Scale dependencies of hydrologic models to spatial variability of precipitation

Koren, Victor; Finnerty, Bryce; Schaake, John C.; Smith, Michael; Seo, Dong Jun; Duan, Qingyun

doi:10.1016/s0022-1694(98)00231-5

Cited by 135 publications

(110 citation statements)

References 18 publications

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“…Liang and Guo (2003) showed that LSMs such as ISBA and VIC, which explicitly represent the subgrid-scale spatial variability of soil, vegetation, and/or atmospheric forcings, can be less sensitive to the choice of parameter values and thereby produce more robust results, and several other studies have supported this conclusion (e.g. Liang et al, 1996bLiang et al, , 2004Koren et al, 1999;. VIC is insensitive to the assumption of different precipitation distributions within the precipitation-covered area (e.g.…”

Section: C Prentice Et Al: Next-generation Land-surface Modellingmentioning

confidence: 97%

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

et al. 2015

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Abstract. Land-surface models (LSMs) are increasingly called upon to represent not only the exchanges of energy, water and momentum across the land-atmosphere interface (their original purpose in climate models), but also how ecosystems and water resources respond to climate, atmospheric environment, land-use and land-use change, and how these responses in turn influence land-atmosphere fluxes of carbon dioxide (CO 2 ), trace gases and other species that affect the composition and chemistry of the atmosphere. However, the LSMs embedded in state-of-the-art climate models differ in how they represent fundamental aspects of the hydrological and carbon cycles, resulting in large inter-model differences and sometimes faulty predictions. These "thirdgeneration" LSMs respect the close coupling of the carbon and water cycles through plants, but otherwise tend to be under-constrained, and have not taken full advantage of robust hydrological parameterizations that were independently developed in offline models. Benchmarking, combining multiple sources of atmospheric, biospheric and hydrological data, should be a required component of LSM development, but this field has been relatively poorly supported and intermittently pursued. Moreover, benchmarking alone is not sufficient to ensure that models improve. Increasing complexity may increase realism but decrease reliability and robustness, by increasing the number of poorly known model parameters. In contrast, simplifying the representation of complex processes by stochastic parameterization (the representation of unresolved processes by statistical distributions of values) has been shown to improve model reliability and realism in both atmospheric and land-surface modelling contexts. We provide examples for important processes in hydrology (the generation of runoff and flow routing in heterogeneous catchments) and biology (carbon uptake by speciesdiverse ecosystems). We propose that the way forward for next-generation complex LSMs will include: (a) representations of biological and hydrological processes based on the implementation of multiple internal constraints; (b) systematic application of benchmarking and data assimilation techniques to optimize parameter values and thereby test the structural adequacy of models; and (c) stochastic parameterization of unresolved variability, applied in both the hydrological and the biological domains.

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Section: C Prentice Et Al: Next-generation Land-surface Modellingmentioning

confidence: 97%

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

et al. 2015

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“…Calibration is the process of adjusting selected model parameters to minimize the difference between the simulated and observed variables of interest [1,2]. 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].…”

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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“…Each has been associated with some form of approximation e.g. the Bayesian forecasting system (Krzysztofowicz, 2002;Kyriakidis et al, 2001); the US National Weather service ensemble forecasting (Koren et al, 1999); the River Flow Forecasting system (Moore et al, 1990); Transfer Function methods (Young, 2002;Young and Beven, 1994); the Generalized Likelihood Uncertainty Estimation (GLUE) method Beven, 1998, 2003;Romanowicz et al, 1994). However, all the references mentioned constrain the uncertainty analysis to only two model components (e.g.…”

Section: Introductionmentioning

confidence: 99%

Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS)

Pappenberger

Beven

Hunter

et al. 2005

Hydrol. Earth Syst. Sci.

298

213

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The political pressure on the scientific community to provide medium to long term flood forecasts has increased in the light of recent flooding events in Europe. Such demands can be met by a system consisting of three different model components (weather forecast, rainfall runoff forecast and flood inundation forecast) which are all liable to considerable uncertainty in the input, output and model parameters. Thus, an understanding of cascaded uncertainties is a necessary requirement to provide robust predictions. In this paper, 10-day ahead rainfall forecasts, consisting of one deterministic, one control and 50 ensemble forecasts, are fed into a rainfallrunoff model (LisFlood) for which parameter uncertainty is represented by six different parameter sets identified through a Generalised Likelihood Uncertainty Estimation (GLUE) analysis and functional hydrograph classification. The runoff of these 52 * 6 realisations form the input to a flood inundation model (LisFlood-FP) which acknowledges uncertainty by utilising ten different sets of roughness coefficients identified using the same GLUE methodology. Likelihood measures for each parameter set computed on historical data are used to give uncertain predictions of flow hydrographs as well as spatial inundation extent. This analysis demonstrates that a full uncertainty analysis of such an integrated system is limited mainly by computer power as well as by how well the rainfall predictions represent potential future conditions. However, these restrictions may be overcome or lessened in the future and this paper establishes a computationally feasible methodological approach to the uncertainty cascade problem.

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Scale dependencies of hydrologic models to spatial variability of precipitation

Cited by 135 publications

References 18 publications

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

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

Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS)

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