1) Assess the performance and sensitivity of the physically-based Distributed Hydrology Soil Vegetation Model (DHSVM) implemented at two different spatial scales: 10m resolution over the Entiat Experimental Forest (EEF) alone and 100m resolution over the entire Entiat watershed;
2) Prepare model simulations for the historical period using gridded meteorological data produced from both coop station records and much higher quality measurements from the EEF;
3) Assess the sensitivity of the simulated water balance to historical changes in temperature and precipitation first using the historical record, and then using climate model projections;
4) Evaluate the role of data quality and calibration of soil parameters in determining model sensitivity and performance.
5) Assess model resolution by comparing model sensitivity to climatic variations between the high- and low-resolution implementations;
6) On the basis of observed spatial heterogeneity of recovery from major disturbance events in the Entiat watershed, develop a better understanding of the role of hydroclimatological processes on vegetation recruitment and related biogeophysical recovery processes.
7) Incorporate these disturbance / recovery dynamics into land surface models to project future climate change impacts on fire regimes, forest recruitment, snowpack, streamflow, and water quality
Project Extension
parts
type
Objectives
value
1) Assess the performance and sensitivity of the physically-based Distributed Hydrology Soil Vegetation Model (DHSVM) implemented at two different spatial scales: 10m resolution over the Entiat Experimental Forest (EEF) alone and 100m resolution over the entire Entiat watershed;
2) Prepare model simulations for the historical period using gridded meteorological data produced from both coop station records and much higher quality measurements from the EEF;
3) Assess the sensitivity of the simulated water balance to historical changes in temperature and precipitation first using the historical record, and then using climate model projections;
4) Evaluate the role of data quality and calibration of soil parameters in determining model sensitivity and performance.
5) Assess model resolution by comparing model sensitivity to climatic variations between the high- and low-resolution implementations;
6) On the basis of observed spatial heterogeneity of recovery from major disturbance events in the Entiat watershed, develop a better understanding of the role of hydroclimatological processes on vegetation recruitment and related biogeophysical recovery processes.
7) Incorporate these disturbance / recovery dynamics into land surface models to project future climate change impacts on fire regimes, forest recruitment, snowpack, streamflow, and water quality
