Premium
A synthetic hydrologic‐response dataset
Author(s) -
Mirus Benjamin B.,
Loague Keith,
Cristea Nicoleta C.,
Burges Stephen J.,
Kampf Stephanie K.
Publication year - 2011
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.8185
Subject(s) - hydrological modelling , surface runoff , environmental science , computer science , hydrology (agriculture) , forcing (mathematics) , range (aeronautics) , synthetic data , climatology , geology , artificial intelligence , ecology , materials science , geotechnical engineering , composite material , biology
Synthetic data have long been employed in hydrology for model development and testing. The objective of this study was to generate a synthetic dataset of hydrologic response with higher spatial and temporal resolution than could presently be obtained in the field, spanning a longer period than the typical duration of monitoring campaigns in experimental catchments. The synthetic dataset was generated for a rangeland catchment with the Integrated Hydrology Model (InHM), and is presented for future use by the community. The InHM boundary‐value problem is based upon the previously reported hypothetical reality of Tarrawarra‐like hydrologic response. Whereas the emphasis in developing the hypothetical reality was on parameterising InHM to reproduce observations from the Tarrawarra catchment, the emphasis in generating the synthetic dataset is on developing an internally valid hydrologic‐response dataset that extends well beyond the period of observations at Tarrawarra. The synthetic dataset spans 11 years of continuous forcing and response data (e.g. integrated response, distributed fluxes, state variable dynamics). The dataset should be useful for a wide range of problems including evaluation of simple rainfall runoff modelling techniques, design of measurement networks, development of data‐assimilation algorithms, and studies on information theory. The dataset is available at: ftp://pangea.stanford.edu/pub/loague/ . Copyright © 2011 John Wiley & Sons, Ltd.