THE QUEST FOR MORE POWERFUL VALIDATION OF CONCEPTUAL CATCHMENT MODELS

The power of a validation strategy (that is, its ability to discrimina te 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 pow er and practical applicability. It is argued that validation using mul tiresponse data in a catchment experiencing a shift in hydrologic regi me due to disturbance or extreme climatic inputs is a considerably mor e powerful strategy than traditional split-sample testing using stream flow data alone in undisturbed catchments. A case study testing two mo del hypotheses is presented using paired catchments for which multiple -response data in the form of streamflow, stream chloride, and groundw ater levels were available. The first catchment, Salmon, was maintaine d as an established forest, while the second, Wights, was cIear-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) inclu ding it. It was found that even with three concurrent responses from t he 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 fail ed to conclusively reject H1; parameters could be found which accurate ly tracked the streamflow changes following forest clearing yet produc ed erroneous simulations of responses such as stream chloride and grou ndwater 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, c oincided with multiple-response validation in a disturbed catchment.