Modeling of groundwater heads based on second-order difference time series models

Ahn, Hosung

doi:10.1016/s0022-1694(00)00242-0

Cited by 32 publications

(15 citation statements)

References 9 publications

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“…A range of techniques can be used to model groundwater hydrographs at a site, i.e. non-distributed groundwater models, including statistical models (Ahn, 2000;Bloomfield et al, 2003), artificial neural network models (Sreekanth et al, 2009) and "blackbox" models (Mackay et al, 2014). The hydrograph cluster analysis could be used in combination with any of these techniques for groundwater drought forecasting.…”

Section: Discussionmentioning

confidence: 99%

Regional analysis of groundwater droughts using hydrograph classification

Bloomfield

Marchant

Bricker

et al. 2015

Hydrol. Earth Syst. Sci.

113

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Abstract. Groundwater drought is a spatially and temporally variable phenomenon. Here we describe the development of a method to regionally analyse and quantify groundwater drought. The method uses a cluster analysis technique (nonhierarchical k-means) to classify standardised groundwater level hydrographs (the standardised groundwater level index, SGI) prior to analysis of their groundwater drought characteristics, and has been tested using 74 groundwater level time series from Lincolnshire, UK. Using the test data set, six clusters of hydrographs have been identified. For each cluster a correlation can be established between the mean SGI and a mean standardised precipitation index (SPI), where each cluster is associated with a different SPI accumulation period. Based on a comparison of SPI time series for each cluster and for the study area as a whole, it is inferred that the clusters are independent of the driving meteorology and are primarily a function of catchment and hydrogeological factors. This inference is supported by the observation that the majority of sites in each cluster are associated with one of the principal aquifers in the study region. The groundwater drought characteristics of the three largest clusters, which constitute ∼ 80 % of the sites, have been analysed. There are differences in the distributions of drought duration, magnitude and intensity of groundwater drought events between the three clusters as a function of autocorrelation of the mean SGI time series for each cluster. In addition, there are differences between the clusters in their response to three major multi-annual droughts that occurred during the analysis period. For example, sites in the cluster with the longest SGI autocorrelation experience the greatest-magnitude droughts and are the slowest to recover from major droughts, with groundwater drought conditions typically persisting at least 6 months longer than at sites in the other clusters. Membership of the clusters is shown to be related to unsaturated zone thickness at individual boreholes. This last observation emphasises the importance of catchment and aquifer characteristics as (non-trivial) controls on groundwater drought hydrographs. The method of analysis is flexible and can be adapted to a wide range of hydrogeological settings while enabling a consistent approach to the quantification of regional differences in response of groundwater to meteorological drought.

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Section: Discussionmentioning

confidence: 99%

Regional analysis of groundwater droughts using hydrograph classification

Bloomfield

Marchant

Bricker

et al. 2015

Hydrol. Earth Syst. Sci.

113

View full text Add to dashboard Cite

show abstract

“…The model utilized in their study was a first-order difference ARIMA model. However, some groundwater head data may also be fitted adequately by a second-order difference time series model [37]. Five-time series models were applied: autoregressive (AR), moving average (MA), auto-regressive moving-average (ARMA), autoregressive integrated moving-average (ARIMA) and seasonal auto-regressive integrated moving-average (SARIMA).…”

Section: Linkages Between Climate Variability and Water Resourcesmentioning

confidence: 99%

The Influence of Climate Variability Effects on Groundwater Time Series in the Lower Central Plains of Thailand

Taweesin

Seeboonruang

Saraphirom

2018

Water

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Abstract:This research studies the relationship between the climate index and the groundwater level of the lower Chao Phraya basin, in order to forecast the groundwater level in the studied area by using Autoregressive Integrated Moving Average with Explanatory (ARIMAX). The combination of 6 climate indices-Dipole Mode Index, Indian Summer Monsoon Index, Multivariate ENSO Index, Sea Surface Temperature NINO4, Southern Oscillation Index and the Western North Pacific Monsoon Index-were used, along with the groundwater level data from 14 stations during the period 1980-2011 to develop the forecast model and verify it with the data of 2012.The first step was correlation of the ARIMA model with Autocorrelation Function and Partial Autocorrelation Function. The possible model was then selected using BIC statistics. Diagnostic Checking was done to consider the white noise characteristic of estimated residuals by using the statistics of Box and Ljung (Q-statistic). If the selected models were found to be proper, then the Granger Causality Test of the leading parameters or the climate index would be performed as the next step. The results show that there is a relationship between the groundwater level and the climate index. The model could be used to forecast effectively the average RMSE value at 0.6. The last step was to develop the MODFLOW for a conceptual model and synthesize groundwater levels in the study area, which covers around 43,000 km 2 and has 8 layers of groundwater, with Bangkok clay on the top. All other boundary values were set to be steady. The calibration was done using the data of 325 observed wells. The normalized RMS value was 9.705%. The results were verified by the data using ARIMAX over the same time periods. To conclude, the simulated results of the monthly groundwater level in 2012 of the wells have a confidence interval of around 95%, which is near the result from the ARIMAX model. The advantages of the ARIMAX model include high accuracy, no requirement for a large amount of data and inexpensive implementation. It is one of the effective tools for the groundwater prediction.

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“…Ahn and Salas 1997;Ahn 2000;Bidwell 2005;Adamowski and Chan 2011;Shirmohammadi et al 2013). Other studies proposed a multiple linear regression (MLR) model for groundwater level forecasting (e.g.…”

Section: Introductionmentioning

confidence: 99%

Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models

et al. 2014

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Several groundwater-level forecasting studies have shown that data-driven models are simpler, faster to develop, and provide more accurate and precise results than physical or numerical-based models. Five data-driven models were examined for the forecasting of groundwater levels as a result of recharge via tailings from an abandoned mine in Quebec, Canada, for lead times of 1 day, 1 week and 1 month. The five models are: a multiple linear regression (MLR); an artificial neural network (ANN); two models that are based on de-noising the model predictors using the wavelet-transform (W-MLR, W-ANN); and a W-ensemble ANN (W-ENN) model. The tailing recharge, total precipitation, and mean air temperature were used as predictors. The ANN models performed better than the MLR models, and both MLR and ANN models performed significantly better after denoising the predictors using wavelet-transforms. Overall, the W-ENN model performed best for each of the three lead times. These results highlight the ability of wavelettransforms to decompose non-stationary data into discrete wavelet-components, highlighting cyclic patterns and trends in the time-series at varying temporal scales, rendering the data readily usable in forecasting. The good performance of the W-ENN model highlights the usefulness of ensemble modeling, which ensures model robustness along with improved reliability by reducing variance. a Original time series b Approximation component at the seventh decomposition level c Approximation component at the ninth decomposition level d Based on the approximation component at the sixth decomposition level 132

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Modeling of groundwater heads based on second-order difference time series models

Cited by 32 publications

References 9 publications

Regional analysis of groundwater droughts using hydrograph classification

Regional analysis of groundwater droughts using hydrograph classification

The Influence of Climate Variability Effects on Groundwater Time Series in the Lower Central Plains of Thailand

Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models

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