Reconstruction of springs discharge using tree-rings and earlywood vessel chronologies in an alluvial aquifer

Abstract Monitoring changes in groundwater storage is necessary for water resource management plans. The temporal records of spring discharge are restricted by the absence or paucity in many parts of the world. To address this shortcoming, various models and methods have been implemented to reconstruct past hydrologic data. In this study, temporal variations of spring discharge were reconstructed using tree-rings and earlywood vessel chronologies in an alluvial aquifer on the southern coast of the Caspian sea. The dendrochronological analysis was performed using both cross-sections and core samples of Zelkova carpinifolia (Pall.) K. Koch species, including tree-rings width and vessel features (vessels diameter, area, and perimeter) as proxy data. The distance between the sampling trees varied between 20 and 1000 m. An artificial neural network (ANN) was used to establish a relationship between the temporal dendrochronological records of Z. carpinifolia and corresponding discharge obtained from two alluvial springs in the vicinity of sampling trees. The ANN model was optimized and tested by comparing the recorded and predicted values in the training and testing stage of the modeling process. Results show the high performance of the ANN model in the training (R-squared = 0.98, MSE = 0.009) and test stage (R-squared = 0.81, MSE = 1.26). The results also indicated the higher correlation of springs discharge with vessel features (R = 0.76) compared to tree-rings (R = 0.59). Dendrohydrology works better for those springs with notable discharge changes during the growing seasons. Further, the combination of tree-ring width and vessel features as inputs in modeling will increase the model's performance. The tested model was applied to the dendrochronological records of past decades (1982 to 2001) to reconstruct the spring discharge data during the growing seasons for those years.

[1]  S. St. George,et al.  Vessel anomalies in Quercus macrocarpa tree rings associated with recent floods along the Red River of the North, United States , 2013 .

[2]  F. Hawley.  Relationship of Southern Cedar Growth to Precipitation and Run Off , 1937 .

[3]  V. Gholami,et al.  Soil erosion modeling using erosion pins and artificial neural networks , 2021 .

[4]  J. Sykes,et al.  The impact of climate change on spatially varying groundwater recharge in the grand river watershed (Ontario) , 2007 .

[5]  Shengxia Jiang,et al.  A 475-year tree-ring-width record of streamflow for the Qingshui River originating in the southern slope of the central Tianshan Mountains, China , 2020, Geografiska Annaler: Series A, Physical Geography.

[6]  E. Jobbágy,et al.  Climate and groundwater effects on the establishment, growth and death of Prosopis caldenia trees in the Pampas (Argentina) , 2011 .

[7]  M. Carrer,et al.  Wood anatomical traits in black spruce reveal latent water constraints on the boreal forest , 2019, Global change biology.

[8]  R. Burgy,et al.  The Relationship between oak tree roots and groundwater in fractured rock as determined by tritium tracing , 1964 .

[9]  P. Jones,et al.  Summer Temperature Patterns over Europe: A Reconstruction from 1750 A.D. Based on Maximum Latewood Density Indices of Conifers , 1988, Quaternary Research.

[10]  J. Sperry,et al.  Functional and Ecological Xylem Anatomy , 2015, Cambridge International Law Journal.

[11]  Chen‐Loung Chen,et al.  Formation of Chloro-Organics During Chlorine Bleaching of Softwood Kraft Pulp. Part 1. Chlorination of Lignin Model Compounds , 1994 .

[12]  Evaluating the suitability of nine shelterbelt species for dendrochronological purposes in the Canadian Prairies , 2013, Agroforestry Systems.

[13]  V. Gholami,et al.  Simulation of precipitation time series using tree-rings, earlywood vessel features, and artificial neural network , 2018, Theoretical and Applied Climatology.

[14]  E. Rastetter,et al.  Seasonal variation in net carbon exchange and evapotranspiration in a Brazilian rain forest: a modelling analysis , 1998 .

[15]  Shu-long Yu,et al.  Tree-ring width based streamflow reconstruction for the Kaidu River originating from the central Tianshan Mountains since A.D. 1700 , 2020, Dendrochronologia.

[16]  Jan Tumajer,et al.  Influence of artificial alteration of groundwater level on vessel lumen area and tree-ring width of Quercus robur , 2017, Trees.

[17]  E. Cook,et al.  THE SMOOTHING SPLINE: A NEW APPROACH TO STANDARDIZING FOREST INTERIOR TREE -RING WIDTH SERIES FOR DENDROCLIMATIC STUDIES , 1981 .

[18]  J. Tardif,et al.  Influence of climate on tree rings and vessel features in red oak and white oak growing near their northern distribution limit, southwestern Quebec, Canada , 2006 .

[19]  H. Fritts,et al.  Tree Rings and Climate. , 1978 .

[20]  P. Fonti,et al.  Suitability of chestnut earlywood vessel chronologies for ecological studies. , 2004, The New phytologist.

[21]  Qinhua Tian,et al.  Moisture changes over the past 467 years in the central Hexi Corridor, northwestern China , 2020 .

[22]  Rajesh Kumar,et al.  Comparison of regression and artificial neural network models for estimation of global solar radiations , 2015 .

