NCA-LDAS: An Integrated Terrestrial Water Analysis System for Development, Evaluation, and Dissemination of Climate Indicators
Abstract
An Integrated Terrestrial Water Analysis System, or NCA-LDAS, has been created to enable development, evaluation, and dissemination of terrestrial hydrologic climate indicators focusing on the continental U.S. The purpose is to provide quantifiable indicators of states and estimated trends in our nation's water stores and fluxes over a wide range of scales and locations, to support improved understanding and management of water resources and numerous related sectors such as agriculture and energy. NCA-LDAS relies on improved modeling of terrestrial hydrology through assimilation of satellite imagery, building upon the legacy of the Land Information System modeling framework (Kumar et al, 2006; Peters-Lidard et al, 2007). It currently employs the Noah or Catchment Land Surface Model, run with a number of satellite data assimilation scenarios. The domain for NCA-LDAS is the continental U.S. at 1/8 degree grid for the period 1979 to present. Satellite-based variables that are assimilated are soil moisture and snow water equivalent from principally microwave sensors such as SMMR, SSM/I and AMSR, snow covered area from multispectral sensors such as AVHRR, and MODIS, and terrestrial water storage from GRACE. Once simulated, output are evaluated in comparison to independent datasets using a variety of metrics using the Land Surface Verification Toolkit (LVT). LVT schemes within NCA-LDAS also include routines for computing standard statistics of time series such means, max, and linear trends, at various scales. The dissemination of the NCA-LDAS, including model descriptions, forcings, parameters, daily output, indicator results and LVT tools, have been made available to the public through dissemination on NASA GES-DISC.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2014
- Bibcode:
- 2014AGUFMGC51B0405J
- Keywords:
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- 0850 Geoscience education research;
- 1630 Impacts of global change;
- 1640 Remote sensing;
- 6309 Decision making under uncertainty