An integrated approach for identifying homogeneous regions of extreme rainfall events and estimating IDF curves in Southern Ontario, Canada: Incorporating radar observations

Journal of Hydrology 528 (2015) 734–750

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Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol

An integrated approach for identifying homogeneous regions of extreme rainfall events and estimating IDF curves in Southern Ontario, Canada: Incorporating radar observations Edson Paixao a,⇑, M. Monirul Qader Mirza b, Mark W. Shephard c, Heather Auld d, Joan Klaassen c, Graham Smith e a

Department of Physical and Environmental Sciences, University of Toronto at Scarborough, Toronto, Ontario M1C 1A4, Canada Climate Research Division, Environment Canada, Toronto, Ontario M3H 5T4, Canada c Environment Canada, Toronto, Ontario M3H 5T4, Canada d Risk Sciences International, Richmond Hill, Ontario L4E 4L3, Canada e Grand River Conservation Authority (GRCA), Cambridge, Ontario N1R 5W6, Canada b

a r t i c l e

i n f o

Article history: Received 24 July 2014 Received in revised form 14 March 2015 Accepted 7 June 2015 Available online 28 June 2015 This manuscript was handled by Konstantine P. Georgakakos, Editor-in-Chief, with the assistance of Alan Seed, Associate Editor Keywords: Extreme rainfall events Radar rainfall rates Flood TBRG Intensity–Duration–Frequency (IDF)

s u m m a r y Reliable extreme rainfall information is required for many applications including infrastructure design, management of water resources, and planning for weather-related emergencies in urban and rural areas. In this study, in situ TBRG sub-daily rainfall rate observations have been supplemented with weather radar information to better capture the spatial and temporal variability of heavy rainfall events regionally. Comparison of extreme rainfall events show that the absolute differences between the rain gauge and radar generally increase with increasing rainfall. Better agreement between the two observations is found when comparing the collocated radar and TBRG annual maximum values. The median difference is <18% for the annual maximum rainfall values 650 mm. The median of difference of IDF estimates obtained through the Gumbel distribution for 10-year return period values computed from TBRG and radar are also found to be 4%. The overall results of this analysis demonstrates the potential value of incorporating remotely sensed radar with traditional point source TBRG network observations to provide additional insight on extreme rainfall events regionally, especially in terms of identifying homogeneous regions of extreme rainfall. The radar observations are particularly useful in areas where there is insufficient TBRG station density to statistically capture the extreme rainfall events. Ó 2015 Elsevier B.V. All rights reserved.

1. Introduction The accuracy and efficiency of water infrastructure design and flood management planning are highly dependent on reliable rainfall information. Most meteorological and hydrological applications require sub-daily and 24-h rainfall data in order to capture the spatial and temporal variability of heavy rainfall events (Pedersen et al., 2010; Bonnin et al., 2006; Watt et al., 2003; Sun et al., 2000). For example, in regions where finer scale convective rainfall processes dominate, site-specific extreme rainfall Intensit y–Duration–Frequency (IDF) values require a representative network of stations to statistically capture the spatial and temporal variability of events in the region (Konrad, 2001; Wheater et al., 2000). Typically rain gauge networks have been used to capture

⇑ Corresponding author. E-mail address: [email protected] (E. Paixao). http://dx.doi.org/10.1016/j.jhydrol.2015.06.015 0022-1694/Ó 2015 Elsevier B.V. All rights reserved.

rainfall events, however, depending on balance between the density of the in-situ point source network and the homogeneity of the extreme rainfall events, other additional sources of extreme rainfall information may need to be considered in order statistically represent the extreme events for a region. In recent years, there has been a growing demand for reliable spatial and temporal rainfall data, especially those related to heavy convective storms for engineering designs and disaster management. During the last two decades, weather radar and satellite approaches have received special attention by the meteorological and hydrological communities for identification of spatial and temporal patterns associated with extreme rainfall events and flooding over large regions (Villarini et al., 2010; Corral et al., 2009; Endreny and Imbeah, 2009; Huffman et al., 2007; Hughes, 2006). In general, the intensity of rainfall that reaches the ground is dependent on the amount of moisture in the air and the rate at which atmospheric lifting processes can convert this moisture into rainfall. Typically, a variety

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of dynamic atmospheric processes can lead to the lifting of moisture-laden air and result in a range of rainfall intensities, durations, and seasonal tendencies over a given area. Radar information is being utilized to provide additional insight on the spatial patterns of the short duration extreme rainfall. Some flood estimation examples include, the Sun et al. (2000) study that compared different approaches to estimate the input rainfall in the Finniss River catchment in Darwin, Australia using rain gauge data only, Kriging of the rain gauge data, radar data alone, and Cokriging of both radar and rain gauge data. Their results indicated that the Cokriging method significantly improves flood estimates as it combines the information provided by both the radar and rain gauge data to provide a better estimate of the sub-catchment rainfall. Durrans et al. (2002) analyzed eight years of radar rainfall data for various durations in estimating 1-, 2-, and 4-h rainfall depth-area relationships and 2-, 10-, 50- and 100-year storm return periods. The analysis demonstrated reasonable consistency of the calculated rainfall depth-area curves with those published in the U.S. Weather Bureau technical paper TP 29 (U.S. Weather Bureau, 1957). Overeem et al. (2009) estimated rainfall depth–dura tion–frequency (DDF) curves in the Netherlands using 11 years of radar data to calculate precipitation depths for 15-min to 24-h durations and concluded that despite large uncertainties in the long duration rainfall, the radar data was found to be suitable to develop DDF curves. In Southern Ontario, the Grand River Conservation Authority (GRCA) has been using 1-h to 7-day accumulated radar rainfall information from NEXRAD radar system operated by the United States federal agency National Oceanic and Atmospheric Administration (NOAA) for flood forecasting (GRCA, 2013). The goal of this analysis is to demonstrate to potential value of incorporating the spatial coverage provide by radar rainfall rate observations in a synergistic manner with traditional in-situ point source observations to provide additional insight on extreme rainfall values in a region. This includes exploring the radar’s capability of remotely monitoring a large region (250 km) to examine spatially the different spatial patterns of the extreme rainfall events over the study region of Southern Ontario. Identifying different homogeneous regions of extreme rainfall in an area is important for a number of applications, such as regional frequency analysis (RFA) approaches (i.e. Hosking and Wallis, 1997), and regional trend analysis (Shephard et al., 2014). Recent homogeneous extreme rainfall zones have been derived in Southern Ontario using the Ward (1963) clustering method with rain gauge observations using the Hosking and Wallis (1997) approach (Paixao et al.,

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2011). Also, Additional corroborative information on the extreme rainfall over this area can be been gained through analyses of the meteorological processes associated with the events (e.g. lightning climatologies (Burrows and Kochtubajda, 2010; Shephard et al., 2013), forensic analyses of weather maps, etc.). Note, this study is not intended to provide an exhaustive analysis of the attribution of the differences between in-situ point source rain gauge and remotely sensed radar observations as this has been extensively research (Chokngamwong and Chiu, 2008; Hughes, 2006; Jensen and Pedersen, 2005; Llasat et al., 2005), and is not part of the scope of this analysis. However, direct comparisons of the radar with TBRG (Tipping Bucket Rain Gauge) observations on an extreme event and annual maximum basis are presented in order to provide additional information on the radar observations given its different sampling compared with traditional point source measurements, which are commonly used in water management and infrastructure design. Both point-by-point extreme event comparisons and annual maximum comparisons are presented. The point-by-point event comparison of radar and rain gauges can unveil how well they capture the same annual maximum rainfall events. The annual maximum values from the two sources are also compared to explore the possible directly using the radar values in IDF calculations, which is important for regions with insufficient coverage by TBRG stations. Note, even with perfect radar and TBRG observations there are expected differences in extreme rainfall values event-by-event comparisons given the different sampling of the atmospheric processes (i.e. synoptic scale frontal processes and convective precipitation processes) in a particular region. For example, Jensen and Pedersen (2005) investigated the spatial irregularity of rainfall of nine high-resolution TBRG stations within a single radar pixel of 500  500 m and found that there can be up to 100% variation between neighboring rain gauges within a given radar pixel.

