Time series trend analysis and prediction of water quality in a managed canal system, Florida (USA)

To understand the algae migration characteristics in the fresh water red tide, we performed a field survey in the Shorenji Reservoir located in Nabari City, Japan. From the analysis of the field data, it is found that the patterns of vertical distributions of the indices representing biomass are very different in the morning and the afternoon. Since some water quality indices have reverse fluctuations between the surface and the bottom layer in respect of the time series changes and the total biomass of the vertical water column is relatively constant, it is concluded that vertical and daily biomass variation of red tide alga is caused by its daily migration, that is the movement from the bottom layer to the surface in the morning and the reverse movement in the afternoon.

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Assessment of Automatically Monitored Water Levels and Water Quality Indicators in Rivers with Different Hydromorphological Conditions and Pollution Levels in Greece

Hydrology ◽

10.3390/hydrology8020086 ◽

2021 ◽

Vol 8 (2) ◽

pp. 86

Author(s):

Angeliki Mentzafou ◽

George Varlas ◽

Anastasios Papadopoulos ◽

Georgios Poulis ◽

Elias Dimitriou

Keyword(s):

Water Quality ◽

Time Series ◽

Quality Indicators ◽

High Frequency ◽

Automatic Monitoring ◽

Water Levels ◽

High Temporal Resolution ◽

Water Quality Indicators ◽

Pollution Levels ◽

Riverine Ecosystems

Water resources, especially riverine ecosystems, are globally under qualitative and quantitative degradation due to human-imposed pressures. High-temporal-resolution data obtained from automatic stations can provide insights into the processes that link catchment hydrology and streamwater chemistry. The scope of this paper was to investigate the statistical behavior of high-frequency measurements at sites with known hydromorphological and pollution pressures. For this purpose, hourly time series of water levels and key water quality indicators (temperature, electric conductivity, and dissolved oxygen concentrations) collected from four automatic monitoring stations under different hydromorphological conditions and pollution pressures were statistically elaborated. Based on the results, the hydromorphological conditions and pollution pressures of each station were confirmed to be reflected in the results of the statistical analysis performed. It was proven that the comparative use of the statistics and patterns of the water level and quality high-frequency time series could be used in the interpretation of the current site status as well as allowing the detection of possible changes. This approach can be used as a tool for the definition of thresholds, and will contribute to the design of management and restoration measures for the most impacted areas.

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

Arabian Journal of Geosciences ◽

10.1007/s12517-021-06935-5 ◽

2021 ◽

Vol 14 (6) ◽

Author(s):

Majed AlSubih ◽

Madhuri Kumari ◽

Javed Mallick ◽

Raghu Ramakrishnan ◽

Saiful Islam ◽

...

Keyword(s):

Time Series ◽

Saudi Arabia ◽

Trend Analysis

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

Remote Sensing ◽

10.3390/rs13091618 ◽

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.

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Seasonal trend analysis of image time series

International Journal of Remote Sensing ◽

10.1080/01431160902755338 ◽

2009 ◽

Vol 30 (10) ◽

pp. 2721-2726 ◽

Cited By ~ 85

Author(s):

J. Ronald Eastman ◽

Florencia Sangermano ◽

Bardan Ghimire ◽

Honglei Zhu ◽

Hao Chen ◽

...

Keyword(s):

Time Series ◽

Trend Analysis ◽

Seasonal Trend

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

Water ◽

10.3390/w13010095 ◽

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.

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