Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model

Benke, Kurt K.; Lowell, Kim; Hamilton, Andrew J.

doi:10.1016/j.mcm.2007.05.017

Cited by 121 publications

(81 citation statements)

References 8 publications

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“…Further, we carried out the perturbation analysis in order to account for the variations of the model parameters estimating the approximated solutions (p′ ) for each parameter set (p). The perturbation analysis has been used to evaluate how variations of the model input parameters affect model outputs [de Kroon et al, 1986;Caswell, 2000;Benke et al, 2008]. The perturbed parameters were calculated as…”

Section: Modified-microga For Estimating Optimal Parameters and Theirmentioning

confidence: 99%

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

2015

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Various hydrological models have been developed for estimating root zone soil moisture dynamics. These models, however, incorporated their own parameterization approaches indicating that the output from the different model inherent structures might include uncertainties because we do not know which model structure is correct for describing the real system. More recently, multimodel approaches using a Bayesian Model Averaging (BMA) scheme can improve the overall predictive skill while individual models retain their own uncertainties for simulated soil moisture based on a single set of weights in modeling under different land surface wetness conditions (e.g., wet, moderately wet, and dry conditions). In order to overcome their limitations, we developed a BMA-based multimodel simulation approach based on various soil wetness conditions for estimating effective surface soil moisture dynamics (0-5 cm) and quantifying uncertainties efficiently based on the land surface wetness conditions. The newly developed approach adapts three different hydrological models (i.e., Noah Land Surface Model, Noah LSM; Soil-Water-Atmosphere-Plant, SWAP; and Community Land Model, CLM) for simulating soil moisture. These models were integrated with a modified-microGA (advanced version of original Genetic Algorithm (GA)) to search for optimized soil parameters for each model. Soil moisture was simulated from the estimated soil parameters using the hydrological models in a forward mode. It was found that SWAP performed better than others during wet condition, while Noah LSM and CLM showed a good agreement with measurements during dry condition. Thus, we inferred that performance of individual models with different model structures can be different with land surface wetness. Taking into account the effects of soil wetness on different model performances, we categorized soil moisture measurements and estimated different weights for each category using the BMA scheme. Effective surface soil moisture dynamics were obtained by aggregating multiple weighted soil moisture. Our findings demonstrated that the effective soil moisture estimates derived by this approach showed a better match with the measurements compared to the original models and single-weighted outputs. Multimodel simulation approach based on land surface wetness enhances the ability to predict reliable soil moisture dynamics and reflects the strengths of different hydrological models under various soil wetness conditions.

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Section: Modified-microga For Estimating Optimal Parameters and Theirmentioning

confidence: 99%

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

2015

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“…As a result, these six parameters (i.e., ranked seventh to twelfth) were not considered for uncertainty analysis in this study. Benke et al [12] also noted that when parameters have little impact on the modeling output value, they can be easily ignored for simplification of the model structure.…”

Section: Sensitivity Analysis Of Gw-sw Interactionmentioning

confidence: 99%

“…For instance, Scibek and Allen [3] model; Van Roosmalen et al [4] used the DK model (The National Water Resource model for Denmark); Goderniaux et al [5] used the HydroGeoSphere model; Stoll et al [6] used the MIKE Système Hydrologique Européen (MIKE-SHE) model; Jackson et al [7] used the coupled Zoom Object-Oriented Distributed Recharge Model (ZOODRM) and Zoom Object-Oriented Quasi-3-Dimensional Model (ZOOMQ3D); Vansteenkiste et al [8] used the MIKE-SHE, and Water and Energy Transfer between Soil, Plants and Atmosphere (WetSpa) models; El Hassan et al [9] used the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model; Wu et al [10] used Groundwater and Surface-water FLOW (GSFLOW) model; Faramarzi et al [11] used the Soil and Water Assessment Tool (SWAT) model. Most of the parameters (e.g., soil properties, surface roughness) in hydrologic models used for GW-SW interaction simulations require intensive field measurements [12], and they are always associated with uncertainty. Such uncertainty could also lead to uncertainty in modeling outputs [13], which could jeopardize the decision-making of water resources management.…”

