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The capability and synergistic use of multisource satellite observations for flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. Here, surface fractional water cover (FW) retrievals derived from Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) brightness temperatures were used for flood assessment over southeast Africa during the Cyclone Idai event. We then focused on five subcatchments of the Pungwe basin and developed a machine learning based approach with the support of Google Earth Engine for daily (24-h) forecasting of FW and 30-m inundation downscaling and mapping. The Classification and Regression Trees model was selected and trained using retrievals derived from SMAP and Landsat coupled with rainfall forecasts from the NOAA Global Forecast System. Independent validation showed that FW predictions over randomly selected dates are highly correlated (R = 0.87) with the Landsat observations. The forecast results captured the flood temporal dynamics from the Idai event; and the associated 30-m downscaling results showed inundation spatial patterns consistent with independent satellite synthetic aperture radar observations. The data-driven approach provides new capacity for flood monitoring and forecasts leveraging synergistic satellite observations and big data analysis, which is particularly valuable for data sparse regions.

Poorly monitored river flows in many regions of the world have been hindering our ability to accurately estimate global water budgets as well as the variability of the global water cycle. In situ gauging sites, as well as a number of satellite-based systems, make observations of river discharge throughout the globe; however, these observations are often sparse due to, for example, the sampling frequencies of sensors or a lack of reporting. Recently, efforts have been made to develop methods to integrate these discrete observations to gain a better understanding of the underlying processes. This paper presents an application of a fixed interval Kalman smoother-based model, called inverse streamflow routing (ISR), to generate spatially and temporally continuous river discharge fields from discrete observations. The method propagates the observed information across all reachable parts of the river network (up/downstream from gauging point) and all reachable times (before/after observation time) using a two-sweep procedure that first propagates information backward in time to the furthest upstream locations (inverse routing) and then propagates it forward in time to the furthest downstream locations (forward routing). The ISR methodology advances prediction of streamflow in ungauged basins by accounting for a physical representation of the river system that is not generally handled explicitly in more-commonly applied statistically based models. The key advantages of this approach are that it (1) maintains all the physical consistencies embodied by a diffusive wave routing model (flow confluence relationships on the river network and the resulting mass balance, wave velocity, and diffusivity), (2) updates the lateral influx (runoff) at the pixel level (furthest upstream) to guarantee exhaustive propagation of observed information, and (3) works both with a first guess of initial river discharge conditions from a routing model (assimilation) and without a first guess (pure interpolation of observations). Two sets of experiments are carried out under idealized conditions and under real-world conditions provided by United States Geological Survey (USGS) observations. Results show that the method can effectively reproduce the spatial and temporal dynamics of river discharge in each of the experiments presented. The performance is driven by the density of the gauge network as well as the quality of the data being assimilated. We find that when assimilating the actual USGS observations, the performance decreases relative to our idealized scenario; however, we are still able to produce an improved discharge product at each validation site. With further testing, as well as global application, ISR may prove to be a useful method for extending our current network of global river discharge observations.

Spatiotemporally continuous global river discharge estimates across the full spectrum of stream orders are vital to a range of hydrologic applications, yet they remain poorly constrained. Here we present a carefully designed modeling effort (Variable Infiltration Capacity land surface model and Routing Application for Parallel computatIon of Discharge river routing model) to estimate global river discharge at very high resolutions. The precipitation forcing is from a recently published 0.1° global product that optimally merged gauge‐, reanalysis‐, and satellite‐based data. To constrain runoff simulations, we use a set of machine learning‐derived, global runoff characteristics maps (i.e., runoff at various exceedance probability percentiles) for grid‐by‐grid model calibration and bias correction. To support spaceborne discharge studies, the river flowlines are defined at their true geometry and location as much as possible—approximately 2.94 million vector flowlines (median length 6.8 km) and unit catchments are derived from a high‐accuracy global digital elevation model at 3‐arcsec resolution (~90 m), which serves as the underlying hydrography for river routing. Our 35‐year daily and monthly model simulations are evaluated against over 14,000 gauges globally. Among them, 35% (64%) have a percentage bias within ±20% (±50%), and 29% (62%) have a monthly Kling‐Gupta Efficiency ≥0.6 (0.2), showing data robustness at the scale the model is assessed. This reconstructed discharge record can be used as a priori information for the Surface Water and Ocean Topography satellite mission's discharge product, thus named “Global Reach‐level A priori Discharge Estimates for Surface Water and Ocean Topography”. It can also be used in other hydrologic applications requiring spatially explicit estimates of global river flows., Key Points Thirty‐five‐year global reconstruction of river flows at unprecedented 2.94 million reaches extracted from 90‐m high‐accuracy DEMNovel bias correction (“sparse CDF matching”) is conducted grid‐by‐grid against machine learning‐derived global runoff characteristics mapsEvaluation against >14,000 gauges shows data robustness for hydrologic applications and utility in spaceborne discharge estimation efforts

The capability of synergistic satellite flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. In this study, the Soil Moisture Active Passive (SMAP) fractional water (FW) data sets were used for flood mapping over southeast Africa during the Cyclone Idai event. We then developed a machine-learning approach with the support of Google Earth Engine (GEE) for 24-hour flood forecasting and 30-m inundation mapping using observations from SMAP and Landsat coupled with rainfall forecasts from Global Forecast System (GFS) 384-Hour Predicted Atmosphere Data. The forecast results for the Idai event captured the flood dynamics at 30-m resolution and showed inundation patterns consistent with independent satellite Synthetic Aperture Radar (SAR) observations. The approach provides new capacity for flood monitoring and forecasts from synergistic satellite observations and is particularly valuable for data sparse regions.

