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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

We used environmental metrics developed from multi-source satellite observations to quantify the global influence of El Niño-Southern Oscillation (ENSO) events on surface wetting and drying anomalies, and their impact on vegetation health. The environmental metrics included a microwave surface wetness index (ASWI) incorporating near-surface atmospheric vapor pressure deficit (VPD), volumetric soil moisture (VSM), and land surface fractional water cover (FW) derived from Advanced Microwave Scanning Radiometer (AMSR) observations, and the vegetation health index (VHI) derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) observations. The combined ASWI and VHI analysis reveals complex ENSO related impacts on the distribution of water availability to plant communities, and variable vegetation sensitivity to associated drought and pluvial events. A delayed VHI response to changes in surface wetness (up to 3.4 months) was observed, whereby the ASWI may provide an effective forecast predictor of climate impacts on vegetation health. The intense 2015/2016 El Niño event coincided with strong ASWI and VHI latitudinal correspondence (R ≥ 0.73). The cascading impacts of climate anomalies on water cycle components and vegetation were further investigated over ENSO-sensitive sub-regions including Amazonia, Australia, southern Africa, and the South American Paraná delta region. The ASWI component information linked the effect of drought and pluvial events on vegetation health to underlying changes in surface water inundation, soil moisture and atmospheric moisture deficits. The new satellite-based assessments reveal the global complexity of ENSO-related impacts on surface water storages, and the influence of these climate and hydrologic perturbations on ecosystem productivity.

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

Accurate global mapping of soil moisture is the goal of the Soil Moisture Active Passive (SMAP) mission, which is expected to improve the estimation of water, energy, and carbon exchanges between the land and the atmosphere. Like other satellite products, the SMAP soil moisture retrievals need to be validated, with the validation relying heavily on in situ measurements. However, a one-to-one comparison is ill advised due to the spatial mismatch of the large SMAP footprint (∼40 km) and the point scale in situ measurements. This study uses a recently developed hyper-resolution land surface model—HydroBlocks—as a tool to upscale in situ soil moisture measurements for the SMAPVEX15 (SMAP Validation Experiment 2015) field campaign during 2–18 August 2015. Calibrated against in situ observation, HydroBlocks shows a satisfactory Kling-Gupta efficiency (KGE) of 0.817 and RMSE of 0.019 m3/m3 for the calibration period. These results indicate that HydroBlocks can be used to upscale in situ measurements for this site. Different from previous studies, here in situ measurements are upscaled using a land surface model without bias correction. The upscaled soil moisture is then used to evaluate SMAP (passive) soil moisture products. The comparison of the upscaled network to SMAP shows that the retrievals are generally able to capture the areal-averaged soil moisture temporal variations. However, SMAP appears to be oversensitive to summer precipitation. We expect these findings can be used to improve the SMAP soil moisture product and thus facilitate its usage in studying the water, energy, and carbon cycles.

The fate of the terrestrial biosphere is highly uncertain given recent and projected changes in climate. This is especially acute for impacts associated with changes in drought frequency and intensity on the distribution and timing of water availability. The development of effective adaptation strategies for these emerging threats to food and water security are compromised by limitations in our understanding of how natural and managed ecosystems are responding to changing hydrological and climatological regimes. This information gap is exacerbated by insufficient monitoring capabilities from local to global scales. Here, we describe how evapotranspiration (ET) represents the key variable in linking ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, and highlight both the outstanding science and applications questions and the actions, especially from a space-based perspective, necessary to advance them.

Publisher's version (útgefin grein), This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come., We would like to thank the members of the IAHS, EGU, AGU and IAH for supporting this initiative. The LinkedIn group and overall secretariat was hosted by the IAHS, the Splinter meeting by EGU and the Vienna Catchment Science Symposium by the Vienna Doctoral Programme on Water Resource Systems (DK W1219-N28) funded by the Austrian Science Funds (FWF)., "Peer Reviewed"

The first issue of WRR appeared eight years after the launch of Sputnik, but by WRR's 25th anniversary, only seven papers that used remote sensing had appeared. Over the journal's second 25 years, that changed remarkably, and remote sensing is now widely used in hydrology and other geophysical sciences. We attribute this evolution to production of data sets that scientists not well versed in remote sensing can use, and to educational initiatives like NASA's Earth System Science Fellowship program that has supported over a thousand scientists, many in hydrology. We review progress in remote sensing in hydrology from a water balance perspective. We argue that progress is primarily attributable to a creative use of existing and past satellite sensors to estimate such variables as evapotranspiration rates or water storage in lakes and reservoirs and to new and planned missions. Recent transforming technologies include the Gravity Recovery and Climate Experiment (GRACE), the European Soil Moisture and Ocean Salinity (SMOS) and U.S. Soil Moisture Active Passive (SMAP) missions, and the Global Precipitation Measurement (GPM) mission. Future missions include Surface Water and Ocean Topography (SWOT) to measure river discharge and lake, reservoir, and wetland storage. Measurement of some important hydrologic variables remains problematic: retrieval of snow water equivalent (SWE) from space remains elusive especially in mountain areas, even though snow cover extent is well observed, and was the topic of 4 of the first 5 remote sensing papers published in WRR. We argue that this area deserves more strategic thinking from the hydrology community.

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.

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 spatial heterogeneity of soil moisture remains a persistent challenge in the design of in situ measurement networks, spatial downscaling of coarse estimates (e.g., satellite retrievals), and hydrologic modeling. To address this challenge, we analyze high-resolution (∼9 m) simulated soil moisture fields over the Little River Experimental Watershed (LREW) in Georgia, USA, to assess the role and interaction of the spatial heterogeneity controls of soil moisture. We calibrate and validate the TOPLATS distributed hydrologic model with high to moderate resolution land and meteorological data sets to provide daily soil moisture fields between 2004 and 2008. The results suggest that topography and soils are the main drivers of spatial heterogeneity over the LREW. We use this analysis to introduce a novel network design method that uses land data sets as proxies of the main drivers of local heterogeneity (topography, land cover, and soil properties) to define unique and representative hydrologic similar units (subsurface, surface, and vegetation) for probe placement. The calibration of the hydrologic model and network design method illustrates how the use of hydrologic similar units in hydrologic modeling could minimize computation and guide efforts toward improved macroscale land surface modeling.

