Development of an Apparent Recharge Coefficient (ARC) for Estimating Groundwater Storage Changes Due to Precipitation Events Using Time Series Monitoring Data
Significant variation in the precipitation events caused by global climate change has made it difficult to manage water resources due to the increased frequency of unexpected droughts and floods. Under these conditions, groundwater is needed to ensure a sustainable water supply; thus, estimates of p...
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doaj-c0edbf6072174b5eae25e163bac05e492020-11-25T02:39:55ZengMDPI AGWater2073-44412020-06-01121675167510.3390/w12061675Development of an Apparent Recharge Coefficient (ARC) for Estimating Groundwater Storage Changes Due to Precipitation Events Using Time Series Monitoring DataJae Min Lee0Sunjoo Cho1Hyun A Lee2Nam C. Woo3Groundwater Research Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon 34132, KoreaDepartment of Earth System Sciences, Yonsei University, Seoul 03722, KoreaDepartment of Earth System Sciences, Yonsei University, Seoul 03722, KoreaDepartment of Earth System Sciences, Yonsei University, Seoul 03722, KoreaSignificant variation in the precipitation events caused by global climate change has made it difficult to manage water resources due to the increased frequency of unexpected droughts and floods. Under these conditions, groundwater is needed to ensure a sustainable water supply; thus, estimates of precipitation recharge are essential. In this study, we derived an apparent recharge coefficient (ARC) from a modified water table fluctuation equation to predict groundwater storage changes due to precipitation events. The ARC is calculated as the ratio of the recharge rate over the specific yield (<i>R</i>/<i>S</i><sub>y</sub>); therefore, it implicitly expresses variation in <i>S</i><sub>y</sub>. The ARC varies spatially and temporally, corresponding to the precipitation events and hydrogeological characteristics of unsaturated materials. ARCs for five monitoring wells from two basins in Korea in different seasons were calculated using a 10-year groundwater level and weather dataset for 2005–2014. Then, the reliability of the ARCs was tested by the comparison of the predicted groundwater level changes for 2015 and 2016 with observed data. The root mean square error ranged from 0.03 to 0.09 m, indicating that the predictions were acceptable, except for one well, which had thick clay layers atop the soil layer; the low permeability of the clay slowed the precipitation recharge, interfering with groundwater level responses. We performed a back-calculation of <i>R</i> from the <i>S</i><sub>y</sub> values of the study areas; the results were similar to those obtained via other methods, confirming the practical applicability of the ARC. In conclusion, the ARC is a viable method for predicting groundwater storage changes for regions where long-term monitoring data are available, and subsequently will facilitate advanced decision making for allocating and developing water resources for residents, industry, and groundwater-dependent ecosystems.https://www.mdpi.com/2073-4441/12/6/1675groundwater rechargecumulative precipitationtime-series monitoringwater resources management |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jae Min Lee Sunjoo Cho Hyun A Lee Nam C. Woo |
spellingShingle |
Jae Min Lee Sunjoo Cho Hyun A Lee Nam C. Woo Development of an Apparent Recharge Coefficient (ARC) for Estimating Groundwater Storage Changes Due to Precipitation Events Using Time Series Monitoring Data Water groundwater recharge cumulative precipitation time-series monitoring water resources management |
author_facet |
Jae Min Lee Sunjoo Cho Hyun A Lee Nam C. Woo |
author_sort |
Jae Min Lee |
title |
Development of an Apparent Recharge Coefficient (ARC) for Estimating Groundwater Storage Changes Due to Precipitation Events Using Time Series Monitoring Data |
title_short |
Development of an Apparent Recharge Coefficient (ARC) for Estimating Groundwater Storage Changes Due to Precipitation Events Using Time Series Monitoring Data |
title_full |
Development of an Apparent Recharge Coefficient (ARC) for Estimating Groundwater Storage Changes Due to Precipitation Events Using Time Series Monitoring Data |
title_fullStr |
Development of an Apparent Recharge Coefficient (ARC) for Estimating Groundwater Storage Changes Due to Precipitation Events Using Time Series Monitoring Data |
title_full_unstemmed |
Development of an Apparent Recharge Coefficient (ARC) for Estimating Groundwater Storage Changes Due to Precipitation Events Using Time Series Monitoring Data |
title_sort |
development of an apparent recharge coefficient (arc) for estimating groundwater storage changes due to precipitation events using time series monitoring data |
publisher |
MDPI AG |
series |
Water |
issn |
2073-4441 |
publishDate |
2020-06-01 |
description |
Significant variation in the precipitation events caused by global climate change has made it difficult to manage water resources due to the increased frequency of unexpected droughts and floods. Under these conditions, groundwater is needed to ensure a sustainable water supply; thus, estimates of precipitation recharge are essential. In this study, we derived an apparent recharge coefficient (ARC) from a modified water table fluctuation equation to predict groundwater storage changes due to precipitation events. The ARC is calculated as the ratio of the recharge rate over the specific yield (<i>R</i>/<i>S</i><sub>y</sub>); therefore, it implicitly expresses variation in <i>S</i><sub>y</sub>. The ARC varies spatially and temporally, corresponding to the precipitation events and hydrogeological characteristics of unsaturated materials. ARCs for five monitoring wells from two basins in Korea in different seasons were calculated using a 10-year groundwater level and weather dataset for 2005–2014. Then, the reliability of the ARCs was tested by the comparison of the predicted groundwater level changes for 2015 and 2016 with observed data. The root mean square error ranged from 0.03 to 0.09 m, indicating that the predictions were acceptable, except for one well, which had thick clay layers atop the soil layer; the low permeability of the clay slowed the precipitation recharge, interfering with groundwater level responses. We performed a back-calculation of <i>R</i> from the <i>S</i><sub>y</sub> values of the study areas; the results were similar to those obtained via other methods, confirming the practical applicability of the ARC. In conclusion, the ARC is a viable method for predicting groundwater storage changes for regions where long-term monitoring data are available, and subsequently will facilitate advanced decision making for allocating and developing water resources for residents, industry, and groundwater-dependent ecosystems. |
topic |
groundwater recharge cumulative precipitation time-series monitoring water resources management |
url |
https://www.mdpi.com/2073-4441/12/6/1675 |
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