Trend In June Research Articles

Potential Impacts of Future Climate Change on Super-Typhoons in the Western North Pacific: Cloud-Resolving Case Studies Using Pseudo-Global Warming Experiments

Potential impacts of projected long-term climate change toward the end of the 21st century on rainfall and peak intensity of six super-typhoons in the western North Pacific (WNP) are assessed using a cloud-resolving model (CRM) and the pseudo-global warming (PGW) method, under two representative concentration pathway (RCP) emission scenarios of RCP4.5 and RCP8.5. Linear long-term trends in June–October are calculated from 38 Coupled Model Intercomparison Project phase 5 (CMIP5) models from 1981–2000 to 2081–2100, with warmings of about 3 °C in sea surface temperature, 4 °C in air temperature in the lower troposphere, and increases of 20% in moisture in RCP8.5. The changes in RCP4.5 are about half the amounts. For each typhoon, three experiments are carried out: a control run (CTL) using analysis data as initial and boundary conditions (IC/BCs), and two future runs with the trend added to the IC/BCs, one for RCP4.5 and the other for RCP8.5, respectively. Their results are compared for potential impacts of climate change. In future scenarios, all six typhoons produce more rain rather consistently, by around 10% in RCP4.5 and 20% in RCP8.5 inside 200–250 km from the center, with increased variability toward larger radii. Such increases are tested to be highly significant and can be largely explained by the increased moisture and water vapor convergence in future scenarios. However, using this method, the results on peak intensity are mixed and inconsistent, with the majority of cases becoming somewhat weaker in future runs. It is believed that in the procedure to determine the best initial time for CTL, which yielded the strongest TC, often within a few hPa in minimum central sea-level pressure to the best track data, an advantage was introduced to the CTL unintentionally. Once the long-term trends were added in future runs, the environment of the storm was altered and became not as favorable for subsequent intensification. Thus, the PGW approach may have some bias in assessing the peak intensity of such super-typhoon cases, and caution should be practiced.

Crop planning using innovative trend analysis of 62-years rainfall data

The success of climate-smart agriculture in high rainfall zone lies in understanding the rainfall trend and planning or modifying the cropping system for maximum yield. Moisture stress in critical crop growth stages is detrimental to the crop and drastically reduces the yield. Udhagamandalam region in Western Ghats is a high rainfall area and is largely cultivated by vegetable crops. Rainfall trend based crop planning would enhance the crop yield without water stress. A study was carried out at ICAR-Indian Agricultural Research Institute, Dhemaji, Assam focused on assessing the long-term seasonal and monthly rainfall trends of Udhagamandalam region, Tamil Nadu using non-parametric tests and Innovative Trend Analysis (ITA). Daily rainfall of 62 years from 1960–2021 was analyzed with non-parametric tests, viz. Mann-Kendall and modified Mann-Kendall and ITA to find the seasonal rainfall characteristics. Mann- Kendall (3.055) and modified Mann-Kendall (3.055) tests showed a significantly increasing trend in the annual and seasonal monsoonal rainfall. ITA revealed either a significant positive or a negative trend in all the months except February, with the highest trend in June (2.625). In contrast to standard non-parametric tests, ITA detected a significant positive trend in all seasons and annual rainfall, except in cold winters where the trend is negative. The long-term trend analysis results suggest that the ITA is more precise for rainfall trend analysis than standard non-parametric tests and can be used to evaluate hidden variations of rainfall trends. Hence, ITA is recommended for analyzing rainfall trends for crop planning in high-rainfall regions. IT analysis of 62 years of rainfall data of Udhagamandalam suggested that vegetable crop planning can be done by farmers from August–November months as the rainfall trend during this period is assured as an increasing trend of rainfall pattern was observed.