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.

This paper presents a continental-scale phenological analysis of African savannas and woodlands. We apply an array of synergistic vegetation and hydrological data records from satellite remote sensing and model simulations to explore the influence of rainy season timing and duration on regional land surface phenology and ecosystem structure. We find that (i) the rainy season onset precedes and is an effective predictor of the growing season onset in African grasslands. (ii) African woodlands generally have early green-up before rainy season onset and have a variable delayed senescence period after the rainy season, with this delay correlated nonlinearly with tree fraction. These woodland responses suggest their complex water use mechanisms (either from potential groundwater use by relatively deep roots or stem-water reserve) to maintain dry season activity. (iii) We empirically find that the rainy season length has strong nonlinear impacts on tree fractional cover in the annual rainfall range from 600 to 1800 mm/yr, which may lend some support to the previous modeling study that given the same amount of total rainfall to the tree fraction may first increase with the lengthening of rainy season until reaching an “optimal rainy season length,” after which tree fraction decreases with the further lengthening of rainy season. This nonlinear response is resulted from compound mechanisms of hydrological cycle, fire, and other factors. We conclude that African savannas and deciduous woodlands have distinctive responses in their phenology and ecosystem functioning to rainy season. Further research is needed to address interaction between groundwater and tropical woodland as well as to explicitly consider the ecological significance of rainy season length under climate change.

Summary The North American Land Data Assimilation System (NLDAS) phase 2 (NLDAS-2) has generated 31-years (1979–2008) of water and energy products from four state-of-the-art land surface models (Noah, Mosaic, SAC, VIC). The soil moisture data from these models have been used for operational drought monitoring activities, but so far have not yet been comprehensively evaluated. In this study, three available in situ soil moisture observation data sets in the United States were used to evaluate the model-simulated soil moisture for different time scales varying from daily to annual. First, we used the observed multiple layer monthly and annual mean soil moisture from the Illinois Climate Network to evaluate 20-years (January 1985–December 2004) of model-simulated soil moisture in terms of skill and analysis of error statistics. Second, we utilized 6-years (1 January 1997–31 December 2002) of daily soil moisture observed from 72 sites over the Oklahoma Mesonet network to assess daily and monthly simulation skill and errors for 3 model soil layers (0–10 cm, 10–40 cm, 40–100 cm). Third, we extended the daily assessment to sites over the continental United States using 8-years (1 January 2002–31 December 2009) of observations for 121 sites from the Soil Climate Analysis Network (SCAN). Overall, all models are able to capture wet and dry events and show high skill (in most cases, anomaly correlation is larger than 0.7), but display large biases when compared to in situ observations. These errors may come from model errors (i.e., model structure error, model parameter error), forcing data errors, and in situ soil moisture measurement errors. For example, all models simulate less soil moisture due to lack of modeled irrigation and ground water processes in Illinois, Oklahoma, and the other Midwest states.

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 (

[1] Budyko's framework has been widely used to study basin-scale water and energy balances and one of the formulations of the Budyko curve is Fu's equation. The curve shape parameter ϖ in Fu's equation controls how much of the available water will be evaporated given the available energy. Previous studies have found that land surface characteristics significantly affect variations in the parameter ϖ. In this study, we focus on the vegetation impact and examine the conditions under which vegetation plays a major role in controlling the variability of ϖ. Using data from 26 major global river basins that are larger than 300,000 km2, the basin-specific ϖ parameter is found to be linearly correlated with the long-term averaged annual vegetation coverage. A simple parameterization for the ϖ parameter based solely on remotely sensed vegetation information is proposed, which improves predictions of annual actual evapotranspiration by reducing the root mean square error (RMSE) from 76 mm to 47 mm as compared to the default ϖ value used in the Budyko curve method. The controlling impact of vegetation on the basin-specific ϖ parameter is diminished in small catchments with areas less than 50,000 km2, which suggests a scale-dependence of the role of vegetation in affecting water and energy balances. In small catchments, other key ecohydrological processes need to be taken into account in order to fully capture the variability of the ϖ parameter in Fu's equation.

Land surface spatial heterogeneity plays a significant role in the water, energy, and carbon cycles over a range of temporal and spatial scales. Until now, the representation of this spatial heterogeneity in land surface models has been limited to over simplistic schemes due to computation and environmental data limitations. This study introduces HydroBlocks—a novel land surface model that represents field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). HydroBlocks is a coupling between the Noah-MP land surface model and the Dynamic TOPMODEL hydrologic model. The HRUs are defined by clustering proxies of the drivers of spatial heterogeneity using high-resolution land data. The clustering mechanism allows for each HRU's results to be mapped out in space, facilitating field-scale application and validation. The Little Washita watershed in the United States is used to assess HydroBlocks’ performance and added benefit from traditional land surface models. A comparison between the semi-distributed and fully distributed versions of the model suggests that using 1000 HRUs is sufficient to accurately approximate the fully distributed solution. A preliminary evaluation of model performance using available in-situ soil moisture observations suggests that HydroBlocks is generally able to reproduce the observed spatial and temporal dynamics of soil moisture. Model performance deficiencies can be primarily attributed to parameter uncertainty. HydroBlocks’ ability to explicitly resolve field-scale spatial heterogeneity while only requiring an increase in computation of one to two orders of magnitude when compared to existing land surface models is encouraging—ensemble field-scale land surface modeling over continental extents is now possible.

