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Title: Recognition of Regional Water Table Patterns for Estimating Recharge Rates in Shallow Aquifers: Recognition of Regional Water Table Patterns for Estimating Recharge Rates in Shallow Aquifers
NSF-PAR ID:
10067481
Author(s) / Creator(s):
;  ;  
Publisher / Repository:
Wiley-Blackwell
Date Published:
Journal Name:
Groundwater
ISSN:
0017-467X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Abstract

    Groundwater recharge moves downward from the land surface and reaches the groundwater to replenish aquifers. Despite its importance, methods to directly measure recharge remain cost and time‐intensive. Recharge is usually estimated using indirect methods, such as the widely used water‐table fluctuation (WTF) method, which is based on the premise that rises in groundwater levels are due to recharge. In the WTF method, recharge is calculated as the difference between the observed groundwater hydrograph and the hydrograph obtained in the absence of water input. The hydrograph in the absence of rise‐producing input is estimated based on a characteristic master recession curve (MRC), which describes an average behavior for a declining water‐table. Previous studies derive MRC using recession data from all seasons. We hypothesize that for sites where groundwater table is shallow, using recession data from periods with high groundwater‐influenced evapotranspiration (ET) rates versus all periods will yield significantly different MRC, and consequently different estimates of recharge. We test this hypothesis and show that groundwater recession rates are significantly greater in warm months when the groundwater‐influenced ET rates are higher. Since obtaining seasonal recession rates is challenging for locations with a limited amount of data and is prohibitive if it is to be obtained for any given season of a particular year, we propose two novel parsimonious methods to obtain recession time constants for distinct seasons. The proposed methods show the potential to significantly improve the estimates of seasonal recession time constants and provide a better understanding of seasonal variations in recharge estimates.

     
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