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.

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 Glacier recession and water resources in Peru’s Cordillera Blanca

2012 ◽  
Vol 58 (207) ◽  
pp. 134-150 ◽  
Author(s):  
Michel Baraer ◽  
Bryan G. Mark ◽  
Jeffrey M. McKenzie ◽  
Thomas Condom ◽  
Jeffrey Bury ◽  
...  
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Hydrologic Model ◽  
Dry Season ◽  
Glacier Retreat ◽  
Cordillera Blanca ◽  
Tropical Glaciers ◽  
Daily Discharge ◽  
Synthetic Time Series ◽  
Nonparametric Statistical

AbstractThe tropical glaciers of the Cordillera Blanca, Peru, are rapidly retreating, resulting in complex impacts on the hydrology of the upper Río Santa watershed. The effect of this retreat on water resources is evaluated by analyzing historical and recent time series of daily discharge at nine measurement points. Using the Mann-Kendall nonparametric statistical test, the significance of trends in three hydrograph parameters was studied. Results are interpreted using synthetic time series generated from a hydrologic model that calculates hydrographs based on glacier retreat sequences. The results suggest that seven of the nine study watersheds have probably crossed a critical transition point, and now exhibit decreasing dry-season discharge. Our results suggest also that once the glaciers completely melt, annual discharge will be lower than present by 2-30% depending on the watershed. The retreat influence on discharge will be more pronounced during the dry season than at other periods of the year. At La Balsa, which measures discharge from the upper Río Santa, the glacier retreat could lead to a decrease in dry-season average discharge of 30%.

Wavelet-based trend analysis of hydrological processes at different timescales

2015 ◽  
Vol 6 (3) ◽  
pp. 414-435 ◽  
Author(s):  
Vahid Nourani ◽  
Nasrin Nezamdoost ◽  
Maryam Samadi ◽  
Farnaz Daneshvar Vousoughi
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Stream Flow ◽  
Discrete Wavelet ◽  
Precipitation Data ◽  
Short Term ◽  
Mk Test ◽  
Starting Point ◽  
Annual Time Series ◽  
Annual Time

This study analyzes involved trends in stream flow and precipitation data at monthly, seasonal and annual timescales observed at six precipitation and four stream flow stations of Tampa Bay using non-parametric Mann–Kendall (MK) and discrete wavelet transform (DWT) methods. The MK test and sequential MK analysis were applied to different combinations of DWT after removing the effect of significant lag-1 serial correlation to calculate components responsible for trend of the time series. Also, the sequential MK test was used to find the starting point of changes in annual time series. The results showed that negative trend is prevalent in the case study; generally, short-term periods were important in the involved trend at original time series. Thus, the precipitation data at three scales showed short-term periods of 2 months, 6 months and 2 years in monthly, seasonal and annual scales, respectively. In the greatest stream-flow time series at three timescales, wavelet-based detail at level 2 plus the approximations time series was conceded as the dominant periodic component. Finally, the results of Sen's trend analysis, applied to the original annual time series, also confirmed the results of the proposed wavelet-based MK test in most cases.

scholarly journals TREND DETECTION OF METROLOGICAL DROUGHT IN NORTH OF IRAQ

2021 ◽  
Vol 25 (3) ◽  
pp. 60-73
Author(s):  
Ihsan F. Hasan ◽  
Keyword(s):  
Time Series ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Kendall Test ◽  
Northern Region ◽  
Trend Detection ◽  
Precipitation Data ◽  
Study Region ◽  
Slope Estimator ◽  
Mild Drought

This study presents an analysis of meteorological drought using multi time-scales of Standardized Precipitation Index SPI (6, 9 and 12 month), based on observed 49-year daily mean precipitation data records at 11 stations over the Northern region of Iraq. The detection of drought trends in results of SPI analysis was studied to identify whether there is any increase or decrease in the severity of drought at the selected meteorological Stations; Mann Kendall test and Sen's slope estimator were used to detect statistically significant trends. The results indicate that there is a statistically significant decreasing trend of SPI time series at 5% significant level in most of the selected stations. Based on drought categories the meteorological drought in the study region can be classified as mild drought.

