Spatial variability and rainfall characteristics of Kerala

Geographical regions of covariability in precipitation over the Kerala state are exposed using factor analysis. The results suggest that Kerala can be divided into three unique rainfall regions, each region having a similar covariance structure of annual rainfall. Stations north of 10‡N (north Kerala) fall into one group and they receive more rainfall than stations south of 10‡N (south Kerala). Group I stations receive more than 65% of the annual rainfall during the south-west monsoon period, whereas stations falling in Group II receive 25–30% of annual rainfall during the pre-monsoon and the north-east monsoon periods. The meteorology of Kerala is profoundly influenced by its orographical features, however it is difficult to make out a direct relationship between elevation and rainfall. Local features of the state as reflected in the rainfall distribution are also clearly brought out by the study.     

Spatial variability analysis of precipitation in northwest Iran

The spatial variability of precipitation was investigated in the northwestern corner of Iran using data collected at 24 synoptic stations from 1986 to 2015. Principal component analysis (PCA) and cluster analysis (CA) were used to regionalize precipitation in the study area. Eleven precipitation variables were averaged and arranged as an input matrix for the R-mode PCA to identify the precipitation patterns. Results suggest that the study area can be divided into four spatially homogeneous sub-zones. In addition, the spatial patterns of annual precipitation were identified by applying the T-mode PCA and CA to the annual precipitation data. The delineated spatial patterns revealed three distinct sub-regions. The resultant maps were compared with the spatial distribution of the rotated principal components (PCs). Results pointed out that the delineated clusters are characterized by different precipitation variability; and using different precipitation parameters can lead to different spatial patterns of precipitation over northwest Iran.     

Multiple time scales in rainfall variability

Monthly rainfall data averaged over a selected number of stations in peninsular Malaysia with a long record was subjected to singular spectrum analysis to determine the different modes of fluctuations in the rainfall. The analysis highlights the presence of fluctuations in the QBO time scale to a very long term time scale of 18–5 years which is possibly linked to lunar tidal forcing. There is also evidence of the Malaysian rainfall responding to El-Nino Southern Oscillation. An oscillation with a 7 to 10 year cycle is also evident. The annual cycle as a regular periodic oscillation is well established by the SSA.     

Spatial and temporal variability of rainfall: case of Bizerte-Ichkeul Basin (Northern Tunisia)

This study examines spatial and temporal variability of rainfall in Bizerte-Ichkeul Watershed. The basin, located in the extreme north of Tunisia, covers an area of 3084 km². Thirteen rainfall stations, with continuous monthly precipitation records over the period (1970–2011), were considered in the analysis. Two methods were used. In the first, the dimensionless standardized precipitation ratio is applied to examine precipitation temporal variation. The second method is represented by continuous wavelet analysis for the precipitation spatial analysis and the identification of the origin of its variability. The study of temporal variability of annual rainfall showed severe persistent and recurrent drought episodes over the period (1977–2001). Wavelet analysis resulted in detecting the modes and origins of precipitation variability. Three energy bands were clearly identified: (1, 2–4, and 4–8 years) for the entire watershed. The visualization of the power distribution showed that the observed modes of variability are different in their power distributions from one station to another. The approach adopted allowed the identification of two groups with the same precipitation frequency and temporal variation. These groups were defined according to the difference in occurrence of the frequency band for each station.     

Analysis of rainfall and temperature time series to detect long-term climatic trends and variability over semi-arid Botswana

Arid and semi-arid environments have been identified with locations prone to impacts of climate variability and change. Investigating long-term trends is one way of tracing climate change impacts. This study investigates variability through annual and seasonal meteorological time series. Possible inhomogeneities and years of intervention are analysed using four absolute homogeneity tests. Trends in the climatic variables were determined using Mann–Kendall and Sen’s Slope estimator statistics. Association of El Niño Southern Oscillation (ENSO) with local climate is also investigated through multivariate analysis. Results from the study show that rainfall time series are fully homogeneous with 78.6 and 50% of the stations for maximum and minimum temperature, respectively, showing homogeneity. Trends also indicate a general decrease of 5.8, 7.4 and 18.1% in annual, summer and winter rainfall, respectively. Warming trends are observed in annual and winter temperature at 0.3 and 1.5% for maximum temperature and 1.7 and 6.5% for minimum temperature, respectively. Rainfall reported a positive correlation with Southern Oscillation Index (SOI) and at the same time negative association with Sea Surface Temperatures (SSTs). Strong relationships between SSTs and maximum temperature are observed during the El Niño and La Niña years. These study findings could facilitate planning and management of agricultural and water resources in Botswana.     

