GLDAS Validation Publications

For reprints of any article listed on this page, contact Matthew Rodell.

Primary Reference:

Rodell, M., P.R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J.K. Entin, J.P. Walker, D. Lohmann, and D. Toll, The Global Land Data Assimilation System, Bull. Amer. Meteor. Soc., 85(3), 381-394, 2004. Abstract

GLDAS validation publications are marked with a "V".

Non-exhaustive list of GLDAS-related references:

Chen, Y., K. Yang, J. Qin, L. Zhao, W. Tang, and M. Han, Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau, J. Geophys. Res. Atmos., 118, 44664475, doi:10.1002/jgrd.50301, 2013. Abstract - V

Rodell, M., E.B. McWilliams, J.S. Famiglietti, H.K. Beaudoing, and J. Nigro, Estimating evapotranspiration using an observation based terrestrial water budget, Hydrol. Proc., 25, 4082-4092, 2011. Abstract - V

Jimenez, C., C. Prigent, B. Mueller, S. I. Seneviratne, M. F. McCabe, E.F. Wood, W. B. Rossow, G. Balsamo, A. K. Betts, P. A. Dirmeyer, J. B. Fisher, M. Jung, M. Kanamitsu, R. H. Reichle, M. Reichstein, M. Rodell, J. Sheffield, K. Tu, and K. Wang, Global inter-comparison of 12 land surface heat flux estimates, J. Geophys. Res., 116, D02102, doi:10.1029/2010JD014545, 2011. Abstract - V

Mueller, B., S.I. Seneviratne, C. Jimenez, T. Corti, M. Hirschi, G. Balsamo, A. Beljaars, A.K. Betts, P. Ciais, P. Dirmeyer, J.B. Fisher, Z. Guo, M. Jung, C.D. Kummerow, F. Maignan, M.F. McCabe, R. Reichle, M. Reichstein, M. Rodell, W.B. Rossow, J. Sheffield, A. J. Teuling, K. Wang, and E.F. Wood, Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations, Geophys. Res. Lett., 38, L06402, doi:10.1029/2010GL046230, 2011. Abstract - V

Wang, F., L. Wang, T. Koike, H. Zhou, K. Yang, A. Wang, and W. Li, Evaluation and application of a fine-resolution global data set in a semiarid mesoscale river basin with a distributed biosphere hydrological model, J. Geophys. Res., 116, D21108, doi:10.1029/2011JD015990, 2011. Abstract - V

Zaitchik, B.F., M. Rodell, and F. Olivera, Evaluation of the Global Land Data Assimilation System using global river discharge data and a source to sink routing scheme, Water Resour. Res., 46, W06507, doi:10.1029/2009WR007811, 2010. Abstract - V

Ozdogan, M., M. Rodell, H.K. Beaudoing, and D. Toll, Simulating the effects of irrigation over the U.S. in a land surface model based on satellite derived agricultural data, J. Hydrometeor., 11 (1), 171-184, doi: 10.1175/2009JHM1116.1, 2010. Abstract

Zaitchik, B.F., and M. Rodell, Forward-looking assimilation of MODIS-derived snow covered area into a land surface model, J. Hydrometeor., 10 (1), 130-148, 2009. Abstract

Syed, T.H., J.S. Famiglietti, M. Rodell, J.L. Chen, and C.R. Wilson, Analysis of terrestrial water storage changes from GRACE and GLDAS, Wat. Resour. Res., 44, W02433, doi:10.1029/2006WR005779, 2008. Abstract - V

Rodell, M., and H. Kato, GLDAS output supports CEOP studies, CEOP Newsletter, 10, 2006. Full Text

Goncalves, L., J. Shuttleworth, S.C. Chou, Y. Xue, P. Houser, D. Toll, J. Marengo, and M. Rodell, Impact of different initial soil moisture fields on Eta model weather forecasts for South America, J. Geophys. Res., 111, D17102, doi:10.1029/2005JD006309, 2006. Abstract

Goncalves, L., J. Shuttleworth, E. Burke, P. Houser, D. Toll, M. Rodell, and K. Arsenault, Towards a South America land data assimilation system (SALDAS): aspects of land surface model spin up using the Simplified Simple Biosphere (SSiB), J. Geophys. Res., 111, D17110, doi:10.1029/2005JD006297, 2006. Abstract

