The Global Land Data Assimilation System

Abstract

This powerful new land surface modeling system integrates data from advanced observing systems to support improved forecast model initialization and hydrometeorological investigations. Land surface temperature and wetness conditions affect and are affected by numerous climatological, meteorological, ecological, and geophysical phenomena. Therefore, accurate, high-resolution estimates of terrestrial water and energy storages are valuable for predicting climate change, weather, biological and agricultural productivity, and flooding, and for performing a wide array of studies in the broader biogeosciences. In particular, terrestrial stores of energy and water modulate fluxes between the land and atmosphere and exhibit persistence on diurnal, seasonal, and interannual time scales. Furthermore, because soil moisture, temperature, and snow are integrated states, biases in land surface forcing data and parameterizations accumulate as errors in the representations of these states in operational numerical weather forecast and climate models and their associated coupled data assimilation systems. That leads to incorrect surface water and energy partitioning, and, hence, inaccurate predictions. Reinitialization of land surface states would mollify this problem if the land surface fields were reliable and available globally, at high spatial resolution, and in near-real time.A Global Land Data Assimilation System (GLDAS) has been developed jointly by scientists at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) in order to produce such fields. GLDAS makes use of the new generation of groundand space-based observation systems, which provide data to constrain the modeled land surface states. Constraints are applied in two ways. First, by forcing the land surface models (LSMs) with observationbased meteorological fields, biases in atmospheric model-based forcing can be avoided. Second, by employing data assimilation techniques, observations of land surface states can be used to curb unrealistic model states. Through innovation and an ever-improving conceptualization of the physics underlying earth system processes, LSMs have continued to evolve and to display an improved ability to simulate complex phenomena. Concurrently, increases in computing power and affordability are allowing global simulations to be run more routinely and with less processing time, at spatial resolutions that could only be simulated using supercomputers five years ago. GLDAS harnesses this low-cost computing power to integrate observationbased data products from multiple sources within a sophisticated, global, high-resolution land surface modeling framework.What makes GLDAS unique is the union of all of these qualities: it is a global, high-resolution, offline (uncoupled to the atmosphere) terrestrial modeling system that incorporates satellite-and ground-based observations in order to produce opt...

Hydrological monitoring and seasonal forecasting: Progress and perspectives

“…Moreover, for a comprehensive identification of drought, a GRACE Data Assimilation System, based on the incorporation of the GRACE-based TWS into the Catchment Land Surface Model (CLSM) (Zaitchik et al, 2008), has been successfully applied into the North American Drought Monitor (NADM) system to fill up the ignored subsurface water storage information (Houborg et al, 2012). Coincidently, there are also a large number of operational data assimilation systems that have been made publicly available at large scale to provide the multi-source-based optimal fields, such as the Global Land Data Assimilation System (GLDAS) (Rodell et al, 2004), North American Land Data Assimilation System (NLDAS) (now upgraded to Phase 2 (NLDAS-2)) (Mitchell et al, 2004), European Land Data Assimilation System (ELDAS), and West China Land Data Assimilation System (WCLDAS) . Therefore, the data assimilation approach is being a promising area to yield the 'best' hydrological monitoring.…”

Estimating soil moisture storage change using quasi-terrestrial water balance method

“…Note that all the storage change terms here are treated as averages in both time and space (Yeh et al, 1998). The terrestrial component of the hydrologic cycle is likewise expressed in terms of precipitation, evapotranspiration and runoff as (Yeh et al, 1998;Rodell et al, 2004aRodell et al, , 2004bSeneviratne et al, 2004;Moiwo et al, 2011):…”

“…Unlike field surveys, satellite observations provide cost-and labor-effective means of monitoring soil moisture at sufficient spatial and temporal scales (Becker, 2006;Zhao et al, 2010). In fact, there is a huge potential for monitoring soil moisture and other land-state processes from space (Rodell et al, 2004a(Rodell et al, , 2004b. Space-borne or remote sensing (RS)-based data resolutions are often refined for higher sensor accuracies.…”

“…ETWatch is now used in the continuous monitoring of ET in the Hai River Basin (HRB) (Wu et al, 2008b). Hence the advent of RS-based data along with the related energy balance algorithms has greatly enhanced the estimation of ET and other land-surface processes for sustainable hydro-ecological management (Yeh et al, 1998;Rodell et al, 2004aRodell et al, , 2004bZhang et al, 2008).…”

“…Groundwater and root-zone soil moisture storage exhibits the degree of variability needed to characterize terrestrial water storage change in space and time (Rodell et al, 2004b(Rodell et al, , 2009Swenson et al, 2006). Compared with precipitation, irrigation and groundwater, establishing credible soil moisture measurements at basin-to-regional scale is difficult especially in regions under water stress, storage depletion and freezing winter conditions (Yang et al, 2003;Han et al, 2008;Zhang et al, 2008;Zhao et al, 2010).…”