distribution are crucial not only for agriculture and food security, but also for
hydrological aspects (Gedefaw et al. 2018; Nikumbh et al. 2019).
Extensive research has addressed the spatiotemporal dynamism of rainfall in the
Indian subcontinent, emphasizing seasonality, periodicity, and global and local
trends using a nonparametric statistical approach such as the Mann–Kendall
(MK) or modified Mann–Kendall (mMK) test, rank correlation test, Sen’s slope
estimation, and the Spearman rank correlation test (Gedefaw et al. 2018; Das and
Bhattacharya 2018; Das et al. 2019, 2020c, d). A few studies have also detected
trends in annual and seasonal rainfall in India using both parametric methods (i.e.,
linear regression) and nonparametric methods (i.e., MK). Many researchers have
documented that there is no evidence of a considerable trend in annual rainfall in
India as a whole (Krishnan et al. 2016; Wang et al. 2020), although some studies
show a decreasing trend in summer monsoon rainfall (Dash et al. 2009) with
pre-monsoon, post-monsoon, and winter rainfall showing an increasing trend at
the national scale. Many studies show that the frequency of extreme rainfall events
has increased in the past 50 years, but the seasonal mean rainfall does not show any
significant trend in central India (Sahoo et al. 2020). However, these trend analysis
methods are purely statistical and do not detect trends at high, medium and low
values at a single computation process. Hence, for effective and efficient water
resource management, an advanced and flexible graphical technique of trend analysis at high, medium, and low values with such traditional methods is mandatory
(Şen 2012). For that reason, a new approach, innovative trend analysis (ITA), was
introduced by Şen for hydrometeorological analysis. ITA is a new and robust trend
detection techniques that have already been used several studies in the field of
hydrometeorological data such as rainfall (Ahmad et al. 2018; Das et al. 2020b;
Ay and Kisi 2015; Öztopal and Şen 2017; Mandal et al. 2020), temperature (Cui
et al. 2017; Mohorji et al. 2017), evapotranspiration (Kisi 2015; Pour et al. 2020;
Tabari et al. 2011), streamflow (Changnon and Demissie 1996; Machiwal and Jha
2012; Şen 2012), groundwater level (Das et al. 2020a, b; Satishkumar and Rathnam
2020), and water quality parameters (Kisi and Ay 2014) in several parts of the world.
The main objectives of this study are to identify rainfall trends using the ITA for
quantifying the subtrends considering 36 meteorological subdivisions in India
(1901–2015) and make a comparison with the traditional nonparametric methods.
The graphical trend detection method classify the given hydrometeorological data
into “low,” “medium,” and “high” cluster for more convenient interpretation. In this
study, the nonparametric Mann–Kendall (MK) test (Mann 1945; Kendall 1975) or
the modified MK test (Hamed and Rao 1998) has been used to assess the reliability
of the ITA (De Leo et al. 2020). Furthermore, the percent bias (P BIAS ) technique is
also used for estimating the changes in rainfall following the first and second half of
the ITA pre-process data, as well as the slope of changes estimated by Sen’s
estimator method (Şen 1968).
7 Comparison of Classical Mann–Kendal Test and Graphical Innovative Trend. . .
157
hydrological aspects (Gedefaw et al. 2018; Nikumbh et al. 2019).
Extensive research has addressed the spatiotemporal dynamism of rainfall in the
Indian subcontinent, emphasizing seasonality, periodicity, and global and local
trends using a nonparametric statistical approach such as the Mann–Kendall
(MK) or modified Mann–Kendall (mMK) test, rank correlation test, Sen’s slope
estimation, and the Spearman rank correlation test (Gedefaw et al. 2018; Das and
Bhattacharya 2018; Das et al. 2019, 2020c, d). A few studies have also detected
trends in annual and seasonal rainfall in India using both parametric methods (i.e.,
linear regression) and nonparametric methods (i.e., MK). Many researchers have
documented that there is no evidence of a considerable trend in annual rainfall in
India as a whole (Krishnan et al. 2016; Wang et al. 2020), although some studies
show a decreasing trend in summer monsoon rainfall (Dash et al. 2009) with
pre-monsoon, post-monsoon, and winter rainfall showing an increasing trend at
the national scale. Many studies show that the frequency of extreme rainfall events
has increased in the past 50 years, but the seasonal mean rainfall does not show any
significant trend in central India (Sahoo et al. 2020). However, these trend analysis
methods are purely statistical and do not detect trends at high, medium and low
values at a single computation process. Hence, for effective and efficient water
resource management, an advanced and flexible graphical technique of trend analysis at high, medium, and low values with such traditional methods is mandatory
(Şen 2012). For that reason, a new approach, innovative trend analysis (ITA), was
introduced by Şen for hydrometeorological analysis. ITA is a new and robust trend
detection techniques that have already been used several studies in the field of
hydrometeorological data such as rainfall (Ahmad et al. 2018; Das et al. 2020b;
Ay and Kisi 2015; Öztopal and Şen 2017; Mandal et al. 2020), temperature (Cui
et al. 2017; Mohorji et al. 2017), evapotranspiration (Kisi 2015; Pour et al. 2020;
Tabari et al. 2011), streamflow (Changnon and Demissie 1996; Machiwal and Jha
2012; Şen 2012), groundwater level (Das et al. 2020a, b; Satishkumar and Rathnam
2020), and water quality parameters (Kisi and Ay 2014) in several parts of the world.
The main objectives of this study are to identify rainfall trends using the ITA for
quantifying the subtrends considering 36 meteorological subdivisions in India
(1901–2015) and make a comparison with the traditional nonparametric methods.
The graphical trend detection method classify the given hydrometeorological data
into “low,” “medium,” and “high” cluster for more convenient interpretation. In this
study, the nonparametric Mann–Kendall (MK) test (Mann 1945; Kendall 1975) or
the modified MK test (Hamed and Rao 1998) has been used to assess the reliability
of the ITA (De Leo et al. 2020). Furthermore, the percent bias (P BIAS ) technique is
also used for estimating the changes in rainfall following the first and second half of
the ITA pre-process data, as well as the slope of changes estimated by Sen’s
estimator method (Şen 1968).
7 Comparison of Classical Mann–Kendal Test and Graphical Innovative Trend. . .
157