Kato, H., M. Rodell, F. Beyrich, H. Cleugh, E. van Gorsel, H. Liu, and T.P. Meyers, Sensitivity of Land Surface Simulations to Model Physics, Parameters, and Forcings, at Four CEOP Sites, J. Meteor. Soc. Japan, 85A, 187-204, 2007. Abstract - V

Rodell, M., P.R. Houser, A.A. Berg, and J.S. Famiglietti, Evaluation of ten methods for initializing a land surface model, J. Hydromet., 6(2), 146-155, 2005. Abstract

Berg A.A., J.S. Famiglietti, M. Rodell, R.H. Reichle, U. Jambor, S.L. Holl, and P.R. Houser, Development of a hydrometeorological forcing data set for global soil moisture estimation, Int. J. Climatol., 25(13), 1697-1714, 2005. Abstract

Gottschalck, J., J. Meng, M. Rodell, and P. Houser, Analysis of multiple precipitation products and preliminary assessment of their impact on Global Land Data Assimilation System (GLDAS) land surface states, J. Hydromet., 6(5), 573-598, 2005. Abstract

Rodell, M., and P.R. Houser, Updating a land surface model with MODIS derived snow cover, J. Hydromet., 5(6), 1064-1075, 2004. Abstract

Rodell, M., J.S. Famiglietti, J. Chen, S. Seneviratne, P. Viterbo, S. Holl, and C. R. Wilson, Basin scale estimates of evapotranspiration using GRACE and other observations, Geophys. Res. Lett., 31, L20504, doi:10.1029/2004GL020873, 2004. Abstract - V

Koster, R.D., M.J. Suarez, P. Liu, U. Jambor, M. Kistler, A. Berg, R. Reichle, M. Rodell, and J. Famiglietti, Realistic initialization of land surface states: impacts on subseasonal forecast skill, J. Hydromet., 5(6), 1049-1063, 2004. Abstract

ABSTRACTS

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 hours of the present). GLDAS is also a test bed for innovative modeling and assimilation capabilities. A vegetation-based "tiling" approach is used to simulate sub-grid 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 employed as forcing data. The high-quality, global land surface fields provided by GLDAS will be used to initialize weather and climate prediction models and will promote various hydrometeorological studies and applications. The 2001-forward GLDAS archive of modeled and observed, global, surface meteorological data, parameter maps, and output is publicly available.

A multi-scale soil moisture and temperature monitoring network, consisting of 55 soil moisture and temperature measurement stations, has been established in central Tibetan Plateau (TP). In this study, the station-averaged surface soil moisture data from the network are used to evaluate four soil moisture products retrieved from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and four land surface modeling products from the Global Land Data Assimilation System (GLDAS). Major findings are (1) none of the four AMSR-E products provides reliable estimates in the unfrozen season, in terms of the mission requirement of the root mean square error (RMSE) < 0.06 m3m−3. These algorithms either evidently overestimate soil moisture or obviously underestimate it, although some of them showed the soil moisture dynamic range, indicating that the retrieval algorithms have much space to be improved for the cold semi-arid regions. (2) The four GLDAS models tend to systematically underestimate the surface soil moisture (05 cm) while well simulate the soil moisture for 2040 cm layer. In comparison with the satellite surface soil moisture products, three among the four models give low RMSE and BIAS values, but still falling out of the acceptable range. The causes for the modeling biases in this cold region were discussed.

Rodell, M., E.B. McWilliams, J.S. Famiglietti, H.K. Beaudoing, and J. Nigro, Estimating evapotranspiration using an observation based terrestrial water budget, Hydrol. Proc., 25, 4082-4092, 2011.

