Satellite Land Surface Climatology Project Research Articles

Estimation of Turbulent Heat Fluxes via Assimilation of Air Temperature and Specific Humidity into an Atmospheric Boundary Layer Model

AbstractA number of studies have used time series of air temperature and specific humidity observations to estimate turbulent heat fluxes. These studies require the specification of surface roughness lengths for heat and momentum (that are directly related to the neutral bulk heat transfer coefficientCHN) and/or ground heat flux, which are often unavailable. In this study, sequences of air temperature and specific humidity are assimilated into an atmospheric boundary layer model within a variational data assimilation (VDA) framework to estimateCHN, evaporative fraction (EF), turbulent heat fluxes, and atmospheric boundary layer (ABL) height, potential temperature, and humidity. The developed VDA approach needs neither the surface roughness parameterization (as it is optimized by the VDA approach) nor ground heat flux measurements. The VDA approach is tested over the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) site in the summers of 1987 and 1988. The results indicate that the estimated sensible and latent heat fluxes agree fairly well with the corresponding measurements. For FIFE 1987 (1988), the daily sensible and latent heat fluxes estimates have a root-mean-square error of 25.72 W m−2(27.77 W m−2) and 53.63 W m−2(48.22 W m−2), respectively. In addition, the ABL height, specific humidity, and potential temperature estimates from the VDA system are in good agreement with those inferred from the radiosondes both in terms of magnitude and diurnal trend.

Estimation of surface heat fluxes via variational assimilation of land surface temperature, air temperature and specific humidity into a coupled land surface-atmospheric boundary layer model

Numerous studies have estimated surface heat fluxes by assimilating land surface temperature (LST) observations (as the state variable of land surface). A number of other studies have focused on the estimation of surface energy balance components by assimilating air temperature and specific humidity (as the state variables of atmosphere) into an atmospheric boundary layer model. This study advances these existing data assimilation approaches by the synergistic assimilation of LST, air temperature, and specific humidity into a coupled land surface-atmospheric boundary layer model. The unknown parameters are the neutral bulk heat transfer coefficient (CHN) and evaporative fraction (EF). CHN scales the sum of turbulent heat fluxes, and EF represents their partitioning. The developed approach is tested at the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) site in the summer of 1987 and 1988. The results indicate that the new approach performs well in both wet and dry periods because it uses the implicit information in both the land surface and atmospheric state variables (i.e., LST, air temperature, and specific humidity). The root-mean-square-errors (RMSEs) of estimated daily sensible and latent heat fluxes are 21.80 Wm−2 (22.10 Wm−2) and 39.32 Wm−2 (36.89 Wm−2) for FIFE 87 (88). The corresponding mean-absolute-percentage-errors (MAPEs) are 22.16% (18.64%) and 13.98% (13.44%) for FIFE 87 (88). The new variational data assimilation (VDA) system outperforms the previous studies that assimilated either LST or air temperature/specific humidity. For FIFE 87, this study decreases the RMSEs of daily sensible and latent heat fluxes estimates by 12.5% and 24.4% compared to assimilating only LST, and by 15.2% and 26.7% compared to assimilating only air temperature and specific humidity. A similar improvement is obtained for FIFE 88. The atmospheric boundary layer height, potential temperature, and specific humidity estimates from the VDA approach are also in good agreement with the corresponding radiosonde observations, and can capture their variations during the course of the day.

How can the First ISLSCP Field Experiment contribute to present-day efforts to evaluate water stress in JULESv5.0?

Abstract. The First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), Kansas, US, 1987–1989, made important contributions to the understanding of energy and CO2 exchanges between the land surface and the atmosphere, which heavily influenced the development of numerical land-surface modelling. Now, 30 years on, we demonstrate how the wealth of data collected during FIFE and its subsequent in-depth analysis in the literature continue to be a valuable resource for the current generation of land-surface models. To illustrate, we use the FIFE dataset to evaluate the representation of water stress on tallgrass prairie vegetation in the Joint UK Land Environment Simulator (JULES) and highlight areas for future development. We show that, while JULES is able to simulate a decrease in net carbon assimilation and evapotranspiration during a dry spell, the shape of the diurnal cycle is not well captured. Evaluating the model parameters and results against this dataset provides a case study on the assumptions in calibrating “unstressed” vegetation parameters and thresholds for water stress. In particular, the responses to low water availability and high temperatures are calibrated separately. We also illustrate the effect of inherent uncertainties in key observables, such as leaf area index, soil moisture and soil properties. Given these valuable lessons, simulations for this site will be a key addition to a compilation of simulations covering a wide range of vegetation types and climate regimes, which will be used to improve the way that water stress is represented within JULES.

