[show abstract][hide abstract] ABSTRACT: Evaporation from water or soil surfaces and transpiration from plants combine to return available water at the surface layer
back to the bulk atmosphere in a process called evapotranspiration. Much of our understanding of the complex feedback mechanisms
between the Earth’s surface and the surrounding atmosphere is focused on quantifying this process. At its most fundamental
level, evapotranspiration is the loss of water from a surface to the atmosphere, achieved through vaporization. The complex
nature of the evaporative process, however, includes mechanisms such as turbulent transport, feedback between the surface
and atmosphere, and the biophysical nature of transpiration – all of which combine to make both measurement and estimation
a difficult task.
Land Remote Sensing and Global Environmental Change. 01/2011;
[show abstract][hide abstract] ABSTRACT: Vertisols are clay soils that are common in the monsoonal and dry warm regions of the world. One of the characteristics of these soil types is to form deep cracks during periods of extended dry, resulting in significant variation of the soil and hydrologic properties. Understanding the influence of these varying soil properties on the hydrological behavior of the system is of considerable interest, particularly in the retrieval or simulation of soil moisture. In this study we compare surface soil moisture (θ in m3 m−3) retrievals from AMSR-E using the VUA-NASA (Vrije Universiteit Amsterdam in collaboration with NASA) algorithm with simulations from the Community Land Model (CLM) over vertisol regions of mainland Australia. For the three-year period examined here (2003–2005), both products display reasonable agreement during wet periods. During dry periods however, AMSR-E retrieved near surface soil moisture falls below values for surrounding non-clay soils, while CLM simulations are higher. CLM θ are also higher than AMSR-E and their difference keeps increasing throughout these dry periods. To identify the possible causes for these discrepancies, the impacts of land use, topography, soil properties and surface temperature used in the AMSR-E algorithm, together with vegetation density and rainfall patterns, were investigated. However these do not explain the observed θ responses. Qualitative analysis of the retrieval model suggests that the most likely reason for the low AMSR-E θ is the increase in soil porosity and surface roughness resulting from cracking of the soil. To quantitatively identify the role of each factor, more in situ measurements of soil properties that can represent different stages of cracking need to be collected. CLM does not simulate the behavior of cracking soils, including the additional loss of moisture from the soil continuum during drying and the infiltration into cracks during rainfall events, which results in overestimated θ when cracks are present. The hydrological influence of soil physical changes are expected to propagate through the modeled system, such that modeled infiltration, evaporation, surface temperature, surface runoff and groundwater recharge should be interpreted with caution over these soil types when cracks might be present. Introducing temporally dynamic roughness and soil porosity into retrieval algorithms and adding a "cracking clay" module into models are expected to improve the representation of vertisol hydrology.
[show abstract][hide abstract] ABSTRACT: Vertisols are clay soils that are common in the monsoonal and dry warm regions of the world. A defining feature of these soils is the development of shrinking cracks during dry periods, the effects of which are not described in land surface models nor considered in the surface soil moisture estimation from passive microwave satellite observations. To investigate the influence of this process we compared the soil moisture (θ in m3 m−3) from AMSR-E observations and the Community Land Model (CLM) simulations over vertisols across mainland Australia. Both products agree reasonably well during wet seasons. However, during dry periods, AMSR-E θ falls below values for surrounding non-clays, while CLM simulations are higher. The impacts of soil property used in the AMSR-E algorithm, vegetation density and rainfall patterns were investigated, but do not explain the observed θ patterns. Analysis of the retrieval model suggests that the most likely reason for the low AMSR-E θ is the increase in soil porosity and surface roughness through cracking. CLM does not consider the behavior of cracking clay, including the further loss of moisture from soil and extremely high infiltration rates that would occur when cracks develop. Analyses show that the corresponding water fluxes can be different when cracks occur and therefore modeled evaporation, surface temperature, surface runoff and groundwater recharge should be interpreted with caution. Introducing temporally dynamic roughness and soil porosity into retrieval algorithms and adding a "cracking clay" module into models, respectively, may improve the representation of vertisol hydrology.
