Paul R. Houser

The Global Land Data Assimilation System

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...

Correcting eddy-covariance flux underestimates over a grassland

Independent measurements of the major energy balance flux components are not often consistent with the principle of conservation of energy. This is referred to as a lack of closure of the surface energy balance. Most results in the literature have shown the sum of sensible and latent heat fluxes measured by eddy covariance to be less than the difference between net radiation and soil heat fluxes. This under-measurement of sensible and latent heat fluxes by eddy-covariance instruments has occurred in numerous field experiments and among many different manufacturers of instruments. Four eddy-covariance systems consisting of the same models of instruments were set up side-by-side during the Southern Great Plains 1997 Hydrology Experiment and all systems under-measured fluxes by similar amounts. One of these eddy-covariance systems was collocated with three other types of eddy-covariance systems at different sites; all of these systems under-measured the sensible and latent-heat fluxes. The net radiometers and soil heat flux plates used in conjunction with the eddy-covariance systems were calibrated independently and measurements of net radiation and soil heat flux showed little scatter for various sites. The 10% absolute uncertainty in available energy measurements was considerably smaller than the systematic closure problem in the surface energy budget, which varied from 10 to 30%. When available-energy measurement errors are known and modest, eddy-covariance measurements of sensible and latent heat fluxes should be adjusted for closure. Although the preferred method of energy balance closure is to maintain the Bowen‐ratio, the method for obtaining closure appears to be less important than assuring that eddy-covariance measurements are consistent with conservation of energy. Based on numerous measurements over a sorghum canopy, carbon dioxide fluxes, which are measured by eddy covariance, are underestimated by the same factor as eddy covariance evaporation measurements when energy balance closure is not achieved. Published by Elsevier Science B.V.

The common land model

The Common Land Model (CLM) was developed for community use by a grassroots collaboration of scientists who have an interest in making a general land model available for public use and further development. The major model characteristics include enough unevenly spaced layers to adequately represent soil temperature and soil moisture, and a multilayer parameterization of snow processes; an explicit treatment of the mass of liquid water and ice water and their phase change within the snow and soil system; a runoff parameterization following the TOPMODEL concept; a canopy photo synthesis-conductance model that describes the simultaneous transfer of CO2 and water vapor into and out of vegetation; and a tiled treatment of the subgrid fraction of energy and water balance. CLM has been extensively evaluated in offline mode and coupling runs with the NCAR Community Climate Model (CCM3). The results of two offline runs, presented as examples, are compared with observations and with the simulation of three other la...

Integration of soil moisture remote sensing and hydrologic modeling using data assimilation

The feasibility of synthesizing distributed fields of soil moisture by the novel application of four-dimensional data assimilation (4DDA) applied in a hydrological model is explored. Six 160-km2 push broom microwave radiometer (PBMR) images gathered over the Walnut Gulch experimental watershed in southeast Arizona were assimilated into the Topmodel-based Land-Atmosphere Transfer Scheme (TOPLATS) using several alternative assimilation procedures. Modification of traditional assimilation methods was required to use these high-density PBMR observations. The images were found to contain horizontal correlations that imply length scales of several tens of kilometers, thus allowing information to be advected beyond the area of the image. Information on surface soil moisture also was assimilated into the subsurface using knowledge of the surface- subsurface correlation. Newtonian nudging assimilation procedures are preferable to other techniques because they nearly preserve the observed patterns within the sampled region but also yield plausible patterns in unmeasured regions and allow information to be advected in time.

Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water

Monitoring Earths terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earths terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

Ground‐based investigation of soil moisture variability within remote sensing footprints During the Southern Great Plains 1997 (SGP97) Hydrology Experiment

