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Estimation of mountain front recharge to regional aquifers: 2. A Maximum Likelihood Approach incorporating prior information
Author(s) -
Chavez Adolfo,
Sorooshian Soroosh,
Davis Stanley N.
Publication year - 1994
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 - 10.1029/93wr03370
Subject(s) - identifiability , estimation theory , groundwater recharge , weighting , streamflow , mathematics , maximum likelihood , bayesian information criterion , statistics , mathematical optimization , computer science , geology , aquifer , geography , groundwater , geotechnical engineering , medicine , drainage basin , cartography , radiology
In this two‐part series a stochastic estimation procedure applicable to the analytic streamflow model derived in the companion is introduced. The parameter estimation problem is posed in the framework of maximum likelihood theory, where prior information about the model parameters and a suitable weighting scheme for the error terms in the estimation criterion are included. Various optimization methods are combined for parameter estimation. The issues of model and parameter identifiability, uniqueness, and stability are addressed, and strategies to mitigate identifiability problems in our modeling are discussed. The seasonal streamflow model is applied to a mountainous watershed in southern Arizona, and maximum likelihood estimates of mountain front recharge and other model and statistical parameters are obtained. The analysis of estimation errors is performed in both the eigenspace and the original space of the parameters.