Estimating evapotranspiration (ET) at continental to global scales is central to understanding the partitioning of energy and water at the earth's surface and the feedbacks with the atmosphere and biosphere, especially in the context of climate change. Recent evaluations of global estimates from remote sensing, upscaled observations, land surface models and atmospheric reanalyses indicate large uncertainty across the datasets of the order of 50% of the global annual mean value. In this paper, we explore the uncertainties in global land ET estimates using three process-based ET models and a set of remote sensing and observational based radiation and meteorological forcing datasets. Input forcings were obtained from International Satellite Cloud Climatology Project (ISCCP) and Surface Radiation Budget (SRB). The three process-based ET models are: a surface energy balance method (SEBS), a revised Penman–Monteith (PM) model, and a modified Priestley–Taylor model. Evaluations of the radiation products from ISCCP and SRB show large differences in the components of surface radiation, and temporal inconsistencies that relate to changes in satellite sensors and retrieval algorithms. In particular, step changes in the ISCCP surface temperature and humidity data lead to spurious increases in downward and upward longwave radiation that contributes to a step change in net radiation, and the ISCCP data are not used further. An ensemble of global estimates of land surface ET are generated at daily time scale and 0.5 degree spatial resolution for 1984–2007 using two SRB radiation products (SRB and SRBqc) and the three models. Uncertainty in ET from the models is much larger than the uncertainty from the radiation data. The largest uncertainties relative to the mean annual ET are in transition zones between dry and humid regions and monsoon regions. Comparisons with previous studies and an inferred estimate of ET from long-term inferred ET indicate that the ensemble mean value is reasonable, but generally biased high globally. Long-term changes over 1984–2007 indicate a slight increase over 1984–1998 and decline thereafter, although uncertainties in the forcing radiation data and lack of direct linkage with soil moisture limitations in the models prevents attribution of these changes. Copyright © 2011 John Wiley & Sons, Ltd.

The Model Parameter Estimation Experiment (MOPEX) is an international project aimed at developing enhanced techniques for the a priori estimation of parameters in hydrologic models and in land surface parameterization schemes of atmospheric models. The MOPEX science strategy involves three major steps: data preparation, a priori parameter estimation methodology development, and demonstration of parameter transferability. A comprehensive MOPEX database has been developed that contains historical hydrometeorological data and land surface characteristics data for many hydrologic basins in the United States (US) and in other countries. This database is being continuously expanded to include more basins in all parts of the world. A number of international MOPEX workshops have been convened to bring together interested hydrologists and land surface modelers from all over world to exchange knowledge and experience in developing a priori parameter estimation techniques. This paper describes the results from the second and third MOPEX workshops. The specific objective of these workshops is to examine the state of a priori parameter estimation techniques and how they can be potentially improved with observations from well-monitored hydrologic basins. Participants of the second and third MOPEX workshops were provided with data from 12 basins in the southeastern US and were asked to carry out a series of numerical experiments using a priori parameters as well as calibrated parameters developed for their respective hydrologic models. Different modeling groups carried out all the required experiments independently using eight different models, and the results from these models have been assembled for analysis in this paper. This paper presents an overview of the MOPEX experiment and its design. The main experimental results are analyzed. A key finding is that existing a priori parameter estimation procedures are problematic and need improvement. Significant improvement of these procedures may be achieved through model calibration of well-monitored hydrologic basins. This paper concludes with a discussion of the lessons learned, and points out further work and future strategy.

An Ensemble Kalman filter (EnKF) is used to assimilate airborne measurements of 1.4 GHz surface brightness temperature ðTBÞ acquired during the 1997 Southern Great Plains Hydrology Experiment (SGP97) into the TOPMODEL-based Land–Atmosphere Transfer Scheme (TOPLATS). In this way, the potential of using EnKF-assimilated remote measurements of TB to compensate land surface model predictions for errors arising from a climatological description of rainfall is assessed. The use of a real remotely sensed data source allows for a more complete examination of the challenges faced in implementing assimilation strategies than previous studies where observations were synthetically generated. Results demonstrate that the EnKF is an effective and computationally competitive strategy for the assimilation of remotely sensed TB measurements into land surface models. The EnKF is capable of extracting spatial and temporal trends in root-zone (40 cm) soil water content from TB measurements based solely on surface (5 cm) conditions. The accuracy of surface state and flux predictions made with the EnKF, ESTAR TB measurements, and climatological rainfall data within the Central Facility site during SGP97 are shown to be superior to predictions derived from open loop modeling driven by sparse temporal sampling of rainfall at frequencies consistent with expectations of future missions designed to measure rainfall from space (6–10 observations per day). Specific assimilation challenges posed by inadequacies in land surface model physics and spatial support contrasts between model predictions and sensor retrievals are discussed. 2002 Elsevier Science Ltd. All rights reserved.

The Export Coefficient model (ECM) is capable of gen-erating reasonable estimates of annual phosphorous loading simplyfrom a watershed’s land cover data and export coefficient values(ECVs). In its current form, the ECM assumes that ECVs arehomogeneous within each land cover type, yet basic nutrient runoffand hydrological theory suggests that runoff rates have spatial pat-terns controlled by loading and filtering along the flow paths fromthe upslope contributing area and downslope dispersal area. Usinga geographic information system (GIS) raster, or pixel, modelingformat, these contributing area and dispersal area (CADA) controlswere derived from the perspective of each individual watershedpixel to weight the otherwise homogeneous ECVs for phosphorous.Although the CADA-ECM predicts export coefficient spatial varia-tion for a single land use type, the lumped basin load is unaffectedby weighting. After CADA weighting, a map of the new ECVsaddressed the three fundamental criteria for targeting critical pol-lutant loading areas: (1) the presence of the pollutant, (2) the likeli-hood for runoff to carry the pollutant offsite, and (3) the likelihoodthat buffers will trap nutrients prior to their runoff into the receiv-ing water body. These spatially distributed maps of the most impor-tant pollutant management areas were used within New York’sWest Branch Delaware River watershed to demonstrate how theCADA-ECM could be applied in targeting phosphorous critical load-ing areas. (KEY TERMS: NPS modeling; watershed management; spatiallydistributed; topographic index; buffer index; phosphorous.)Endreny, Theodore A. and Eric F. Wood, 2003. Watershed Weighting of ExportCoefficients to Map Critical Phosphorous Loading Areas. J. of the AmericanWater Resources Association (JAWRA) 39(1):165-181.

