
Research Spotlight: Computational techniques can yield large errors in hydrological models
Author(s) -
Kumar Mohi,
Tretkoff Ernie
Publication year - 2010
Publication title -
eos, transactions american geophysical union
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.316
H-Index - 86
eISSN - 2324-9250
pISSN - 0096-3941
DOI - 10.1029/eo091i051p00512-01
Subject(s) - conceptualization , computer science , robustness (evolution) , flood myth , hydrological modelling , computational model , management science , focus (optics) , econometrics , operations research , industrial engineering , hydrology (agriculture) , algorithm , mathematics , geology , artificial intelligence , climatology , geography , biochemistry , chemistry , physics , geotechnical engineering , archaeology , engineering , optics , economics , gene
Hydrological models are frequently used in flood forecasting, water resources assessments, and other environmental management projects. They are also useful tools for advancing scientific understanding of hydrological processes. In many applications to date, especially those using conceptual models, data uncertainty and model conceptualization are tacitly assumed to be the main sources of modeling error. However, in a recent review of practical model robustness, Kavetski and Clark focus on errors arising from the computational technique used to approximate the time‐dependent catchment dynamics and on how the lack of numerical error control can affect model behavior. They found that unless careful attention is paid to numerical calculations, troublesome artifacts arise and severely deform the response characteristics of hydrological models.