VARIABILIDADE E TENDÊNCIAS PLUVIOMÉTRICAS OBSERVADAS NO SEMÁRIDO PERNAMBUCANO BRASILEIRO

Groundwater recharge using the chloride mass balance method in the Kanye area, in southeast Botswana

Meghalaya is known to receive the most torrential rainfall in the world, but the region suffers from water shortage as soon as the rain recedes, and the dry season starts. Changes in rainfall patterns and distribution can have a profound impact on water availability in a watershed, and therefore, examining spatial and temporal variations in rainfall is essential. However, the long-term rainfall variations in Meghalaya are not well explored. In this study, we take up two important watersheds in Meghalaya, i.e. Umiam and Umtru watersheds, to study the spatial and temporal rainfall variations. Using the gridded rainfall data from the Indian Meteorological Department from 1901 to 2018, we show that annual, winter, pre-monsoon, and monsoon rainfall is decreasing, whereas the post-monsoon rainfall is increasing. We use the innovative trend analysis (ITA) method to identify the trends in low-, medium-, and high-intensity rainfall. We find that low- and medium-intensity rainfall is in decreasing trend while high-intensity rainfall is increasing across annual and seasonal time scales. Lastly, we cross-check the trends detected using the innovative trend analysis method with a widely accepted Mann-Kendall (MK) test. We find that the results obtained by using the two methods generally concur; however, the ITA can detect non-monotonic trends in different rainfall intensities and is more sensitive to hidden patterns than the MK test.

Six in situ precipitation time series of varying time periods in the northwestern region and the Global Precipitation Climatology Centre (GPCC) v6 0.5° monthly dataset (1901–2010) were statistically examined for monotonic trends in Trinidad. The Pettit test was used to investigate the abrupt changes in the mean while the Mann–Kendall test was employed to assess the monotonic trends. It was found that three in situ stations and the six grids experienced abrupt changes in the rainfall patterns and that there was an apparent shift in the seasons. In addition, for five out of the six in situ stations no monotonic change was detected in the monthly, seasonal, and annual rainfall patterns. Gradual decreases were detected in the calculated weighted area average for five stations, the GPCCv6 dataset and St. Ann’s time series. The GPCCv6 data indicated that the dry season in the southern Trinidad is becoming drier. Results also suggested that the range between the greatest and lowest recorded rainfall values for some months have increased while others decreased. The gridded dataset appears to give a good representation of the dry season (January to May) rainfall compared with the wet season (June to December) and was found to be negatively biased for the north-western region but may not necessarily be so for the entire island. The results suggested that in the north-western region mirco-climates may exist. It is recommended that further investigations are needed using in situ data.

Spatial and temporal patterns of rainfall are governed by complex interactions between climate and landscape perturbations including deforestation, fire, and drought. Previous research demonstrated that rainfall in portions of the Amazon Basin has intensified, resulting in more extreme droughts and floods. The basin has global impacts on climate and hydrologic cycles; thus, it is critical to understand how precipitation patterns and intensity are changing. Due to insufficient precipitation gauges, we analyzed the variability and seasonality of rainfall over the Amazon Basin from 1982 to 2018 using high-resolution gridded precipitation products. We developed several precipitation indices and analyzed their trends using the Mann–Kendall test (Mann 1945; Kendall, 1975) to identify significant changes in rainfall patterns over time and space. Our results show landscape scale changes in the timing and intensity of rainfall events. Specifically, wet areas of the western Basin have become significantly wetter since 1982, with an increase of 182 mm of rainfall per year. In the eastern and southern regions, where deforestation is widespread, a significant drying trend is evident. Additionally, local alterations to precipitation patterns were also observed. For example, the Tocantins region has had a significant increase in the number of dry days during both wet and dry seasons, increasing by about 1 day per year. Surprisingly, changes in rainfall amount and number of dry days do not consistently align. Broadly, over this 37-year period, wet areas are trending wetter and dry areas are trending drier, while spatial anomalies show structure at the scale of hundreds of kilometers.