Soil moisture is widely recognized as an important land surface variable that provides a deeper knowledge of land-atmosphere interactions and climate change. Space-borne passive and active microwave sensors have become valuable and essential sources of soil moisture observations at global scales. Over the past four decades, several active and passive microwave sensors have been deployed, along with the recent launch of two fully dedicated missions (SMOS and SMAP). Signifying the four decades of microwave remote sensing of soil moisture, this Part 2 of the two-part review series aims to present an overview of how our knowledge in this field has improved in terms of the design of sensors and their accuracy for retrieving soil moisture. The first part discusses the developments made in active and passive microwave soil moisture retrieval algorithms. We assess the evolution of the products of various sensors over the last four decades, in terms of daily coverage, temporal performance, and spatial performance, by comparing the products of eight passive sensors (SMMR, SSM/I, TMI, AMSR-E, WindSAT, AMSR2, SMOS and SMAP), two active sensors (ERS-Scatterometer, MetOp-ASCAT), and one active/passive merged soil moisture product (ESA-CCI combined product) with the International Soil Moisture Network (ISMN) in-situ stations and the Variable Infiltration Capacity (VIC) land surface model simulations over the Contiguous United States (CONUS). In the process, the regional impacts of vegetation conditions on the spatial and temporal performance of soil moisture products are investigated. We also carried out inter-satellite comparisons to study the roles of sensor design and algorithms on the retrieval accuracy. We find that substantial improvements have been made over recent years in this field in terms of daily coverage, retrieval accuracy, and temporal dynamics. We conclude that the microwave soil moisture products have significantly evolved in the last four decades and will continue to make key contributions to the progress of hydro-meteorological and climate sciences. (C) 2017 Elsevier Ltd. All rights reserved.

Using a spatially distributed water and energy balance model, we investigate the spatial structure of surface fluxes and states for the Washita '92 field experiment and the August campaign of the Washita '94 field experiments. For Washita '92, the model is validated against gravimetric and remotely sensed soil moisture, and for Washita '94, the model is validated against gravimetric soil moisture and measured energy fluxes. The model is shown to reasonably represent land–atmosphere interactions during the experimental periods. Scaling analysis of remotely sensed and modeled soil moisture and modeled latent heat flux is indicative of multiscaling behavior. The temporal behavior of the soil moisture scaling exponents for various moments suggests the existence of three distinct regimes during a dry-down. The multiscaling behavior inferred from simulated soil moisture and latent heat flux is hypothesized as a relationship which is a function of average soil moisture. Similar scaling analysis of important land surface properties indicates simple scaling for porosity, field capacity and wilting point, and multiscaling for residual soil moisture, leaf area index and the soils-topographic index. This is consistent with model results, which indicate a transition from simple scaling to multiscaling with dry-down. It is hypothesized that this transition is governed by the scaling properties which in wet conditions control infiltration (porosity, field capacity, leaf area index) to properties which in dry conditions control drainage (residual moisture content and soils-topographic index) and evaporation (wilting point, leaf area index). Land surface models which fail to incorporate these features will most likely be unable to capture the dynamic nature of soil moisture spatial variability.

Vertical inaccuracies in terrain data propagate through dispersal area subroutines to create uncertainties in runoff flowpath predictions. This study documented how terrain error sensitivities in the D8, Multiple Flow (MF), DEMON, D-Infinity and two hybrid dispersal area algorithms, responded to changes in terrain slope and error magnitude. Runoff dispersal areas were generated from convergent and divergent sections of low, medium, and high gradient 64-ha parcels using a 30 m pixel scale control digital elevation model (DEM) and an ensemble of alternative realizations of the control DEM. The ensemble of alternative DEM realizations was generated randomly to represent root mean square error (RMSE) values ranging from 0·5 to 6 m and spatial correlations of 0 to 0·999 across 180 m lag distances. Dispersal area residuals, derived by differencing output from control and ensemble simulations, were used to quantify the spatial consistency of algorithm dispersal area predictions. A maximum average algorithm consistency of 85% was obtained in steep sloping convergent terrain, and two map analysis techniques are recommended in maintaining high spatial consistencies under less optimum terrain conditions. A stochastic procedure was developed to translate DEM error into dispersal area probability maps, and thereby better represent uncertainties in runoff modelling and management. Two uses for these runoff probability maps include watershed management indices that identify the optimal areas for intercepting polluted runoff as well as Monte-Carlo-ready probability distributions that report the cumulative pollution impact of each pixel's downslope dispersal area. Copyright © 2001 John Wiley & Sons, Ltd.

Hydrological models can benefit from satellite-derived digital elevation models (DEMs) only after determining the hydrological model sensitivity to DEM inaccuracies. This study examined how vertical errors within a SPOT satellite-derived DEM of the 532 km2 Little Washita River, OK, watershed affected hydrological predictions in the TOPLATS (topographically based land–atmosphere transfer scheme) water and energy balance model. Model predictions based on SPOT-derived DEM inputs were compared with US Geological Survey (USGS) 7·5-minute level 1 and level 2 DEM-based predictions to determine model sensitivity. Ten-year simulation runs using a statistical formulation of TOPLATS indicated that while DEM inaccuracies had little effect on basin average output, they had a significant effect on the upper and lower quartiles of predicted water table depth. In 12-day simulation runs using a spatially explicit formulation of TOPLATS, which used 30-m grid cells across a 600 000 pixel model domain, elevation errors propagated into model predictions of soil moisture, runoff, evapotranspiration, incoming solar radiation and surface skin temperature. Aggregation of the 30-m pixel model output to scales of 0·25 km2, however, reduced differences between model-predicted vadose zone hydrology. Agreement between model-predicted water table hydrology was achieved at much larger scales of 5 km2, indicating that topography and its associated error structure may have a greater influence on saturated rather than unsaturated hydrological modelling. Copyright © 2000 John Wiley & Sons, Ltd.

Digital elevation models (DEMs) are currently generated using satellite imagery, but little is known about how errors in satellite-derived DEMs affect hydrogeomorphological products such as relief, elevation contours, basin boundaries and stream networks. This study identified and minimized vertical errors for a SPOT-derived DEM of the 532 km2 Little Washita River, OK, watershed and then assessed how elevation inaccuracies affected hydrogeomorphological analyses. SPOT-derived DEM errors were identified using a set of ground control points (GCPs) and by comparing the 600 000 pixels comprising the SPOT image of the study area to high and low accuracy US Geological Survey (USGS) 7·5-minute airborne-derived DEMs. The comparative analysis identified a basin-wide error structure in the SPOT product that post-processing of the SPOT image then reduced from an RMSE of 8·7 to 4·5 m. Although SPOT- and USGS-derived topographic relief images had a poor correlation at small spatial scales, at larger hillslope scales nearly 90% of the image pixels overlapped. For basin-scale descriptors, such as catchment area, stream length, stream density and Horton ratios, SPOT- and USGS-derived estimates differed by no more than 3%. At smaller spatial scales, however, an overlay of SPOT-derived vector images of basin boundaries and stream networks with equivalent higher accuracy products revealed that the products were incongruent on average at 100 m and at most up to distances of 1 km. In summary, the accuracy of the SPOT-derived DEM was adequate for deriving estimates of basin average hydrogeomorphology but was unable to match equivalent products derived from USGS 7·5-minute DEMs at scales finer than 100 m. Copyright © 2000 John Wiley & Sons, Ltd.

