In this study, three kinds of hourly precipitation series with the spatial
resolution of 0.1
Extreme weather and climatic events have drawn broader concerns during past years, particularly on the regional and local scales. It has been recognized that changes in extreme events are more likely to cause damages for human lives and their properties than gradual changes (Bonsal et al., 2001). In order to obtain a better understanding of potential risks for decision making in terms of societal adaptation to future climate change, the detection and attribution of past changes become increasingly significant (Madsen et al., 2014).
Extreme precipitation, which is regarded as the main factor contributing to water security, reflects the homogeneity of temporal and spatial distribution of precipitation. Extreme precipitation indices, such as extreme precipitation threshold, extreme precipitation days, extreme precipitation amount, are widely used to assess the variations of extremes in several studies (You et al., 2014). As an identical classification of extremes is not comparable among the areas with greatly varying climates, empirical ranking methods are recommended to determine the extreme threshold at different percentiles. Therefore, the formula introduced by Beard (1943) has come into wide use because it is more suitable for studies on the changing climate extremes (Folland and Anderson, 2002).
Map of Study Area. (The square grid represents the spatial resolution of the assimilated data, which covers 205 grids in the whole area. The areas in the northern area surrounded by the blue line stand for the urban areas. Elevation is also shown in this figure.)
There have been plenty of studies on the analysis for variations and trends of extreme precipitation over global or regional scales. With respect to extreme precipitation, most of studies are based on in situ observations or large scaled gridded data downscaled from climate models (Koteswara et al., 2014; Li et al., 2015). In the local area, both the length of the data series and the spatial representative are limited due to the finite number of long-term observational stations. An urgent demand occurs for high resolution datasets for extremes studies especially in the rapidly urbanized regions with insufficient data. Under this circumstance, data assimilation technique undergoes a rapid development. It supplies an alternative way to study the impact of climate changes in these kinds of areas.
Beijing, the capital of China, has experienced tremendous changes due to the accelerated development of socio-economics and the rapid expansion of population during the past fifty years. However, negative consequences, such as sever water scarcity, serious floods and urban water-logging, are all along with the rapid growth of economics and urbanization. Therefore, accurate quantifications of recent changes in extreme precipitation can be benefit to clarify the mechanism of climate change and enhance decision-making for sustainable development of water resources and environment protection in Beijing.
Due to the fact that surface precipitation changes exhibit obvious regional characteristics, few temporal and spatial studies with higher resolution data have been made so far in Beijing. The main objective of this study is to: (1) analyze the tempo-spatial variability of the annual extreme precipitation based on assimilated datasets with high resolution in Beijing; (2) qualitatively indicate the local-scale effects on extreme precipitation, such as topography, urbanization and local climate. Jenkinson's ranking formula and Theil–Sen Estimator are employed in this study. The findings will probably contribute to reduce uncertainties on floods and droughts induced by the variations of extreme precipitation.
Beijing, the capital of the People's Republic of China, is composed of
16 districts, with most of the urban areas lying in the western area. It is
located at 39
The city is in the semi-humid warm continental monsoon climate zone. This place experiences four distinct seasons, with a cold and dry winter accompanied by northward wind blowing from high-latitude area, while a hot and wet summer because of the east-southeast toward airflow from the southern Pacific Ocean and the Indian Ocean. Due to the interaction of these cold and hot airflows, the precipitation is maily concentrated in summer, which accounts for 60–80 % of total precipitation amount.
In this study, a high resolution assimilated dataset (1979–2012) was used to analyze the variation of extreme precipitation. For each grid, Jenkinson's ranking formula was employed to estimate the 95th percentiles of daily precipitation distribution. The temporal and spatial characteristics and trends of surface annual extreme precipitation indices were then analyzed by Theil–Sen slope estimator method. A brief introduction on the dataset and methods are as follows.
In this study, three hourly assimilated datasets (1979–2012) with
0.1
According to Bonsal et al. (2001), daily precipitation for each year should
be firstly ranked in ascending order
The Theil–Sen estimator is an unbiased estimator of the true slope in simple linear regression. For many distributions of the response error, this estimator has high asymptotic efficiency relative to least-squares. Estimators with low efficiency require more independent observations to attain the same sample variance of efficient unbiased estimators. Besides, it is more robust because it is much less sensitive to outliers: it can tolerate arbitrary corruption of up to 29.3 % of the input data without degradation of the accuracy.
