M. Sayedur Rahman

Empirical mode decomposition analysis of climate changes with special reference to rainfall data

We have used empirical mode decomposition (EMD) method, which is especially well fitted for analyzing time-series data representing nonstationary and nonlinear processes. This method could decompose any time-varying data into a finite set of functions called “intrinsic mode functions” (IMFs). The EMD analysis successively extracts the IMFs with the highest local temporal frequencies in a recursive way. The extracted IMFs represent a set of successive low-pass spatial filters based entirely on the properties exhibited by the data. The IMFs are mutually orthogonal and more effective in isolating physical processes of various time scales. The results showed that most of the IMFs have normal distribution. Therefore, the energy density distribution of IMF samples satisfies χ2-distribution which is statistically significant. This study suggested that the recent global warming along with decadal climate variability contributes not only to the more extreme warm events, but also to more frequent, long lasting drought and flood.

Markov Chain Analysis of Weekly Rainfall Data in Determining Drought-proneness

Markov chain models have been used to evaluate probabilities of getting a sequence of wet and dry weeks during South-West monsoon period over the districts Purulia in West Bengal and Giridih in Bihar state and dry farming tract in the state of Maharashtra of India. An index based on the parameters of this model has been suggested to indicate the extend of drought-proneness of a region. This study will be useful to agricultural planners and irrigation engineers to identifying the areas where agricultural development should be focused as a long term drought mitigation strategy. Also this study will contribute toward a better understanding of the climatology of drought in a major drought-prone region of the world.

A rainfall simulation model for agricultural development in Bangladesh

A rainfall simulation model based on a first-order Markov chain has been developed to simulate the annual variation in rainfall amount that is observed in Bangladesh. The model has been tested in the Barind Tract of Bangladesh. Few significant differences were found between the actual and simulated seasonal, annual and average monthly. The distribution of number of success is asymptotic normal distribution. When actual and simulated daily rainfall data were used to drive a crop simulation model, there was no significant difference of rice yield response. The results suggest that the rainfall simulation model perform adequately for many applications.

Logistic regression estimation of a simulated Markov chain model for daily rainfall in Bangladesh

Abstract The simulation model has been used successfidly to estimate daily rainfall. The use of multivariate logistic regression has been made to estimate the probability that it has raining; The logistic regression technique is used to compare the actual results and simulation results for a rainfall from January to December in Bangladesh. For this limited data set the results are quite good and the model is doing a reasonably good job of simulating daily rainfall. The probability can be used for both instantaneous and climate time scale retrievals.

A stochastic simulated first-order Markov chain model for daily rainfall at Barind, Bangladesh

Abstract Sustainable agro-hydrological and water management planning requires knowledge of possible long term rainfall patterns. A stochastic model based on a flrst-order Markov chain was developed to simulate daily rainfall. The model is capable of simulating a daily rainfall data of any length, based on the estimated transitional probabilities, mean, standard deviation and skew coefficients of rainfall amounts. In this paper we investigate methods to obtain estimates ofthe conditional probabilities, the probability of success and its probability distribution to describe the seasonal variability in the parameters for a stochastic rainfall model. Parameters are obtained from a two-state Markov chain model for wet and dry day occurrence. The procedure is demonstrated on 12 meteorological stations scattered across the high Barind region in Bangladesh. The simulated data have statistical properties similar to those of time series data.

Markov Chain Analysis of Weekly Rainfall Data for Predicting Agricultural Drought

In the semiarid Barind region, episodes of agricultural droughts of varying severity have occurred. The occurrence of these agricultural droughts is associated with rainfall variability and can be reflected by soil moisture deficit that significantly affects crop performance and yield. In the present study, an analysis of long-term (1971–2010) rainfall data of 12 rain monitoring stations in the Barind region was carried out using a Markov chain model which provides a drought index for predicting the spatial and temporal extent of agricultural droughts. Inverse distance weighted interpolation was used to map the spatial extent of drought in a GIS environment. The results indicated that in the Pre-Kharif season drought occurs almost every year in different parts of the study area. Though occurrence of drought is less frequent in the Kharif season the minimum probability of wet weeks leads to reduction in crop yields. Meanwhile, the calculation of 12 months drought suggests that severe to moderate drought is a common phenomenon in this area. Drought index is also found to vary depending on the length of period. The return period analysis suggests that chronic drought is more frequent in the Pre-Kharif season and the frequency of moderate droughts is higher in the Kharif season. On the contrary severe drought is more frequent for a 12-month period.

