Investigation of Extreme Rainfall Patterns around Antalya and Muğla Cities in Türkiye by Using C Band Doppler Radar Data

<p>As is known, rainfall varies spatially and temporally with regard to intensity and frequency. Floods, related to extreme rainfall cases, cause stress on geophysical system and community if climate change is considered. For this reason determining of extreme rainfall patterns is very important. While obtaining three dimensional status of hydrometors in atmosphere is not possible only by using ground station networks, it is possible by using weather radars. Therefore, weather radars provide significant contribution to studies about getting cloud and rainfall patterns. The aim of this study is to investigate spatial patterns of extreme rainfall events in Antalya and Muğla cities which are located on the Mediterranean coast of Türkiye. Firstly, hourly rainfall (RN1) and rain rate (SRI) products of 2 C band doppler radars and raingauge data between 2015 and 2020 will be processed by a software named MeteoRadar which is developed by İstanbul Technical University. It is capable of reading, decoding, parallel processing and visualization. Secondly, extreme rainfall patterns will be obtained over 2 study areas. Finally, after validation by using raingauge data, results will be discussed in detail.</p><p><strong>Key Words</strong>: Antalya, Extreme rainfall, MeteoRadar, Muğla, RN1, SRI, Weather radar.</p>

A spatial bootstrap technique for parameter estimation of rainfall annual maxima distribution

Estimation of extreme event distributions and depth-duration-frequency (DDF) curves is achieved at any target site by repeated sampling among all available raingauge data in the surrounding area. The estimate is computed over a gridded domain in Northern Italy, using precipitation time series from 1929 to 2011, including data from historical analog stations and from the present-day automatic observational network. The presented local regionalisation naturally overcomes traditional station-point methods, with their demand of long historical series and their sensitivity to very rare events occurring at very few stations, possibly causing unrealistic spatial gradients in DDF relations. At the same time, the presented approach allows for spatial dependence, necessary in a geographical domain such as Lombardy, complex for both its topography and its climatology. The bootstrap technique enables evaluating uncertainty maps for all estimated parameters and for rainfall depths at assigned return periods.

A spatial bootstrap technique for parameter estimation of rainfall annual maxima distribution

Estimation of extreme event distributions and depth-duration-frequency (DDF) curves is achieved at any target site by repeated sampling among all available raingauge data in the surrounding area. The estimate is computed over a gridded domain in Northern Italy, using precipitation time series from 1929 to 2011, including data from historical analog stations and from the present-day automatic observational network. The presented local regionalisation naturally overcomes traditional station-point methods, with their demand of long historical series and their sensitivity to very rare events occurring at very few stations, possibly causing unrealistic spatial gradients in DDF relations. At the same time, the presented approach allows for spatial dependence, necessary in a geographical domain such as Lombardy, complex for both its topography and its climatology. The bootstrap technique enables evaluating uncertainty maps for all estimated parameters and for rainfall depths at assigned return periods.

Radar–raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology

The applicability of the operational radar and raingauge networks for urban hydrology is insufficient. Radar rainfall estimates provide a good description of the spatiotemporal variability of rainfall; however, their accuracy is in general insufficient. It is therefore necessary to adjust radar measurements using raingauge data, which provide accurate point rainfall information. Several gauge-based radar rainfall adjustment techniques have been developed and mainly applied at coarser spatial and temporal scales; however, their suitability for small-scale urban hydrology is seldom explored. In this paper a review of gauge-based adjustment techniques is first provided. After that, two techniques, respectively based upon the ideas of mean bias reduction and error variance minimisation, were selected and tested using as case study an urban catchment (∼8.65 km2) in North-East London. The radar rainfall estimates of four historical events (2010–2012) were adjusted using in situ raingauge estimates and the adjusted rainfall fields were applied to the hydraulic model of the study area. The results show that both techniques can effectively reduce mean bias; however, the technique based upon error variance minimisation can in general better reproduce the spatial and temporal variability of rainfall, which proved to have a significant impact on the subsequent hydraulic outputs. This suggests that error variance minimisation based methods may be more appropriate for urban-scale hydrological applications.