Bayesian recursive parameter estimation for hydrologic models

By M. Thiemann, M. Trosset, Hoshin V. Gupta, Soroosh Sorooshian. 2001

463 citations recorded by the source corpus.

The uncertainty in a given hydrologic prediction is the compound effect of the parameter, data, and structural uncertainties associated with the underlying model. In general, therefore, the confidence in a hydrologic prediction can be improved by reducing the uncertainty associated with the parameter estimates. However, the classical approach to doing this via model calibration typically requires that considerable amounts of data be collected and assimilated before the model can be used. This limitation becomes immediately apparent when hydrologic predictions must be generated for a previously ungauged watershed that has only recently been instrumented. This paper presents the framework for a Bayesian recursive estimation approach to hydrologic prediction that can be used for simultaneous parameter estimation and prediction in an operational setting. The prediction is described in terms of the probabilities associated with different output values. The uncertainty associated with the parameter estimates is updated (reduced) recursively, resulting in smaller prediction uncertainties as measurement data are successively assimilated. The effectiveness and efficiency of the method are illustrated in the context of two models: a simple unit hydrograph model and the more complex Sacramento soil moisture accounting model, using data from the Leaf River basin in Mississippi.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/823943d847939c2f05437830de39f0558b5c1b4d

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Functional classification and evaluation of hydrographs based on Multicomponent Mapping (Mx) (2004, methodology): “This method aims to increase the information content which can be retrieved from a hydrograph in a way similar to other current approaches like the Dynamic Identifiability Analysis [37] or other recursive approaches [3,38].”
The role of hydrograph indices in parameter estimation of rainfall–runoff models (2005, background): “…caused by model structure inaccuracies and data errors has led to the development of techniques that simultaneously identify parameters and estimate their associated uncertainties (e.g., Kuczera, 1983; Beven and Binley, 1992; Uhlenbrook et al., 1999; Thiemann et al., 2001; Vrugt et al., 2003).”
Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors (2010, methodology): “Generally speaking, hydrologic modeling is affected by four main sources of uncertainty: (1) input uncertainty, e.g., sampling and measurement errors in catchment rainfall estimates; (2) output uncertainty, e.g., rating curve errors affecting runoff estimates; (3) structural uncertainty (sometimes r”
Dual state-parameter estimation of hydrological models using ensemble Kalman filter (2005, background): “[35] emphasized another limitation of batch calibration in hydrological prediction of an ungauged watershed where the lack of sufficient historical data makes the batch method infeasible.”
Reconciling theory with observations: elements of a diagnostic approach to model evaluation (2008, methodology): “…1994), and various Bayesian approaches such as particle filtering (Gordon et al., 1993; Moradkhani et al., 2005), Bayesian recursive estimation (Thiemann et al., 2001), and the data-based mechanistic (DBM) approach to stochastic modeling (Young, 2002, 2003; Romanowicz et al., 2006; Young and…”
Validity of First-Order Approximations to Describe Parameter Uncertainty in Soil Hydrologic Models (2002, background): “This approach isvariance between the q sequence means, i, each especially popular in the field of rainfall-runoff modelingbased on n samples i (see for example, Thiemann et al., 2001).”
Quantifying hydrological modeling errors through a mixture of normal distributions (2007, methodology): “In parameter inference studies, the catchment response simulated through a hydrological model is generally represented as a non-linear regression of the following form (e.g., Sorooshian and Dracup, 1980; Thiemann et al., 2001):”
Application of stochastic parameter optimization to the Sacramento Soil Moisture Accounting model (2006, methodology): “In keeping with previous studies (Boyle et al., 2000; Thiemann et al., 2001; Vrugt et al., 2003), the hydrological data used for model calibration and uncertainty assessment of the parameters of the SAC-SMA model consists of approximately 36 years (28 July, 1952–30 September, 1988) of streamflow d”
Influence of uncertain boundary conditions and model structure on flood inundation predictions. (2006, background): “GLUE has been criticised for the subjectivity of this distinction [5,81], but as Freer et al.”
Confronting the Challenge of Modeling Cloud and Precipitation Microphysics (2020, methodology): “…Bayesian methods have been used to estimate parameters and quantify uncertainty in land surface and hydrology models (e.g., Beven et al., 2007; Franks & Beven, 1997; Kavetski et al., 2006; Knorr & Kattge, 2005; Raoult et al., 2016; Shi et al., 2015; Smith & Marshall, 2008; Thiemann et al., 2001).”
Estimating information entropy for hydrological data: One‐dimensional case (2014, methodology): “…the entropy estimation methods to a widely used 40 year (October 1948 to September 1988) daily time step data set from the 1944 km2 humid Leaf River basin located north of Collins, Mississippi, USA [see, e.g., Sorooshian et al., 1993; Thiemann et al., 2001; Vrugt et al., 2003; Gong et al., 2013].”
Application of temporal streamflow descriptors in hydrologic model parameter estimation (2005, methodology): “, Dawdy and O’Donnell, 1965; Diskin and Simon, 1977; Sorooshian and Dracup, 1980; Rao and Han, 1987], (3) utilizing probabilistic methods to account for parameter uncertainty [e.g., Kuczera, 1988; Beven and Binley, 1992; Uhlenbrook et al., 1999; Thiemann et al., 2001; Vrugt et al., 2002], (4) calibr”