The land use and land cover (LULC) changes in the upper Gibe catchment were studied intricately using the MIKE SHE model and analyzed through statistical tests. The Mann–Kendall test was used to identify potential trends, while the Pettit test was used to detect abrupt changes in rainfall, temperature, and streamflow. A set of three maps of LULC change (1990, 2003, and 2018) was developed to observe how they affect the hydrological pattern of the catchment. Statistical analyses were conducted at mean annual time scales to establish relationships among the anomalies. It has been noted that certain temperature gauges showed statistically significant increases in temperature. Change points in the 1980s and 1990s were identified during the study period. The annual streamflow trends displayed an increasing trend that was not that significant. LULC changes contributed to increased surface runoff, attributed to agricultural, settlement, and water body expansion, as well as reductions in bare land, forest, shrubland, and grassland. The MIKE SHE model performed well during both the calibration and validation periods on the monthly time scale. The alterations in LULC had a noticeable impact on stream flows during both wet and dry seasons, resulting in increased mean monthly stream flows during the wet season and decreased flows during the dry season. The MIKE SHE study and related statistical analysis is quite a different method to perceive the results.

Rainfall is a climatic variable that dictates the daily rhythm of urban areas in Northeastern Brazil (NEB) and, therefore, understanding its dynamics is fundamental. The objectives of the study were (i) to validate the CHELSA product with data in situ, (ii) assess the spatial-temporality of the rains, and (iii) assess the trends and socio-environmental implications in the Metropolitan Region of Maceió (MRM). The monthly rainfall data observed between 1960 and 2016 were flawed and were filled with the imputation of data. These series were subjected to descriptive and exploratory statistics, statistical indicators, and the Mann-Kendall (MK) and Pettitt tests. CHELSA product was validated for MRM, and all stations obtained satisfactory determination coefficients (R2) and Pearson correlation (r). The standard error of the estimate (SEE), root mean square error (RMSE), and mean absolute error (MAE) were satisfactory. The highest annual rainfall accumulated occurred near the Mundaú and Manguaba lagoons. The Pettitt test identified that abrupt changes occur in El Niño and La Niña years (strong and weak). The monthly rain boxplots showed high variability in the rainy season (April-July). Outliers have been associated with extreme rainfall at MRM. The drought period was 5 months in all MRM seasons, except in Satuba and Pilar. The Mann-Kendall test and the Sen method showed a tendency for a significant increase in rainfall in Satuba and not significant in the Pilar, while in the others, there was a tendency for a decrease in rainfall. The MRM rainfall depends on physiographic factors, multiscale meteorological systems, and the coastal environment. These results will assist in planning conservationist practices, especially in areas of socio-environmental vulnerability.

Overgrazing by livestock and the changing patterns of rainfall, characterized by severe drought and floods during dry and wet seasons, respectively, threaten the sustainable productivity of the savannas. To understand the implications of such changes in Lambwe Valley—Kenya, we simulated 50% decrease (50%) and increase (150%) in ambient rainfall (100%), respectively, in grazed (G) and ungrazed (U) sites during dry and wet months. CO2 exchange and biomass production were quantified using chamber method and direct biomass sampling technique, respectively. Plots were named by combining the first letters of the sites followed by rainfall amount, i.e., U150%. Soil moisture (VWC) increased along a rainfall gradient of 50–150%. Grazing reduced the VWC, net ecosystem exchange (NEE), and total biomass by 19.07%, 57.14%, and 37.03%, respectively, with severe effects during the dry months. 50% rainfall strongly influenced the VWC, NEE (negative and positive signs indicate CO2 uptake and net carbon loss, respectively), and biomass compared to 150% rainfall. The U150% plot reported the highest mean NEE (– 8.80 ± 2.26 µmol m−2 s−1), AGB (1208.41 g m−2), and total biomass (1589.06 g m−2) during the wet months. Lower VWC in the G50% plot triggered a net carbon loss of 3.68 ± 0.81 µmol m−2 s−1 (NEE). Our results show that livestock grazing during the dry months hinders herbaceous CO2 uptake and standing biomass. Proper understanding of the interaction between livestock grazing and rainfall variability in humid savannas is essential for sustainable management strategies to regulate the herbaceous productivity.