The ECOsystem Space Thermal Radiometer Experiment on the Space Station (ECOSTRESS) was launched to the International Space Station on June 29, 2018 by the National Aeronautics and Space Administration (NASA). The primary scientific focus of ECOSTRESS is on evapotranspiration (ET), which occurs as Level 3 (L3) latent heat flux (LE) data products. This data is generated from the Level 2 Earth Surface Temperature and Emissivity Product (L2_LSTE), along with the atmospheric and ancillary surface data. Here, we provide the first validation (Stage 1, preliminary) of the global product ECOSTRESS clear - sky ET (L3_ET_PT - JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against site measurements (instant clear sky / flyover time: r 2 = 0.88; overall bias = 8%; normalized root mean square error, RMSE = 6%) . ET uncertainty was generally constant across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), although temperate sites are over-represented. ECOSTRESS 70 m high spatial resolution improved correlations by 85% and RMSE by 62%, relative to 1 km pixels. This document serves as a reference for the accuracy of ECOSTRESS L3 ET and the Stage 1 validation status for subsequent science that continues to use this data. Fil: Fisher, Joshua B. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Lee, Brian. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Purdy, Adam J. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Halverson, Gregory H. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Dohlen, Matthew B. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Cawse - Nicholson, Kerry. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Wang, Audrey. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Anderson, Ray G. U.S. Department of Agriculture; Estados Unidos Fil: Aragon, Bruno. King Abdullah University of Science and Technology. Division of Biological and Environmental Science and Engineering. Hydrology, Agriculture and Land Observation Group (HALO); Arabia Saudita Fil: Arain, M. Alfat. McMaster University. School of Geography and Earth Sciences; Canadá Fil: Baldocchi, Dennis D. University of California, Berkeley. Department of Environmental Science, Policy and Management; Estados Unidos Fil: Baker, John M. U.S. Department of Agriculture; Estados Unidos Fil: Barral, Helene. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: Bernacchi, Carl J. University of Illinois Urbana‐Champaign. Institute for Sustainability, Energy, and Environment and Carl R. Woese Institute for Genomic Biology. Center for Advanced Bioenergy and Bioproducts Innovation; Estados Unidos. U.S. Department of Agriculture, Global Change and Photosynthesis Research Unit; Estados Unidos Fil: Bernhofer, Christian. Technische Universität Dresden, Institute of Hydrology and Meteorology; Alemania Fil: Biraud, Sebastien C. Lawrence Berkeley National Laboratory; Estados Unidos Fil: Bohrer, Gil. Ohio State University. Department of Civil, Environmental and Geodetic Engineering; Estados Unidos Fil: Brunsell, Nathaniel. University of Kansas. Department of Geography and Atmospheric Science; Estados Unidos Fil: Cappelaere, Bernard. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: Castro - Contreras, Saulo. University of Alberta. Earth and Atmospheric Sciences Department. Centre for Earth Observation Sciences (CEOS); Canadá Fil: Chum, Junghwa. National Institute of Forest Science; Corea del Sur Fil: Conrad, Bryan. University of Kansas. Department of Geography and Atmospheric Science; Estados Unidos Fil: Cremonese, Edoardo. Environmental Protection Agency of Aosta Valley; Italia Fil: Demarty, Jerome. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: Desai, Ankur R. University of Wisconsin‐Madison. Department of Atmospheric and Oceanic Sciences; Estados Unidos Fil: De Ligne, Anne. University of Liege. TERRA Teaching and Research Centre. Gembloux Agro‐Bio Tech; Bélgica Fil: Foltynova, Lenka. Czech Academy of Sciences. Global Change Research Institute ; República Checa Fil: Goulden, Michael L. University of California Irvine. Department of Earth System Science; Estados Unidos Fil: Griffis, Timothy J. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos Fil: Grunwald, Thomas. Technische Universität Dresden, Institute of Hydrology and Meteorology; Alemania Fil: Johnson, Mark S. University of British Columbia. Department of Earth, Ocean, and Atmospheric Sciences; Canadá Fil: Kang, Minseok. National Center for AgroMeteorology; Corea del Sur Fil: Kelbe, Dave. Xerra Earth Observation Institute; Nueva Zelanda Fil: Kowalska, Natalia. Czech Academy of Sciences. Global Change Research Institute ; República Checa Fil: Jong - Hwan, Lim. National Institute of Forest Science; Corea del Sur Fil: Mainassara, Ibrahim. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: McCabe, Matthew F. King Abdullah University of Science and Technology. Division of Biological and Environmental Science and Engineering. Hydrology, Agriculture and Land Observation Group (HALO); Arabia Saudita Fil: Missik, Justine E.C. Washington State University. Department of Civil and Environmental Engineering. Laboratory for Atmospheric Research; Estados Unidos Fil: Mohanty, Binayak P. Texas A&M University. Texas Water Observatory; Estados Unidos Fil: Moore, Caitlin E. University of Illinois Urbana‐Champaign. Institute for Sustainability, Energy, and Environment and Carl R. Woese Institute for Genomic Biology. Center for Advanced Bioenergy and Bioproducts Innovation; Estados Unidos Fil: Morillas, Laura. University of British Columbia. Department of Earth, Ocean, and Atmospheric Sciences; Canadá Fil: Morrison, Ross. Centre for Ecology and Hydrology; Reino Unido Fil: Munger, J. Willians. Harvard University. School of Engineering and Applied Sciences; Estados Unidos Fil: Posse, Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Richardson, Andrew D. Northern Arizona University Center for Ecosystem Science and Society; Estados Unidos Fil: Russell, Eric. S. Washington State University. Department of Civil and Environmental Engineering. Laboratory for Atmospheric Research; Estados Unidos Fil: Ryu, Youngryel. Seoul National University . Department of Landscape Architecture and Rural Systems Engineering; Corea del Sur Fil: Sanchez - Azofeifa, Arturo. University of Alberta. Earth and Atmospheric Sciences Department. Centre for Earth Observation Sciences (CEOS); Canadá Fil: Schmidt, Marius. IInstitute of Bio‐ and Geosciences: Agrosphere (IBG‐3) Forschungszentrum Jülich IBG‐3; Alemania Fil: Schwartz, Efrat. Weizmann Institute of Science. Department of Earth and Planetary Sciences, ; Israel Fil: Sharp, Iain. University of Alberta. Earth and Atmospheric Sciences Department. Centre for Earth Observation Sciences (CEOS); Canadá Fil: Sigut, Ladislav. Czech Academy of Sciences. Global Change Research Institute ; República Checa Fil: Tang, Yao. Georgia Institute of Technology. School of Civil and Environmental Engineering; Estados Unidos Fil: Hulley, Glynn. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Anderson, Martha. U.S. Department of Agriculture‐Agricultural Research Service; Estados Unidos Fil: Hain, Christopher. NASA Marshall Space Flight Center; Estados Unidos Fil: French, Andrew. U.S. Department of Agriculture; Estados Unidos Fil: Wood, Eric. Princeton University. Department of Civil and Environmental Engineering; Estados Unidos Fil: Hook, Simón. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos

Author(s): Pan, M; Cai, X; Chaney, NW; Entekhabi, D; Wood, EF | Abstract: At the end of its first year of operation, we compare soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission to simulations from a land surface model with meteorological forcing downscaled from observations/reanalysis and in situ observations from sparse monitoring networks within continental United States (CONUS). The radar failure limits the duration of comparisons for the active and combined products (~3 months). Nevertheless, the passive product compares very well against in situ observations over CONUS. On average, SMAP compares to the in situ data even better than the land surface model and provides significant added value on top of the model and thus good potential for data assimilation. At large scale, SMAP is in good agreement with the model in most of CONUS with less-than-expected degradation over mountainous areas. Lower correlation between SMAP and the model is seen in the forested east CONUS and significantly lower over the Canadian boreal forests.

The increasing availability and reliability of satellite remote sensing products [e.g., precipitation (P), evapotranspiration (ET), and the total water storage change (TWSC)] make it feasible to estimate the global terrestrial water budget at fine spatial resolution. In this study, we start from a reference water budget dataset that combines all available data sources, including satellite remote sensing, land surface model (LSM) and reanalysis, and investigate the roles of different non-satellite remote sensing products in closing the terrestrial water budget through a sensitivity analysis by removing/replacing one or more categories of products during the budget estimation. We also study the differences made by various satellite products for the same budget variable. We find that the gradual removal of non-satellite data sources will generally worsen the closure errors in the budget estimates, and remote sensing retrievals of P, ET, and TWSC together with runoff (R) from LSM give the worst closure errors. The gauge-corrected satellite precipitation helps to improve the budget closure (4.2–9 % non-closure errors of annual mean precipitation) against using the non-gauge-corrected precipitation (7.6–10.4 % non-closure errors). At last, a data assimilation technique, the constrained Kalman filter, is applied to enforce the water balance, and it is found that the satellite remote sensing products, though with worst closure, yield comparable budget estimates in the constrained system to the reference data. Overall, this study provides a first comparison between the water budget closure using the satellite remote sensing products and a full combination of remote sensing, LSM, and reanalysis products on a quasi-global basis. This study showcases the capability and potential of the satellite remote sensing in closing the terrestrial water budget at fine spatial resolution if properly constrained.

The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project aims to advance the development of land evaporation estimates on global and regional scales. Its main objective is the derivation, validation, and intercomparison of a group of existing evaporation retrieval algorithms driven by a common forcing data set. Three commonly used process-based evaporation methodologies are evaluated: the Penman–Monteith algorithm behind the official Moderate Resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Global Land Evaporation Amsterdam Model (GLEAM), and the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL). The resulting global spatiotemporal variability of evaporation, the closure of regional water budgets, and the discrete estimation of land evaporation components or sources (i.e. transpiration, interception loss, and direct soil evaporation) are investigated using river discharge data, independent global evaporation data sets and results from previous studies. In a companion article (Part 1), Michel et al. (2016) inspect the performance of these three models at local scales using measurements from eddy-covariance towers and include in the assessment the Surface Energy Balance System (SEBS) model. In agreement with Part 1, our results indicate that the Priestley and Taylor products (PT-JPL and GLEAM) perform best overall for most ecosystems and climate regimes. While all three evaporation products adequately represent the expected average geographical patterns and seasonality, there is a tendency in PM-MOD to underestimate the flux in the tropics and subtropics. Overall, results from GLEAM and PT-JPL appear more realistic when compared to surface water balances from 837 globally distributed catchments and to separate evaporation estimates from ERA-Interim and the model tree ensemble (MTE). Nonetheless, all products show large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into its different components. This observed inter-product variability, even when common forcing is used, suggests that caution is necessary in applying a single data set for large-scale studies in isolation. A general finding that different models perform better under different conditions highlights the potential for considering biome- or climate-specific composites of models. Nevertheless, the generation of a multi-product ensemble, with weighting based on validation analyses and uncertainty assessments, is proposed as the best way forward in our long-term goal to develop a robust observational benchmark data set of continental evaporation.