Section: Introductionmentioning

confidence: 99%

“…Vrugt et al [16] used the Markov Chain Monte Carlo (MCMC) method in the HYdrological MODel (HYMOD) to find out the variation of stream flow under the parameter uncertainty in the Leaf River watershed, Mississippi, USA. Benke et al [12] used the Monte Carlo simulation (MCS) method as an uncertainty analysis method in the 2C hydrological model to investigate the impacts of parameter uncertainty on the prediction of stream flow in Eastern Australia. Mishra [17] used the first-order second-moment (FOSM) and MCS methods in the Natural Systems Regional Simulation Model (NSRSM) to compare the impacts of parameter uncertainty on stream flow prediction in Southern Florida, USA.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the Effects of Parameter Uncertainty on Temporal Dynamics of Groundwater-Surface Water Interaction

Saha

Thring

2017

Hydrology

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This study presents the understanding of temporal dynamics of groundwater-surface water (GW-SW) interaction due to parameter uncertainty by using a physically-based and distributed gridded surface subsurface hydrologic analysis (GSSHA) model combined with a Monte Carlo simulation. A study area along the main stem of the Kiskatinaw River of the Kiskatinaw River watershed, Northeast British Columbia, Canada, was used as a case study. Two different greenhouse gas (GHG) emission scenarios (i.e., A2: heterogeneous world with self-reliance and preservation of local identities, and B1: a more integrated and environmental-friendly world) of the Special Report on Emissions Scenarios (SRES) from the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) for 2013 were used as case scenarios. Before conducting uncertainty analysis, a sensitivity analysis was performed to find the most sensitive parameters to the model output (i.e., mean monthly groundwater contribution to stream flow). Then, a Monte Carlo simulation was used to conduct the uncertainty analysis. The uncertainty analysis results under both case scenarios revealed that the pattern of the cumulative relative frequency distribution of the mean monthly and annual groundwater contributions to stream flow varied monthly and annually, respectively, due to the uncertainties of the sensitive model parameters. In addition, the pattern of the cumulative relative frequency distribution of a particular month's groundwater contribution to the stream flow differed significantly between both scenarios. These results indicated the complexities and uncertainties in the GW-SW interaction system. Therefore, it is of necessity to use such uncertainty analysis results rather than the point estimates for better water resources management decision-making.

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“…Other uncertainty estimation techniques may employ a combination of these approaches (Del Giudice et al, 2013). Some techniques focus on one source of uncertainty, such as the model parameter uncertainty (Benke et al, 2008) or the model structure uncertainty (Butts et al, 2004), while others focus on combined uncertainties stemming from model parameters, model structure deficits and inputs (Schoups and Vrugt, 2010;Evin et al, 2013;Del Giudice et al, 2013). In this context, it is important to note that apart from estimating uncertainty of model parameters during calibration, uncertainty estimation for hydrologic forecasting requires quantification of predictive uncertainty, which includes uncertain system response in addition to different combinations of model parameters (Renard et al, 2010;Coccia and Todini, 2011;.…”

mentioning

confidence: 99%

Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting

Wani

Beckers

Weerts

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. A non-parametric method is applied to quantify residual uncertainty in hydrologic streamflow forecasting. This method acts as a post-processor on deterministic model forecasts and generates a residual uncertainty distribution. Based on instance-based learning, it uses a k nearestneighbour search for similar historical hydrometeorological conditions to determine uncertainty intervals from a set of historical errors, i.e. discrepancies between past forecast and observation. The performance of this method is assessed using test cases of hydrologic forecasting in two UK rivers: the Severn and Brue. Forecasts in retrospect were made and their uncertainties were estimated using kNN resampling and two alternative uncertainty estimators: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Results show that kNN uncertainty estimation produces accurate and narrow uncertainty intervals with good probability coverage. Analysis also shows that the performance of this technique depends on the choice of search space. Nevertheless, the accuracy and reliability of uncertainty intervals generated using kNN resampling are at least comparable to those produced by QR and UNEEC. It is concluded that kNN uncertainty estimation is an interesting alternative to other post-processors, like QR and UNEEC, for estimating forecast uncertainty. Apart from its concept being simple and well understood, an advantage of this method is that it is relatively easy to implement.

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Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model

Cited by 121 publications

References 8 publications

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

Understanding the Effects of Parameter Uncertainty on Temporal Dynamics of Groundwater-Surface Water Interaction

Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting

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