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

Abstract

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.

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

“…Nevertheless it was found that the joint calibration results in more robust calibration than the calibration using streamflow only case [145,146]. While studies by Parajka et al [145,146] used low temporal resolution SAR data in a semi-distributed system, further attention has been paid to calibrate fully distributed hydrologic models using higher temporal resolution microwave data [136,144,147]. Sutanudjaja et al [144] used SCAT/ERS derived soil moisture data for a coupled groundwater-land surface model and found that the joint calibration using flow and soil moisture can reproduce good soil moisture and streamflow, as well as ground water head predictions.…”

“…Thus, one aspect of the improved robustness in batch calibration is to address the equifinality issue. This has been explored by Silvestro et al [147], who tested the use of EUMETSAT soil moisture data to calibrate a distributed hydrologic model using the brute-force calibration algorithm. They demonstrated that some parameters are only weakly dependent on streamflow measurements, and that the use of both ground gauges and remote sensing data is able to additionally constrain the parameters and reduce the number of equifinal solutions.…”

“…However, it is a common issue in both batch calibration and data assimilation for conceptual forecasting models with a single soil water layer. One group of studies have used exponential filtering techniques to convert the remote sensing data to a root-zone soil wetness index [103,147,180,186,191]. This method is simple and easy to be implemented for all conceptual models.…”

“…In spite of the challenge in improving streamflow prediction by calibration using soil moisture, several explorations have been carried out recently [136,[144][145][146][147] to examine the potential of improving short-term streamflow forecasting by calibrating model parameters using soil moisture remote sensing data (Table 3). A relatively early study conducted by Parajka et al [146] found that joint-calibration of a semi-distributed model using both streamflow and SAR/ERS derived soil moisture data improved the accuracy of the soil moisture predictions without degradation of the streamflow predictions.…”

Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding

“…Decisions regarding how to constrain ranges and distributions for priors on model parameters often depend on the availability of field measurements, awareness of the catchment or model, and may involve sensitivity analysis to identify which parameters are most influential to simulations (Tang et al, 2007;Saltelli et al, 2008;Cuo et al, 2011). A subset of distributed modeling studies has directly incorporated parameter uncertainty into final simulations, sampling across ranges of values obtained either from literature or measurements (e.g., Surfleet et al, 2010;Shields and Tague, 2015;Gharari et al, 2014;Silvestro et al, 2015). Very few studies have shown the implications of this equifinality during model calibration, an important consideration given that selection of a parameter set or sets will influence conclusions made for validation and scenario analysis.…”