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Dendrohydrogeology can be used to simulate historical groundwater depth, water table drawdown, groundwater recharge and piezometric lines. We simulated paleohydrogeologic conditions via tree-rings and vessel chronologies using an artificial neural network (ANN) in the alluvial aquifer of the Caspian southern coast of Iran during the past century. Tree-ring width, vessel features, secondary piezometric well data, and precipitation from different sites within the study area were evaluated. After cross-dating, standardization and time series analysis, the relationships between tree-rings and vessel chronologies with groundwater depth were defined and simulated. Additionally, paleohydrogeologic records during the past century were simulated. The results generally demonstrate that tree-ring width is a better index than vessel features. However, we obtained the most exact groundwater depth modeling results by using the combination of tree-rings and earlywood vessel diameter from periods of low precipitation and groundwater fluctuations and significant temperature fluctuations. We also found that dendrohydrogeology has more applicability in groundwater modeling in areas where groundwater depth fluctuates 10–20 m below ground surface (based on root depth and water access). Moreover, using the simulated groundwater depths, piezometric lines in 1927 and 2000 (the years with maximum natural recharge and maximum drawdown respectively) were extracted using an interpolation technique and Geographic Information System (GIS). Finally, we suggest applying dendrohydrogeology for paleohydrogeologic modeling in alluvial aquifers. [ABSTRACT FROM AUTHOR]

Summary Groundwater is the most important water resource in semi-arid and arid regions such as Iran. It is necessary to study groundwater level fluctuations to manage disasters (such as droughts) and water resources. Dendrochronology, which uses tree-rings to reconstruct past events such as hydrologic and climatologic events, can be used to evaluate groundwater level fluctuations. In this study, groundwater level fluctuations are simulated using dendrochronology (tree-rings) and an artificial neural network (ANN) for the period from 1912 to 2013. The present study was undertaken using the Quercus Castaneifolia species, which is present in an alluvial aquifer of the Caspian southern coasts, Iran. A multilayer percepetron (MLP) network was adopted for the ANN. Tree-ring diameter and precipitation were the input parameters for the study, and groundwater levels were the outputs. After the training process, the model was validated. The validated network and tree-rings were used to simulate groundwater level fluctuations during the past century. The results showed that an integration of dendrochronology and an ANN renders a high degree of accuracy and efficiency in the simulation of groundwater levels. The simulated groundwater levels by dendrochronology can be used for drought evaluation, drought period prediction and water resources management. [ABSTRACT FROM AUTHOR]