Satellite remote sensing allows many hydrological variables to be retrieved at regional and global scales, and these retrievals can contribute to our knowledge of these quantities and their spatial and temporal variation. However, the availability and precision of measurements from remote sensing are still limited. Other important sources of information are represented by reanalysis products and simulations from land surface models (LSMs), which are continuously improving their ability to reproduce the terrestrial hydrological and energy cycles. Such global datasets are very informative and they can be used to integrate the data provided by remote sensing. Among these datasets, the NCEP Climate Forecast System Reanalysis (CFSR) provides many hydrological variables covering the period from 1979 to 2011 with hourly temporal resolution and ~ 0.3-degree spatial resolution. This paper addresses the frequent challenge arising from remote sensing data: how can we create a long-term, global dataset starting with remote sensing data that are sparse in space and time, and provided by sensors flown on different satellites? This was the challenge for creating a long-term Land Surface Temperature (LST) dataset at an hourly resolution based on the High-Resolution Infrared Radiation Sounder (HIRS). To this end, a methodology is presented that merges the continuous CFSR LST estimates with the HIRS retrievals that come from 11 different NOAA satellites. The goals of the analysis are to have a long-term LST dataset consistent with the HIRS retrievals, and to assess the uncertainty associated with the reconstructed data through a Bayesian uncertainty post-processing methodology. The result of this approach is a temporally and spatially continuous dataset, with no step changes associated to the different NOAA satellites, and a dataset described in terms of a probability distribution function whose expected value is unbiased with respect to the original HIRS LST retrievals.

Seasonal hydrologic extremes in the form of droughts and wet spells have devastating impacts on human and natural systems. Improving understanding and predictive capability of hydrologic extremes, and facilitating adaptations through establishing climate service systems at regional to global scales are among the grand challenges proposed by the World Climate Research Programme (WCRP) and are the core themes of the Regional Hydroclimate Projects (RHP) under the Global Energy and Water Cycle Experiment (GEWEX). An experimental global seasonal hydrologic forecasting system has been developed that is based on coupled climate forecast models participating in the North American Multimodel Ensemble (NMME) project and an advanced land surface hydrologic model. The system is evaluated over major GEWEX RHP river basins by comparing with ensemble streamflow prediction (ESP). The multimodel seasonal forecast system provides higher detectability for soil moisture droughts, more reliable low and high f low ensemble forecasts, and better “real time” prediction for the 2012 North American extreme drought. The association of the onset of extreme hydrologic events with oceanic and land precursors is also investigated based on the joint distribution of forecasts and observations. Climate models have a higher probability of missing the onset of hydrologic extremes when there is no oceanic precursor. But oceanic precursor alone is insufficient to guarantee a correct forecast—a land precursor is also critical in avoiding a false alarm for forecasting extremes. This study is targeted at providing the scientific underpinning for the predictability of hydrologic extremes over GEWEX RHP basins and serves as a prototype for seasonal hydrologic forecasts within the Global Framework for Climate Services (GFCS).

Rainfall and soil moisture are two key elements in modeling the interactions between the land surface and the atmosphere. Accurate and high-resolution real-time precipitation is crucial for monitoring and predicting the onset of floods, and allows for alert and warning before the impact becomes a disaster. Assimilation of remote sensing data into a flood-forecasting model has the potential to improve monitoring accuracy. Space-borne microwave observations are especially interesting because of their sensitivity to surface soil moisture and its change. In this study, we assimilate satellite soil moisture retrievals using the Variable Infiltration Capacity (VIC) land surface model, and a dynamic assimilation technique, a particle filter, to adjust the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) real-time precipitation estimates. We compare updated precipitation with real-time precipitation before and after adjustment and with NLDAS gauge-radar observations. Results show that satellite soil moisture retrievals provide additional information by correcting errors in rainfall bias. The assimilation is most effective in the correction of medium rainfall under dry to normal surface conditions, while limited/negative improvement is seen over wet/saturated surfaces. On the other hand, high-frequency noises in satellite soil moisture impact the assimilation by increasing rainfall frequency. The noise causes larger uncertainty in the false-alarmed rainfall over wet regions. A threshold of 2 mm day−1 soil moisture change is identified and applied to the assimilation, which masked out most of the noise.

Summary The impact of model structure and parameterization on the estimation of evaporation is investigated across a range of Penman–Monteith type models. To examine the role of model structure on flux retrievals, three different retrieval schemes are compared. The schemes include a traditional single-source Penman–Monteith model (Monteith, 1965), a two-layer model based on Shuttleworth and Wallace (1985) and a three-source model based on Mu et al. (2011). To assess the impact of parameterization choice on model performance, a number of commonly used formulations for aerodynamic and surface resistances were substituted into the different formulations. Model response to these changes was evaluated against data from twenty globally distributed FLUXNET towers, representing a cross-section of biomes that include grassland, cropland, shrubland, evergreen needleleaf forest and deciduous broadleaf forest. Scenarios based on 14 different combinations of model structure and parameterization were ranked based on their mean value of Nash–Sutcliffe Efficiency. Results illustrated considerable variability in model performance both within and between biome types. Indeed, no single model consistently outperformed any other when considered across all biomes. For instance, in grassland and shrubland sites, the single-source Penman–Monteith model performed the best. In croplands it was the three-source Mu model, while for evergreen needleleaf and deciduous broadleaf forests, the Shuttleworth–Wallace model rated highest. Interestingly, these top ranked scenarios all shared the simple lookup-table based surface resistance parameterization of Mu et al. (2011), while a more complex Jarvis multiplicative method for surface resistance produced lower ranked simulations. The highly ranked scenarios mostly employed a version of the Thom (1975) formulation for aerodynamic resistance that incorporated dynamic values of roughness parameters. This was true for all cases except over deciduous broadleaf sites, where the simpler aerodynamic resistance approach of Mu et al. (2011) showed improved performance. Overall, the results illustrate the sensitivity of Penman–Monteith type models to model structure, parameterization choice and biome type. A particular challenge in flux estimation relates to developing robust and broadly applicable model formulations. With many choices available for use, providing guidance on the most appropriate scheme to employ is required to advance approaches for routine global scale flux estimates, undertake hydrometeorological assessments or develop hydrological forecasting tools, among many other applications. In such cases, a multi-model ensemble or biome-specific tiled evaporation product may be an appropriate solution, given the inherent variability in model and parameterization choice that is observed within single product estimates.

Soil moisture retrievals, delivered as a CATDS (Centre Aval de Traitement des Donnees SMOS) Level-3 product of the Soil Moisture and Ocean Salinity (SMOS) mission, form an important information source, particularly for updating land surface models. However, the coarse resolution of the SMOS product requires additional treatment if it is to be used in applications at higher resolutions. Furthermore, the remotely sensed soil moisture often does not reflect the climatology of the soil moisture predictions, and the bias between model predictions and observations needs to be removed. In this paper, a statistical framework is presented that allows for the downscaling of the coarse-scale SMOS soil moisture product to a finer resolution. This framework describes the interscale relationship between SMOS observations and model-predicted soil moisture values, in this case, using the va riable infiltration capacity (VIC) model, using a copula. Through conditioning, the copula to a SMOS observation, a probability distribution function is obtained that reflects the expected distribution function of VIC soil moisture for the given SMOS observation. This distribution function is then used in a cumulative distribution function matching procedure to obtain an unbiased fine-scale soil moisture map that can be assimilated into VIC. The methodology is applied to SMOS observations over the Upper Mississippi River basin. Although the focus in this paper is on data assimilation apcations, the framework developed could also be used for other purposes where downscaling of coarse-scale observations is required.

Hydrologic extremes in the form of flood and drought have large impacts on society that can be reduced through preparations made possible by seasonal prediction. However, the skill of seasonal predictions from global climate models is uncertain, which severely limits their practical use. In the past, the skill assessment has been limited to a single temporal or spatial resolution for a short hindcast period, which is prone to sampling errors, and noise that leads to uncertainty. In this work a framework that uses “canonical” forecast events, or averages in space–time, to provide a more certain assessment of when and where models are skillful is developed. This framework is demonstrated by using NCEP’s Climate Forecast System, version 2, hindcast dataset for precipitation and temperature over the contiguous United States (CONUS). As part of the canonical event analyses, the probabilistic predictability metric (PPM) is used to define spatial and seasonal variability of forecast skill and its attribution to El Niño–Southern Oscillation (ENSO) over the CONUS. The PPM indicates that there are clear seasonal and spatial patterns of model skill that provide a better understanding of when and where to have confidence in model predictions as compared to a skill metric based on a single temporal and spatial scale. Furthermore, the canonical event analysis also facilitates the attribution of spatiotemporal variations of precipitation predictive skill to the antecedent ENSO conditions. This work illustrates the importance of using canonical event analysis to diagnose seasonal predictions and discusses its extensions for model development.

