Characteristics of rainfall events in an ensemble of 23 regional climate model (RCM) simulations are evaluated against observed data in the Czech Republic for the period 1981–2000. Individual rainfall events are identified using the concept of minimum inter-event time (MIT) and only heavy events (15 % of events with the largest event depths) during the warm season (May–September) are considered. Inasmuch as an RCM grid box represents a spatial average, the effects of areal averaging of rainfall data on characteristics of events are investigated using the observed data. Rainfall events from the RCM simulations are then compared to those from the at-site and area-average observations. Simulated number of heavy events and seasonal total precipitation due to heavy events are on average represented relatively well despite the higher spatial variation compared to observations. RCM-simulated event depths are comparable to the area-average observations, while event durations are overestimated and other characteristics related to rainfall intensity are significantly underestimated. The differences between RCM-simulated and at-site observed rainfall event characteristics are in general dominated by the biases of the climate models rather than the areal-averaging effect. Most of the rainfall event characteristics in the majority of the RCM simulations show a similar altitude-dependence pattern as in the observed data. The number of heavy events and seasonal total precipitation due to heavy events increase with altitude, and this dependence is captured better by the RCM simulations with higher spatial resolution.
Potential changes in characteristics of precipitation due to climate change
may have significant societal impacts. Several studies have reported
significant changes in daily precipitation extremes in observed data
The climate change scenarios for precipitation are frequently based on
simulations of regional climate models (RCMs). Even as the majority of RCM
simulations available are conducted in resolution coarser than 10
Many studies of precipitation extremes across Europe examine daily data from
RCM simulations
The existing studies on RCM-simulated sub-daily rainfall are typically
looking at precipitation maxima in a number of temporal aggregations
The purpose of this study is to assess heavy rainfall event characteristics
(considering 15 % of events with the largest event depths) in an ensemble of
RCM simulations using hourly data conducted within the ENSEMBLES
The paper is organized as follows: Section
Rainfall event characteristics are analysed for
the Czech Republic (78 800
Average annual precipitation totals for the period 1961–2000 vary from about
420
In the present study, we used hourly
precipitation data provided by the Czech Hydrometeorological Institute. The
original data in 10
Given the unreliability of the pluviograph records in the winter period
RCM simulations analysed.
In order to increase the number of stations available for spatial averaging,
a longer period (1961–2009) was considered for analysing the areal-averaging
effects. This resulted in making 26 additional stations available (each of
which has records shorter than 10 years or ending before 1981). Figure
Moreover, we examined the influence of the number of stations considered in
the areal averaging using a dense rain gauge network for Prague (22 stations
within 500
An ensemble of 23 RCM simulations was examined
(see Table
The RCMs' outputs are available on a rotated latitude–longitude grid with
horizontal resolutions ranging from 12.5 to 50
The HIRHAM5, HadRM3, and RACMO2 simulations were conducted within the
ENSEMBLES project
Two of the HadRM3 simulations were driven by the GCM versions with perturbed
physics parameterizations
This section defines rainfall events in the
observed and RCM-simulated data (Sect.
Several methods exist for defining
individual rainfall events
The value of MIT considerably influences rainfall event characteristics
Using the MIT concept for determination of individual events requires choice
of the wet-hour threshold (i.e. precipitation amount below which the hour is
considered dry). For the observed data, the choice often follows naturally
from rain gauge precision leading frequently to wet-hour threshold of 0.1
Our attention is aimed only at events potentially causing soil erosion or
flooding (denoted heavy rainfall events further). The identification of such
events was based on one of the criteria used in USLE, i.e. considering only
those events with total depth larger than 12.7
We focused on the following basic
characteristics of heavy rainfall events:
event depth event duration event mean rainfall rate maximum 60
As our definition of a rainfall event is in general consistent with the USLE methodology, we consider also indicators of rainfall event erosivity:
event rainfall energy where event rainfall erosivity index EI
Note that in the USLE methodology, maximum 30
In addition to the aforementioned rainfall event characteristics, we analysed
also the following seasonal (May–September) characteristics:
number of heavy rainfall events per season seasonal total precipitation due to heavy rainfall events
Areal averaging of rainfall data can significantly affect such
characteristics of rainfall events as depth
The whole procedure can be summarized as follows:
Square regions with area corresponding to the considered resolutions (12.5, 25, and 50 Time series of areal average rainfall were calculated for each neighbourhood by averaging the data from
included stations (for periods where station data sets overlapped). Rainfall events were determined and the rainfall event
characteristics calculated for this areal average as well as at-site for the central station. To quantify the difference between the area-average and at-site characteristics at each neighbourhood,
we evaluated the following indices describing the differences in mean as well as in the whole distribution of rainfall event characteristics:
Ratio of mean areal to mean at-site (event and seasonal) characteristics. This ratio is further denoted rt Ratio of the Ratio of frequencies of corresponding bins of the histograms of areal and at-site event characteristics, further denoted as histogram ratio rt
The RCM-simulated event characteristics (representing areal averages) were
compared to the observed at-site characteristics considering the same indices
as described in point 3 in Sect.
