On the deterministic and stochastic use of hydrologic models

Environmental simulation models, such as precipitation-runoff watershed models, are increasingly used in a deterministic manner for environmental and water resources design, planning, and management. In operational hydrology, simulated responses are now routinely used to plan, design, and manage a very wide class of water resource systems. However, all such models are calibrated to existing data sets and retain some residual error. This residual, typically unknown in practice, is often ignored, implicitly trusting simulated responses as if they are deterministic quantities. In general, ignoring the residuals will result in simulated responses with distributional properties that do not mimic those of the observed responses. This discrepancy has major implications for the operational use of environmental simulation models as is shown here. Both a simple linear model and a distributed-parameter precipitation-runoff model are used to document the expected bias in the distributional properties of simulated responses when the residuals are ignored. The systematic reintroduction of residuals into simulated responses in a manner that produces stochastic output is shown to improve the distributional properties of the simulated responses. Every effort should be made to understand the distributional behavior of simulation residuals and to use environmental simulation models in a stochastic manner. This article is protected by copyright. All rights reserved.

[1]  Richard M. Vogel,et al.  Stochastic and Deterministic World Views , 1999 .

[2]  J. Stedinger,et al.  Appraisal of the generalized likelihood uncertainty estimation (GLUE) method , 2008 .

[3]  Wilbert O. Thomas,et al.  An Evaluation of Flood Frequency Estimates Based on Rainfall/runoff Modeling , 1982 .

[4]  Paul Dunne,et al.  Bootstrap Position Analysis for Forecasting Low Flow Frequency , 1997 .

[5]  T. Gneiting,et al.  Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling , 2013, 1302.7149.

[6]  Teresa J. Rasmussen,et al.  Estimation of Constituent Concentrations, Loads, and Yields in Streams of Johnson County, Northeast Kansas, Using Continuous Water-Quality Monitoring and Regression Models, October 2002 through December 2006 , 2008 .

[7]  D. E. Prudic,et al.  GSFLOW - Coupled Ground-Water and Surface-Water Flow Model Based on the Integration of the Precipitation-Runoff Modeling System (PRMS) and the Modular Ground-Water Flow Model (MODFLOW-2005) , 2008 .

[8]  Tyler Smith,et al.  Modeling residual hydrologic errors with Bayesian inference , 2015 .

[9]  N. Matalas,et al.  A correlation procedure for augmenting hydrologic data , 1964 .

[10]  Vazken Andréassian,et al.  Transferring global uncertainty estimates from gauged to ungauged catchments , 2014 .

[11]  James M. Sherwood ESTIMATION OF PEAK-FREQUENCY RELATIONS, FLOOD HYDROGRAPHS, AND VOLUME-DURATION-FREQUENCY RELATIONS OF UNGAGED SMALL URBAN STREAMS IN OHIO , 1993 .

[12]  Dmitri Kavetski,et al.  A unified approach for process‐based hydrologic modeling: 1. Modeling concept , 2015 .

[13]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[14]  Alberto Montanari,et al.  Uncertainty of Hydrological Predictions , 2011 .

[15]  R. Vogel,et al.  L moment diagrams should replace product moment diagrams , 1993 .

[16]  R. Hirsch A Comparison of Four Streamflow Record Extension Techniques , 1982 .

[17]  J. Schaake,et al.  Precipitation and temperature ensemble forecasts from single-value forecasts , 2007 .

[18]  Climate driver informed short‐term drought risk evaluation , 2013 .

[19]  G. Blöschl,et al.  Spatiotemporal topological kriging of runoff time series , 2007 .

[20]  J. R. Wallis,et al.  Just a Moment , 2013 .

[21]  Günter Blöschl,et al.  Runoff models and flood frequency statistics for design flood estimation in Austria – Do they tell a consistent story? , 2012 .

[22]  L. Shawn Matott,et al.  Evaluating uncertainty in integrated environmental models: A review of concepts and tools , 2009 .

[23]  Paul D. Bates,et al.  Flood-plain mapping: a critical discussion of deterministic and probabilistic approaches , 2010 .

[24]  J. Stedinger,et al.  Minimum variance streamflow record augmentation procedures , 1985 .

[25]  Martyn P. Clark,et al.  Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models , 2008 .

[26]  Keith Beven,et al.  A framework for uncertainty analysis , 2014 .

[27]  H. N. Nagaraja,et al.  Order Statistics, Third Edition , 2005, Wiley Series in Probability and Statistics.

[28]  Robert M. Hirsch Stochastic Hydrologic Model for Drought Management , 1981 .

[29]  Jery R. Stedinger,et al.  Synthetic streamflow generation: 1. Model verification and validation , 1982 .

[30]  Majid Mirzaei,et al.  Application of the generalized likelihood uncertainty estimation (GLUE) approach for assessing uncertainty in hydrological models: a review , 2015, Stochastic Environmental Research and Risk Assessment.

[31]  Yuqiong Liu,et al.  Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework , 2007 .

[32]  Soroosh Sorooshian,et al.  General Review of Rainfall-Runoff Modeling: Model Calibration, Data Assimilation, and Uncertainty Analysis , 2009 .

[33]  Julie E. Kiang,et al.  A comparison of methods to predict historical daily streamflow time series in the southeastern United States , 2015 .

[34]  Dmitri Kavetski,et al.  A unified approach for process‐based hydrologic modeling: 2. Model implementation and case studies , 2015 .

[35]  A rainfall-runoff modeling procedure for improving estimates of T-year (annual) floods for small drainage basins , 1978 .

[36]  Demetris Koutsoyiannis,et al.  A blueprint for process‐based modeling of uncertain hydrological systems , 2012 .

[37]  Jery R. Stedinger,et al.  Appraisal of the generalized likelihood uncertainty estimation (GLUE) method , 2008 .

[38]  J. Vrugt,et al.  A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors , 2010 .

[39]  Robert M. Hirsch,et al.  An evaluation of some record reconstruction techniques , 1979 .

[40]  David,et al.  [Wiley Series in Probability and Statistics] Order Statistics (David/Order Statistics) || Basic Distribution Theory , 2003 .

[41]  M. Clark,et al.  The Schaake Shuffle: A Method for Reconstructing Space–Time Variability in Forecasted Precipitation and Temperature Fields , 2004 .