Detection and attribution of non-stationarity in intensity and frequency of daily and 4-h extreme rainfall of Hyderabad, India

Agilan, V.; Umamahesh, N. V.

doi:10.1016/j.jhydrol.2015.10.028

Cited by 50 publications

(34 citation statements)

References 40 publications

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“…Likewise, the linear best fit of the Hyderabad city annual maximum rainfall series of all durations are having increasing trend and, except 1-h duration linear best fit, they are statistically significant at 95% confidence level. Moreover, Agilan and Umamahesh (2015) detected and attributed non-stationarity present in the Hyderabad city, India (one of the study area of this study) extreme Besides, the previous studies indicate that the precipitation extremes in several regions of the world have increased (Westra et al, 2013). Nevertheless, most ground-based stations do not exhibit a strong trend and only limited stations show a statistically significant non-stationary behaviour (Westra et al, 2013;Cheng and AghaKouchak, 2014).…”

Section: Trend Analysismentioning

confidence: 53%

“…Likewise, the linear best fit of the Hyderabad city annual maximum rainfall series of all durations are having increasing trend and, except 1‐h duration linear best fit, they are statistically significant at 95% confidence level. Moreover, Agilan and Umamahesh () detected and attributed non‐stationarity present in the Hyderabad city, India (one of the study area of this study) extreme rainfall intensity and frequency, and they reported that the stationary statistical model is not even qualified as a considerable model when compared with non‐stationary statistical model for modelling extreme rainfall of the Hyderabad city, India. Further, Cheng and AghaKouchak () detected non‐stationarity in the Wilmington city (another study area of this study) extreme rainfall series and developed non‐stationary IDF curves.…”

Section: Resultsmentioning

confidence: 79%

“…

Δ_{i} = AICc - normalmin ((), AICc)

where min(AICc) is the smallest AICc among all the three models. The model which has Δ i value zero is the best model, and the models having Δ i ≤ 2 are considered reasonable choices (Burnham and Anderson, ; Agilan and Umamahesh, ).…”

Section: Methodsmentioning

confidence: 99%

“…The extreme rainfall is associated with different physical processes, namely, ENSO cycle (Kenyon and Hegerl, 2010;Zhang et al, 2010;Villafuerte and Matsumoto, 2015), IOD (Ajayamohan and Rao, 2008), global warming (Trenberth, 2011) and urbanization (Lei et al, 2008;Miao et al, 2011;Agilan and Umamahesh, 2015). Except urbanization, all other physical processes have a periodic component in it.…”

Section: Gev-3: Nonlinear Trend Using Mogamentioning

confidence: 99%

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Modelling nonlinear trend for developing non‐stationary rainfall intensity–duration–frequency curve

Agilan

Umamahesh

2016

Intl Journal of Climatology

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The infrastructure design is primarily based on rainfall intensity–duration–frequency (IDF) curves, and the existing IDF curves are based on the concept of stationary extreme value theory (EVT) (i.e. the occurrence probability of extreme precipitation is not expected to change significantly over time). But, the extreme precipitation events are increasing due to global climate change and questioning the reliability of our current infrastructure design. Based on recent developments in the EVT, recent studies proposed a method for developing non‐stationary rainfall IDF curve by incorporating linear trend in the location parameter of the generalized extreme value (GEV) distribution. Upon detecting a significant trend in the extreme rainfall series, directly applying the linear trend to develop non‐stationary IDF curves may increase the bias of the non‐stationary model. Hence, it is important to develop non‐stationary GEV model which has less bias than the stationary model by modelling nonlinear trend in the series. In this study, we try to develop non‐stationary GEV models with less bias by modelling nonlinear trend in the series using multi‐objective genetic algorithm (MOGA). In addition, the proposed GEV model is compared with the stationary GEV model and the linear trend‐based non‐stationary GEV model. Furthermore, the Wilmington city and the Hyderabad city non‐stationary IDF curves are developed and compared with stationary IDF curves. From the study results, it is observed that the proposed MOGA‐based method is able to build the good quality and less bias non‐stationary GEV models by modelling nonlinear trend in the series. In addition, it is also observed that the usage of linear trend for modelling non‐stationarity in the time series sometimes increase the bias of non‐stationary model.