FOREST LITTER PRODUCTION VARIES WITH SEASON AND ELEVATION GRADIENT IN CHIAPAS, MEXICO

<p><strong>Background.</strong> Forest litterfall is a fundamental process of ecosystem nutrient cycling, also, it is a source of energy for the development and propagation of wildfire. Understanding the temporal dynamics of litter production and storage is critical for sustainable management and conservation of forest ecosystems. <strong>Objective. </strong>To quantify<strong> </strong>the monthly production and storage of forest litter in an elevation gradient. <strong>Methodology.</strong> We selected forest sites at three elevations: 670, 775, and 1010 masl, corresponding to pine, oak, and tropical lowland forest ecosystems in Nambiyugua hill, Chiapas, Mexico. Sixteen sampling sites with a radius of 11.28 m were established for tree measurements, and 48 litter traps of 0.50 m<sup>2 </sup>were installed to collect monthly litterfall for a year. To sample ground litter eight 30 by 30 cm<sup>2</sup> quadrats were used in each site. The fallen woody material was measured with the planar intersection method. Litter samples were oven-dried at 60 °C for 72 h and separated into leaves and other plant parts. One-way ANOVA was used to test the significant differences between forests.<strong> Results.</strong> The highest total loads of litter and fallen woody material were obtained in the pine forests of upper elevation with 29.01 t ha<sup>-1</sup>. The highest litter production was obtained in January and April, with a mean of 1.34 ± 0.19 and 0.74 ± 0.13 t ha<sup>-1</sup> respectively in pine forests. In the oak forest, the highest production occurred in March, with 1.08 ± 0.25 t ha<sup>-1</sup>; while the lowland forest reached the highest production in January with 0.85 ± 0.26 t ha<sup>-1</sup>, with a decreasing trend in June. <strong>Implications.</strong> Understanding the seasonal variability in litter production and forest fuel loads is crucial for forest productivity, carbon sequestration, and wildfire prevention <strong>Conclusions.</strong> The production of forest fuels was different among the ecosystems representing the elevation gradients. The highest monthly production of litter was registered during the January-May period for the pine and oak ecosystems but in November - January in tropical lowland forests.</p>

Estimation of regional meteorological aridity and drought characteristics in Baluchistan province, Pakistan.

Droughts and prevailing arid conditions have a significant impacts on the natural environment, agriculture, and human life. To analyze the regional characteristics of drought in Baluchistan province, the aridity index (AI) and standardized potential evapotranspiration index (SPEI) were used in. The study analyzed the rainfall, temperature, and potential evapotranspiration (PET) data and the same were used for the calculation of AI as well as SPEI to find out the drought spells during the study period. The linear regression and Mann-Kendall test were applied to calculate the trend in AI as well as in SPEI results. The AI results revealed that most of the meteorological stations are arid and semi-arid, where the highest increasing aridity is noted at Kalat (0.0065/year). The results of the SPEI at 1 and 6-months identified the extreme to severe drought spell during 1998-2002 in all meteorological stations of Baluchistan province. The distinct drought spells identified from the SPEI results were in the years 1998-2003, 2006-2010, 2015-2016 and 2019. The drought frequency results showed highest frequency percentage at Lasbella (46%) of extreme to severe drought. The Mann-Kendall trend results showed negative trend in monthly AI and 1-month SPEI results and most significant trend was observed in April and October months, this shows that aridity and drought in the region are decreasing to some extent except Dalbandin and Lasbella observed increasing trend in winter season (November to January months) and Kalat met-station observed increasing trend in June. Prior investigation and planning of drought situations can help in controlling the far-reaching consequences on environment and human society.

Homogeneity and Trend Analysis of Climatic Variables in Cap-Bon Region of Tunisia

As a semi-arid Mediterranean country, Tunisia is affected by the impacts of climate change, particularly the coastal regions like the Cap-Bon. Irregular rainfall, rising temperatures and the recurrence of extreme events are all indicators that affect ecosystems and populations and make them more vulnerable to the influence of climatic variables. Therefore, an analysis of the trends of climate variables can contribute to facilitating the development of effective adaptation strategies. In this matter, this study was conducted to assess the homogeneity and trends of minimum and maximum air temperature (Tmin and Tmax) and precipitation (P) in the Cap-Bon region. Daily data were collected from the meteorological station of Nabeul for the period of 1982–2020. Pettitt and SNHT tests for homogeneity were applied to identify the breakpoints in multi-time scales of Tmax, Tmin and P data series. The Mann–Kendall (MK) test was used to detect the change in the time-series trend. A modified Mann–Kendall (mMK) test was used to remove the autocorrelation effect from the data series. Both the MK and mMK tests were used at the 5% significant level. The magnitude of the climatic trend was estimated using the non-parametric Sen’s slope estimator. Contrary to Tmin and P, the results of the homogeneity tests revealed the existence of significant breakpoints in the annual, seasonal and monthly Tmax time series. For most cases, the breakpoint occurred around the year 2000. For Tmin, significant breakpoints were recorded in March and April, while a significant shift in the P time series was detected in December. The Mann–Kendall results show a significant warming trend in annual Tmax, with magnitudes equal to 0.065 and 0.045 °C/year before and after the breakpoint, respectively. Nevertheless, non-significant tendencies were observed in the annual Tmin and P time series. On the monthly time scale, Tmax exhibited a significant upward trend in June and August, before the observed breakpoints, with Sen’s slope values equal to 0.065 and 0.045 °C/year, respectively. Regarding the Tmin data, a significant positive trend was observed in July at a rate of 0.033 °C/year.