Climate-model-based seasonal hydrologic forecasting (CM-SHF) is an emerging area in recent decade because of the development of coupled atmosphere-ocean-land general circulation models (CGCMs) and land surface hydrologic models, and increasing needs for transferring the advances in climate research into hydrologic applications within the framework of climate services. In order to forecast terrestrial hydrology from monthly to seasonal time scales, a CM-SHF system should take advantage of important information from initial land surface conditions (ICs) as well as skillful seasonal predictions of atmospheric boundary conditions that mostly rely on the predictability of large-scale climate precursors such as the El Nino Southern Oscillation (ENSO). The progresses in the understanding of seasonal hydrologic predictability in terms of ICs and climate precursors are reviewed, and future emphases are discussed. Both the achievements and challenges of the CM-SHF system development, including multimodel ensemble prediction, seamless hydrologic forecasting, dynamical downscaling, hydrologic post-processing, and seasonal forecasting of hydrologic extremes with the hyper-resolution modeling framework that is able to address both the climate change and water resources management impacts on terrestrial hydrology, are presented. Regardless of great strides in CM-SHF, a grand challenge is the effective dissemination of the information provided by the seasonal hydrologic forecasting system to the decision-makers, which cannot be resolved without cross-disciplinary dialog and collaboration. WIREs Water 2015, 2:523–536. doi: 10.1002/wat2.1088 This article is categorized under: Engineering Water > Planning Water Science of Water > Water Extremes

The contents of this paper reflect the discussions that were held among the authors during and after the Workshop ‘Hyper-resolution global hydrological modelling: the next step’ held on 13–14 February 2014 in Utrecht, the Netherlands.

The satellite based passive (radiometer) and active (radar) microwave sensors enhanced our ability to retrieve soil moisture at global scales. It has been almost four decades since the first passive microwave satellite sensor was launched in 1978. Since then soil moisture has gained considerable attention in hydro-meteorological, climate, and agricultural research resulting in the deployment of two dedicated missions in the last decade, SMOS and SMAP. Microwave retrievals require an algorithm to estimate soil moisture from satellite measurements. In this Part 1 of a two-part review series, we provide a synthesis of four decades of research and development on the passive and active microwave soil moisture retrieval algorithms. The algorithms associated with passive sensors use the radiometer brightness temperatures, while active sensors use the radar backscatter measurements to retrieve soil moisture. The physics of both algorithm classes are based on the fact that the microwave measurements at lower frequencies are influenced by the soil dielectric property, which acts as a proxy for the surface soil moisture content. In this review effort, the emphasis is laid on the physical models of the passive and the active retrieval algorithms. These algorithms facilitate to obtain the individual radiative contributions from soil, vegetation, and atmosphere that reach satellite sensors after mixing (roughness), scattering, and attenuation. In the process, we looked into the current research efforts to improve individual aspects of the algorithms, followed by a description of different retrieval procedures. In Part 2 of this review series, performance evaluation and inter-sensor comparisons of soil moisture of eight passive and two active sensors are carried out using 1058 stations along with model soil moisture data in the Contiguous United States (CONUS) region. (C) 2017 Elsevier Ltd. All rights reserved.

We undertook a comprehensive evaluation of 23 gridded (quasi-)global (sub-)daily precipitation ( P ) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another ten gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the conceptual model HBV against streamflow records for each of 9053 small to medium-sized ( 2 ) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gaugebased CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed those indirectly incorporating gauge data through other multi-source datasets (PERSIANN-CDR V1R1 and PGF). Our results highlight large differences in estimation accuracy, and hence, the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite- and reanalysis-based P estimates.

Seamless hydrologic forecasting is explored through integrating medium-range weather forecasts from NOAA's Global Ensemble Forecast System Reforecast version 2 (GEFSRv2) and seasonal climate predictions from the Climate Forecast System version 2 (CFSv2). A set of 25 year hydrologic reforecasts over the Ohio basin shows that incorporating GEFSRv2 14 day forecasts into the Ensemble Streamflow Prediction (ESP) and CFSv2-based seasonal forecast systems improves efficiency scores for month-1 streamflow by up to 32.6% and 11.2%, respectively. For the second biweekly forecast, the combination of GEFSRv2 and CFSv2 is superior to that of GEFSRv2 and ESP by increasing efficiency score up to 17.2%, suggesting that the climate prediction usefully extends the medium-range hydrologic forecast. As compared with ESP, incorporation of either weather or climate prediction improves the month-1 soil moisture drought prediction significantly. The potential of seamless hydrologic forecast should be further investigated from the operational service perspective and improved understanding of underlying physical processes.

The implementation of a multimodel drought monitoring system is described, which provides near-real-time estimates of surface moisture storage for the global land areas between 50°S and 50°N with a time lag of about 1 day. Near-real-time forcings are derived from satellite-based precipitation estimates and modeled air temperatures. The system distinguishes itself from other operational systems in that it uses multiple land surface models (Variable Infiltration Capacity, Noah, and Sacramento) to simulate surface moisture storage, which are then combined to derive a multimodel estimate of drought. A comparison of the results with other historic and current drought estimates demonstrates that near-real-time nowcasting of global drought conditions based on satellite and model forcings is entirely feasible. However, challenges remain because hydrological droughts are inherently defined in the context of a long-term climatology. Changes in observing platforms can be misinterpreted as droughts (or as excessively wet periods). This problem cannot simply be addressed through the addition of more observations or through the development of new observing platforms. Instead, it will require careful (re)construction of long-term records that are updated in near–real time in a consistent manner so that changes in surface meteorological forcings reflect actual conditions rather than changes in methods or sources.

Assessing changes in the frequency and intensity of extreme meteorological events and their impact on water resources, agriculture, and infrastructure is necessary to adequately prepare and adapt to future change. This is a challenge in data-sparse regions such as sub-Saharan Africa, where a lack of high-density and temporally consistent long-term in situ measurements complicates the analysis. To address this, a temporally homogenous and high-temporal- and high-spatial-resolution meteorological dataset is developed over sub-Saharan Africa (5°S–25°N), covering the time period between 1979 and 2005. It is developed by spatially downscaling the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis to a 0.1° spatial resolution, detecting and correcting for temporal inhomogeneities, and by removing random errors and biases by assimilating quality-controlled and gap-filled Global Summary of the Day (GSOD) in situ measurements. The dataset is then used to determine the statistical significance and magnitude of changes in climate extremes between 1979 and 2005. The results suggest a shift in the distribution of daily maximum and minimum temperatures toward a warmer mean with a faster increase in warm than cold events. Changes in the mean annual precipitation and heavy rainfall events are significant only in regions affected by the Sahel droughts of the 1970s and 1980s.

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2...

a b s t r a c t We evaluated the performance of four commonly applied land surface evaporation models using a high-quality dataset of selected FLUXNET towers. The models that were examined include an energy balance approach (Surface Energy Balance System; SEBS), a combination-type technique (single-source Penman-Monteith; PM), a complementary method (advection-aridity; AA) and a radiation based approach (modified Priestley-Taylor; PT-JPL). Twenty FLUXNET towers were selected based upon sat- isfying stringent forcing data requirements and representing a wide range of biomes. These towers encompassed a number of grassland, cropland, shrubland, evergreen needleleaf forest and deciduous broadleaf forest sites. Based on the mean value of the Nash-Sutcliffe efficiency (NSE) and the root mean squared difference (RMSD), the order of overall performance of the models from best to worst were: ensemble mean of models (0.61, 64), PT-JPL (0.59, 66), SEBS (0.42, 84), PM (0.26, 105) and AA (0.18, 105) (statistics stated as (NSE, RMSD in W m −2 )). Although PT-JPL uses a relatively simple and largely empirical formulation of the evaporative process, the technique showed improved performance com- pared to PM, possibly due to its partitioning of total evaporation (canopy transpiration, soil evaporation, wet canopy evaporation) and lower uncertainties in the required forcing data. The SEBS model showed low performance over tall and heterogeneous canopies, which was likely a consequence of the effects of the roughness sub-layer parameterization employed in this scheme. However, SEBS performed well overall. Relative to PT-JPL and SEBS, the PM and AA showed low performance over the majority of sites, due to their sensitivity to the parameterization of resistances. Importantly, it should be noted that no single model was consistently best across all biomes. Indeed, this outcome highlights the need for further evaluation of each model's structure and parameterizations to identify sensitivities and their appropriate application to different surface types and conditions. It is expected that the results of this study can be used to inform decisions regarding model choice for water resources and agricultural management, as well as providing insight into model selection for global flux monitoring efforts.

