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  • IFloodS 2013: A Field Campaign to Support the NASA-JAXA Global Precipitation Measurement Mission x
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Huan Wu, Robert F. Adler, Yudong Tian, Guojun Gu, and George J. Huffman

-routing model to form the Dominant River Tracing-Routing Integrated with VIC Environment (DRIVE) model system. The DRIVE model serves as the core of the GFMS ( ) driven by the real-time TMPA satellite-based precipitation, routinely providing global flood information every 3 h at ⅛° (or ~12 km) resolution. The GFMS has been available to a wide range of users and has been providing essential inputs in catastrophe response activities by various humanitarian relief agencies such as the

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Munir A. Nayak, Gabriele Villarini, and A. Allen Bradley

; Hou et al. 2014 ) core satellite. Our study focuses on atmospheric rivers (ARs) and rainfall during the IFloodS period. ARs are narrow (less than 400 km in width) and long (1000+ km in length) regions in the lower levels of the troposphere that transport large amounts of water vapor from the tropics and extratropics ( Newell et al. 1992 ; Newell and Zhu 1994 ). In extratropical cyclones, ARs generally form in the warm sector in the presence of low-level jets (LLJs) ahead of cold fronts. Because

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Evan J. Coopersmith, Michael H. Cosh, Walt A. Petersen, John Prueger, and James J. Niemeier

soil. For the study region, soil textures fall between 23% and 28% clay, with most stations presenting a value of 27%. On the left, an image of a representative soil moisture station is included. This paper focuses its analysis on a Long-Term Agroecosystem Research site in the South Fork Iowa River in central Iowa. The ARS monitors this test watershed with 15 in situ soil moisture and precipitation stations, each of which provides hourly estimates of soil moisture profiles and precipitation from

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Felipe Quintero, Witold F. Krajewski, Ricardo Mantilla, Scott Small, and Bong-Chul Seo

Anagnostou 2012 ). The hydrological models that partition rainfall into runoff components and route runoff to predict streamflow fluctuations represent the second key component of global flood forecasting systems. These models are constructed to obey basin boundaries, which are defined by the selection of points of interest (e.g., major cities) along the river network. In a global system, all of the millions of possible basins should be represented, which poses a computational challenge. Current global

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Andrea Thorstensen, Phu Nguyen, Kuolin Hsu, and Soroosh Sorooshian

–monitoring networks have been providing local soil moisture measurements for years. As part of the Oklahoma Mesonet ( Brock et al. 1995 ), soil moisture–monitoring instruments have been deployed since 1996 ( Scott et al. 2013 ). The NOAA Hydrometeorology Testbed program has developed soil moisture observation networks in the Russian River and North Fork American River basins in California as well as the San Pedro River basin in Arizona ( Zamora et al. 2011 ). Since the 1990s, the U.S. Department of Agriculture

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Bong-Chul Seo, Witold F. Krajewski, Felipe Quintero, Mohamed ElSaadani, Radoslaw Goska, Luciana K. Cunha, Brenda Dolan, David B. Wolff, James A. Smith, Steven A. Rutledge, and Walter A. Petersen

RR estimates in this study because the comparison between individual (e.g., NPOL) and composite products would not be fair, and individual radar products are often affected by significant range effects (e.g., Fabry et al. 1992 ) that are less impactful for composite products. A detailed evaluation and the performance of NPOL estimates are documented in Chen et al. (2017) . We also drive the IFC hillslope-link model (HLM) using the reference product over the Turkey River basin in Iowa and assess

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Phu Nguyen, Andrea Thorstensen, Soroosh Sorooshian, Kuolin Hsu, and Amir AghaKouchak

systems use hydrologic models at kilometer resolutions with or without one-dimensional routing schemes forced with satellite precipitation data to provide basic warnings on where there is the potential for floods to occur. None of them can show the details of the floods, for example, spatiotemporal distribution of water depth and flow velocity at river scale, which are crucially important in flood analysis and warnings. Together with the rapid evolution in remote sensing technologies, more data with

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Luciana K. Cunha, James A. Smith, Witold F. Krajewski, Mary Lynn Baeck, and Bong-Chul Seo

the late spring and early summer of 2013, present rain gauge versus radar data comparisons, and demonstrate how rainfall uncertainties propagate through the river network. In the last section, we present our conclusions and a discussion on the main technical challenges we foresee in DP QPE that should be resolved in the coming years. 2. Study area, data, and methods We focus on the IFloodS spatial domain shown in Fig. 1 and the period from 1 April to 30 July. Iowa was chosen as a GPM ( Hou et al

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Di Wu, Christa Peters-Lidard, Wei-Kuo Tao, and Walter Petersen

for the IFloodS campaign were the Cedar and Iowa Rivers basin, the Turkey River basin, and South Fork Iowa River ( Fig. 1 ). The Iowa–Cedar Rivers basin is 12 620 mi 2 with a population of about 1 million ( Iowa–Cedar Watershed Interagency Coordination Team 2015 ). The Turkey River drains 1545 mi 2 . According to the Iowa Flood Center, the annual maximum discharges often occur in March or April. The maximum is likely caused by snowmelt or heavy spring rainfall when soils are often near saturation

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Bong-Chul Seo, Brenda Dolan, Witold F. Krajewski, Steven A. Rutledge, and Walter Petersen

volume data (see, e.g., Kelleher et al. 2007 ) from NEXRAD that cover the IFloodS study area shown in Fig. 1 . The key differences in the structure and specifications between the IFC-SP and CSU-DP processing algorithms are presented and discussed to account for the observed similarities and discrepancies. We use high-quality, dense rain gauge networks that cover the Turkey River basin and the vicinity of the Iowa City area (see Fig. 1 ) as ground reference to assess the capability of the two RR

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