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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. Key Points: Exploring cascading impacts of climate anomalies on water cycle and vegetation using synergistic satellite observationsDiversity in vegetation sensitivity and responses to anomalies of key water cycle variables associated with El Niño‐Southern Oscillation (ENSO) eventsPotential global monitoring and forecasting of ENSO climate and hydrologic impacts on terrestrial ecosystems [ABSTRACT FROM AUTHOR]

Water resources management (WRM) for sustainable development presents many challenges in areas with sparse in situ monitoring networks. The exponential growth of satellite based information over the past decade provides unprecedented opportunities to support and improve WRM. Furthermore, traditional barriers to the access and usage of satellite data are lowering as technological innovations provide opportunities to manage and deliver this wealth of information to a wider audience. We review data needs for WRM and the role that satellite remote sensing can play to fill gaps and enhance WRM, focusing on the Latin American and Caribbean as an example of a region with potential to further develop its resources and mitigate the impacts of hydrological hazards. We review the state‐of‐the‐art for relevant variables, current satellite missions, and products, how they are being used currently by national agencies across the Latin American and Caribbean region, and the challenges to improving their utility. We discuss the potential of recently launched, upcoming, and proposed missions that are likely to further enhance and transform assessment and monitoring of water resources. Ongoing challenges of accuracy, sampling, and continuity still need to be addressed, and further challenges related to the massive amounts of new data need to be overcome to best leverage the utility of satellite based information for improving WRM. Key Points: Satellite remote sensing is being incorporated into water resources management but is generally underutilizedNew and proposed missions have the potential to transform water resources management for sustainable development, especially in data‐poor regionsOngoing challenges of accuracy, sampling, and continuity and capacity development need to be addressed, as well as new challenges of information volume and diversity [ABSTRACT FROM AUTHOR]

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 Land- Flux 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 PTJPL provided the highest overall statistical performance (0.72; 61Wm-2; 0.65), followed closely by GLEAM (0.68; 64Wm-2; 0.62), with values in parentheses representing the R2, RMSD and Nash-Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78Wm-2; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101Wm-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. [ABSTRACT FROM AUTHOR]

The WACMOS-ET project aims to advance the development of land evaporation estimates at global and regional scales. Its main objective is the derivation, validation and inter-comparison 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 oficial Moderate Resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Global Land Evaporation Amsterdam Model (GLEAM), and the Priestley and 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. (2015) inspect the performance of these three models at local scales using measurements from eddy-covariance towers, and include the assessment the Surface Energy Balance System (SEBS) model. In agreement with Part 1, our results here indicate that the Priestley and Taylor based products (PT-JPL and GLEAM) perform overall best for most ecosystems and climate regimes. While all three products adequately represent the expected average geographical patterns and seasonality, there is a ten dency from PM-MOD to underestimate the flux in the tropics and subtropics. Overall, results from GLEAM and PT-JPL appear more realistic when compared against surface water balances from 837 globally-distributed catchments, and against separate evaporation estimates from ERA-Interim and the Model Tree Ensemble (MTE). Nonetheless, all products manifest 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, implies caution 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. Yet, 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. [ABSTRACT FROM AUTHOR]

The WACMOS-ET project has compiled a forcing data set covering the period 2005- 2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run 4 estab- lished ET algorithms: the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman-Monteith algorithm from the MODIS evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in-situ meteorological data from 24 FLUXNET towers was used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed across several time scales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement to the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements (R² =0.67), the agreement of the satellite-based ET estimates is only marginally lower (R² =0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. An extension of the evaluation to a larger selection of 85 towers (model inputs re-sampled to a common grid to facilitate global estimates) confirmed the original findings. [ABSTRACT FROM AUTHOR]

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 falsealarmed 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. [ABSTRACT FROM AUTHOR]

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 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 (PMMu) and the Global Land Evaporation: the Amsterdam Methodology (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multimodel 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 gridbased forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to 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 forty-five globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overally statistical performance (0.72; 61Wm-2; 0.65), followed closely by GLEAM (0.68; 64Wm-2; 0.62), with values in parenthesis representing the R2, RMSD and Nash-Sutclifie Effciency (NSE) and respectively. PM-Mu (0.51; 78Wm-2; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101Wm-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 towerand 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. [ABSTRACT FROM AUTHOR]

