Characterizing the Spatial Scales of Extreme Daily Precipitation in the United States

被引:53
作者
Touma, Danielle [1 ]
Michalak, Anna M. [2 ]
Swain, Daniel L. [3 ,4 ]
Diffenbaugh, Noah S. [1 ,5 ]
机构
[1] Stanford Univ, Dept Earth Syst Sci, Stanford, CA 94305 USA
[2] Carnegie Inst Sci, Dept Global Ecol, Stanford, CA USA
[3] Univ Calif Los Angeles, Inst Environm & Sustainabil, Los Angeles, CA USA
[4] Nature Conservancy, 1815 N Lynn St, Arlington, VA USA
[5] Stanford Univ, Woods Inst Environm, Stanford, CA 94305 USA
关键词
North America; Extreme events; Precipitation; HIGH-RESOLUTION; HOURLY PRECIPITATION; CLIMATE; VARIABILITY; UNCERTAINTY; PATTERNS; EVENTS; EXTENT; RADAR; WELL;
D O I
10.1175/JCLI-D-18-0019.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The spatial extent of an extreme precipitation event can be important for a basin's hydrologic response and subsequent flood risk, and may yield insights into underlying atmospheric processes. Using a relaxed moving-neighborhood approach, we develop indicator semivariograms based on precipitation records from the Global Historical Climatology Network-Daily (GHCN-D) station network to directly quantify the climatological length scales of extreme daily precipitation over the United States during 1965-2014. We find that the length scales of extreme (90th percentile) daily precipitation events vary both regionally and seasonally. Over the eastern half of the United States, daily extreme precipitation length scales reach 400 km during the winter months, but are approximately half as large during the summer months. The Northwest region, on the other hand, exhibits little seasonal variation, with extreme precipitation length scales of approximately 150 km throughout the year. By leveraging in situ station measurements, our study avoids some of the uncertainties associated with satellite or interpolated precipitation data, and provides the longest climatological assessment of length scales of extreme daily precipitation over the United States to date. Although the length scales that we calculate can be sensitive to station density, neighborhood size, and neighborhood relaxation, we find that the interregional and interseasonal differences in length scales are relatively robust. Our method could be extended to quantify changes in the spatial extent of extreme daily precipitation in the recent past, and to investigate the underlying causes of any changes that are detected.
引用
收藏
页码:8023 / 8037
页数:15
相关论文
共 72 条
[1]   Climatology of Daily Precipitation and Extreme Precipitation Events in the Northeast United States [J].
Agel, Laurie ;
Barlow, Mathew ;
Qian, Jian-Hua ;
Colby, Frank ;
Douglas, Ellen ;
Eichler, Timothy .
JOURNAL OF HYDROMETEOROLOGY, 2015, 16 (06) :2537-2557
[2]   Evaluation of satellite-retrieved extreme precipitation rates across the central United States [J].
AghaKouchak, A. ;
Behrangi, A. ;
Sorooshian, S. ;
Hsu, K. ;
Amitai, E. .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2011, 116
[3]   A global evaluation of the regional spatial variability of column integrated CO2 distributions [J].
Alkhaled, Alanood A. ;
Michalak, Anna M. ;
Kawa, S. Randolph ;
Olsen, Seth C. ;
Wang, Jih-Wang .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2008, 113 (D20)
[4]  
[Anonymous], 1997, Technometrics
[5]  
[Anonymous], 2017, U.S. Billion -Dollar Weather & Climate Disasters 1980-2016
[6]  
[Anonymous], 2012, Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups I and II of the Intergovernmental Panel on Climate Change
[7]  
[Anonymous], ENCY CLIMATE WEATHER
[8]   Evaluation of downscaled, gridded climate data for the - conterminous United States [J].
Behnke, R. ;
Vavrus, S. ;
Allstadt, A. ;
Albright, T. ;
Thogmartin, W. E. ;
Radeloff, V. C. .
ECOLOGICAL APPLICATIONS, 2016, 26 (05) :1338-1351
[9]   CPLFD-GDPT5: High-resolution gridded daily precipitation and temperature data set for two largest Polish river basins [J].
Berezowski, Tomasz ;
Szczesniak, Mateusz ;
Kardel, Ignacy ;
Michalowski, Robert ;
Okruszko, Tomasz ;
Mezghani, Abdelkader ;
Piniewski, Mikolaj .
EARTH SYSTEM SCIENCE DATA, 2016, 8 (01) :127-139
[10]   Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios [J].
Berndt, Christian ;
Rabiei, Ehsan ;
Haberlandt, Uwe .
JOURNAL OF HYDROLOGY, 2014, 508 :88-101