2. Data and methods 2.1. Study region: Southern Ontario, Canada The study area is located in the south of the Province of Ontario, Canada (Fig. 1). This region was chosen as it has good radar coverage, a network of both dense and sparse TBRGs, and is characterized by a range of meteorological processes. Therefore, this location is selected to evaluate the integrated approach of utilizing radar with other meteorological process information to

Fig. 1. Map of Canada, Ontario (left) and Southern Ontario (right).

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provide additional insight on short duration rainfall rates compared to only using traditional TBRG observations. The area of the region is approximately 130,000 km2 and its boundaries extend from the western extremes of Windsor to the Cornwall area located in the easternmost part of Ontario (Fig. 1). The elevation of the study area ranges from 75 m above mean sea level to the highest 526 m elevation in south-central Ontario Upland region. The study area is the most populous region in Canada with more than one-third of the country’s population, and the largest industrialized area in the country. 2.2. Datasets The rainfall data used in this study is comprised of weather radar and TBRG rain gauge networks. Since the TBRGs typically only operate in the warm season (April 1–September 30 in the Southern Ontario), we only analyzed data from these months for both the radar and TBRG stations. The radar observations were obtained from the U. S. National Weather Service and the in situ TBRG data used in this study were extracted from Environment Canada’s National Data Archive. 2.2.1. Radar Radar-rainfall data is collected by a network of S-band Doppler weather radars that measure reflectivity in the atmosphere from the radar stations. This Next-Generation Radar (NEXRAD) network is operated by the National Weather Service – NWS, which is an agency of the National Oceanic and Atmospheric Administration (NOAA) of the United States of America (USA). The system consists of 159 radar stations covering the entire land area of the USA and areas along the USA–Canada and USA–Mexico boundaries. The Southern Ontario study area is almost completely covered by NEXRAD radar range of 230 km and located in the cities of

Detroit, Cleveland, Buffalo and Montague (Fig. 2). The first three radar stations have collected data from 1996 and data collection in Montague started in 2001. These radars cover large areas in Southern Ontario and include regions with a few or no TBRG stations. In this analysis, the radar Digital Precipitation Array (DPA) product, which is one of four Level-III precipitation products available from the National Climatic Data Center (NCDC), was utilized. This DPA product is a moving 1-h total of rainfall accumulation, with a spatial resolution of approximately 4  4 km2 (individual pixel) and a temporal sampling of 6 min. The DPA data for Detroit (KDTX), Cleveland (KCLE), Buffalo (KBUF), and Montague (KTYX) radar stations were obtained from the NEXRAD network (http:// hurricane.ncdc.noaa.gov/pls/plhas/has.dsselect). The rainfall rate observations are estimated using the reflectivity (Z) equation (Eq. (1); for a more detailed description see OFCM, 2005). Due to the contamination of high reflectivity in rainfall estimation by hail, Fulton et al., 1998 suggested that a maximum rate of 53 dBZ be set in the radar data, which is equivalent to 4.09 in./h or 104 mm/h of rainfall.

Z ¼ aRb ;

ð1Þ

where a and b are coefficients that may vary from location to location and from one season to the next, but they are independent of rainfall rate R itself. The NEXRAD DPA precipitation data was selected for this analysis as it is: (i) calibrated with surface in situ rain gauge observations, which accounts for several sources of errors (e.g. Fulton et al., 1998 and Anagnostou and Krajewski, 1998); (ii) readily available and efficiently accessed; and (iii) the data is generated from S-band radars that suffer less attenuation of the radio signal than C-band radars. The utilization of the Canadian radar observations should be investigated in the future, especially in regions without

Fig. 2. Study area with tipping bucket rain gauge stations (gray area) and the domain of radars in Detroit Buffalo and Montague used in this study (large cycles).

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NEXRAD coverage providing that the higher attenuation of the C-band is not a barrier to monitor extreme rainfall events. 2.2.2. Rain gauges The in situ point source surface rainfall observations were obtained from 35 TBRG stations distributed throughout Southern Ontario, Canada (Fig. 2) maintained by Environment Canada’s Meteorological Service of Canada (MSC). Annual maximum series for storm durations of 1- and 24-h during the warm season were used in this analysis. Although most TBRG stations have long records of rainfall data, 1996 was selected as the start of the period for this analysis as the radar observations are only available from 1996. The ending period for the analysis is 2006 as the latest short duration rainfall observations updates presently only available up to 2006 for Ontario. 2.2.3. Sources of error to the dataset The radar and rain gauge data have very different sampling and other well documented errors (Pedersen et al., 2010; Llasat et al., 2005). The datasets are incorporated as provided with no additional adjustments made for the different sampling and measurements errors. However, it is helpful to keep in mind some of the important common sources of errors in the radar and TBRG rain gauge rainfall observations. Radar – There are several sources of measurement errors associated with radars (i.e. electronics stability, antenna accuracy, signal processing accuracy, clutter, anomalous propagation), but the most significant errors are the vertical profile of reflectivity, the drop size distribution, and attenuation by precipitations (Michelson et al., 2005). The errors in the vertical profile of reflectivity occur when a high elevation reflectivity scan is applied at long ranges where it increases the height of observation and normally causes an underestimation of the accumulated precipitation. The drop size distribution can also be a source of error as the radar reflectivity, Z, and rainfall rate, R, are estimated from it. Finally, the radar beam can be attenuated by very extreme rain and/or hail. This decrease in intensity of radar signal happens as a result of scattering and absorption by intense storm cells. Rain gauge – The measurement errors in the TBRG station(s) includes wind effects, electric signal malfunction, mechanical problems, evaporation losses, and rain intensity (Duchon and Biddle, 2010). The most relevant measurement error is caused by the wind, since it induces undercatch by gauges due to the deflection of precipitation (Duchon and Biddle, 2010). In addition to measurement errors, there can be large random errors in the reported TBRG extremes (i.e. inclusion of frozen precipitation, errors in data archiving, etc.) that need to be identified and quality controlled (Shephard et al., 2014). 2.3. Methodology 2.3.1. Event comparisons For the analysis, point-by-point event comparisons of 1- and 24-h annual maximum rainfall were performed based on (i) the TBRG identified extremes (i.e. TBRG data as the reference) and (ii) extreme rainfall events identified by the radar (i.e. radar as the reference). For the TBRG identified extreme event comparison mode each annual maximum extreme event identified by the TBRG stations was matched with the corresponding 4  4 km radar pixels. For the radar identified extreme event, the radar pixel containing the annual maximum extreme was compared with the values recorded at the corresponding TBRG station. Note, the main objective of this comparison is to investigate characteristics of the radar estimated observations compared with the rain gauge point source observations to determine what information is provided by the radar, but not as an alternative to rain gauges. Rain gauge