[23]  François Anctil,et al.  Evaluation of Neural Network Streamflow Forecasting on 47 Watersheds , 2005 .

[24]  Malcolm K. Hughes,et al.  SACRAMENTO RIVER FLOW RECONSTRUCTED TO A.D. 869 FROM TREE RINGS 1 , 2001 .

[25]  Puneet Srivastava,et al.  Modeling effects of changing land use/cover on daily streamflow: An Artificial Neural Network and curve number based hybrid approach , 2013 .

[26]  Edward R. Cook,et al.  Hydrometeorological Reconstructions for Northeastern Mongolia Derived from Tree Rings: 1651–1995* , 2001 .

[27]  Y. Bergeron,et al.  Continuous earlywood vessels chronologies in floodplain ring-porous species can improve dendrohydrological reconstructions of spring high flows and flood levels , 2016 .

[28]  D. Spittlehouse,et al.  Estimating Douglas-fir wood production from soil and climate data , 1990 .

[29]  M. Khaleghi.  Application of dendroclimatology in evaluation of climatic changes , 2018 .

[30]  M. Panayotov,et al.  First measurements of Blue intensity from Pinus peuce and Pinus heldreichii tree rings and potential for climate reconstructions , 2020 .

[31]  Xuxiang Li,et al.  Tree-ring hydrologic reconstructions for the Heihe River watershed, western China since AD 1430. , 2010, Water research.

[32]  M. Azodi,et al.  Modeling of karst and alluvial springs discharge in the central Alborz highlands and on the Caspian southern coasts , 2008 .

[33]  Hui-Hai Liu.  Impact of climate change on groundwater recharge in dry areas: An ecohydrology approach , 2011 .

[34]  Holger Gärtner,et al.  Studying global change through investigation of the plastic responses of xylem anatomy in tree rings. , 2010, The New phytologist.

[35]  Mohamed Sultan,et al.  Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia , 2020, Remote. Sens..

[36]  T. Wigley,et al.  On the Average Value of Correlated Time Series, with Applications in Dendroclimatology and Hydrometeorology , 1984 .

[37]  Z. Yin,et al.  Reconstruction of a 1436‐year soil moisture and vegetation water use history based on tree‐ring widths from Qilian junipers in northeastern Qaidam Basin, northwestern China , 2007 .

[38]  S. Bijak,et al.  Groundwater Level Fluctuations Affect the Mortality of Black Alder (Alnus glutinosa Gaertn.) , 2020 .

[39]  V. Gholami,et al.  A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer , 2020 .

[40]  H. Kooi,et al.  Beneath the surface of global change: Impacts of climate change on groundwater , 2011 .

[41]  M. Bierkens,et al.  Global depletion of groundwater resources , 2010 .

[42]  E. Wise.  Spatiotemporal variability of the precipitation dipole transition zone in the western United States , 2010 .

[43]  J. Dracup,et al.  Alternative principal components regression procedures for dendrohydrologic reconstructions , 2000 .

[44]  M. Therrell,et al.  A record of flooding on the White River, Arkansas derived from tree-ring anatomical variability and vessel width , 2020, Physical Geography.

[45]  E. Gutiérrez,et al.  Vessel features of Quercus ilex L. growing under Mediterranean climate have a better climatic signal than tree-ring width , 2010, Trees.

[46]  H. Fritts,et al.  Tree Rings and Climate. , 1978 .

[47]  P. Corona,et al.  Climatic and anthropogenic influence on tree-ring growth in riparian lake forest ecosystems under contrasting disturbance regimes , 2020 .

[48]  George Hardman.  The Relationship Between Tree-Growths And Stream-Runoff In The Truckee River Basin, California-Nevada , 1936 .

[49]  H. Grissino-Mayer.  An Updated List of Species Used in Tree-Ring Research , 1993 .

[50]  C. Turney,et al.  Palaeoclimate potential of New Zealand Manoao colensoi (silver pine) tree rings using Blue-Intensity (BI) , 2020 .

[51]  Ali Akbar Safavi,et al.  A simple neural network model for the determination of aquifer parameters , 2007 .

[52]  D. Eckstein,et al.  Climatic signal of earlywood vessels of oak on a maritime site. , 2003, Tree physiology.

[53]  L. Conkey.  Response of Tree-Ring Density to Climate in Maine, U.S.A. , 1979 .

[54]  K. Chau,et al.  Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers , 2015 .

[55]  J. Betancourt,et al.  Annual precipitation in the yellowstone National Park region since AD 1173 , 2007, Quaternary Research.

[56]  V. Gholami,et al.  Dendrohydrogeology in paleohydrogeologic studies , 2017 .

[57]  K. Kipfmueller,et al.  Reconstructed Temperature And Precipitation On A Millennial Timescale From Tree-Rings In The Southern Colorado Plateau, U.S.A. , 2005 .

[58]  E. Cook,et al.  A 1,200-year perspective of 21st century drought in southwestern North America , 2010, Proceedings of the National Academy of Sciences.

[59]  A. Oubeidillah,et al.  Tree-Ring Reconstructions of Streamflow for the Tennessee Valley , 2019, Hydrology.