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Christina Bandaragoda

Publications and source records attributed to Christina Bandaragoda.

2 recordsLinked to original sources

How does precipitation variability control bedload response across a mountainous channel network in a maritime climate?

Modeled stream discharge is often used to drive sediment transport models across channel networks. Because sediment transport varies non-linearly with flow rates, discharge modeled from daily total precipitation distributed evenly over 24-hrs may significantly underestimate actual bedload transport capacity. In this study, we assume bedload transport capacity determined from a hydrograph resulting from the use of hourly (1-h) precipitation is a close approximation of actual transport capacity and quantify the error introduced into a network-scale bedload transport model driven by daily precipitation at channel network locations varying from lowland pool-riffle channels to upland colluvial channels in a watershed where snow accumulation and melt can affect runoff processes. Transport capacity is determined using effective stresses and the Wilcock and Crowe (2003) equations and expressed in terms of transport capacity normalized by the bankfull value. We find that, depending on channel network location, cumulative error can range from 10 - 20% to more than two orders of magnitude. Surprisingly, variation in flow rates due to differences in hillslope and channel runoff do not seem to dictate the network locations where the largest errors in predicted bedload transport capacity occur. Rather, spatial variability of the magnitude of the effective-bankfull-excess shear stress and changes in runoff due to snow accumulation and melt exert the greatest influence. These findings have implications for flood-hazard and aquatic habitat models that rely on modeled sediment transport driven by coarse-temporal-resolution climate data.

Washington

Technical Manual for the Geospatial Stream Flow Model (GeoSFM)

The monitoring of wide-area hydrologic events requires the use of geospatial and time series data available in near-real time. These data sets must be manipulated into information products that speak to the location and magnitude of the event. Scientists at the U.S. Geological Survey Earth Resources Observation and Science (USGS EROS) Center have implemented a hydrologic modeling system which consists of an operational data processing system and the Geospatial Stream Flow Model (GeoSFM). The data processing system generates daily forcing evapotranspiration and precipitation data from various remotely sensed and ground-based data sources. To allow for rapid implementation in data scarce environments, widely available terrain, soil, and land cover data sets are used for model setup and initial parameter estimation. GeoSFM performs geospatial preprocessing and postprocessing tasks as well as hydrologic modeling tasks within an ArcView GIS environment. The integration of GIS routines and time series processing routines is achieved seamlessly through the use of dynamically linked libraries (DLLs) embedded within Avenue scripts. GeoSFM is run operationally to identify and map wide-area streamflow anomalies. Daily model results including daily streamflow and soil water maps are disseminated through Internet map servers, flood hazard bulletins and other media.

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