NASA - National Aeronautics and Space Administration

On February 16, 2006, monsoon rains and flooding in the Philippines led to mudslides responsible for hundreds of lives lost, hundreds more missing, thousands of displaced persons, and the worldwide appeal of relief officials for water, food and medical assistance. Many similar such natural disasters occur across the globe each year, responsible for over 300,000 casualties between 1985 and 2005 alone.

Landslides and floods wreak havoc, and are especially felt in parts of the world without extensive flood and rainfall monitoring ground networks. Using a variety of advanced new observations from space, scientists are beginning to build early warning systems with potentially global reach.

Researchers from NASA, the U.S. Geological Survey, and Dartmouth University are scratching the surface of cutting-edge efforts to turn satellite observations of rainfall, rivers, and surface topography into warning systems that can save lives, particularly from the hazards of impending floods and landslides. The scientists are also facing the challenge of integrating these sophisticated tools into operational natural hazard networks around the world.

Specifically, scientists are now employing a new strategy to detect floods using satellite microwave sensors to gauge water discharge from rivers by measuring almost daily changes in river widths. NASA has also led research that puts forward a groundbreaking and cost-effective method of mapping floods around the globe, with computational efficiency, using a combination of satellite-based data.

With heavy rainfall as a primary cause of flooding around the world, scientists also will discuss advances in flood monitoring using satellite-based precipitation estimation as perhaps the best source in many countries of rainfall data to improve warning systems. Researchers are also now beginning to use satellite observations to map and detect rainfall conditions that may trigger landslides and the flow of debris from natural disasters that are responsible for thousands of deaths and billions of dollars in destroyed property each year.

Abstracts

Satellite Microwave Detection and Measurement of River Floods

LEAD: Brakenridge, R
EM: Robert.Brakenridge@Dartmouth.edu
AF: Dartmouth College, Fairchild Building, Hanover, NH 03755 United States

AU: Anderson, E
EM: Elaine.Anderson@Dartmouth.edu
AF: Dartmouth College, Fairchild Building, Hanover, NH 03755 United States

AU: Nghiem, S V
EM: nghiem@solar.jpl.nasa.gov
AF: Jet Propulsion Laboratory, 4800 Oak Grove Drive, MS 300-235, Pasadena, CA 91109 United States

Click image to view PDF of slides Satellite microwave sensors provide global coverage of the Earth's land surface on a near-daily basis and without severe interference from cloud cover. Using a strategy first developed for wide-area optical sensors, such data can be used to measure river discharge changes and, in particular, floods. River width varies with discharge, total reach water surface area increases as flow widens, and any well-calibrated observation of such water area monitors discharge. The spatial imaging resolution obtained by the sensor is less important than scene-to-scene calibration and the contrast between water and land signal returns. In our prototype operational system for global "critical reach" monitoring, we use the Advanced Microwave Scanning Radiometer aboard NASA's Aqua satellite (AMSR-E). The band at 36.5 Ghz is optimal for this purpose, and only descending orbit, horizontal polarization data are utilized. The discharge estimator is a ratio of: calibration target brightness temperature ("C", for a local land parcel unaffected by the river), to measurement target brightness temperature ("M", for a pixel centered over the river reach). Due to low emission from water surfaces and wet soil, C/M values increase as discharge increases and in-pixel water area increases; they increase sharply once overbank flow conditions occur. The initiation and removal of river ice cover can also be detected. Transformation of the remote sensing signal to discharge values is accomplished by comparison of monthly means, where at least intermittent gaging station data are available. The sensitivity, accuracy, and precision of the orbital measurements are tested along U.S., European, and Australian rivers presently being measured by in situ gaging stations.

Mapping Severe Flood Events With NASA TRMM and SRTM Data

LEAD: Asante
EM: asante@usgs.gov
AF: SAIC TSSC Contract, USGS/EROS, 47914 252nd St., Sioux Falls, SD 57198 United States

AU: Lietzow, R
EM: lietzow@usgs.gov
AF: SAIC TSSC Contract, USGS/EROS, 47914 252nd St., Sioux Falls, SD 57198 United States

AU: Verdin, J P
EM: verdin@usgs.gov
AF: USGS EROS, 47914 252nd St., Sioux Falls, SD 57198 United States

Click image to view PDF of slides Remote sensing of precipitation is making it possible to monitor severe flood events around the world by integrating satellite-derived forcing data with hydrologic models. However, the development of effective early warning systems requires the use of hazard maps that express model flows in terms inundation extent and impacts on vulnerable communities. While high-resolution topographic datasets with near global coverage such the SRTM have been available for several years now, their application in wide-area flood applications has been rather limited because of the absence of computationally efficient methods for their exploitation. This paper describes a computationally efficient method of mapping floods using a combination of TRMM and SRTM data. The key to computational efficiency of the approach lies in the simplification of channel characterization, hydraulic computations and inundation mapping processes. While these simplifications introduce some computational errors, it is demonstrated through sensitivity analysis that the errors are minor compared to the benefits accrued from computation efficiency. The approach offers a practical solution to the significant challenge of developing cost-effective, early warning systems in data scarce, rural settings.

A Conceptual Framework for Space-borne Flood Detection/Monitoring System

LEAD: Hong
EM: yanghong@agnes.gsfc.nasa.gov
AF: GEST/UMBC and GSFC/NASA, NASA Goddard Space Flight Center Code 613.1, Greenbelt, MD 20771 United States

AU: Adler, R
EM: adler@agnes.gsfc.nasa.gov
AF: NASA Goddard Space Flight Center, NASA Goddard Space Flight Center, code 613.1,Greenbelt, MD 20771, United States

AU: Huffman, G
EM: huffman@agnes.gsfc.nasa.gov
AF: SSAI and NASA Goddard Space Flight Center, NASA Goddard Space Flight Center, code 613.1,Greenbelt, MD 20771, United States

AU: Negri, A
EM: negri@agnes.gsfc.nasa.gov
AF: NASA Goddard Space Flight Center, NASA Goddard Space Flight Center, code 613.1,Greenbelt, MD 20771, United States

Click image to view PDF of slides Floods account for the largest number of natural disasters and affect more people than any other types of natural disasters in many regions of the world. Heavy rainfall is the primary causative factor for floods in many temperate and tropical regions across the world. Advances in flood monitoring/forecasting have been constrained by the difficulty of estimating rainfall continuously over space (catchment-, national-, continental-, or even global-scale areas) and time (daily to hourly). In many countries around the world, satellite-based precipitation estimation may be the best source of rainfall data due to insufficient hydrometeorological networks, long delays in data transmission and absence of data sharing in many trans-boundary river basins. In this presentation, a conceptual framework for utilizing space-borne data sets in testing of global flood detection/monitoring systems is proposed to evaluate options and implement a first-cut (prototype) macro- scale flood detection algorithm. Three major components included in this framework are 1) NASA TRMM- based Multi-satellite Precipitation Analysis (TMPA), a state-of-the-art quasi-global precipitation at fine time and space scales (3-hr, 0.25¢X „e 0.25¢X latitude¡Vlongitude) over the latitude band 50¢XN-S; 2) land surface characteristics: elevation aggregated from a 30 arc-second digital elevation model (DEM) of the world, DEM- based derivatives of hydrologic parameters (flow direction, flow accumulation, slope, basin, river network etc.); 3) a spatially distributed rainfall-runoff model to generate surface runoff and route excess precipitation from upper stream to outlet. This framework is evaluated with several flooding events worldwide. It is planned that this preliminary work will lead to wide interdisciplinary efforts and multi-agency collaboration to improve existing regional decision support systems, leading to a near real-time space-borne flood detection/monitoring/forecasting system for disaster management, response, and mitigation activities around the globe.

Towards a Quasi-global precipitation-induced Landslide Detection System using Remote Sensing Information

LEAD: Adler, Bob
EM: adler@agnes.gsfc.nasa.gov
AF: NASA GSFC, NASA Goddard Space Flight Center, Code 613.1, Greenbelt, Maryland 20771

AU: * Hong, Y
EM: yanghong@agnes.gsfc.nasa.gov
AF: UMBC GEST and NASA GSFC, NASA Goddard Space Flight Center, Code 613.1, Greenbelt, Maryland 20771, United States

AU: Huffman, G
EM: huffman@agnes.gsfc.nasa.gov
AF: SSAI and NASA GSFC, NASA Goddard Space Flight Center, Code 613.1, Greenbelt, Maryland 20771

AU: Negri, A
EM: negri@agnes.gsfc.nasa.gov
AF: NASA GSFC, NASA Goddard Space Flight Center, Code 613.1, Greenbelt, Maryland 20771

AU: Pando, M
EM: mpando@uprm.edu
AF: University of Puerto Rico at Mayaguez, University of Puerto Rico at Mayaguez Department of Civil Engineering P.O. Box 9041 Mayaguez, Puerto Rico 00681-9041

