Spatio -temporal trend detection of hydro -meteorological parameters for climate change assessment in Lower Tapi river basin of Gujarat state, India

Chandole, Vishal; Joshi, Geeta S.; Rana, Shilpesh C.

doi:10.1016/j.jastp.2019.105130

Cited by 24 publications

(7 citation statements)

References 37 publications

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“…The test results using four methods indicates that ST method was very sensitive and superior to the LT, MK, and MMK test for the determination of the change in lake levels. This superior aspect of the ST method has been seen in many other studies in the literature [31][32][33][34]. In addition, all data ranges can be displayed graphically in the ST Cartesian coordinate system, so visual inspection and interpretation can also be made.…”

Section: Discussionmentioning

confidence: 73%

Trend Analyses of Meteorological Variables and Lake Levels for Two Shallow Lakes in Central Turkey

Yağbasan

Demir

Yazıcıgil

2020

Water

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Trend analyses of meteorological variables play an important role in assessing the long-term changes in water levels for sustainable management of shallow lakes that are extremely vulnerable to climatic variations. Lake Mogan and Lake Eymir are shallow lakes offering aesthetic, recreational, and ecological resources. Trend analyses of monthly water levels and meteorological variables affecting lake levels were done by the Mann-Kendall (MK), Modified Mann-Kendall (MMK), Sen Trend (ST), and Linear trend (LT) methods. Trend analyses of monthly lake levels for both lakes revealed an increasing trend with the Mann-Kendall, Linear, and Sen Trend tests. The Modified Mann-Kendall test results were statistically significant with an increasing trend for Eymir lake levels, but they were insignificant for Mogan lake due to the presence of autocorrelation. While trend analyses of meteorological variables by Sen Test were significant at all tested variables and confidence levels, Mann-Kendall, Modified Mann-Kendall, and Linear trend provided significant trends for only humidity and wind speed. The trend analyses of Sen Test gave increasing trends for temperature, wind speed, cloud cover, and precipitation; and decreasing trends for humidity, sunshine duration, and pan evaporation. These results show that increasing precipitation and decreasing pan evaporation resulted in increasing lake levels. The results further demonstrated an inverse relationship between the trends of air temperature and pan evaporation, pointing to an apparent “Evaporation Paradox”, also observed in other locations. However, the increased cloud cover happens to offset the effects of increased temperature and decreased humidity on pan evaporation. Thus, all relevant factors affecting pan evaporation should be considered to explain seemingly paradoxical observations.

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Section: Discussionmentioning

confidence: 73%

Trend Analyses of Meteorological Variables and Lake Levels for Two Shallow Lakes in Central Turkey

Yağbasan

Demir

Yazıcıgil

2020

Water

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“…The MK test is a non-parametric test that does not require the data to obey a normal distribution [31] and is insensitive to the interference of a few outliers, especially effective for short-term time series data [32]. In this study, FVC trends and p-values were calculated in MATLAB for Windows Version 2019b (MathWorks, Natick, MA, USA).…”

Section: Trend Analysismentioning

confidence: 99%

Sensitivity of Grassland Coverage to Climate across Environmental Gradients on the Qinghai-Tibet Plateau

Rihan

Ganjurjav

et al. 2023

Remote Sensing

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Grassland cover is strongly influenced by climate change. The response of grassland cover to climate change becomes complex with background climate. There have been some advances in research on the sensitivity of grassland vegetation to climate change around the world, but the differences in climate sensitivity among grassland types are still unclear in alpine grassland. Therefore, we applied MODIS NDVI data and trend analysis methods to quantify the spatial and temporal variation of grassland vegetation cover on the Qinghai-Tibet Plateau. Then, we used multiple regression models to analyze the sensitivity of fractional vegetation cover (FVC) to climatic factors (Temperature, Precipitation, Solar radiation, Palmer drought severity index) and summarized the potential mechanisms of vegetation sensitivity to different climatic gradients. Our results showed (1) a significant increasing trend in alpine desert FVC from 2000–2018 (1.12 × 10−3/a, R2 = 0.56, p < 0.001) but no significant trend in other grassland types. (2) FVC sensitivity to climatic factors varied among grassland types, especially in the alpine desert, which had over 60% of the area with positive sensitivity to temperature, precipitation and PDSI. (3) The sensitivity of grassland FVC to heat factors decreases with rising ambient temperature while the sensitivity to moisture increases. Similarly, the sensitivity to moisture decreases while the sensitivity to thermal factors increases along the moisture gradient. Furthermore, the results suggest that future climate warming will promote grassland in cold and wet areas of the Qinghai-Tibet Plateau and may suppress vegetation in warmer areas. In contrast, the response of the alpine desert to future climate is more stable. Studying the impact of climate variation at a regional scale could enhance the adaptability of vegetation in future global climates.

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“…This method is not restricted to any sample sizes, distribution types, valid for autocorrelated data and also provides graphical illustration of trends. This method has been applied in many studies to discover trends in climate variables, mostly along with the MK test for comparison purpose [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of the Rank-based and Slope-based Nonparametric Tests for Trend Detection in Climate Time Series

Ali,

Abdul Ghani

2024

AMCI

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Trend detection in climate time series data is crucial for understanding climate change, predicting future climate patterns, assessing impacts, managing resources, and formulating policies. Several trend detection methods have been introduced in the literature, including parametric and non-parametric approaches. Nonparametric trend detection methods are often considered more preferable than parametric methods in certain situations due to their flexibility and robustness. Comparing various nonparametric methods of trend detection is vital in data analysis because different techniques can yield divergent results based on the same dataset. In this study, three nonparametric trend tests which were the MannKendall (MK), Sen’s Innovative Trend Analysis (ITA) and Modified Mann-Kendall by Sen’s Innovative Trend Analysis (MMK_ITA) were compared based on their power. The MK test is a rank-based test and the ITA is a slope-based test. Meanwhile, the combination of rank-based and slope-based methods is known as the MMK_ITA test. The power analysis was conducted through Monte Carlo simulation on normal, non-normal and autocorrelated time series. The simulation results indicated that test power relied on magnitude of linear trend slope, sample sizes, distribution type and variation in time series. These tests were then applied to monthly maximum temperature from 2002 until 2021 for Selangor, Malaysia. This study found that the slope-based test performed better compared to the rank-based test and their combined methods from the simulation studies and real data application based on the calculated power.

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Spatio -temporal trend detection of hydro -meteorological parameters for climate change assessment in Lower Tapi river basin of Gujarat state, India

Cited by 24 publications

References 37 publications

Trend Analyses of Meteorological Variables and Lake Levels for Two Shallow Lakes in Central Turkey

Trend Analyses of Meteorological Variables and Lake Levels for Two Shallow Lakes in Central Turkey

Sensitivity of Grassland Coverage to Climate across Environmental Gradients on the Qinghai-Tibet Plateau

A Comparison of the Rank-based and Slope-based Nonparametric Tests for Trend Detection in Climate Time Series

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