Investigation of the effect of climate change on reference evapotranspiration in Tekirdag, Turkiye

It is important to determine the impact of global climate change on water resources. In the effective use of water resources, the amount of water required for agricultural irrigation should be planned under these conditions. In this study, the trend analysis of the climate data required for the calculation of reference evapotranspiration (ET0) between 2011 and 2020 for the meteorology station of Tekirdag, Turkey was carried out. Analysis of change in the study was carried out according to the methods of Linear Regression Test, Mann-Kendall Test and Sen T Test. As a result of the change analysis, it was determined that the reference evapotranspiration (ET0) values had a decreasing trend in June and July and this decrease was statistically significant. As a result of the analysis of the changes in the climate parameters used in the calculation of reference evapotranspiration (ET0), it was determined that the values of maximum temperature, average relative humidity and average sunshine duration were in a decreasing trend, while minimum temperature and wind speed values were in an increasing trend. In the study, the change in monthly precipitation amounts during the measurement periods was also examined. As a result of the analysis of the changes, it was determined that the monthly precipitation amounts have a decreasing trend and the changes especially in April, September, October and December are statistically significant. It is thought that the results obtained will contribute to the future agricultural production planning in the regional conditions.

Interdecadal Variation of Summer Extreme Heat Events in the Beijing–Tianjin–Hebei Region

Extreme heat events are frequent in the Beijing–Tianjin–Hebei (BTH) region due to global warming and accelerated urbanization. While previous studies have analyzed the trend of extreme heat events in the Beijing–Tianjin–Hebei (BTH) region, the interdecadal changes of these events remain unclear. Therefore, this study aims to analyze the interdecadal temporal and spatial characteristics of summer extreme heat events in the BTH region using daily mean and maximum temperature datasets from 174 stations over the period 1979–2020. The results are shown as follows: (1) From 1979 to 2020, extreme heat events showed an overall upward trend in the BTH region. There were similarities in the changes in the extreme maximum temperature (TXx) and the number of high-temperature days (Htd) between different generations, and both were low until the mid-1990s. (2) In terms of the spatial pattern, TXx and Htd both showed the spatial distribution characteristics of being high in the south and low in the north. Extreme heat events in the BTH region were mainly concentrated in Beijing City, Tianjin City, and the eastern region of Hebei, and the TXx increase in most areas reached 1.5–2.0 °C. (3) The number of high-temperature days (Htd) increased significantly in the background of global warming, especially in Beijing, Tianjin, and Shijiazhuang Cities. (4) Extreme heat events in the BTH region mainly occurred in June and July, and the interdecadal changes showed a decreasing trend in June and an increasing trend in July. A high proportion of Htd was concentrated in Northern Hebei Province in July.

Long-term variations of cloud top patterns associated with heavy rainfall over the Korean peninsula

Study regionKorean peninsula Study focusAlthough heavy rainfall systems over Korea were known to be characterized by warm-type heavy rainfall associated with a relatively low cloud top, this feature can vary under climate change. This study primarily investigated the mean climatology and linear trends of heavy rainfall and cloud top distributions over the Korean peninsula. The rainy seasons from June to September for the period of 1983–2021 were analyzed in this study. To understand the climatology and changes of heavy rainfall events, thermodynamic variables and synoptic forcings were analyzed. New hydrological insights for the regionLong-term consistent geostationary satellite data provides a unique opportunity to understand the global cloud variations associated with climate change. This study examined the mean climatology and decadal trends of cloud tops inferred from geostationary satellite observations, along with heavy rainfall events over the Korean Peninsula. Rainfall and cloud top heights exhibited a decreasing trend in June, whereas an increase in vigorous deep convection was observed in August/September, mainly in southern Korea. The decadal trends in August and September were associated with large-scale circulation changes, while the trend in September was attributed to increasing typhoon visits over the Korean Peninsula. The cloud top changes, with lowered cloud tops in June and elevated cloud tops in August, suggested a shift of deep convective systems toward the late summer over Korea.

