Does precipitation pattern foretell climatic shift over Punjab state ?

ABSTRACT. Statistical analysis of 82-years (1901-1982) record of precipitation from 27 rain-recording stations in Punjab state of lndia has been carried out to assess the climate shift if any in the state. The central part of the study is the trend and spectrum analysis of annual. monsoon and winter rainfall of different stations in the region. It is seen that frequency distribution of 19 rainfall series out of 81 series is normally distributed. Maikov linear type of persistence is observed in some of the rainfall series. Marin-Kendall test indicates the decreasing trend in winter rainfall of all the stations and is found to be significant in case of Amritsar, Taran Taran, Tanda, Ludhiana and Ranike. Low-pass filter reveals that trend is not linear but oscillatory consisting of periods of 10 years or more. It is seen that winter rainfall of most of the stations exhibits the decreasing trend from 1935-40 to 1965-70. It is also revealed by the low-pass filter curves that winter rainfall of all t1le sla1ions remained below average from 1960 till the end of the study period. The spectral analysis indicates a significant cycle of 4.1 to 27 years in some of the stations and Quasi-Biennial Oscillations (QBO) over many stations.

Trends and periodicities in sub-divisional rainfall

The monthly and annual rainfall data for 35 meteorological sub-divisions for the 87-year period (1901-1987) have been used to study the trends and periodicities of monsoon and annual rainfall series. A number of distribution-free statistical tests have been applied to the rainfall series for testing non-randomness. Comparison of the decadewise means with the mean of the whole period showed that, for the country as a whole, the annual rainfall indicated four different climatic periods -two periods of above normal rainfall from 1960-1965 and from 1975 onwards and two periods of below normal rainfall from 1901-1915 and 1965-1975 whereas the monsoon rainfall showed two different climatic periods-a period of below normal rainfall from 1901-1920 and a period of above normal rainfall from 1920 onwards. The series were also subjected to low-passfilters which showed the presence of significant long term trend for a few sub-divisions. The power spectrum analysis for the annual and monthly rainfall series for a large number of sub-divisions showed significant periodicities of 2. 1-3.6 years, which correspond to the frequency range of the QBO. In addition, periodicities of 5.1 to 10.0 years and 19.3 years or more were also significant for a number of sub-divisions.

Influence of some circulation anomalies on Indian northeast monsoon rainfall

The relationship between Indian northeast monsoon rainfall over Tamil Nadu (TNR) and southeast India (SER) as well as two indices of southern oscillation (SOI), and sea surface temperature (SST) anomalies over different Nino regions of equatorial Pacific Ocean and seven tropical circulation indices (TCI), have been studied for different periods. The study indicates that northeast monsoon rainfall (TNR) shows significant inverse relationship with SOI (I-D) during previous MAM (March- April-May) season. significant direct relationship with SST anomalies over Nino-4 region during previous JJA (June-July- August) and significant direct relationship with TCI (C-N) during previous DJF, The SOI (I-D), MAM correlates I significantly and negatively with both the northeast monsoon rainfall series, the TNR rainfall series displaying the better correlation. The strongest correlation is observed during 1961-90. For SSTA, the strongest correlation is during 1964-85 and for TCI, the highest correlation is observed during 1958-82.

Examining optimum prediction time of rainfall dynamics based on chaotic perspective at different temporal scales: a case study in Bojonegoro, Indonesia

As the driving force of the hydrological system, rain has severe impact when dealing with petroleum mining activities, especially in protecting assets and safety. Rainfall has high variability, both spatial and temporal (chaotic data). Due to this reason, ones can only create long-range prediction using the stochastic method. Here we use the Lyapunov exponent to analyze the nonlinear pattern of rainfall dynamics. This method is useful for identifying chaotic deportment in rainfall data. This study uses rainfall data for six years obtained from one of the largest petroleum mining sites in Bojonegoro, Indonesia. Rainfall dynamics have been analyzed on three different time scales, namely daily data, 5-day, and 10-day. The time delay (τ) was obtained by using the Average Mutual Information (AMI) method for the three-rainfall series (3, 2, 3, respectively). The observed rainfall data in Bojonegoro show signs of chaos as the finite correlation dimensions (m) attain values about 4 for all time scales. The maximum Lyapunov exponent λmax for each of three-rainfall series in Bojonegoro is 0.111, 0.057, 0.062, respectively. These values were analyzed to find the optimum prediction time of rainfall occurrence to perform better forecasting. The result shows that the optimum range of prediction time for daily, 5-day, and 10-day have 9, 18, and 16 times longer than their temporal scale.

Calculation method of short-duration rainstorm intensity formula considering nonstationarity of rainfall series: impacts on the simulation of urban drainage system

The changing nature of the earth's climate and rapid urbanization lead to the change of rainfall characteristics in urban areas, the stability of rainfall series is destroyed and it is a difficult challenge to consider this change in urban drainage simulation. A generalized additive model (GAMLSS) with time as covariant was established to calculate and predict the design values of extreme rainstorm return period, and the nonstationary short-duration rainstorm intensity formula of three periods was fitted and compared with the stationary formula. The urban water simulation model and the Mike 21 two-dimensional surface flow model are coupled to simulate the urban flood under different formulas and different return periods. The results show that the nonstationary results are worse in the same period. In the 5-year return period rainfall–runoff simulation performance, the nonstationary inundation area is 18.5% more than the stationary, and inundation water is 23.5% more than the stationary. The nonstationary simulation results show higher inundation depth and slower flood recession process. These gaps will widen in the future, but they will become less significant as the return period increases. It can provide a reference for the study of flood control work and the design of existing drainage infrastructure in the region.