The presence of nonlinear relationships between surface soil moisture and various hydrologic processes suggests that grid-scale water and energy fluxes cannot be accurately modeled without subgrid-scale soil moisture information. For land surface and energy balance models run over continental- to global-scale domains, accurate fine-scale soil moisture observations are nearly impossible to obtain on a consistent basis and will likely remain so through the next generation of soil moisture remote sensors. In the absence of such data sets, an alternative approach is to generalize the statistical behavior of soil moisture fields across the relevant range of spatial scales. Downscaling procedures offer the possibility that the fine-scale statistical properties of soil moisture fields can be inferred from coarse-scale data. Such an approach was used for a 29×200 km transect of 25 m active radar data acquired over Oklahoma by NASA's spaceborne imaging radar imaging (SIR-C) mission on April 12, 1994. Using a soil dielectric inversion model, the radar data were processed to provide estimates of surface soil dielectric values, which can be equated to volumetric soil moisture content. The soil moisture field along each strip was analyzed for evidence of spatial scaling for scales ranging from 100 to 6400 m. Results suggest that a spatial scaling assumption may not always be an appropriate basis for a downscaling approach. Prospects for the development of a more robust downscaling procedure for soil moisture are discussed.

This paper describes the development of a process-based water and energy balance model for use in high latitudes under both summertime and wintertime conditions. The model is developed as a part of the Boreal Ecosystem-Atmosphere Study (BOREAS). The model differs from its original version in the representation of hydrological processes specific to climatic and ecologic conditions in high latitudes: the impact of an organic layer on the water and energy balance of the boreal forest; the parameterization of frozen ground and snow accumulation and ablation; and the effect of open water bodies on the calculation of the radiative energy and water budget. The model can be run either in a fully distributed or an aggregated statistical mode. The resulting macroscale hydrological model can be used, off line, to study the energy and water balance of the boreal forest and potentially can be used as a land-atmosphere parameterization in atmospheric models.

In support of the overall scientific objective of the Boreal Ecosystem-Atmosphere Study (BOREAS), which encompasses the improved understanding of the interactions between the boreal forest and the atmosphere, a process-based water and energy balance model is applied to observed forcing data, and the results are presented and discussed. Observed tower forcing and validation data are analyzed. A consistent diurnal pattern in the energy balance closure of the validation data is obtained. Simulations are performed for a number of BOREAS flux tower sites. The model successfully simulates the temporally averaged Bowen ratio and the evaporative part of precipitation over the different BOREAS flux tower sites during the 1994 and 1996 intensive field campaigns. At finer temporal scales a small phase shift in sensible heat flux and net radiation exists between the observed and model-derived quantities. The ground heat flux is found to be slightly larger than the observations during the course of the day. It is suggested that the sensitivity of the model to parameters such as the moss thickness, thermal conductivity, and heat capacity is responsible for these differences. The moss moisture content and the different components of the energy balance were very well matched for a continuous simulation during 1996. Overall, the accuracy performance of the model is equivalent to the accuracy of the input forcing data.

A new ground heat flux parameterization for land surface schemes, such as those used in climate and numerical weather prediction models, is described. Compared with other approaches that lump the canopy layer and ground surface, or empirically based approaches that consider the effect of radiation attenuation through the canopy layer, the new parameterization has several advantages. First, the reduction of radiation available for conducting soil surface exchange under vegetated areas is represented in a manner that assures that heat is conserved in the long term. Second, problems in representing properly the phase of the ground heat flux are alleviated. Finally, the approach is relatively simple and is computationally efficient, requiring only two soil thermal layers. Comparison of the method with analytical solutions for special cases shows that the new method approximates the analytical solution very well for different conditions, and that the new method is superior to the force-restore and the Crank-Nicholson method. Model-derived ground heat heat fluxes for the French HAPEX-MOBILHY (Hydrology-Atmosphere Pilot Experiment - Modelisation de Bilan Hydrique) site and the Brazilian ABRACOS (Anglo-Brazilian Amazonian Climate Observation Study) cleared ranch land site are shown to be in close agreement with observations. Sensitivity analyses show that if the attenuation of radiation under vegetation and soil heat storage are ignored, the daytime peak and nighttime minima of ground heat flux, latent and sensible heat fluxes, and surface temperature can be significantly in error. In particular, neglecting the radiation attenuation through the canopy layer can result in significant overestimation (underestimation) of daytime (nighttime) ground heat flux, while neglecting soil heat storage can result in significant phase errors.

Watershed areas that generate nonpoint source (NPS) polluted runoff need to be identified prior to the design of basin-wide water quality projects. Current watershed-scale NPS models lack a variable source area (VSA) hydrology routine, and are therefore unable to identify spatially dynamic runoff zones. The TOPLATS model used a watertable-driven VSA hydrology routine to identify runoff zones in a 17.5 km 2 agricultural watershed in central Oklahoma. Runoff areas were identified in a static modeling framework as a function of prestorm watertable depth and also in a dynamic modeling framework by simulating basin response to 2, 10, and 25 yr return period 6 h design storms. Variable source area expansion occurred throughout the duration of each 6 h storm and total runoff area increased with design storm intensity. Basin-average runoff rates of 1 mm h -1 provided little insight into runoff extremes while the spatially distributed analysis identified saturation excess zones with runoff rates equaling effective precipitation. The intersection of agricultural landcover areas with these saturation excess runoff zones targeted the priority potential NPS runoff zones that should be validated with field visits. These intersected areas, labeled as potential NPS runoff zones, were mapped within the watershed to demonstrate spatial analysis options available in TOPLATS for managing complex distributions of watershed runoff. TOPLATS concepts in spatial saturation excess runoff modeling should be incorporated into NPS management models.