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Section: Trend Analysismentioning

confidence: 53%

Section: Resultsmentioning

confidence: 79%

“…

Δ_{i} = AICc - normalmin ((), AICc)

Section: Methodsmentioning

confidence: 99%

Section: Gev-3: Nonlinear Trend Using Mogamentioning

confidence: 99%

See 2 more Smart Citations

Modelling nonlinear trend for developing non‐stationary rainfall intensity–duration–frequency curve

Agilan

Umamahesh

2016

Intl Journal of Climatology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Dourte et al (2012) suggest that increases in rainfall intensities may be one of the many factors contributing to the regional groundwater depletion in Hyderabad. Further, the catchment areas of the city (mostly peri-urban) are under water stress due to rapid urbanisation and climate variability (Ramachandraiah & Prasad, 2004;Agilan & Umamahesh, 2015). Franco et al's (2015) study suggests that water bodies coverage in Hyderabad city has reduced by 67%.…”

Section: Context: Urban and Peri-urban Hyderabadmentioning

confidence: 99%

Whose water? Whose profits? The role of informal water markets in groundwater depletion in peri-urban Hyderabad

Vij

John²,

Barua

2019

Water Policy

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Urbanising cities of India are engulfing the peri-urban land and water resources. Informal water sellers, who transfer water from peri-urban to urban areas, meet the growing water demand in Hyderabad, one of the fastest growing cities in India. This article qualitatively explores how informal water tankers are changing the flows of water, posing challenges to water access for peri-urban residents. We conclude that apart from the state's infrastructural and capacity challenges to provide piped water, power interplay between actors is responsible for the mushrooming of informal water markets. The transfer of water has contributed to groundwater depletion as well as to the water insecurity of peri-urban residents.

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Non‐Stationary Flash Drought Analysis and Its Teleconnection With Low‐Frequency Climatic Oscillations

Priya,

Reddy,

Ray

et al. 2024

Hydrological Processes

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Flash droughts, characterised by their rapid onset and significant impacts on local communities and agriculture, pose challenges for monitoring and mitigation efforts due to their unpredictable nature. Therefore, this study aims to investigate the occurrence, characteristics and influencing factors of flash droughts in the Ganga River Basin (GRB) for the period 1981–2020. Flash droughts are identified using the pentad averaged root zone soil moisture (PRZSM). The Mann‐Kendall trend test is used to determine the spatial and temporal pattern of flash drought characteristics. Furthermore, a multivariate flash drought index (MFDI) is developed to account for the combined effects of flash drought characteristics. Finally, wavelet coherence analysis evaluates the relationship between climatic oscillations and MFDI at the sub‐basin scale. Utilising a revised flash drought identification approach incorporating non‐stationary cumulative distribution functions (CDFs), the study identifies flash droughts in the GRB, particularly emphasising higher occurrences in the Chambal and Upper Yamuna Sub‐basins. Analysis of flash drought characteristics under stationary and non‐stationary conditions reveals increased frequency, severity and decline rates, highlighting the impact of evaporation and latent heat flux. Furthermore, the Upper Ganga Sub‐basin demonstrates coherence with the DMI at shorter time scales (1 to 4‐year time scales), while the Lower Ganga Sub‐basin displays a pronounced association with the NINO3.4 index (5.65‐year time scale), indicating the impact of climate oscillations on flash drought dynamics in these regions. These findings provide valuable insights for drought monitoring, prediction and management strategies in a changing climate, emphasising the need for integrated approaches to address the complex interplay between climate variability and flash drought occurrences in the GRB.

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Detection and attribution of non-stationarity in intensity and frequency of daily and 4-h extreme rainfall of Hyderabad, India

Cited by 50 publications

References 40 publications

Modelling nonlinear trend for developing non‐stationary rainfall intensity–duration–frequency curve

Modelling nonlinear trend for developing non‐stationary rainfall intensity–duration–frequency curve

Whose water? Whose profits? The role of informal water markets in groundwater depletion in peri-urban Hyderabad

Non‐Stationary Flash Drought Analysis and Its Teleconnection With Low‐Frequency Climatic Oscillations

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