Residual layer effects on the modeling of convective boundary layer growth rates with a slab model using FIFE data

The ability of slab models for the growth of the convective boundary layer (CBL) to work in the presence of residual layers above is analyzed in detail with a large data set from the First International Satellite Land Surface Climatology Project Field Experiment (FIFE)‐87 and FIFE‐89 experiments. We confirm that the critical element that allows the models to predict the growth as the CBL coalesces with the residual layer is the adoption of a variable lapse rate above the growing CBL. This is not a new finding per se, as several previous studies in the literature have adopted this strategy. Different parameterizations of a slab model, based on the turbulence kinetic energy budget at the top of the CBL, were tested, and the inclusion of the storage and dissipation terms did not improve the model. Among the alternatives for choosing the lapse rate as the CBL grows, fair results are obtained with a simple choice of two values, with absolute mean errors of the order of 270 m for cases with and without a residual layer. Both lapse rates, representative of the residual layer and the free atmosphere, are obtained from the first morning sounding. Finally, an alternative model for the CBL growth into a residual layer that assumes a zero‐lapse rate above the CBL did not perform well with the FIFE data.

Analysis of BRDF and Albedo Retrieved by Kernel-Driven Models Using Field Measurements

The algorithm for Model Bidirectional Reflectance Anisotropies of the Land Surface (AMBRALS) includes a series of kernel-driven bidirectional reflectance distribution function (BRDF) models. Among these models, the RossThick-LiSparse-Reciprocal (RTLSR) model has been selected as the operational MODIS BRDF/Albedo algorithm. With the newly developed LiTransit kernel and Ross kernel with the hotspot effect by Breon, there is a need to further evaluate models' potentials in retrieving land surface properties for user community. In this paper, 16 kernel-driven models in reciprocal form have been analyzed using 70 measurements mainly from ground campaigns of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) and the Boreal Ecosystem Atmosphere Study (BOREAS). Results show that all models under investigation generally fit the ground measurements with reasonable accuracy—although the “sparse” geometric-optical (GO) kernels appear to present better behaviors compared with the “dense” GO kernels—in combining with volumetric shapes. Estimated albedos from these models show high correlations, while the black-sky albedos (BSAs) present higher correlations in small solar zenith angles than in large ones, indicating that the difference in these kernels may arise from large solar geometry. A further investigation shows that the hotspot effects for these models display a significant discrepancy. Although the additional hotspot factor for Ross kernels significantly improves the hotspot effects, LiTransit kernel can well characterize the hotspot effects in combination with Ross kernels without the hotspot factor. Such an assessment may be helpful in understanding the models' potentials in retrieving vegetation structural characteristics.

Variational assimilation of land surface temperature and the estimation of surface energy balance components

Recently, numerous studies have focused on the estimation of surface energy flux components, based on the assimilation of land surface temperature (LST) within a variational data assimilation (VDA) framework. Unlike the previous investigations based on the force-restore equation, in this study, the full heat diffusion equation is employed in the VDA scheme as an adjoint (constraint). In addition, a model error term is added to the surface energy balance (SEB) equation and the VDA scheme to include the model uncertainty. Both VDA schemes (with and without the model uncertainty) are tested over the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) site. The comprehensive comparisons between the present model (with heat diffusion equation) and previous model (with the force-restore equation) demonstrate that the present model will decrease the phase error associated with the ground heat flux diurnal cycle, and improve the evaporative fraction and heat fluxes estimation. The numerical examples also conclude that the errors caused by model structures and noisy data in the SEB equation can be detected and quantified in the present model (with model uncertainty).