The objective of this paper is to study the variation of evaporation in time and space. A two-layer model for solving energy and water balance is presented. The vertical soil column between the soil surface and the water table is divided into the root zone and the transmission zone. The variable infiltration capacity (VIC) concept is used to introduce a spatially varied distribution of soil moisture in the root zone layer. The soil moisture is distributed uniformly in space in the transmission zone layer. The model is used to simulate the fluxes for the King's Creek catchment in Manhattan, Kansas for a period between June through October 1987 (for the four intensive field campaigns), during which the first ISLSCP (International Satelite Land Surface Climatology Project) field experiment (FIFE) was conducted. The model is calibrated using the observed data during the first intensive field campaign (IFC) and validated over the next three IFCS. The energy and water balance equations are solved to vield the time series of fluxes which are compared to their observed counterparts. The model predicted diurnal variation of the evaporative fluxes and the variation of the fluxes after rainfall events is compared with the observations. The model computed fluxes match fairly well with the observed fluxes.

A grid network version of the two-layer variable infiltration capacity (VIC-2L) macroscale hydrologic model is described. VIC-2L is a hydrologically based soil- vegetation-atmosphere transfer scheme designed to represent the land surface in numerical weather prediction and climate models. The grid network scheme allows streamflow to be predicted for large continental rivers. Off-line (observed and estimated surface meteorological and radiative forcings) applications of the model to the Columbia River (1° latitude-longitude spatial resolution) and Delaware River (0.5° resolution) are described. The model performed quite well in both applications, reproducing the seasonal hydrograph and annual flow volumes to within a few percent. Difficulties in reproducing observed streamflow in the arid portion of the Snake River basin are attributed to groundwater-surface water interactions, which are not modeled by VIC-2L.

Regional and global scale studies of land-surface-atmosphere interactions require the use of observations for calibration and validation. In situ field observations are not representative of the distributed nature of land surface characteristics, and large-scale field experiments are expensive undertakings. In light of these requirements and shortcomings, satellite observations serve our purposes adequately. The use of satellite data in land surface modeling requires developing a hydrological model with a thin upper layer to be compatible with the nature of the satellite observations and that would evaluate the soil moisture and soil temperature of a thin layer close to the surface. This paper outlines the formulation of a thin layer hydrological model for use in simulating the soil moistures and soil temperatures. This thin layer hydrological model is the first step in our attempt to use microwave brightness temperature data for regional soil moisture estimation. The hydrological model presented here has been calibrated using five years (1980–1984) of daily streamflow data for the Kings Creek catchment. The calibrated parameters are used to validate the daily streamflows for the next 5 year period (1985–1989). The comparison of surface soil moistures and surface temperatures for the period of the Intensive Field Campaigns (IFCs) during the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) in 1987 is carried out and yields good results. The thin layer hydrological model is coupled with a canopy radiative transfer model and an atmospheric attenuation model to create a coupled soil-canopy-atmosphere model in order to study the effect of the vegetation and the soil characteristics on the Special Sensor Microwave Imager (SSM/I) brightness temperatures. The sensitivities of the brightness temperatures to the soil and vegetation is examined in detail. The studies show that increasing leaf area index masks the polarization difference signal originating at the soil surface.

The effects of small-scale heterogeneity in land surface characteristics on the large-scale fluxes of water and energy in the land-atmosphere system has become a central focus of many climatology research experiments. The acquisition of high resolution land surface data through remote sensing and intensive land-climatology field experiments (like HAPEX, FIFE, and BOREAS) has provided data to investigate the interactions between microscale land-atmosphere interactions and macroscale models. To determine the effect of small scale heterogeneities, the spatially averaged evaporative fraction is analytically derived for spatially variable soil moisture and soil-atmospheric controls on evaporation at low soil moisture. This average evaporative fraction is compared with the evaporative fraction determined using the spatially averaged soil moisture, as if from a lumped, or aggregated, land surface model. Results show that the lumped-model based evaporation will over estimate evaporation during periods of low atmospheric demands (early morning/late afternoon, Winter periods, etc.) and under estimate evaporation during periods of high demand (midday Summer periods.) The accuracy of using ‘effective’ parameters in lumped macroscale models depends on the variability of soil moisture and the sensitivity of the soil-vegetation system to low soil moisture.

In support of the eventual goal to integrate remotely sensed observations with coupled land-atmosphere models, a soil-vegetation-atmosphere transfer scheme is presented which can represent spatially variable water and energy balance processes on timescales of minutes to months. This scheme differs from previous schemes developed to address similar objectives in that it: (1) represents horizontal heterogeneity and transport in a TOPMODEL framework, and (2) maintains computational efficiency while representing the processes most important for our applications. The model is based on the original TOPMODEL-based land surface-atmosphere transfer scheme [Famiglietti and Wood, 1994a] with modifications to correct for deficiencies in the representation of ground heat flux, soil column geometry, soil evaporation, transpiration, and the effect of atmospheric stability on energy fluxes. These deficiencies were found to cause errors in the model predictions in quantities such as the sensible heat flux, to which the development of the atmospheric boundary layer is particularly sensitive. Application of the model to the entire First International Satellite Land Surface Climatology Project Field Experiment 1987 experimental period, focusing on Intensive Field Campaigns 3 and 4, shows that it successfully represents the essential processes of interest.

The two-layer variable infiltration capacity (VIC-2L) model is extended to incorporate a representation of subgrid variability in precipitation, using an analytical one-dimensional statistical dynamic representation for partial area coverage of precipitation. The analytical approach allows the effects of subgrid-scale spatial variability of precipitation on surface fluxes, runoff production, and soil moisture to be represented explicitly. With this method, spatially integrated representations of surface fluxes, runoff, and soil moisture due to subgrid-scale spatial variability in precipitation, infiltration, and vegetation cover are obtained. The results are compared with those obtained using an exhaustive pixel-based approach, and the results obtained by applying uniform precipitation over the precipitation-covered area. The precipitation coverage over a grid cell is shown to play a primary role in estimating the surface fluxes, runoff, and soil moisture. In general, the spatial distribution of precipitation within the precipitation-covered area plays a secondary role, in part because VIC-2L represents the subgrid spatial variability of soil properties. While the analytical approach gives good approximations to the pixel-based approach, and is superior to the uniform precipitation approach in general, the differences are not large.

