Groundwater level data for Watershed-9 (W-9) in the Sleepers River Research Watershed (Vermont)

2018 ◽  
Author(s):  
James B. Shanley ◽  
Stephen D. Sebestyen ◽  
Thor E. Smith ◽  
Ann T. Chalmers ◽  
Stew F. Clark ◽  
...  
Keyword(s):  
Groundwater Level ◽  
Level Data

scholarly journals A novel deseasonalized time series model with an improved seasonal estimate for groundwater level predictions

2019 ◽  
Vol 2 (1) ◽  
pp. 25-44 ◽  
Author(s):  
S. Mohanasundaram ◽  
G. Suresh Kumar ◽  
Balaji Narasimhan
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Moving Average ◽  
Seasonal Component ◽  
Univariate Time Series ◽  
Level Data ◽  
Proposed Model ◽  
Interval Approach ◽  
Prediction And Forecasting ◽  
Observation Wells

Abstract Groundwater level prediction and forecasting using univariate time series models are useful for effective groundwater management under data limiting conditions. The seasonal autoregressive integrated moving average (SARIMA) models are widely used for modeling groundwater level data as the groundwater level signals possess the seasonality pattern. Alternatively, deseasonalized autoregressive and moving average models (Ds-ARMA) can be modeled with deseasonalized groundwater level signals in which the seasonal component is estimated and removed from the raw groundwater level signals. The seasonal component is traditionally estimated by calculating long-term averaging values of the corresponding months in the year. This traditional way of estimating seasonal component may not be appropriate for non-stationary groundwater level signals. Thus, in this study, an improved way of estimating the seasonal component by adopting a 13-month moving average trend and corresponding confidence interval approach has been attempted. To test the proposed approach, two representative observation wells from Adyar basin, India were modeled by both traditional and proposed methods. It was observed from this study that the proposed model prediction performance was better than the traditional model's performance with R2 values of 0.82 and 0.93 for the corresponding wells' groundwater level data.

scholarly journals Earthquake prediction from high frequency groundwater level data: A case study from Gujarat, India

HydroResearch ◽  
2020 ◽  
Vol 3 ◽  
pp. 118-123
Author(s):  
M. Senthilkumar ◽  
D. Gnanasundar ◽  
B. Mohapatra ◽  
A.K. Jain ◽  
Anoop Nagar ◽  
...  
Keyword(s):  
Earthquake Prediction ◽  
Groundwater Level ◽  
High Frequency ◽  
Level Data

Preliminary identification of groundwater drought events in unconfined aquifer with standardized drought indices in single multilevel groundwater station

2020 ◽  
Author(s):  
Jānis Bikše ◽  
Andis Kalvāns ◽  
Inga Retike ◽  
Alise Babre ◽  
Konrāds Popovs ◽  
...  
Keyword(s):  
Climate Change ◽  
Groundwater Level ◽  
Monitoring Station ◽  
Data Set ◽  
Temporal Prediction ◽  
Level Data ◽  
Small Streams ◽  
Groundwater Drought ◽  
Standardised Precipitation Index ◽  
Groundwater Wells

<p>More severe and frequent drought events are one of the challenges faced worldwide in the context of climate change. There are multiple anecdotal evidence of dug wells and small streams running dry during  drought events in years 2015 and 2018 in Latvia. However, no comprehensive research has been made to assess groundwater drought and its ecological and socioeconomic impacts in Latvia and wider Baltic region. More intensive irrigation can further exaggerate the groundwater drought problem in the future. </p><p>We aim to analyse past drought events from meteorological and groundwater drought perspective. Groundwater drought development and propagation is complex, however, we try to find the best simple predictors that can be used for evaluating purposes. We examine groundwater level data set from “Dricani” monitoring station with 14 groundwater wells uncovering unconfined heterogenous quaternary aquifer with well depths ranging from 2.5 to 15 m and monthly data records starting from 1970.-ies. Such a high number of wells in a single monitoring station permit detailed groundwater level analysis with a focus on local scale disturbances and groundwater drought propagation that could be caused by heterogeneous sediments in the aquifer, terrain and other drivers. </p><p>We us “Dricani” groundwater level data series to calculate Standardized groundwater level index (SGI) (Bloomfield, Marchant 2013) revealing several major groundwater drought events during the last 50 years. Although largest groundwater drought events shows similar pattern within all the wells, minor changes in SGI can be identified that can be attributed to different depths of groundwater wells. </p><p>The study is supported by fundamental and applied science research programme, project No. lzp-2019/1-0165 “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”.</p><p>References</p><p>Bloomfield JP, Marchant BP. 2013. Analysis of groundwater drought building on the standardised precipitation index approach. Hydrology and Earth System Sciences 17 (12): 4769–4787 DOI: 10.5194/hess-17-4769-2013</p>

scholarly journals Spatiotemporal Variation of the Meteorological and Groundwater Droughts in Central Taiwan

2021 ◽  
Vol 3 ◽  
Author(s):  
Hsin-Fu Yeh
Keyword(s):  
River Basin ◽  
Groundwater Level ◽  
Standardized Precipitation Index ◽  
Extreme Drought ◽  
Water Shortages ◽  
Level Data ◽  
Groundwater Drought ◽  
Central Taiwan ◽  
The Standardized Precipitation Index

