Scott Curtis, PhD
Associate Professor, East Carolina University
Assistant Director, Center for Natural Hazards Research
Center for Natural Hazards Research
15 October
Virtual Seminar
CDRE, Millersville University
 Established in 2004, with the impetus being the
1999 Hurricane Floyd flood
 CNHR’s mission is to promote research and
analysis that ultimately reduces the harm caused
by forces of nature to life, communities, and the
environment
 Dr. Jamie Kruse (director, temporarily at NOAA),
Dr. Craig Landry (interim director), Dr. Scott
Curtis (assistant director)
 Hurricane Floyd Symposium
 www.ecu.edu/hazards
Center for Natural Hazards Research
 Floods and landslides represent the most
extreme hazards, especially in terms of loss of
life
 Overpopulation and socio-economic
pressures have forced some of the most
vulnerable populations into areas of high risk
(flood plains and hillsides)
 Further, land degradation and climate change
have exacerbated the problem
Center for Natural Hazards Research
Pakistan, July-August, 2010
China, August, 2010
Center for Natural Hazards Research
Percentage within
IRBs
Number of Countries
 Lack of in situ data
90-100
39
80-90
11
 Transboundary
70-80
14
complications
 Modeling complexities
60-70
11
50-60
17
40-50
10
30-40
10
20-30
13
10-20
9
0-10
11
(Wolf et al. 1999; Hossain et al. 2007)
Center for Natural Hazards Research
Hossain et al. (2007)
Center for Natural Hazards Research
 The majority of floods and landslides are
ultimately caused by extreme rainfall
 The Tropical Rainfall Measuring Mission was
launched in 1997 and is still flying today
 Through passive and active sensors onboard as
well as complementary satellites, we have
learned more about tropical rainfall than ever
before
 TRMM Multi-Satellite Precipitation Analysis
(TMPA)
Center for Natural Hazards Research
To obtain and study multi-year science data sets of tropical
and subtropical rainfall measurements.
To understand how interactions between the ocean, air, and
land masses produce changes in global rainfall and climate.
To improve modeling of tropical rainfall processes and their
influence on global circulation in order to predict rainfall
and its variability at various space and time scales.
To test, evaluate, and improve satellite rainfall
measurement techniques.
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ENSO Variability
(Curtis et al. 2007)
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(Curtis, Crawford, Lecce 2007)
Center for Natural Hazards Research
Basin
Volumetric rainfall x109 m3
(bias % w/ gauge)
Discharge x109 m3
TRMM
RADAR
GAUGE
USGS
Tar - Greenville
2.12 (+6%)
1.91 (-5%)
2.00
0.92
Neuse - Ft. Barnwell
2.88 (+10%)
2.57 (-2%)
2.61
0.92
Cape Fear - Kelley
2.83 (+49%)
1.73 (-9%)
1.90
0.47
 Curve numbers are based on empirical rainfall-runoff
relationships
 Q = (P - 0.2S)2 / (P + 0.8S) and CN = 1000 / (S + 10);
where Q = discharge, P = precipitation, S = maximum
retention, and assuming initial abstraction is 20% of S.
 CN 55, which fits the gauge and radar data reasonably
well represents woodlands in good hydrologic condition.
TMPA curve numbers are lower. Thus, if we had global
CN numbers, they could be adjusted to account for the
difference
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radar
TAR
gauge
TRMM
NEUSE
CAPE FEAR
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 Hong et al. (2007a) extended this work by deriving global CN
numbers from remotely sensed data sets of soil and land use.
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Center for Natural Hazards Research
Hong et al. (2007b)
Center for Natural Hazards Research
4% of the globe is in the “High” susceptibility
category, concentrated in the tropics and
subtropics
Hong et al. (2007b)
Hong , Adler, Huffman (2007)
Center for Natural Hazards Research
 “In the future, the increasing availability of
improved yet low-cost remote sensing products
that can support geographic information
system (GIS)–based landslide models will likely
be useful for disaster prevention for landslideprone regions”

Hong, Adler, and Huffman (2007)
Center for Natural Hazards Research
Hong et al. (2007b)
http://trmm.gsfc.nasa.gov/publications_dir/potential_flood_hydro.html
Center for Natural Hazards Research
 To improve ongoing efforts to predict climate by
providing near-global measurement of
precipitation, its distribution, and physical
processes
 To improve the accuracy of weather and
precipitation forecasts through more accurate
measurement of rain rates and latent heating
 To provide more frequent and complete
sampling of Earth’s precipitation
 Scheduled to launch 2013
 Curtis, S., T.W. Crawford, and S.A. Lecce. 2007. A comparison of TRMM to other

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basin-scale rainfall during the 1999 Hurricane Floyd flood. Natural Hazards,
43:187-198.
Curtis, S., A. Salahuddin, R.F. Adler, G.J. Huffman, G. Gu, and Y. Hong. 2007.
Precipitation extremes estimated by GPCP and TRMM: ENSO relationships.
Journal of Hydrometeorology, 8:678-689.
Hong, Y., R.F. Adler, F. Hossain, S. Curtis, and G.J. Huffman. 2007a. Estimate
gridded and time-variant runoff curve numbers using satellite remote sensing
and geospatial data. Water Resources Research, 43:W08502.
Hong, Y., R.F. Adler, A. Negri, G.J. Huffman. 2007b. Flood and landslide
applications of near real-time satellite rainfall products. Natural Hazards,
43:285-294.
Hong, Y., R.F. Adler, and G.J. Huffman. 2007. Satellite remote sensing for global
landslide monitoring. EOS, 88:357-358.
Hossain, F., N. Katiyar, Y. Hong, and A. Wolf. 2007. The emerging role of satellite
rainfall data in improving the hydro-political situation of flood monitoring in the
under-developed regions of the world. Natural Hazards, 43:199-210.
Wolf, A., J. Nathrius, J. Danielson, B. Ward, and J. Pender. 1999. International
river basins of the world. International Journal of Water Resource Development,
15:387-427.
Center for Natural Hazards Research