Yong B.,Hohai University |
Yong B.,University of Oklahoma |
Ren L.-L.,Hohai University |
Hong Y.,University of Oklahoma |
And 6 more authors.
Water Resources Research | Year: 2010
Two standard Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products, 3B42RT and 3B42V6, were quantitatively evaluated in the Laohahe basin, China, located within the TMPA product latitude band (50NS) but beyond the inclined TRMM satellite latitude band (36NS). In general, direct comparison of TMPA rainfall estimates to collocated rain gauges from 2000 to 2005 show that the spatial and temporal rainfall characteristics over the region are well captured by the 3B42V6 estimates. Except for a few months with underestimation, the 3B42RT estimates show unrealistic overestimation nearly year round, which needs to be resolved in future upgrades to the real-time estimation algorithm. Both model-parameter error analysis and hydrologic application suggest that the three-layer Variable Infiltration Capacity (VIC-3L) model cannot tolerate the nonphysical overestimation behavior of 3B42RT through the hydrologic integration processes, and as such the 3B42RT data have almost no hydrologic utility, even at the monthly scale. In contrast, the 3B42V6 data can produce much better hydrologic predictions with reduced error propagation from input to streamflow at both the daily and monthly scales. This study also found the error structures of both RT and V6 have a significant geo-topography-dependent distribution pattern, closely associated with latitude and elevation bands, suggesting current limitations with TRMM-era algorithms at high latitudes and high elevations in general. Looking into the future Global Precipitation Measurement (GPM) era, the Geostationary Infrared (GEO-IR) estimates still have a long-term role in filling the inevitable gaps in microwave coverage, as well as in enabling sub-hourly estimates at typical 4-km grid scales. Thus, this study affirms the call for a real-time systematic bias removal in future upgrades to the IR-based RT algorithm using a simple scaling factor. This correction is based on MW-based monthly rainfall climatologies applied to the combined monthly satellite-gauge research products. © 2010 by the American Geophysical Union. Source
Liao Z.,University of Oklahoma |
Hong Y.,University of Oklahoma |
Hong Y.,Center for Natural Hazard and Disaster Research |
Wang J.,University of Oklahoma |
And 5 more authors.
Landslides | Year: 2010
An early warning system has been developed to predict rainfall-induced shallow landslides over Java Island, Indonesia. The prototyped early warning system integrates three major components: (1) a susceptibility mapping and hotspot identification component based on a land surface geospatial database (topographical information, maps of soil properties, and local landslide inventory, etc.); (2) a satellite-based precipitation monitoring system (http://trmm. gsfc.nasa.gov) and a precipitation forecasting model (i. e., Weather Research Forecast); and (3) a physically based, rainfall-induced landslide prediction model SLIDE. The system utilizes the modified physical model to calculate a factor of safety that accounts for the contribution of rainfall infiltration and partial saturation to the shear strength of the soil in topographically complex terrains. In use, the land-surface "where" information will be integrated with the "when" rainfall triggers by the landslide prediction model to predict potential slope failures as a function of time and location. In this system, geomorphologic data are primarily based on 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, digital elevation model (DEM), and 1-km soil maps. Precipitation forcing comes from both satellite-based, real-time National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM), and Weather Research Forecasting (WRF) model forecasts. The system's prediction performance has been evaluated using a local landslide inventory, and results show that the system successfully predicted landslides in correspondence to the time of occurrence of the real landslide events. Integration of spatially distributed remote sensing precipitation products and in-situ datasets in this prototype system enables us to further develop a regional, early warning tool in the future for predicting rainfall-induced landslides in Indonesia. © 2010 Springer-Verlag. Source
Liu W.,Northwest University, China |
Liu W.,University of Oklahoma |
Liu W.,Center for Natural Hazard and Disaster Research |
Liu W.,Chinese Academy of Sciences |
And 10 more authors.
Water Resources Management | Year: 2011
The objective of this study is to evaluate the potential utility of the USGS Global Data Assimilation System (GDAS) 1-degree, daily reference Evapotranspiration (ET 0) products by comparing them with observed Oklahoma mesonet daily ET 0 over a 2 year period (2005-2006). The comparison showed a close match between the two independent ET 0 products, with bias within a range of 10% for most of the sites and the overall bias of - 2.80%. The temporal patterns are strongly correlated, with a correlation coefficient above 0.9 for all groups. In summary, we conclude that (1) the consistent low bias shows the original GDAS ET 0 products have high potentials to be used in land surface modeling; (2) the high temporal correlations demonstrate the capability of GDAS ET 0 to represent the major atmospheric processes that control the daily variation of surface hydrology; (3) The temporal and spatial correspondences in trend between independent datasets (GDAS and MESONET) were good. The finding in Oklahoma, a different hydro-climate region from a similar regional study conducted in California by Senay et al. (J Am Water Res Assoc 44(4):969-979, 2008), reconfirms the reliability and potential of using GDAS reference ET for regional energy balance and water resources management in many parts of the world. © 2011 Springer Science+Business Media B.V. Source