Figure 2 shows the spatial distribution of extreme precipitation indices in Beijing. The spatial distribution of median annual precipitation (PTV), with a range from 500 to 825 mm, is opposite to that of local topography, which increases from the northwest to the southeast. Results by using Principal Component Analysis (PCA) method indicate that the local climate and topography are two main factors influencing the spatial distributions of precipitation.
Extreme precipitation threshold (Ex_pv95) calculated as the
upper 95 percentile (15.0–32.5 mm day
Extreme precipitation amount (Ex_ptv95) is defined as the total amount of daily precipitation which exceeds Ex_pv95. Figure 1 shows that extreme precipitation amount has a parallel spatial distribution to average annual precipitation, with maximum values concentrated on urban area and the eastern mountain area, and minimum values in the north-western area. It accounts for 40–48 % of total precipitation amount within only 5 to 7 days, which indirectly suggests the inhomogeneous temporal characteristics of precipitation. It is worthwhile to notice that Ex_ptv95 of the urban areas occupies the largest proportion of total precipitation amount. Moreover, the total precipitation also has the maximum value of 825 mm, displaying a strong feature of urban wet island effect. The reason for this is partly owing to the effect of urbanisation in terms of urban heat island, the obstacles of high-rise buildings and the increase of condensation nucleus.
Climatological annual-median values of extreme precipitation based
on 3 h gridded data during 1979–2012. (From the left to the right and from
the up to the bottom, the figures referred to
Trends of extreme precipitation based on 3 h gridded data during 1979–2012. (The variables are same as Fig. 2.)
Extreme precipitation intensity (Ex_pi95) is an important
measurement of extreme precipitation, since larger Ex_pi95
implies higher risk caused by extreme precipitation. It is clear that the
spatial characteristic of Ex_pi95 is similar to that of
Ex_pv95, which suggests that areas with larger
Ex_pv95 may experience heavy storm. The maximum values appear
at the urban area and some north-eastern areas, which is just under 70 mm day
A significant downward trend can be found in both PTV and Ex_ptv95
in Beijing, with sharply decreasing rate (90–110 mm
According to the formula given by Jenkinson, the decrease of
Ex_pv95 indicates reducing daily precipitation intensity,
while the increase of Ex_pv95 represents a rise of daily
precipitation. As it can be seen from Fig. 3, the northern and
north-eastern districts have experienced an upward tendency in daily
intensity, while the regions in north and east fell with the value of
3.0 mm day
Compared the trends of PTV with that of Ex_ptv95, it is clear
that the downward rate of PTV is nearly 30 mm
The spatial distribution of median annual precipitation increases from
the northwest to the southeast. Results obtained by using Principal
Component Analysis (PCA) method indicate that the local climate and
topography are two main factors influencing the spatial distributions of
precipitation in Beijing. Ex_pv95 presents an apparent opposite distribution to
Ex_pd95, which means that areas with greater precipitation
threshold may have shorter precipitation days. The maximum Ex_pv95
appears at most of urban areas and some districts in the northeast
plain area. The piedmont areas have the largest Ex_pd95
because of the effect of local monsoon climate and significant uplift of terrain. Ex_ptv95 has a similar spatial distribution to average
annual precipitation, with maximum values concentrated on the urban area and
the eastern mountain area. It accounts for 40–48 % of total
precipitation amount within only 5 to 7 days, which indirectly suggests the
inhomogeneous temporal characteristics of precipitation. The spatial characteristics of Ex_pi95 are similar to
that of Ex_pv95, with the maximum values appearing at the
urban area and some north-eastern areas. These areas may experience heavy
storm since larger Ex_pi95 implies higher risk caused by
extreme precipitation. Significant downward trends are detected in both PTV and
Ex_ptv95, with sharply decreasing rate (90–110 mm The northern and north-eastern districts have experienced an upward
tendency in daily intensity, while the regions in north and east fell with
the value of 3.0 mm day Ex_ptv95 contributed the largest part in the decrease of
PTV. However, the proportion of Ex_ptv95 (Ex_maxper95) varied slightly during this period, indicating that the risk of
extreme precipitation was still high, especially in the areas with the
increase of Ex_pv95.
This study was supported by the research project from Beijing Natural Science Foundation (No. 8141003). The authors thank the Data Assimilation and Modelling Centre for Tibetan Multi-spheres (DAM) for providing high resolution dataset of surface precipitation.