Space‐time variability of rainfall at local scale is affected by several regional factors such as aerosol concentration, greenhouse gases, land cover changes, etc., along with large scale atmospheric ...circulations. Predictive ability of regional circulation models can be significantly improved and efficient management of water resources can be assured by identifying dominant variables controlling spatiotemporal variations in rainfall among aforementioned factors. The present study aims to investigate dominant climate system(s) controlling trends in rainfall over Chhattisgarh state (a semi‐arid region) in India over the period of 115 years (1901–2015). Discrete wavelet transform in conjunction with Mann–Kendall test is applied to the rainfall data series at different time scales (monthly, seasonal, annual, pre‐monsoon, monsoon, post‐monsoon and winter) in order to identify the long term trends and dominant periodic components influencing the trend. In the results, negative trends are found to exist in all rainfall time series at majority of districts (except for annual and monsoon rainfall at Bijapur and Sukma district). In addition, analysis of trend in actual evapotranspiration and soil moisture in the region does not exhibits the effect of anthropogenic variables (such as land cover change, irrigation projects, etc.) on the rainfall as significant negative trend are also observed in soil moisture for majority of districts. Overall, 2‐year and 4‐year periodic components have been detected to be dominating the trends in most of the rainfall time series (annual, monsoon, post‐monsoon and winter). On comparing the identified dominating components with the existing climate systems (Atlantic multidecadal oscillations, Indian ocean dipole, Madden‐Julian oscillations, Inter‐Tropical Convergence Zone, etc.), El Niño‐Southern Oscillations has been recognized as predominant climate circulation influencing the rainfall trends over the study region. The study outcomes are expected to improve the regional precipitation forecasts and should be useful in various hydro‐meteorological analyses and decision making at regional scale.
A better understanding of behaviour of seasonal rainfall over Indian sub‐continent requires identification of trends and dominant weather systems influencing those trends in rainfall. Traditional methods of trend analysis assume the variable to be stationary, which is not a valid assumption in the context of changing climate. Discrete Wavelet transform has proven to be a handy technique for such analyses. The study results indicate the predominance of El Niño‐Southern Oscillations on the rainfall over the semi‐arid region in India.
The non-parametric Mann-Kendall (MK) statistical test has been popularly used to assess the significance of trend in hydrological time series. The test requires sample data to be serially ...independent. When sample data are serially correlated, the presence of serial correlation in time series will affect the ability of the test to correctly assess the significance of trend. To eliminate the effect of serial correlation on the MK test, effective sample size (ESS) has been proposed to modify the MK statistic. This study investigates the ability of ESS to eliminate the influence of serial correlation on the MK test by Monte Carlo simulation. Simulation demonstrates that when no trend exists within time series, ESS can effectively limit the effect of serial correlation on the MK test. When trend exists within time series, the existence of trend will contaminate the estimate of the magnitude of sample serial correlation, and ESS computed from the contaminated serial correlation cannot properly eliminate the effect of serial correlation on the MK test. However, if ESS is computed from the sample serial correlation that is estimated from the detrended series, ESS can still effectively reduce the influence of serial correlation on the MK test.PUBLICATION ABSTRACT
The aim of this study is the detection of trends of precipitation from (1986-2020) over Târgu Mureș city. Precipitation data for 35 years were processed with MS Excel spreadsheets to find monthly, ...seasonal and annual variability of rainfall. The Mann-Kendall test was used for trend analysis of precipitation and the Sen’s slope estimator was used for the magnitude of variation. The calculations of the two methods were performed using the MAKESENS program. The standard deviation and the coefficient of variation were used to highlight the dispersion. Results show that all three scales (annual, seasonal and monthly show a tendency to increase rainfall. The highest monthly average of precipitation is 227.70 mm (August, 2005), and the lowest monthly average of precipitation is 0.80 mm (November, 2011). The maximum value of annual precipitation is 852.60 mm and was registered in 2005, and the minimum value was 408.70 mm registered in 2000.
Land desertification is a significant research area among current global ecological and environmental issues. Desertification not only affects the ecological environment, but is also closely related ...to human society. With the development of new technology, outstanding progress has been made in the development of desertification monitoring and evaluation indicators. The objective of this study was to assess the trends and distribution of land desertification in northern China using time-series GIMMS NDVI 3 g and MODIS-NDVI remote sensing data from 1991 to 2019. The Dimidiate Pixel Model was chosen to calculate the Vegetation Coverage Index (VCI). The Sen's slope estimator and the Mann–Kendall statistical test were used to analyze the spatial trends of the VCI. The residual trend method was applied to assess human and climate factor induced land degradation. Data from the study area showed that the total area of desertification in northern China decreased year by year from 1991 to 2019, but the area of very severe desertification demonstrated an increasing trend. At the same time, areas of severe, moderate, and mild desertification showed a decreasing trend. From the overall analysis, a trend of overall decrease and local aggravation of land desertification was revealed. The residual analysis showed that desertification control measures and favorable climatic conditions have played key roles in the process of desertification reversion; while climate fluctuations, reclamation and livestock have led to the development of further desertification.
