Precipitation Trend Analysis by Mann-Kendall Test: A Case of Tianchang County Anhui Province, China

2013 ◽  
Vol 864-867 ◽  
pp. 2218-2223 ◽  
Author(s):  
Elsie Akwei ◽  
Bao Hong Lu ◽  
Han Wen Zhang
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Trend Analysis ◽  
Null Hypothesis ◽  
Kendall Test ◽  
Anhui Province ◽  
Significant Trend ◽  
Precipitation Data ◽  
Precipitation Time Series ◽  
Mann Kendall Test

The purpose of this research is to study the temporal variability of precipitation time series of Tianchang County in Anhui Province, China to aid in the understanding of the state of the hydrology of the catchment. Trend analysis of one of the main component of the water balance of a catchment and a climate variable, precipitation was conducted with the aim of detecting a possible trend in the precipitation time series of Tianchang County, the non-parametric Mann-Kendall test was applied to precipitation series from 1951-2010 of Tianchang County. It was performed using Trend (version 1.0.2) to identify the significant positive or negative trends in the precipitation data if any. The 59 years period of precipitation data for the different towns in whole area showed, on the whole, some significant trend at an alpha level of 0.01 and 0.05 when grouped into the four seasons present in the area. The trend analysis revealed an overall upward and significant trend in five towns namely Datong, Xinjie, Shiliang, Qinlan and Tongcheng with downward statistically non-significant trend in the other ten areas .Using hypothesis testing, the null hypothesis states that there is no trend and alternative state there is a trend. From the results we reject the null hypothesis within the level of confidence 0.05 and 0.01. The rising rate of precipitation in some months and decreasing in others signifies an overall random pattern in the time series. This result is a part contribution to the effect of Climate change on hydrology and indicates that there is still room for research on the impact of climate change to ensure sustainable development in future.

scholarly journals Trend Analysis of Hydroclimatic Variables in the Kamo River Basin, Japan

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1782 ◽  
Author(s):  
Maochuan Hu ◽  
Takahiro Sayama ◽  
Sophal TRY ◽  
Kaoru Takara ◽  
Kenji Tanaka
Keyword(s):  
Trend Analysis ◽  
River Basin ◽  
River Discharge ◽  
Water Resource Management ◽  
Potential Evapotranspiration ◽  
Kendall Test ◽  
Winter Precipitation ◽  
Significant Trend ◽  
Mann Kendall Test ◽  
Proactive Measures

Understanding long-term trends in hydrological and climatic variables is of high significance for sustainable water resource management. This study focuses on the annual and seasonal trends in precipitation, temperature, potential evapotranspiration, and river discharge over the Kamo River basin from the hydrological years 1962 to 2017. Homogeneity was examined by Levene’s test. The Mann–Kendall and a modified Mann–Kendall test as well as Sen’s slope estimator were used to analyze significant trends (p < 0.05) in a time series with and without serial correlation and their magnitudes. The results indicate that potential evapotranspiration calculated by the Penman–Monteith equation was highly related to temperature, and significantly increased in the annual and summer series. Annual river discharge significantly decreased by 0.09 m3/s. No significant trend was found at the seasonal scale. Annual, autumn, and winter precipitation at Kumogahata station significantly increased, while no significant trend was found at Kyoto station. Precipitation was least affected by the modified Mann–Kendall test. Other variables were relatively highly autocorrelated. The modified Mann–Kendall test with a full autocorrelation structure improved the accuracy of trend analysis. Furthermore, this study provides information for decision makers to take proactive measures for sustainable water management.

scholarly journals Comparison of Statistical, Graphical, and Wavelet Transform Analyses for Rainfall Trends and Patterns in Badulu Oya Catchment, Sri Lanka

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ashika M. Ruwangika ◽  
Anushka Perera ◽  
Upaka Rathnayake
Keyword(s):  
Climate Change ◽  
Sri Lanka ◽  
Trend Analysis ◽  
Wavelet Transforms ◽  
Kendall Test ◽  
Discrete Wavelet ◽  
Rainfall Trend ◽  
Analysis Methods ◽  
Rainfall Trends ◽  
Mann Kendall Test

