Integration of Observation Data Model and Distributed Hydrology Soil Vegetation Model for Dry Creek Experimental Watershed, ID USA

Citation data:

CONFERENCE: Spring Runoff Conference

Publication Year:
2008
Usage 3
Abstract Views 3
Repository URL:
https://digitalcommons.usu.edu/runoff/2008/AllAbstracts/45
Author(s):
Tesfa, Teklu K.
Publisher(s):
Hosted by Utah State University Libraries
Tags:
Life Sciences; Physical Sciences and Mathematics
conference paper description
The Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) hydrologic information system (HIS) observations data model (ODM) provides a new format for the storage and retrieval of environmental observations in a relational database designed to facilitate integrated analyses of large environmental datasets. This has created an opportunity for the hydrologic community in general and hydrologic modelers in particular to store hydrologic data from various sources and formats in a relational database that can be coupled to hydrologic models. With this coupling input data can be directly retrieved from the database and output can be stored in the database for further analyses. Coupling of ODM with hydrologic models requires development of interface tools that can extract input data from the relational database and transform into the formats, time steps and units required by the hydrologic model as well as import model outputs into the relational database. Here we present the implementation of ODM for Dry Creek Experimental Watershed (DCEW) describing how the data was formatted and loaded into ODM and illustrating the visualization and sharing of this data enabled by doing this. We also discuss the approach and some initial results from the work to build a tool to connect the Distributed Hydrology Soil Vegetation Model (DHSVM) with ODM. This work is intended to make application of DHSVM less cumbersome by enhancing the efficiency of procedures for preparation of input data and evaluate the potential for improvements of hydrologic forecasts using DHSVM model driven by data from ODM.