1. Extreme events: detection

In addition to global warming, global climate change is also characterized by frequently occurring extreme events, such as droughts and floods. This sub-theme presents evidence on changes in precipitation, soil moisture, evapotranspiration, streamflow, water table, lake levels, agriculture, vegetation, and social-economic factors through in situ measurements, surveys, and satellite remote sensing (e.g., MODIS, AMSR-E, GRACE, SMOS). Errors in observations will also be discussed.

Chair: Bob Su, University of Twente
Keynote 1: Matthew Rodell, NASA Goddard Space Flight Center: “Detection of Extreme Events with GRACE and Data Assimilation”
A unique aspect of GRACE is its ability to quantify changes in all forms of water storage, including groundwater and water ponded on the surface.  Thus GRACE is well suited for identifying both hydrological droughts, when total water storage is low, and floods, when total water storage is high.  The potential for GRACE to detect and help predict droughts and floods is clear, but first it is necessary to overcome GRACE’s low spatial and temporal resolutions (relative to other hydrological observations) and data latency.  To do so we synthesize GRACE data with other ground and space based meteorological observations within a land surface model.  The resulting high resolution, near real-time fields of soil moisture and groundwater storage variations are then used to generate wetness index maps, which are now being distributed through the University of Nebraska’s National Drought Mitigation Center website and incorporated into the U.S. and North American Drought Monitors.  At present, such wetness index maps are scarce outside of North America.  We intend to address this need by expanding our drought indicator production to the global scale over the next 1-2 years.
Keynote 2: John Nielsen-Gammon, Texas A&M University: “Detection of Drought at High Spatial Resolution Using Bias-Adjusted Stage IV Precipitation”
The National Weather Service produces a Stage IV Mosaic of precipitation estimates on an hourly and daily basis across the contiguous United States for the purposes of flood and river forecasting.  In the central and eastern United States, the precipitation analysis is based largely on radar estimates of precipitation, adjusted to agree with gauge values.  However, the signal-to-noise ratio of 24-hour precipitation is low, and biases accumulate over time.  We have developed a three-step process to minimize long-term biases in the Stage IV mosaic for accumulation periods of one month or longer, making the product suitable for drought monitoring.  The three-step process eliminates spatial patterns of bias inherent in radar observations before performing a conventional two-dimensional bias correction procedure.  The resulting analyses are used to produce Standardized Precipitation Index (SPI) maps of drought intensity.  We have also developed a new drought index, the SPI Blend, that merges different accumulation periods to give a more realistic depiction of drought development.  The bias-adjusted analyses are also useful as input to high-resolution land surface and hydrologic models.
Oral 1: David Arctur, The University of Texas at Austin, USA: “Advancing Flood Detection and Preparedness Through GEOSS Water Services”
Oral 2: Xingang Dai, IAP/Chinese Academy of Sciences, China: “Climate Change Adaptation Involving Land Use Management and Grazing Strategies by Use of Social Survey, Statistics, and Numerical Modeling”