Land surface temperature representativeness in a heterogeneous area through a distributed energy-water balance model and remote sensing data

Corbari, Chiara; Sobrino, José A.; Mancini, Marco; Hidalgo, Vanesa

doi:10.5194/hess-14-2141-2010

Cited by 28 publications

(12 citation statements)

References 36 publications

(40 reference statements)

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“…In that study, they found that the consideration of spatially averaged T s , besides streamflow, improved monthly evapotranspiration predictions by up to 20%. Similar efforts were undertaken by Corbari et al () and Silvestro et al ().…”

Section: Introductionmentioning

confidence: 61%

Conditioning a Hydrologic Model Using Patterns of Remotely Sensed Land Surface Temperature

Zink

Mai

Cuntz

et al. 2018

Water Resources Research

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Hydrologic models are usually calibrated using observed river runoff at catchment outlets. Streamflow, however, represents an integral response of the entire catchment and is observed at a few locations worldwide. Parameter estimation based on streamflow has the disadvantage that it does not consider the spatiotemporal variability of hydrologic states and fluxes such as evapotranspiration. Remotely sensed data, in contrast, include these variabilities and are broadly available. In this study, we assess the predictive skill of satellite‐derived land surface temperature (Ts) with respect to river runoff (Q). We developed a bias‐insensitive pattern‐matching criterion to focus the parameter optimization on spatial patterns of Ts. The proposed method is extensively tested in six distinct large German river basins and cross validated in 222 additional basins in Germany. We conclude that land surface temperature calibration outperforms random drawn parameter sets, which could be meaningful for calibrating hydrologic models in ungauged locations. A combined calibration with Q and Ts reduces the root mean squared error in the predicted evapotranspiration by 8% compared to flux tower observations but reduces the NSEs of the streamflow predictions by 6% on average for the six large basins. Our results show that patterns of Ts better constrain model parameters when considered in a calibration next to Q, which finally reduces parametric uncertainty.

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Section: Introductionmentioning

confidence: 61%

Conditioning a Hydrologic Model Using Patterns of Remotely Sensed Land Surface Temperature

Zink

Mai

Cuntz

et al. 2018

Water Resources Research

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“…The FEST-EWB model is a distributed hydrological energy water balance model (Corbari et al 2010(Corbari et al , 2011(Corbari et al , 2013 and was developed starting from FEST-WB (Mancini 1990, Rabuffetti et al 2008.…”

Section: Fest-ewb Hydrological Modelmentioning

confidence: 99%

“…The FEST-EWB model was proved to make accurate predictions of actual evapotranspiration by comparisons against energy and mass exchange measurements acquired by an eddy covariance station (Corbari et al 2011), and also at the agricultural district scale by comparisons with ground and remote sensing information (Corbari et al 2010). …”

Section: Fest-ewb Hydrological Modelmentioning

confidence: 99%

Can satellite land surface temperature data be used similarly to river discharge measurements for distributed hydrological model calibration?