Decadal trend analysis of rainfall patterns of past 115 years & its impact on Sikkim, India

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 72:73-82 (2017) - DOI: https://doi.org/10.3354/cr01451 Drought assessment in northwest China during 1960-2013 using the standardized precipitation index Peng Yang1,2, Jun Xia1,3,*, Yongyong Zhang1, Longfeng Wang1,2 1Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, PR China 2University of Chinese Academy of Sciences, Beijing 100049, PR China 3State Key Laboratory of Water Resources & Hydropower Engineering Sciences, Wuhan University, Wuhan 430000, PR China *Corresponding author: xiaj@igsnrr.ac.cn ABSTRACT: The standardized precipitation index (SPI) can be used to analyze the spatiotemporal characteristics of regional water resources. Monthly precipitation data obtained from 96 weather stations in northwest China from 1960 to 2013 were used to calculate the SPI. Changes in the SPI were analyzed using the Mann-Kendall (MK) test and the Pettitt test. The results indicated that 50 stations had a significant increasing trend in the annual SPI series. Analysis of seasonal SPI trends revealed the prevalence of serious drought conditions in the spring, while most stations exhibited a wetting trend in the winter. Significant (at α = 0.05 level) abrupt changes in the annual and seasonal SPI series occurred mostly in 1981-1985. Additionally, a significant abrupt change occurred in the year 1986 in 5 sub-basins (the Turpan-Hami Basin, Gurbantunggut Desert Basin, Northern Tianshan Mountains, Northern Kunlun Mountains, and the Tarim Desert Basin). KEY WORDS: Standardized precipitation index · Pettitt test · Mann-Kendall · Northwest China Full text in pdf format PreviousCite this article as: Yang P, Xia J, Zhang Y, Wang L (2017) Drought assessment in northwest China during 1960-2013 using the standardized precipitation index. Clim Res 72:73-82. https://doi.org/10.3354/cr01451 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 72, No. 1. Online publication date: April 19, 2017 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2017 Inter-Research.

In recent years, studies focused on the climate of the Amazon have been the focus of major research mainly to identify possible temperature trends. The objective of this study was to analyze the trends and the years of abrupt changes in temperature that occurred in the Southwest Amazon from 1971 to 2016 in the municipality of Porto Velho-RO. The study area is located in the municipality of Porto Velho at the Meteorological Station of Surface, we used average daily temperature data and subdivided into climatological series from 1971 to 2006 and 1981 to 2016. The Mann-Kendall and Pettitt tests were used to verify trends. A positive trend was observed for the Mann-Kendall test for the months of January, February, March, April, June, September, October and December of 1971 to 2006 and for the years 1981 to 2016 only the months of March and June presented a trend temperature increase. The Pettitt test indicated a sudden change in the data series coinciding with most of the months that showed a tendency to increase the minimum air temperature by the Mann-Kendall test in the years 1971 to 2006. From 1981 to 2016 the Pettitt test indicated the months of March and June with changes in the minimum air temperature, this result also coincided with the months with positive trends.

Drought characterization for the state of Rio de Janeiro based on the annual SPI index: trends, statistical tests and its relation with ENSO

The water shortage in the Yellow River, China, has been aggravated by rapid population growth and global climate changes. To identify the characteristics of streamflow change in the Yellow River, approximately 50 years of natural and observed streamflow data from 23 hydrological stations were examined. The Mann-Kendall and Pettitt tests were used to detect trends and abrupt change points. The results show that both the natural and the observed streamflow in the Yellow River basin present downward trends from 1956 to 2008, and the decreasing rate of observed streamflow is generally faster than that of the natural streamflow. Larger drainage areas have higher declining rates, and the declining trends are intensified downstream within the mainstream. The possibility of abrupt changes in observed streamflow is higher than in natural streamflow, and streamflow series in the mainstream are more likely to change abruptly than those in the tributaries. In the mainstream, all the significant abrupt changes appear in the middle and latter half of the 1980s, but the abrupt changes occur somewhat earlier for observed streamflow than for natural streamflow. The significant abrupt change for the observed streamflow in the tributaries is almost isochronous with the natural streamflow and occurs from the 1970s to 1990s. It is implied that the slight reduction in precipitation is not the only direct reason for the streamflow variation. Other than the effects of climate change, land-use and land-cover changes are the main reasons for the natural streamflow change. Therefore, the increasing net water diversion by humans is responsible for the observed streamflow change. It is estimated that the influence of human activity on the declining streamflow is enhanced over time. Editor Z.W. Kundzewicz Citation Miao, C.Y., Shi, W., Chen, X.H., and Yang, L., 2012. Spatio-temporal variability of streamflow in the Yellow River: possible causes and implications. Hydrological Sciences Journal, 57 (7), 1355–1367.

