Exploring for the Future—Groundwater level data release for the McBride and Nulla basalt provinces, Upper Burdekin region, North Queensland

2020 ◽  
Author(s):  
S.B. Cook
Keyword(s):  
Groundwater Level ◽  
Level Data ◽  
Data Release ◽  
The Future

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 Weather and Stochastic Forecasting Method for Generated Discharge Level Data at Sathanur Dam

2019 ◽  
Vol 8 (10) ◽  
pp. 3797-3802
Keyword(s):  
Trend Analysis ◽  
Error Estimates ◽  
Data Sets ◽  
Forecasting Models ◽  
Level Data ◽  
Forecasting Method ◽  
The Future ◽  
Current Scenario ◽  
Stochastic Forecasting

This article forecasts the future values using stochastic forecasting models for specified fitted values by using downscaling data, which are collected from Sathanoor Dam gauging site. Due to the demand of the water in this current scenario, this study analyzed the perdays Discharge level data collected from Sathanoor Dam where the outcome is predicted in a downscaling data sets in hydrology, extended Thomas –Fiering, ARIMA, MLE models, is used to estimate perdays discharge level data of each month. The error estimates RMSE, MAE of forecasts from above models is compared to identify the most suitable approaches for forecasting trend analysis.

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

scholarly journals Prediction of astrometric microlensing events from Gaia DR2 proper motions

2018 ◽  
Vol 620 ◽  
pp. A175 ◽  
Author(s):  
J. Klüter ◽  
U. Bastian ◽  
M. Demleitner ◽  
J. Wambsganss
Keyword(s):  
Proper Motion ◽  
Proper Motions ◽  
Stellar Objects ◽  
Gravitational Microlensing ◽  
Data Release ◽  
The Future ◽  
Nested Intervals ◽  
Excellent Tool ◽  
Gaia Dr2

Context. Astrometric gravitational microlensing is an excellent tool to determine the mass of stellar objects. Using precise astrometric measurements of the lensed position of a background source in combination with accurate predictions of the positions of the lens and the unlensed source it is possible to determine the mass of the lens with an accuracy of a few percent. Aims. Making use of the recently published Gaia Data Release 2 (DR2) catalogue, we want to predict astrometric microlensing events caused by foreground stars with high proper motion passing a background source in the coming decades. Results. We selected roughly 148 000 high-proper-motion stars from Gaia DR2 with μtot > 150 mas yr−1 as potential lenses. We then searched for background sources close to their paths. Using the astrometric parameters of Gaia DR2, we calculated the future positions of source and lens. With a nested-intervals algorithm we determined the date and separation of the closest approach. Using Gaia DR2 photometry we determined an approximate mass of the lens, which we used to calculate the expected microlensing effects. Conclusions. We predict 3914 microlensing events caused by 2875 different lenses between 2010 and 2065, with expected shifts larger than 0.1 mas between the lensed and unlensed positions of the source. Of those, 513 events are expected to happen between 2014.5 and 2026.5 and might be measured by Gaia. For 127 events we also expect a magnification between 1 mmag and 3 mag.

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>

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