Estimating surface energy fluxes using a dual‐source data assimilation approach adjoined to the heat diffusion equation

Recently, a number of studies have assimilated land surface temperature (LST) within a variational data assimilation (VDA) framework to estimate turbulent heat fluxes. These VDA models have mainly considered soil and vegetation as a combined source (CS) and have not accounted for the difference between soil and canopy temperatures and turbulent exchange rates, although soil and canopy can exhibit very different behaviors. Hence, in this study the contribution of soil and canopy to the LST and turbulent heat fluxes is taken into account separately by developing a dual‐source (DS) VDA model. The unknown model parameters are the neutral bulk heat transfer coefficient (that scales the sum of turbulent heat fluxes) and the evaporative fractions for soil and canopy (which represent partitioning among the turbulent fluxes over soil and vegetation). The model as developed has been tested with area‐averaged measurements of turbulent heat fluxes obtained from the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) during the summers of 1987 and 1988. The results show that the predicted turbulent heat fluxes match well with observations. For FIFE 1987 (1988), the half‐hourly latent heat flux estimates from the new model have a root‐mean square‐error (RMSE) of 57.4 Wm−2 (66.8 Wm−2), which represents a significant improvement over the previous study.

Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters

The estimation of surface heat fluxes based on the assimilation of land surface temperature (LST) has been achieved within a variational data assimilation (VDA) framework. Variational approaches require the development of an adjoint model, which is difficult to derive and code in the presence of thresholds and discontinuities. Also, it is computationally expensive to obtain the background error covariance for the variational approaches. Moreover, the variational schemes cannot directly provide statistical information on the accuracy of their estimates. To overcome these shortcomings, we develop an alternative data assimilation (DA) procedure based on ensemble Kalman smoother (EnKS) with the state augmentation method. The unknowns of the assimilation scheme are neutral turbulent heat transfer coefficient (that scales the sum of turbulent heat fluxes) and evaporative fraction, EF (that represents partitioning among the turbulent fluxes). The new methodology is illustrated with an application to the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) that includes areal average hydrometeorological forcings and flux observations. The results indicate that the EnKS model not only provides reasonably accurate estimates of EF and turbulent heat fluxes but also enables us to determine the uncertainty of estimations under various land surface hydrological conditions. The results of the EnKS model are also compared with those of an optimal smoother (a dynamic variational model). It is found that the EnKS model estimates are less than optimal. However, the degree of suboptimality is small, and its outcomes are roughly comparable to those of an optimal smoother. Overall, the results from this test indicate that EnKS is an efficient and flexible data assimilation procedure that is able to extract useful information on the partitioning of available surface energy from LST measurements and eventually provides reliable estimates of turbulent heat fluxes.

Estimating soil thermal properties from sequences of land surface temperature using hybrid Genetic Algorithm–Finite Difference method

Most models used in land surface hydrology, vadose zone hydrology, and hydro-climatology require an accurate representation of soil thermal properties (soil thermal conductivity and volumetric heat capacity). Various empirical relations have been suggested to estimate soil thermal properties. However, they require many input parameters such as soil texture, mineralogical composition, porosity and water content, which are not always available from laboratory experiments and field measurements. In this paper, to overcome the above challenge, a hybrid numerical method, Genetic Algorithm–Finite Difference (GA–FD), is proposed to estimate soil thermal properties using land surface temperature (LST) as the only input. The genetic algorithm (GA) optimization method coupled with the finite difference (FD) modeling technique is a viable hybrid approach for estimating soil thermal properties. The finite difference method is employed to solve the heat diffusion equation and simulate LST, while a robust optimization technique (GA) is used to retrieve soil thermal properties by minimizing the difference between observed and simulated LST. Furthermore, a generalization of the hybrid model is developed for inhomogeneous soil, in which soil thermal properties are not constant throughout the soil slab. The proposed model is applied to the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). The results show that the proposed hybrid numerical method is able to estimate soil thermal properties accurately, and therefore effectively eliminate the need for the unavailable soil parameters which are required by empirical methods for determining the soil thermal conductivity and volumetric heat capacity. Remarkably, the temporal variation of the retrieved soil thermal conductivity is consistent with the volumetric water content, even though no water content information is used in the model.

