Estimation of large‐scale evaporation fields based on assimilation of remotely sensed land temperature

Sini, Francesca; Boni, Giorgio; Caparrini, Francesca; Entekhabi, Dara

doi:10.1029/2006wr005574

Cited by 50 publications

(92 citation statements)

References 33 publications

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“…The same considerations can be done for c f . The c t shows an evident trend: this is reasonable since this parameter strongly influences the time of permanence of water in the soil and the LST diurnal dynamics (Caparrini et al, 2004;Sini et al, 2008;Silvestro et al, 2013).…”

Section: Uncertainty Analysismentioning

confidence: 99%

“…The overland flow (hillslopes) is described by a linear reservoir scheme, while the channel flow (channel), a schematization derived by the kinematic wave approach (Wooding, 1965;Todini and Ciarapica, 2001), is used. The energy balance is solved explicitly at cell scale by means of the force-restore equation, which allows the LST to be a distributed state variable of the model (e.g., Lin, 1980;Dickinson, 1988;Sini et al, 2008).…”

Section: Model Overviewmentioning

confidence: 99%

“…However, the ability to calibrate a model using satellite data, even in combination with traditional in situ data, is still a challenging topic. Scientific work in this field goes in many directions: Rhoads et al (2001) used satellite-derived LST to validate a land-surface model, Caparrini et al (2004) and Sini et al (2008) assimilated remotely sensed measurements into a land-surface model to estimate the surface turbulent fluxes, Brocca et al (2011a) analyzed different remotely sensed soil humidity estimations with the perspective of using them in hydrological modeling, White and Lewis (2011) used satellite imagery to monitor the dynamics of wetlands of the Australian Great Artesian basin and Khan et al (2011) have recently proposed a procedure to calibrate a fully distributed hydrological model using satellite-derived flood maps.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data

Silvestro

Gabellani

Rudari

et al. 2015

Hydrol. Earth Syst. Sci.

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115

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Abstract. During the last decade the opportunity and usefulness of using remote-sensing data in hydrology, hydrometeorology and geomorphology has become even more evident and clear. Satellite-based products often allow for the advantage of observing hydrologic variables in a distributed way, offering a different view with respect to traditional observations that can help with understanding and modeling the hydrological cycle. Moreover, remote-sensing data are fundamental in scarce data environments. The use of satellite-derived digital elevation models (DEMs), which are now globally available at 30 m resolution (e.g., from Shuttle Radar Topographic Mission, SRTM), have become standard practice in hydrologic model implementation, but other types of satellite-derived data are still underutilized. As a consequence there is the need for developing and testing techniques that allow the opportunities given by remote-sensing data to be exploited, parameterizing hydrological models and improving their calibration.In this work, Meteosat Second Generation land-surface temperature (LST) estimates and surface soil moisture (SSM), available from European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) H-SAF, are used together with streamflow observations (S. N.) to calibrate the Continuum hydrological model that computes such state variables in a prognostic mode. The first part of the work aims at proving that satellite observations can be exploited to reduce uncertainties in parameter calibration by reducing the parameter equifinality that can become an issue in forecast mode. In the second part, four parameter estimation strategies are implemented and tested in a comparative mode: (i) a multi-objective approach that includes both satellite and ground observations which is an attempt to use different sources of data to add constraints to the parameters; (ii and iii) two approaches solely based on remotely sensed data that reproduce the case of a scarce data environment where streamflow observation are not available; (iv) a standard calibration based on streamflow observations used as a benchmark for the others.Two Italian catchments are used as a test bed to verify the model capability in reproducing long-term (multi-year) simulations.The results of the analysis evidence that, as a result of the model structure and the nature itself of the catchment hydrologic processes, some model parameters are only weakly dependent on discharge observations, and prove the usefulness of using data from both ground stations and satellites to additionally constrain the parameters in the calibration process and reduce the number of equifinal solutions.

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Section: Uncertainty Analysismentioning

confidence: 99%

Section: Model Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data

Silvestro

Gabellani

Rudari

et al. 2015

Hydrol. Earth Syst. Sci.