Land surface modeling, in conjunction with numerical weather forecasting and satellite remote sensing, is playing an increasing role in global monitoring and prediction of extreme hydrologic events (i.e., floods and droughts). However, uncertainties in the meteorological forcings, model structure, and parameter identifiability limit the reliability of model predictions. This study focuses on the latter by assessing two potential weaknesses that emerge due to limitations in our global runoff observations: (1) the limits of identifying model parameters at coarser timescales than those at which the extreme events occur, and (2) the negative impacts of not properly accounting for model parameter equifinality in the predictions of extreme events. To address these challenges, petascale parallel computing is used to perform the first global-scale, 10 000 member ensemble-based evaluation of plausible model parameters using the VIC (Variable Infiltration Capacity) land surface model, aiming to characterize the impact of parameter identifiability on the uncertainty in flood and drought predictions. Additionally, VIC's global-scale parametric sensitivities are assessed at the annual, monthly, and daily timescales to determine whether coarse-timescale observations can properly constrain extreme events. Global and climate type results indicate that parameter uncertainty remains an important concern for predicting extreme events even after applying monthly and annual constraints to the ensemble, suggesting a need for improved prior distributions of the model parameters as well as improved observations. This study contributes a comprehensive evaluation of land surface modeling for global flood and drought monitoring and suggests paths forward to overcome the challenges posed by parameter uncertainty. [ABSTRACT FROM AUTHOR]

Climate change is expected to modify intra-seasonal rainfall variability, arising from shifts in rainfall frequency, intensity and seasonality. These intra-seasonal changes are likely to have important ecological impacts on terrestrial ecosystems. Yet, quantifying these impacts across biomes and large climate gradients is largely missing. This gap hinders our ability to better predict ecosystem services and their responses to climate change, especially for arid and semi-arid ecosystems. Here we use a synthetic weather generator and an independently validated vegetation dynamic model (SEIB-Dynamic Global Vegetation Model, DGVM) to virtually conduct a series of "rainfall manipulation experiments" to study how changes in the intra-seasonal rainfall variability affect continent-scale ecosystem responses across Africa. We generate different rainfall scenarios with fixed total annual rainfall but shifts in (i) frequency vs. intensity, (ii) rainy season length vs. frequency, (iii) intensity vs. rainy season length. These scenarios are fed into SEIB-DGVM to investigate changes in biome distributions and ecosystem productivity. We find a loss of ecosystem productivity with increased rainfall frequency and decreased intensity at very low rainfall regimes (< 400 mm year"1) and low frequency (< 0.3 event day-1); beyond these very dry regimes, most ecosystems benefit from increased frequency and decreased intensity, except in the wet tropics (> 1800 mm year-1) where radiation limitation prevents further productivity gains. This result reconciles seemingly contradictory findings in previous field studies on the impact of rainfall frequency/intensity on ecosystem productivity. We also find that changes in rainy season length can yield more dramatic ecosystem responses compared with similar percentage changes in rainfall frequency or intensity, with the largest impacts in semi-arid woodlands. This study demonstrates that intra-seasonal rainfall characteristics play a significant role in influencing ecosystem function and structure through controls on ecohydrological processes. Our results suggest that shifts in rainfall seasonality have potentially large impacts on terrestrial ecosystems, and these understudied impacts should be explicitly examined in future studies of climate impacts. [ABSTRACT FROM AUTHOR]

The process whereby the spatially distributed runoff (generated through saturation/infiltration excesses, subsurface flow, etc.) travels over the hillslope and river network and becomes streamflow is generally referred to as "routing". In short, routing is a runoff-to-streamflow process, and the streamflow in rivers is the response to runoff integrated in both time and space. Here we develop a methodology to invert the routing process, i.e., to derive the spatially distributed runoff from streamflow (e.g., measured at gauge stations) by inverting an arbitrary linear routing model using fixed interval smoothing. We refer to this streamflowto- runoff process as "inverse routing". Inversion experiments are performed using both synthetically generated and real streamflow measurements over the Ohio River basin. Results show that inverse routing can effectively reproduce the spatial field of runoff and its temporal dynamics from suf- ficiently dense gauge measurements, and the inversion performance can also be strongly affected by low gauge density and poor data quality. The runoff field is the only component in the terrestrial water budget that cannot be directly measured, and all previous studies used streamflow measurements in its place. Consequently, such studies are limited to scales where the spatial and temporal difference between the two can be ignored. Inverse routing provides a more sophisticated tool than traditional methods to bridge this gap and infer finescale (in both time and space) details of runoff from aggregated measurements. Improved handling of this final gap in terrestrial water budget analysis may potentially help us to use space-borne altimetry-based surface water measurements for cross-validating, cross-correcting, and assimilation with other space-borne water cycle observations. [ABSTRACT FROM AUTHOR]