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data offers the most accurate measurement for point rainfall values. The radar with its scanning spatial coverage provides the best option to identify the spatial variability of rainfall over large areas. However, it will generally only provide an estimate a point source rainfall amounts at a given location due to measurement uncertainties in the remotely sensed rainfall rate observations and the fact that it is sampling an area of 4  4 km. 2.3.2. Annual maximum comparisons In addition to the event comparison shown above, it is also beneficial at each TBRG location to compare the annual maximum values for both the TBRG and the radar (note here that the AMS values identified by both measurements can occur on different dates). In this comparison one is investigating the differences in the AMS values at a location if there were either TBRG or radar observations available. This AMS-by-AMS comparison is interesting as it is the AMS values that are commonly used in infrastructure design values (i.e. IDF). 2.3.3. Spatial analysis Given the limited period-of-record of the radar data in this study, applying the Hosking and Wallis (1997) approach for identifying homogeneous region, as was done for the rain gauges over this region (Paixao et al., 2011), does not provide reliable results. Thus, to extract the wealth of spatial information on extreme rainfall events in the contiguous remotely sensed radar observations the Getis–Ord spatial autocorrelation clustering statistic is utilized to identify spatial patterns (Getis and Ord, 1992; Ord and Getis, 1995; Mitchell, 2005). This type of clustering analysis is used to indicate spatial aggregations of high or low values within the region of interest. Many different spatial autocorrelation methods have been used with remotely sensed data (Wulder and Boots, 1998). Wulder and Boots (1998) showed that the Getis–Ord statistic was an effective technique in assessing the spatial autocorrelation of remotely sensed data. Some notable applications of the technique are for: lightning (Shephard et al., 2013), snow (Derksen et al., 1998), coral reef stress (LeDrew et al., 2004), satellite calibration sites (Bannari et al., 2005), and freshwater chlorophyll concentrations (Anttila and Kairesalo, 2010). Since the radar extreme rainfall rate observations can occur from a number of atmospheric processes (from small meso-scale convection to larger synoptic systems), direct application of a general spatial autocorrelation method to the radar observations is desirable. The Getis–Ord statistic, G⁄i , for a parameter x (e.g. radar rainfall amounts) within the neighborhood distance d around pixel (location or cell) i is computed as,

Gi ðdÞ ¼

P

j wij ðdÞxj

P

j xj

;

ð2Þ

where w is the spatial weights assigned to all pixels within d of pixel i. For remote sensing applications the Getis statistic is computed for a specified moving window around the center observational pixel i (e.g. Wulder and Boots, 1998; Derksen et al., 1998). In this application an inverse distance weighing was selected for radar pixels within the neighborhood distance. Ord and Getis (1995) provide a standardized version of G*i that can be expressed in terms of a Z score as the statistic minus its expected value E(G*i ) divided by its standard deviation r(G*i )

zGi ðdÞ ¼

P P    j wij ðdÞ Gi  E Gi j wij ðdÞxj  x ¼ 2  2 31=2 ; rðGi Þ P 2  P n wij ðdÞ  wij ðdÞ j j 7 6 7 s6 ðn1Þ 5 4

ð3Þ

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where x and s2 are the global sample variance and mean computed from the entire scene,

3.1. Rain gauges and radar event comparisons

 x2 :

ð4Þ

G⁄i

Therefore, if the computed for a pixel within a local neighborhood window distance is greater (less) than the expected values for the local neighborhood region, which are based on the entire region, then the standardized test statistic zG⁄i will be positive (negative) indicating a potential cluster of high (or low) areas. Since the zG⁄i is an actual Z score (normal distribution), the value of the zG⁄i provides the statistical significance of the clustering (either high or low) above a normal random distribution. Under the null hypothesis, a zG⁄i = 0 indicates that there is no significant clustering occurring in the neighborhood distance around pixel i. For zG⁄i values outside the range ± 1.96 (at 95% confidence level), the clustering for the neighborhood distance around pixel i exceeds the null hypothesis and is not random. Contour lines equal to the 95% confidence level are used to define regions of maximum and minimum frequency of extreme rainfall events in Southern Ontario, and compared with the regional climatological zones identified by Paixao et al. (2011). For this analysis, the highest radar rainfall rate values were identified for 1 and 24-h durations, both monthly and annually, in the warm season from 1996 to 2009. Data for 1996–2009 and 1996–2006 was used to validate the homogeneous regions and to compare TBRG and radar values, respectively. The maximum radar values were identified for each pixel for the four weather radars. Furthermore, the highest maximum values were selected in the case of overlapping radars. All events with rainfall rates P50 mm/h and P100 mm/day were selected as extreme rainfall events. The rationale is that these threshold values are usually associated with 100-year return period at locations in the Southern Ontario area for the 1-h and 24-h durations, respectively. Villarini et al. (2010) indicated that extreme flood response in urban watersheds is characterized by pronounced nonlinearities in runoff generation for rainfall accumulations exceeding 50 mm. The number of extreme rainfall events in each individual radar pixel for each year was identified and used to create a map of the extreme rainfall (P50 mm/h) spatial distribution within the study area. Based on this information, the Getis–Ord Gi statistic was applied to identify possible spatial autocorrelations between extreme rainfall events in Southern Ontario. These radar derived climatological regions are then compared with homogeneous regions previously determined using the RFA analysis (Paixao et al., 2011). The information provided from TBRG and radar observations along with the corresponding meteorological processes (obtained from forensic analysis of weather maps, lightning observations, and satellite cloud observations) are integrated to refine the homogeneous climatological regions previously generated.

2.3.4. Estimated return periods In addition to the main objective of this analysis, we also provided a preliminary example of the potential of direct application of the radar rainfall rates to compute 10-year return period values for 1-h duration without accounting the measurement errors and the different sampling methods. The dataset used in this section was composed of AMS from 1996 to 2006 as the EC archive of short duration rainfall rates was limited to 2006. The Gumbel extreme value distribution was used to estimate the return periods at several TBRG locations maintained by Environment Canada to produce the national set of IDF tables and graphs (Hogg et al., 1989).