Evapotranspiration (ET) is difficult to measure at the scales of climate models and climate variability. While satellite retrieval algorithms do exist, their accuracy is limited by the sparseness of in situ observations available for calibration and validation, which themselves may be unrepresentative of 500 m and larger scale satellite footprints and grid pixels. Here, we use a combination of satellite and ground-based observations to close the water budgets of seven continental scale river basins (Mackenzie, Fraser, Nelson, Mississippi, Tocantins, Danube, and Ubangi), estimating mean ET as a residual. For any river basin, ET must equal total precipitation minus net runoff minus the change in total terrestrial water storage (TWS), in order for mass to be conserved. We make use of precipitation from two global observation-based products, archived runoff data, and TWS changes from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. We demonstrate that while uncertainty in the water budget-based estimates of monthly ET is often too large for those estimates to be useful, the uncertainty in the mean annual cycle is small enough that it is practical for evaluating other ET products. Here, we evaluate five land surface model simulations, two operational atmospheric analyses, and a recent global reanalysis product based on our results. An important outcome is that the water budget-based ET time series in two tropical river basins, one in Brazil and the other in central Africa, exhibit a weak annual cycle, which may help to resolve debate about the strength of the annual cycle of ET in such regions and how ET is constrained throughout the year. The methods described will be useful for water and energy budget studies, weather and climate model assessments, and satellite-based ET retrieval optimization.

A global intercomparison of 12 monthly mean land surface heat flux products for the period 19931995 is presented. The intercomparison includes some of the first emerging global satellite-based products (developed at Paris Observatory, Max Planck Institute for Biogeochemistry, University of California Berkeley, University of Maryland, and Princeton University) and examples of fluxes produced by reanalyses (ERA-Interim, MERRA, NCEP-DOE) and off-line land surface models (GSWP-2, GLDAS CLM/Mosaic/Noah). An intercomparison of the global latent heat flux (Qle) annual means shows a spread of ∼20 W m−2 (all-product global average of ∼45 W m−2). A similar spread is observed for the sensible (Qh) and net radiative (Rn) fluxes. In general, the products correlate well with each other, helped by the large seasonal variability and common forcing data for some of the products. Expected spatial distributions related to the major climatic regimes and geographical features are reproduced by all products. Nevertheless, large Qle and Qh absolute differences are also observed. The fluxes were spatially averaged for 10 vegetation classes. The larger Qle differences were observed for the rain forest but, when normalized by mean fluxes, the differences were comparable to other classes. In general, the correlations between Qle and Rn were higher for the satellite-based products compared with the reanalyses and off-line models. The fluxes were also averaged for 10 selected basins. The seasonality was generally well captured by all products, but large differences in the flux partitioning were observed for some products and basins.

Quantification of global land evapotranspiration (ET) has long been associated with large uncertainties due to the lack of reference observations. Several recently developed products now provide the capacity to estimate ET at global scales. These products, partly based on observational data, include satellite-based products, land surface model (LSM) simulations, atmospheric reanalysis output, estimates based on empirical upscaling of eddy-covariance flux measurements, and atmospheric water balance datasets. The LandFlux-EVAL project aims to evaluate and compare these newly developed datasets. Additionally, an evaluation of IPCC AR4 global climate model (GCM) simulations is presented, providing an assessment of their capacity to reproduce flux behavior relative to the observations-based products. Though differently constrained with observations, the analyzed reference datasets display similar large-scale ET patterns. ET from the IPCC AR4 simulations was significantly smaller than that from the other products for India (up to 1 mm/d) and parts of eastern South America, and larger in the western USA, Australia and China. The inter-product variance is lower across the IPCC AR4 simulations than across the reference datasets in several regions, which indicates that uncertainties may be underestimated in the IPCC AR4 models due to shared biases of these simulations.

Accurate estimates of basin-wide water and energy cycles are essential for improving the integrated water resources management (IWRM), especially for relatively dry conditions. This study aims to evaluate and apply a fine-resolution global data set (Global Land Data Assimilation System with Noah Land Surface Model, GLDAS/Noah; 3-h, 0.25-degree) in a semiarid mesoscale basin (∼15000 km2). Four supporting objectives are proposed: (1) validating a Water and Energy Budget-based Distributed Hydrological Model (WEB-DHM) for GLDAS/Noah evaluation and application; (2) evaluating GLDAS forcing data (precipitation; near-surface air temperature, Tair; downward shortwave radiation, Rsw,d; downward longwave radiation, Rlw,d); (3) investigating GLDAS/Noah outputs (land surface temperature, LST; evapotranspiration; fluxes); (4) evaluating the applicability of GLDAS forcing in modeling basin-wide water cycles. Japanese 25-year reanalysis and in situ observations (precipitation; Tair; Rsw,d; discharge) are used for GLDAS/Noah evaluation. Main results include: (1) WEB-DHM can reproduce daily discharge, 8-day LST and monthly surface soil moisture (point scale) fairly well; (2) the GLDAS is of high quality for daily and monthly precipitation, Tair, monthly Rlw,d, while it overestimates monthly Rsw,d; (3) the GLDAS/Noah agrees well with the verified WEB-DHM and JRA-25 in terms of LST, upward shortwave and longwave radiation. While the net radiation, evapotranspiration, latent and sensible heat fluxes modeled by GLDAS/Noah are larger than WEB-DHM and JRA-25 simulations in wet seasons; (4) the basin-integrated discharges and evapotranspiration can be reproduced reasonably well by WEB-DHM fed with GLDAS forcing except linear corrections of Rsw,d. These findings would benefit the IWRM in ungauged or poorly gauged river basins around the world.