Climate change has adversely influenced many activities. It has increased the intensified precipitation events in some places and decreased the precipitation in some other places. In addition, some research studies revealed that the climate change has moved seasons in the temporal scale. Therefore, the changes can be seen in both spatial and temporal scales. Thus, analyzing climate change in the localized environments is highly essential. Rainfall trend analysis in a localized catchment can improve many aspects of water resource management not only to the catchment itself but also to some of the related other catchments. This research is carried to identify the rainfall trends in Badulu Oya catchment, Sri Lanka. The catchment is important as it is in the intermediate climate zone and rich in agricultural productions. Four rain gauges (namely, Badulla, Kandekatiya, Lower Spring Valley, and Ledgerwatte Estate) were used to analyze the rainfalls in the resolutions of monthly, seasonally, and annually. 30-year monthly cumulative rainfall data for the above four gauging stations are analyzed using various standard tests. Nonparametric tests including Mann–Kendall test and sequential Mann–Kendall test and innovative trend analysis methods are used to identify the potential rainfall trends in Badulu Oya catchment. In addition, continuous wavelet transforms and discrete wavelet transforms tests are carried out to check the patterns on rainfall to the catchment. The trend analysis methods are compared against each other to identify the better technique. The results reveal that the nonparametric Mann–Kendall test is powerful to produce the statistically significant rainfall trends in qualitative and quantitative manner. Mann–Kendall analysis shows a positive trend to Ledgerwatte Estate in monthly (3.7 mm in February and 7.4 mm in October), seasonal (6.9 mm in the 2ndintermonsoon), and annual (3 mm annually) scales. However, the analysis records one decreasing rainfall trend to Kandekatiya (8.1 mm in December) only in monthly scale. Nevertheless, it was found that the graphical method can be easily used in qualitative analysis, while discrete wavelet transformations are efficient in identifying the rainfall patterns effectively.

scholarly journals Teste de Mann-Kendall aplicado à dados hidrológicos – Desempenho dos filtros TFPW e CV2 na análise de tendências

2020 ◽  
Vol 42 ◽  
pp. e87
Author(s):  
Thais Vieira Dos Santos ◽  
Lília Dos Anjos De Freitas ◽  
Roger Dias Gonçalves ◽  
Hung Kiang Chang
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Trend Analysis ◽  
Serial Correlation ◽  
Kendall Test ◽  
Precipitation Data ◽  
Accurate Analysis ◽  
Parametric Test ◽  
Trend Magnitude ◽  
Synthetic Time Series

This study brings an original comparison related to the performance of two filters on trend analysis regarding hydrological time series. We applied the Mann-Kendall test for trend analysis, a non-parametric test widely used in hydrological studies, and Sen’s slope in order to extract the trend magnitude. The presence of autocorrelation tends to impact on trend interpretation erroneously. As most of water resources data presents serial correlation, the use of filters is essential to achieve an accurate analysis regarding temporal variation of the dataset. The filters trend free pre-whitening (TFPW) and variance correction approach (CV2) were applied on monthly time series of precipitation, streamflow, storage and evapotranspiration, from 2002 to 2014, plus eighty synthetic time series. The comparison of the filters performances showed the TFPW filter as much superior, reducing the autocorrelation by at least 71.1%. While the CV2 filter, despite strongly reducing the variance, did not impact the serial correlation (in fact, reduced less than 1% in almost half of the performed simulations). The main difference was related to the precipitation data, from which CV2 suggested a negative trend, while TFPW, besides drastically reducing autocorrelation, showed that the time series does not have a statistically significant trend.

scholarly journals VEGETATION DYNAMICS TREND USING SATELLITE TIME SERIES IMAGERY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 783-788
Author(s):  
Z. Najafi ◽  
P. Fatehi ◽  
A. A. Darvishsefat
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Vegetation Dynamics ◽  
Kendall Test ◽  
Significant Trend ◽  
Least Square ◽  
Positive Trend ◽  
Vegetation Dynamic ◽  
Ordinary Least Square ◽  
Ordinary Least Square Regression

Abstract. In this study, the trend of vegetation dynamics in Kermanshah city assessed using NDVI MOD13Q1 product over the time period of 2000–2017. Based on time series imagery the pick of vegetation phenology stage (maximum NDVI) identified, then the trend of vegetation dynamic was investigated using the Ordinary Least Square regression and the Theil-Sen approaches. To generate a pixel-wise trend map, a pixel-based vegetation dynamics was also implemented. A non-parametric Mann-Kendall statistics approach was used to examine a statistically significant trend analysis. The mean maximum NDVI observed for the first half or second half of April. Trend analysis using regression and Theil-Sen methods indicated a no-trend in vegetation fractions. The pixel-based trend assessment using regression showed that a 50% of the study area faced a positive trend and reaming part faced a negative trend. The Theil-Sen method revealed the no-trend for a large majority of area. The Mann-Kendall test indicated that only 20 percent of the area shows a statistically significant trend.

scholarly journals Effects of the prewhitening method, the time granularity and the time segmentation on the Mann-Kendall trend detection and the associated Sen's slope

2020 ◽  
Author(s):  
Martine Collaud Coen ◽  
Elisabeth Andrews ◽  
Alesssandro Bigi ◽  
Gonzague Romanens ◽  
Giovanni Martucci ◽  
...  
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Statistical Significance ◽  
Parametric Method ◽  
Kendall Test ◽  
Trend Detection ◽  
Temporal Segmentation ◽  
Sen’S Slope ◽  
Power Of The Test ◽  
Mann Kendall Test