Modeling of agricultural drought risk pattern using Markov chain and GIS in the western part of Bangladesh

The aim of the study is to assess the agricultural drought risk condition in the context of global climate change in the western part of Bangladesh that covers about 45% area of the country for the period of 1960–2011. Drought Index (DI) and Drought Hazard Index (DHI) have been calculated by Markov Chain analysis and that of Drought Vulnerability Index (DVI) from socioeconomic and physical indicators. The DI values show that the northern part in general is more drought-prone, having less crops prospect, whereas the southern part is less drought-prone with high crop potentiality. The probability of extreme drought occurrence increases in recent decades in some parts as a result the drought events become more frequent in the areas. The DHI ranges from 15 to 32, and northern part suffers from more extreme drought hazards than that of southern part. DVI also indicates that northern part is exposed to high to very high drought vulnerability as higher percentage of illiterate people are involved in agricultural practices and high percentage of irrigation to cultivable land, but southern part exposed to moderate to low vulnerability because of low values of vulnerability indicators. Finally, agricultural drought exists at high risk condition in northern part and low in southern parts and 21.63, 26.54 and 29.68% of the area poses very high, high and moderate risk, respectively. So, immediate adaptation measures are needed keeping in mind climate features like rainfall and temperature variability, drought risk and risk ranking to make viable adaptation measures.

Analysis of Inter-Annual Climate Variability Using Discrete Wavelet Transform

This chapter presents a data adaptive filtering technique to extract annual cycles and the analysis of inter-annual climate variability based on different climate signals using discrete wavelet transform (DWT). The annual cycle is considered as higher energy trend in a climate signal and separated by implementing a threshold-driven filtering technique. The fractional Gaussian noise (fGn) is used here as a reference signal to determine adaptive threshold without any prior training constraint. The climate signal and fGn are decomposed into a finite number of subband signals using the DWT. The subband energy of the fGn and its confidence intervals are computed. The upper bound of the confidence interval is set as the threshold level. The energy of individual subband of a climate signal is compared with the threshold. The lowest order subband of which the energy is greater than the threshold level is selected yielding the upper frequency limit of the trend representing annual cycle. All the lower frequency subbands starting from the selected one are used to reconstruct the annual cycle of the corresponding climate signal. The distance between adjacent peaks in the extracted cycles refers to the inter-annual variation of the climate condition. The experimental results illustrate the efficiency of the proposed data adaptive approach to separate the annual cycle and the quantitative analysis of climate variability.

Impact study of volatility modelling of Bangladesh stock index using non-normal density

This article examines a wide variety of popular volatility models for stock index return, including the random walk (RW), autoregressive, generalized autoregressive conditional heteroscedasticity (GARCH), and asymmetric GARCH models with normal and non-normal (Students t and generalized error) distributional assumption. Fitting these models to the Chittagong stock index return data from the period 2 January 1999 to 29 December 2005, we found that the asymmetric GARCH/GARCH model fits better under the assumption of non-normal distribution than under normal distribution. Non-parametric specification tests show that the RW-GARCH, RW-TGARCH, RW-EGARCH, and RW-APARCH models under the Students t-distributional assumption are significant at the 5% level. Finally, the study suggests that these four models are suitable for the Chittagong Stock Exchange of Bangladesh. We believe that this study would be of great benefit to investors and policy makers at home and abroad.

Markov chain analysis of succession for a university admission test of Bangladesh

Abstract One hundred students are selected from a large number of applicants on the basis of an admission test in the Department of Statistics, Rajshahi University, Bangladesh every year. The academic performance of the students vary from one to another. Markov chain analysis that sacrifice all information about the position of observation within the succession. The statistic –2 log λ has an asymptotic x2-distribution with (m – 1)2 d.f. and the hypothesis of independence of successive state is correct. There is a statistically significant tendency for certain states not be preferentially followed by certain other states. This study has therefore, revealed the need for undertaking a variety of further investigations, if we wish to gain a deeper insight into the influencing teachers which cause variation. The results suggest that Markov chain analysis is important for policy making in education system and in designing future monitoring programs.