This study performs three experiments to calibrate the drought area percentages in the continental United States (CONUS), six U.S. Drought Monitor (USDM) regions, and 48 states downloaded from the USDM archive website. The corresponding three experiments are named CONUS, Region, and State, respectively. The data sets used in these experiments are from the North American Land Data Assimilation System Phase 2 (NLDAS-2). The main purpose is to develop an automated USDM-based approach to objectively generate and reconstruct USDM-style drought maps using NLDAS-2 data by mimicking 10 year (2000–2009) USDM statistics. The results show that State and Region have larger correlation coefficients and smaller root-mean-square error (RMSE) and bias than CONUS when compared to the drought area percentages derived from the USDM, indicating that State and Region perform better than CONUS. In general, State marginally outperforms Region in terms of RMSE, bias, and correlation. Analysis of normalized optimal weight coefficients shows that soil moisture percentiles (top 1 m and total column) play the dominant role in most of the 48 states. The optimal blended NLDAS drought index (OBNDI) has higher simulation skills (correlation coefficient and Nash-Sutcliffe efficiency) in the South, Southeast, High Plains, and Midwest regions when compared to those in the West and Northeast. The highest simulation skills appear in TX and OK. By using optimal equations, we can reconstruct the long-term drought area percentages and OBNDI over the continental United States for the entire period of the NLDAS-2 data sets (January 1979 to present).

[1] The capability of seasonal forecasting of global drought onset at local scales (1°) has been investigated using multiple climate models with 110 realizations. Climate models increase the global mean probability of drought onset detection from the climatology forecast by 31%–81%, but only increase equitable threat score by 21%–50% due to a high false alarm ratio. The multimodel ensemble increases the drought detectability over some tropical areas where individual models have better performance, but cannot help more over most extratropical regions. On average, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with low potential predictability and weak antecedent El Nino–Southern Oscillation signal. Given the high false alarms, the reliability is very important for a skillful probabilistic drought onset forecast. This raises the question of whether seasonal forecasting of global drought onset is essentially a stochastic forecasting problem.

National Centers for Environmental Prediction recently upgraded its operational seasonal forecast system to the fully coupled climate modeling system referred to as CFSv2. CFSv2 has been used to make seasonal climate forecast retrospectively between 1982 and 2009 before it became operational. In this study, we evaluate the model’s ability to predict the summer temperature and precipitation over China using the 120 9-month reforecast runs initialized between January 1 and May 26 during each year of the reforecast period. These 120 reforecast runs are evaluated as an ensemble forecast using both deterministic and probabilistic metrics. The overall forecast skill for summer temperature is high while that for summer precipitation is much lower. The ensemble mean reforecasts have reduced spatial variability of the climatology. For temperature, the reforecast bias is lead time-dependent, i.e., reforecast JJA temperature become warmer when lead time is shorter. The lead time dependent bias suggests that the initial condition of temperature is somehow biased towards a warmer condition. CFSv2 is able to predict the summer temperature anomaly in China, although there is an obvious upward trend in both the observation and the reforecast. Forecasts of summer precipitation with dynamical models like CFSv2 at the seasonal time scale and a catchment scale still remain challenge, so it is necessary to improve the model physics and parameterizations for better prediction of Asian monsoon rainfall. The probabilistic skills of temperature and precipitation are quite limited. Only the spatially averaged quantities such as averaged summer temperature over the Northeast China of CFSv2 show higher forecast skill, of which is able to discriminate between event and non-event for three categorical forecasts. The potential forecast skill shows that the above and below normal events can be better forecasted than normal events. Although the shorter the forecast lead time is, the higher deterministic prediction skill appears, the probabilistic prediction skill does not increase with decreased lead time. The ensemble size does not play a significant role in affecting the overall probabilistic forecast skill although adding more members improves the probabilistic forecast skill slightly.

There is a long history of debate on the usefulness of climate model–based seasonal hydroclimatic forecasts as compared to ensemble streamflow prediction (ESP). In this study, the authors use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2), and its previous version, CFSv1, to investigate the value of climate models by conducting a set of 27-yr seasonal hydroclimatic hindcasts over the conterminous United States (CONUS). Through Bayesian downscaling, climate models have higher squared correlation R2 and smaller error than ESP for monthly precipitation, and the forecasts conditional on ENSO have further improvements over southern basins out to 4 months. Verification of streamflow forecasts over 1734 U.S. Geological Survey (USGS) gauges shows that CFSv2 has moderately smaller error than ESP, but all three approaches have limited added skill against climatology beyond 1 month because of overforecasting or underdispersion errors. Using a postprocessor, 60%–70% of probabilistic streamflow forecasts are more skillful than climatology. All three approaches have plausible predictions of soil moisture drought frequency over the central United States out to 6 months, and climate models provide better results over the central and eastern United States. The R2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R2 of drought severity accumulated over CONUS is higher during winter, and climate models present added value, especially at long leads. This study indicates that climate models can provide better seasonal hydroclimatic forecasts than ESP through appropriate downscaling procedures, but significant improvements are dependent on the variables, seasons, and regions.

Ideally, a seasonal streamflow forecasting system would ingest skilful climate forecasts and propagate these through calibrated hydrological models initialized with observed catchment conditions. At global scale, practical problems exist in each of these aspects. For the first time, we analyzed theoretical and actual skill in bimonthly streamflow forecasts from a global ensemble streamflow prediction (ESP) system. Forecasts were generated six times per year for 1979-2008 by an initialized hydrological model and an ensemble of 1 degrees resolution daily climate estimates for the preceding 30 years. A post-ESP conditional sampling method was applied to 2.6% of forecasts, based on predictive relationships between precipitation and 1 of 21 climate indices prior to the forecast date. Theoretical skill was assessed against a reference run with historic forcing. Actual skill was assessed against streamflow records for 6192 small (

With their origins in numerical weather prediction and climate modeling, land surface models aim to accurately partition the surface energy balance. An overlooked challenge in these schemes is the role of model parameter uncertainty, particularly at unmonitored sites. This study provides global parameter estimates for the Noah land surface model using 85 eddy covariance sites in the global FLUXNET network. The at-site parameters are first calibrated using a Latin Hypercube based ensemble of the most sensitive parameters, determined by the Sobol method, to be the minimum stomatal resistance (rs,min), the Zilinkitevich empirical constant (Czil), and the bare soil evaporation exponent (fxexp). Calibration leads to an increase in the mean Kling-Gupta Efficiency (KGE) performance metric from 0.54 to 0.71. These calibrated parameter sets are then related to local environmental characteristics using the Extra-Trees machine learning algorithm. The fitted Extra-Trees model is used to map the optimal parameter sets over the globe at a 5 km spatial resolution. The leave-one-out cross-validation of the mapped parameters using the Noah land surface model suggests that there is the potential to skillfully relate calibrated model parameter sets to local environmental characteristics. The results demonstrate the potential to use FLUXNET to tune the parameterizations of surface fluxes in land surface models and to provide improved parameter estimates over the globe.

Reanalysis precipitation is routinely used as a surrogate for observations due to its high spatial and temporal resolution and global coverage, and it has been widely used in hydrologic and agricultural applications. The resultant product and analysis are largely dependent on the accuracy of the reanalysis precipitation data set. In this study, we analyze the impact of reanalysis precipitation uncertainties on drought depiction. Five reanalyses precipitation data sets (Climate Forecast System Reanalysis, R1: National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research reanalysis, R2: NCEP/Department of Energy reanalysis II, 20CR: The Twentieth Century Reanalysis version 2 and Modern-Era Retrospective Analysis for Research and Applications) are evaluated from 1979 to 2012 over sub-Saharan Africa against an observational-based reference: The Princeton Global Meteorological Forcing data set. Results show that all the reanalyses precipitation data sets provide a relatively good representation of the long-term statistics of spatiotemporal drought characteristics in sub-Saharan Africa. However, deficiencies are found in the estimation of drought severity, the spatial pattern, and temporal persistency of droughts. Drought depiction over central Africa appears more problematic due to a lack of observational data. A comparison of drought depiction based on the Standardized Precipitation Index reveals higher monthly to seasonal precipitation variability that further increases after ~1999 due to changes in the observations included in the reanalyses assimilation systems.

Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, four commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman–Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72; 61 W m−2; 0.65), followed closely by GLEAM (0.68; 64 W m−2; 0.62), with values in parentheses representing the R2, RMSD and Nash–Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78 W m−2; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101 W m−2; 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Challenges related to the robust assessment of the LandFlux product are also discussed.

