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

Silvestro, Francesco; Gabellani, Simone; Delogu, Fabio; Rudari, Roberto; Boni, Giorgio

doi:10.5194/hess-17-39-2013

Cited by 74 publications

(79 citation statements)

References 65 publications

(70 reference statements)

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“…The parameters c t and c f regulate the dynamics of saturation at cell scale. Since both f 0 and V max are distributed parameters estimated as functions of curve number (Gabellani et al, 2008), the pattern of f 1 and V fc is spatially modulated by the pattern of curve number maps (Silvestro et al, 2013) which are a synthetic representation of the local soil properties.…”

Section: Parametermentioning

confidence: 99%

“…The parameters V W max and R f govern the deep flow and the water-table dynamic (Silvestro et al, 2013). V W max represents the absolute maximum water content of the aquifer on the whole investigated area; the maximum water content on each cell is estimated based on V W max and on the slope (Saulnier et al, 1997).…”

Section: Parametermentioning

confidence: 99%

“…This kind of data is by now available for a very high percentage of the earth's surface, and covers most of the areas where the density of ground stations is poor. This leads to a panorama in which estimating the parameters of a hydrological model by using only satellite information is a real possibility (Silvestro et al, 2013). However, the ability to calibrate a model using satellite data, even in combination with traditional in situ data, is still a challenging topic.…”

Section: Introductionmentioning

confidence: 99%

“…The second and third methods are two attempts at using only satellite data without any streamflow measurements, simulating the case of a basin in a scarce data environment. These last experiments are conceived along the lines of Silvestro et al (2013), but with a more comprehensive approach that exploits both LST and SSM estimates from satellites. The results of the presented methods are then compared with those obtained using a fourth standard calibration methodology based on streamflow data.…”

Section: Introductionmentioning

confidence: 99%

“…The hydrological model used in the study is Continuum (Silvestro et al, 2013). It is a distributed continuous model conceived to satisfy the principle of parsimony in parameterization (Perrin et al, 2001;Coccia et al, 2009;Efstratiadis and Koutsoyiannis, 2010) and to be balanced between a good representation of physical processes and the simplicity of the schematizations and implementation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

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: Parametermentioning

confidence: 99%

Section: Parametermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

107

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Toward a true spatial model evaluation in distributed hydrological modeling: Kappa statistics, Fuzzy theory, and EOF‐analysis benchmarked by the human perception and evaluated against a modeling case study

Koch

Jensen

Stisen

2015

Water Resources Research

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The hydrological modeling community is aware that the validation of distributed hydrological models has to move beyond aggregated performance measures, like hydrograph assessment by means of Nash-Suitcliffe efficiency toward a true spatial model validation. Remote sensing facilitates continuous data and can be measured on a similar spatial scale as the predictive scale of the hydrological model thereby it can serve as suitable data for the spatial validation. The human perception is often described as a very reliable and well-trained source for pattern comparison, which this study wants to exploit. A web-based survey that is interpreted based on approximately 200 replies reflects the consensus of the human perception on map comparisons of a reference map and 12 synthetic perturbations. The resulting similarity ranking can be used as a reference to benchmark various spatial performance metrics. This study promotes Fuzzy theory as a suitable approach because it considers uncertainties related to both location and value in the simulated map. Additionally, an EOF-analysis (Empirical Orthogonal Function) is conducted to decompose the map comparison into its similarities and dissimilarities. A modeling case study serves to further examine the metrics capability to assess the goodness of fit between simulated and observed land surface temperature maps. The EOF-analysis unambiguously identifies a systematic depth to groundwater table-related model deficiency. Kappa statistic extended by Fuzziness is a suitable and commonly applied measure for map comparison. However, its apparent bias sensitivity limits it's capability as a diagnostic tool to detect the distinct deficiency.

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Spatial validation of large‐scale land surface models against monthly land surface temperature patterns using innovative performance metrics

et al. 2016

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Land surface models (LSMs) are a key tool to enhance process understanding and to provide predictions of the terrestrial hydrosphere and its atmospheric coupling. Distributed LSMs predict hydrological states and fluxes, such as land surface temperature (LST) or actual evapotranspiration (aET), at each grid cell. LST observations are widely available through satellite remote sensing platforms that enable comprehensive spatial validations of LSMs. In spite of the great availability of LST data, most validation studies rely on simple cell to cell comparisons and thus do not regard true spatial pattern information. The core novelty of this study is the development and application of two innovative spatial performance metrics, namely, empirical orthogonal function (EOF) and connectivity analyses, to validate predicted LST patterns by three LSMs (Mosaic, Noah, Variable Infiltration Capacity (VIC)) over the contiguous United States. The LST validation data set is derived from global High‐Resolution Infrared Radiometric Sounder retrievals for a 30 year period. The metrics are bias insensitive, which is an important feature in order to truly validate spatial patterns. The EOF analysis evaluates the spatial variability and pattern seasonality and attests better performance to VIC in the warm months and to Mosaic and Noah in the cold months. Further, more than 75% of the LST variability can be captured by a single pattern that is strongly correlated to air temperature. The connectivity analysis assesses the homogeneity and smoothness of patterns. The LSMs are most reliable at predicting cold LST patterns in the warm months and vice versa. Lastly, the coupling between aET and LST is investigated at flux tower sites and compared against LSMs to explain the identified LST shortcomings.

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

Cited by 74 publications

References 65 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

Toward a true spatial model evaluation in distributed hydrological modeling: Kappa statistics, Fuzzy theory, and EOF‐analysis benchmarked by the human perception and evaluated against a modeling case study

Spatial validation of large‐scale land surface models against monthly land surface temperature patterns using innovative performance metrics

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