Self Cite

115

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“…A suitable alternative to "in situ" measurement of land surface fluxes is remote sensing observations. Among the numerous techniques developed to make quantitative estimates of surface fluxes using remotely sensed observations (e.g., [8]- [12]), data assimilation (DA) techniques, such as ensemble Kalman filter (EnKF) and variational data assimilation (VDA) (e.g., [11], [13]- [16]) have gained substantial success in recent years. DA techniques for the estimation of land surface heat fluxes are commonly focused on estimation of land surface state variables [e.g., land surface temperature (LST)], while keeping the model parameters fixed [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification in Land Surface Hydrologic Modeling: Toward an Integrated Variational Data Assimilation Framework

Abdolghafoorian

Farhadi

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Variational data assimilation (VDA) is an effective technique for the estimation of land surface heat fluxes. In this method, sequences of remotely sensed land surface temperature measurements are assimilated into a dynamic surface energy balance model to estimate the key unknown parameters of the turbulent heat fluxes. Despite the advantages of the VDA technique in the retrieval of land surface heat fluxes, it suffers from a key limitation, which is its tendency to be ill posed. Moreover, unlike ensemble-based schemes, the VDA method itself does not provide estimates of the predictive uncertainty of estimated parameters and, thus, retrieved fluxes. This research addresses these shortcomings by proposing an uncertainty quantification (UQ) framework for the VDA technique. The proposed framework utilizes uncertainty analysis and analysis of error covariance approximation as a tool to quantify the uncertainty of estimated parameters and to guide the formulation of a well-posed estimation problem. It provides a calibration-free tool to assess the performance of the VDA technique in retrieving land surface heat fluxes over a range of land surfaces and climatic conditions. The UQ framework suggests that the VDA approach performs poorly over wet and highly vegetated land surface regions and when the difference between land surface and air temperature is low. Moreover, it reveals that characterizing the effect of vegetation dynamics on the bulk heat transfer coefficient reduces the correlation between unknown parameters and, hence, leads to a more robust estimation of parameters.Index Terms-Land surface hydrology, uncertainty analysis, variational data assimilation (VDA).

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“…In practice, the volume is extended to the unit cell of the numerical scheme used. This approximation is a fair trade-off between parsimony in parameterization and accuracy in the description of the processes (Boni et al, 2001;Sini et al, 2008).…”

Section: Energy Balance and Evapotranspirationmentioning

confidence: 99%

Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the Continuum model

Silvestro

Gabellani

Delogu

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. Full process description and distributed hydrological models are very useful tools in hydrology as they can be applied in different contexts and for a wide range of aims such as flood and drought forecasting, water management, and prediction of impact on the hydrologic cycle due to natural and human-induced changes. Since they must mimic a variety of physical processes, they can be very complex and with a high degree of parameterization. This complexity can be increased by necessity of augmenting the number of observable state variables in order to improve model validation or to allow data assimilation.In this work a model, aiming at balancing the need to reproduce the physical processes with the practical goal of avoiding over-parameterization, is presented. The model is designed to be implemented in different contexts with a special focus on data-scarce environments, e.g. with no streamflow data.All the main hydrological phenomena are modelled in a distributed way. Mass and energy balance are solved explicitly. Land surface temperature (LST), which is particularly suited to being extensively observed and assimilated, is an explicit state variable.A performance evaluation, based on both traditional and satellite derived data, is presented with a specific reference to the application in an Italian catchment. The model has been firstly calibrated and validated following a standard approach based on streamflow data. The capability of the model in reproducing both the streamflow measurements and the land surface temperature from satellites has been investigated.The model has been then calibrated using satellite data and geomorphologic characteristics of the basin in order to test its application on a basin where standard hydrologic observations (e.g. streamflow data) are not available. The results have been compared with those obtained by the standard calibration strategy based on streamflow data.

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Estimation of large‐scale evaporation fields based on assimilation of remotely sensed land temperature

Cited by 50 publications

References 33 publications

Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data

Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data

Uncertainty Quantification in Land Surface Hydrologic Modeling: Toward an Integrated Variational Data Assimilation Framework

Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the Continuum model

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