An inversion procedure is presented for estimating surface soil water content (as surface moisture availability, Mo ), fractional vegetation cover ( Fr ), and the instantaneous surface energy fluxes, using remote multispectral measurements made from an aircraft. The remotely derived values of these fluxes and the soil water content are compared with field measurements from two intensive field measurement programs FIFE and MONSOON '90. The measurements from the NS001 multispectral radiometer were reduced to fractional vegetation cover, surface soil water content (surface moisture availability), and turbulent energy fluxes, with the application of a soil vegetation atmosphere transfer (SVAT) model. A further step in the inversion process involved 'stretching' the SVAT results between pre-determined boundaries of the distribution of normalized difference vegetation index (NDVI) and surface radiant temperature ( To ). Comparisons with measurements at a number of sites from two field experiments show standard errors, between derived and measured fluxes, generally between 25 and 55Wm-2, or about 10-30 per cent of the magnitude of the fluxes and for surface moisture availability of 16 per cent.
Harshburger, Brian J., Karen S. Humes, Von P. Walden, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2010. Evaluation of Short‐to‐Medium Range Streamflow Forecasts Obtained Using an Enhanced Version of SRM. Journal of the American Water Resources Association (JAWRA) 46(3):603‐617. DOI: 10.1111/j.1752‐1688.2010.00437.x Abstract: As demand for water continues to escalate in the western United States, so does the need for accurate streamflow forecasts. Here, we describe a methodology for generating short‐to‐medium range (1 to 15 days) streamflow forecasts using an enhanced version of the Snowmelt Runoff Model (SRM), snow‐covered area data derived from MODIS products, data from Snow Telemetry stations, and meteorological forecasts. The methodology was tested on three mid‐elevation, snowmelt‐dominated basins ranging in size from 1,600 to 3,500 km 2 . To optimize the model performance and aid in its operational implementation, two enhancements have been made to SRM: (1) the use of an antecedent temperature index method to track snowpack cold content, and (2) the use of both maximum and minimum critical temperatures to partition precipitation into rain, snow, or a mixture of rain and snow. The comparison of retrospective model simulations with observed streamflow shows that the enhancements significantly improve the model performance. Streamflow forecasts generated using the enhanced version of the model compare well with the observed streamflow for the earlier leadtimes; forecast performance diminishes with leadtime due to errors in the meteorological forecasts. The three basins modeled in this research are typical of many mid‐elevation basins throughout the American West, thus there is potential for this methodology to be applied successfully to other mountainous basins.
Remotely sensed data in the visible, near‐infrared, and thermal‐infrared wave bands were collected from a low‐flying aircraft during the Monsoon '90 field experiment. Monsoon '90 was a multidisciplinary experiment conducted in a semiarid watershed. It had as one of its objectives the quantification of hydrometeorological fluxes during the “monsoon” or wet season. The remote sensing observations along with micrometeprological and atmospheric boundary layer (ABL) data were used to compute the surface energy balance over a range of spatial scales. The procedure involved averaging multiple pixels along transects flown over the meteorological and flux (METFLUX) stations. Average values of the spectral reflectance and thermal‐infrared temperatures were computed for pixels of order 10 −1 to 10 1 km in length and were used with atmospheric data for evaluating net radiation ( R n ), soil heat flux ( G ), and sensible ( H ) and latent ( LE ) heat fluxes at these same length scales. The model employs a single‐layer resistance approach for estimating H that requires wind speed and air temperature in the ABL and a remotely sensed surface temperature. The values of R n and G are estimated from remote sensing information together with near‐surface observations of air temperature, relative humidity, and solar radiation. Finally, LE is solved as the residual term in the surface energy balance equation. Model calculations were compared to measurements from the METFLUX network for three days having different environmental conditions. Average percent differences for the three days between model and the METFLUX estimates of the local fluxes were about 5% for R n , 20% for G and H , and 15% for LE . Larger differences occurred during partly cloudy conditions because of errors in interpreting the remote sensing data and the higher spatial and temporal variation in the energy fluxes. Minor variations in modeled energy fluxes were observed when the pixel size representing the remote sensing inputs changed from 0.2 to 2 km. Regional scale estimates of the surface energy balance using bulk ABL properties for the model parameters and input variables and the 10‐km pixel data differed from the METFLUX network averages by about 4% for R n , 10% for G and H , and 15% for LE . Model sensitivity in calculating the turbulent fluxes H and LE to possible variations in key model parameters (i.e., the roughness lengths for heat and momentum) was found to be fairly significant. Therefore the reliability of the methods for estimating key model parameters and potential errors needs further testing over different ecosystems and environmental conditions.
Harshburger, Brian J., Von P. Walden, Karen S. Humes, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2012. Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model. Journal of the American Water Resources Association (JAWRA) 48(4): 643‐655. DOI: 10.1111/j.1752‐1688.2012.00642.x Abstract: As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1‐15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt‐dominated basins in Idaho. Model inputs are derived from meteorological forecasts, snow cover imagery, and surface observations from Snowpack Telemetry stations. The model performed well at lead times up to 7 days, but has significant predictability out to 15 days. The timing of peak flow and the streamflow volume are captured well by the model, but the peak‐flow value is typically low. The model performance was assessed by computing the coefficient of determination ( R 2 ), percentage of volume difference (Dv%), and a skill score that quantifies the usefulness of the forecasts relative to climatology. The average R 2 value for the mean ensemble is >0.8 for all three basins for lead times up to seven days. The Dv% is fairly unbiased (within ±10%) out to seven days in two of the basins, but the model underpredicts Dv% in the third. The average skill scores for all basins are >0.6 for lead times up to seven days, indicating that the ensemble model outperforms climatology. These results validate the usefulness of the ensemble forecasting approach for basins of this type, suggesting that the ensemble version of SRM might be applied successfully to other basins in the Intermountain West.