The Cooling Over Northeast Asia in June Over the Most Recent Decade: A Possible Response to Declining Bering Sea Sea Ice in March

AbstractIn sharp contrast to rapid global warming, the surface air temperature (SAT) in Northeast Asia (NEA) has exhibited strong cooling in June since 2010. Our results show that this cooling is a possible response to declining Bering Sea sea ice concentrations (BSICs) in March. A physical process analysis shows that decreasing BSICs provoke a south‐eastward‐propagating Rossby wave train and induce a strong anomalous anticyclone over the midlatitude Northwest Atlantic, resulting in tripolar anomalous sea surface temperature (SST) pattern with warm anomalies in the midlatitude Northwest Atlantic. These persistent tripolar SST anomalies generate a new Rossby wave across Eurasia in June, which enhances the cyclonic atmosphere circulation anomaly over NEA and leads to the cold SAT anomalies observed in this area. Correspondingly, in response to the sharp decline in BSIC in March, the NEA has exhibited an unprecedented cooling trend in June over the most recent decade.

Streamflow Changes of Small and Large Rivers in the Aldan River Basin, Eastern Siberia

The flow of large northern rivers has increased, but regional patterns of changes are not well understood. The aim of this study is the estimation of monthly discharge changes of the 11 river catchments in the Aldan River basin in Eastern Siberia, the largest Lena River tributary and the sixth largest river in Russia. We considered the trend dependence on month, number of years in the sample, finish and start years, and basin area. The median fraction of samples with no trend, positive and negative trends are 70.5%, 28.5%, and 1%, respectively. Longer samples tend to show more positive trends than shorter ones. There is an increasing fraction of samples with positive trends as a function of later sample end year, whereas the start year does not result in a similar pattern. The larger basins, with one exception, have more positive trends than smaller ones. The trends in monthly streamflow have prominent seasonality with absence of positive trends in June and increasing fraction of samples with positive trends from October till April. The study reports the recent streamflow changes on the rarely analyzed rivers in Eastern Siberia, where air temperature rises faster than in average on the globe. The study results are important for water resources management in the region and better understanding of current environmental changes.

ANALYSIS OF VARIATIONS IN TREND IN NOVEL CORONAVIRUS DISEASE IN SOUTHERN TAMIL NADU, INDIA

Corona Virus Disease (COVID) is the newly identified communicable disease which originally found in China first and its spread was found all over the world. The spread made a huge impact among people around the world. In this study, the variation of trends in novel COVID spread is determined in the southern region of Tamilnadu state of India. Tamilnadu ranks in the seventh position in the total population among the 29 states in India. This can be one of the reasons for the spread of the disease. In this study, the trend patterns in the spread of disease are determined from May to November for the year 2020 in southern districts of Tamilnadu. The results of the CUSUM test indicated that all the districts had a statistically significant step jump in June. The Mann-Kendall test indicated that there is an increasing trend in June in all the districts and decreasing trend in October and November. From the results, it is suggested that the first phase vaccination can be implemented in Madurai, Virudhunagar, Kanyakumari and Thoothukudi districts.

Can innovative trend analysis identify trend change points?

Trends in temperature series are the main cause of climate change. Because solar energy directs hydro-meteorological events and increasing variations in this resource change the balance between events such as evaporation, wind, and rainfall. There are many methods for calculating trends in a time series such as Mann-Kendall, Sen's slope estimator, Spearman's rho, linear regression and the new Sen innovative trend analysis (ITA). In addition, Mann-Kendall's variant, the sequential Mann Kendall, has been developed to identify trend change points; however, it is sensitive to related data as specified by some researchers. Şen_ITA is a new trend detection method and does not require independent and normally distributed time series, but has never been used to detect trend change points. In the literature, multiple, half-time and multi-durations ITA methods are used to calculate partial trends in a time series without identifying trend change points. In this study, trend change points are detected using the Şen_ITA method and named ITA_TCP. This approach may allow researchers to identify trend change points in a time series. Diyarbakır (Turkey) is selected as a study area, and ITA_TCP has detected trends and trends change points in monthly average temperatures. Although ITA detects only a significant upward trend in August, given the 95% statistical significance level, ITA_TCP shows three upward trends in June, July and August, and a decreasing trend in September. Critical trend slope values are obtained using the bootstrap method, which does not require the normal distribution assumption.