The ratios between the RCM-simulated and observed at-site rainfall characteristics represent the combination of the bias in the RCM simulation with the effect of areal averaging of rainfall data. Therefore, ratios for RCM-simulated characteristics were further compared to those for area-average observations.
Finally, we also evaluated the dependence of the RCM-simulated (event and
seasonal) characteristics on altitude. A linear regression model of the
dependence of the
Seasonal number of heavy rainfall events (
This section presents findings related to areal averaging of rainfall data
(Sect.
Quantile ratios rt
The number of heavy rainfall events (
Mean characteristics of rainfall events considered for the at-site and
area-average observations are shown in the top four rows of Table
Ratios rt
The quantile ratios rt
To demonstrate how spatial resolution influences the area-average
characteristics, box plots of rt
Figure
In the RCM simulations, the event depths (
The number of heavy events per season (
Note that the differences in seasonal and event characteristics may be
considerably larger for individual RCM simulations, in particular at grid
boxes with high altitude. For instance
Area-average quantile ratios rt
The coefficient of variation (CV; not shown) of rt
Figure
The correspondence between simulated and area-average event depths (
For the longest events, the RCM-simulated event duration (
The difference between RCM-simulated and area-average rainfall rate (
Differences in distributions of rainfall event characteristics between the
RCM simulations and observations are characterized by histogram ratio
rt
Considerably higher numbers of events with smaller depths (
Simulated numbers of events with short duration are underestimated. Only
0.3–7.5 % (1.6 % on average) of events considered for the RCM simulations
are shorter than 6
Dependence of rainfall event characteristics on altitude. Dependence
is expressed by the change of characteristic per 100
Events with the smallest rainfall rates (
Most of the simulated events (84–99 %) have maximum 60
Figure
Although the RCM simulations generally show a similar pattern of altitude
dependence as that for the at-site observations regarding most
characteristics (with changes between
Number of heavy events per season (
Heavy rainfall event characteristics were assessed in an ensemble of 23 RCM
simulations. Events were identified while considering 6
While the same 6
Estimates of the effect of areal averaging are influenced by several sources
of uncertainties
The number of stations included into the calculation of the areal average
influenced most of the estimated event characteristics. Mean event depths
(
Several conclusions can be drawn from the comparison of area-average and
at-site characteristics in general:
More heavy rainfall events are identified in area-average observations while the area-average
seasonal total precipitation due to heavy events corresponds well with that from the at-site observations. Area-average event characteristic values are on average lower than are those for at-site
observed characteristics, except that area-average event duration is longer for the shortest events and rainfall rate is comparable for events with low rates. For most of the rainfall event characteristics, the difference between the area-average and at-site
observations grows with increasing non-exceedance probability (the exception being event depth, for which
the difference is comparable across the whole distribution). These findings complement other studies using
areal reduction factors that point out larger differences between area-average and at-site rainfall maxima
for longer return periods Considerably fewer events with high maximum 60 The effect of areal averaging (lower values of characteristics with larger area, except for event duration)
is generally in agreement with the review published by
Differences between the RCM-simulated and at-site observed characteristics are in general considerably larger than are those between the at-site and area-average observations, i.e. these differences are dominated by the RCMs' bias rather than the areal-averaging effect.