Monsoon rainfall and its variability in Godavari river basin

Rainfall variability over a river basin has greater impact on the water resource in that basin. With this in view, the variability of the monsoon rainfall over the Godavari river basin has been studied on different time scales. As expected, the monsoon rainfall in Godavari basin is more variable (17%) than the all-India monsoon rainfall (11%) during the period of study (1951–90). Similarly, inter-annual variability of the monsoon rainfall on smaller time scales is found to be still higher and increases while going on from seasonal to daily scales. An interesting observation is that the intra-seasonal variability of the monsoon rainfall has a significant negative relationship (CC= −0.53) with the total seasonal rainfall in the basin.     

Interannual variability of summer monsoon rainfall over Myanmar

Observed summer (May–October) rainfall in Myanmar for the period 1981–2010 was used to investigate the interannual variability of summer monsoon rainfall over Myanmar. Empirical orthogonal function, the sequential Mann-Kendall test, power spectrum analysis, and singular value decomposition (SVD) were deployed in the study. Results from spectral analysis showed that the variability of rainfall over Myanmar exhibits a 2- to 6-year cycle. An abrupt change in rainfall over the country was noted in 1992. There was a notable increasing rainfall trend from 1989. After the sudden change, the mean rainfall increased by 36.1 mm, compared with the mean rainfall before the sudden change, and was associated with a rise in temperature of about 0.2 °C. An increase in heavy rainfall days was observed from the early 1990s to 2010. IOD and ENSO play an important role in the interannual variability of the summer rainfall over Myanmar. The covariability between rainfall over Myanmar and Indian Ocean SST generally suggests that a positive IOD mode is associated with suppressed rainfall in the central and northern parts of Myanmar. During a negative IOD mode, nearly the whole Myanmar experiences enhanced rainfall, which is associated with devastating socioeconomic impacts. The covariability between the rainfall over Myanmar and the sea surface temperature in the Pacific Ocean in the first and second SVD modes was dominated by warming in the east and central Pacific—an El Niño-like pattern—resulting in dry conditions in central Myanmar.     

Analyzing rainfall seasonality and trends in the North Maharashtra region

The North Maharashtra region comprises three districts, namely Jalgaon, Dhule, and Nandurbar. The region comprises 25 talukas, which are mostly covered by agricultural fields. Seasonality and trend analysis of rainfall over North Maharashtra region from 1901 to 2016 is conducted in the present work. The data were analyzed on the basis of season, i.e. winter, pre-monsoon, monsoon, and post monsoon. For trend detection, the Sen’s slope estimator and the Mann–Kendall test are used. The largest negative rainfall trends are found in the talukas Nandurbar and Jamner. In Akkalkuwa, the strongest positive trend is found. The rainfall trends in the recent years are discussed in the present work.     

Spatial variability of climatic hazards in Bangladesh

Natural Hazards - Climate change is evident with the extreme climatic indices changing all over the world. Bangladesh is one of the most vulnerable countries to climate change. The patterns of...     

Application of principal component analysis to understand variability of rainfall

The usefulness of principal component analysis for understanding the temporal variability of monsoon rainfall is studied. Monthly rainfall data of Karnataka, spread on 50 stations for a period of 82 years have been analysed for interseasonal and interannual variabilities. A subset of the above data comprising 10 stations from the coherent west zone of Karnataka has also been investigated to bring out statistically significant interannual signals in the southwest monsoon rainfall. Conditional probabilities are proposed for a few above normal/below normal transitions. A sample prediction exercise for June–July using such a transition probability has been found to be successful.     

Seasonal variability of soil phosphate stable oxygen isotopes in rainfall manipulation experiments

Phosphorus (P) availability limits productivity in many ecosystems worldwide. As a result, improved understanding of P cycling through soil and plants is much desirable. The use of the oxygen isotopes associated to phosphate can be used to study the cycle of P in terrestrial systems. However, changes with time in the oxygen isotopes associated to available P have not yet been evaluated under field conditions. Here we present the variations in available-P oxygen isotopes, based on resin extractions, in a semi-arid site that included plots in which the amount of rainfall reaching the soil was modified. In addition, the oxygen isotopes in the less dynamic fraction which is extractable by HCl, were also measured. The δ¹⁸O of the HCl-extractable phosphate shows no seasonal pattern and corresponds to the average value of the available phosphate of 16.5‰. This value is in the expected range for equilibration with soil water at the prevailing temperatures in the site. The δ¹⁸O values of resin-extractable P showed a range of 14.5-19.1‰ (SMOW), and evidence of seasonal variability, as well as variability induced by rainfall manipulation experiments. We present a framework for analyzing the isotopic ratios in soil phosphate and explain the variability as mainly driven by phosphate equilibration with soil water, and by the isotopic effects associated with extracellular mineralization. Additional isotopic effects result from fractionation in uptake, and the input to the soil of phosphate equilibrated in leaves. These results suggest that the δ¹⁸O of resin-extractable P is an interesting marker for the rate of biological P transformations in soil systems.     