The temporal dynamics of anthropogenic impacts on the Pchelina Reservoir is assessed based on chemical element analysis of three sediment cores at a depth of about 100-130 cm below the surface water. ...The
Cs activity is measured to identify the layers corresponding to the 1986 Chernobyl accident. The obtained dating of sediment cores gives an average sedimentation rate of 0.44 cm/year in the Pchelina Reservoir. The elements' depth profiles (Ti, Mn, Fe, Zn, Cr, Ni, Cu, Mo, Sn, Sb, Pb, Co, Cd, Ce, Tl, Bi, Gd, La, Th and U
) outline the Struma River as the main anthropogenic source for Pchelina Reservoir sediments. The principal component analysis reveals two groups of chemical elements connected with the anthropogenic impacts. The first group of chemical elements (Mn, Fe, Cr, Ni, Cu, Mo, Sn, Sb and Co) has increasing time trends in the Struma sediment core and no trend or decreasing ones at the Pchelina sampling core. The behavior of these elements is determined by the change of the profile of the industry in the Pernik town during the 1990s. The second group of elements (Zn, Pb, Cd, Bi and U
) has increasing time trends in Struma and Pchelina sediment cores. The increased concentrations of these elements during the whole investigated period have led to moderate enrichments for Pb and U
, and significant enrichments for Zn and Cd at the Pchelina sampling site. The moderately contaminated, according to the geoaccumulation indexes, Pchelina Reservoir surface sediment samples have low ecotoxicity.
Against the backdrop of global climate change, the frequency of drought events is increasing, leading to significant impacts on human society and development. Therefore, it is crucial to study the ...propagation patterns and trends of drought characteristics over a long timescale. The main objective of this study is to delineate the dynamics of drought characteristics by examining their propagation patterns in China from 1951 to 2020. In this study, precipitation data from meteorological stations across mainland China were used. A comprehensive dataset consisting of 700 stations over the past 70 years was collected and analyzed. To ensure data accuracy, the GPCC (the Global Precipitation Climatology Center) database was employed for data correction and gap-filling. Long-term drought evolution was assessed using both the SPI-12 (standardized precipitation index) and SPEI-12 (standardized precipitation evapotranspiration index) to detect drought characteristics. Two Moran indices were applied to identify propagation patterns, and the MK (the Mann–Kendall) analysis method, along with the Theil–Sen slope estimator, was utilized to track historical trends of these indices. The findings of this study reveal the following key results: (i) Based on the SPI-12, the main areas of China that are prone to drought are mostly concentrated around the Hu Huanyong Line, indicating a tendency towards drying based on the decadal change analysis. (ii) The distribution of drought-prone areas in China, as indicated by the SPEI-12, is extensive and widely distributed, with a correlation to urbanization and population density. These drought-prone areas are gradually expanding. (iii) Between 2010 and 2011, China experienced the most severe drought event in nearly 70 years, affecting nearly 50% of the country’s area with a high degree of severity. This event may be attributed to atmospheric circulation variability, exacerbated by the impact of urbanization on precipitation and drought. (iv) The frequency of drought occurrence in China gradually decreases from south to north, with the northeast and northern regions being less affected. However, areas with less frequent droughts experience longer and more severe drought durations. In conclusion, this study provides valuable insights into the characteristics and propagation patterns of drought in China, offering essential information for the development of effective strategies to mitigate the impacts of drought events.
Analysis of the spatiotemporal pattern of burned areas over time is necessary to understand how fire behavior in the Himalayan region has altered as a result of the complex climatic variables. The ...differenced Normalized Burnt Ratio (NBR) is calculated utilizing the cloud-based platform Google Earth Engine (GEE) to quantify the extent of burned regions. The spatial distribution of burnt areas in the Himalayan region over the last 21 years has been examined and correlated with climatic and edaphic factors in the current study. The area affected by forest fire has shown a direct correlation with the land surface temperature, but an inverse relationship with surface soil moisture, pre-fire precipitation, pre-fire Normalized Difference Vegetation Index (NDVI) and pre-fire Enhanced Vegetation Index (EVI). The p-value for 9 of the 20 regions in which the research area has been divided for the spatial analysis is less than 0.05, implying that the regression model is statistically significant. Trend analysis done using Mann–Kendall test and Theil–Sen estimator state the distinct trends of burnt area and other meteorological and edaphic parameters in the Western, Central and Eastern Himalaya. The assessment of burned areas aids forest managers in mitigating the impacts and managing the forest fires, as well as in the implementation of the restoration methods following a forest fire.