Can innovative trend analysis identify trend change points?

Trends in temperature series are the main cause of climate change. Because solar energy directs hydro-meteorological events and increasing variations in this resource change the balance between events such as evaporation, wind, and rainfall. There are many methods for calculating trends in a time series such as Mann-Kendall, Sen's slope estimator, Spearman's rho, linear regression and the new Sen innovative trend analysis (ITA). In addition, Mann-Kendall's variant, the sequential Mann Kendall, has been developed to identify trend change points; however, it is sensitive to related data as specified by some researchers. Şen_ITA is a new trend detection method and does not require independent and normally distributed time series, but has never been used to detect trend change points. In the literature, multiple, half-time and multi-durations ITA methods are used to calculate partial trends in a time series without identifying trend change points. In this study, trend change points are detected using the Şen_ITA method and named ITA_TCP. This approach may allow researchers to identify trend change points in a time series. Diyarbakır (Turkey) is selected as a study area, and ITA_TCP has detected trends and trends change points in monthly average temperatures. Although ITA detects only a significant upward trend in August, given the 95% statistical significance level, ITA_TCP shows three upward trends in June, July and August, and a decreasing trend in September. Critical trend slope values are obtained using the bootstrap method, which does not require the normal distribution assumption.

Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product

Abstract. This study presents a spatio-temporal continuous data set for snow cover in Iceland based on the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2018. Cloud cover and polar darkness are the main limiting factors for data availability of remotely sensed optical data at higher latitudes. In Iceland the average cloud cover is 75 % with some spatial variations, and polar darkness reduces data availability from the MODIS sensor from late November until mid January. In this study MODIS snow cover data were validated over Iceland with comparison to manned in situ observations and Landsat 7/8 and Sentinel 2 data. Overall a good agreement was found between in situ observed snow cover, with an average agreement of 0.925. Agreement of Landsat 7/8 and Sentinel 2 was found to be acceptable, with R2 values 0.96, 0.92 and 0.95, respectively, and in agreement with other studies. By applying daily data merging from Terra and Aqua and a temporal aggregation of 7 d, unclassified pixels were reduced from 75 % to 14 %. The remaining unclassified pixels after daily merging and temporal aggregation were removed with classification learners trained with classified data, pixel location, aspect and elevation. Various snow cover characteristic metrics were derived for each pixel such as snow cover duration, first and last snow-free dates, deviation and dynamics of snow cover and trends during the study period. On average the first snow-free date in Iceland is 27 June, with a standard deviation of 19.9 d. For the study period a trend of increasing snow cover duration was observed for all months except October and November. However, statistical testing of the trends indicated that there was only a significant trend in June.

Changes in Soil Arthropod Abundance and Community Structure across a Poplar Plantation Chronosequence in Reclaimed Coastal Saline Soil

Poplar plantations have the capacity to improve the properties of soils in muddy coastal areas; however, our understanding of the impacts of plantation development on soil arthropods remains limited. For this study, we determined the community dynamics of soil dwelling arthropods across poplar plantations of different ages (5-, 10-, and 21-years) over the course of one year in Eastern Coastal China. The total abundance of soil arthropods differed with stand development. Further, there were some interactions that involved the sampling date. On average, total abundance was highest in the 10-year-old stands and lowest in the 5-year-old stands. Total abundance exhibited strong age-dependent trends in June and September, but not in March or December. The abundance of Prostigmata and Oribatida increased in the 5- to 21-year-old stands, with the highest levels being in the 10-year-old stands. The abundance of Collembola increased with stand development; however, the stand age had no significant impact on the abundance of epedapic, hemiedaphic, and euedaphic Collembola. Order richness (Hill number q = 0) curve confidence intervals overlapped among three stand ages. Shannon and Simpson diversity (Hill numbers q = 1 and q = 2) differed between 10- and 21-year-old stand age. They showed almost similar trends, and the highest and lowest values were recorded in the 21- and 10-year-old stand ages, respectively. Permutational multivariate analysis of variance demonstrated that composition also varied significantly with the sampling date and stand age, and the 10-year-old stands that were sampled in June stood well-separated from the others. Indicator analysis revealed that Scolopendromorpha and Prostigmata were indicators in June for the 10-year-old stands, while Collembola were indicators for the 21-year-old stands sampled in September. Our results highlight that both stand development and climate seasonality can significantly impact soil arthropod community dynamics in the reclaimed coastal saline soils of managed poplar plantations.

