A Global Land Data Assimilation System (GLDAS) has been developed. Its purpose is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes. GLDAS is unique in that it is an uncoupled land surface modeling system that drives multiple models, integrates a huge quantity of observation-based data, runs globally at high resolution (0.25°), and produces results in near–real time (typically within 48 h of the present). GLDAS is also a test bed for innovative modeling and assimilation capabilities. A vegetation-based “tiling” approach is used to simulate subgrid-scale variability, with a 1-km global vegetation dataset as its basis. Soil and elevation parameters are based on high-resolution global datasets. Observation-based precipitation and downward radiation and output fields from the best available global coupled atmospheric data assimilation systems are employe...

The Global Land Data Assimilation System

Investigating soil moisture-climate interactions in a changing climate: A review

The Intergovernmental Panel on Climate Change (IPCC) Technical Paper Climate Change and Water draws together and evaluates the information in IPCC Assessment and Special Reports concerning the impacts of climate change on hydrological processes and regimes, and on freshwater resources – their availability, quality, use and management. It takes into account current and projected regional key vulnerabilities, prospects for adaptation, and the relationships between climate change mitigation and water. Its objectives are:

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Climate change and water.

Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts.

https://science.sciencemag.org/content/sci/305/5687/1138.full.pdf

Regions of Strong Coupling Between Soil Moisture and Precipitation

Monthly gravity field estimates made by the twin Gravity Recovery and Climate Experiment (GRACE) satellites have a geoid height accuracy of 2 to 3 millimeters at a spatial resolution as small as 400 kilometers. The annual cycle in the geoid variations, up to 10 millimeters in some regions, peaked predominantly in the spring and fall seasons. Geoid variations observed over South America that can be largely attributed to surface water and groundwater changes show a clear separation between the large Amazon watershed and the smaller watersheds to the north. Such observations will help hydrologists to connect processes at traditional length scales (tens of kilometers or less) to those at regional and global scales.

https://science.sciencemag.org/content/sci/305/5683/503.full.pdf

GRACE measurements of mass variability in the Earth system.

Soil moisture is an important variable in the climate system. Understanding and predicting variations of surface temperature, drought, and flood depend critically on knowledge of soil moisture variations, as do impacts of climate change and weather forecasting. An observational dataset of actual in situ measurements is crucial for climatological analysis, for model development and evaluation, and as ground truth for remote sensing. To that end, the Global Soil Moisture Data Bank, a Web site (http://climate.envsci.rutgers.edu/soil—moisture) dedicated to collection, dissemination, and analysis of soil moisture data from around the globe, is described. The data bank currently has soil moisture observations for over 600 stations from a large variety of global climates, including the former Soviet Union, China, Mongolia, India, and the United States. Most of the data are in situ gravimetric observations of soil moisture; all extend for at least 6 years and most for more than 15 years. Most of the stat...

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The Global Soil Moisture Data Bank

Evaluation of Global Soil Wetness Project Soil Moisture Simulations

We analyze the large-scale spatial variation of soil moisture in eastern China using geostatistical techniques with observations at 99 stations for the top 0.1 m and top 1 m from 1987 to 1989. Sample variograms are found to have a clear sill and a nugget in many cases. A spherical variogram model, including a nugget in some cases, fits the sample variograms closely. Using a quantitative method to select the separation interval for variogram analysis, we find that the average range is 200-400 km for the top 0.1 m and 400-700 km for the top 1 m. The averaged coefficient of variation of soil moisture in the top 0.1 m is larger than for the top 1 m, showing that the range for the top 0.1 m is less than for the top 1 m. The range in summer is less than in winter. By calculating the ratio of the nugget effect to the spatial variance, we find that the ratios for the top 0.1 m data are smaller than that for the top 1 m data, showing that in most cases the spatial variation of the top 0.1 m is more strongly autocorrelated than of the top 1 m, and that the measurement errors are much larger in the top 1 m data than in the top 0.1 m. For more than half of the measured dates, the ratio of the nugget effect to the variance is less than 20 percent, indicating that the spatially correlated variation on those dates can explain more than 80 percent of the total variance.

Suxia Liu

Papers

The Global Soil Moisture Data Bank

Evaluation of Global Soil Wetness Project Soil Moisture Simulations

Spatial variation of soil moisture in China: Geostatistical characterization