Estimation of spatially distributed latent energy flux over complex terrain using a scanning water-vapor Raman lidar
Evapotranspiration is one of the critical variables in both water and energy balance models of the hydrological system. The hydrologic system is driven by the soil-plant-atmosphere continuum, and as such is a spatially distributed process. Traditional techniques rely on point sensors to collect information that is then averaged over a region. The assumptions involved in spatially average point data is of limited value (1) because of limited sensors in the arrays, (2) the inability to extend and interpret the Measured scalars and estimated fluxes at a point over large areas in complex terrain, and (3) the limited understanding of the relationship between point measurements of spatial processes. Remote sensing technology offers the ability to collect detailed spatially distributed data. However, the Los Alamos National Laboratory`s volume-imaging, scanning water-vapor Raman lidar has been shown to be able to estimate the latent energy flux at a point. The extension of this capability to larger scales over complex terrain represents a step forward. This abstract Outlines the techniques used to estimate the spatially resolved latent energy flux. The following sections describe the site, model, data acquired, and lidar estimated latent energy ``map``.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 218708
- Report Number(s):
- LA-UR-95-2912; CONF-960542-1; ON: DE95017850
- Resource Relation:
- Conference: Agricultural and forest meterological conference, Atlanta, GA (United States), May 1996; Other Information: PBD: [1995]
- Country of Publication:
- United States
- Language:
- English
Similar Records
Field evaluation of remote wind sensing technologies: Shore-based and buoy mounted LIDAR systems
Final Report: Phase II Nevada Water Resources Data, Modeling, and Visualization (DMV) Center