Click image to view PDF of slides Landslides and debris flows are one of the most widespread natural hazards on Earth, responsible for thousands of deaths and billions of dollars in property damage per year. Currently, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides. In this study, global landslide susceptibility is mapped using USGS GTOPO30 Digital Elevation, hydrological derivatives (slopes and wetness index etc.) from HYDRO1k data, soil type information downscaled from Digital Soil Map of the World (Sand, Loam, Silt, or Clay etc.), and MODIS land cover/use classification data. These variables are then combined with empirical landslide inventory data, if available, to derive a global landslide susceptibility map at elemental resolution of 1 x 1 km. This map can then be overlain with the driving force, namely rainfall estimates from the TRMM-based Multiple-satellite Precipitation Analysis to identify when areas with significant landslide potential receive heavy rainfall. The relations between rainfall intensity and rainstorm duration are regionally specific and often take the form of a power-law relation. Several empirical landslide-triggering Rainfall Intensity-Duration thresholds are implemented regionally using the 8-year TRMM-based precipitation with or without the global landslide susceptibility map at continuous space and time domain. Finally, the effectiveness of this system is validated by studying several recent deadly landslide/mudslide events. This study aims to build up a prototype quasi-global potential landslide warning system. Spatially-distributed landslide susceptibility maps and regional empirical rainfall intensity-duration thresholds, in combination with real-time rainfall measurements from space and rainfall forecasts from models, will be the basis for this experimental system.

Biographies

Robert Brakenridge

Robert Brakenridge is the director of the Dartmouth Flood Observatory in the Geography Department of Dartmouth College, Hanover, N.H. The Observatory detects, maps, and measures major flood events worldwide using satellite remote sensing. It is used by a wide variety of governmental and non-governmental flood disaster responders, and its web site, www.dartmouth.edu/~floods, is part of many university curricula in hydrology and natural hazards.

Brakenridge received his B.S. in Environmental Geology from Beloit College, Wisconsin, his M.S. in Geosciences from the University of Arizona, Tuscson, and his Ph.D. in Geosciences from the University of Arizona, Tucson. He taught in the Geological Sciences Department at Wright State University from 1983-1987, and joined the Dartmouth College faculty in 1988. Brakenridge has authored many peer-reviewed journal articles or book chapters on the topic of floodplains and floods.

Kwabena O. Asante

Kwabena O. Asante is a senior scientist of Hydrology and Geospatial Analysis at the U.S. Geological Survey (USGS) in Sioux Falls, S.D. Monitoring and assessment of large-area climatic hazards. Asante specializes in integration of hydrologic fluxes and land surface data into decision support tools.

Asante holds a B.S. in Civil Engineering from the University of Nairobi, Kenya, a M.S. and Ph.D. in Civil Engineering from the University of Texas at Austin.

Yang Hong

Yang Hong is a research scientist at NASA's Goddard Earth Science and Technology (GEST) Center and Goddard Space Flight Center in Greenbelt, MD. His research interests include: Surface Hydrology; Remote Sensing and Spatial Analysis; Satellite-based Precipitation Retrieval Algorithm Development and Validation; Flood Forecasting and Landslide Analysis; Digital Image Processing and Analysis; and Sustainable Development and Water Resources Management.

Hong received his Ph.D. in Hydrology and Water Resources in the College of Engineering at the University of Arizona, Tucson. He also holds a M.A. in Geophysics and Environmental Sciences from Beijing University in the People's Republic of China. Hong has completed post-doctoral work at the University of California, Irvine, and at the University of Arizona, Tucson.

Robert F. Adler

Robert F. Adler is the Tropical Rainfall Measuring Mission (TRMM) Project Scientist at NASA's Goddard Space Flight Center in Greenbelt, Md. Adler's research focuses on the analysis of precipitation observations from space on global and regional scales using TRMM data along with data from other satellites. He studies precipitation variations in relation to phenomena such as El Nino/Southern Oscillation (ENSO), volcanoes and tropical cyclones, as well as longer, inter-decadal changes or variations. He also leads the group that produces the global monthly and daily precipitation analyses for the World Climate Research Program's Global Precipitation Climatology Project (GPCP). Adler has published 80 papers in scientific journals on these topics.

Adler holds B.S. and M.S. degrees from Pennsylvania State University and a Ph.D. from Colorado State University at Boulder. He is a Fellow of the American Meteorological Society and is the recipient of a number of NASA achievement awards.