A History and Review of the Global Soil Wetness Project (GSWP)

Abstract The Global Soil Wetness Project (GSWP) is an international land surface modeling research effort involving dataset production, validation, model comparison, and scientific investigation in the areas of land surface hydrology and climatology. GSWP is characterized by the integration of multiple land surface models on a latitude–longitude grid in a stand-alone uncoupled mode, driven by meteorological forcing data constructed by combining atmospheric analyses and gridded observed data products. The models produce time series of gridded estimates of land surface fluxes and state variables that are then studied and compared. Defining characteristics that have distinguished GSWP include its global scale, application of land surface models in the same gridded structure as they are used in weather and climate models, and the multimodel approach, which included production of a multimodel analysis in its second phase. This paper gives an overview of the history of GSWP beginning with its inception within the International Satellite Land Surface Climatology Project. Various phases of the project are described, and a review of scientific results stemming from the project is presented. Musings on future directions of research are also discussed.

The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements

[1] This first paper of the two‐part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah‐MP). The Noah‐MP’s performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long‐term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture‐groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local‐scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah‐MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah‐MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah‐MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah‐MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework.

Effects of Vegetation Cover on Hydrological Processes in a Large Region: Huaihe River Basin, China

AbstractVegetation plays an important role in water and energy cycles on land surfaces. Nevertheless, the physical effects of vegetation are not explicitly considered in many hydrologic-modeling works. In this study, a coupled land-surface-hydrologic model was used to investigate the vegetation effects on hydrologic processes over the years 1980–1987 in the Huaihe River basin, China. The vegetation coverage of the basin was assessed by the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II historic and potential land cover data set. Farmland was declared the dominant vegetation type of the basin in 1970 by the historic land cover scenario, and the potential vegetation cover is mixed forest. Firstly, the coupled model was calibrated by observed streamflow at Bengbu station. The correlation coefficient and Nash-Sutcliffe coefficient of efficiency of multiannual daily series were 0.987 and 0.968, respectively, which indicate that the capability of the coupled model system is acce...

A calibrated advection‐aridity evaporation model requiring no humidity data

A modified advection‐aridity model was tested with 24 h averaged data from the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment‐87 and the Cooperative Atmospheric‐Surface Exchange Study (CASES) ‐97 experiments. Two modifications were made. First, the need for humidity measurements in the “drying power” term of the equation was circumvented by assuming the daily average specific humidity for a 24 h day is equal to the specific humidity at the minimum temperature during the day. When compared to measured latent heat fluxes, this modification resulted in no deterioration in advection‐aridity estimates compared to versions using measured humidity. A second modification followed previous researchers by formulating the drying power in the Penman equation using Monin‐Obukhov similarity (MOS) theory. However, this version calibrated kB−1 (≡ln(zo/zov) by setting the Priestley‐Taylor evapotranspiration rate equal (on average) to the Penman evapotranspiration on several moist days and solving for the value of kB−1 as the only unknown. The calibration involved no latent heat flux measurements. The results suggest that the advection‐aridity model performs modestly better in the calibrated MOS version than with Penman's original wind function. Further investigation is recommended because MOS theory accounts for varying momentum roughness lengths, and the calibration was not done under ideal (well‐watered or nearly saturated) conditions that deteriorated the results somewhat with the CASES data set.

An Improved Method for Estimating Global Evapotranspiration Based on Satellite Determination of Surface Net Radiation, Vegetation Index, Temperature, and Soil Moisture

