Uncertainty Recognition and Quantification of Hydrologic Prediction (original) (raw)
Related papers
2005
We propose a methodology to identify prediction uncertainty through recognizing and quantifying the different uncertainty sources in a hydrologic model. Statistical second moment is used as a measure of uncertainty; also an index which originated from Nash coefficient of efficiency named Model Structure Indicating Index (MSII) is proposed to evaluate the model predicting uncertainty. The results show that MSII can well reflect the goodness of model structure, while a larger value of MSII indicating a poorer structure of hydrologic model. The index can be used as a tool for implementing model quantitative comparison.
An Approach to Uncertainty Identification in Hydrological Modeling
PROCEEDINGS OF HYDRAULIC ENGINEERING, 2005
We propose a methodology to identify prediction uncertainty through recognizing and quantifying the different uncertainty sources in a hydrologic model. Statistical second moment is used as a measure of uncertainty; also an index which originated from Nash coefficient of efficiency named Model Structure Indicating Index (MSII) is proposed to quantify model structure uncertainty. The results show that MSII can well reflect the goodness of model structure, while a larger value of MSII indicating a poorer structure of hydrologic model. The index can be used as a tool for implementing model quantitative comparison (selection).
Hydrological model performance comparison through uncertainty recognition and quantification
Hydrological Processes, 2007
In this study, a methodology which is capable of identifying the uncertainty of prediction through recognizing and quantifying the different sources of uncertainty in hydrologic models is applied for model comparison. The methodology is developed to recognize and quantify different uncertainty sources through observing hydrologic model behaviour under increasing input uncertainty levels. Based on the methodology, an index, which originates from the Nash–Sutcliffe efficiency named Model Structure Indicating Index (MSII) developed by the authors is applied to evaluate the reliability of model structure. A ranking of the adequacy of the hydrologic models to the watershed can be achieved by applying MSII. The hydrologic models Storage Function Method (SFM), TOPMODEL and KW-GIUH are used for model quantitative comparison in this study. Of these, a parameter-constrained SFM is used as an example of a poor-structured model; and two versions of TOPMODEL with different vertical flux calculation processes are used to demonstrate the behaviour of different model components. The results show that, at small input uncertainties, no distinction can be made between the capabilities of the hydrologic models to adapt themselves to error-contaminated data. As the input uncertainty increased, however, the distinction between the models becomes larger and the accuracy of the model structures could be quantified through MSII. The results prove that the index can be used as a tool for implementing quantitative model comparison/selection. Copyright © 2007 John Wiley & Sons, Ltd.
Evaluating the information content of data for uncertainty reduction in hydrological modelling
The inclusion of additional information in a model should improve the model's performance and reduce associated uncertainties. The additional information may take the form of higher temporal and/or spatial resolution of data already used, or additional types of data (including soft data). However, additional data is not necessarily equivalent with additional information as a model may only be able to make use of a fraction of the information in the data, or the approach chosen to extract data might be inefficient. Consideration of the information-to-noise ratio of the data is needed in order to evaluate whether the information will indeed improve the model's performance or reduce uncertainty. Evaluating the information-to-noise ratio and the information content of data in general are often difficult tasks. One simple but effective approach to this problem is to compare the performance and uncertainty of a model with and without additional information. This workshop will focu...
Uncertainty in hydrological modelling: A review
International Journal of Hydrology Research
Availability of hydrological data and various soft wares for developing models make easy way to answer frequently asked questions to hydrologists. A great deal of concentration has given to the development of models in the last decades. But the thorough study regarding uncertainty of simulations has not carried out in comparison with the development of models. Uncertainty in models emanates from input data, calibrated data, parameters and from the structure of models. The sources of uncertainty, cause of generation and how these can be dealt with are reviewed here. This also comprises a review about five different methods viz. Monte Carlo sampling, Bayesian approach, Generalized Likelihood Uncertainty Estimation, Bootstrap Approach and Machine learning methods which were applied in the estimation of the model and parameter uncertainty. This will indicate the comparison between the methods which were applied to measure the uncertainty of hydrological models and highlight the strength...
