Yilan River basins; the severity of rain events in the Lanyang River basin increased after 2002, and the severity of drought events in the Yilan River basin exhibited a gradual upward trend. In the study of drought severity, analysis results from periods of three months (November to January) and six months (November to April) have shown significant drought characteristics. In addition, analysis of drought occurrence probabilities using the method of Markov chains has shown that the occurrence probabilities of drought events are higher in the Lanyang River basin than in the Yilan River basin; particularly for extreme events, the occurrence probability of an extreme drought event is 20.6% during the dry season (November to April) in the Lanyang River basin, and 3.4% in the Yilan River basin. This study shows that for analysis of drought/wet occurrence probabilities, the results obtained for the drought frequency and occurrence OPEN ACCESSSustainability 2015, 7 10790 probability using short-term data with the method of Markov chains can be used to predict the long-term occurrence probability of drought/wet events.

show abstract

“…The approach can be satisfactorily used as a predictive tool for forecasting transitions among drought severity classes up to 3 months ahead (Paulo and Pereira, 2007). An early warning system for drought management was developed using the Markov chain, in two climatic areas of Virginia, USA (Lohani and Loganathan, 1997;Lohani et al 1998). Liu et al (2009) demonstrated two advantages of the Markov chain technique for forecasting drought and rainfall conditions.…”

Section: Markov Chain Modelmentioning

confidence: 99%

Markov chain analysis of the rainfall patterns of five geographical locations in the south eastern coast of Ghana

et al. 2017

View full text Add to dashboard Cite

This study develops an objective rainfall pattern assessment through Markov chain analysis using daily rainfall data from 1980 to 2010, a period of 30 years, for five cities or towns along the south eastern coastal belt of Ghana; Cape Coast, Accra, Akuse, Akatsi and Keta. Transition matrices were computed for each town and each month using the conditional probability of rain or no rain on a particular day given that it rained or did not rain on the previous day. The steady state transition matrices and the steady state probability vectors were also computed for each town and each month. It was found that, the rainy or dry season pattern observed using the monthly steady state rainfall vectors tended to reflect the monthly rainfall time series trajectory. Overall, the probability of rain on any day was low to average: Keta 0.227, Akuse 0.382, Accra 0.467, Cape Coast, 0.50 and Akatsi 0.50. In particular, for Accra, the rainy season was observed to be in the months of May to June and September to October. We also determined that the probability of rainfall generally tended to increase from east to west along the south eastern coast of Ghana.

show abstract

Long-Term Analysis and Short-Term Forecasting of Dry Spells by Palmer Drought Severity Index

Cited by 62 publications

References 12 publications

SPI Drought Class Predictions Driven by the North Atlantic Oscillation Index Using Log-Linear Modeling

SPI Drought Class Predictions Driven by the North Atlantic Oscillation Index Using Log-Linear Modeling

SDI and Markov Chains for Regional Drought Characteristics

Markov chain analysis of the rainfall patterns of five geographical locations in the south eastern coast of Ghana

Contact Info

Product

Resources

About