Calibrating a soil–vegetation–atmosphere transfer model with remote sensing estimates of surface temperature and soil surface moisture in a semi arid environment

Ridler, Marc; Sandholt, Inge; Butts, M. B.; Lerer, Sara Maria; Mougin, E.; Timouk, Franck; Kergoat, Laurent; Madsen, Henrik

doi:10.1016/j.jhydrol.2012.01.047

Cited by 45 publications

(33 citation statements)

References 42 publications

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“…This approach follows and improves the investigations carried out by other authors (Crow et al, 2003;Santanello et al, 2007;Koren et al, 2008;Flores et al, 2010;Montzka et al, 2011;Ridler et al, 2012;Corbari and Mancini, 2014;Sutanudjaja et al, 2014;Wanders et al, 2014) who attempted to combine, in the calibra- Figure 3. Scheme of calibration of the surface parameters in R. S. approach based on the reproduction of the basin lag time at two different closure sections using synthetic rainfall events.…”

Section: Multi-objective Calibration (M O)mentioning

confidence: 65%

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.

107

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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: Multi-objective Calibration (M O)mentioning

confidence: 65%

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.

107

View full text Add to dashboard Cite

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“…Certain attempts at incorporating spatial distributed data derived through remote sensing into hydrological models, either through data assimilation or calibration, have already been made but they were mostly focused on soil moisture (e.g. Draper et al, 2011;Corato et al, 2013), LST (Stisen et al, 2011a;Ridler et al, 2012) or LAI (Boegh et al, 2004). Pipunic et al (2008) have looked at assimilating simulated H and LE estimates into a land surface model, however this was done with a one-dimensional single column model, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…The distributed physically based hydrological models, in contrast to the remote sensing models, are heavily parametrized and calibrated for each individual catchment or study area (Refsgaard, 1997). Besides evapotranspiration, and other land surface fluxes, they can model a host of other hydrological processes such as channel flow, unsaturated zone flow or ground water flow and the interactions between those processes (Graham and Butts, 2005).…”

Section: R Guzinski Et Al: Inter-comparison Of Energy Balance and Hmentioning

confidence: 99%

Inter-comparison of energy balance and hydrological models for land surface energy flux estimation over a whole river catchment

Guzinski

Nieto

Stisen

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. Evapotranspiration (ET) is the main link between the natural water cycle and the land surface energy budget. Therefore water-balance and energy-balance approaches are two of the main methodologies for modelling this process. The water-balance approach is usually implemented as a complex, distributed hydrological model, while the energybalance approach is often used with remotely sensed observations of, for example, the land surface temperature (LST) and the state of the vegetation. In this study we compare the catchment-scale output of two remote sensing models based on the two-source energy-balance (TSEB) scheme, against a hydrological model, MIKE SHE, calibrated over the Skjern river catchment in western Denmark. The three models utilize different primary inputs to estimate ET (LST from different satellites in the case of remote sensing models and modelled soil moisture and heat flux in the case of the MIKE SHE ET module). However, all three of them use the same ancillary data (meteorological measurements, land cover type and leaf area index, etc.) and produce output at similar spatial resolution (1 km for the TSEB models, 500 m for MIKE SHE). The comparison is performed on the spatial patterns of the fluxes present within the catchment area as well as on temporal patterns on the whole catchment scale in 8-year long time series. The results show that the spatial patterns of latent heat flux produced by the remote sensing models are more similar to each other than to the fluxes produced by MIKE SHE. The temporal patterns produced by the remote sensing and hydrological models are quite highly correlated (r ≈ 0.8). This indicates potential benefits to the hydrological modelling community of integrating spatial information derived through remote sensing methodology (contained in the ET maps derived with the energy-balance models, satellite based LST or another source) into the hydrological models. How this could be achieved and how to evaluate the improvements, or lack of thereof, is still an open research question.

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“…At present, SVATs are able to describe the multifarious transfer processes through varying degrees of complexity, including the energy, water and carbon dioxide (CO 2 ) fluxes between the ground surface covered by different vegetation types and the atmosphere over different temporal and spatial scales (Olchev et al, 2008). These require an application context constrained by input variables (atmospheric forcing and vegetation) and input parameters (soil and vegetation properties, initialisation) to simulate the water and energy budget at the surface (Coudert et al, 2008;Ridler et al, 2012).…”

Section: G P Petropoulos Et Al: Quantifying the Prediction Accuracmentioning

confidence: 99%

Quantifying the prediction accuracy of a 1-D SVAT model at a range of ecosystems in the USA and Australia: evidence towards its use as a tool to study Earth's system interactions

et al. 2015

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Abstract. This paper describes the validation of the SimSphere SVAT (Soil-Vegetation-Atmosphere Transfer) model conducted at a range of US and Australian ecosystem types. Specific focus was given to examining the models' ability in predicting shortwave incoming solar radiation (R g ), net radiation (R net ), latent heat (LE), sensible heat (H ), air temperature at 1.3 m (T air 1.3 m ) and air temperature at 50 m (T air 50 m ). Model predictions were compared against corresponding in situ measurements acquired for a total of 72 selected days of the year 2011 obtained from eight sites belonging to the AmeriFlux (USA) and OzFlux (Australia) monitoring networks. Selected sites were representative of a variety of environmental, biome and climatic conditions, to allow for the inclusion of contrasting conditions in the model evaluation.Overall, results showed a good agreement between the model predictions and the in situ measurements, particularly so for the R g , R net , T air 1.3 m and T air 50 m parameters. The simulated R g parameter exhibited a root mean square deviation (RMSD) within 25 % of the observed fluxes for 58 of the 72 selected days, whereas an RMSD within ∼ 24 % of the observed fluxes was reported for the R net parameter for all days of study (RMSD = 58.69 W m −2 ). A systematic underestimation of R g and R net (mean bias error (MBE) = −19.48 and −16.46 W m −2 ) was also found. Simulations for the T air 1.3 m and T air 50 m showed good agreement with the in situ observations, exhibiting RMSDs of 3.23 and 3.77 • C (within ∼ 15 and ∼ 18 % of the observed) for all days of analysis, respectively. Comparable, yet slightly less satisfactory simulation accuracies were exhibited for the H and LE parameters (RMSDs = 38.47 and 55.06 W m −2 , ∼ 34 and ∼ 28 % of the observed). Highest simulation accuracies were obtained for the open woodland savannah and mulga woodland sites for most of the compared parameters. The Nash-Sutcliffe efficiency index for all parameters ranges from 0.720 to 0.998, suggesting a very good model representation of the observations.To our knowledge, this study presents the most detailed evaluation of SimSphere done so far, and the first validation of it conducted in Australian ecosystem types. Findings are important and timely, given the expanding use of the model both as an educational and research tool today. This includes ongoing research by different space agencies examining its synergistic use with Earth observation data towards the development of global operational products.

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Calibrating a soil–vegetation–atmosphere transfer model with remote sensing estimates of surface temperature and soil surface moisture in a semi arid environment

Cited by 45 publications

References 42 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

Inter-comparison of energy balance and hydrological models for land surface energy flux estimation over a whole river catchment

Quantifying the prediction accuracy of a 1-D SVAT model at a range of ecosystems in the USA and Australia: evidence towards its use as a tool to study Earth's system interactions

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