Advanced land surface models (LSMs) offer detailed estimates of distributed hydrological fluxes and storages. These estimates are extremely valuable for studies of climate and water resources, but they are difficult to verify as field measurements of soil moisture, evapotranspiration, and surface and subsurface runoff are sparse in most regions. In contrast, river discharge is a hydrologic flux that is recorded regularly and with good accuracy for many of the world′s major rivers. These measurements of discharge spatially integrate all upstream hydrological processes. As such, they can be used to evaluate distributed LSMs, but only if the simulated runoff is properly routed through the river basins. In this study, a rapid, computationally efficient source-to-sink (STS) routing scheme is presented that generates estimates of river discharge at gauge locations based on gridded runoff output. We applied the scheme as a postprocessor to archived output of the Global Land Data Assimilation System (GLDAS). GLDAS integrates satellite and ground-based data within multiple offline LSMs to produce fields of land surface states and fluxes. The application of the STS routing scheme allows for evaluation of GLDAS products in regions that lack distributed in situ hydrological measurements. We found that the four LSMs included in GLDAS yield very different estimates of river discharge and that there are distinct geographic patterns in the accuracy of each model as evaluated against gauged discharge. The choice of atmospheric forcing data set also had a significant influence on the accuracy of simulated discharge.

A novel method is introduced for integrating satellite-derived irrigation data and high-resolution crop-type information into a land surface model (LSM). The objective is to improve the simulation of land surface states and fluxes through better representation of agricultural land use. Ultimately, this scheme could enable numerical weather prediction (NWP) models to capture landatmosphere feedbacks in managed lands more accurately and thus improve forecast skill. Here, it is shown that the application of the new irrigation scheme over the continental United States significantly influences the surface water and energy balances by modulating the partitioning of water between the surface and the atmosphere. In this experiment, irrigation caused a 12% increase in evapotranspiration (QLE) and an equivalent reduction in the sensible heat flux (QH) averaged over all irrigated areas in the continental United States during the 2003 growing season. Local effects were more extreme: irrigation shifted more than 100 W m−2 from QH to QLE in many locations in California, eastern Idaho, southern Washington, and southern Colorado during peak crop growth. In these cases, the changes in ground heat flux (QG), net radiation (RNET), evapotranspiration (ET), runoff (R), and soil moisture (SM) were more than 3 W m−2, 20 W m−2, 5 mm day−1, 0.3 mm day−1, and 100 mm, respectively. These results are highly relevant to continental-to-global-scale water and energy cycle studies that, to date, have struggled to quantify the effects of agricultural management practices such as irrigation. On the basis of the results presented here, it is expected that better representation of managed lands will lead to improved weather and climate forecasting skill when the new irrigation scheme is incorporated into NWP models such as NOAA’s Global Forecast System (GFS).

Zaitchik, B.F., and M. Rodell, Forward-looking assimilation of MODIS-derived snow covered area into a land surface model, J. Hydrometeor., 10 (1), 130-148, 2009

Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow-covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observationSCA indicates only the presence or absence of snow, not snow water equivalentand by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to nonphysical artifacts in the local water balance. In this paper, a novel assimilation algorithm is presented that introduces Moderate Resolution Imaging Spectroradiometer (MODIS) SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm uses observations from up to 72 h ahead of the model simulation to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes during the snow season and, in some regions, on into the following spring.

Syed, T.H., J.S. Famiglietti, M. Rodell, J.L. Chen, and C.R. Wilson, Analysis of terrestrial water storage changes from GRACE and GLDAS, Wat. Resour. Res., 44, W02433, doi:10.1029/2006WR005779, 2008.