Abstract. The most widely used non-parametric method for trend analysis is the Mann-Kendall test associated with the Sen's slope. The Mann-Kendall test requires serially uncorrelated time series, whereas most of the atmospheric processes exhibit positive autocorrelation. Several prewhitening methods have been designed to overcome the presence of lag-1 autocorrelation. These include a prewhitening, a detrending and/or a correction for the detrended slope and the original variance of the time series. The choice of which prewhitening method and temporal segmentation to apply has consequences for the statistical significance, the value of the slope and of the confidence limits. Here, the effects of various prewhitening methods are analyzed for seven time series comprising in-situ aerosol measurements (scattering coefficient, absorption coefficient, number concentration and aerosol optical depth), Raman Lidar water vapor mixing ratio and the tropopause and zero degree levels measured by radio-sounding. These time series are characterized by a broad variety of distributions, ranges and lag-1 autocorrelation values and vary in length between 10 and 60 years. A common way to work around the autocorrelation problem is to decrease it by averaging the data over longer time intervals than in the original time series. Thus, the second focus of this study is evaluation of the effect of time granularity on long-term trend analysis. Finally, a new algorithm involving three prewhitening methods is proposed in order to maximize the power of the test, to minimize the amount of erroneous detected trends in the absence of a real trend and to ensure the best slope estimate for the considered length of the time series.

scholarly journals Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

2016 ◽  
Vol 20 (4) ◽  
pp. 1387-1403 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Ole Bøssing Christensen ◽  
Karsten Arnbjerg-Nielsen ◽  
Peter Steen Mikkelsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Weather Generator ◽  
Spatial Extent ◽  
Urban Hydrology ◽  
Precipitation Time Series ◽  
Spatio Temporal

Abstract. Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a spatio-temporal Neyman–Scott rectangular pulses weather generator (WG). Precipitation time series used as input to the WG are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 km  ×  60 km model domain. The WG simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from regional climate models (RCMs) with spatial resolutions of 25 and 8 km, respectively. Six different RCM simulation pairs are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology.

scholarly journals Innovative Trend Analysis of Precipitation in the Lake Issyk-Kul Basin, Kyrgyzstan

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 332 ◽  
Author(s):  
Yilinuer Alifujiang ◽  
Jilili Abuduwaili ◽  
Balati Maihemuti ◽  
Bilal Emin ◽  
Michael Groll
Keyword(s):  
Trend Analysis ◽  
Resource Planning ◽  
Kendall Test ◽  
Trend Test ◽  
Lake Basin ◽  
Positive Trend ◽  
Monthly Precipitation ◽  
Innovative Trend Analysis ◽  
Mann Kendall Test ◽  
Increasing Trends

The analysis of various characteristics and trends of precipitation is an essential task to improve the utilization of water resources. Lake Issyk-Kul basin is an upper alpine catchment, which is more susceptible to the effects of climate variability, and identifying rainfall variations has vital importance for water resource planning and management in the lake basin. The well-known approaches linear regression, Şen’s slope, Spearman’s rho, and Mann-Kendall trend tests are applied frequently to try to identify trend variations, especially in rainfall, in most literature around the world. Recently, a newly developed method of Şen-innovative trend analysis (ITA) provides some advantages of visual-graphical illustrations and the identification of trends, which is one of the main focuses in this article. This study obtained the monthly precipitation data (between 1951 and 2012) from three meteorological stations (Balykchy, Cholpon-Ata, and Kyzyl-Suu) surrounding the Lake Issyk-Kul, and investigated the trends of precipitation variability by applying the ITA method. For comparison purposes, the traditional Mann–Kendall trend test also used the same time series. The main results of this study include the following. (1) According to the Mann-Kendall trend test, the precipitation of all months at the Balykchy station showed a positive trend (except in January (Zc = −0.784) and July (Zc = 0.079)). At the Cholpon-Ata and Kyzyl-Suu stations, monthly precipitation (with the same month of multiple years averaged) indicated a decreasing trend in January, June, August, and November. At the monthly scale, significant increasing trends (Zc > Z0.10 = 1.645) were detected in February and October for three stations. (2) The ITA method indicated that the rising trends were seen in 16 out of 36 months at the three stations, while six months showed decreasing patterns for “high” monthly precipitation. According to the “low” monthly precipitations, 14 months had an increasing trend, and four months showed a decreasing trend. Through the application of the ITA method (January, March, and August at Balykchy; December at Cholpon-Ata; and July and December at Kyzyl-Suu), there were some significant increasing trends, but the Mann-Kendall test found no significant trends. The significant trend occupies 19.4% in the Mann-Kendall test and 36.1% in the ITA method, which indicates that the ITA method displays more positive significant trends than Mann–Kendall Zc. (3) Compared with the classical Mann-Kendall trend results, the ITA method has some advantages. This approach allows more detailed interpretations about trend detection, which has benefits for identifying hidden variation trends of precipitation and the graphical illustration of the trend variability of extreme events, such as “high” and “low” values of monthly precipitation. In contrast, these cannot be discovered by applying traditional methods.

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