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Spatial and Temporal Forecasting of Groundwater Levels

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inferredcites →https://doi.org/10.1002/2014wr015339openalex-extracted
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inferredcites →https://doi.org/10.1002/2015wr017096openalex-extracted
Improving the representation of hydrologic processes in Earth System Models
inferredcites →https://doi.org/10.1002/2015wr017198openalex-extracted
A unified approach for process‐based hydrologic modeling: 1. Modeling concept
inferredcites →https://doi.org/10.1002/2017gl074071openalex-extracted
Annual Irrigation Dynamics in the U.S. Northern High Plains Derived from Landsat Satellite Data
inferredcites →https://doi.org/10.1002/hyp.13151openalex-extracted
Factors controlling seasonal groundwater and solute flux from snow‐dominated basins
inferredcites →https://doi.org/10.1002/hyp.13726openalex-extracted
Wavelet analyses of neural networks based river discharge decomposition
inferredcites →https://doi.org/10.1002/hyp.14411openalex-extracted
On the hydrological difference between catchments above and below the intermittent‐persistent snow transition
inferredcites →https://doi.org/10.1002/hyp.554openalex-extracted
A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models
inferredcites →https://doi.org/10.1002/hyp.6686openalex-extracted
Modelling groundwater levels in an urban coastal aquifer using artificial neural networks
inferredcites →https://doi.org/10.1002/joc.1325openalex-extracted
Long‐term memory of the hydrological cycle and river runoffs in China in a high‐resolution climate model
inferredcites →https://doi.org/10.1002/joc.3370050602openalex-extracted
Climatology of the terrestrial seasonal water cycle
inferredcites →https://doi.org/10.1007/s10040-016-1397-8openalex-extracted
Groundwater flow systems theory: research challenges beyond the specified-head top boundary condition
inferredcites →https://doi.org/10.1007/s10462-020-09838-1openalex-extracted
A review on the long short-term memory model
inferredcites →https://doi.org/10.1007/s10661-015-5049-6openalex-extracted
GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran
inferredcites →https://doi.org/10.1007/s11269-011-9808-zopenalex-extracted
Operational Prediction of Groundwater Fluctuation in South Florida using Sequence Based Markovian Stochastic Model
inferredcites →https://doi.org/10.1007/s11269-018-2169-0openalex-extracted
A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming
inferredcites →https://doi.org/10.1007/s11269-019-2193-8openalex-extracted
A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model
inferredcites →https://doi.org/10.1016/j.agsy.2014.01.004openalex-extracted
Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture
inferredcites →https://doi.org/10.1016/j.agwat.2019.06.010openalex-extracted
Estimating irrigation demand with geospatial and in-situ data: Application to the high plains aquifer, Kansas, USA
inferredcites →https://doi.org/10.1016/j.ejrh.2021.100930openalex-extracted
Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States
inferredcites →https://doi.org/10.1016/j.jhydrol.2010.11.002openalex-extracted
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
inferredcites →https://doi.org/10.1016/j.jhydrol.2019.124540openalex-extracted
Stochastic simulation on reproducing long-term memory of hydroclimatological variables using deep learning model
inferredcites →https://doi.org/10.1016/j.jog.2011.05.001openalex-extracted
Impact of water withdrawals from groundwater and surface water on continental water storage variations
inferredcites →https://doi.org/10.1016/j.rse.2020.111740openalex-extracted
Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates
inferredcites →https://doi.org/10.1016/j.scitotenv.2016.05.127openalex-extracted
Complex water management in modern agriculture: Trends in the water-energy-food nexus over the High Plains Aquifer
inferredcites →https://doi.org/10.1016/j.scitotenv.2020.139109openalex-extracted
Influence of surface water – groundwater interactions on the spatial distribution of pesticide metabolites in groundwater
inferredcites →https://doi.org/10.1016/j.tree.2019.03.006openalex-extracted
Uncovering Ecological Patterns with Convolutional Neural Networks
inferredcites →https://doi.org/10.1016/s0166-2481(08)00006-8openalex-extracted
Chapter 6 Basic Land-Surface Parameters
inferredcites →https://doi.org/10.1016/s0309-1708(01)00034-3openalex-extracted
Spatio-temporal evolution and time-stable characteristics of soil moisture within remote sensing footprints with varying soil, slope, and vegetation
inferredcites →https://doi.org/10.1017/9781108631129openalex-extracted
Satellite Remote Sensing for Conservation Action
inferredcites →https://doi.org/10.1029/2008gl035296openalex-extracted
Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets
inferredcites →https://doi.org/10.1029/2010gl046427openalex-extracted
Classifying the water table at regional to continental scales
inferredcites →https://doi.org/10.1029/2010jd013892openalex-extracted
Evidence of enhanced precipitation due to irrigation over the Great Plains of the United States
inferredcites →https://doi.org/10.1029/2018sw001829openalex-extracted
Quantitative Prediction of High‐Energy Electron Integral Flux at Geostationary Orbit Based on Deep Learning