Point-by-point event comparisons of the radar and rain gauges are useful in determining generally how well the two sources capture the same annual maximum rainfall events, even if they have different sampling. The comparisons are also provide insight on the general characteristics of the different rainfall rate observations, and demonstrate how an integrated approach can provide additional information on the estimates of the rainfall rate over a given region. In this comparison method the rain gauge (TBRG) and radar event comparisons were performed two ways, first the rain gauge is the reference (defining the annual extreme event) and is matched with the co-located radar observation for the same location and time. Then for each TBRG location the radar is used as the reference to identify annual extreme events and compared with the TBRG observation for the same time period. Fig. 3 shows scatter plots of the annual maximum rainfall values of the TBRG stations plotted against the corresponding observations from the weather radar stations for the 1-h duration. The same values in Fig. 3 are also plotted in bins as a box-and-whisker plot in Fig. 4. A general result clearly seen in Fig. 4 is that the median difference value in each 10-mm bin is positive (with the only possible exception being RADAR–TBRG 10 mm, which does not have enough samples). This states that the instrument used to select the annual extreme event on average measures a larger amount of rainfall with no large overall bias toward the TBRG or radar extreme rainfall observations. Another general result is that the absolute comparison differences increase with increasing rainfall. The comparisons with the TBRG as a reference (blue bins) show that for rainfall events with <50 mm the TBRG is 4–22% larger than the radar. For this same range to identify the annual maximum events, the radar is larger than the TBRG with the fraction difference increases with magnitude from 18%, 44%, 50%, and 55% for the values <50 mm. One apparent result in Figs. 3 and 4 is that there are larger differences between the two observations for extreme rainfall observations >50 mm, with the larger values corresponding to which ever measurement was used to identify the extreme event. For the large extremes in Fig. 3 when the TBRG identifies (blue circles) and measures rainfall events >50 mm, these same events 100

n

80

2 j xj

60

P

40

and s2 ¼

2 j ðxj  xÞ ¼ n

RADAR Rainfall (mm)

P

20

x ¼

j xj ; n

0

P

3. Results

0

20

40

60

80

100

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TBRG Rainfall (mm) Fig. 3. Scatterplot graph for 1-h annual maximum rainfall events (i) recorded by TBRG stations vs respective rainfall values estimated by weather radar (blue open circle) and (ii) rainfall estimated by weather radar vs respective rainfall values observed by TBRG stations in the study area (red + sign). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Rainfall (mm)

60 40 20 0 -20

RADAR-TBRG >50 mm

TBRG-RADAR >50 mm

RADAR-TBRG 41-50 mm

TBRG-RADAR 41-50 mm

RADAR-TBRG 31-40 mm

TBRG-RADAR 31-40 mm

RADAR-TBRG 21-30 mm

TBRG-RADAR 21-30 mm

RADAR-TBRG 11-20 mm

TBRG-RADAR 11-20 mm

RADAR-TBRG 0-10 mm

TBRG-RADAR 0-10 mm

-40

100 80 60 40 20 0

1-Hour RADAR Rainfall (mm)

are generally captured by the radar (co-located red + symbols), however, the TBRG values are on average larger by 30 mm as shown in the (TBRG–RADAR > 50 mm) blue bin in Fig. 4. A comparison example where the extreme rainfall event was captured by TBRG and underestimated by radar is Toronto on August 19, 2005. A case study of this event is provided in Appendix A. The corresponding annual maximum events in Fig. 3 scatter plot identified by the radar (radar > 50 mm; red + symbol) is not generally the same annual maximum event captured by the TBRG (blue O), with the radar values on average being larger than the TBRG by 36 mm (as shown in the RADAR–TBRG > 50 mm bin in Fig. 4). Often in these situations where the radar observations show much higher rainfall values than the observed values from TBRG stations the rainfall event was not captured by the TBRG. An example of when the annual maximum event identified by the radar that was not captured by the in-situ TBRG observations occurred in Windsor on August 10, 1998 and is provided as a case study in Appendix A. The 1-h rainfall bins (TBRG as a reference) ranging from 11 to 40 mm that contains 89% of the observations show event median comparison differences of 4–21%. For the 1-h rainfall with the radar selected extreme events (Radar–TBRG) the median differences are larger with differences ranging from 18% to 50%. To further compare the 1-h rainfall characteristics of TBRG and radar, the following additional analyses have been conducted. For visual examination, the ranked annual maximum values for the TBRG and radar are plotted against each other (Fig. 5). The result shows that for values less than 50 mm, the slope is slightly greater than the 1:1 line. However, the slope significantly decreases for values greater than 50 mm. To examine their distributions, we fitted the generalized extreme value distribution (GEV) to both TBRG and radar data with L-moments. The Q–Q plots of the residuals have been generated (Figs. 6 and 7) to check validity of the fitted model. The plotted points are near-linear with slight deviations. The model quantiles (approximately up to 3rd) fit very well the empirical quantiles for both the TBRG and the radar. Above that threshold, the TBRG and radar over- and under-estimated the empirical values, respectively. The calculated average of the radar rainfall values is found to be 0.5 mm higher than that of

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Fig. 4. Box-and-whisker plots of the differences in the 1-h annual maximum rainfall values (in mm) of various ranges (i) recorded by TBRG stations vs respective rainfall values estimated by weather radar (blue) and (ii) rainfall estimated by weather radar vs respective rainfall values observed by TBRG stations in the study area (red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

0

20

40

60

80

100

120

1-Hour TBRG Rainfall (mm) Fig. 5. The ranked series of annual maximum of 1-h TBRG and radar rainfalls.

the TBRG rainfall. To assess whether this difference is statistically significant, we applied the Wilcoxon (Mann–Whitney) non-parametric test. The p value is found significant at 5% level and the alterative hypothesis is accepted. Note that although this difference is significant, it is small relative to general measurement errors (Shephard et al., 2014). The range in the differences between the two measurements are not unexpected given the potential measurement errors and sampling differences of the in-situ point source and radar remotely sensed 4  4 km2 observations of 1-h convective events that are generally on the scale of a single or few thunderstorms. However, given that the measurement source used to identify the annual extreme event provides on average the larger values, and that comparison results provide a consistent pattern of increasing absolute errors with increasing rainfall rates, these

E. Paixao et al. / Journal of Hydrology 528 (2015) 734–750

120

740

80 60 20

40

4 3 1

0

2

Exponential Quantiles

5

RADAR Rainfall (mm)

100

6

QQ Plot of Residuals

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60

80

100

120

0

TBRG Rainfall (mm)

0

1

2

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Ordered Data Fig. 6. The Q–Q plot of residuals of the fitted GEV model for 1-h TBRG rainfall values.

greater than that of 1-h rainfall events. In both modes of observation, the medians of the differences for all (except 11–20 mm for TBRG) bins are positive and increase with increasing rainfall amounts. Fig. 9 also shows that the magnitude of differences between the two modes of comparison in each bin is similar. The median differences in the TBRG identified events (blue bins) vary in the range of 2.5–27%, while for the radar identified events (red bins) vary in the range of 7–29%.

4

5

6

QQ Plot of Residuals

2

3

3.2. TBRG and radar AMS-by-AMS comparisons

0

1

Exponential Quantiles

Fig. 8. Scatterplot graph for 24-h annual maximum rainfall events (i) recorded by TBRG stations vs respective rainfall values estimated by weather radar (blue open circle) and (ii) rainfall estimated by weather radar vs respective rainfall values observed by TBRG stations in the study area (red + sign). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

0

1

2

3

4

Ordered Data Fig. 7. The Q–Q plot of residuals of the fitted GEV model for 1-h radar rainfall values.

results provide further support for utilizing both observations over a region in order to gain insight into convective events, especially for defining homogeneous regions. Fig. 8 is a scatter plot of the annual maximum rainfall values of the TBRG stations plotted against the corresponding observations from the weather radar stations for the 24-h duration. The same values in Fig. 8 are also plotted in bins as box-and-whisker plots in Fig. 9. Like the 1-h extreme rainfall results, there are large differences between the two modes of observation for high 24-h rainfall amounts. However, the values of the differences are comparable (Fig. 8). For example, the median of difference between TBRG and radar for rainfall >50 mm is 27 mm when the TBRG identifies the AMS, while when the radar identifies the event the difference is 24 mm (Fig. 9). Fig. 8 also shows that extreme rainfall events >50 mm captured by TBRG (blue circles) are also captured by the RADAR (co-located red + symbols). The number of such events is

Since the extreme event comparisons show for both modes of comparison that the instrument that is selected as the reference captures larger values, then the overall annual maximum comparison differences are less than the event comparisons. Fig. 10 displays that overall 89% of the 1-h annual maximum values occur in the 11–40 mm range, with the number of annual maximum values decreases with increasing amount of rainfall values >10 mm. This results show that the radar derived AMS values provide a reasonable estimate of the expected point source observations as determined by the TBRG with increasing median differences in the annual maximum with more extreme events. For extreme events with AMS values 650 mm, the median difference is <18%. 3.3. Spatial analyses of extreme rainfall values detected by weather radars The analyses of DPA radar data in the warm season demonstrates presence of many localities across Southern Ontario where high and low intensity rainfall values for 1- and 24-h annual maximum rainfalls persist (Fig. 11). The areas with the highest predominant intensity of 1- and 24-h annual maximum rainfall values tend to be consistently located in the southern extremes of Ontario and in the land area south of Georgian Bay and northwest/southeast of Lake Simcoe. The localities with the lowest intensity of 1- and 24-h annual maximum rainfall values in Fig. 11 are predominant around the Niagara valley and in the extreme eastern portion of Ontario. Over the 11-year period, some locations in the southernmost portion of the study area did report random occurrence of reflectivities higher than the maximum rate

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TBRG-RADAR >50 mm

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TBRG-RADAR 21-30 mm

RADAR-TBRG 11-20 mm

TBRG-RADAR 11-20 mm

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20 40 60 80 100 120 0

Number of Observations for Each Bin

Fig. 9. Box-and-whisker plots of differences in the 24-h annual maximum rainfall values (in mm) of various ranges (i) recorded by TBRG stations vs respective rainfall values estimated by weather radar (blue) and (ii) rainfall estimated by weather radar vs respective rainfall values observed by TBRG stations in the study area (red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

TBRG-RADAR >50 mm

TBRG-RADAR 41-50 mm

TBRG-RADAR 31-40 mm

TBRG-RADAR 21-30 mm

TBRG-RADAR 11-20 mm

40 20 0 -20 -40 -60 TBRG-RADAR 0-10 mm

Rainfall (in mm)

(a)

(b) Fig. 10. (a) Histogram of the number of observations for each bin and (b) Box-and-whiskers plot of the differences in 1-h annual maximum rainfall values (in mm) of various ranges recorded by TBRG stations vs respective rainfall values estimated by weather radar.

of 53 dBZ or 104 mm/h. The sparse density of TBRG stations did not provide additional insight on atmospheric processes (e.g. synoptic tracks, convective and topographic) that might be associated with these maximum values. Based on forensic analyses, most of the highest 1-h annual maximum extreme rainfall events in the study area were associated with convective thunderstorms that advanced over Lake Huron or Georgian Bay and intensified during their progression

onto the warmer land. These extreme events generally moved east-southeastward when they arrived over the land in the study region (Southwestern Ontario and the western and eastern shores of Lake Simcoe, see Fig. 2). The intensity maps of 1- and 24-h maximum rainfall values in Fig. 11 provide guidance on where the highest rainfall events have occurred; however, these maps only show the highest rainfall values in each individual pixel. An additional spatial analysis of

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Fig. 11. Largest rainfall accumulations P50 mm/1 h (a) and P100 mm/24 h (b) detected by the NEXRAD network. This figure was generated using the radar data from 1996 to 2009.

severe rainfall events was completed to include the frequency of the extremes. The combined radar information was used to identify the distribution of the maximum rainfall values throughout Southern Ontario, and to determine whether the areas of maximum intensity also contain a high frequency of extreme events. Fig. 12 shows the frequency of heavy rainfall events that are P50 mm/h and events P100 mm/24-h corresponding to the areas of the highest and lowest intensity of extreme rainfall values in Fig. 11. These additional results show that the areas with the highest frequency of extreme rainfall values are similar to Fig. 11 with the extreme rainfall events occurring in southwestern Ontario, and along the western and eastern shorelines of Lake Simcoe. Some extreme rainfall events were also observed in eastern Ontario (Fig. 11), but these few events do not cover as large of a domain as the two previously identified areas. The largest areas with low frequency of extreme rainfall events are restricted to the Niagara Valley and eastern Ontario areas.

3.3.1. Spatial clusters of extreme rainfall values The Getis–Ord statistic was applied to the 1-h maximum extreme rainfall values, from the radar data, with the objective of identifying patterns in the study area. This approach identified

areas with the highest and lowest frequency of extreme rainfall values (Fig. 12). The G statistical score (Getis and Ord, 1992; Mitchell, 2005) was used to determine whether statistically significant high or low values tended to cluster in Southern Ontario. Different autocorrelation neighborhood distances were assessed with the objective of identifying the best distance for the radar data. For this analysis, the distance band that reflects the maximum spatial autocorrelation was selected after using the Incremental Spatial Autocorrelation Global Moran’s I (Moran, 1950) statistic for a series of increasing clustering distances, where the intensity of spatial clustering determined by the Z-score was computed for each distance. This analysis indicated that the clustering distance of 8 km is the shortest significant clustering distance (Fig. 13) at a 95% confidence level. This corresponds to a moving window of 5  5 radar pixels, which is the neighborhood Euclidian distance of 8 km or 2 radar pixels (2  4 km) around the center pixel. Fig. 14 shows a spatial clustering of extreme rainfall values for 8-km peak. Note that when the next significantly peaked region starting at 16 km is selected as the distance band (results not shown) the number of homogeneous regions remains the same with the general spatial pattern being very similar with the clustering patterns of the regions being smoother. The regions of

Fig. 12. Frequency of heavy rainfall events P50 mm/h (a) and P100 mm/24 h (b) detected by the NEXRAD network. This figure was generated using the radar data from 1996 to 2009.

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2.0

2.2

2.4

2.6

to represent the extreme events that appear to be captured by the radar during this period. As an example, from the previous RFA analyses, the Trenton TBRG station in eastern Ontario could be placed in either of two regions as defined by the RFA method (i.e. was in a ‘‘transition’’ zone for climatological processes that drive extreme rainfall). The statistical algorithms from the RFA analyses were further challenged by the relatively low density of TBRG stations in this region, requiring supplementary information, or data such as this radar analyses to define the climatology of extreme rainfall event near this TBRG station.