Hydrology and Earth System Sciences Discussions. 01/2010;
[show abstract][hide abstract] ABSTRACT: A multi-sensor/multi-platform approach to water and energy cycle prediction is demonstrated in an effort to understand the variability and feedback of land surface and atmospheric processes over large space and time scales. Remote sensing-based variables including soil moisture (from AMSR-E), surface heat fluxes (from MODIS) and precipitation rates (from TRMM) are combined with North American Regional Reanalysis derived atmospheric components to examine the degree of hydrological consistency throughout these diverse and independent hydrologic data sets. The study focuses on the influence of the North American Monsoon System (NAMS) over the southwestern United States, and is timed to coincide with the SMEX04 North American Monsoon Experiment (NAME). The study is focused over the Arizona portion of the NAME domain to assist in better characterizing the hydrometeorological processes occurring across Arizona during the summer monsoon period. Results demonstrate that this multi-sensor approach, in combination with available atmospheric observations, can be used to obtain a comprehensive and hydrometeorologically consistent characterization of the land surface water cycle, leading to an improved understanding of water and energy cycles within the NAME region and providing a novel framework for future remote observation and analysis of the coupled land surface–atmosphere system.
[show abstract][hide abstract] ABSTRACT: In this paper, the Coordinated Enhanced Observing Period (CEOP) data during an Enhanced Observ- ing Period (EOP-1) is used to assess the Surface Energy Balance System (SEBS) model. The purpose of this study is to evaluate the adaptability of SEBS to different climatic zones and land cover classifica- tions at two different scales. The SEBS model was examined at the field (tower) scale based primarily on in-situ observations from CEOP sites. To examine a broader scale application, remotely sensed land surface temperature (LST) from the MODIS sensor and surface meteorology from the Global Land Data Assimilation System (GLDAS) were used for the required forcing datasets. Comparisons at tower scale show that the model predictions of the energy fluxes agree reasonably well with the observations. The root mean square error (RMSE) of the ET prediction based on MODIS Land Surface Temperature (LST) plus CEOP meteorological observations is about 61 W m� 2 at a grassland site (Cabauw) and a nee- dle leaf forest site (BERMS). The RMSE of ET predication at a corn site (Bondville) is 96 W m� 2 and the corresponding percentage error is 28.9%. When GLDAS forcing was used instead of the CEOP tower ob- servations, the RMSEs of ET prediction at Cabauw, BERMS and Bondville are increased to 82, 84 and 140 W m� 2 respectively. The negative bias of surface downward radiative forcing from GLDAS contrib- uted much to the larger deviation of the ET prediction when compared to tower based values. The inno- vative aspects of our study in this paper are: a) No similar work on evaluating remote sensing based ET model under a diverse climate and land cover condition has been done before; b) ET modeling was as- sessed in different scales ranging from site scale to GLDAS grid cell; c) The framework of estimating the spatial distribution of ET combining satellite data and available ground meteorology is tested.
Journal of the Meteorological Society of Japan 01/2007; 85:439-459. · 0.80 Impact Factor
[show abstract][hide abstract] ABSTRACT: Study of Evapotranspiration (ET) is important in Earth system science, not only because ET returns about 64% of land-based precipitation to the atmosphere, but because it acts as a strong link between biosphere, hydrosphere and atmosphere. Accurate estimation of ET at regional or continental scales is essential to understand the land-surface-atmosphere interactions. Quality control or quantifying the uncertainty in the ET prediction from ET model, as well as the ET predictions, is especially necessary when the model begins providing ET operationally. As is well known, the error in model predictions is composed of two parts. One is caused by the model itself and the other is due to the error in the forcing data. Most investigations of ET model have been focused on analyzing the system error sourced from the model itself by validating it at site scale using in-situ measurements, however, few has been done on the error propagation of the uncertainty in forcing data during the ET estimation. In this study, the sensitivity of ET predictions to the forcing data at regional scale is analyzed by numerical simulation, i.e. introducing Gaussian random errors into the original forcing data and testing the variation of the model outputs. The meteorological forcing and its valid range are extracted from NLDAS (North American Land Data Assimilation System) forcing datasets. The remote sensing data, including land surface temperature, albedo, leaf area index, landcover classification, are from MODIS land products. Three different models (SEBS, modified Priestley-Taylor, Penman-Monteith) are studied in the experiment. The sensitivity analysis will form the basis of quality control for the MODIS ET product (MOD16) which is under development.
[show abstract][hide abstract] ABSTRACT: Both observations and theoretical simulation show that the surface hydrologic condition and vegetation cover have a major control on surface energy flux patterns. Remote sensing techniques provide a basis for assessing these controls, and the subsequent patterns of surface fluxes at scales ranging from kilometres to regional and, potentially, to global scales. These remotely sensed data, and their derived hydrologic variables can be compared to and combined with hydrological models to estimate the surface water and energy balances at continental to global scales. In this presentation, regional estimates of the evapotranspiration, based on a combination of remote sensing measurements and operational surface observations, will be presented using the Surface Energy Balance System. Results will be obtained for a diverse set sites identified as part of the GEWEX Global Energy and Water Experiments (GEWEX) Coordinated Enhanced Observation Period (CEOP), and represent a range of hydroclimatologies and surface condition making them ideally suited to the assessment of routine retrieval of remote sensing based evapotranspiration. Surface temperature and vegetation information is based on MODIS satellite retrievals and are used in conjunction with the Global Land Data Assimilation (GLDAS) project data to estimate daily values of evapotranspiration. These estimates are compared with both tower flux data and GLDAS derived estimates forcing.
