Groundwater level trend analysis using the statistical auto-regressive HARTT method
In this study, the Hydrograph Analysis: Rainfall and Time-Trends (HARTT) model was used to determine the contribution of climatic and non-climatic stresses on groundwater levels in the Lake Haramaya well-field, Ethiopia. Monthly precipitation and monitored water-level data were used as explanatory v...
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| Veröffentlicht in: | Hydrological Research Letters 2020, Vol.14(1), pp.17-22 |
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| creator | Zeru, Guesh Alamirew, Tena Shishaye, Haile A. Olmana, Megersa Tadesse, Nata Reading, Michael J. |
| description | In this study, the Hydrograph Analysis: Rainfall and Time-Trends (HARTT) model was used to determine the contribution of climatic and non-climatic stresses on groundwater levels in the Lake Haramaya well-field, Ethiopia. Monthly precipitation and monitored water-level data were used as explanatory variables of the method. Variability in rainfall explained 81.3% of groundwater levels using 2-month average time-delay. The coefficient of the impact of rainfall on groundwater level (K1) was found to be 0.00562 ± 0.0007 mm. This K1 value indicates that a 1 mm increase in rainfall from the annual average rainfall raises the groundwater-level by 0.00562 ± 0.0007 mm, while 1 mm decrease in rainfall causes a 0.00562 ± 0.0007 mm drop in groundwater-level in the area. However, the average falling trend of the groundwater level (K2) was 1.51 ± 0.133 m/year, even with rainfall causing water-levels to rise between 1.01 to 3.29 m/year. With decreased rainfall, rainfall accounted for about 19.5% of the total-drawdown, while 80.5% was due to cumulative effects of non-climatic variables. This shows that rainfall inputs are negated by cumulative non-climatic stresses leading to the long-term net decline in groundwater level. Projected water-level results show that groundwater levels will be below pumping positions in |
| doi_str_mv | 10.3178/hrl.14.17 |
| format | Article |
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Monthly precipitation and monitored water-level data were used as explanatory variables of the method. Variability in rainfall explained 81.3% of groundwater levels using 2-month average time-delay. The coefficient of the impact of rainfall on groundwater level (K1) was found to be 0.00562 ± 0.0007 mm. This K1 value indicates that a 1 mm increase in rainfall from the annual average rainfall raises the groundwater-level by 0.00562 ± 0.0007 mm, while 1 mm decrease in rainfall causes a 0.00562 ± 0.0007 mm drop in groundwater-level in the area. However, the average falling trend of the groundwater level (K2) was 1.51 ± 0.133 m/year, even with rainfall causing water-levels to rise between 1.01 to 3.29 m/year. With decreased rainfall, rainfall accounted for about 19.5% of the total-drawdown, while 80.5% was due to cumulative effects of non-climatic variables. This shows that rainfall inputs are negated by cumulative non-climatic stresses leading to the long-term net decline in groundwater level. Projected water-level results show that groundwater levels will be below pumping positions in <24 years which may have dire consequences for local landowners.</description><identifier>ISSN: 1882-3416</identifier><identifier>EISSN: 1882-3416</identifier><identifier>DOI: 10.3178/hrl.14.17</identifier><language>eng</language><publisher>Tokyo: Japan Society of Hydrology and Water Resources (JSHWR) / Japanese Association of Groundwater Hydrology (JAGH) / Japanese Association of Hydrological Sciences (JAHS) / Japanese Society of Physical Hydrology (JSPH)</publisher><subject>Annual rainfall ; Climate change ; Climate effects ; Drawdown ; Groundwater ; Groundwater levels ; Hydrograph analysis ; Hydrologic data ; Lakes ; Monthly precipitation ; Rain ; Rainfall impact ; Regression analysis ; Statistical analysis ; Stresses ; Trend analysis ; Trends ; Water levels ; Water table</subject><ispartof>Hydrological Research Letters, 2020, Vol.14(1), pp.17-22</ispartof><rights>2020 The Author(s) CC-BY 4.0 (Before 2017: Copyright © Japan Society of Hydrology and Water Resources)</rights><rights>Copyright Japan Science and Technology Agency 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c512t-ea3b45698b379b3eab628e84ccf673942c094716b46851011577aa81e6a6c1073</citedby><cites>FETCH-LOGICAL-c512t-ea3b45698b379b3eab628e84ccf673942c094716b46851011577aa81e6a6c1073</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,1877,4010,27900,27901,27902</link.rule.ids></links><search><creatorcontrib>Zeru, Guesh</creatorcontrib><creatorcontrib>Alamirew, Tena</creatorcontrib><creatorcontrib>Shishaye, Haile A.</creatorcontrib><creatorcontrib>Olmana, Megersa</creatorcontrib><creatorcontrib>Tadesse, Nata</creatorcontrib><creatorcontrib>Reading, Michael J.