1.6

1.8

Zscore

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6

8

10

12

14

16

18

Distance Fig. 13. The Z-score for the spatial autocorrelation distance computed from the Global Moran’s I statistic. A Z-score of 1.96 corresponds to a significance level of 5% (confidence interval of 95%).

maximum and minimum rainfall values are similar to the results obtained from a previous clustering analyses using TBRG point source observations for the same study region (Paixao et al., 2011). There are two small regions, around Waterloo and Renfrew that do not agree with the previous defined climatological zones for this region. The two possible explanations for these differences are: (i) the previous RFA analysis covered a longer period of TBRG record than the current radar analyses (e.g. Waterloo, Guelph and Fergus TBRG stations showed a low frequency of rainfall events P50 mm/h that is likely linked to the low occurrence of events P50 mm/h since 1996), and (ii) these regions also have a relatively low density of TBRG stations, which might not be suffice

3.3.2. Analyses of meteorological processes Meteorological information (i.e. weather maps) is also used to provide further insight when evaluating annual maximum rainfall values over a region. Therefore, in order to provide more information on the high and low frequency of 1-h extreme rainfall values identified by the Getis–Ord statistic, analyses of the meteorological processes driving the extreme rainfall events were incorporated to evaluate and further refining the homogeneous regions in the study area. In this analysis, 25 mm/h and 50 mm/h were chosen as thresholds to separate extreme rainfall convective events from the weaker ones in the region from 1996 to 2009. Fig. 15a shows the annual average of 1-h events with values P25 mm in Southern Ontario. This map identifies three large areas (shown in green and dark blue pixels) representing high radar detected rainfall values that are easily identifiable and distinct from the other areas. However, in Fig. 15b, when the annual average of 1-h events with values P50 mm/h are considered as the threshold level, the analyses shows the presence of a low frequency of extreme 1-h rainfall events in eastern Ontario. These results highlighted the existence of relatively lower frequency of extreme rainfall events that are produced by convective atmospheric processes in this large area.

Fig. 14. Spatial clustering of frequency of extreme rainfall values equal to or higher than 50 mm/h. The radar analyses were generated using the radar data from 1996 to 2009.

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Fig. 15. Annual average of 1-h events with values (a) equal to or higher 25 mm/h, and (b) 50 mm/h. The radar analyses were generated using the radar data from 1996 to 2009.

Fig. 16. Homogeneous regions for extreme rainfall climatology of south-central Ontario by Paixao et al. (2011) (a) and annual average of lightning occurrences (number of flashes/5 km2) (b).

Fig. 17. Homogeneous regions of extreme rainfall events defined by different data sources such as TBRG stations, radar data and climatic regions presented in Fig. 16a.

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The results also show the apparent influence of breezes of Lake Huron and Georgian Bay on extreme rainfall, as indicated by the highest radar rainfall values identified in the southern extremes of Ontario, and in the area south of Georgian Bay. The higher values and frequency of those events were often associated with storms that come off Lake Huron and Georgian Bay and intensify rapidly, and are often restricted to areas near Lake Huron, Lake Erie, and Georgian Bay. 3.3.3. Homogeneous regions of extreme rainfall The radar spatial analyses are compared with other the homogeneous extreme rainfall regions defined in Southern Ontario. Fig. 16 shows the nine climatic homogeneous regions defined through the application of the Ward clustering using rain gauge observations as part of a RFA method (Paixao et al., 2011). The climate zones derived using the Getis–Ord spatial clustering of remotely sensed radar observations (Fig. 14) and a previous study for this region using the Ward clustering of rain gauge observations (Fig. 16a) are for the most part similar. Additional comparison information was obtained using the annual average of lightning in 5 km bins from 1999 to 2008 (Burrows and Kochtubajda, 2010; Shephard et al., 2013). The lightning information map shows that the highest annual flash density values are concentrated in the extreme southern region of Ontario and the land area south of Georgian Bay. This suggests the presence of cumulonimbus clouds and thunderstorms in the area. This information was assimilated with an additional tenth region in Eastern Ontario was determined from the Montague NEXRAD radar analysis, shown in Fig. 17. This map of extreme rainfall climatological regions was produced by analyzing the similarities and dissimilarities associated with each of the different sources of information. Convective events are predominant in the extreme south region of Ontario and the land area south of Georgian Bay for the 1- and 24-h durations. These regions are briefly described in Table 1 along with selected remarks for data and information coverage for each region (Fig. 17). For example, Table 1 identifies: (i) areas that are more or less affected by intense rainfall episodes; (ii) the main meteorological systems responsible for extreme rainfall events in each area; and (iii) some differences from the extreme rainfall information provided by the maps in Fig. 16. Note that the radar and TBRG stations did not show similar characteristics in the intensity and frequency of extreme rainfall events for homogeneous region ‘‘G’’ (Fig. 17), which is likely due to the highest values in this region were reported before 1996 (obtained from long-term climate station data). In addition, region ‘‘J’’ was not identified in the earlier Ward rain gauge analyses since there were no gauge observations for this particular region. 3.4. Comparison of 1-h duration IDF values using radar and TBRG observations Even though IDF calculations are by definition for a point source location, and not 4  4 km2 region, it is still interesting to investigate the differences between extreme rainfall return period values calculated from remotely sensed radar with TBRG observations in order to further assess the potential value of integrating radar information. For example, based on these TBRG and radar comparisons, radar estimated IDF values might provide valuable information in a location where there is not a representative point source rain gauge observation. For this IDF comparison the Gumbel extreme value distribution and Method-of-Moments (MOM) was selected to calculate the return periods as this is the procedure that has been traditionally used it for point location IDF values by Environment Canada. As noted in Fig. 4, the annual maximum 1- and 24-h rainfall values observed by the TBRG and NEXRAD

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radar stations during the study period (1996–2006) do not necessarily occur on the same date. Thus, the IDF values estimated from TBRG and radar data were only calculated from the following two annual maximum rainfall series: (1) annual maximum 1-h rainfall values observed by the TBRG stations; (2) annual maximum 1-h rainfall values obtained by the radar stations. Fig. 18 compares the 10-year return period values and their confidence intervals for the 1-h rainfall duration from both TBRG stations and corresponding radar observations in Southern Ontario. Note that in this analysis, stations with data length shorter than a 10-year period was included to obtain a sufficient sample size. In Fig. 18, the blue and red +/ bars represent the confidence limits of the 10-year return period values for TBRG and radar, respectively. In general, the values estimated by radar data are Table 1 Summary information for the 10 homogeneous regions of extreme rainfall events in the Southern Ontario pilot study and eastern Ontario. Zone

Main characteristics and comments

A

– The heaviest rain falls in this area linked to ‘tropical remnants’ or storms with a tropical moisture source. A relatively lower frequency of intense rainfall events occur over shorter durations (e.g. 1 h) – The rainfall values estimated by Buffalo and Montague radar station showed similar characteristics as TBRG station. However, the extreme rainfall values associated with tropical storms tend to be underestimated by the weather radar, likely due to signal attenuation

B

– The area is characterized by the presence of a high frequency intense short duration rainfall (e.g. intense thunderstorms) – The radar and TBRG data showed similar characteristics in the intensity and frequency of extreme rainfall events