[show abstract][hide abstract] ABSTRACT: Accurate estimation of surface energy fluxes from space at high spatial resolution has the potential to improve prediction of the impact of land-use changes on the local environment and to provide a means to assess local crop conditions. To achieve this goal, a combination of physically based surface flux models and high-quality remote-sensing data are needed. Data from the ASTER sensor are particularly well-suited to the task, as it collects high spatial resolution (15-90 m) images in visible, near-infrared, and thermal infrared bands. Data in these bands yield surface temperature, vegetation cover density, and land-use types, all critical inputs to surface energy balance models for assessing local environmental conditions. ASTER is currently the only satellite sensor collecting multispectral thermal infrared images, a capability allowing unprecedented surface temperature estimation accuracy for a variety of surface cover types. Availability of ASTER data to study surface energy fluxes allows direct comparisons against ground measurements and facilitates detection of modeling limitations, both possible because of ASTER's higher spatial resolution. Surface energy flux retrieval from ASTER is demonstrated using data collected over an experimental site in central Iowa, USA, in the framework of the Soil Moisture Atmosphere Coupling Experiment (SMACEX). This experiment took place during the summer of 2002 in a study of heterogeneous agricultural croplands. Two different flux estimation approaches, designed to account for the spatial variability, are considered: the Two-Source Energy Balance model (TSEB) and the Surface Energy Balance Algorithm or Land model (SEBAL). ASTER data are shown to have spatial and spectral resolution sufficient to derive surface variables required as inputs for physically based energy balance modeling. Comparison of flux model results against each other and against ground based measurements was promising, with flux values commonly agreeing within approximately 50 W m- 2. Both TSEB and SEBAL showed systematic agreement and responded to spatially varying surface temperatures and vegetation densities. Direct comparison against ground Eddy Covariance data suggests that the TSEB approach is helpful over sparsely vegetated terrain.
[show abstract][hide abstract] ABSTRACT: Developing a globally robust algorithm for the prediction of surface heat fluxes is a significant challenge. Difficulties in capturing the hydro-climatic variability inherent at global scales have limited the extensive application of remote sensing approaches for characterizing surface heat flux behavior. An increased ability to capture the land surface variability has arisen with the development of a number of key remote sensing based products. Spatial and temporal fields of the surface temperature, land surface cover and vegetation distribution are globally available, offering increased ability to monitor hydrological patterns. Coupled with improved data availability is the growing availability of high quality, in-situ validation datasets, essential for the robust evaluation of model responses. Two such data sets are the WCRP/GEWEX/CEOP reference tower data and the FLUXNET tower data. The Coordinated Enhanced Observing Period (CEOP) activity is an element of the World Climate Research Program (WCRP), initiated by the Global Energy and Water Cycle Experiment (GEWEX). Along with FLUXNET, a global network of carbon dioxide and micro-meteorological tower sites, these programs provide measurements of water vapor and energy exchanges over diverse environments across the globe. Combined, the two datasets form a unique hydro-climatological database with global consistency over a range of climatic and vegetation conditions, making them ideally suited to robustly evaluate regional and meso-scale hydrological models and applications. In this study, observations from CEOP and FLUXNET are used to assess estimates of the evapotranspiration, determined using a number of approaches. The purpose of this analysis is to evaluate the adaptability of varied techniques to different climatic conditions and land cover types and conditions. Forcing data from validation tower sites and widely available remote sensing products are used to produce estimates of the land surface fluxes. Daily and 10-day averaged surface fluxes are computed and compared to in-situ observations. Monthly mean diurnal fluxes are also determined to assess the level of temporal variability throughout the observation period at each of the investigation sites. Comparisons show that model predictions of the energy fluxes indicate some promise towards the development of a robust algorithm to derive a global land surface evapotranspiration product.