scholarly journals Rainfall trend analysis

2021 ◽  
Vol 17 (1) ◽  
pp. 121-125
Author(s):  
Virendra N. Barai ◽  
Rohini M. Kalunge
Keyword(s):  
Trend Analysis ◽  
Kendall Test ◽  
River Basins ◽  
Annual Rainfall ◽  
Positive Trend ◽  
Rainfall Trend ◽  
Trend Magnitude ◽  
Increasing Trends ◽  
Multiple Units ◽  
Rainy Days

This article aims to review studies pertaining to trends in rainfall, rainy days over India. Non-parametric tests such as Sen’s Slope were used as estimator of trend magnitude which was supported by Mann-Kendall test. The findings of various studies indicate variance with respect to the rainfall rate, which contributes to an uncertain picture of the rainfall trend. In the study of monsoon of different locations in India some places showed increasing trends however, there is signifying decrease in trend all over India. It was also mentioned that analysis can vary from for a location if done using different source or types of collection of data. Spatial units range from station results and sub-division to sub-basin/river basins for trend analysis. The outcomes of the different experiments vary and a simple and reliable picture of the trend of rainfall has not appeared. While there can be a non-zero slope value for the multiple units (sub-basins or sub-divisions), few values are statistically important. In a basin-wise trend analysis report, some basins had a declining annual rainfall trend; at a 95 per cent confidence stage, only one basin showed a strong decreasing trend. Out of the six basins exhibiting a rising trend saw a major positive trend in one basin. Many of the basins have the same pattern direction on the annual and seasonal scale for rainfall and rainy days.

scholarly journals Over-whitening or Pre-whitening in Hydro-climatological Trend Analysis?

2022 ◽  
Author(s):  
Zekai Sen
Keyword(s):  
Time Series ◽  
White Noise ◽  
Trend Analysis ◽  
Serial Correlation ◽  
Original Time Series ◽  
Trend Component ◽  
The Difference ◽  
Temperature Records ◽  
Original Time ◽  
Trend Identification

Abstract To meet the basic assumption of classical Mann-Kendall (MK) trend analysis, which requires serially independent time series, a pre-whitening (PW) procedure is proposed to alleviate the serial correlation structure of a given hydro-meteorological time series records for application. The procedure is simply to take the lagged differences in a given time series in the hope that the new time series will have an independent serial correlation coefficient. The whole idea was originally based on the first-order autoregressive AR (1) process, but such a procedure has been documented to damage the trend component in the original time series. On the other hand, the over-whitening procedure (OW) proposes a white noise process superposition of the same length with zero mean and some standard deviation on the original time series to convert it into serially independent series without any damage to the trend component. The stationary white noise addition does not have any trend components. For trend identification, annual average temperature records in New Jersey and Istanbul are presented to show the difference between PW and OW procedures. It turned out that the OW procedure was superior to the PW procedure, which did not cause a loss in the original trend component.

scholarly journals A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data

2018 ◽  
Vol 22 (1) ◽  
pp. 757-766 ◽  
Author(s):  
Yan-Fang Sang ◽  
Fubao Sun ◽  
Vijay P. Singh ◽  
Ping Xie ◽  
Jian Sun
Keyword(s):  
Time Series ◽  
Statistical Significance ◽  
Kendall Test ◽  
Wavelet Spectrum ◽  
Spatiotemporal Variability ◽  
Discrete Wavelet ◽  
Potential Evaporation ◽  
Monotonic Trend ◽  
Warming Hiatus ◽  
Synthetic Time Series