Although the RCM-simulated number of heavy events and seasonal total
precipitation due to heavy events averaged across the Czech Republic
correspond relatively well with the area-average observations (they are only
slightly larger), large differences between individual grid boxes may be
found (especially in areas with complex orography). Generally good simulation
of extremes (mean annual maxima, 20-year return values) in total
precipitation amounts (from both convective and stratiform daily
precipitation data together) was reported earlier for the Czech Republic by
Recent studies considering different spatial resolutions of RCM simulations
suggest that hourly precipitation characteristics of extreme events are
represented better in RCMs with higher spatial resolution
The RCM-simulated maximum 60
Overestimation of event duration (
It should be noted that when event duration (
Most analysed characteristics in most of the RCM simulations show a pattern
of altitude dependence similar to that for the at-site observations, and the
differences in strength of the altitude dependence for different quantiles of
rainfall event characteristics are in general small (largest differences
compared to at-site observations appear for simulations with the coarse 50
RCM simulations driven by reanalysis do not in general show better results in
simulating individual rainfall events compared to the GCM-driven RCMs. That
is in agreement with
The results and discussion presented so far were focused on the assessment of RCM performance in simulating individual rainfall events over a relatively small domain – the Czech Republic. It should be noted that at present there is no available data set allowing for assessment of RCM performance at hourly timescale over substantially larger domains or even whole Europe. However, although the bias in the RCM simulations is known to vary regionally, a number of findings can probably be transferred to other locations. This includes in particular the results concerning the effect of area averaging on the rainfall event characteristics but also the general deficiencies in the simulated event characteristics, such as strong underestimation of event rainfall rates, maxima and erosivity indices, overestimation of event duration, and dependence of the biases on the exceedance probability of event characteristics for event duration, rainfall rate and maxima.
Large part of the presented analysis considered spatial average event characteristics, especially due to lack of observed data with sufficient spatial coverage. For future research it may be an option to use radar data, provided that sufficiently long and homogenous data exist.
The bias in temporal structure of sub-daily rainfall, revealed in this study,
impairs in practice the use of simulated sub-daily rainfall in hydrological
applications even after standard bias correction (e.g. quantile mapping),
correcting the distribution of rainfall at sub-daily timescales. It was
shown in several studies that despite the correspondence of the distribution
at the corrected scale, the resulting simulated hydrological response may be
severely biased even for daily data
This study presents a methodology for analysis of precipitation
characteristics in RCM simulations from an event-based perspective.
Individual rainfall events and their characteristics are important with
respect to many hydrological applications and rainfall impact assessment
studies. Although it is generally not expected that the current RCMs would
simulate sub-daily variability and rainfall event characteristics properly
The results suggest that representation of individual heavy-rainfall events
(15 % events with largest event depth) in the RCM simulations suffers
from several deficiencies which have been only partly discussed in previous
studies dealing with precipitation characteristics and extremes. The most
important findings are summarized as follows:
Differences between RCM-simulated and at-site observed rainfall event characteristics are dominated
by the biases of the climate models rather than the areal-averaging effect. The RCMs on average represent the number of heavy rainfall events, seasonal
total precipitation due to heavy events and event depths relatively well; however,
the number of heavy events as well as the corresponding seasonal totals are overestimated at higher-elevated grid boxes. Simulated event durations are overestimated, while the event mean rainfall
rate, maximum 60 The underestimation is larger for larger rainfall rates and maximum 60 The largest deficiencies are found for events with short duration, which are
longer in the RCM simulations compared to the area-average observations. Therefore,
the numbers of events with shortest duration (below 10 The increase in number of heavy rainfall events and seasonal total precipitation
due to heavy events with altitude is considerably overestimated in all RCM simulations
except those with the highest spatial resolution.
The limitations in RCM-simulated rainfall event characteristics should be taken into consideration when applying their outputs in hydrological studies and climate change assessments.
The RCM data used in the paper were provided on personal request and cannot be redistributed. The modelling groups should be contacted for information on data availability. The observed data are property of the Czech Hydrometeorological Institute and Pražská vodohospodářská společnost a.s. and cannot be published due to licence.
The authors declare that they have no conflict of interest.
The research was supported by the Czech Science Foundation (project number 14-18675S);
observed data were prepared within a project supported by the Ministry of the
Interior of the Czech Republic (project number VG20122015092). We thank E. Buonomo
(Met Office Hadley Centre), O. B. Christensen (DMI), E. van Meijgaard (KNMI), and
G. Nikulin (SMHI) for providing the sub-daily RCM data. We acknowledge the ENSEMBLES
project, funded by the European Commission's Sixth Framework Programme through contract
GOCE-CT-2003-505539, and the World Climate Research Programme's Working Group on
Regional Climate, and the Working Group on Coupled Modelling, former coordinating
body of CORDEX and responsible panel for CMIP5. We also thank the climate modelling
groups and institutions (listed in Table