Assessing the state of homogeneity,variability and trends in the rainfall time series from 1969 to 2017 and its significance for groundwater in north-east India

Natural Hazards - Rainfall is the key climatic variable, on which water availability, food security and livelihood depend, especially in an agrarian society like the northeast region of India. It...     

Spatial variability of flow parameters in a stratified sand

The spatial variability in porosity, hydraulic conductivity, compressibility, and various grain size fractions is analyzed for several sets of samples from the Quadra Sand. This unit is a well-sorted, medium grained, horizontally stratified sand with relatively few silt or gravel interbeds. Both random and uniformly spaced sample plans are used. The heterogeneity of the flow parameters is characterized by frequency histograms and their estimated moments, by their sample autocorrelation functions, and the estimated power spectra. Emphasis is placed on the nature of the spatial dependence between neighboring values of the flow parameters. A nearest neighbor stochastic process model is fit to the data to consider its adequacy in describing the spatial dependence within the porosity and hydraulic conductivity sequences. Even though the Quadra Sand is relatively uniform, a fairly complex spatial structure is observed. A simple monotonically decaying autocorrelation function may not adequately represent the spatial continuity. Statistical anisotropy is observed in both the extent of the spatial autocorrelation and in its functional form. Results show the importance of scale in constructing a probability model to describe the spatial variability.     

Identification of trends in rainfall,rainy days and 24 h maximum rainfall over subtropical Assam in Northeast India

Trends in rainfall, rainy days and 24 h maximum rainfall are investigated using the Mann-Kendall non-parametric test at twenty-four sites of subtropical Assam located in the northeastern region of India. The trends are statistically confirmed by both the parametric and non-parametric methods and the magnitudes of significant trends are obtained through the linear regression test. In Assam, the average monsoon rainfall (rainy days) during the monsoon months of June to September is about 1606 mm (70), which accounts for about 70% (64%) of the annual rainfall (rainy days). On monthly time scales, sixteen and seventeen sites (twenty-one sites each) witnessed decreasing trends in the total rainfall (rainy days), out of which one and three trends (seven trends each) were found to be statistically significant in June and July, respectively. On the other hand, seventeen sites witnessed increasing trends in rainfall in the month of September, but none were statistically significant. In December (February), eighteen (twenty-two) sites witnessed decreasing (increasing) trends in total rainfall, out of which five (three) trends were statistically significant. For the rainy days during the months of November to January, twenty-two or more sites witnessed decreasing trends in Assam, but for nine (November), twelve (January) and eighteen (December) sites, these trends were statistically significant. These observed changes in rainfall, although most time series are not convincing as they show predominantly no significance, along with the well-reported climatic warming in monsoon and post-monsoon seasons may have implications for human health and water resources management over bio-diversity rich Northeast India.     

地质灾害与不同尺度降雨时空分布关系

降雨是地质灾害最重要的诱发因素之一,开展地质灾害与不同尺度降雨的时空分布研究,分析降雨诱发地质灾害的特点规律,对于提升地质灾害区域预警水平和防灾减灾实践具有重要意义。本文以2006～2007年汛期地质灾害的实际发生情况为例,通过统计分析方法,对区域地质灾害与年降雨量、月降雨量、月暴雨日数、典型降雨过程之间的时空分布关系开展系统分析,研究表明:降雨是群发型地质灾害发生的重要诱发因素,地质灾害的发育密度与年均雨量成正比,地质灾害发育密度最大的区域,其年均雨量最大;地质灾害分布与月降雨量、月暴雨日数的分布总体上具备一定的对应关系,但并不是完全对应的,主要受到典型强降雨过程的落区控制;在地质灾害多发区出现典型强降雨过程时,地质灾害群发。     

Synoptic analysis of unexampled super-heavy rainfall on April 1, 2019, in west of Iran

Natural Hazards - The aim of this study is to analyze the synoptic and analyzing the mechanisms of flood occurrence on April 1, 2019, in the western half of Iran. In order to statistically analyze...