Introduction: Studying long-term trend changes of meteorological parameters is one of the routine methods in atmospheric studies, especially in the climate change subject. Among the meteorological parameters, temperature is always considered as one of the most atmospheric elements and studying it in order to gain a better understanding of the climate change phenomenon, has been effective. In addition to identifying trends, extraction of oscillatory patterns in the atmospheric phenomena and parameters occurrence can be an applicable and reliable method to explore the complex relations between atmospheric-oceanic cycles and short term or long term consequences of meteorological parameters. Materials and Methods: In this paper, monthly average temperature time series in Mashhad synoptic station in 55 years period (from 1956 to 2010) in monthly, seasonal, annual and seasons separately (winter, spring, summer and autumn) have been analyzed. Discrete wavelet transform and Mann-Kendall trend test were the main methods for performing this research. Wavelet transform is a powerful method in signal processing and it is an advanced version of short time Fourier transforms. Moreover, it has many improvements and more capabilities compared with Fourier transform. In the first step, temperature time series in various time scales (which was mentioned above) have been decomposed via discrete wavelet transforms into approximation (A) and detail (D) components. For the second step, Mann-Kendall trend test was applied to the various combinations of these decomposed components. For detecting the most dominant periodic component for each of the time scales datasets, results of Mann-Kendall test for the original time series and the decomposed components were compared to each other. The nearest value indicated the most dominant periodicity based on the D component’s level. To detect the similarity between results of the Mann-Kendall test, relative error method was employed. Additionally, it must be noted that before applying Mann-Kendall test, time series has to be assessed for its autocorrelation status. If there are seasonality patterns in the studied time series or lag-1 autocorrelation coefficient of data is significant, then some modified versions of the Mann-Kendall test have to be employed. Results and Discussion: Results of this study showed that the temperature trend at every time scaled dataset (monthly, seasonal, annual and seasons separately) is positive and significant. Autocorrelation coefficients indicated that only seasonal time series and winter datasets did not have significant ACFs. On the other hand, monthly and seasonal datasets had seasonality pattern. Based on these results, Hirsch and Slack’s modified version of Mann-Kendall test was employed for monthly and seasonal time series and for the winter temperature data, the original version of the Mann-Kendall test was applied. For the remaining time series, the Hamed and Rao’s modified version of the Mann-Kendall trend test was employed. Dominant periodicities in monthly, seasonal and annual, confirmed the oscillatory behavior of each other. However, in the seasons, it seems that periodic patterns with the same temperature ranges are more similar. On the other hand, due to the greater similarity between the results of the Mann-Kendall test in the warmer seasons and the data with monthly, seasonal and annual time scale, it seems that yearly warm period has more noticeable impacts on the positive and significant trend of temperature in the study area. It must be noted that in any of the studied time series, results of the Mann-Kendall test for detail (D) component was not significant and after adding approximation (A) component, Mann-Kendall statistics turned to a significant value. This happens because the long term variations or trends appear in approximation components in most of the time series. Conclusion: In this study, a powerful signal processing method called wavelet transform was employed to detect the most dominant periodic components in temperature time series in various time scales, in Mashhad synoptic station. Results showed that using frequency-time analysis methods has more benefits compared with the use of only classic statistical methods, since one can explore any time series with more accuracy. Because most of the meteorological variables have periodic structures, it seems that using advanced signal processing methods like wavelet for analysis of these variables can have many advantages compared with linear-based methods. It can be suggested for future studies to use and employ signal processing methods for exploring the large scaled phenomena (e.g. ENSO, NAO, etc.) and discovering the relationship between these phenomena and climate change in recent decades. Keywords: Discrete wavelet transforms, Mann-Kendall test, Oscillatory pattern, Trend