Land evapotranspiration (ET) estimates are available from several global data sets. Here, monthly global land ET synthesis products, merged from these individual data sets over the time periods 1989-1995 (7 yr) and 1989-2005 (17 yr), are presented. The merged synthesis products over the shorter period are based on a total of 40 distinct data sets while those over the longer period are based on a total of 14 data sets. In the individual data sets, ET is derived from satellite and/or in situ observations (diagnostic data sets) or calculated via land-surface models (LSMs) driven with observations-based forcing or output from atmospheric reanalyses. Statistics for four merged synthesis products are provided, one including all data sets and three including only data sets from one category each (diagnostic, LSMs, and reanalyses). The multi-annual variations of ET in the merged synthesis products display realistic responses. They are also consistent with previous findings of a global increase in ET between 1989 and 1997 (0.13mm yr-2 in our merged product) followed by a significant decrease in this trend (-0.18 mm yr-2), although these trends are relatively small compared to the uncertainty of absolute ET values. The global mean ET from the merged synthesis products (based on all data sets) is 493 mm yr-1 (1.35 mm d-1) for both the 1989-1995 and 1989-2005 products, which is relatively low compared to previously published estimates. We estimate global runoff (precipitation minus ET) to 263 mm yr-1 (34 406 km³ yr-1) for a total land area of 130 922 000 km². Precipitation, being an important driving factor and input to most simulated ET data sets, presents uncertainties between single data sets as large as those in the ET estimates. In order to reduce uncertainties in current ET products, improving the accuracy of the input variables, especially precipitation, as well as the parameterizations of ET, are crucial. [ABSTRACT FROM AUTHOR]

Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1-6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs -- soil moisture and snow water content - and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961-2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydro-logic forecast skill over many regions of the globe at least during some parts of the year. [ABSTRACT FROM AUTHOR]

Whether the global runoff (or freshwater discharge from land to the ocean) is currently increasing and the global water cycle is intensifying is still a controversial issue. Here we compute land--atmosphere and ocean--atmosphere water budgets and derive two independent estimates of the global runoff over the period 1993-2009. Water storage variations in the land, ocean and atmosphere reservoirs are estimated from different types of data sets: atmospheric reanalyses, land surface models, satellite altimetry and in situ ocean temperature data (the difference between altimetry based global mean sea level and ocean thermal expansion providing an estimate of the ocean mass component). These data sets are first validated using independent data, and then the global runoff is computed from the two methods. Results for the global runoff show a very good correlation between both estimates. More importantly, no significant trend is observed over the whole period. Besides, the global runoff appears to be clearly impacted by large-scale climate phenomena such as major ENSO events. To infer this, we compute the zonal runoff over four latitudinal bands and set up for each band a new index (combined runoff index) obtained by optimization of linear combinations of various climate indices. Results show that, in particular, the intertropical and northern mid-latitude runoffs are mainly driven by ENSO and the Atlantic multidecadal oscillation (AMO) with opposite behavior. Indeed, the zonal runoff in the intertropical zone decreases during major El Niño events, whereas it increases in the northern mid-latitudes, suggesting that water masses over land are shifted northward/southward during El Niño/La Niña. In addition to this study, we propose an innovative method to estimate the global ocean thermal expansion. The method is based on the assumption that the difference between both runoff estimates is mainly due to the thermal expansion term not accounted for in the estimation of the ocean mass. We find that our reconstructed thermal expansion time series compares well with two existing data sets in terms of year-to-year fluctuations but somewhat differs on longer (multi-year) time scales. Possible explanations include non negligible steric variations from the deep ocean. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) satellite has opened the era of soil moisture products from passive L-band observations. In this paper, validation of SMOS products over continental U.S. is done by using the Soil Climate Analysis Network (SCAN)/SNOwpack TELemetry (SNOTEL) soil moisture monitoring stations. The SMOS operational products and the SMOS reprocessing products are both used and compared over year 2010. First, a direct node-to-site comparison is performed by taking advantage of the oversampling of the SMOS product grid. The comparison is performed over several adjacent nodes to site, and several representative couples of site-node are identified. The impact of forest fraction is shown through the analysis of different cases across the U.S. Also, the impact of water fraction is shown through two examples in Florida and in Utah close to Great Salt Lake. A radiometric aggregation approach based on the antenna footprint and spatial description is used. A global comparison of the SCAN/SNOTEL versus SMOS is made. Statistics show an underestimation of the soil moisture from SMOS compared to the SCAN/SNOTEL local measurements. The results suggest that SMOS meets the mission requirement of 0.04 m3/m3 over specific nominal cases, but differences are observed over many sites and need to be addressed. [ABSTRACT FROM AUTHOR]

