Shallow groundwater affects terrestrial ecosystems by sustaining river base-flow and root-zone soil water in the absence of rain, but little is known about the global patterns of water table depth and where it provides vital support for land ecosystems We present global observations of water table depth compiled from government archives and literature, and fill in data gaps and infer patterns and processes using a groundwater model forced by modern climate, terrain, and sea level Patterns in water table depth explain patterns in wetlands at the global scale and vegetation gradients at regional and local scales Overall, shallow groundwater influences 22 to 32% of global land area, including ~15% as groundwater-fed surface water features and 7 to 17% with the water table or its capillary fringe within plant rooting depths

http://science.sciencemag.org/content/sci/339/6122/940.full.pdf

Global patterns of groundwater table depth.

A wavelet neural network conjunction model for groundwater level forecasting

A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer

http://ira.lib.polyu.edu.hk/bitstream/10397/5259/1/EAAI7.pdf

Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon

A review of the artificial intelligence methods in groundwater level modeling

Forecasting the ground water level fluctuations is an important requirement for planning conjunctive use in any basin. This paper reports a research study that investigates the potential of artificial neural network technique in forecasting the groundwater level fluctuations in an unconfined coastal aquifer in India. The most appropriate set of input variables to the model are selected through a combination of domain knowledge and statistical analysis of the available data series. Several ANN models are developed that forecasts the water level of two observation wells. The results suggest that the model predictions are reasonably accurate as evaluated by various statistical indices. An input sensitivity analysis suggested that exclusion of antecedent values of the water level time series may not help the model to capture the recharge time for the aquifer and may result in poorer performance of the models. In general, the results suggest that the ANN models are able to forecast the water levels up to 4 months in advance reasonably well. Such forecasts may be useful in conjunctive use planning of groundwater and surface water in the coastal areas that help maintain the natural water table gradient to protect seawater intrusion or water logging condition.

/pdf/groundwater-level-forecasting-in-a-shallow-aquifer-using-1nlubdecym.pdf

Y. R. Satyaji Rao

Papers

Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach