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Physically based hydrologic modeling: 2. Is the concept realistic?
Author(s) -
Grayson Rodger B.,
Moore Ian D.,
McMahon Thomas A.
Publication year - 1992
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/92wr01259
Subject(s) - computer science , process (computing) , hydrological modelling , field (mathematics) , a priori and a posteriori , publication , scale (ratio) , data science , simulation modeling , management science , industrial engineering , data mining , operations research , mathematics , engineering , climatology , geology , philosophy , physics , mathematical economics , epistemology , quantum mechanics , advertising , pure mathematics , business , operating system
Future directions for physically based, distributed‐parameter models intended for use as hydrologic components of sediment and nutrient transport models are discussed. The attraction of these models is their potential to provide information about the flow characteristics at points within catchments, but current representations in process‐based models are often too crude to enable accurate, a priori application to predictive problems. The difficulties relate to both the perception of model capabilities and the fundamental assumptions and algorithms used in the models. In addition, the scale of measurement for many parameters is often not compatible with their use in hydrologic models. The most appropriate uses of process‐based, distributed‐parameter models are to assist in the analysis of data, to test hypotheses in conjunction with field studies, to improve our understanding of processes and their interactions and to identify areas of poor understanding in our process descriptions. The misperception that model complexity is positively correlated with confidence in the results is exacerbated by the lack of full and frank discussion of a model's capability /limitations and reticence to publish poor results. This may ultimately diminish the opportunity to advance understanding of natural processes because the managers of research resources are given the impression that the answers are already known and are being provided by models. Model development is often not carried out in conjunction with field programs designed to test complex models, so the link with reality is lost.