Premium
Automating Recession Curve Displacement Recharge Estimation
Author(s) -
Smith Brennan,
Schwartz Stuart
Publication year - 2016
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/gwat.12439
Subject(s) - groundwater recharge , recession , displacement (psychology) , estimation , geodesy , geology , computer science , economics , environmental science , keynesian economics , geotechnical engineering , aquifer , groundwater , psychology , management , psychotherapist
Recharge estimation is an important and challenging element of groundwater management and resource sustainability. Many recharge estimation methods have been developed with varying data requirements, applicable to different spatial and temporal scales. The variability and inherent uncertainty in recharge estimation motivates the recommended use of multiple methods to estimate and bound regional recharge estimates. Despite the inherent limitations of using daily gauged streamflow, recession curve displacement methods provide a convenient first‐order estimate as part of a multimethod hierarchical approach to estimate watershed‐scale annual recharge. The implementation of recession curve displacement recharge estimation in the United States Geologic Survey ( USGS ) RORA program relies on the subjective, operator‐specific selection of baseflow recession events to estimate a gauge‐specific recession index. This paper presents a parametric algorithm that objectively automates this tedious, subjective process, parameterizing and automating the implementation of recession curve displacement. Results using the algorithm reproduce regional estimates of groundwater recharge from the USGS Appalachian Valley and Piedmont Regional Aquifer‐System Analysis, with an average absolute error of less than 2%. The algorithm facilitates consistent, completely automated estimation of annual recharge that complements more rigorous data‐intensive techniques for recharge estimation.