Spatial Downscaling of GRACE TWSA Data to Identify Spatiotemporal Groundwater Level Trends in the Upper Floridan Aquifer, Georgia, USA

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

[2]  S. Swenson,et al.  Remote sensing of groundwater storage changes in Illinois using the Gravity Recovery and Climate Experiment (GRACE) , 2006 .

[3]  Seyed Amir Naghibi,et al.  A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping , 2015, Water Resources Management.

[4]  Marc F. P. Bierkens,et al.  Global hydrology 2015: State, trends, and directions , 2015 .

[5]  Alexander Y. Sun,et al.  Toward calibration of regional groundwater models using GRACE data , 2012 .

[6]  A. Milewski,et al.  Improved methods for estimating local terrestrial water dynamics from GRACE in the Northern High Plains. , 2017 .

[7]  Michael N. Fienen,et al.  A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA , 2015 .

[8]  A. Milewski,et al.  A quantitative assessment of groundwater resources in the Middle East and North Africa region , 2018, Hydrogeology Journal.

[9]  L. Breiman.  Arcing classifier (with discussion and a rejoinder by the author) , 1998 .

[10]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .

[11]  Alexander Y. Sun,et al.  Predicting groundwater level changes using GRACE data , 2013 .

[12]  C. Jackson,et al.  Effects of irrigation withdrawals on streamflows in a karst environment: lower Flint River Basin, Georgia, USA , 2012 .

[13]  Mohamed Sultan,et al.  A remote sensing solution for estimating runoff and recharge in arid environments , 2009 .

[14]  Victor Zlotnicki,et al.  Time‐variable gravity from GRACE: First results , 2004 .

[15]  Racha Elkadiri,et al.  Assessment and Comparison of TMPA Satellite Precipitation Products in Varying Climatic and Topographic Regimes in Morocco , 2015, Remote. Sens..

[16]  Todd C. Rasmussen,et al.  Delineating groundwater/surface water interaction in a karst watershed: Lower Flint River Basin, southwestern Georgia, USA , 2016 .

[17]  J. Famiglietti,et al.  Improving parameter estimation and water table depth simulation in a land surface model using GRACE water storage and estimated base flow data , 2010 .

[18]  Jerome H Friedman,et al.  Multiple additive regression trees with application in epidemiology , 2003, Statistics in medicine.

[19]  F. Landerer,et al.  Accuracy of scaled GRACE terrestrial water storage estimates , 2012 .

[20]  Adam Milewski,et al.  Downscaling GRACE TWSA Data into High-Resolution Groundwater Level Anomaly Using Machine Learning-Based Models in a Glacial Aquifer System , 2019, Remote. Sens..

[21]  J. Dozier,et al.  Inroads of remote sensing into hydrologic science during the WRR era , 2015 .

[22]  T. Hastie,et al.  Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees , 2006 .

[23]  M. Watkins,et al.  The gravity recovery and climate experiment: Mission overview and early results , 2004 .

[24]  Youssef,et al.  Hydrological Processes and Model Representation: Impact of Soft Data on Calibration , 2015 .

[25]  Marine Lacoste,et al.  Extrapolation at regional scale of local soil knowledge using boosted classification trees: A two-step approach , 2012 .

[26]  James S. Famiglietti,et al.  Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California's Central Valley , 2018, Remote. Sens..

[27]  N. Jarvis,et al.  Using boosted regression trees to explore key factors controlling saturated and near‐saturated hydraulic conductivity , 2015 .

[28]  Lynn Crawford,et al.  Causes of delay and cost overruns in construction of groundwater projects in a developing countries; Ghana as a case study , 2003 .

[29]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[30]  S. Swenson,et al.  Satellites measure recent rates of groundwater depletion in California's Central Valley , 2011 .

[31]  Mohamed Sultan,et al.  Modern recharge to fossil aquifers: Geochemical, geophysical, and modeling constraints , 2011 .

[32]  Seyed Amir Naghibi,et al.  GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran , 2015, Environmental Monitoring and Assessment.

[33]  Saro Lee,et al.  Application of Decision-Tree Model to Groundwater Productivity-Potential Mapping , 2015 .

[34]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[35]  S. Swenson,et al.  Post‐processing removal of correlated errors in GRACE data , 2006 .

[36]  R. Reedy,et al.  Groundwater depletion and sustainability of irrigation in the US High Plains and Central Valley , 2012, Proceedings of the National Academy of Sciences.

[37]  G. De’ath,et al.  CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .

[38]  R. Houborg,et al.  Drought indicators based on model‐assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations , 2012 .

[39]  Hayley J. Fowler,et al.  Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling , 2007 .

[40]  W. Feng,et al.  Evaluation of groundwater depletion in North China using the Gravity Recovery and Climate Experiment (GRACE) data and ground‐based measurements , 2013 .

[41]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[42]  A. Milewski,et al.  The groundwater risk index: Development and application in the Middle East and North Africa region. , 2018, The Science of the total environment.

[43]  Vijay P. Singh,et al.  Assessment of Informativeness of Groundwater Monitoring in Developing Regions (Gaza Strip Case Study) , 2005 .

[44]  J. Famiglietti,et al.  Satellite-based estimates of groundwater depletion in India , 2009, Nature.

[45]  Nicolas Le Moigne,et al.  Time-lapse microgravity surveys reveal water storage heterogeneity of a karst aquifer , 2010 .

[46]  Ralph Mac Nally,et al.  Groundwater change forecasts widespread forest dieback across an extensive floodplain system , 2011 .

[47]  V. Lakshmi,et al.  Estimating Groundwater Abstractions at the Aquifer Scale Using GRACE Observations , 2018, Geosciences.