Development of a hydrometeorological forcing data set for global soil moisture estimation

Abstract

Off-line land surface modeling simulations require accurate meteorological forcing with consistent spatial and temporal resolutions. Although reanalysis products present an attractive data source for these types of applications, bias to many of the reanalysis fields limits their use for hydrological modeling. In this study, we develop a global 0.5°forcing data sets for the time period 1979-1993 on a 6-hourly time step through application of a bias correction scheme to reanalysis products. We then use this forcing data to drive a land surface model for global estimation of soil moisture and other hydrological states and fluxes. The simulated soil moisture estimates are compared to in situ measurements, satellite observations and to a modeled data set of root zone soil moisture produced within a separate land surface model, using a different data set of hydrometeorological forcing. In general, there is good agreement between anomalies in modeled and observed (in situ) root zone soil moisture. Similarly, for the surface soil wetness state, modeled estimates and satellite observations are in general statistical agreement; however, correlations decline with increasing vegetation amount. Comparisons to a modeled data set of soil moisture also demonstrates that both simulations present estimates that are well correlated for the soil moisture in the anomaly time series, despite being derived from different land surface models, using different data sources for meteorological forcing, and with different specifications of the land surfaces properties.

Assessment and Enhancement of MERRA Land Surface Hydrology Estimates

“…We therefore use the GPCP estimates to construct a corrected version of the MERRA precipitation. The approach used here is similar in concept to that applied in the Global Soil Wetness Project (Dirmeyer et al 2006) and other global land modeling studies (Berg et al 2005;Guo et al 2006;Qian et al 2006;Sheffield et al 2006). Based on results from these earlier studies, we recognize that corrections to surface radiation and surface air temperature have a much smaller effect than precipitation corrections.…”

“…Besides estimates of atmospheric conditions, reanalysis products also provide estimates of land surface fields, including surface meteorological forcing data (such as precipitation, radiation, air temperature, and humidity) as well as land surface states and fluxes (such as soil moisture, snow, and runoff). Reanalysis estimates can be used for a large variety of research and applications, for example, the generation of enhanced land surface meteorological datasets (Berg et al 2005;Guo et al 2006;Sheffield et al 2006), the study of the land surface water budget, including streamflow, droughts, soil moisture, and snow processes (Dai and Trenberth 2002;Su and Lettenmaier 2009;Sheffield and Wood 2008;Burke et al 2010;Brown et al 2010), the estimation of the land carbon budget Yi et al 2011), and, possibly, the calibration and verification of seasonal climate forecasting systems (Saha et al 2006) and the generation of climate data records (Thorne and Vose 2010;Dee et al 2010).…”

“…On average, global precipitation from MERRA is no worse than estimates from other reanalysis products ). There have been many similar efforts to improve global offline land surface simulations through corrected analysis or reanalysis forcing data (e.g., Dirmeyer and Tan 2001;Berg et al 2005;Guo et al 2006;Qian et al 2006;Sheffield et al 2006). Our paper focuses on the land surface hydrology estimates from MERRA and how they can be improved through simple corrections to land model parameters and the precipitation forcing.…”

A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies

“…The MODIS science team must currently disaggregate the coarse-resolution (1.008 ) 1.258) Data Assimilation Office (DAO) (now the Global Modeling and Assimilation Office) reanalysis solar radiation product to provide the forcing data to produce the 1-km ET product from MODIS data (MOD16) (Mu et al 2007;Mu, Zhao, and Running 2011). Berg et al (2005) pointed out that a bias on many of the reanalysis fields limits their use for hydrological modeling. Liu, Chen, and Cihlar (2003) bilinearly interpolated NCEP reanalysis data of around 0.98 into 1km to calculate daily ET at 1km resolution over the entire Canadian landmass in 1996 using the boreal ecosystem productivity simulator (BEPS).…”

Estimating groundwater storage changes in the Mississippi River basin (USA) using GRACE

“…A spatially and temporally downscaled version (Gottschalck et al 2005) of the NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP; Xie and Arkin 1997) product replaced the GDAS precipitation; observation based downward radiation products derived using Air Force Weather Agency fields and procedures (Rodell et al 2004a) replaced the GDAS radiation. Each simulation was performed on a 1°global grid and initialized in 1979, forced by bias-corrected reanalysis products (Berg et al 2005) prior to 2000.…”