A physically based approach to drought modelling shows that there has been little change in drought from 1950 to 2008, contradicting previous work that suggested an increase in recent years. Published assessments of historic changes in drought during recent decades have suggested that the frequency and area of droughts have been increasing. Here Justin Sheffield et al. show that this prior work was flawed, because of an inappropriate calculation of drought metrics. Using a more physically based approach, the team shows that there has in fact been little change in drought during the period 1950 to 2008. Drought is expected to increase in frequency and severity in the future as a result of climate change, mainly as a consequence of decreases in regional precipitation but also because of increasing evaporation driven by global warming1,2,3. Previous assessments of historic changes in drought over the late twentieth and early twenty-first centuries indicate that this may already be happening globally. In particular, calculations of the Palmer Drought Severity Index (PDSI) show a decrease in moisture globally since the 1970s with a commensurate increase in the area in drought that is attributed, in part, to global warming4,5. The simplicity of the PDSI, which is calculated from a simple water-balance model forced by monthly precipitation and temperature data, makes it an attractive tool in large-scale drought assessments, but may give biased results in the context of climate change6. Here we show that the previously reported increase in global drought is overestimated because the PDSI uses a simplified model of potential evaporation7 that responds only to changes in temperature and thus responds incorrectly to global warming in recent decades. More realistic calculations, based on the underlying physical principles8 that take into account changes in available energy, humidity and wind speed, suggest that there has been little change in drought over the past 60 years. The results have implications for how we interpret the impact of global warming on the hydrological cycle and its extremes, and may help to explain why palaeoclimate drought reconstructions based on tree-ring data diverge from the PDSI-based drought record in recent years9,10.

The recently available Soil Moisture and Ocean Salinity (SMOS) 1.4 GHz based soil moisture retrievals for the year of 2010 and the first nine months of 2011 are assessed over the continental United States (CONUS) region, along with soil moisture retrievals produced at Princeton University based on the Advanced Microwave Scanning Radiometer (AMSR-E) 10.7 GHz channel using the Land Surface Microwave Emission Model (LSMEM) and in-situ measurements from the Natural Resource Conservation Service's (NRCS) Soil Climate Analysis Network (SCAN). The assessment is carried out using a performance metric developed by Crow (J. Hydromet., 2007), which calculates the ability of soil moisture estimates to correct errors in surface moisture predictions through a linear Kalman filter. Within the Crow framework, SMOS retrievals show the same level of skill as AMSR-E/LSMEM or SCAN when evaluated on the days where both are available. But the SMOS product is significantly less available than AMSR-E/LSMEM or SCAN, especially on rainy days, therefore it is less able to reproduce the rainfall-moisture dynamics and consequently achieves a lower performance metric if all available data are used from all products. Detailed analysis shows that, with uncertainties, the performance of both SMOS and AMSR-E/LSMEM generally decays with thicker vegetation and wetter climate but is not significantly influenced by topography. We expect SMOS to further improve its accuracy through validation studies and its availability under rainy conditions as well.

Land–atmosphere coupling strength or the degree to which land surface anomalies influence boundary layer development—and in extreme cases, rainfall—is arguably the single most fundamental criterion for evaluating hydrological model performance. The Global Land–Atmosphere Coupling Experiment (GLACE) showed that strength of coupling and its representation can affect a model’s ability to simulate climate predictability at the seasonal time scale. And yet, the lack of sufficient observations of coupling at appropriate temporal and spatial scales has made achieving “true” coupling in models an elusive goal. This study uses Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) soil moisture (SM), multisensor remote sensing (RS) evaporative fraction (EF), and Atmospheric Infrared Sounder (AIRS) lifting condensation level (LCL) to evaluate the realism of coupling in the Global Land Data Assimilation System (GLDAS) suite of land surface models (LSMs), Princeton Global Forcing Variable Infiltration Capacity model (PGF–VIC), seven global reanalyses, and the North American Regional Reanalysis (NARR) over a 5-yr period (2003–07). First, RS and modeled estimates of SM, EF, and LCL are intercompared. Then, emphasis is placed on quantifying RS and modeled differences in convective-season daily correlations between SM–LCL, SM–EF, and EF–LCL for global, regional, and conditional samples. RS is found to yield a substantially weaker state of coupling than model products. However, the rank order of basins by coupling strength calculated from RS and models do roughly agree. Using a mixture of satellite and modeled variables, a map of hybrid coupling strength was produced, which supports the findings of GLACE that transitional zones tend to have the strongest coupling.

A systematic method is proposed to optimally combine estimates of the terrestrial water budget from different data sources and to enforce the water balance constraint using data assimilation techniques. The method is applied to create global long-term records of the terrestrial water budget by merging a number of global datasets including in situ observations, remote sensing retrievals, land surface model simulations, and global reanalyses. The estimation process has three steps. First, a conventional analysis on the errors and biases in different data sources is conducted based on existing validation/error studies and other information such as sensor network density, model physics, and calibration procedures. Then, the data merging process combines different estimates so that biases and errors from different data sources can be compensated to the greatest extent and the merged estimates have the best possible confidence. Finally, water balance errors are resolved using the constrained Kalman filter technique. The procedure is applied to 32 globally distributed major basins for 1984–2006. The authors believe that the resulting global water budget estimates can be used as a baseline dataset for large-scale diagnostic studies, for example, integrated assessment of basin water resources, trend analysis and attribution, and climate change studies. The global scale of the analysis presents significant challenges in carrying out the error analysis for each water budget variable. For some variables (e.g., evapotranspiration) the assumptions underpinning the error analysis lack supporting quantitative analysis and, thus, may not hold for specific locations. Nevertheless, the merging and water balance constraining technique can be applied to many problems.

A simple method was developed to forecast 3- and 6-month standardized precipitation indices (SPIs) for the prediction of meteorological drought over the contiguous United States based on precipitation seasonal forecasts from the NCEP Climate Forecast System (CFS). Before predicting SPI, the precipitation (P) forecasts from the coarse-resolution CFS global model were bias corrected and downscaled to a regional grid of 50 km. The downscaled CFS P forecasts, out to 9 months, were appended to the P analyses to form an extended P dataset. The SPIs were calculated from this new time series. Five downscaling methods were tested: 1) bilinear interpolation; 2) a bias correction and spatial downscaling (BCSD) method based on the probability distribution functions; 3) a conditional probability estimation approach using the mean P ensemble forecasts developed by J. Schaake, 4) a Bayesian approach that bias corrects and downscales P using all ensemble forecast members, as developed by the Princeton University group; and 5) multimethod ensemble as the equally weighted mean of the BCSD, Schaake, and Bayesian forecasts. For initial conditions from April to May, statistical downscaling methods were compared with dynamic downscaling based on the NCEP regional spectral model and forecasts from a high-resolution CFS T382 model. The skill is regionally and seasonally dependent. Overall, the 6-month SPI is skillful out to 3–4 months. For the first 3-month lead times, forecast skill comes from the P analyses prior to the forecast time. After 3 months, the multimethod ensemble has small advantages, but forecast skill may be too low to be useful in practice.

The lack of observational data for use in evaluating the realism of model-based land–atmosphere feedback signal and strength has been deemed a major obstacle to future improvements to seasonal weather prediction by the Global Land–Atmosphere Coupling Experiment (GLACE). To address this need, a 7-yr (2002–09) satellite remote sensing data record is exploited to produce for the first time global maps of predominant coupling signals. Specifically, a previously implemented convective triggering potential (CTP)–humidity index (HI) framework for describing atmospheric controls on soil moisture–rainfall feedbacks is revisited and generalized for global application using CTP and HI from the Atmospheric Infrared Sounder (AIRS), soil moisture from the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E), and the U.S. Climate Prediction Center (CPC) merged satellite rainfall product (CMORPH). Based on observations taken during an AMSR-E-derived convective rainfall season, the global land area is categorized into four convective regimes: 1) those with atmospheric conditions favoring deep convection over wet soils, 2) those with atmospheric conditions favoring deep convection over dry soils, 3) those with atmospheric conditions that suppress convection over any land surface, and 4) those with atmospheric conditions that support convection over any land surface. Classification maps are produced using both the original and modified frameworks, and later contrasted with similarly derived maps using inputs from the National Aeronautics and Space Administration (NASA) Modern Era Retrospective Analysis for Research and Applications (MERRA). Both AIRS and MERRA datasets of CTP and HI are validated using radiosonde observations. The combinations of methods and data sources employed in this study enable evaluation of not only the sensitivity of the classification schemes themselves to their inputs, but also the uncertainty in the resultant classification maps. The findings are summarized for 20 climatic regions and three GLACE coupling hot spots, as well as zonally and globally. Globally, of the four-class scheme, regions for which convection is favored over wet and dry soils accounted for the greatest and least extent, respectively. Despite vast differences among the maps, many geographically large regions of concurrence exist. Through its ability to compensate for the latitudinally varying CTP–HI–rainfall tendency characteristics observed in this study, the revised classification framework overcomes limitations of the original framework. By identifying regions where coupling persists using satellite remote sensing this study provides the first observationally based guidance for future spatially and temporally focused studies of land–atmosphere interactions. Joint distributions of CTP and HI and soil moisture, rainfall occurrence, and depth demonstrate the relevance of CTP and HI in coupling studies and their potential value in future model evaluation, rainfall forecast, and/or hydrologic consistency applications.