Whether the global runoff (or freshwater discharge from land to the ocean) is currently increasing and the global water cycle is intensifying is still a controversial issue. Here we compute land-atmosphere and ocean-atmosphere water budgets and derive two independent estimates of the global runoff over the period 1993-2009. Water storage variations in the land, ocean and atmosphere reservoirs are estimated from different types of datasets: atmospheric reanalyses, land surface models, satellite altimetry and in situ ocean temperature data (the difference between altimetry based global mean sea level and ocean thermal expansion providing an estimate of the ocean mass component). Results for the global runoff from the two methods show a very good correlation between both estimates. More importantly, no significant trend is observed over the whole period. Besides, the global runoff appears to be clearly impacted by large-scale climate phenomena such as major ENSO events. To infer this, we compute the zonal runoff over four latitudinal bands and set up for each band a new index (Combined Runoff Index) obtained by optimization of linear combinations of various climate indices. Results show that, in particular, the intertropical and northern mid-latitude runoffs are mainly driven by ENSO and the Atlantic Multidecadal Oscillation (AMO) with opposite behavior. Indeed, the zonal runoff in the intertropical zone decreases during major El Niño events whereas it increases in the northern mid-latitudes, suggesting that water masses over land are shifted northward/southward during El Niño/La Niña. In addition to this study, we propose an innovative method to estimate the global ocean thermal expansion. The method is based on the assumption that the difference between both runoff estimates is mainly due the thermal expansion term not accounted for in the estimation of the ocean mass. Comparison of our reconstructed thermal expansion with two existing datasets shows the relevance of this new method. [ABSTRACT FROM AUTHOR]

Multitemporal RADARSAT Synthetic Aperture Radar (SAR) imagery was evaluated for monitoring soil moisture during the Southern Great Plains 1997 Hydrology Experiment (SGP97). Over the 1-month experiment a total of 10 RADARSAT (C-band; 5.3 GHz; HH polarized) acquisitions were obtained. After co-registration of the images, the dependence of the backscattering coefficient (σ°) on incidence angle (θ) was evaluated. An angular correction scheme was applied to the data and the correlation of σ° values with volumetric soil moisture measurements was determined. A high positive correlation between the two was observed for wheat stubble fields, whereas no correlation could be observed for pasture fields. A change detection approach yielded a correlation of soil moisture changes to σ° changes for wheat stubble fields, indicating the potential of this method for soil moisture monitoring. [ABSTRACT FROM AUTHOR]

Presents information on field experiments (FIFE) while demonstrating the sensitivity of sensible and latent heat fluxes and surface temperatures to the parameterization of the soil thermal conductivity. Application of soil vegetation atmosphere transfer scheme (SVATS) to intensive field campaigns (IFCs) 3 and 4.

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. [ABSTRACT FROM AUTHOR]

The models presented in the previous paper (Famiglietti and Wood, this issue) are applied at their appropriate scales for evapotranspiration modeling at the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) site. The local soil-vegetation-atmospheric transfer scheme is applied at five flux measurement stations in the northwest quadrant of the FIFE site. Simulations were performed for three of the four FIFE 'golden (cloud-free) days' with good results. The spatially distributed model was applied at the 11.7-km2 King's Creek catchment, also located in the northwest quadrant of the FIFE site, during FIFE Intensive Field Campaigns (IFCs) 1-4. Simulated catchment average evapotranspiration was compared to an average of observations made at the five aforementioned measurement stations with good results. The macroscale formulation was applied to both the King's Creek catchment and the entire 15-km FIFE site for evapotranspiration simulations. Macroscale model simulations for King's Creek were nearly identical to the spatially distributed results, implying that at this location and at this scale, the assumptions invoked in the development of the macroscale formulation are reasonable. The macroscale model was also employed to simulate evapotranspiration from the entire 15-km site for the four golden days. Simulated evapotranspiration rates show reasonably good agreement with the 22-station average of observations. However, it is suggested that at 15-km and larger scales, simulation error may arise as a result of the macroscale assumptions of areally averaged atmospheric forcing, vegetation parameters, soil parameters, and the methods by which these data and other flux observations are aggregated. A methodology to combat these problems at larger scales is reviewed. [ABSTRACT FROM AUTHOR]