Surface soil moisture content is highly variable in both space and time. While remote sensing provides an effective methodology for mapping surface moisture content over large areas, it averages within-pixel variability thereby masking the underlying heterogeneity observed at the land surface. This variability must be better understood in order to rigorously evaluate sensor performance and to enhance the utility of the larger-scale remotely sensed averages by quantifying the underlying variability that remote sensing cannot record explicitly. In support of the Southern Great Plains 1997 (SGP97) Hydrology Experiment (a surface soil moisture mapping mission conducted between June 18 and July 17, 1997, in central Oklahoma) an investigation was conducted to characterize soil moisture variability within remote sensing footprints (approximately 0.64 km2) with more certainty than would be afforded with conventional gravimetric moisture content sampling. Nearly every day during the experiment period, portable impedance probes were used to intensively monitor volumetric moisture content in the 0- to 6-cm surface soil layer at six footprint-sized fields scattered over the SGP97 study area. A minimum of 49 daily moisture content measurements were made on most fields. Higher-resolution grid and transect data were also collected periodically. In total, more than 11,000 impedance probe measurements of volumetric moisture content were made at the six sites by over 35 SGP97 participants. The wide spatial distribution of the sites, combined with the intensive, near-daily monitoring, provided a unique opportunity (relative to previous smaller-scale and shorter-duration soil moisture studies) to characterize variations in surface moisture content over a range of wetness conditions. In this paper the range and temporal dynamics of the variability in moisture content within each of the six fields are described, as are general relationships between the variability and footprint-mean moisture content. Results indicate that distinct differences in mean moisture content between the six sites are consistent with variations in soil type, vegetation cover, and rainfall gradients. Within fields the standard deviation, coefficient of variation, skewness, and kurtosis increased with decreasing moisture content; the distribution of surface moisture content evolved from negatively skewed/nonnormal under very wet conditions, to normal in the midrange of mean moisture content, to positively skewed/nonnormal under dry conditions; and agricultural practices of row tilling and terracing were shown to exert a major control on observed moisture content variations. Results presented here can be utilized to better evaluate sensor performance, to extrapolate estimates of subgrid-scale variations in moisture content across the entire SGP97 region, and in the parameterization of soil moisture dynamics in hydrological and land surface models.

Land information system: An interoperable framework for high resolution land surface modeling

Abstract Knowledge of land surface water, energy, and carbon conditions are of critical importance due to their impact on many real world applications such as agricultural production, water resource management, and flood, weather, and climate prediction. Land Information System (LIS) is a software framework that integrates the use of satellite and ground-based observational data along with advanced land surface models and computing tools to accurately characterize land surface states and fluxes. LIS employs the use of scalable, high performance computing and data management technologies to deal with the computational challenges of high resolution land surface modeling. To make the LIS products transparently available to the end users, LIS includes a number of highly interactive visualization components as well. The LIS components are designed using object-oriented principles, with flexible, adaptable interfaces and modular structures for rapid prototyping and development. In addition, the interoperable features in LIS enable the definition, intercomparison, and validation of land surface modeling standards and the reuse of a high quality land surface modeling and computing system.

Extended versus Ensemble Kalman Filtering for Land Data Assimilation

Abstract The performance of the extended Kalman filter (EKF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation. In a twin experiment for the southeastern United States synthetic observations of near-surface soil moisture are assimilated once every 3 days, neglecting horizontal error correlations and treating catchments independently. Both filters provide satisfactory estimates of soil moisture. The average actual estimation error in volumetric moisture content of the soil profile is 2.2% for the EKF and 2.2% (or 2.1%; or 2.0%) for the EnKF with 4 (or 10; or 500) ensemble members. Expected error covariances of both filters generally differ from actual estimation errors. Nevertheless, nonlinearities in soil processes are treated adequately by both filters. In the application presented herein the EKF and the EnKF with four ensemble members are equally accurate at comparable computational cost. Because of its flexibility and its performance in this study, the EnKF is a promising ...

The hydrosphere State (hydros) Satellite mission: an Earth system pathfinder for global mapping of soil moisture and land freeze/thaw

The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Administration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earths soil moisture at 10-km resolution with two- to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture. The mission data will enable advances in weather and climate prediction and in mapping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39/spl deg/ with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented.

A methodology for initializing soil moisture in a global climate model: Assimilation of near‐surface soil moisture observations

Because of its long-term persistence, accurate initialization of land surface soil moisture in fully coupled global climate models has the potential to greatly increase the accuracy of climatological and hydrological prediction. To improve the initialization of soil moisture in the NASA Seasonal-to-Interannual Prediction Project (NSIPP), a one-dimensional Kalman filter has been developed to assimilate near-surface soil moisture observations into the catchment-based land surface model used by NSIPP. A set of numerical experiments was performed using an uncoupled version of the NSIPP land surface model to evaluate the assimilation procedure. In this study, “true” land surface data were generated by spinning-up the land surface model for 1987 using the International Satellite Land Surface Climatology Project (ISLSCP) forcing data sets. A degraded simulation was made for 1987 by setting the initial soil moisture prognostic variables to arbitrarily wet values uniformly throughout North America. The final simulation run assimilated the synthetically generated near-surface soil moisture “observations” from the true simulation into the degraded simulation once every 3 days. This study has illustrated that by assimilating near-surface soil moisture observations, as would be available from a remote sensing satellite, errors in forecast soil moisture profiles as a result of poor initialization may be removed and the resulting predictions of runoff and evapotranspiration improved. After only 1 month of assimilation the root-mean-square error in the profile storage of soil moisture was reduced to 3% vol/vol, while after 12 months of assimilation, the root-mean-square error in the profile storage was as low as 1% vol/vol.