In recent years, Taiwan has been facing severe water shortages due to extreme drought. In addition, changes in rainfall patterns have resulted in an increasingly notable drought phenomenon, which affects the management and utilization of water resources. Therefore, this work examines basins in Central Taiwan. Long-term records from 13 rainfall and 17 groundwater stations were selected. The Standardized Precipitation Index (SPI) and Standardized Groundwater Level Index (SGI) were used to analyze the drought characteristics of this region. The rainfall and groundwater level data from basins in Central Taiwan were analyzed in this study. The results show that the year 2015 experienced extreme drought conditions due to a correlation with SPI and SGI signals. In addition, with regard to groundwater drought, more drought events occurred in the Da'an River basin; however, the duration and intensity of these events were relatively low, in contrast to those of the Wu River basin. Finally, the correlation between SPI and SGI was observed to vary in different basins, but a certain degree of correlation was observed in all basins. The results show that drought intensity increases with longer drought durations. Moreover, severe droughts caused by rainfall tend to occur at a greater frequency than those caused by groundwater.

scholarly journals Spatial-temporal variation of groundwater and land subsidence evolution in Beijing area

2015 ◽  
Vol 372 ◽  
pp. 7-11
Author(s):  
K. Lei ◽  
Y. Luo ◽  
B. Chen ◽  
M. Guo ◽  
G. Guo ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Land Subsidence ◽  
Groundwater Level ◽  
Meteorological Data ◽  
Rapid Expansion ◽  
Persistent Scatterer Interferometry ◽  
Systematic Analysis ◽  
Groundwater Levels ◽  
Level Data ◽  
Regional Land

Abstract. Precipitation is the main recharge source of groundwater in the plain of Beijing, China. Rapid expansion of urbanization has resulted in increased built-up area and decreased amount of effective recharge of precipitation to groundwater, indirectly leading to the long-term over-exploitation of groundwater, and induced regional land subsidence. Based on the combination of meteorological data, groundwater level data, interferometric synthetic aperture radar (InSAR; specifically persistent scatterer interferometry, PSI), geographic information system (GIS) spatial analysis method and rainfall recharge theory, this paper presents a systematic analysis of spatial-temporal variation of groundwater level and land subsidence evolution. Results show that rainfall has been decreasing annually, while the exploitation of groundwater is increasing and the groundwater level is declining, which is has caused the formation and evolution of land subsidence. Seasonal and interannual variations exist in the evolution of land subsidence; the subsidence is uneven in both spatial and temporal distribution. In 2011, at the center of mapped subsidence the subsidence rate was greater than 120 mm a−1. The results revealed good correlation between the spatial distribution of groundwater level declines and subsidence. The research results show that it is beneficial to measure the evolution of land subsidence to dynamic variations of groundwater levels by combining InSAR or PSI, groundwater-level data, and GIS. This apprpach provides improved information for environmental and hydrogeologic research and a scientific basis for regional land subsidence control.

scholarly journals Characterizing groundwater and surface-water interaction using hydrograph-separation techniques and groundwater-level data throughout the Mississippi Delta, USA

2019 ◽  
Vol 27 (6) ◽  
pp. 2167-2179 ◽  
Author(s):  
Courtney D. Killian ◽  
William H. Asquith ◽  
Jeannie R. B. Barlow ◽  
Gardner C. Bent ◽  
Wade H. Kress ◽  
...  
Keyword(s):  
Surface Water ◽  
Groundwater Level ◽  
Mississippi Delta ◽  
Hydrograph Separation ◽  
Separation Techniques ◽  
Water Interaction ◽  
Level Data

scholarly journals Analysis of groundwater drought building on the standardised precipitation index approach

2013 ◽  
Vol 17 (12) ◽  
pp. 4769-4787 ◽  
Author(s):  
J. P. Bloomfield ◽  
B. P. Marchant
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Precipitation Index ◽  
Monitoring Networks ◽  
Level Data ◽  
Index Approach ◽  
Groundwater Drought ◽  
Standardised Precipitation Index ◽  
Positive Linear Correlation ◽  
And Storage

Abstract. A new index for standardising groundwater level time series and characterising groundwater droughts, the Standardised Groundwater level Index (SGI), is described. The SGI builds on the Standardised Precipitation Index (SPI) to account for differences in the form and characteristics of groundwater level and precipitation time series. The SGI is estimated using a non-parametric normal scores transform of groundwater level data for each calendar month. These monthly estimates are then merged to form a continuous index. The SGI has been calculated for 14 relatively long, up to 103 yr, groundwater level hydrographs from a variety of aquifers and compared with SPI for the same sites. The relationship between SGI and SPI is site specific and the SPI accumulation period which leads to the strongest correlation between SGI and SPI, qmax, varies between sites. However, there is a consistent positive linear correlation between a measure of the range of significant autocorrelation in the SGI series, mmax, and qmax across all sites. Given this correlation between SGI mmax and SPI qmax, and given that periods of low values of SGI can be shown to coincide with previously independently documented droughts, SGI is taken to be a robust and meaningful index of groundwater drought. The maximum length of groundwater droughts defined by SGI is an increasing function of mmax, meaning that relatively long groundwater droughts are generally more prevalent at sites where SGI has a relatively long autocorrelation range. Based on correlations between mmax, average unsaturated zone thickness and aquifer hydraulic diffusivity, the source of autocorrelation in SGI is inferred to be dependent on dominant aquifer flow and storage characteristics. For fractured aquifers, such as the Cretaceous Chalk, autocorrelation in SGI is inferred to be primarily related to autocorrelation in the recharge time series, while in granular aquifers, such as the Permo–Triassic sandstones, autocorrelation in SGI is inferred to be primarily a function of intrinsic saturated flow and storage properties of aquifer. These results highlight the need to take into account the hydrogeological context of groundwater monitoring sites when designing and interpreting data from groundwater drought monitoring networks.

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