Corbari

Mancini

et al. 2014

Hydrological Sciences Journal

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A new methodology is proposed for the calibration of distributed hydrological models at the basin scale by constraining an internal model variable using satellite data of land surface temperature (LST). The model algorithm solves the system of energy and mass balances in terms of a representative equilibrium temperature that governs the fluxes of energy and mass over the basin domain. This equilibrium surface temperature, which is a critical model state variable, is compared to operational satellite LST, while calibrating soil hydraulic parameters and vegetation variables differently in each pixel, minimizing the errors. This procedure is compared to the traditional calibration using only discharge measurements. The distributed energy water balance model, Flashflood Event-based Spatially-distributed rainfall-runoff Transformation -Energy Water Balance model (FEST-EWB), is used to test this approach. This methodology is applied to the Upper Yangtze River basin (China) using MODIS LST retrieved from satellite data in the framework of the NRSCC-ESA DRAGON-2 Programme. The calibration procedure based on LST seems to outperform the calibration based on discharge, with lower relative error and higher Nash-Sutcliffe efficiency index on cumulated volume.Key words energy water balance model; satellite land surface temperature; river discharge measurements Les données satellitaires de température de surface peuvent-elles être utilisées de la même manière que les mesures de débit au sol pour le calage de modèles hydrologiques distribués ?Résumé Cette étude propose une nouvelle méthodologie de calage des modèles hydrologiques distribués à l'échelle du bassin versant, en contraignant une variable interne du modèle par des données satellitaires de température de surface. L'algorithme du modèle résout le système de bilan de masse et d'énergie au niveau d'une température représentative d'équilibre, qui gouverne les flux de masse et d'énergie sur le domaine du bassin versant. Cette température de surface d'équilibre, qui est une variable d'état clé du modèle, est comparée à la température de surface fournie en routine par des satellites, en calant les paramètres hydrauliques du sol et les variables de végétation différemment dans chaque pixel par minimisation des erreurs. Cette procédure a été comparée au calage traditionnel utilisant seulement les mesures de débit. Le modèle distribué de bilan eauénergie, FEST-EWB (Flash-flood Event-based Spatially-distributed rainfall-runoff Transformation -Energy Water Balance model ; Modèle événementiel et spatialement distribué pour la transformation pluie-débit et le bilan eau-énergie lors des crues éclair) a été utilisé pour tester cette approche, appliquée au bassin amont du fleuve Yangtze, en Chine, en utilisant la température de surface MODIS obtenue à partir de données satellitaires dans le cadre du programme NRSCC-ESA DRAGON-2. La procédure de calage exploitant la température de surface semble être plus performante que le calage basé sur le débit, avec une erreur relative plus faible ...

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“…It is simply a composite generated by averaging eight days of daily LST product (MOD11A1). The MODIS LST products are validated over a range of representative conditions with well-defined product uncertainties (Wan 2008), and they have been satisfactorily used in a wide variety of scientific studies (Hall et al 2008;Bookhagen and Burbank 2010;Neteler 2010;Corbari et al 2010;Gutmann and Small 2010). Tile h25v06 covers the entire study area and both MODIS datasets were downloaded from the WIST of NASA.…”

Section: B Model Evaluationmentioning

confidence: 99%

Modeling the Spatial Distribution of Snow Cover in the Dudhkoshi Region of the Nepal Himalayas

Shrestha

Wang

Koike

et al. 2012

Journal of Hydrometeorology

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In this study, a distributed biosphere hydrological model with three-layer energy-balance snow physics [an improved version of the Water and Energy Budget-based Distributed Hydrological Model (WEB-DHM-S)] is applied to the Dudhkoshi region of the eastern Nepal Himalayas to estimate the spatial distribution of snow cover. Simulations are performed at hourly time steps and 1-km spatial resolution for the 2002/03 snow season during the Coordinated Enhanced Observing Period (CEOP) third Enhanced Observing Period (EOP-3). Point evaluations (snow depth and upward short-and longwave radiation) at Pyramid (a station of the CEOP Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region. The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating the model's capability to accurately capture the spatiotemporal variations in snow cover across the study area. The qualitative pixelto-pixel comparisons for the snow-free and snow-covered grids reveal that the simulations agree well with the MODIS data to an accuracy of 90%. Simulated nighttime land surface temperatures (LST) are comparable to the MODIS LST (MOD11A2) with mean absolute error of 2.428C and mean relative error of 0.778C during the study period. The effects of uncertainty in air temperature lapse rate, initial snow depth, and snow albedo on the snow-cover area (SCA) and LST simulations are determined through sensitivity runs. In addition, it is found that ignoring the spatial variability of remotely sensed cloud coverage greatly increases bias in the LST and SCA simulations. To the authors' knowledge, this work is the first to adopt a distributed hydrological model with a physically based multilayer snow module to estimate the spatial distribution of snow cover in the Himalayan region.

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Land surface temperature representativeness in a heterogeneous area through a distributed energy-water balance model and remote sensing data

Cited by 28 publications

References 36 publications

Conditioning a Hydrologic Model Using Patterns of Remotely Sensed Land Surface Temperature

Conditioning a Hydrologic Model Using Patterns of Remotely Sensed Land Surface Temperature

Can satellite land surface temperature data be used similarly to river discharge measurements for distributed hydrological model calibration?

Modeling the Spatial Distribution of Snow Cover in the Dudhkoshi Region of the Nepal Himalayas

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