S.R. Fassnacht's Research - Colorado State University

previous work:

Develop routines for snow processes and data assimilation in hydrologic and soil-vegetation atmospheric transfer models (WATFLOOD, CLASS, USGS-PRMS)
Derive snow water equivalent (SWE) estimates across various mountain watersheds in gridded format and assess gridding techniques in terms of snowpack volumes
Combine gridded SWE data with remotely sensed snowcovered area maps as input to a hydrologic model for snowmelt streamflow estimation (Upper Colorado River basin)
Use weather radar to predict snowfall quantities for distributed hydrologic modelling, to assess the snowpack properties and to predict snowmelt streamflow (SW Ontario watersheds)

AVHRR and TM imagery processing for snow-covered area (SCA) estimation with commercial information systems and in-house software
GOES imagery analysis for SCA estimation and cloud-cover investigation
RADARSAT imagery for soil moisture analysis
Ground based weather radar data interpolation and analysis

Snow surveying and soil moisture sampling (with Environment Canada)
Develop a snow sampling program for research and education
Design, construct, test a laboratory assembly to measure fresh snow characteristics

current interests:

Runoff Forecasting and Hydrologic Modelling - emphasizing Snow and Winter Processes

The overall goal is to improve runoff forecasting for water resources managers and other water users, in order to know how much water is in system and to better allocate resources.
There are two main foci for modelling. Hydrological modelling efforts use both conceptual or index models and physically-based models to simulate state variables and streamflow. The first focus is to improve the representation of snow and winter processes in models, for more accurate simulation. The second focus is to incorporate these lessons learned from these models into simpler spatial runoff models.
The existing runoff models use point data and spatial models are being developed to use distributed snowpack data.

The different model types use a variety of data and thus spatial analysis and commercial information systems are important components prior to modelling. For example, conceptual models use temperature and precipitation data (and at times net radiation), while physically based models use radiation (long and short wave), pressure, humidity and wind data.
These spatial data are being further developed for use in forecast models, as well as for evaluation of hydrological models. Others research are using these data to evaluate atmospheric models, and remotely sensed precipitation estimates. These data include snow water equivalent and snow covered area.

There exists a good understanding of the physics behind most snow and winter hydrological processes. However, this understanding has not always been translated into a relationship that can be used for modelling. Fieldwork will build the knowledge-base to better understand the processes and how to incorporate the understanding into models.
Experience from fieldwork will supplement data mining efforts. There are numerous existing datasets that are very comprehensive. These datasets are being explored to provide additional information. Fieldwork will provide the hands on training to understand the datasets, and to collect additional information.
Field studies will investigate lesser understood processes and will support other research efforts.

srf@cnr.colostate.edu