The non-hydrostatic Weather Research and Forecasting model (WRF) was nested into NCEP’s operational seasonal forecast model Climate Forecast System (CFS) to downscale seasonal prediction of winter precipitation over continental China. Using the same initial conditions, 16 ensemble downscaling forecasts configured with two alternative schemes of microphysics, cumulus, land surface and radiation in WRF were conducted at 30 km for 27-cold seasons (December–February) during 1982–2008. On average, WRF downscaling forecasts reduced wet bias of seasonal mean precipitation from CFS prediction by 25–71%, decreased errors by up to 33%, and increased equitable threat score by 0.1 for low threshold. With appropriate physical configurations, WRF could improve interannual variations over the region where CFS has correct anomaly signal. The spatial distribution of daily precipitation characteristics such as rainy frequency and extremes highlighted the sensitivity of downscaling forecasts to physical configurations, and the dominant uncertainties were introduced by land surface and radiation schemes. The differences in convective and resolved rainfall between alternative land surface and radiation schemes were consistent with differences of surface downwelling shortwave and longwave radiation through cloud-radiation feedback. Such feedback was strengthened in the land surface sensitivity experiments due to different parameterizations of surface albedo. As compared with CFS ensemble predictions with different initial conditions, the WRF ensemble downscaling forecasts with various physical schemes had larger spread, and some schemes could complement each other in different regions that provided a promising opportunity to enhance the prediction through optimization. The optimized WRF reduced error from the optimized CFS by 30% and increased pattern correlation by 0.12. Moreover, WRF physical configuration ensemble increased percentage of skillful probabilistic forecasts from CFS initial condition ensemble.

Recent retrievals of multiple satellite products for each component of the terrestrial water cycle provide an opportunity to estimate the water budget globally. In this study, we estimate the water budget from satellite remote sensing over ten global river basins for 2003–2006. We use several satellite and non-satellite precipitation (P) and evapo-transpiration (ET) products in this study. The satellite precipitation products are the GPCP, TRMM, CMORPH and PERSIANN. For ET, we use four products generated from three retrieval models (Penman–Monteith (PM), Priestley–Taylor (PT) and the Surface Energy Balance System (SEBS)) with data inputs from the Earth Observing System (EOS) or the International Satellite Cloud Climatology Project (ISCCP) products. GPCP precipitation and PM (ISCCP) ET have less bias and errors over most of the river basins. To estimate the total water budget from satellite data for each basin, we generate merged products for P and ET by combining the four P and four ET products using weighted values based on their errors with respect to non-satellite merged product. The water storage change component is taken from GRACE satellite data, which are used directly with a single pre-specified error value. In the absence of satellite retrievals of river discharge, we use in-situ gauge measurements. Closure of the water budget over the river basins from the combined satellite and in-situ discharge products is not achievable with errors of the order of 5–25% of mean annual precipitation. A constrained ensemble Kalman filter is used to close the water budget and provide a constrained best-estimate of the water budget. The non-closure error from each water budget component is estimated and it is found that the merged satellite precipitation product carries most of the non-closure error.

The skill of instantaneous Atmospheric Infrared Sounder (AIRS) retrieved near-surface meteorology, including surface skin temperature (Ts), air temperature (Ta), specific humidity (q), and relative humidity (RH), as well as model-derived surface pressure (Psurf) and 10-m wind speed (w), is evaluated using collocated National Climatic Data Center (NCDC) in situ observations, offline data from the North American Land Data Assimilation System (NLDAS), and geostationary remote sensing (RS) data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Such data are needed for RS-based water cycle monitoring in areas without readily available in situ data. The study is conducted over the continental United States and Africa for a period of more than 6 years (2002–08). For both regions, it provides for the first time the geographic distribution of AIRS retrieval performance. Through conditional sampling, attribution of retrieval errors to scene atmospheric and surface conditions is performed. The findings support previous assertions that performance degrades with cloud fraction and that (positive) bias enhances with altitude. In general AIRS is biased warm and dry. In certain regions, strong AIRS–NCDC correlation suggests that bias-driven errors, which can be substantial, are correctable. The utility of the error characteristics for prescribing the input-induced uncertainty of RS retrieval models is demonstrated through two applications: a microwave soil moisture retrieval algorithm and the Penman–Monteith evapotranspiration model. An important side benefit of this study is the verification of NLDAS forcing.

The development and evaluation of a long-term high-resolution dataset of potential and actual evapotranspiration for Mexico based on remote sensing data are described. Evapotranspiration is calculated using a modified version of the Penman–Monteith algorithm, with input radiation and meteorological data from the International Satellite Cloud Climatology Project (ISCCP) and vegetation distribution derived from Advanced Very High Resolution Radiometer (AVHRR) products. The ISCCP data are downscaled to ⅛° resolution using statistical relationships with data from the North American Regional Reanalysis (NARR). The final product is available at ⅛°, daily, for 1984–2006 for all Mexico. Comparisons are made with the NARR offline land surface model and measurements from approximately 1800 pan stations. The remote sensing estimate follows well the seasonal cycle and spatial pattern of the comparison datasets, with a peak in late summer at the height of the North American monsoon and highest values in low-lying and coastal regions. The spatial average over Mexico is biased low by about 0.3 mm day−1, with a monthly rmse of about 0.5 mm day−1. The underestimation may be related to the lack of a model for canopy evaporation, which is estimated to be up to 30% of total evapotranspiration. Uncertainties in both the remote sensing–based estimates (because of input data uncertainties) and the true value of evapotranspiration (represented by the spread in the comparison datasets) are up to 0.5 and 1.2 mm day−1, respectively. This study is a first step in quantifying the long-term variation in global land evapotranspiration from remote sensing data.

Part I of this series of studies developed procedures to implement the multiscale filtering algorithm for land surface hydrology and performed assimilation experiments with rainfall ensembles from a climate model. However, a most important application of the multiscale technique is to assimilate satellite-based remote sensing observations into a land surface model—and this has not been realized. This paper focuses on enabling the multiscale assimilation system to use remotely sensed precipitation data. The major challenge is the generation of a rainfall ensemble given one satellite rainfall map. An acceptable rainfall ensemble must contain a proper multiscale spatial correlation structure, and each ensemble member presents a realistic rainfall process in both space and time. A pattern-based sampling approach is proposed, in which random samples are drawn from a historical rainfall database according to the pattern of the satellite rainfall and then a cumulative distribution function matching procedure is applied to ensure the proper statistics for the pixel-level rainfall intensity. The assimilation system is applied using Tropical Rainfall Measuring Mission real-time satellite rainfall over the Red–Arkansas River basin. Results show that the ensembles so generated satisfy the requirements for spatial correlation and realism and the multiscale assimilation works reasonably well. A number of limitations also exist in applying this generation method, mainly stemming from the high dimensionality of the problem and the lack of historical records.

The multiscale autoregressive (MAR) framework was introduced in the last decade to process signals that exhibit multiscale features. It provides the method for identifying the multiscale structure in signals and a filtering procedure, and thus is an efficient way to solve the optimal estimation problem for many high-dimensional dynamic systems. Later, an ensemble version of this multiscale filtering procedure, the ensemble multiscale filter (EnMSF), was developed for estimation systems that rely on Monte Carlo samples, making this technique suitable for a range of applications in geosciences. Following the prototype study that introduced EnMSF, a strategy is devised here to implement the multiscale method in a hydrologic data assimilation system, which runs a land surface model. Assimilation experiments are carried out over the Arkansas–Red River basin, located in the central United States (∼645 000 km2), using the Variable Infiltration Capacity (VIC) model with a computing grid of 1062 pixels. A synthetic data assimilation experiment is performed, driven by meteorological forcing fields downscaled from the ensemble forecasts made by the NOAA/National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The classic full-rank ensemble Kalman filter is used as the benchmark to evaluate the multiscale filter performance, and comparisons are also made with a horizontally uncoupled filter. It is demonstrated that the multiscale filter is able to closely approximate the full-rank solution with a low computational cost (∼1/20 of the full-rank filter) in an experiment in which the top-layer soil moisture is assimilated, whereas the horizontally uncoupled filter fails to approximate the full-rank solution.