Trends and homogeneity of monthly, seasonal, and annual rainfall over arid region of Rajasthan, India

Knowledge of rainfall variability is important for regional-scale planning and management of water resources in agriculture. This study explores spatio-temporal variations, trends, and homogeneity in monthly, seasonal, and annual rainfall series of 62 stations located in arid region of Rajasthan, India using 55 year (1957–2011) data. Box-whisker plots indicate presence of outliers and extremes in annual rainfall, which made the distribution of annual rainfall right-skewed. Mean and coefficient of variation (CV) of rainfall reveals a high inter-annual variability (CV > 200%) in the western portion where the mean annual rainfall is very low. A general gradient of the mean monthly, seasonal, and annual rainfall is visible from northwest to southeast direction, which is orthogonal to the gradient of CV. The Sen’s innovative trend test is found over-sensitive in evaluating statistical significance of the rainfall trends, while the Mann-Kendall test identifies significantly increasing rainfall trends in June and September. Rainfall in July shows prominently decreasing trends although none of them are found statistically significant. Monsoon and annual rainfall show significantly increasing trends at only four stations. The magnitude of trends indicates that the rainfall is increasing at a mean rate of 1.11, 2.85, and 2.89 mm year−1 in August, monsoon season, and annual series. The rainfall is found homogeneous over most of the area except for few stations situated in the eastern and northwest portions where significantly increasing trends are observed. Findings of this study indicate that there are few increasing trends in rainfall of this Indian arid region.

Mathematical modeling - food price analysis

This paper mainly uses the idea of pedigree clustering analysis, gray prediction and principal component analysis. The clustering analysis model, GM (1,1) model and principal component analysis model were established by using SPSS software to analyze the correlation matrices and principal component analysis. MATLAB software was used to calculate the correlation matrices. In January, The difference in price changes of major food prices in cities is calculated, and had forecasted the various food prices in June 2016. For the first issue, the main food is classified and the data are processed. After that, the SPSS software is used to classify the 27 kinds of food into four categories by using the pedigree cluster analysis model and the system clustering. The four categories are made by EXCEL. The price of food changes over time with a line chart that analyzes the characteristics of food price volatility. For the second issue, the gray prediction model is established based on the food classification of each kind of food price. First, the original data is cumulated, test and processed, so that the data have a strong regularity, and then establish a gray differential equation, and then use MATLAB software to solve the model. And then the residual test and post-check test, have C <0.35, the prediction accuracy is better. Finally, predict the price trend in June 2016 through the function. For the third issue, we analyzed the main components of 27 kinds of food types by celery, octopus, chicken (white striped chicken), duck and Chinese cabbage by using the data of principal given and analyzed by principal component analysis. It can be detected by measuring a small amount of food, this predict CPI value relatively accurate. Through the study of the characteristics of the region, select Shanghai and Shenyang, by looking for the relevant CPI and food price data, using spss software, principal component analysis, the impact of the CPI on several types of food, and then calculated by matlab algorithm weight, and then the data obtained by the analysis and comparison, different regions should be selected for different types of food for testing.

Assessing operative natural and anthropogenic forcing factors from long-term climate time series of Uttarakhand (India) in the backdrop of recurring extreme rainfall events over northwest Himalaya