Since March 2002, the Gravity Recovery and Climate Experiment (GRACE) has provided first estimates of land water storage variations by monitoring the time-variable component of Earth's gravity field. Here we characterize spatial-temporal variations in terrestrial water storage changes (TWSC) from GRACE and compare them to those simulated with the Global Land Data Assimilation System (GLDAS). Additionally, we use GLDAS simulations to infer how TWSC is partitioned into snow, canopy water and soil water components, and to understand how variations in the hydrologic fluxes act to enhance or dissipate the stores. Results quantify the range of GRACE-derived storage changes during the studied period and place them in the context of seasonal variations in global climate and hydrologic extremes including drought and flood, by impacting land memory processes. The role of the largest continental river basins as major locations for freshwater redistribution is highlighted. GRACE-based storage changes are in good agreement with those obtained from GLDAS simulations. Analysis of GLDAS-simulated TWSC illustrates several key characteristics of spatial and temporal land water storage variations. Global averages of TWSC were partitioned nearly equally between soil moisture and snow water equivalent, while zonal averages of TWSC revealed the importance of soil moisture storage at low latitudes and snow storage at high latitudes. Evapotranspiration plays a key role in dissipating globally averaged terrestrial water storage. Latitudinal averages showed how precipitation dominates TWSC variations in the tropics, evapotranspiration is most effective in the midlatitudes, and snowmelt runoff is a key dissipating flux at high latitudes. Results have implications for monitoring water storage response to climate variability and change, and for constraining land model hydrology simulations.

Two 7-day weather simulations were made for South America in July 2003 and January 2004 (in the Southern Hemisphere summer and winter) to investigate the impacts of using different soil moisture initialization fields in the Eta model coupled to the Simplified Simple Biosphere (SSiB) land surface model. The alternative initial soil moisture fields were (1) the soil moisture climatology used operationally by the Centro de Previsão do Tempo e Estudos Climáticos in Brazil and (2) the soil moisture fields generated by a South American Land Data Assimilation System (SALDAS) based on SSiB. When the SALDAS soil moisture fields were used, there was an increase in the model performance relative to climatology in the equitable threat score calculated with respect to observed surface precipitation fields and a decrease (up to 53%) in the root-mean-square error relative to the NCEP analysis of the modeled geopotential height at 500 hPa and mean sea level pressure. However, there was small change in the model skill in positioning the primary South American weather systems because of a change in the upper troposphere circulation caused by SALDAS initialization, most noticeably in the South Atlantic Convergence Zone.

This paper describes a spin-up experiment conducted over South America using the Simplified Simple Biosphere (SSiB) land surface model to study the process of model adjustment to atmospheric forcing data. The experiment was carried out as a precursor to the use of SSiB in a South American Land Data Assimilation System (SALDAS). The results from an 11 year long recursive simulation using three different initial conditions of soil wetness (control, wet and dry) are examined. The control run was initiated by interpolation of the NCEP/DOE Global Reanalysis-2 (NCEP/DOE R-2) soil moisture data set. In each case the time required for the model to reach equilibrium was calculated. The wet initialization leads to a faster adjustment of the soil moisture field, followed by the control and then the dry initialization. Overall, the final spin-up states using the SSiB-based SALDAS are generally wetter than both the NCEP/DOE R-2 and the Centro de Previsao do Tempo e Estudos Climaticos (CPTEC-Brazilian Center for Weather Forecast and Climate Studies) operational initial soil moisture states, consequently modeled latent heat is higher and sensible heat lower in the final year of simulation when compared with the first year. Selected regions, i.e., in semiarid northeastern Brazil, the transition zone to the south of the Amazon tropical forest, and the central Andes were studied in more detail because they took longer to spin up (up to 56 months) when compared with other areas (less than 24 months). It is shown that there is a rapid change in the soil moisture in all layers in the first 2 months of simulation followed by a subsequent slow and steady adjustment: This could imply there are increasing errors in medium range simulations. Spin-up is longest where frozen soil is present for long periods such as in the central Andes.