Existing forward snow emission models (SEMs) are limited by knowledge of both the temporal and spatial variability of snow microphysical parameters, with grain size being the most difficult to measure or estimate. This is due to the sparseness of in situ data and the lack of simple operational parameterizations for the evolution of snowpack properties. This paper compares snow brightness temperatures predicted by three SEMs using, as inputs, predicted snowpack characteristics from the Variable Infiltration Capacity (VIC) model. The latter is augmented by a new parameterization for the evolution of snow grain morphology and density. The grain size dynamics are described using a crystal growth equation. The three SEMs used in the study are the Land Surface Microwave Emission Model (LSMEM), the Dense Media Radiative Transfer (DMRT) model, and the Microwave Emission Model of Layered Snowpacks (MEMLS). Estimated brightness temperature is validated against the satellite [Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E)] data at two sites from the Cold Land Processes Experiment (CLPX), conducted in Colorado in the winter of 2003. In addition, a merged multimodel estimate, based on Bayesian model averaging, is developed and compared to the measured brightness temperatures. The advantages of the Bayesian approach include the increase in the mean prediction accuracy as well as providing a nonparametric estimate of the error distributions for the brightness temperature estimates.

Skillful seasonal hydrologic predictions are useful in managing water resources, preparing for droughts and their impacts, energy planning, and many other related sectors. In this study, a seasonal hydrologic ensemble prediction system is developed and evaluated over the eastern United States, with a focus on the Ohio River basin. The system uses a hydrologic model (i.e., the Variable Infiltration Capacity model) as the central element for producing ensemble predictions of soil moisture, snow, and streamflow with lead times up to six months. One unique feature of this system is in the method for generating ensemble atmospheric forcings for the forecast period. It merges seasonal climate forecasts from multiple climate models with observed climatology in a Bayesian framework, such that the uncertainties related to the atmospheric forcings can be better quantified while the signals from individual models are combined. Simultaneously, climate model forecasts are downscaled to an appropriate spatial scale for hydrologic predictions. When generating daily meteorological forcing, the system uses the rank structures of selected historical forcing records to ensure reasonable weather patterns in space and time. Seasonal hydrologic predictions are made with this system, using seasonal climate forecast from the NCEP Climate Forecast System (CFS), and from a combination of the NCEP CFS and seven climate models in the European Union’s Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (CFS+DEMETER). Forecasts of these two types are made for the summer periods (May to October) of 1981–99 and are compared to forecasts produced with the traditional Ensemble Streamflow Prediction (ESP) approach used in operational seasonal streamflow predictions. The forecasts from this system for the summer of 1988 show very promising skill in precipitation, soil moisture, and streamflow over the Ohio River basin, especially the multimodel CFS+DEMETER forecast. The evaluation with all 19 summer forecasts shows that the multimodel CFS+DEMETER forecast is significantly better than the ESP forecast during the first two months of the forecasts. The advantage is marginal to moderate when only the CFS forecast is used. This study validates the approach of using seasonal climate predictions from dynamic climate models in hydrological predictions, and it also emphasizes the need for international collaborations to develop multimodel seasonal predictions.

An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190–199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.

An integrated data assimilation system is implemented over the Red-Arkansas river basin to estimate the regional scale terrestrial water cycle driven by multiple satellite remote sensing data. These satellite products include the Tropical Rainfall Measurement Mission (TRMM), TRMM Microwave Imager (TMI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Also, a number of previously developed assimilation techniques, including the ensemble Kalman filter (EnKF), the particle filter (PF), the water balance constrainer, and the copula error model, and as well as physically based models, including the Variable Infiltration Capacity (VIC), the Land Surface Microwave Emission Model (LSMEM), and the Surface Energy Balance System (SEBS), are tested in the water budget estimation experiments. This remote sensing based water budget estimation study is evaluated using ground observations driven model simulations. It is found that the land surface model driven by the bias-corrected TRMM rainfall produces reasonable water cycle states and fluxes, and the estimates are moderately improved by assimilating TMI 10.67 GHz microwave brightness temperature measurements that provides information on the surface soil moisture state, while it remains challenging to improve the results by assimilating evapotranspiration estimated from satellite-based measurements.

A multi-sensor/multi-platform approach to water and energy cycle prediction is demonstrated in an effort to understand the variability and feedback of land surface and atmospheric processes over large space and time scales. Remote sensing-based variables including soil moisture (from AMSR-E), surface heat fluxes (from MODIS) and precipitation rates (from TRMM) are combined with North American Regional Reanalysis derived atmospheric components to examine the degree of hydrological consistency throughout these diverse and independent hydrologic data sets. The study focuses on the influence of the North American Monsoon System (NAMS) over the southwestern United States, and is timed to coincide with the SMEX04 North American Monsoon Experiment (NAME). The study is focused over the Arizona portion of the NAME domain to assist in better characterizing the hydrometeorological processes occurring across Arizona during the summer monsoon period. Results demonstrate that this multi-sensor approach, in combination with available atmospheric observations, can be used to obtain a comprehensive and hydrometeorologically consistent characterization of the land surface water cycle, leading to an improved understanding of water and energy cycles within the NAME region and providing a novel framework for future remote observation and analysis of the coupled land surface–atmosphere system.

A seasonal streamflow monitoring and forecasting component is implemented into the Drought Monitoring and Prediction System (Luo and Wood, 2007a), which supplements the existing soil-moisture-based analysis framework by providing real-time streamflow monitoring and forecasting up to 6 months lead time. Evaluations were conducted over four basins in eastern USA to understand the forecast skill of the system for the extensive hydrologic droughts in 2007. Consistent with the agricultural drought forecasts reported by Luo and Wood (2007a), the streamflow subsystem can forecast low-flow conditions for up to three months in advance, with Brier scores ranging from 0.10 to 0.49. Copyright © 2008 Royal Meteorological Society

Assimilating soil moisture from satellite remote sensing into land surface models (LSMs) has potential for improving model predictions by providing real-time information at large scales. However, the majority of the research demonstrating this potential has been limited to datasets based on either airborne data or synthetic observations. The limited availability of satellite-retrieved soil moisture and the observed qualitative difference between satellite-retrieved and modeled soil moisture has posed challenges in demonstrating the potential over large regions in actual applications. Comparing modeled and satellite-retrieved soil moisture fields shows systematic differences between their mean values and between their dynamic ranges, and these systematic differences vary with satellite sensors, retrieval algorithms, and LSMs. This investigation focuses on generating observation operators for assimilating soil moisture into LSMs using a number of satellite–model combinations. The remotely sensed soil moisture products come from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the NASA/Earth Observing System (EOS) Advanced Microwave Scanning Radiometer (AMSR-E). The soil moisture model predictions are from the Variable Infiltration Capacity (VIC) hydrological model; the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); and the NCEP North American Regional Reanalysis (NARR). For this analysis, the satellite and model data are over the southern Great Plains region from 1998 to 2003 (1998–2002 for ERA-40). Previous work on observation operators used the matching of cumulative distributions to transform satellite-retrieved soil moisture into modeled soil moisture, which implied perfect correlations between the ranked values. In this paper, a bivariate statistical approach, based on copula distributions, is employed for representing the joint distribution between retrieved and modeled soil moisture, allowing for a quantitative estimation of the uncertainty in modeled soil moisture when merged with a satellite retrieval. The conditional probability distribution of model-based soil moisture conditioned on a satellite retrieval forms the basis for the soil moisture observation operator. The variance of these conditional distributions for different retrieval algorithms, LSMs, and locations provides an indication of the information content of satellite retrievals in assimilation. Results show that the operators vary by season and by land surface model, with the satellite retrievals providing more information in summer [July–August (JJA)] and fall [September–November (SON)] than winter [December–February (DJF)] or spring [March–May (MAM)] seasons. Also, the results indicate that the value of satellite-retrieved soil moisture is most useful to VIC, followed by ERA-40 and then NARR.