Water Resources Research, 2013
1] With growing interest in understanding the magnitudes and sources of uncertainty in hydrological modeling, the difficult problem of characterizing model structure adequacy is now attracting considerable attention. Here, we examine this problem via a model-structureindependent approach based in information theory. In particular, we (a) discuss how to assess and compute the information content in multivariate hydrological data, (b) present practical methods for quantifying the uncertainty and shared information in data while accounting for heteroscedasticity, (c) show how these tools can be used to estimate the best achievable predictive performance of a model (for a system given the available data), and (d) show how model adequacy can be characterized in terms of the magnitude and nature of its aleatory uncertainty that cannot be diminished (and is resolvable only up to specification of its density), and its epistemic uncertainty that can, in principle, be suitably resolved by improving the model. An illustrative modeling example is provided using catchment-scale data from three river basins, the Leaf and Chunky River basins in the United States and the Chuzhou basin in China. Our analysis shows that the aleatory uncertainty associated with making catchment simulations using this data set is significant ($50%). Further, estimated epistemic uncertainties of the HyMod, SAC-SMA, and Xinanjiang model hypotheses indicate that considerable room for model structural improvements remain.
Development Of A Repository For Hydrologic Model Uncertainty Data
2014
Hydrologic processes are complex and when modeling them using a deterministic or stochastic approach one invariably introduces errors because of simplifications and assumptions made. However, not all assumptions and simplifications in the approach chosen produce the amount of errors; in fact the impact of deviations from the truth on a final output set of variables varies greatly. In addition, not every catchment behaves alike adding another layer of complexity to the modeling effort. Hence, every approach exhibits a degree of uncertainty in their results. While this uncertainty can be examined systematically in this technical note we focus on the development of a repository for modeling uncertainty data. We store information about the model used (lumped, semi distributed, fully distributed), the objective function (Nash Sutcliff, Root Mean Square Error, ...) used to calculate the fitness of an approach, a Pareto best parameter combination, and also some statistical values that arri...
A New Uncertainty Measure for Assessing the Uncertainty Existing in Hydrological Simulation
Water
The absence of aggregated uncertainty measures restricts the assessment of uncertainty in hydrological simulation. In this work, a new composite uncertainty measure is developed to evaluate the complex behaviors of uncertainty existing in hydrological simulation. The composite uncertainty measure is constructed based on a framework, which includes three steps: (1) identification of behavioral measures by analyzing the pairwise correlations among different measures and removing high correlations; (2) weight assignment by means of a new hierarchical weight assembly (HWA) approach incorporating the intra-class and inter-class weights; (3) construction of a composite uncertainty measure through incorporating multiple properties of the measure matrix. The framework and the composite uncertainty measure are demonstrated by case studies in uncertainty assessment for hydrological simulation. Results indicate that the framework is efficient to generate a composite uncertainty index (denoted ...
A PSEUDO VALIDATION ALGORITHM FOR HYDROLOGICAL MODEL RELIABILITY ASSESSMENT
2006
A pseudo validation algorithm, which is capable of identifying the prediction uncertainty through recognizing and quantifying the different uncertainty sources in a hydrologic model, is manipulated as an instrument for hydrological model reliability assessment. For implementation, the pseudo validation algorithm is manipulated in order to compare TOPMODEL with different vertical flux calculation components, which have been applied to two Japanese basins. An index, which originates from the Nash-Sutcliffe efficiency, named Model Structure Indicating Index (MSII), is used to quantify the model reliability under different magnitudes of input uncertainty. The results show that within a small magnitude of input uncertainty, the reliability of a five parameter TOPMODEL is worse than a six parameter TOPMODEL. However, within larger magnitudes of the input uncertainty, the reliability of the five parameter TOPMODEL is better than that of the six parameter TOPMODEL, this shows that the pseudo validation algorithm can be used as a reference for hydrological modeling.