Numerical land surface models (LSMs) are abundant and in many cases highly sophisticated, yet their output has not converged towards a consensus depiction of reality. Addressing this matter is complicated by the huge number of possible combinations of input land characteristics, forcings, and physics packages available. The Global Land Data Assimilation System (GLDAS) and its sister project the Land Information System (LIS) have made it straightforward to test a variety of configurations with multiple LSMs. In order to compare the impacts of the choice of LSM, land cover, soil, and elevation information, and precipitation and downward radiation forcing datasets on simulated evapotranspiration, sensible heat flux, and top layer soil moisture, a set of experiments was designed which made use of high quality, physically coherent, 1-year datasets from four reference sites of the Coordinated Enhanced Observing Period (CEOP) initiative. As in previous studies, it was shown that the LSM itself is generally the most important factor governing output. Beyond that, evapotranspiration seems to be most sensitive to precipitation, land cover, and radiation (in that order); sensible heat flux is most sensitive to radiation, precipitation, and land cover; and soil moisture is most sensitive to precipitation, soil, and land cover. Various seasonal and model specific dependencies and other caveats are discussed. Output fields were also compared with observations in order to test whether the LSMs are capable of simulating an observed reality given a plausible set of inputs. In general, that potential was fair for evapotranspiration, good for sensible heat flux but problematic given its strong sensitivity to the inputs, and poor for soil moisture. The results emphasize that improving the LSMs themselves, and not just the inputs, will be essential if we hope to model land surface water and energy processes accurately.

Rodell, M., P.R. Houser, A.A. Berg, and J.S. Famiglietti, Evaluation of ten methods for initializing a land surface model, J. Hydromet., 6(2), 146-155, 2005.

Improper initialization of numerical models can cause spurious trends in the output, inviting erroneous interpretations of the Earth system processes that one wishes to study. In particular, soil moisture memory is considerable, so that accurate initialization of this variable in land surface models (LSMs) is critical. The most commonly employed method for initializing a LSM is to spin-up by looping through a single year repeatedly until a predefined equilibrium is achieved. The downside to this technique, when applied to continental to global scale simulations, is that regional annual anomalies in the meteorological forcing accumulate as artificial anomalies in the land surface states, including soil moisture. Nine alternative approaches were tested and compared using the Mosaic LSM and 15 years of global meteorological forcing. Results indicate that the most efficient way to initialize a LSM, if possible and given that multiple years of preceding forcing are not available, is to use climatological average states from the same model for the precise time of year of initialization. Three other approaches were also determined to be preferable to the single year spin-up method. In addition, low resolution spin-up scenarios were devised and tested, and based on the results an effective yet computationally economical technique is proposed.

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.

Precipitation is arguably the most important meteorological forcing variable in land surface modeling. Many types of precipitation datasets exist (with various pros and cons) and include those from atmospheric data assimilation systems, satellites, rain gauges, ground radar, and merged products. These datasets are being evaluated in order to choose the most suitable precipitation forcing for real-time and retrospective simulations of the Global Land Data Assimilation System (GLDAS). This paper first presents results of a comparison for the period from March 2002 to February 2003. Later, GLDAS simulations 14 months in duration are analyzed to diagnose impacts on GLDAS land surface states when using the Mosaic land surface model (LSM).

A comparison of seasonal total precipitation for the continental United States (CONUS) illustrates that the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) has the closest agreement with a CPC rain gauge dataset for all seasons except winter. The European Centre for Medium-Range Weather Forecasts (ECMWF) model performs the best of the modeling systems. The satellite-only products [the Tropical Rainfall Measuring Mission (TRMM) Real-time Multi-satellite Precipitation Analysis and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)] suffer from a few deficiencies most notably an overestimation of summertime precipitation in the central United States (200-400 mm). CMAP is the most closely correlated with daily rain gauge data for the spring, fall, and winter seasons, while the satellite-only estimates perform best in summer. GLDAS land surface states are sensitive to different precipitation forcing where percent differences in volumetric soil water content (SWC) between simulations ranged from -75% to +100%. The percent differences in SWC are generally 25-75% less than the percent precipitation differences, indicating that GLDAS and specifically the Mosaic LSM act to generally "damp" precipitation differences. Areas where the percent changes are equivalent to the percent precipitation changes, however, are evident. Soil temperature spread between GLDAS runs was considerable and ranged up to ±3.0 K with the largest impact in the western United States.