The objective of this paper is to numerically evaluate the spatially filtered flow theory of Beckie et al. [this issue]. We perform two suites of tests to evaluate the theory. In the first suite of tests we examine the accuracy of the spatially filtered Darcy's law approximations. To do this, we use exact filtered conductivities and head gradients in the spatially filtered Darcy's laws to calculate the approximate filtered flux and then compare the approximate flux field to the exact flux field. We determine the exact fields by numerically filtering the primitive fields. In the second suite of tests we solve the model equations to determine the approximate flux solution. We present a simple numerical-perturbation strategy to solve the model equations. The first suite of tests shows that over a range of filter widths the error in the resolved-scale flux computed from the second-order approximate spatially filtered Darcy's law is up to 3 orders of magnitude lower than the zeroth-order approximation. The second suite of tests, which more closely correspond to the way the theory would be applied in practice, show that the model accuracy and convergence properties are degraded by error from the numerical-perturbation strategy.

An approach for estimation of the parameters of a macroscale land surface hydrology model is illustrated for the Global and Water Cycle Experiment (GEWEX) Continental Scale International Project (GCIP) large-scale area southwest (LSA-SW) which essentially comprises the Arkansas-Red River basin. The macroscale land surface hydrology model parameters were estimated for 44 catchments within LSA-SW with areas ranging from 180 to 7100 km2 using an automated search procedure. The catchment parameters were then linearly interpolated and overlaid on a one degree grid, which was used to represent the drainage network. The macroscale grid network model was run off-line at a daily time step, forced by gridded station precipitation and potential evapotranspiration. The model-generated long-term mean streamflows were compared with observations (corrected for management effects such as reservoir storage and diversions) and were found to agree to within one percent for the Arkansas River and about two percent for the Red River. For both rivers, the model underestimates the seasonal peak streamflow in late spring, and overestimates the late summer and early fall minimum. Model-derived evapotranspiration, spatially averaged over the entire Arkansas-Red basin, was compared to evapotranspiration derived from an atmospheric moisture budget of the Arkansas-Red River basin. On an average annual basis, for the period 1973–1986, the two agree to within one percent. The mean seasonal cycles for the two estimates agree quite closely from late winter to midsummer. However, the hydrologic model estimates less evapotranspiration in the fall, and more in midwinter, than the atmospheric budget.

[1] The progress in forecasting seasonal climate by using coupled atmosphere-ocean-land general circulation models (CGCMs) has increased the use of CGCM-based hydrologic forecasting in recent years. A common procedure is to downscale the meteorological forcings and use them as inputs to hydrologic models to provide ensemble forecasts. Less attention has been paid to bias correcting the hydrologic forecasts directly generated by CGCM. In this study, we show that either downscaling precipitation for hydrologic model or directly bias-correcting CGCM streamflow increases the efficiency skill score greatly as compared to the original CGCM streamflow forecast, and bias correcting the streamflow from hydrologic model with downscaled precipitation leads to a further skill increase. Bias-correcting CGCM streamflow is more skillful and reliable than downscaling precipitation for hydrologic modeling in terms of ensemble forecasts, as verified by the ranked probability skill score and the rank histogram. While bias-correcting streamflow from CGCM can provide useful forecasts, combining the downscaled CGCM forcings and bias-corrected hydrologic output through the CGCM-hydrology forecasting approach does gain additional skill of accuracy and discrimination.

Addressing scaling issues in hydrological modelling involves, among other things, the study of problems related to hydrological similarity between catchments of different scales. Recent research about catchment similarity relationships is based on distributed conceptual models of surface runoff production. In this type of hydrological modelling both infiltration excess and saturation excess runoff production mechanisms are considered. In many humid lowland areas overland flow is a rare phenomenon because of the specific conditions that prevail: moderate rainfall, high infiltration capacity and low relief. The complete drainage system in these regions consists of surface and subsurface components which have organized themselves in a given geological, geomorphological and climatic situation. A surface drainage network has developed through sapping erosion at the zone of groundwater exfiltration. The resulting hierarchical stream network is in equilibrium with large time-scale conditions and adjusts itself dynamically to the inter-year and seasonal meteorological fluctuations. Greater understanding of the interrelationships that underlie the storm response of catchments in humid lowland regions can be expected by focusing on stream network morphology as a function of topography, geology and climate. This paper applies the physically based mathematical model of stream network morphology, developed by De Vries (1977), to the Zwalmbeek catchment, Belgium. Based on this model and for different climatic conditions (expressed in terms of rainfall characteristics) the first-order stream spacing versus average water-table depth relationship is calculated. From field observations, digital elevation model derived channel network drainage densities and flood event analysis it is concluded that the 1% exceedance probability rainfall can be suggested as representative for the shaping climatic conditions in the catchment under study. The corresponding curves relating channel network characteristics, such as stream spacing, drainage density and channel geometry, to average water-table depth are basin descriptors and could be used for comparative studies (e.g. regional flood frequency analysis). The model further allows for the prediction of the expansion and shrinkage of the first-order channel network as a function of catchment wetness expressed in terms of the effective water-table depth.

The effects of small-scale heterogeneity in land surface characteristics on the large-scale fluxes of water and energy in the land-atmosphere system has become a central focus of many of the climatology research experiments. The acquisition of high resolution land surface data through remote sensing and intensive land-climatology field experiments (such as HAPEX and FIFE) has provided data to investigate the interactions between microscale land-atmosphere interactions and macroscale models. One essential research question is how to account for the small-scale heterogeneities and whether 'effective' parameters can be used in the macroscale models. To address this question of scaling, is important to carry out modelling studies by analysing the spatial behaviour of process-based, distributed land-atmospheric models and available data from land surface climate experiments such as those designed under ISLSCP (e.g. FIFE and BOREAS) and HAPEX (e.g. HAPEX-MOBILY, HAPEX-SAHEL) and GEWEX (e.g. GCIP) and from smaller scale remote sensing experiments. Using data from FIFE'87 and WASHITA'92, a soil moisture remote sensing experiment, analyses are presented on how the land surface hydrology during rain events and between rain event varies; specifically, runoff during rain events, evaporation between rain events and soil moisture. The analysis with FIFE'87 data suggests that the scale at which a macroscale model becomes valid, the representative elementary scale (REA), is of the order of 1.5-3 correlation lengths, which for the land processes investigated appear to be about 750-1250 m. For the Washita catchment data, analysis of soil-based infiltration data supports an REA of this spatial scale, but model derived and remotely sensed soil moisture data appear to suggest a larger scale. Statistical self-similarity is investigated to further understand how soil moisture scales over the Washita catchment and to provide a basis for macroscale models.

