During the last decade the opportunity and use- fulness of using remote-sensing data in hydrology, hydrom- eteorology and geomorphology has become even more ev- ident 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 model- ing 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 conse- quence there is the need for developing and testing tech- niques 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 Ex- ploitation 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 estima- tion strategies are implemented and tested in a comparative mode: (i) a multi-objective approach that includes both satel- lite and ground observations which is an attempt to use dif- ferent 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 stan- dard 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) sim- ulations. The results of the analysis evidence that, as a result of the model structure and the nature itself of the catchment hydro- logic processes, some model parameters are only weakly de- pendent on discharge observations, and prove the usefulness of using data from both ground stations and satellites to ad- ditionally constrain the parameters in the calibration process and reduce the number of equifinal solutions.

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

GABELLANI, SIMONE;RUDARI, ROBERTO;LAIOLO, PAOLA;BONI, GIORGIO
2015-01-01

Abstract

During the last decade the opportunity and use- fulness of using remote-sensing data in hydrology, hydrom- eteorology and geomorphology has become even more ev- ident 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 model- ing 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 conse- quence there is the need for developing and testing tech- niques 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 Ex- ploitation 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 estima- tion strategies are implemented and tested in a comparative mode: (i) a multi-objective approach that includes both satel- lite and ground observations which is an attempt to use dif- ferent 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 stan- dard 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) sim- ulations. The results of the analysis evidence that, as a result of the model structure and the nature itself of the catchment hydro- logic processes, some model parameters are only weakly de- pendent on discharge observations, and prove the usefulness of using data from both ground stations and satellites to ad- ditionally constrain the parameters in the calibration process and reduce the number of equifinal solutions.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/813709
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