Non‐stationary modelling of extreme precipitation by climate indices during rainy season in Hanjiang River Basin, China

Abstract

The extreme precipitation regimes have been changing as the climate system has warmed. Investigating the non-stationarity and better estimating the changes of the extreme precipitation are valuable for informing policy decisions. In this study, two precipitation indices are employed to describe the extreme events, including maximum 5-day precipitation amount (RX5day) and the number of very heavy precipitation days (R20). The generalized additive models for location, scale and shape (GAMLSS) is employed to characterize non-stationarities in extreme precipitation events and related climate indices in 13 stations in the Hanjiang River basin (HJRB). Three models including stationary model without change (M0), nonstationary models over time (M1) and non-stationarity models with large-scale climate indices (M2) as predictors, respectively, are considered to analyse occurrence rates of extreme precipitation. The optimal model and the significant predictors were selected by the Akaike information criterion (AIC). To investigate the main predictors at regional scale, the homogeneous subregions for precipitation extremes are identified by clustering analysis. Results indicate that: (a) the non-stationarities of RX5day series and R20 series at all stations are identified in the HJRB; (b) extreme precipitation behaviour is significantly influenced by climate indices and non-stationary model 2 to describe the changes of extreme precipitation is better than non-stationary model 1, indicating the impact of large-scale climate forcing on the changes of extreme precipitation regimes; (c) the HJRB can be categorized into three homogenous regions. The optimal distributions and the main predictors of extreme precipitation events in most stations of each subregion are basically the same; (d) the dominated climate indices influencing the extreme precipitation events are different in different regions and have regional patterns. The results highlight the modelling of extreme precipitation events under non-stationarity conditions and provide information for developing strategies of mitigation and adaptation to climate change impacts on extreme precipitation.

Non‐stationary frequency analysis of extreme streamflow disturbance in a typical ecological function reserve of China under a changing climate

“…Guo et al (2008) found that climate change played a dominant role in the changes of streamflow in the Poyang Lake Basin. At present, some studies have considered the simulation of extreme hydro-meteorological events based on non-stationary model (Hao et al, 2019;Tan and Gan, 2017), yet with limited research efforts on exploring the non-stationary probabilistic characteristics of extreme streamflow under a changing climate. Therefore, the study of extreme streamflow frequency analysis under the conditions of climate change is of great importance for ecological environment management and water resources planning in the basin.…”

Regional Agroclimate Characteristic and Its Multiple Teleconnections: A Case Study in the Jianghan Plain (JHP) Region

“…Tharu and Dhakal [84] also used Bayesian quantile regression to explore the effects of ENSO and NAO on the extreme precipitation at different quantile levels in the United States. Hao et al [85] analyzed the non-stationarities in extreme precipitation events and related climate indices at 13 stations in the Hanjiang river basin, based on the generalized additive model (GAMLSS). They found that better performance of simulating extreme precipitation intensity and scale after considering climate indices.…”

Evaluation and projection of the annual maximum streamflow in response to anthropogenic and climatic effects under nonstationary conditions in the Hanjiang River Basin, China

“…Thus, nonstationary analysis of hydrologic time series has been attracting increasing attention and numerous studies have been reported which attempt to address frequency analysis with the consideration of nonstationarity (Galloway 2011;López & Francés 2013;Panagoulia et al 2014;Hao et al 2019b). The most common ways to analyze nonstationarity of hydrologic time series are to associate the distribution parameters with time by linear relationship in changing environment (Villarini et al 2010;Xu & Huang 2011;Zhang et al 2014;Mullan et al 2016), but unfortunately cannot help to reveal the physical mechanisms behind the changing properties.…”

“…The irrationality of this method is that the different trends such as rising, falling and jumping identified based on the historical observations cannot be revealed in the future, making their usefulness to understand the risks of future extreme events limited. To date, many studies have reported that the distribution parameters of candidate probability distribution, in terms of extreme precipitation, flood event, extreme water level and drought event, are dependent on time (Cunderlik & Ouarda 2006) or some physical covariates (e.g., climate indices, urbanization and water reservoir construction; see, for example, Zhang et al 2015b;Liu et al 2017;Hao et al 2019b). Moreover, Hao et al (2019b) and Zhang et al (2015b) pointed out that adopting the physical factors as covariates in modeling the hydrologic events can better describe the changing properties of hydrological events and understand the physical mechanisms behind the changing properties.…”