The success of an automatic calibration procedure is highly dependent on the choice of the objective function and the nature (quantity and quality) of the data used. The objective function should be selected on the basis of the stochastic properties of the errors present in the data and in the model. Also, the data should be chosen so as to contain as much valuable information about the process as possible. In this paper we compare the performance of two maximum likelihood estimators, the AMLE, which assumes the presence of first lag autocorrelated homogeneous variance errors, and the HMLE, which assumes the presence of uncorrelated inhomogeneous variance errors, to the commonly used simple least squares criterion, SLS. The model calibrated was the soil moisture accounting model of the U.S. National Weather Service's river forecast system (SMA‐NWSRFS). The results indicate that a properly chosen objective function can enhance the possibility of obtaining unique and conceptually realistic parameter estimates. Furthermore, the sensitivity of the estimation results to various characteristics of the calibration data, such as hydrologic variability and length, are substantially reduced.
Water Resources Research – Wiley
Published: Feb 1, 1983
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera