This study assesses the temporal behaviour in terms of inter-decadal variability of extreme daily rainfall of stated return period relevant for hydrologic risk analysis using a novel regional parametric approach. The assessment is carried out based on annual maximum daily rainfall series of 180 meteorological stations of Yangtze River Basin over a 50-year period (1961–2010). The outcomes of the analysis reveal that while there were effects present indicating higher quantile values when estimated from data of the 1990s, it is found not to be noteworthy to exclude the data of any decade from the extreme rainfall estimation process for hydrologic risk analysis.
The temporal assessment of rainfall extremes is necessary to understand its temporal behavior which will lead to better assess the frequency of extreme rainfall under climate change, and subsequently the flood risk. Since the climate change (e.g. IPCC, 1995) phenomena emerges, a large number of studies appear to find its impact on rainfall. Among them, a substantial research was carried out on rainfall extremes (Bülow et al., 2015; Das et al., 2013; Feng et al., 2007; Ntegeka and Willems, 2008; Su et al., 2008; Tabari et al., 2014; Zhang et al., 2008) as they impact the society most. The outcome is not one dimensional- a significant variability is observed in some regions whereas in some cases, a number of regions do not show much variation that prove to be significant (e.g. Alexander et al., 2006; Damberg and AghaKouchak, 2014; Feng et al., 2007). The data length and type of diagnostic tools can also influence the outcome (Ntegeka and Willems, 2008). A regional behavior is also recognized in those studies (Alexander et al., 2006).
The Mann Kendall and the Spearman rho tests are mostly used approaches to assess the temporal variability. In a more recent time, the quantile perturbation method (Ntegeka and Willems, 2008; Tabari et al., 2014) is used to assess the decadal variability in terms of identify the changes in the extremes for particular return periods. The peak-over-threshold (POT) data was used so that a longer data series can be gathered in a decade. All the above approaches are non-parametric.
Locations of meteorological stations on the map of Yangtze River Basin.
This study introduces a new parametric approach to assess the temporal
variability of rainfall extremes for the Yangtze River Basin, a major
development area of China. A number of studies were carried out to
investigate observed long-term trends and patterns in extreme precipitation
events in china (e.g. Fu et al., 2013) including the Yangtze Basin (e.g. Su
et al., 2008) in order to assess the regional response to climate change. A
summary of the outcomes was reported, among others, by Chen et al. (2012) who
also recognized that only a few studies were concentrated on to assess
decadal variability of rainfall extremes. This study aims to investigate the
issue further in terms of inter-decadal variability of extreme daily rainfall
of stated return period relevant for hydrologic risk analysis. In particular,
the study aims to address the following research objectives:
whether climate change has a significant role in decadal quantile
variability of daily rainfall extremes in Yangtze Basin context
whether recent datasets really provide more reliable predictions of
design extreme rainfall to be used in engineering purposes
L-moment ratio diagram indicating the average value of L-moment ratios of 1-day AM rainfall series fall on the theoretical line of the GEV distribution.
Variation of X50 with decades from each individual stations' pooling group.
The approach examines whether rainfall quantiles estimated for a decade as
well as the most recent changes in extreme quantiles can be qualified as
statistically significant in comparison with the natural temporal
variability. A major drawback of performing decadal variability in a
parametric setup is that a limited number of data set is available in a
decade to fit with the selected probability model. In this study, a regional
approach in region-of-influence (ROI) form (Burn, 1990; Das and Cunnane,
2012; Institute of Hydrology, 1999) is used to study the decadal quantile
behavior. The ROI method is an objective way of forming a homogeneous pooling
group/region for a site with limited data aiming to perform a frequency
analysis. In this approach, annual maximum rainfall series are pooled from
other homogeneous stations and perform frequency analysis based on
index-flood method (Dalrymple, 1960). The estimation of
With this approach, a homogeneous pooling/regional group for a particular decade is selected from gauged stations that are available in that particular decade and a frequency analysis is performed for a target site. A decade consists of 10 annual maximum (AM) data points but with this approach a large number of AM data is available for that particular decade which will lead to achieve a reliable estimate.
In order to test whether the variability is statistically significant, the study outlines confidence intervals based on a parametric Monte Carlo method. In this test, a baseline AM rainfall series is first fitted to a distribution after which random samples are generated from the distribution, and confidence intervals are estimated. In the present study, the baseline series is chosen as the long-term historical datasets from which the base quantile is estimated while the other one is from the group of series taken from a particular decade of interest. After assessing the decadal variability, the confidence intervals are also estimated and overlaid on the same chart. It is therefore graphically possible to identify periods that demonstrate significant departures under the hypothesis of no trend of rainfall extremes.
This study selects Yangtze River Basin to analyze decadal behavior in annual maximum (AM) daily rainfall series of over a 50-year period (1961–2010). The basin is analyzed with two major distinct climatic conditions: upper and middle-lower. A total number of 180 stations are available in the study area. They are displayed on the map in Fig. 1. The datasets were obtained from the National Meteorological Information Center of China Meteorological Administration (CMA). Data of these stations have different record length and have different starting and ending year.
Decadal datasets.
Table 1 lists the number of stations that are available for each decade. Only stations which have a minimum of 9 years of data available are included for that particular decade. There are 144 stations for which AM data are available for 5 decades.
In a parametric approach, a suitable probability distribution is needed to describe the data. The L-moment ratio diagram (Das, 2016; Hosking and Wallis, 1997) is used to identify an appropriate distribution. The LMRD diagram shown in Fig. 2 identifies the generalized extreme value (GEV) as the most suitable distribution for the Yangtze Basin. The formula for estimating growth factors and the associated quantiles based on the GEV can be obtained from Das and Cunnane (2011).
Growth factors,
This method also permits to identify whether the decadal variability occurred
in an individual station is significant or not. A number of stations shown
bold in Fig. 1 are chosen to represent for that particular region. They can
be classified as Upper (Upper-West stations 56144, 56167, 56374; Upper-South
station 56671; Upper-North station 57206; Upper-East stations 57614, 57606),
Middle (stations 57562, 57584, 57799) and Lower (stations 58345, 58436). The
decadal quantile,
The different decade's values (anomalies) came out significant for different
location of Yangtze basin. In the Upper-West basin, the 1980 decade value of
This study examines the decadal variability in daily AM rainfall series of Yangtze River Basin over a 50-year period (1961–2010) with a novel regional parametric approach. Based on the dimensionless standardized growth factor of 50 year return period value, the average largest value occurred in the 1990s but a dip occurred in the following decade. There are some case studies conducted to see the nature of the decadal variability in several stations of the Yangtze Basin with significant test. In general, the 1980s decadal quantile value came out as the largest in the far Upper, the 1990s value came out as the largest in the Middle with some being proved significant, and the 2000s value came out as the largest for the lower basin. So far the outcome of the analysis reveal that while there were small effects present indicating higher quantile values when estimated from data of decade 1990, it is found not to be worthwhile to exclude the data of any decade from the extreme rainfall estimation process for hydrologic risk analysis.
The observed daily rainfall data series of selected stations were obtained from the National Meteorological Information Center of China Meteorological Administration (CMA). CMA owns the datasets and a formal request is required to obtain the required datasets.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Innovative water resources management – understanding and balancing interactions between humankind and nature”. It is a result of the 8th International Water Resources Management Conference of ICWRS, Beijing, China, 13–15 June 2018.
The research is funded by Nanjing University of Information Science and Technology in the form of a grant (Grant no. 2243141501015) of the first author. Edited by: Dingzhi Peng Reviewed by: Qinglan Li and one anonymous referee