Detection of abrupt shift and non-parametric analyses of trends in runoff time series in the Dez river basin

Gholami, Hooman; Moradi, Yahya; Lotfirad, Morteza; Gandomi, Mohammad Amin; Bazgir, Nooredin; Hajibehzad, Manoochehr Shokrian

doi:10.2166/ws.2021.357

Cited by 14 publications

(6 citation statements)

References 61 publications

(71 reference statements)

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“…Climatic and hydrological data analysis techniques, such as the MK and Pettit tests, have been extensively explored in the scientific literature. Nonetheless, these tests present some differences, as underscored by Gholami et al [57]. While the MK test might not have a high sensitivity toward sudden changes, the Pettit test can detect them [58].…”

Section: Discussionmentioning

confidence: 99%

Evaluating Non-Stationarity in Precipitation Intensity-Duration-Frequency Curves for the Dallas–Fort Worth Metroplex, Texas, USA

Ghimire,

Kharel,

Gebremichael

et al. 2023

Hydrology

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Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning.

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Section: Discussionmentioning

confidence: 99%

Evaluating Non-Stationarity in Precipitation Intensity-Duration-Frequency Curves for the Dallas–Fort Worth Metroplex, Texas, USA

Ghimire,

Kharel,

Gebremichael

et al. 2023

Hydrology

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“…The null hypothesis states that there is no shift or displacement in mean monthly temperature (MT min or MT max) data and the alternate hypothesis states that a certain data may be distinguished as a change point and mean monthly temperature data set is significantly displaced at the break point [22].…”

Section: Mann Whitney Pettit Methods (Mwp)homogeneity Testmentioning

confidence: 99%

Change Point Detection of Temperature in Karnataka State in India During the Period 1979-2019

Rani,

Kumar,

Singh

et al. 2024

IJECC

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This paper deals with study of exposure of Karnataka state to climate change for a period 1979-2019. The Mann Whitney Pettit’s homogeneity test (MWP) was analysed for 240 data sets for monthly data of minimum (MTmin) and maximum temperature (MT max) across ten agro climatic zones) to estimate the year of structural break or year of shift in mean monthly temperature from one level to next higher level during the forty years of study period i.e., 1979-2019. About 77 data sets were identified to show year of structural break The annual mean temperature recorded anupward shift in all the agro climatic zones of Karnataka except in hilly zone. The break year was chosen based on its frequent occurrence in data sets of minimum and maximum temperature. It is observed to be 1998 for North Eastern Transition Zone and is 1997, 1994, 1996, 1995, 1996, 1999, 1999, 1999 and 1997 for North Eastern Dry Zone, Northern Dry Zone, Central Dry Zone, Eastern Dry Zone, Southern Dry Zone, Southern Transition Zone, Northern Transition Zone, Hilly Zone and Coastal Zone respectively. Therefore, it is a evidential picture reflecting the increase in temperatures across the zones. Researchers should develop crop varieties that are insensitive to temperature changes and should develop packages of practices which will mitigate adverse effect of fluctuations in climate parameters on crop productivity.

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“…A non-parametric technique called the Mann-Kendall trend test is frequently used to identify monotonic patterns in environmental data sets [9]. The Mann-Kendall trend test, which can deal with values below the detection limit and missing values, is frequently used for the analysis of climatological and hydrological time series [10].…”

Section: Trend Test Methodsmentioning

confidence: 99%

Spatial and temporal changes of runoff in the Yellow River in recent 15 years

Liu

Qian

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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In this study, the runoff data from 12 representative stations in the Yellow River from 2005 to 2020 is utilized, and the Mann-Kendall trend test is used to examine the changes in the observed runoff of the Yellow River. The results show that the annual runoff has grown in the upper reaches and has gradually reduced along the river flow direction. It is because the runoff is mainly affected by precipitation and human activities. In addition, the runoff mutation of each hydrological station is not consistent. This may be caused by many factors, resulting in the complexity of annual runoff changes. The findings can reveal runoff sequence variation of the Yellow River and provide a theoretical basis for river management.

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Detection of abrupt shift and non-parametric analyses of trends in runoff time series in the Dez river basin

Cited by 14 publications

References 61 publications

Evaluating Non-Stationarity in Precipitation Intensity-Duration-Frequency Curves for the Dallas–Fort Worth Metroplex, Texas, USA

Evaluating Non-Stationarity in Precipitation Intensity-Duration-Frequency Curves for the Dallas–Fort Worth Metroplex, Texas, USA

Change Point Detection of Temperature in Karnataka State in India During the Period 1979-2019

Spatial and temporal changes of runoff in the Yellow River in recent 15 years

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