Abstract A simple and accurate method to estimate regional or global latent heat of evapotranspiration (ET) from remote sensing data is essential. The authors proposed a method in an earlier study that utilized satellite-determined surface net radiation (Rn), a vegetation index, and daytime-averaged/daily maximum air temperature (Ta) or land surface temperature (Ts) data. However, the influence of soil moisture (SM) on ET was not considered and is addressed in this paper by incorporating the diurnal Ts range (DTsR). ET, measured by the energy balance Bowen ratio method at eight enhanced facility sites on the southern Great Plains in the United States and by the eddy covariance method at four AmeriFlux sites during 2001–06, is used to validate the improved method. Site land cover varies from grassland, native prairie, and cropland to deciduous forest and evergreen forest. The correlation coefficient between the measured and predicted 16-day daytime-averaged ET using a combination of Rn, enhanced vegetation index (EVI), daily maximum Ts, and DTsR is about 0.92 for all the sites, the bias is −1.9 W m−2, and the root-mean-square error (RMSE) is 28.6 W m−2. The sensitivity of the revised method to input data error is small. Implemented here is the revised method to estimate global ET using diurnal Ta range (DTaR) instead of DTsR because DTsR data are not available yet, although DTaR-estimated ET is less accurate than DTsR-estimated ET. Global monthly ET is calculated from 1986 to 1995 at a spatial resolution of 1° × 1° from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary monthly dataset and is compared with the 15 land surface model simulations of the Global Soil Wetness Project-2. The results of the comparison of 118 months of global ET show that the bias is 4.5 W m−2, the RMSE is 19.8 W m−2, and the correlation coefficient is 0.82. Incorporating DTaR distinctively improves the accuracy of the estimate of global ET.

Simulação na Bacia Amazônica com Dados Limitados: Rio Madeira

Hydrological simulation of the rain-discharge transformation using a hydrological model is applied to water resources management in a number of activities such as estimate, forecast and evaluation of the hydrological processes. The large basins (> 10,000 km2 ) have many effects such as climate, geology and human activities. The Amazon is a region where there are major human impacts due to the expansion of economic development. The Hydrological Model for Large Basins (MGB-IPH) was applied in the Amazon basin in order to evaluate its potential in this basin. Different hydrological data sources were tested. Daily precipitation data were used from the hydrometeorological network of the National Water Agency (ANA) and from NCEP/NCAR re-analysis corrected by the Center for Ocean Land Atmosphere (COLA). Climatological data from the International Satellite Land Surface Climatology Project (ISLSCP) and from NCEP/NCAR reanalysis were used to calculate evapotranspiration. The model was applied in the Madeira river basin, one of the most important sub-basins of the Amazon River. The results showed that daily COLA precipitation series have values very close to ANA precipitation series for the 1979-1990 period. In the portion of the basin located outside Brazil, COLA precipitation was compared with monthly data bases. At some points in the basin we had to correct the precipitation using these data bases. The model simulations showed good quality despite the limited hydrological information in the Amazon region

Large-scale characteristics of the distribution of blowing-snow sublimation

AbstractTo consider the large-scale characteristics of blowing-snow sublimation and its importance in the hydrological cycle in the cryosphere, we investigated the sublimation of blowing snow particles on a global scale using the global datasets of the European Centre for Medium-RangeWeather Forecasts (ECMWF) re-analysis data and the International Satellite Land Surface Climatology Project (ISLSCP) Initiative I data for 1987. The sublimation fluxes of blowing snow particles were estimated globally with 2.5˚ resolution at 6 hour intervals. We found that the sublimation of blowing snow particles occurs more widely in the Northern Hemisphere than in the Southern Hemisphere, does not increase monotonously with latitude, and becomes more active in the polar coast regions and highlands, although the annual mean sublimation fluxes of the Northern and Southern Hemispheres are almost equal. In addition, we confirmed the characteristic seasonal changes in the area of sublimation in the Northern Hemisphere. Although we need to incorporate continuous parameters from systematic ground-based studies of the structure of blowing snow in specific fields to reduce uncertainty regarding the characteristics of blowing snow, our results point to a need to review the current understanding of the hydrological cycle.

Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites

Numerous models of evapotranspiration have been published that range in data-driven complexity, but global estimates require a model that does not depend on intensive field measurements. The Priestley–Taylor model is relatively simple, and has proven to be remarkably accurate and theoretically robust for estimates of potential evapotranspiration. Building on recent advances in ecophysiological theory that allow detection of multiple stresses on plant function using biophysical remote sensing metrics, we developed a bio-meteorological approach for translating Priestley–Taylor estimates of potential evapotranspiration into rates of actual evapotranspiration. Five model inputs are required: net radiation ( R n), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), maximum air temperature ( T max), and water vapor pressure (ea). Our model requires no calibration, tuning or spin-ups. The model is tested and validated against eddy covariance measurements (FLUXNET) from a wide range of climates and plant functional types—grassland, crop, and deciduous broadleaf, evergreen broadleaf, and evergreen needleleaf forests. The model-to-measurement r 2 was 0.90 (RMS = 16 mm/month or 28%) for all 16 FLUXNET sites across 2 years (most recent data release). Global estimates of evapotranspiration at a temporal resolution of monthly and a spatial resolution of 1° during the years 1986–1993 were determined using globally consistent datasets from the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP-II) and the Advanced Very High Resolution Spectroradiometer (AVHRR). Our model resulted in improved prediction of evapotranspiration across water-limited sites, and showed spatial and temporal differences in evapotranspiration globally, regionally and latitudinally.

Use of ISLSCP II data to intercompare and validate the terrestrial net primary production in a land surface model coupled to a general circulation model

Using the global terrestrial NPP and climate data from International Satellite Land Surface Climatology Project Initiative II (ISLSCP II) and additional NPP data, we validated the NPP simulations and explored the relationship between NPP and climate variation in a global two‐way coupled model AVIM‐GOALS. The strength of this study is that the global simulations produced will enhance interactive climate and vegetation study; however, the weakness is that the NPP distribution is not fully reproduced in some regions, because of the coarse model resolution and climate biases. Global NPP is spatially consistent with IGBP NPP and MODIS data, though there is a discrepancy in NPP (significantly lower values) for boreal forests and tundra, due to the underestimated temperature. The NPP distribution in China indicates agreement with IGBP data, but the IGBP data in northeast China (around 48°N) seem to be slightly high in contrast with other modeled and estimated NPP. The spatial structure of NPP in USA and Australia roughly corresponds to the IGBP NPP data and GPPDI Gridded data, and a possible lower value of GPPDI data in central Australia exists, in contrast with other NPP data. The globally averaged NPP of 447.47 g C m−2 year−1 is close to the 450.42 g C m−2 year−1 from IGBP data. The global relative error of simulated NPP against IGBP data is about 20% and is comparable to other global biogeochemical models. The meridional variation of globally zonal mean NPP corresponds more to the meridional change of precipitation than temperature. The global NPP for all vegetation types is highly statistically significant in correlation with precipitation.

ISLSCP Initiative II global data sets: Surface boundary conditions and atmospheric forcings for land‐atmosphere studies

We report herein the publication and evaluation of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary data record. The record consists of 52 data sets, with a common series in the 10‐year period 1985 to 1996. Selected data series extend well beyond this period. All series are coregistered to a common grid and gap‐filled for continuity using uniform procedures. We describe briefly the individual data sets within the collection; provide user guidance; and contrast, compare and evaluate those data sets containing similar parameters (land cover, NDVI, albedo, precipitation and near‐surface meteorology). We also describe the process used to develop the Initiative II collection which involved a broad international scientific community focused on addressing a well‐defined set of carbon, water and energy cycle questions within the context of a specific set of analysis tools. The communities that drove the definition of the Initiative II collection were investigators within the international scientific communities of the Global Energy and Water cycle Experiment, GEWEX, program (http://www.gewex.org/); the International Geosphere/Biosphere Program IGBP (http://www.igbp.kva.se); and the U.S. Global Change Research Program, USGCRP (http://www.usgcrp.gov/). Finally, we report usage statistics based on access and download of files from the ISLSCP Initiative II collection available at http://www.daac.ornl.gov.

Revisiting a hydrological analysis framework with International Satellite Land Surface Climatology Project Initiative 2 rainfall, net radiation, and runoff fields

The International Satellite Land Surface Climatology Project Initiative 2 (ISLSCP‐2) data set provides the data needed to characterize the surface water budget across much of the globe in terms of energy availability (net radiation) and water availability (precipitation) controls. The data, on average, are shown to be consistent with Budyko's decades‐old framework, thereby demonstrating the continuing relevance of Budyko's semiempirical relationships. This consistency, however, appears only when a small subset of the data with hydrologically suspicious behavior is removed from the analysis. In general, the precipitation, net radiation, and runoff data also appear consistent in their interannual variability and in the phasing of their seasonal cycles.