Profile

Hoshin Gupta

Professor, Hydrology / Atmospheric Sciences | Regents Professor, Hydrology / Atmospheric Sciences | Professor, Global Change - GIDP | Member of the Graduate Faculty | Srp Professor, Technology-Public Policy/Markets | Professor, Remote Sensing / Spatial Analysis - GIDP

Hydrology & Atmospheric Sci

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Overview

Research

Collaboration(44)

Page 1 of 9

Grants(8)

Collaborative Research: Frameworks: Building a National Integrated Watershed Evaluation Framework: A Community Platform to Improve Model Evaluation and Decision Making
Active·2024·$228.7K / $1.1M·
External
Co-Investigator (COI)
integrated watershed management, collaborative research, model evaluation, decision making, community platform
Improvement of WRF-Hydro National Water Model Architecture and Calibration Methods for Semi-Arid Environments with Complex Terrain
2017·$122.4K / $367.5K·
External
Co-Investigator (COI)
hydrology, water management, modeling, climate, terrain
Regional Optimization of WRF-Hydro Forecasts for the Contiguous US
2016·$19.9K / $39.9K·
External
Co-Investigator (COI)
weather forecasting, hydrology, meteorology, data analysis, climate modeling
SERVIR Water Africa-Arizona Team (SWAAT)
2012·$275.8K / $1.1M·
External
Co-Investigator (COI)
water management, remote sensing, climate change, collaborative research, geospatial technology
A Scientific Basis for Urban Water PLanning Under Climate Change (SWAN Project)
2012·$277.3K / $554.5K·
External
Principal Investigator (PI)
urban water planning, climate change, scientific basis, water management, environmental impact

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Publications(328)

Recent

Position Paper: Bridging the GaBetween Machine Learning and Sensitivity Analysis

2024

machine learning, sensitivity analysis, bridging methods, integration techniques, data analysis

A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems

2024

machine learning, geoscientific systems, modeling, perceptron, mass conservation

Rainfall distributional properties control hydrologic model parameter importance.

2024

rainfall, hydrologic modeling, parameter importance, distributional properties, hydrology

Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron

2024

hydrological modeling, catchment scale, interpretable modelling, mass-conserving, physical-conceptual

Development of Groundwater Levels Dataset for Chile since 1970

2024

groundwater, water management, data collection, chile, geoscience

em In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance

2023

metrics, hydrological modeling, human preferences, model performance, defense

em Multi-objective assessment of hydrological model performances using Nash-Sutcliffe and Kling-Gupta efficiencies on a worldwide large sample of watersheds

2023

Differentiable modelling to unify machine learning and physical models for geosciences

2023

machine learning, geosciences, differentiable modeling, physical models, unification

A Robust Strategy to Account for Data Sampling Variability in the Development of Hydrological Models

2023

hydrological modeling, data sampling, robust strategy, model development, variability

A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems

2023

machine learning, geoscientific modeling, perceptron, mass conservation, geoscientific systems

People

Profile

Hoshin Gupta

Collaboration(44)

Hao Zhang

Sanlyn Buxner

Nirav Merchant

Salim Hariri

Xubin Zeng

Grants(8)

Collaborative Research: Frameworks: Building a National Integrated Watershed Evaluation Framework: A Community Platform to Improve Model Evaluation and Decision Making

Improvement of WRF-Hydro National Water Model Architecture and Calibration Methods for Semi-Arid Environments with Complex Terrain

Regional Optimization of WRF-Hydro Forecasts for the Contiguous US

SERVIR Water Africa-Arizona Team (SWAAT)

A Scientific Basis for Urban Water PLanning Under Climate Change (SWAN Project)

Publications(328)

Position Paper: Bridging the GaBetween Machine Learning and Sensitivity Analysis

A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems

Rainfall distributional properties control hydrologic model parameter importance.

Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron

Development of Groundwater Levels Dataset for Chile since 1970

em In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance

em Multi-objective assessment of hydrological model performances using Nash-Sutcliffe and Kling-Gupta efficiencies on a worldwide large sample of watersheds

Differentiable modelling to unify machine learning and physical models for geosciences

A Robust Strategy to Account for Data Sampling Variability in the Development of Hydrological Models

A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems