Improved Seasonal Mann–Kendall Tests for Trend Analysis in Water Resources Time Series

2016 ◽  
pp. 215-229 ◽  
Author(s):  
Y. Zhang ◽  
P. Cabilio ◽  
K. Nadeem
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Trend Analysis

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.

Time series trend analysis of rainfall in last five decades and its quantification in Aseer Region of Saudi Arabia

2021 ◽  
Vol 14 (6) ◽  
Author(s):  
Majed AlSubih ◽  
Madhuri Kumari ◽  
Javed Mallick ◽  
Raghu Ramakrishnan ◽  
Saiful Islam ◽  
...  
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Trend Analysis

scholarly journals SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 602
Author(s):  
Luisa Martínez-Acosta ◽  
Juan Pablo Medrano-Barboza ◽  
Álvaro López-Ramos ◽  
John Freddy Remolina López ◽  
Álvaro Alberto López-Lambraño
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Autocorrelation Function ◽  
Information Criterion ◽  
Monthly Rainfall ◽  
Statistical Characteristics ◽  
Rainfall Time Series ◽  
River Watershed ◽  
Statistical Measures ◽  
Sarima Models

Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.

scholarly journals Assessment of Rangeland Degradation in New Mexico Using Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND)

2021 ◽  
Vol 13 (9) ◽  
pp. 1618
Author(s):  
Melakeneh G. Gedefaw ◽  
Hatim M. E. Geli ◽  
Temesgen Alemayehu Abera
Keyword(s):  
Time Series ◽  
New Mexico ◽  
Trend Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rangeland Management ◽  
Environmental Benefits ◽  
Drought Severity ◽  
Assessment Framework ◽  
Rangeland Degradation

Rangelands provide significant socioeconomic and environmental benefits to humans. However, climate variability and anthropogenic drivers can negatively impact rangeland productivity. The main goal of this study was to investigate structural and productivity changes in rangeland ecosystems in New Mexico (NM), in the southwestern United States of America during the 1984–2015 period. This goal was achieved by applying the time series segmented residual trend analysis (TSS-RESTREND) method, using datasets of the normalized difference vegetation index (NDVI) from the Global Inventory Modeling and Mapping Studies and precipitation from Parameter elevation Regressions on Independent Slopes Model (PRISM), and developing an assessment framework. The results indicated that about 17.6% and 12.8% of NM experienced a decrease and an increase in productivity, respectively. More than half of the state (55.6%) had insignificant change productivity, 10.8% was classified as indeterminant, and 3.2% was considered as agriculture. A decrease in productivity was observed in 2.2%, 4.5%, and 1.7% of NM’s grassland, shrubland, and ever green forest land cover classes, respectively. Significant decrease in productivity was observed in the northeastern and southeastern quadrants of NM while significant increase was observed in northwestern, southwestern, and a small portion of the southeastern quadrants. The timing of detected breakpoints coincided with some of NM’s drought events as indicated by the self-calibrated Palmar Drought Severity Index as their number increased since 2000s following a similar increase in drought severity. Some breakpoints were concurrent with some fire events. The combination of these two types of disturbances can partly explain the emergence of breakpoints with degradation in productivity. Using the breakpoint assessment framework developed in this study, the observed degradation based on the TSS-RESTREND showed only 55% agreement with the Rangeland Productivity Monitoring Service (RPMS) data. There was an agreement between the TSS-RESTREND and RPMS on the occurrence of significant degradation in productivity over the grasslands and shrublands within the Arizona/NM Tablelands and in the Chihuahua Desert ecoregions, respectively. This assessment of NM’s vegetation productivity is critical to support the decision-making process for rangeland management; address challenges related to the sustainability of forage supply and livestock production; conserve the biodiversity of rangelands ecosystems; and increase their resilience. Future analysis should consider the effects of rising temperatures and drought on rangeland degradation and productivity.

Seasonal trend analysis of image time series

2009 ◽  
Vol 30 (10) ◽  
pp. 2721-2726 ◽  
Author(s):  
J. Ronald Eastman ◽  
Florencia Sangermano ◽  
Bardan Ghimire ◽  
Honglei Zhu ◽  
Hao Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Seasonal Trend

scholarly journals Trend Analysis of Annual and Seasonal River Runoff by Using Innovative Trend Analysis with Significant Test

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 95
Author(s):  
Yilinuer Alifujiang ◽  
Jilili Abuduwaili ◽  
Yongxiao Ge
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
River Runoff ◽  
Temporal Trends ◽  
Temporal Distribution ◽  
Trend Test ◽  
Data Series ◽  
Mk Test ◽  
Comparison Results ◽  
Innovative Trend Analysis

This study investigated the temporal patterns of annual and seasonal river runoff data at 13 hydrological stations in the Lake Issyk-Kul basin, Central Asia. The temporal trends were analyzed using the innovative trend analysis (ITA) method with significance testing. The ITA method results were compared with the Mann-Kendall (MK) trend test at a 95% confidence level. The comparison results revealed that the ITA method could effectively identify the trends detected by the MK trend test. Specifically, the MK test found that the time series percentage decreased from 46.15% in the north to 25.64% in the south, while the ITA method revealed a similar rate of decrease, from 39.2% to 29.4%. According to the temporal distribution of the MK test, significantly increasing (decreasing) trends were observed in 5 (0), 6 (2), 4 (3), 8 (0), and 8 (1) time series in annual, spring, summer, autumn, and winter river runoff data. At the same time, the ITA method detected significant trends in 7 (1), 9 (3), 6(3), 9 (3), and 8 (2) time series in the study area. As for the ITA method, the “peak” values of 24 time series (26.97%) exhibited increasing patterns, 25 time series (28.09%) displayed increasing patterns for “low” values, and 40 time series (44.94%) showed increasing patterns for “medium” values. According to the “low”, “medium”, and “peak” values, five time series (33.33%), seven time series (46.67%), and three time series (20%) manifested decreasing trends, respectively. These results detailed the patterns of annual and seasonal river runoff data series by evaluating “low”, “medium”, and “peak” values.

Seasonal trend analysis (STA) of MODIS vegetation index time series for the mangrove canopy of the Teacapan-Agua Brava lagoon system, Mexico

2018 ◽  
Vol 56 (3) ◽  
pp. 338-361 ◽  
Author(s):  
César Alejandro Berlanga-Robles ◽  
Arturo Ruiz-Luna ◽  
Marta Rocío Nepita Villanueva
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Vegetation Index ◽  
Seasonal Trend ◽  
Lagoon System ◽  
Index Time Series

scholarly journals Paleo-hydrologic reconstruction of 400 years of past flows at a weekly time step for major rivers of Western Canada

2019 ◽  
Author(s):  
Andrew R. Slaughter ◽  
Saman Razavi
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Tree Ring ◽  
Flow Distribution ◽  
Future Climate Change ◽  
Western Canada ◽  
Tree Ring Chronology ◽  
Time Step ◽  
Water Resources Systems ◽  
Tree Ring Data

Abstract. The assumption of stationarity in water resources no longer holds, particularly within the context of future climate change. Plausible scenarios of flows that fluctuate outside the envelope of variability of the gauging data are required to assess the robustness of water resources systems to future conditions. This study presents a novel method of generating weekly-time-step flows based on tree-ring chronology data. Specifically, this method addresses two long-standing challenges with paleo-reconstruction: (1) the typically limited predictive power of tree-ring data at the annual and sub-annual scale, and (2) the inflated short-term persistence in tree-ring time series and improper use of prewhitening. Unlike the conventional approach, this method establishes relationships between tree-ring chronologies and naturalised flow at a biennial scale to preserve persistence properties and variability of hydrological time series. Biennial flow reconstructions are further disaggregated to weekly, according to the weekly flow distribution of reference two-year instrumental periods, identified as periods with broadly similar tree-ring properties to that of every two-year paleo-period. The Saskatchewan River Basin (SaskRB), a major river in Western Canada, is selected as a study area, and weekly flows in its four major tributaries are extended back to the year 1600. The study shows that the reconstructed flows properly preserve the statistical properties of the reference flows, particularly, short- to long-term persistence and the structure of variability across time scales. An ensemble approach is presented to represent the uncertainty inherent in the statistical relationships and disaggregation method. The ensemble of reconstructed weekly flows are publically available for download from https://doi.org/10.20383/101.0139 (Slaughter and Razavi, 2019).

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