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Member Since 1986
Ty P.A. Ferre
Professor, University of Arizona
AGU Research
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Sequential Optimization Of Temperature Measurements To Estimate Groundwater Surface Water Interactions
ADVANCES IN DATA ASSIMILATION AND UNCERTAINTY QUANTIFICATION FOR WATER RESOURCES MANAGEMENT I ORAL
hydrology | 14 december 2023
Robin Thibaut, Thomas Hermans, Ty P. Ferre, Eric L...
The groundwater-surface water (GW-SW) exchange fluxes are driven by a complex interplay of subsurface processes and their interactions with surface hy...
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Machine Learning Analysis of Geomorphometry and Extreme Flooding Lower Colorado River Basin
ADVANCES IN MACHINE LEARNING FOR EARTH SCIENCE: OBSERVATION, MODELING, AND APPLICATIONS III ELIGHTNING
hydrology | 12 december 2023
Lin Ji, Tao Liu, Victor R. Baker, Hoshin V. Gupta,...
It has been widely acknowledged that geomorphology plays an essential role in understanding hydrology and flood generation processes. However, previou...
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A Model Ensemble Approach to Explore Structural vs. Parameter Uncertainty in Karst Systems with Unmapped Conduits
DIAGNOSTICS, SENSITIVITY, AND UNCERTAINTY ANALYSIS OF EARTH AND ENVIRONMENTAL MODELS III POSTERS
hydrology | 16 december 2020
Chloé Fandel, Ty P. Ferre, Philippe Renard, Nico G...
We present a multi-model ensemble method to represent structural and conceptual uncertainty inherent in simulations of systems with limited spatial in...
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Using Machine Learning Techniques to Optimize Subsurface Hydrologic Data Collection
SCIENTIFIC MACHINE LEARNING FOR FLOW, TRANSPORT, AND COUPLED PROCESSES ACROSS TEMPORAL AND SPATIAL SCALES II
hydrology | 08 december 2020
Ty P. Ferre, Mohammad Mansourmoghaddam
Machine learning (ML) techniques have made a major impact on the field of atmospheric sciences and are gaining a strong foothold in surface hydrology....
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Hydrologic Intermediaries - a Missing Link between Modelers and Stakeholders
ADVANCEMENTS IN WATERSHED MODELING TO SUPPORT WATER RESOURCES MANAGEMENT II
hydrology | 08 december 2020
Vivek Grewal, Chloé Fandel, Ty P. Ferre
Applied hydrologic modelers commonly see their role as providing an objective, quantitative tool that can predict the impacts of changes to hydrologic...
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Machine learning analysis of morphometry-extreme flood links in the Lower Colorado River Basin
DATA SCIENCE AND MACHINE LEARNING FOR NATURAL HAZARD SCIENCES II POSTERS
natural hazards | 08 december 2020
Lin Ji, Victor R. Baker, Hoshin V. Gupta, Ty P. Fe...
Extreme flood hazards characterize the Lower Colorado River basin (LCRB) due to the complex terrain and entrenched river channels. Evaluating basin mo...
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Simulating Air and Water Flow in the Vadose Zone Using Updated MODFLOW 6 and HYDRUS 1-D
ENVIRONMENTAL VADOSE ZONE HYDROLOGY II POSTERS
hydrology | 13 december 2019
Jeffrey Klakovich, Ty P. Ferre, Stephen Farrington...
Transport of gas through the vadose zone can impact plant health, contaminant transport, and atmosphere/soil gas exchange. By modifying the permeabili...
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A comparison of transit time distribution vs. fraction of young water to characterize storage in a mountain headwater catchment: does the tail matter?
ADVANCING SCIENCE AND ENVIRONMENTAL CHANGE DETECTION: LEVERAGING LONG-TERM OBSERVATIONS, MONITORING, AND EXPERIMENTATION IN CATCHMENT, CRITICAL ZONE, AND ECOSYSTEM STUDIES III POSTERS
hydrology | 12 december 2019
Ravindra Dwivedi, Christopher J. Eastoe, John F. K...
Fraction of young water (Fyw) and transit time distribution (TTD) metrics have been widely used to characterize catchment hydrologic behavior, but few...
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