Analyzing Spatiotemporal Patterns of Extreme Precipitation Events in Southeastern Anatolia
- 1ITU, Application and Research Center for Satellite Communications and Remote Sensing,TR 34469 Maslak, Istanbul, Turkey
- 2UC Santa Barbara, Geography Department, Santa Barbara, CA 93106, USA
- 3ITU, Faculty of Civil Engineering, Department of Geomatics Engineering, 34469 Maslak, Istanbul, Turkey
Keywords: TRMM, Extreme events, GEV, Shape parameter, Precipitation, South-eastern Anatolia
Abstract. Extreme environmental events, such as floods, droughts, rainstorms, and strong winds have severe consequences for human society. Changes in extreme weather and climate events have significant impacts and are among the most serious challenges to society in coping with a changing climate. The cost of damage caused by extreme climate events is rising all over the world. The European Environment Agency (EEA) report ("Climate Change, Impacts and Vulnerabilities in Europe 2012") stated that the cost of damage had increased from € 9 billion in the 1980s to € 13 billions in the 2000s. In the United States, the National Oceanic and Atmospheric Administration (NOAA) reported that $ 188 billion in damage was caused by the severe weather events in 2011 and 2012. Understanding and identifying hydrometeorologic extreme events and their changes through time are key in sustaining agriculture and socio-economic development. Planning for weather-related emergencies, agricultural and reservoir management and insurance risk calculations, all rely on knowledge of the frequency of these extreme events. The assessment of extreme precipitation is an important problem in hydrologic risk analysis and design. Erosion and removal of the fertile soil layer through hydroclimatic extreme events is also a serious problem in semi-arid to arid regions, especially in mediterranean climates. Accurate measurements of precipitation on a variety of space and time scales are important to climate scientists and decision makers, including hydrologists, agriculturalists and emergency managers. The historical record of precipitation observations is limited mostly to land areas where rain gauges can be deployed, and measurements from those instruments are sparse over large and meteorologically important regions of the Turkey, such as over the Southeastern Anatolia Region. While rain gauge measurements are often used to tune hydrologic models, they are limited by their spatial coverage. Remote sensing techniques using spaceborne sensors provide an excellent complement to continuous monitoring of rain events both spatially and temporally. In this study we compare ground-station data with Tropical Rainfall Measurement Mission (TRMM) products at the 3-hour time scale to evaluate satellite rain estimates for agricultural and hydrological applications in Turkey. The remote sensing dataset TRMM product 3B42 has been validated with daily rain gauge measurements in order to characterize rainfall variability in the Southeastern Anatolia region. The precipitation retrievals from the TRMM satellite were compared with data from 7 surface rain gauges within the period of 1998–2012. Spatiotemporal patterns through statistical analyses were identified by fitting Generalized Extreme Value (GEV) rainfall distribution to the rainfall time series, and the fitting results were analyzed focusing on the behaviour of the shape parameter. Spatial patterns and correlations of rainfall events across the study area were also analysed by the calculation of the 90th, 95th and 99th percentiles. Regional frequency relationship were developed using the chosen GEV distribution. The recurrence intervals for different years have been estimated using the GEV distribution and their spatial variability has been described. The recurrence intervals of large rainstorms have also been identified for the rain gauge stations with the related TRMM pixel time series and spatial patterns in the study area have been evaluated. Preliminary results indicate that there exist large discrepancies between rain gauge and TRMM data at mean rainfall values; however, least squares fits indicate reliable and quite linear correlation for the 90th, 95th, 99th percentiles (r2 = 0.70, 0.77, and 0.75 respectively) and the annual maximum daily amount of precipitation (r2 = 0.69). In other words, TRMM product 3B42 can be used to assess first-order rainfall statistics and recurrence intervals, but rainfall magnitudes vary significantly from ground measurements.