inferredcites →https://doi.org/10.1029/2019wr025326openalex-extracted
A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning
inferredcites →https://doi.org/10.1029/2019wr026065openalex-extracted
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
inferredcites →https://doi.org/10.1029/2019wr027038openalex-extracted
Training Machine Learning Surrogate Models From a High‐Fidelity Physics‐Based Model: Application for Real‐Time Street‐Scale Flood Prediction in an Urban Coastal Community
inferredcites →https://doi.org/10.1029/2020gl088229openalex-extracted
Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning
inferredcites →https://doi.org/10.1029/2020wr028059openalex-extracted
Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets and Machine Learning
inferredcites →https://doi.org/10.1029/2020wr028148openalex-extracted
Toward Data‐Driven Generation and Evaluation of Model Structure for Integrated Representations of Human Behavior in Water Resources Systems
inferredcites →https://doi.org/10.1029/2020wr029188openalex-extracted
Forecasting Abrupt Depletion of Dissolved Oxygen in Urban Streams Using Discontinuously Measured Hourly Time‐Series Data
inferredcites →https://doi.org/10.1029/2021jb022373openalex-extracted
Machine‐Learning Characterization of Tectonic, Hydrological and Anthropogenic Sources of Active Ground Deformation in California
inferredcites →https://doi.org/10.1029/2022ef002859openalex-extracted
The Role of Irrigation Expansion on Historical Climate Change: Insights From CMIP6
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Assimilation of Remotely Sensed Leaf Area Index Enhances the Estimation of Anthropogenic Irrigation Water Use
inferredcites →https://doi.org/10.1038/415514aopenalex-extracted
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inferredcites →https://doi.org/10.1038/nclimate2196openalex-extracted
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inferredcites →https://doi.org/10.1038/s41467-021-21651-0openalex-extracted
Continental-scale analysis of shallow and deep groundwater contributions to streams
inferredcites →https://doi.org/10.1038/s41558-018-0386-4openalex-extracted
Global patterns and dynamics of climate–groundwater interactions
inferredcites →https://doi.org/10.1038/s43247-021-00225-4openalex-extracted
Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
inferredcites →https://doi.org/10.1080/02626667.2013.803183openalex-extracted
A decade of Predictions in Ungauged Basins (PUB)—a review
inferredcites →https://doi.org/10.1088/1748-9326/10/5/054013openalex-extracted
The impact of climate extremes and irrigation on US crop yields
inferredcites →https://doi.org/10.1109/tkde.2017.2720168openalex-extracted
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
inferredcites →https://doi.org/10.1126/science.aau0323openalex-extracted
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inferredcites →https://doi.org/10.1175/jhm-d-11-013.1openalex-extracted
Incorporating Anthropogenic Water Regulation Modules into a Land Surface Model
inferredcites →https://doi.org/10.1371/journal.pone.0169748openalex-extracted
SoilGrids250m: Global gridded soil information based on machine learning
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Pulse generation models for daily intermittent streamflows / Modèles de génération d’impulsions pour des débits journaliers intermittents
inferredcites →https://doi.org/10.2136/sssaj2017.04.0122openalex-extracted
Soil Property and Class Maps of the Conterminous United States at 100‐Meter Spatial Resolution
inferredcites →https://doi.org/10.2139/ssrn.4481860openalex-extracted
Improving Parameterization of a Regional Irrigation Scheduling Program for Evapotranspiration Estimation with Eddy Covariance Measurements
inferredcites →https://doi.org/10.2478/v10098-010-0026-9openalex-extracted
Comparison of the monitored and modeled soil water storage of the upper soil layer: the influence of soil properties and groundwater table level
inferredcites →https://doi.org/10.3133/ofr70362openalex-extracted
Effects of irrigation on streamflow in the Central Sand Plain of Wisconsin
inferredcites →https://doi.org/10.3133/wsp1811openalex-extracted
Hydrology of the Little Plover River basin, Portage County, Wisconsin, and the effects of water resource development
inferredcites →https://doi.org/10.3390/rs14102433openalex-extracted
An Ionospheric TEC Forecasting Model Based on a CNN-LSTM-Attention Mechanism Neural Network
inferredcites →https://doi.org/10.48550/arxiv.1603.05201openalex-extracted
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inferredcites →https://doi.org/10.5194/gmd-13-5425-2020openalex-extracted
Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6
inferredcites →https://doi.org/10.5194/hess-17-1619-2013openalex-extracted
Climate change impact on groundwater levels: ensemble modelling of extreme values
inferredcites →https://doi.org/10.5194/hess-19-1521-2015openalex-extracted
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inferredcites →https://doi.org/10.5194/hess-21-4169-2017openalex-extracted
Human–water interface in hydrological modelling: current status and future directions
inferredcites →https://doi.org/10.5194/hess-24-1251-2020openalex-extracted
BESS-STAIR: a framework to estimate daily, 30 m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt
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