Global warming and anthropogenic activities have imposed noticeable impacts on rainfall pattern changes at both spatial and temporal scales in recent decades. Systematic diagnosis of rainfall pattern changes is urgently needed at spatiotemporal scales for a deeper understanding of how climate change produces variations in rainfall patterns. The objective of this study was to identify rainfall pattern changes systematically under climate change at a subcontinental scale along a rainfall gradient ranging from 1800 to 200 mm yr−1 by analyzing centennial rainfall data covering 230 sites from 1910 to 2017 in the Northern Territory of Australia. Rainfall pattern changes were characterized by considering aspects of trends and periodicity of annual rainfall, abrupt changes, rainfall distribution, and extreme rainfall events. Our results illustrated that rainfall patterns in northern Australia have changed significantly compared with the early period of the twentieth century. Specifically, 1) a significant increasing trend in annual precipitation associated with greater variation in recent decades was observed over the entire study area, 2) temporal variations represented a mean rainfall periodicity of 27 years over wet to dry regions, 3) an abrupt change of annual rainfall amount occurred consistently in both humid and arid regions during the 1966–75 period, and 4) partitioned long-term time series of rainfall demonstrated a wetter rainfall distribution trend across coastal to inland areas that was associated with more frequent extreme rainfall events in recent decades. The findings of this study could facilitate further studies on the mechanisms of climate change that influence rainfall pattern changes. Significance Statement Characterizing long-term rainfall pattern changes under different rainfall conditions is important to understand the impacts of climate change. We conducted diagnosis of centennial rainfall pattern changes across wet to dry regions in northern Australia and found that rainfall patterns have noticeably changed in recent decades. The entire region has a consistent increasing trend of annual rainfall with higher variation. Meanwhile, the main shifting period of rainfall pattern was during 1966–75. Although annual rainfall seems to become wetter with an increasing trend, more frequent extreme rainfall events should also be noticed for assessing the impacts of climate changes. The findings support further study to understand long-term rainfall pattern changes under climate change.

Rainfall is the most important fundamental physical parameter among the climate, as this parameter determines the environmental condition of the particular region, which affects the agricultural productivity. Global warming or climate change, is one of the most important worldwide issues discussed among scientists and researchers. One of the consequences of climate change is the alteration of rainfall patterns and an increase in temperature. The drastic changes in rainfall pattern showed a significant impact on society, and therefore its up-to-date information is needed to estimate the spatial distribution and variability at all points of the territory. In this paper, a study on trend analysis of rainfall data observed at Anasi, Haliyal, Kadra, and Supa rain gauge stations in Karnataka, India, was carried out. For this purpose, the annual 1-day maximum rainfall (AMR) and annual total rainfall (ATR) series were generated from the daily rainfall data and used in trend analysis. A non-parametric Mann-Kendall (MK) test was applied to evaluate the presence of significant trend in AMR and ATR, while the rate of significant trend was computed by Sen's slope estimator (SSE). The MK test results indicated that there is a decreasing trend in AMR series of Anasi, Haliyal, Kadra, and Supa. The study showed that the rate of decreasing trend in the AMR series of Anasi, Haliyal, Kadra, and Supa is computed as 1.4 mm/year, 0.1 mm/year, 0.6 mm/year, and 0.2 mm/year, respectively. For the ATR series, the rate of decreasing trend for Anasi was computed as 23.8 mm/year, whereas 11.2 mm/year for Supa, whereas the rate of increasing trend was 4.3 mm/year for Haliyal and 2.2 mm/year for Supa. This paper illustrated the application of the MK test and SSE for analyzing the trend in AMR and ATR of Anasi, Haliyal, Kadra, with Supa and the results obtained from the study.