The quest for more powerful validation of conceptual catchment models

The power of a validation strategy (that is, its ability to discriminate between good and bad model hypotheses) depends on what kind of data are available and how the data are used to challenge the hypothesis. Several validation strategies are examined from the perspective of power and practical applicability. It is argued that validation using multiresponse data in a catchment experiencing a shift in hydrologie regime due to disturbance or extreme climatic inputs is a considerably more powerful strategy than traditional split-sample testing using streamflow data alone in undisturbed catchments. A case study testing two model hypotheses is presented using paired catchments for which multiple-response data in the form of streamflow, stream chloride, and groundwater levels were available. The first catchment, Salmon, was maintained as an established forest, while the second, Wights, was clear-felled and converted to pasture about 3 years after monitoring started. The hypotheses consider the same lumped hydrosalinity model with the first (H1) excluding a groundwater discharge zone and the second (H2) including it. It was found that even with three concurrent responses from the undisturbed Salmon catchment, H1 could not be rejected, leaving an important part of the model conceptualization unidentified. Moreover, a streamflow split-sample test for the disturbed Wights catchment failed to conclusively reject H1; parameters could be found which accurately tracked the streamflow changes following forest clearing yet produced erroneous simulations of responses such as stream chloride and groundwater storage. It was only when H1 was subjected to the scrutiny of three catchment responses from the disturbed Wights catchment that it could be rejected. This highlights the importance of challenging model hypotheses under the most demanding of tests, which, in this study, coincided with multiple-response validation in a disturbed catchment.

[1]  Nils Christophersen,et al.  Towards developing a new short-term model for the Birkenes catchment ― lessons learned , 1990 .

[2]  G. Kuczera.  Improved parameter inference in catchment models: 1. Evaluating parameter uncertainty , 1983 .

[3]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[4]  P. R. Johnston,et al.  Parameter optimization for watershed models , 1976 .

[5]  G. R. Hookey,et al.  Prediction of delays in groundwater response to catchment clearing , 1987 .

[6]  George Kuczera,et al.  Modelling yield changes after strip thinning in a mountain ash catchment: an exercise in catchment model validation , 1993 .

[7]  J. Refsgaard,et al.  Operational Validation and Intercomparison of Different Types of Hydrological Models , 1996 .

[8]  R. T. Clarke,et al.  The use of likelihood functions to fit conceptual models with more than one dependent variable , 1976 .

[9]  B. Ambroise,et al.  Multicriterion Validation of a Semidistributed Conceptual Model of the Water Cycle in the Fecht Catchment (Vosges Massif, France) , 1995 .

[10]  G. Kuczera.  Efficient subspace probabilistic parameter optimization for catchment models , 1997 .

[11]  Karel J. Keesman,et al.  Uncertainty propagation and speculation in projective forecasts of environmental change - a lake eutrophication example. , 1991 .

[12]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[13]  Richard P. Hooper,et al.  Assessing the Birkenes Model of stream acidification using a multisignal calibration methodology , 1988 .

[14]  V. Klemeš,et al.  Operational Testing of Hydrological Simulation Models , 2022 .

[15]  M. Kendall,et al.  The Logic of Scientific Discovery. , 1959 .

[16]  William M. Alley,et al.  On the Treatment of Evapotranspiration, Soil Moisture Accounting, and Aquifer Recharge in Monthly Water Balance Models , 1984 .

[17]  A. Jakeman,et al.  How much complexity is warranted in a rainfall‐runoff model? , 1993 .

[18]  Duncan K. Macpherson,et al.  Mechanisms Affecting Streamflow and Stream Water Quality: An Approach via Stable Isotope, Hydrogeochemical,and Time Series Analysis , 1990 .

[19]  Thomas A. McMahon,et al.  Simple Nonlinear Model for Flood Estimation , 1974 .

[20]  D. R. Williamson,et al.  Response of input and output of water and chloride to clearing for agriculture , 1987 .

[21]  Richard P. Hooper,et al.  A multisignal automatic calibration methodology for hydrochemical models: A case study of the Birkenes Model , 1988 .

[22]  Michael J. Brown.  Use of Stream Chemistry to Estimate Hydrologic Parameters , 1986 .

[23]  Chin-Fu Tsang,et al.  The Modeling Process and Model Validation , 1991 .

[24]  P. Flavelle,et al.  A quantitative measure of model validation and its potential use for regulatory purposes , 1992 .

[25]  N. Schofield,et al.  Analysis of streamflow generation following deforestation in southwest Western Australia , 1989 .

[26]  James C. I. Dooge,et al.  Looking for hydrologic laws , 1986 .