Current Research
- Integration
of remote sensing and
snow hydrologic modelling using data assimilation
The importance of
snow to efficient water resources
management, especially in the western US, has long been recognized.
Information on snow properties can be obtained either from hydrologic
models or observations. In situ observation networks cannot explain the
large spatial and temporal variability of snow processes over large
scales. Remote sensing can provide large scale snow observations at an
operational level. However, the need for spatially continuous and
physically consistent estimates has led research towards data
assimilation. The latter uses the model to constrain any available
observations and offers several advantages, such as accounting for
model and observation errors, and utilizing indirect observations (e.g.
brightness temperatures). The objective of this work is the evaluation
of a system that assimilates remotely sensed snow observations into a
macroscale hydrologic model. The ensemble Kalman filter was chosen as
the data assimilation technique, mainly because of its sequential
nature and robustness. MODIS snow cover extent data are going to be
assimilated into VIC to update snow water equivalent estimates over the
Snake river basin. The second step of this work will involve the
assimilation of AMSR-E SWE estimates.
- Spatio-temporal analysis and
characterization of droughts in the U.S. 1916-2003
( working together with Liz Clark )
This project is a continuation of Hyoseok Park's work,
examining the spatial and temporal extent of drought events for the US
from a 88-year simulated hydrologic record. The focus of this work are
agricultural and hydrologic droughts, so soil moisture and runoff
anomalies from the climatological mean will be used as main indices. A
clustering technique will be used to identify droughts over a 0.5
degree grid of the US. Severity- area- duration curves will be
developed and the enveloping curves for each major drought event will
be identified.
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