The entire Indo-Himalayan region from northwest (Kashmir) to northeast (Assam) is facing prevalence of floods and landslides in recent years causing massive loss of property, human and animal lives, infrastructure, and eventually threatening tourist activities substantially. Extremely intense rainfall event of 2013C.E. (between 15 and 17 June) kicked off mammoth flash floods in the Kedarnath area of Uttarakhand state, resulting in huge socioeconomic losses to the state and country. Uttarakhand is an important hilly region attracting thousands of tourists every year owing to numerous shrines and forested mountainous tourist spots. Though recent studies indicate a plausible weakening of Indian summer monsoon rainfall overall, recurrent anomalous high rainfall events over northwest Himalaya (e.g. -2010, 2013, and 2016) point out the need for a thorough reassessment of long-term time series data of regional rainfall and ambient temperatures in order to trace signatures of a shifting pattern in regional meteorology, if any. Accordingly, here we investigate ~100-year-long monthly rainfall and air temperature time series data for a selected grid (28.5°N, 31.25°N; 78.75°E, 81.25°E) covering most parts of Uttarakhand state. We also examined temporal variance in interrelationships among regional meteorological data (temperature and precipitation) and key global climate variability indices using advance statistical methods. Major findings are (i) significant increase in pre-monsoon air temperature over Uttarakhand after 1997, (ii) increasing upward trend in June–July rainfall and its relationship with regional May temperatures (iii) monsoonal rainfall (June, July, August, and September; JJAS) showing covariance with interannual variability in Eurasian snow cover (ESC) extent during the month of March, and (iv) enhancing tendency of anomalous high rainfall events during negative phases of Arctic Oscillation. Obtained results indicate that under warming scenario, JJ rainfall (over AS) may further increase with occasional extreme rainfall spells when AO index (March) is negative.

Inter annual variability and trends of southwest monsoon rainfall over Anand in Gujarat state

Climate is a primary determinant of agricultural productivity. In turn, food and fibber production is essential for sustaining and enhancing human welfare. The trends of maximum atmospheric temperature, rainfall are analysed for meteorological data of Anand district in Central Gujarat India. Rainfall distribution over India is highly erratic and uncertain both in time and space. Heavy rainfall for short period and some times continuous dry spell creates problems for crop growth. Therefore, it is necessary to study the rainfall analysis for developing farming system which may help to increase and stabilize agricultural production through better use of natural resources. Monthly rainfall over Anand showed increasing trend in August and September with decreasing trend in June and July. Such trend was more evident in since in 1971 onwards. It revealed that the monthly rainfall during July decreasing trend while August and September is in increasing trend. Such phenomenon was more evident since 1971 onwards. The positive value of Mann-Kendall test statistic (0.459) indicated that rainfall during southwest monsoon season had an increasing trend tendency which was significant in 0.05 levels. The rainfall of August was highly variable and undependable followed by September. The occurrence of excess and deficient rainfall when compared to that of normal was seen in more or less equal numbers of years. The rainfall range and its variability was less during deficient rainfall years when compared to that of excess rainfall years. During June month, the rainfall series showing significant (0.05) level in sub periods 1961-1990 (-0.340). In July month the series is showing significant level in sub periods like 1901-1930 (0.261), 1931-1960 (0.483) and 1961-1990 (0.261). In the August month the series is showing significant level in sub periods like 1931-1960 (0.655) and 1991-2011 (0.853). In the month of September the rainfall series is showing the significant level in sub periods like 1901-1930 (0.335), 1931-1960 (0.596) and 1991-2011(0.389).

The Female Reproductive Cycle of the Bedriaga Plate-Tailed Gecko,Teratoscincus bedriagai(Sauria: Gekkonidae) in Iran

The Bedriaga Plate-tailed Gecko,Teratoscincus bedriagaiNikolsky, 1900, is distributed in the northern and eastern desert basins of the Central Plateau of Iran, Sistan, and the desert regions of southern Afghanistan. Iranian specimens are believed to be rare in collections. In this study, the reproductive cycle of this species has been investigated through focusing on oogenesis from 5 April to 5 August, 2013. Generally, 15 adult females were collected by hand at midnight from southern parts of Damghan County, situated in Semnan Province of Iran. Ovaries were removed and processed for the purpose of histological and morphometric studies. The results revealed that oocyte growth starts in early April and terminates in late July. Moreover, mating commences in spring, especially at the beginning of May, with oviposition occurring from late May to late July. Approximately, 1 to 2 eggs are laid by females per clutch with the possibility of producing a secondary clutch later in the season. The maximum reproductive activity takes place in May and continues with a decreasing trend in June and more reduction in July and finally ends in August. No significant difference was observed between right and left side of reproductive system. Therefore, oogenesis occurs from April to July, whileT. bedriagaifollows an oogenic cycle typical for temperate species.