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

Abstract

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 ...

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

“…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 (2010Corbari et al ( , 2015 and Silvestro et al (2013Silvestro et al ( , 2015.…”

“…It should be noted that the land surface models employed in those studies explicitly solved the energy balance and thus inherently estimated the land surface temperature Their authors, however, did not specifically focus on the spatial distribution of T s . Consequently, models were either calibrated using basin averaged T s (Silvestro et al, 2013(Silvestro et al, , 2015 or compared observations and simulations using standard error metrics such as bias or root mean squared error (Corbari et al, 2010(Corbari et al, , 2015. Reichle et al (2010), Stisen et al (2011), andKoch et al (2015) on the other hand suggested using biasinsensitive metrics, which only consider the spatial patterns of land surface temperature.…”

Forest thinning impacts on the water balance of Sierra Nevada mixed‐conifer headwater basins

“…Models for projecting runoff response to vegetation changes are often constrained by discharge alone [Corbari et al, 2015]. However, there has also been a concerted effort for continued field studies and increased observational data to adequately support modeling efforts [Dunne, 1983;Grayson et al, 1992;Silberstein, 2006;Burt and McDonnell, 2015].…”

Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach

“…For this purpose, satellite remote sensing comes into play as an independent data source with the required spatial resolution and coverage for many catchment-scale applications. Satellite imagery has been used for estimation of numerous states and fluxes of interest to hydrological modeling, such as snow cover (Immerzeel et al, 2009), groundwater storage change (Chen et al, 2016;Rodell et al, 2009;Sutanudjaja et al, 2013;Richey et al, 2015), soil moisture (SM; Wanders et al, 2014), vegetation water content (Mendiguren et al, 2015), land surface temperature (LST; Corbari et al, 2015) or actual evapotranspiration (ET; Guzinski et al, 2015). The conversions of the remotely sensed signal to hydrological variables are far from trivial and usually require in situ measurements and observations for model evaluation.…”