Automating Recession Curve Displacement Recharge Estimation. Issue 1 (8th July 2016)

• Record Type: Journal Article
• Authors: Smith, Brennan; Schwartz, Stuart
• Is Part Of: Ground water. Volume 55:Issue 1(2017)
• Page Start: 81
• Page End: 87
• Publication Date: 2016-07-08
• Subjects: Groundwater -- Periodicals; Wells -- Periodicals; Eau souterraine -- Périodiques; Puits -- Périodiques; Grondwater; Eau souterraine; Puits; Electronic journals; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 551.49
• DOI: 10.1111/gwat.12439
• Languages: English
• ISSNs: 0017-467X

Abstract

Abstract: 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. Abstract : Article Impact Statement : The technique presented provides consistent, objective recession index estimates for first‐order recharge estimation.