# Hydrological Analysis of Extreme Rain Events in a Medium-Sized Basin
## 1 Introduction  
The city of Toronto is exposed to severe weather systems, producing occasional flash floods mainly due to various thunderstorms over a district, or slow-moving storms crossing a domain [1]. In a recent study, the spatial and temporal analysis of long-term daily rain data, examining various climate oscillations, revealed that extreme rain events over Canada have become even harsher during the period 1950-2012 [2]. The daily data of five large-scale patterns during the period 1958-2013 indicated that summer and fall precipitation was remarkably increased in the Eastern parts of Canada on average by 36.3 mm and 73.3 mm, correspondingly [3]. The survey of various climate indices calculated by historical spatiotemporal daily precipitation observations across Southern Ontario during a 64-year period demonstrated that extreme rainfall became more frequent [4]. Researchers in anotherstudy, using the data of 133 stations all over Ontario, being recorded on average within 26 years, applying the regional time trend methodology for several durations of yearly peak precipitation intensities, concluded that the extreme rainfall intensity raised from 1.25% per decade for a 30 min storm to 1.82% per decade for a 24 h storm [5]. In addition, throughout Ontario, with the use of fifty or more years of raw precipitation data, it was found that short duration events with sub-hourly or less than an hour rainfall intensity displayed greater fluctuation in yearly and monthly peak precipitation values than those of longer duration [6]. [PERSON] et al. [7] suggested the simultaneous use of remotely sensed radar observations and tipping bucket rain gauges recording, to better identify areas with homogeneous severe precipitation. The effectiveness of radar quantitative precipitation estimates (QPEs) for the assessment of reliable rainfall intensity to be used to hydrological software was also punctuated by [PERSON] et al. [8]. Many researchers have investigated the hydrological modeling effects of a changing climate for different watersheds across Canada, based on statistical downscaled methods applied to global climate models (GCMs) daily datasets [9] and various emission scenarios [10], as well as bias-corrected data resulting from pairs of regional climate models (RCMs) and GCMs [11]. Bias-corrected daily precipitation data from the future projections of several GCMs and the worse emissions scenario A2 of AR4 identified that days of heavy and very heavy precipitation, as well as very wet and extremely wet days, will probably raise notably on an annual basis at great areas of Ontario [12]. Some studies have focused on combining various hydrological models with statistical post-processing techniques [13] quantifying uncertainty [14], either with ensemble Kalman filter data assimilation [15] or with operational numerical weather predictions [16], to produce long-term or short-range flood forecasting across Canadian watersheds. Other researchers have already used hydrological modeling and focused on analyzing the changes of the areas which are responsible for eventual fluxes across the studied basins of Ontario temporally [17], and at the same time on a spatial basis [18]. Some other studies have followed empirical methods to determine the event hydrologic discharge. The analysis of 18 rainfall events in a small agricultural basin in southern Ontario displayed that the low primary moisture content of the soil in the summer and fall season contributed to infiltration excess, while the high soil moisture in the spring period generated saturation excess, with additional factors affecting the fluctuation of seasonal runoff; components such as total rain height and rain intensity were critical in summer and fall, whereas, in fall, the determinant factors were the total rain height and the rain duration [19]. Urban lands tend to grow in Southern Ontario, affecting their hydrologic behavior to severe precipitation events.