C

– The area is characterized by the presence of a low frequency of intense rainfall events – The radar and TBRG data indicated similar characteristics in the intensity and frequency of extreme rainfall events

D

– This area is influenced by ‘tropical remnants’ and a low frequency of intense rainfall events – The radar and TBRG data indicated similar characteristics in the intensity and frequency of extreme rainfall events. However, the extreme rainfall amounts associated with tropical storms tend to be underestimated by the weather radar

E

– The area is characterized by the presence of the highest frequency of intense rain events (e.g. severe thunderstorms), in the Lake Huron–Lake Erie lake breeze convergence zone – The radar and TBRG data indicated similar characteristics in the intensity and frequency of extreme rainfall events

F

– The area is characterized by the presence of high frequency of intense rain events (e.g. severe thunderstorms); partly in the lake breeze convergence zone – The radar and TBRG data indicated similar characteristics in the intensity and frequency of extreme rainfall events, with the exception of Sarnia (which may be partially due to the short period of radar information)

G

– The area is characterized by the presence of intense rain events – The radar and TBRG stations did not show similar characteristics in the intensity and frequency of extreme rainfall events, as there were several cases where the TBRG 1-h rainfall P50 mm occurred before 1996

H

– The radar precipitation estimates and daily rainfall climate station data indicate that intense rainfall events occur in this zone, but there were no Environment Canada TBRG stations to compare with the radar results

I

– The area is characterized by the presence of the lowest frequency of intense rainfall episodes – Radar analysis was not undertaken as NEXRAD does not cover this region. This region might benefit from a DPA type precipitation data generated from the Canadian radar observations

J

– Montague NEXRAD showed intense rainfall events in this region – However TBRG data was unavailable to compare the radar estimates

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Barrie

Belleville

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Woodstock

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Fig. 18. Comparison of 10-year return period values for 1-h duration of rainfall for TBRG and radar using the Gumbel distribution. The blue and red vertical bars show TBRG and radar confidence intervals. This analysis was based on a period of record from 1996 to 2006. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

similar to the TBRG values, except in cases where the radar data appears to have underestimated the extreme rainfall (e.g. Goderich, Stratford, Delhi and Toronto North York stations). The confidence intervals (C.I.) estimated from the maximum TBRG data (blue lines) and the corresponding radar data (red lines) in general display similar variability. The exceptions are Barrie, Waterloo Airport, Hamilton RBG, Toronto Pearson Airport, Toronto North York, Peterborough and Airport, which present high differences between the confidence intervals of two datasets. For these examples, the radar rainfall observations generally provide 10-year return period rainfall values that are comparable to the values obtained by the TBRG point source locations. 4. Conclusions This analysis demonstrates the potential value of incorporating radar rainfall rate observations to provide additional insight on extreme rainfall events. The pilot study region of Southern Ontario proved to be a good case study region based on the rain gauge network density characteristics, and the extent of the spatial coverage provided by the remotely sensed radar observations. The extreme rainfall event comparisons of the rain gauge with the radar observations show that the observation method used to select the annual extreme event on average measures a larger amount of rainfall with the median difference values being positive. This indicates that there is no large overall bias in detected events toward one extreme rainfall observation. Another general result is that the absolute event comparison differences between the rain gauge and radar generally increase with increasing rainfall. Thus, the largest bin with 1-h rainfall amounts >50 mm has the largest median event comparison differences reaching 43%, however, it should be noted that this bin accounts for only 2.3% of the total comparison values. Approximately 90% of the observations for 1-h rainfall falling in the 11–40 mm range where the median difference values range from 4% to 21%. The radar selected extreme events (Radar–TBRG) is larger than the differences when the TBRG rain gauges selected as the reference. The 24-h event comparisons show that there are smaller median differences

between the two comparison modes than for the 1-h comparisons, with the bin differences ranging from 2.5% to 29% depending on the reference mode and the magnitude of the rainfall. Comparing the annual maximum radar and TBRG values at a given location (not requiring that it necessarily be from the same event) in general show better agreement than the point-by-point event comparisons. These results show that the radar derived AMS values provide a reasonable estimate of the local TBRG point source observations with increasing median differences in the annual maximum being <18% for extreme events with AMS values <50 mm. This is important and important insight as the AMS values that are commonly used in infrastructure design values (i.e. IDF). This study demonstrations the value of utilizing the radar rainfall rate spatial coverage together with corroborative meteorological information identified regional extreme rainfall climatological zones. These defined climatological zones are important for applications such as: (i) determining which available TBRG station best represents a particular location for its climatic design values governing infrastructure design (e.g. better to pick a TBRG in the same climatological region than the typical practice of using the closest TBRG station), (ii) defining homogeneous or climatological zones of extreme rainfall for use in regional analysis such as regional frequency analysis for IDF, or regional trend analysis, (iii) potentially identifying regions where the TBRG network is not sufficiently dense to statistically represent the extreme events in a region. Computed IDF values derived from radar AMS observations, even without accounting for sampling and measurement errors, are similar to the IDF values estimated by the TBRG data. This indicates the additional potential value of a synergetic approach to supplement in situ TBRG measurements with remotely sensed estimates of the point source measurements, especially in regions where the station network density is sparse and cannot statistically represent the extreme rainfall events. However, further analysis that accounts for the different spatial sampling and measurement errors would have to be investigated to provide additional interpretation of these results.

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This study demonstrated that a synergistic approach utilizing radar rainfall rate observations can provide additional insight on maximum rainfall events, which would be helpful for infrastructure climate design values. There are additional benefits of utilizing the radar observations that are not included in this study that are to be evaluated further in the future. For example, the extreme values generated from the radar observations in this research were only computed during the ‘‘warm’’ season for comparison purposes with the TBRG. However, the radar’s ability to detect extreme rainfall events in general is not limited to providing extreme rainfall events in the ‘warm’’ season and can capture additional extreme rainfall when the TBRG is covered up and not operating. Another added benefit of utilizing the radar that is not explored here is that the radar can provide annual maximum values over the whole domain covered by the remote sensed observations, and not restricted to TBRG point source locations analyzed here for comparison purposes.

Acknowledgments This research was partially supported through the Natural Sciences and Engineering Research Council of Canada (NSERC). We would like to thank Don MacIver (EC), Vincent Cheng (GIS support), Dr. William Burrows (EC lightning data), and Dwight Boyd (Grand River Conservation Authority). We gratefully acknowledge the advice and partnership of several other Conservation Authorities and municipalities in the study region, as well as the Ontario Ministry of Natural Resources.

Appendix A A.1. Case studies demonstrating the strength of an integrated approach Four case studies of annual extreme rainfall events are selected from the event comparisons and used to highlight the need for and advantages of integrating information from rain gauge, radar, and an analysis of the meteorological processes driving these extreme rainfall events. Table 2 provides a summary of example comparison case studies where specific forensic analysis of event comparisons have been performed to demonstrate the value of using both TBRG and radar observations in capturing information on extreme rainfall events in the study area.