</creatorcontrib><title>Groundwater level trend analysis using the statistical auto-regressive HARTT method</title><title>Hydrological Research Letters</title><addtitle>Hydrological Research Letters</addtitle><description>In this study, the Hydrograph Analysis: Rainfall and Time-Trends (HARTT) model was used to determine the contribution of climatic and non-climatic stresses on groundwater levels in the Lake Haramaya well-field, Ethiopia. Monthly precipitation and monitored water-level data were used as explanatory variables of the method. Variability in rainfall explained 81.3% of groundwater levels using 2-month average time-delay. The coefficient of the impact of rainfall on groundwater level (K1) was found to be 0.00562 ± 0.0007 mm. This K1 value indicates that a 1 mm increase in rainfall from the annual average rainfall raises the groundwater-level by 0.00562 ± 0.0007 mm, while 1 mm decrease in rainfall causes a 0.00562 ± 0.0007 mm drop in groundwater-level in the area. However, the average falling trend of the groundwater level (K2) was 1.51 ± 0.133 m/year, even with rainfall causing water-levels to rise between 1.01 to 3.29 m/year. With decreased rainfall, rainfall accounted for about 19.5% of the total-drawdown, while 80.5% was due to cumulative effects of non-climatic variables. This shows that rainfall inputs are negated by cumulative non-climatic stresses leading to the long-term net decline in groundwater level. Projected water-level results show that groundwater levels will be below pumping positions in <24 years which may have dire consequences for local landowners.</description><subject>Annual rainfall</subject><subject>Climate change</subject><subject>Climate effects</subject><subject>Drawdown</subject><subject>Groundwater</subject><subject>Groundwater levels</subject><subject>Hydrograph analysis</subject><subject>Hydrologic data</subject><subject>Lakes</subject><subject>Monthly precipitation</subject><subject>Rain</subject><subject>Rainfall impact</subject><subject>Regression analysis</subject><subject>Statistical analysis</subject><subject>Stresses</subject><subject>Trend analysis</subject><subject>Trends</subject><subject>Water levels</subject><subject>Water table</subject><issn>1882-3416</issn><issn>1882-3416</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNpNkFFLwzAUhYMoOKcP_oOATz609jZpkoIvY-gmDASdzyHN7taOrp1JOtm_t6MyfDqXw8fhnkPIPSQxA6meSlfHwGOQF2QESqUR4yAu_93X5Mb7bZIIladsRD5nru2a1Y8J6GiNB6xpcNisqGlMffSVp52vmg0NJVIfTKh8qKypqelCGzncOPS-OiCdTz6WS7rDULarW3K1NrXHuz8dk6_Xl-V0Hi3eZ2_TySKyGaQhQsMKnolcFUzmBUNTiFSh4tauhWQ5T22Scwmi4EJlkABkUhqjAIURFhLJxuRhyN279rtDH_S27Vz_t9cp47mCPOWipx4HyrrWe4drvXfVzrijhkSfNtP9Zhq4hlPi88Bu-64bPJPG9a1rPJMDfrZtaZzGhv0CJZ11AQ</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Zeru, Guesh</creator><creator>Alamirew, Tena</creator><creator>Shishaye, Haile A.</creator><creator>Olmana, Megersa</creator><creator>Tadesse, Nata</creator><creator>Reading, Michael J.</creator><general>Japan Society of Hydrology and Water Resources (JSHWR) / Japanese Association of Groundwater Hydrology (JAGH) / Japanese Association of Hydrological Sciences (JAHS) / Japanese Society of Physical Hydrology (JSPH)</general><general>Japan Science and Technology Agency</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope></search><sort><creationdate>2020</creationdate><title>Groundwater level trend analysis using the statistical auto-regressive HARTT method</title><author>Zeru, Guesh ; 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Monthly precipitation and monitored water-level data were used as explanatory variables of the method. Variability in rainfall explained 81.3% of groundwater levels using 2-month average time-delay. The coefficient of the impact of rainfall on groundwater level (K1) was found to be 0.00562 ± 0.0007 mm. This K1 value indicates that a 1 mm increase in rainfall from the annual average rainfall raises the groundwater-level by 0.00562 ± 0.0007 mm, while 1 mm decrease in rainfall causes a 0.00562 ± 0.0007 mm drop in groundwater-level in the area. However, the average falling trend of the groundwater level (K2) was 1.51 ± 0.133 m/year, even with rainfall causing water-levels to rise between 1.01 to 3.29 m/year. With decreased rainfall, rainfall accounted for about 19.5% of the total-drawdown, while 80.5% was due to cumulative effects of non-climatic variables. 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| subjects | Annual rainfall Climate change Climate effects Drawdown Groundwater Groundwater levels Hydrograph analysis Hydrologic data Lakes Monthly precipitation Rain Rainfall impact Regression analysis Statistical analysis Stresses Trend analysis Trends Water levels Water table |
| title | Groundwater level trend analysis using the statistical auto-regressive HARTT method |
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