[show abstract][hide abstract] ABSTRACT: The estimation of state variables such as the soil moisture and surface temperature has been a key research area for many years. The importance of this information for land surface studies has motivated the development of different estimation techniques for these variables. Increasingly, there exists the capacity to independently determine components of the hydrological cycle from remote sensing data. Developing techniques to effectively combine the multiple streams of information required for a water budget assessment provides a difficult challenge, particularly given the disparities in spatial and temporal scales between measurements and predictions. The launched EOS Terra and Aqua platforms contain sensors that have an improved capability to retrieve these variables. Coupling these measurements with measurements from TRMM-TMI and GOES satellite platforms with large scale meteorological forcing data available as part of the Global Land Data Assimilation System (GLDAS), offers an increased opportunity to examine global and continental scale hydrological patterns. This presentation reports on recent efforts to measure components of the water budget through remote sensing. The presentation will focus on the estimation of soil moisture and evapotranspiration over the southern Great Plains region of the United States for spring and summer 2002. The retrieved values are compared with available validation data and qualitatively examined against each other to assess their association. A number of potential data assimilation approaches that could be employed to incorporate such information into a robust water balance framework are discussed.
[show abstract][hide abstract] ABSTRACT: Evaporation provides the link between the energy and water budgets at the land surface. Accurate measurements of evaporation rates at large spatial scales are central to understanding the feedback mechanisms between the land and atmosphere. However, with the paucity of available surface observations for many portions of the globe, the use of modeled evaporation using satellite-based remotely sensed inputs is a potentially viable surrogate. Estimates of evaporation are made using two conceptually different models. The Surface Energy Balance System (SEBS) estimates atmospheric turbulent heat fluxes and evaporative fraction using satellite derived surface temperature and near-surface meteorological variables, usually from standard surface stations. The Variable Infiltration Capacity (VIC) model solves the land surface water and energy balances at scales from the point up to several 100km using standard radiative and meteorological inputs. A common set of land surface data, including vegetation type distribution and related parameters such as LAI and albedo, are used to specify the land surface in each model. Comparison of estimated evaporation from the two models is made over the Oklahoma region of the USA for a 2 month warm season period.
[show abstract][hide abstract] ABSTRACT: Evapotranspiration (ET) is the combination of water evaporated from the surface and transpired by plants and it is an important process which links the different components of the energy balance and water cycle for the atmospheric and land surface. Developing a globally robust algorithm for the prediction of surface energy fluxes is a big challenge. Remote sensing techniques provide a possible approach to estimate the surface energy components at a regional to global scale, with the increasing ability of capturing the land surface and atmospheric variables from satellites. To assess the global ET estimation from remote sensing, it requires globally distributed in situ measurement of surface fluxes and surface meteorology. The Coordinated Enhanced Observing Period (CEOP) is an element of the Global Energy and Water Cycle Experiment (GEWEX), which is part of the World Climate Research Program (WCRP). The global network of micro-meteorological tower sites in CEOP provides measurements of water vapor and energy exchanges over diverse environments across the globe. The CEOP observations form a unique hydro-climatological database with global consistency over a range of climatic and vegetation conditions, making it ideally suited to robustly evaluate regional and meso- scale hydrological models and applications. Previous applications of remote sensing ET modeling require that radiation data and surface meteorology come either from local in-situ sites or from 4DDA reanalysis, which is the significant constraint when remote sensing for ET is applied over remote areas where in-situ data is unavailable and reanalysis is unreliable. In this study, all the forcing and input variables of our ET model come from the widely available satellite data products. Observations from CEOP EOP-3/4 (Oct. 1, 2002 through Dec. 31, 2004) are used to assess estimates of the evapotranspiration based purely on remote sensing. The purpose of this study is to evaluate the new implementation of the ET model and the adaptability of the model to different climatic conditions and land cover classifications. Daily and 10-day averaged surface fluxes are computed and compared to in-situ observations. Monthly mean diurnal fluxes are also determined to assess the level of temporal variability throughout the observation period at each of the investigation sites. Comparisons show that model predictions of the energy fluxes indicate good promise towards the development of a robust algorithm to derive a global land surface evapotranspiration product based purely on remotely sensed forcing data.
[show abstract][hide abstract] ABSTRACT: The IAHS initiative Prediction in Ungauged Basins (PUB) addresses a long standing scientific challenge for hydrology: the estimation of water fluxes and states for catchments with insufficient ground-based gauging. PUB has been motivated in part from the decline in ground networks over much of the developing countries, as well as the need to better understand the water and energy budget at regional to continental scales for better understanding of water sustainability under changing demands and climates, and for water cycle research. While ground based networks have declined, the increase in remotely sensed hydrologic data has increased dramatically over the last few years, with additional data imminently available from recently launched satellites. These satellites include NASA's Earth Observing System Terra and Aqua, NASDA's ADEOS-II, and ESA's Envisat. These platforms include a suite of VIS-IR and microwave radiometers that are appropriate for water cycle research. In this presentation a strategy is presented on how these remotely sensed data, combined with hydrologic modeling through data assimilation can be used to estimate water budget terms over ungauged catchments.