This paper explores the effects of spatial variability and scale on areally averaged evapotranspiration. A spatially distributed water and energy balance model is employed to determine the effect of explicit patterns of land surface characteristics and atmospheric forcing on areally averaged evapotranspiration over a range of increasing spatial scales. The analysis is performed from the local scale to the catchment scale. The study area is King's Creek catchment, an 11.7 km2 watershed located on the native tallgrass prairie of Kansas. It is shown that a threshold scale, or representative elementary area (REA) exists for evapotranspiration modeling. It is shown further that the dominant controls on the scaling behavior of catchment-average evapotranspiration, and thus the size of the REA, depend on the dominant controls on its components (bare-soil evaporation, wet canopy evaporation, and dry canopy transpiration) and whether evapotranspiration is occurring at potential rates or soil- and vegetation-controlled rates. The existence of an REA for evapotranspiration modeling suggests that in catchment areas smaller than this threshold scale, actual patterns of model parameters and inputs may be important factors governing catchment-scale evapotranspiration rates in hydrological models. In models applied at scales greater than the REA scale, spatial patterns of dominant process controls can be represented by their statistical distribution functions. It appears that some of our findings are fairly general and will therefore provide a framework for understanding the scaling behavior of areally averaged evapotranspiration at the catchment and larger scales. Our results may have further implications for representing subgrid-scale land surface heterogeneity in hydrological parameterizations for atmospheric models.

[1] Atmospheric Rivers (ARs), narrow plumes of enhanced moisture transport in the lower troposphere, are a key synoptic feature behind winter flooding in midlatitude regions. This article develops an algorithm which uses the spatial and temporal extent of the vertically integrated horizontal water vapor transport for the detection of persistent ARs (lasting 18 h or longer) in five atmospheric reanalysis products. Applying the algorithm to the different reanalyses in the vicinity of Great Britain during the winter half-years of 1980–2010 (31 years) demonstrates generally good agreement of AR occurrence between the products. The relationship between persistent AR occurrences and winter floods is demonstrated using winter peaks-over-threshold (POT) floods (with on average one flood peak per winter). In the nine study basins, the number of winter POT-1 floods associated with persistent ARs ranged from approximately 40 to 80%. A Poisson regression model was used to describe the relationship between the number of ARs in the winter half-years and the large-scale climate variability. A significant negative dependence was found between AR totals and the Scandinavian Pattern (SCP), with a greater frequency of ARs associated with lower SCP values.

[1] Eurasian river discharge into the Arctic Ocean has steadily increased during the 20th century, and many studies have documented the spatial distribution of the trends and hypothesized the causes. There is a large variation in the scope of these studies, including the spatial scale of interest, and they often lack consistency in the time period analyzed. Studies have shown a connection between changes in the seasonal snowpack and discharge, but they have been constrained by the limitations of the snow observational network, which contains few long-term stations. This study overcomes these problems by using both in situ observations and a land surface model to evaluate the role snowpack changes have had on increases in runoff across northern Eurasia from 1936 through 1999. Our analysis shows consistent trends in both observations and model predictions. Increases in cold season precipitation propagate into increases in maximum snow water equivalent, which lead to increases in runoff. A series of model experiments demonstrate that the nonlinear interaction between winter precipitation and temperature has driven changes in the snowpack, which are manifested in the modeled runoff trends. Given that winter precipitation is expected to continue to increase and temperatures to warm during the 21st century in this region, these results point to the importance in understanding how the projected changes will influence the seasonal snowpack, which may have important consequences for streamflow in this region and freshwater export to the Arctic Ocean.

[1] Results are presented from the second phase of the multiinstitution North American Land Data Assimilation System (NLDAS-2) research partnership. In NLDAS, the Noah, Variable Infiltration Capacity, Sacramento Soil Moisture Accounting, and Mosaic land surface models (LSMs) are executed over the conterminous U.S. (CONUS) in realtime and retrospective modes. These runs support the drought analysis, monitoring and forecasting activities of the National Integrated Drought Information System, as well as efforts to monitor large-scale floods. NLDAS-2 builds upon the framework of the first phase of NLDAS (NLDAS-1) by increasing the accuracy and consistency of the surface forcing data, upgrading the land surface model code and parameters, and extending the study from a 3-year (1997–1999) to a 30-year (1979–2008) time window. As the first of two parts, this paper details the configuration of NLDAS-2, describes the upgrades to the forcing, parameters, and code of the four LSMs, and explores overall model-to-model comparisons of land surface water and energy flux and state variables over the CONUS. Focusing on model output rather than on observations, this study seeks to highlight the similarities and differences between models, and to assess changes in output from that seen in NLDAS-1. The second part of the two-part article focuses on the validation of model-simulated streamflow and evaporation against observations. The results depict a higher level of agreement among the four models over much of the CONUS than was found in the first phase of NLDAS. This is due, in part, to recent improvements in the parameters, code, and forcing of the NLDAS-2 LSMs that were initiated following NLDAS-1. However, large inter-model differences still exist in the northeast, Lake Superior, and western mountainous regions of the CONUS, which are associated with cold season processes. In addition, variations in the representation of sub-surface hydrology in the four LSMs lead to large differences in modeled evaporation and subsurface runoff. These issues are important targets for future research by the land surface modeling community. Finally, improvement from NLDAS-1 to NLDAS-2 is summarized by comparing the streamflow measured from U.S. Geological Survey stream gauges with that simulated by four NLDAS models over 961 small basins.