Abstract The knowledge of intensity and frequency of rainfall allows establishing predictive measures to minimize impacts caused by high volume of rainfall totals in a region. Therefore, the ...objective is to evaluate daily rainfall for Paraná slope of the Itararé watershed (PSIW) and to verify the spatiotemporal trend of intense and extreme daily rainfall. Rainfall data from 14 stations collected from 1976 to 2012 were used with less than 4% of data faults. Multivariate analysis based on cluster analysis technique (CA) was used applying the Euclidean distance for the identification of homogeneous groups, and the quantiles technique to classify daily rainfall. The Mann-Kendall (MK) test was used to identify trends for annual rainfall totals, annual number of rainy days (ANRD) and for the occurrence of intense (R95p) and extreme (R99p) rainfall. The CA technique identified three rainfall groups (HG I, II and III). Given the latitudinal position of the area, rainfall at the southern sector is characterized by its greater similarities with the subtropical climate, whereas in the North sector there is a consistent reduction of rainfall totals in autumn and, especially, during winter months, which are characteristic of the tropical climate. The MK test identified the downward trend of ANRD, with greater significance for the south-centered sectors of the basin. The observed trends for the intense (R95p) and extreme (R99p) daily rainfall show the predominance of reduction for the Southwest and central sector, followed by a significant increase in the Southeast and North sectors of the PSIW.
Resumo O conhecimento da intensidade e a frequência das chuvas permitem estabelecer medidas preditivas para minimizar os impactos causados pelos altos totais de chuvas totais em uma região. Portanto, o objetivo deste trabalho é avaliar a precipitação diária para a vertente paranaense da bacia hidrográfica do rio Itararé (BHI) e verificar a tendência espaço-temporal das chuvas diárias intensas e extremas. Os dados de chuva de 14 estações pluviométricas coletadas de 1976 a 2012 foram usados com menos de 4% de falhas de dados. A análise multivariada baseada na técnica de análise de agrupamentos (AA) foi utilizada aplicando a distância euclidiana para a identificação de grupos homogêneos e a técnica de quantis para classificar as chuvas diárias. O teste de Mann-Kendall (MK) foi utilizado para identificar as tendências dos totais anuais pluviométricos, número anual de dias chuvosos (NADC) e ocorrência de chuvas intensas (R95p) e extremas (R99p). A técnica de CA identificou três grupos pluviométricos (HG I, II e III). Dada a posição latitudinal da área, a chuva no setor sul é caracterizada por suas maiores semelhanças com o clima subtropical, enquanto que no setor Norte há uma redução consistente dos totais de chuva no outono e, especialmente, durante os meses de inverno, que são características do clima tropical. O teste MK identificou a tendência de queda da NADC, com maior significância para os setores sul-centrados da bacia. As tendências observadas para as chuvas diárias intensas (R95p) e extremas (R99p) mostram a predominância de redução para o setor sudoeste e central, seguido por um aumento significativo nos setores sudeste e norte do BHI.
•Evaluation of impact of filled precipitation data on long-term trend analysis of extremes.•Trend analysis using filled and unfilled precipitation data and confirmation of biases.•Assessment using ...three WMO-based extreme precipitation indices.•Changes to distributions of filled data and under estimation of extremes are evident.•Variability of precipitation extremes in different phases of AMO with filled data.
This study focuses on the assessment of biases from infilling missing precipitation data on the detection of long-term change using parametric and non-parametric statistical techniques. Long-term historical precipitation data available for almost 100years at 53 rain gages in south Florida, USA, with gages having varying lengths of missing data are used for the study. Precipitation data with gaps and time series with spatial interpolated data are analyzed. Chronologically complete datasets are often used in climate variability studies by analyzing data in multiple temporal windows. The temporal windows selected in this work coincide with Atlantic multi-decadal oscillation (AMO) cool and warm phases that strongly influence precipitation extremes and characteristics in the study region. Selection of these windows has helped in evaluating the extremes derived based on infilled and unfilled data. The frequency of occurrence of precipitation extremes over a pre-specified threshold is also analyzed. Results indicate that infilled precipitation data introduce large biases in the statistical trends and over and under-estimate low and high extremes respectively. Evaluation of three extreme precipitation indices (i.e. Rx1day, R25mm and R50mm) indicates that bias increases with increase in amount of missing data. Nonparametric hypothesis tests indicate that statistical distributions of data of infilled and unfilled data are different when the data infilled is more than 5% of the entire data. Infilled data also introduced high variability in precipitation extremes in AMO cool and warm phases along with the changes in the frequency of occurrence of extreme events over a threshold.
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