An approach to quality analysis, gap filling and homogeneity of monthly rainfall series

The aim of this work was to propose a method for the consistency of climatic series of monthly rainfall using a supervised and unsupervised approach. The methodology was applied for the series (1961-2010) of rainfall from weather stations located in the State of Rio de Janeiro (RJ) and in the borders with the states of São Paulo, Minas Gerais and Espírito Santo with the State of Rio de Janeiro. The data were submitted to quality analysis (physical and climatic limit and, space-time tendency) and gap filling, based on simple linear regression analysis, associated with the prediction band (p < 0.05 or 0.01), in addition to the Z-score (3, 4 or 5). Next, homogeneity analysis was applied to the continuous series, using the method of cumulative residuals. The coefficients of determination (r²) between the assessed series and the reference series were greater than 0.70 for gap filling both for the supervised and unsupervised approaches. In the analysis of data homogeneity, supervised and unsupervised approaches were effective in selecting homogeneous series, in which five out of the nine final stations were homogeneous (p > 0.9). In the other series, the homogeneity break points were identified and the simple linear regression method was applied for their homogenization. The proposed method was effective to consist of the rainfall series and allows the use of these data in climate studies.

Inhomogeneity detection in the rainfall series for the Mae Klong River Basin, Thailand

An accurate and complete rainfall record is prerequisite for climate studies. The purpose of this research study was to evaluate the homogeneity of the rainfall series for the Mae Klong River Basin in Thailand. Monthly rainfall data of eight stations in the Mae Klong River Basin for the period 1971–2015 were used. The double mass curve analysis was used to check the consistency of rainfall data, whereas the absolute homogeneity was assessed using the Pettitt test, standard normal homogeneity test, Buishand test, and von Neumann test at a 5% significance level. The results of these tests were qualitatively classified as ‘useful’, ‘doubtful’, and ‘suspect’ according to the null hypothesis. Results of the monthly time series indicated the rainfall data as ‘useful’ for 75% of the stations, while two stations’ data were classified as ‘doubtful’ (Stn130221) and ‘suspect’ (Stn376401). On an annual scale, seven out of eight stations data were classified as ‘useful,’ while one station (Stn376401) data were classified as ‘suspect’. Double mass curve analysis technique was used for the adjustment of inhomogeneous data. The results of this study can help provide reliable rainfall data for climate studies in the basin.

Simultaneous confidence intervals for all pairwise differences between the coefficients of variation of rainfall series in Thailand

The delta-lognormal distribution is a combination of binomial and lognormal distributions, and so rainfall series that include zero and positive values conform to this distribution. The coefficient of variation is a good tool for measuring the dispersion of rainfall. Statistical estimation can be used not only to illustrate the dispersion of rainfall but also to describe the differences between rainfall dispersions from several areas simultaneously. Therefore, the purpose of this study is to construct simultaneous confidence intervals for all pairwise differences between the coefficients of variation of delta-lognormal distributions using three methods: fiducial generalized confidence interval, Bayesian, and the method of variance estimates recovery. Their performances were gauged by measuring their coverage probabilities together with their expected lengths via Monte Carlo simulation. The results indicate that the Bayesian credible interval using the Jeffreys’ rule prior outperformed the others in virtually all cases. Rainfall series from five regions in Thailand were used to demonstrate the efficacies of the proposed methods.

Comparison of Sub-Series with Different Lengths Using Şen-Innovative Trend Analysis

Climate change causes trends in hydro-meteorological series. Traditional trend analysis methods such as Mann-Kendall and Spearman Rho are sensitive to correlated series and cannot detect non-parametric trends. Şen-innovative trend analysis method is launched to literature in order to overcome these restrictions. It does not require any restrictive assumptions as serial dependence and normal distribution and examines a main series as equally divided two sub-series. Şen multiple innovative trend analyses methodology is improved to detect partial trends on different sub-series but again equal lengths. Climate change nowadays more effects hydro-meteorological parameters according to last two or three decades and gives asymmetric trend change point on main time series. Due to asymmetric trend change points, it may be necessary to analyze sub-series with different lengths to use all measured data. In this study, Şen innovative trend analyses method is revised for these requirements (ITA_DL). The new approach compared with traditional Mann Kendall (MK) and Şen innovative trend analysis (Şen_ITA) gives successful and consistent results. ITA_DL gives four monotonic trends on Oxford May, July, September and October rainfall series although MK gives three monotonic trends on May, July and December and cannot detect trends on September and October. In the ITA_DL visual inspection, the December rainfall series does not show a trend that is monotonic or non-monotonic. Şen_ITA trend results are consistent with ITA_DL except September, although there are different trend slopes.