Modeling non-stationarity in intensity, duration and frequency of extreme rainfall over India

Mondal, Arpita; Mujumdar, P. P.

doi:10.1016/j.jhydrol.2014.11.071

Cited by 160 publications

(82 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, seasonal and annual extreme precipitation in the northern-central and eastern US in 2013 (Knutson et al, 2014), extreme rainfall in the Golden Bay region in New Zealand (Dean et al, 2013), an increase in the summer precipitation rate in northern Europe (Yiou and Cattiaux, 2013), and successive winter storm events in southern England in 2013/14 leading to severe winter floods (Schaller et al, 2016) are primarily attributable to intrinsic natural variability and partly to anthropogenic influences. The asymmetric changes in the distribution of extremes owing to climate change have subsequently been validated for winter temperature extremes over the Northern Hemisphere (Kodra and Ganguly, 2014) and regional short-duration precipitation extremes in India and Australia (Mondal and Mujumdar, 2015;Westra and Sisson, 2011). Two of the recent studies (Deng et al, 2016;Mailhot et al, 2012) analysed a large ensemble of CMIP3 global climate model (GCM) runs and a sub-set of regional climate models that are part of the North American Regional Climate Change Assessment Program (NARCCAP) in terms of impact-relevant metrics over Canada.…”

Section: Introductionmentioning

confidence: 99%

Does nonstationarity in rainfall require nonstationary intensity–duration–frequency curves?

Ganguli

Coulibaly

2017

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. In Canada, risk of flooding due to heavy rainfall has risen in recent decades; the most notable recent examples include the July 2013 storm in the Greater Toronto region and the May 2017 flood of the Toronto Islands. We investigate nonstationarity and trends in the short-duration precipitation extremes in selected urbanized locations in Southern Ontario, Canada, and evaluate the potential of nonstationary intensity-duration-frequency (IDF) curves, which form an input to civil infrastructural design. Despite apparent signals of nonstationarity in precipitation extremes in all locations, the stationary vs. nonstationary models do not exhibit any significant differences in the design storm intensity, especially for short recurrence intervals (up to 10 years). The signatures of nonstationarity in rainfall extremes do not necessarily imply the use of nonstationary IDFs for design considerations. When comparing the proposed IDFs with current design standards, for return periods (10 years or less) typical for urban drainage design, current design standards require an update of up to 7 %, whereas for longer recurrence intervals (50-100 years), ideal for critical civil infrastructural design, updates ranging between ∼ 2 and 44 % are suggested. We further emphasize that the above findings need re-evaluation in the light of climate change projections since the intensity and frequency of extreme precipitation are expected to intensify due to global warming.

show abstract

Section: Introductionmentioning

confidence: 99%

Does nonstationarity in rainfall require nonstationary intensity–duration–frequency curves?

Ganguli

Coulibaly

2017

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…If this problematic method is still used, the frequency analysis may lead to high estimation error in hydrological design. Therefore, considerable literature has introduced the concept of hydrologic nonstationarity into analysis of various hydrological variables, such as annual runoff (Arora, 2002;Jiang et al, 2015a;Xiong et al, 2014;Yang and Yang, 2013), flood (Gilroy and Mccuen, 2012;Kwon et al, 2008;Yan et al, 2017;Zhang et al, 2015), low flow Jiang et al, 2015b;Liu et al, 2015), precipitation (Gu et al, 2017;Mondal and Mujumdar, 2015;Villarini et al, 2010) and so on. Compared with the literature on annual runoff, floods and precipitation, the literature on the nonstationary analysis of low flow is relatively limited.…”

Section: Introductionmentioning

confidence: 99%

Multiple causes of nonstationarity in the Weihe annual low-flow series

Xiong

Chen

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into lowflow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P , mean frequency of precipitation events λ, temperature T , potential evapotranspiration (EP), climate aridity index AI EP , base-flow index (BFI), recession constant K and the recession-related aridity index AI K ). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AI K is of the highest relative importance among these four variables, followed by IAR, BFI and AI EP . We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

show abstract

“…() to investigate the nonlinear relationship between ENSO and extreme rainfall during winter in North America. The GEV distribution from extreme value theory (EVT) has been used to identify the association between extreme precipitation and ENSO in India (Mondal and Mujumdar, ), Australia (Min et al, ) and the Philippines (Villafuerte et al, ); Sun et al . () have analysed the asymmetric effect of ENSO on extreme precipitation on a global scale.…”

Section: Introductionmentioning

confidence: 99%

Changes in extreme precipitation in the Yangtze River basin and its association with global mean temperature and ENSO

Lü

Chen

et al. 2017

Intl Journal of Climatology

View full text Add to dashboard Cite

Extreme precipitation events may lead to secondary disasters such as floods, landslides and erosion. The Yangtze River basin (YRB), which is home to a large population, is susceptible to flooding associated with heavy rainfall. Many dams have been constructed or planned for in the YRB. Understanding the changing behaviour of extreme precipitation and its relationship with other climatic factors will increase planners' ability to plan for, manage and respond to related flood events. This study investigated the trends in annual and seasonal maximum daily precipitation (Mx1day) using the iterative Mann–Kendall test. The non‐stationary Mx1day values were modelled to detect whether such changes were associated with rising local temperature (LTEM), global surface temperature (GTEM) or the El Niño–Southern Oscillation (ENSO). Location and scale parameters were considered individually in the non‐stationary generalized extreme value model during this study. Rising trends in annual Mx1day were identified at 73% of the stations studied. The June–July–August seasonal Mx1day exhibited the most pronounced increase among the four seasons. When location and scale were considered, 78 stations and 31 stations exhibited non‐stationary Mx1day, respectively. The impacts of both LTEM and ENSO on observed annual Mx1day in the YRB did not have field significance, while the impacts of GTEM did. At the YRB scale, the median sensitivity of the annual Mx1day to GTEM was 7.05% °C−1, while the median sensitivity of the annual Mx1day to LTEM was only 0.8% °C−1. The relationship between annual Mx1day and GTEM was more suitable for the Clausius–Clapeyron equation. The relationships between ENSO and observed seasonal Mx1day in March–April–May, June–July–August (JJA) and September–October–November (SON) were not field significant. However, a significant negative correlation between December–January–February Mx1day and ENSO was identified. The opposite effects of ENSO on extreme precipitation in JJA and SON may weaken the influence of ENSO on annual Mx1day.

show abstract

Modeling non-stationarity in intensity, duration and frequency of extreme rainfall over India

Cited by 160 publications

References 68 publications

Does nonstationarity in rainfall require nonstationary intensity–duration–frequency curves?

Does nonstationarity in rainfall require nonstationary intensity–duration–frequency curves?

Multiple causes of nonstationarity in the Weihe annual low-flow series

Changes in extreme precipitation in the Yangtze River basin and its association with global mean temperature and ENSO

Contact Info

Product

Resources

About