Rodell, M., and P.R. Houser, Updating a land surface model with MODIS derived snow cover, J. Hydromet., 5(6), 1064-1075, 2004.

A simple scheme for updating snow water storage in a land surface model using snow cover observations is presented. The scheme makes use of snow cover observations retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra and Aqua satellites. Simulated snow water equivalent is adjusted when and where the model and MODIS observation differ, following an internal accounting of the observation quality, by either removing the simulated snow or adding a thin layer. The scheme is tested in a 101 day global simulation of the Mosaic land surface model driven by the NASA/NOAA Global Land Data Assimilation System. Output from this simulation is compared to that from a control (not updated) simulation, and both are assessed using a conventional snow cover product and data from ground based observation networks over the continental U.S. In general, output from the updated simulation displays more accurate snow coverage and compares more favorably with in situ snow time series. Both the control and updated simulations have serious deficiencies on occasion and in certain areas when and where the precipitation and/or surface air temperature forcing inputs are unrealistic, particularly in mountainous regions. Suggestions for developing a more sophisticated updating scheme are presented.

Evapotranspiration is integral to studies of the Earth system, yet it is difficult to measure on regional scales. One estimation technique is a terrestrial water budget, i.e., total precipitation minus the sum of evapotranspiration and net runoff equals the change in water storage. Gravity Recovery and Climate Experiment (GRACE) satellite gravity observations are now enabling closure of this equation by providing the terrestrial water storage change. Equations are presented here for estimating evapotranspiration using observation based information, taking into account the unique nature of GRACE observations. GRACE water storage changes are first substantiated by comparing with results from a land surface model and a combined atmospheric-terrestrial water budget approach. Evapotranspiration is then estimated for 14 time periods over the Mississippi River basin and compared with output from three modeling systems. The GRACE estimates generally lay in the middle of the models and may provide skill in evaluating modeled evapotranspiration.

Forcing a land surface model (LSM) offline with realistic global fields of precipitation, radiation, and nearsurface meteorology produces realistic fields (within the context of the LSM) of soil moisture, temperature, and other land surface states. These fields can be used as initial conditions for precipitation and temperature forecasts with an atmospheric general circulation model (AGCM). Their usefulness is tested in this regard by performing retrospective 1-month forecasts (for May through September, 1979-1993) with the NASA Global Modeling and Assimilation Office (GMAO) seasonal prediction system. The 75 separate forecasts provide an adequate statistical basis for quantifying improvements in forecast skill associated with land initialization. Evaluation of skill is focused on the Great Plains of North America, a region with both a reliable land initialization and an ability of soil moisture conditions to overwhelm atmospheric chaos in the evolution of the meteorological fields. The land initialization does cause a small but statistically significant improvement in precipitation and air temperature forecasts in this region. For precipitation, the increases in forecast skill appear strongest in May through July, whereas for air temperature, they are largest in August and September. The joint initialization of land and atmospheric variables is considered in a supplemental series of ensemble monthly forecasts. Potential predictability from atmospheric initialization dominates over that from land initialization during the first 2 weeks of the forecast, whereas during the final 2 weeks, the relative contributions from the two sources are of the same order. Both land and atmospheric initialization contribute independently to the actual skill of the monthly temperature forecast, with the greatest skill derived from the initialization of both. Land initialization appears to contribute the most to monthly precipitation forecast skill.

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LDAS datasets are available from the NASA GES DISC

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides access to LDAS datasets using multiple methods, including HTTPS, GDS, and the ability to subset spatially, temporally, and/or by variable:

These datasets are also available via Giovanni. Giovanni is an online application developed by the GES DISC that allows researchers to rapidly explore data, so that spatial-temporal variability, anomalous conditions, and patterns of interest can be directly and easily analyzed online before optionally downloading the data. Supported download formats include NetCDF, GeoTIFF, and KMZ.

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Reprocessed FLDAS Data

Output data for several FLDAS model runs and selected periods have been reprocessed.

Central Asia hydrologic monitoring data set published

McNally, A., et al.: A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan, ESSD, 14, 3115–3135, 2022.

Central Asia Extended Outlook: Reservoir water levels are low with a possible drought looming

Central Asia Extended Outlook: Reservoir water levels are low with a possible drought looming, 2021, https://blog.chc.ucsb.edu/?p=1044 .