Comparison of hydrologic calibration of HSPF using automatic and manual methods

Kim, Sang Min; Benham, Brian Leslie; Brannan, Kevin M.; Zeckoski, Rebecca W.; Doherty, John

doi:10.1029/2006wr004883

Cited by 74 publications

(55 citation statements)

References 11 publications

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“…θ is restricted to the feasible parameter space (Madsen, 2000; Kim et al, 2007;Efstratiadis and Koutsoyiannis, 2010). This calibration approach is based, in our case, on the comparisons of (ground or satellite) observed vs simulated streamflow, LST and SWI.…”

Section: Multi-objective Calibration (M O)mentioning

confidence: 99%

“…Much work has been devoted to determining the best calibration strategy (Yapo et al, 1998;Madesen, 2000;Kim et al, 2007;Singh and Bardossy, 2012;Xu et al, 2013) with some trying to evaluate the uncertainties associated with the parameter estimation process (Beven and Binley, 1992;Vrugt et al, 2003;Carpenter and Georgakakos, 2006;Zappa et al, 2011). This issue has become even more complex with the increasing use of continuous and distributed hydrological models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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.

113

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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: 99%

Section: Introductionmentioning

confidence: 99%

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.

113

View full text Add to dashboard Cite

show abstract

“…Parameter calibration is necessary when using spatially-lumped hydrologic models like the Hydrological Simulation Program-FORTRAN (HSPF) [3,4]. Model calibration may be performed manually, or the processes can be automated using an optimization algorithm [5,6]. Manual calibration can be laborious and time consuming.…”

Section: Introductionmentioning

confidence: 99%

Automatic Calibration Tool for Hydrologic Simulation Program-FORTRAN Using a Shuffled Complex Evolution Algorithm

Seong

Her

Benham

2015

Water

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Hydrologic Simulation Program-Fortran (HSPF) model calibration is typically done manually due to the lack of an automated calibration tool as well as the difficulty of balancing objective functions to be considered. This paper discusses the development and demonstration of an automated calibration tool for HSPF (HSPF-SCE). HSPF-SCE was developed using the open source software "R". The tool employs the Shuffled Complex Evolution optimization algorithm (SCE-UA) to produce a pool of qualified calibration parameter sets from which the modeler chooses a single set of calibrated parameters. Six calibration criteria specified in the Expert System for the Calibration of HSPF (HSPEXP) decision support tool were combined to develop a single, composite objective function for HSPF-SCE. The HSPF-SCE tool was demonstrated, and automated and manually calibrated model performance were compared using three Virginia watersheds, where HSPF models had been previously prepared for bacteria total daily maximum load (TMDL) development. The example applications demonstrate that HSPF-SCE can be an effective tool for calibrating HSPF.

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“…Meteorological stations are sparsely located in some areas and are not all active on any given day. Typical HSPF models rely on very few climate stations, some with only one station to represent the entire watershed [18][19][20][21]. The objective of the meteorological development was to enhance the spatial distribution of daily meteorological data and therefore increase the accuracy of modeled streamflow and suspended sediment.…”

Section: Gridded Meteorological Inputs To Hspfmentioning

confidence: 99%

Characterizing Changes in Streamflow and Sediment Supply in the Sacramento River Basin, California, Using Hydrological Simulation Program—FORTRAN (HSPF)

Stern¹,

Flint²,

Minear³

et al. 2016

Water

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A daily watershed model of the Sacramento River Basin of northern California was developed to simulate streamflow and suspended sediment transport to the San Francisco Bay-Delta. To compensate for sparse data, a unique combination of model inputs was developed, including meteorological variables, potential evapotranspiration, and parameters defining hydraulic geometry. A slight decreasing trend of sediment loads and concentrations was statistically significant in the lowest 50% of flows, supporting the observed historical sediment decline. Historical changes in climate, including seasonality and decline of snowpack, contribute to changes in streamflow, and are a significant component describing the mechanisms responsible for the decline in sediment. Several wet and dry hypothetical climate change scenarios with temperature changes of 1.5 • C and 4.5 • C were applied to the base historical conditions to assess the model sensitivity of streamflow and sediment to changes in climate. Of the scenarios evaluated, sediment discharge for the Sacramento River Basin increased the most with increased storm magnitude and frequency and decreased the most with increases in air temperature, regardless of changes in precipitation. The model will be used to develop projections of potential hydrologic and sediment trends to the Bay-Delta in response to potential future climate scenarios, which will help assess the hydrological and ecological health of the Bay-Delta into the next century.

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Comparison of hydrologic calibration of HSPF using automatic and manual methods

Cited by 74 publications

References 11 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

Automatic Calibration Tool for Hydrologic Simulation Program-FORTRAN Using a Shuffled Complex Evolution Algorithm

Characterizing Changes in Streamflow and Sediment Supply in the Sacramento River Basin, California, Using Hydrological Simulation Program—FORTRAN (HSPF)

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