Abstract. The hydroclimatic process is changing non-monotonically and identifying its trends is a great challenge. Building on the discrete wavelet transform theory, we developed a discrete wavelet spectrum (DWS) approach for identifying non-monotonic trends in hydroclimate time series and evaluating their statistical significance. After validating the DWS approach using two typical synthetic time series, we examined annual temperature and potential evaporation over China from 1961–2013 and found that the DWS approach detected both the “warming” and the “warming hiatus” in temperature, and the reversed changes in potential evaporation. Further, the identified non-monotonic trends showed stable significance when the time series was longer than 30 years or so (i.e. the widely defined “climate” timescale). The significance of trends in potential evaporation measured at 150 stations in China, with an obvious non-monotonic trend, was underestimated and was not detected by the Mann–Kendall test. Comparatively, the DWS approach overcame the problem and detected those significant non-monotonic trends at 380 stations, which helped understand and interpret the spatiotemporal variability in the hydroclimatic process. Our results suggest that non-monotonic trends of hydroclimate time series and their significance should be carefully identified, and the DWS approach proposed has the potential for wide use in the hydrological and climate sciences.

scholarly journals Trend Analysis and Variability of Air Temperature and Rainfall in Regional River Basins

2021 ◽  
Vol 7 (5) ◽  
pp. 816-826
Author(s):  
Benjamin Nnamdi Ekwueme ◽  
Jonah Chukwuemeka Agunwamba
Keyword(s):  
Water Resources ◽  
Trend Analysis ◽  
Air Temperature ◽  
Temporal Trends ◽  
Kendall Test ◽  
River Basins ◽  
Annual Rainfall ◽  
Eastern Region ◽  
South Eastern ◽  
Mann Kendall Test

Global warming and climate variability are emerging as the foremost environmental problems in the 21st century, especially in developing countries. Full knowledge of key climate change variables is crucial in managing water resources in river basins. This study examines the variability of air temperature and rainfall in the five states of South-Eastern region of Nigeria, using the trend analysis approach. For this purpose, temporal trends in annual rainfall and temperature were detected using non-parametric Mann-Kendall test at 5% significance level. The time series rainfall and temperature data for the period 1922-2008 were analyzed statistically for each state separately. The results of Mann Kendall test showed that there is trend in rainfall in all the capital cities in South-East except Owerri and Awka. It is also observed that the trend of rainfall is decreasing for all the study areas in South-East with the lowest trend rate of -0.1153 mm rainfall occurring in Umuahia. In the case of air temperature, it is observed that the trend is increasing for all the study areas in South-East with the highest trend rate of 0.04698 oC/year occurring in Enugu. These findings provide valuable information for assessing the influence of changes on air temperature and rainfall on water resources and references for water management in the South-Eastern river basin of Nigeria. It also proved that Mann-Kendall technique is an effective tool in analyzing temperature and rainfall trends in a regional watershed. Doi: 10.28991/cej-2021-03091692 Full Text: PDF

scholarly journals A stochastic model for drought risk analysis in The Netherlands

2018 ◽  
Author(s):  
Ferdinand L. M. Diermanse ◽  
Marjolein J. P. Mens ◽  
Hector Macian-Sorribes ◽  
Femke Schasfoort
Keyword(s):  
Time Series ◽  
Water Resources ◽  
The Netherlands ◽  
Stochastic Model ◽  
Drought Risk ◽  
Climate Conditions ◽  
Times Series ◽  
Historic Time ◽  
Synthetic Time Series ◽  
Hydrological Variables

Abstract. Population growth and economic developments increase the demand for water resources. Furthermore, climate change is often projected to have negative impacts on the availability of these water resources. Measures to reduce the risk of water shortages can be costly and often require long-term planning strategies. In the decision making process, a thorough understanding of these drought-related risks for the various water users is of crucial importance. Historic time series of climatologic and hydrological variables, used as input for water allocation and drought impact models, are generally too short to provide such a detailed understanding. This makes the case for using lengthy synthetic time series. The challenge is to develop synthetic time series that are realistic and representative for the current and future climate conditions. We present a stochastic model for generating realistic times series of meteorological and hydrological variables that characterise drought events. The model is applied to a case study in the Netherlands, but is generic in set-up and can thus be applied elsewhere as well. It is demonstrated that the main features of the historic time series are well reproduced. The generated synthetic times series provide valuable insights into the frequency and severity of droughts and help improve the assessment of drought risks.

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