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A.2. Case study 1: example of extreme rainfall event captured by TBRG and underestimated by radar (Toronto: August 19, 2005) The extreme rainfall event which occurred within the Greater Toronto Area (GTA) during the afternoon of August 19, 2005 brought record-setting rates of rainfall. Up to 180 mm of rain fell over parts of the City of Toronto and Southern York Region over a 2–3 h period. This event triggered flooding that resulted in the highest insured loss ($500 million) in Ontario, according to the Insurance Bureau of Canada. A warm front over Southern Ontario, associated with a low pressure system crossing central Lake Huron, gave rise to a severe weather outbreak in Southern Ontario during the afternoon of August 19. Tornadoes occurred north and west of Toronto, but the tornadic signature of the storms transitioned to extreme rainfall as the severe weather approached the Greater Toronto area. With the severe thunderstorms being quasi-stationary over the City for a number of hours, new storms cells kept developing on the western flank of the initial thunderstorm cells, creating record rates of rainfall. The maximum 1-h rainfall recorded at the Toronto North York TBRG was 109 mm. Radar imagery for the August 19, 2005 storm event is provided in Fig. 19. The 1-h maximum rainfall amount at Toronto North York was estimated at 65 mm from the Buffalo NEXRAD radar, compared to the observed TBRG 1-h rainfall extreme of 109 mm at the peak of the storm. Given the number of intense thunderstorm cells between the Buffalo radar and the North York TBRG station, it is likely that the radar signal attenuated, resulting in significant underestimations of the extreme rainfall amounts over the City. Radar information from the nearby Canadian radar site, King City Radar to the north of the maximum rainfall locations showed more severe underestimation of the maximum Toronto rainfalls due to attenuation and radome wetting of the radar dome (i.e. heavy rain falling directly on a radar site reduces the signal strength returned to the radar). For example, the Greater Toronto Area (GTA) daily total storm rainfall estimated from the Buffalo radar was approximately 100–125 mm whereas the nearby Canadian King City radar indicated much lower values of 50–75 mm. A.3. Case study 2: example of extreme rainfall event captured by radar that was missed by the TBRG network (Windsor: August 10, 1998)

Case

Example

Summary

1

Toronto Aug/19/2005

The extreme rainfall episode was captured by TBRG and underestimated by radar. This difference was likely caused by radar signal attenuation as a high number of intense thunderstorms occurred between the Buffalo radar station and the TBRG station in North York

2

Windsor Aug/10/1998

Radar observation reported an extreme rainfall event that was not captured by the TBRG station located at Windsor Airport as the thunderstorms were restricted to a particular area north of the station

Extreme rainfall was recorded in Southwestern Ontario on August 10, 1998 as a result of severe thunderstorms associated with a cold front approaching from Michigan. In this example, the NEXRAD network captured the heavy rainfall events in areas without, or with a low density of sub-daily rainfall/TBRG stations. While the Detroit radar station estimated the 1-h annual maximum rainfall value of 84 mm over Windsor on 10 August 1998, the 1-h (and 24-h) maximum rainfall observed at the Windsor Airport station was only 3.6 mm for the same day (Fig. 20). In this case, the extreme convective rainfall event and its driving atmospheric process was very localized to an area north of the Windsor airport and the intense rainfall was missed by the only TBRG station in the vicinity. The Windsor Riverside climate station north of the airport, and closer to the intense rainfall events, reported a daily rainfall amount of 53 mm. However, this station does not have a TBRG, and thus, there is no hourly measurement or record of how the rainfall was distributed during the day.

3

Trenton Jul/31/2000

Extreme rainfall event was recorded by both radar and the TBRG station with comparable rainfall amounts for the maximum 1- and 24-h durations

A.4. Case study 3: example of extreme rainfall event captured by radar and TBRG (Trenton: July 31, 2000)

4

Punkeydoodles Corners Jul/14/1997

The extreme convective event was captured very well by one of the radars. This example also shows the benefit of multiple radar observations in reducing the impact of attenuation

Table 2 Summary of case study examples provided in Appendix A.

On the morning of July 31, 2000 a low pressure system was centered over Southern Lake Michigan. It would remain quasi-stationary through to the early morning of August 1.

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Fig. 19. NEXRAD mosaic image with 1-h maximum rainfall value on 19 August 2005 in the GTA.

Fig. 20. NEXRAD mosaic image with 1-h maximum rainfall value on 10 August 1998 in the Windsor Area.

During this period a warm, humid, and unsettled air mass remained entrenched over Southern and Central Ontario. Severe thunderstorms developed during the afternoon with heavy downpours recorded in several areas. The Muskoka district was inundated with 150 mm of rain falling in only 5–6 h, with the Bracebridge fire weather station (operated at the Muskoka Airport) reporting storm rainfall amounts of 274 mm over the same short period (Klaassen et al., 2003). At the Trenton airport to the southeast recorded 69.8 mm rain during the day, with the majority of this rain, 59.6 mm recorded in early morning thunderstorms. The maximum 1- and 24-h rainfall estimates were 58 mm and 71 mm, respectively from the Buffalo NEXRAD radar (Fig. 21). With no additional thunderstorm cells between the radar and the

Trenton station, the radar signal would not be significantly attenuated. A.5. Case study 4: example of extreme rainfall event captured by radar, corroborated by storm damage but no available TBRG observations (Punkeydoodles Corners: July 14, 1997) The Southern Ontario extreme rainfall event of July 14, 1997 is a good illustration of intense convection triggered by converging lake-breeze fronts (King, 1996; King et al., 2003). The convection developed to the north and east of the Exeter radar site (southwestern Ontario) and was associated with a quasi-stationary cluster of thunderstorms that developed at the merger of the Lake Huron

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Fig. 21. NEXRAD mosaic image with 1-h maximum rainfall value on 31 July 2000 in the Trenton Area.

and Lake Erie breeze convergence zones. An estimated 200 mm of rain fell over a 4–5 h period in a small area (10 km  10 km) around Punkeydoodles Corners, southwest of Kitchener, Ontario. The radar imagery for this event is shown in Fig. 22. Rainfall from this intense convective event was captured by three individual radars; The Detroit, Buffalo and the Canadian Exeter radars. The highest rainfall value, 200 mm for the storm duration, was captured by the Detroit radar and there were no significant thunderstorms between the radar station and Punkeydoodles Corners-location of the maximum rainfall. The radar signal from the Detroit station was not significantly attenuated and therefore, it provided consistent/representative rainfall estimates for the storm event. As there were no TBRGs to directly measure the most extreme rainfall amounts, storm damage from

the event, such as washed out highways, was used to corroborate the rainfall amounts estimated from the radar. Although the Buffalo radar is actually closer to Punkeydoodles Corners than the Cleveland and Detroit radars, estimates from the Buffalo radar were lower (150 mm for the storm duration) for the extreme rainfall events. In this case, radar attenuation due to a number of large storm cells can potentially result in underestimation of storm precipitation amounts. This underscores the importance of an integrated approach to deriving annual maximum values, particularly in the regions where there is a sparse spatial density of the TBRG networks. In the absence of TBRG observations, precipitation estimates from several radars must be carefully evaluated to determine the best estimate of the rain that fell during the extreme event and corroborated with documented storm damage.

Fig. 22. NEXRAD mosaic image with 1-h maximum rainfall value on 14 July 1997 in the Punkeydoodles Corners area.

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