Jonathan Frame
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View article: Time fractional Saint Venant equations reveal the physical basis of hydrograph retardation through model comparison and field data
Time fractional Saint Venant equations reveal the physical basis of hydrograph retardation through model comparison and field data Open
River hydrodynamics are influenced by numerous factors that traditional models often fail to fully capture. Simulating complex hydrographs can benefit from parsimonious upscaling models, such as fractional derivative equations, that reduce…
View article: SWAT Machine Learning-Integrated Modeling for Ranking Watershed Vulnerability to Climate Variability and Land-Use Change in Alabama, USA, in 1990–2023
SWAT Machine Learning-Integrated Modeling for Ranking Watershed Vulnerability to Climate Variability and Land-Use Change in Alabama, USA, in 1990–2023 Open
Understanding streamflow dynamics in watersheds affected by human activity and climate variability is important for sustainable water and environmental resource management. This study evaluates the vulnerability of Alabama watersheds to an…
View article: Climate driven hydrologic nonstationarity patterns across the Contiguous United States
Climate driven hydrologic nonstationarity patterns across the Contiguous United States Open
We calculated metrics of climate change, land use-land cover change, and hydrologic nonstationarity in 671 catchments across the Contiguous United States (CONUS) that are known not to have relatively little urbanization and anthropogenic l…
View article: A Proof of Concept for Improving Estimates of Ungauged Basin Streamflow via an LSTM‐Based Synthetic Network Simulation Approach
A Proof of Concept for Improving Estimates of Ungauged Basin Streamflow via an LSTM‐Based Synthetic Network Simulation Approach Open
This study introduces a machine learning approach to address the critical challenge of limited real‐time flow data in river basins, particularly for calibrating large‐scale hydrologic models. These models often rely on uncertain parameter …
View article: Machine Learning for a Heterogeneous Water Modeling Framework
Machine Learning for a Heterogeneous Water Modeling Framework Open
This technical note describes recent efforts to integrate machine learning (ML) models, specifically long short‐term memory (LSTM) networks and differentiable parameter learning conceptual hydrological models (δ conceptual models), into th…
View article: On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results
On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results Open
Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forec…
View article: Rapid Inundation Mapping Using the US National Water Model, Satellite Observations, and a Convolutional Neural Network
Rapid Inundation Mapping Using the US National Water Model, Satellite Observations, and a Convolutional Neural Network Open
Rapid and accurate maps of floods across large domains, with high temporal resolution capturing event peaks, have applications for flood forecasting and resilience, damage assessment, and parametric insurance. Satellite imagery produces in…
View article: Rapid inundation mapping using the US National Water Model, satellite observations, and a convolutional neural network
Rapid inundation mapping using the US National Water Model, satellite observations, and a convolutional neural network Open
Convolution neural networks (CNN) are suitable for rapid modeling of surface water dynamics for large-scale inundation mapping.• We deploy a CNN for continuous flood mapping across all of California during the devastating 2023 atmospheric …
View article: Machine learning for a heterogeneous water modeling framework
Machine learning for a heterogeneous water modeling framework Open
We explore deep learning for the Next Generation Water Resources Modeling Framework (NextGen). We present results from Random forest-based multi-model ensembles, Long Short-Term Memory (LSTM) and differentiable parameter learning hydrologi…
View article: Rapid inundation mapping using the US National Water Model, satellite observations, and a convolutional neural network
Rapid inundation mapping using the US National Water Model, satellite observations, and a convolutional neural network Open
Rapid and accurate maps of floods across large domains, with high temporal resolution capturing event peaks, have applications for flood forecasting and resilience, damage assessment, and parametric insurance. Satellite imagery produces in…
View article: Comment on egusphere-2023-3084
Comment on egusphere-2023-3084 Open
Abstract. Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles, both in future climate projections and we…
View article: Comment on egusphere-2023-3084
Comment on egusphere-2023-3084 Open
Abstract. Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles, both in future climate projections and we…
View article: On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results
On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results Open
Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles, both in future climate projections and weather fore…
View article: Comment on egusphere-2023-1836
Comment on egusphere-2023-1836 Open
Abstract. Groundwater level (GWL) forecasting with machine learning has been widely studied due to its generally accurate results and little input data requirements. Furthermore, machine learning models for this purpose are set up and trai…
View article: On strictly enforced mass conservation constraints for modelling the <scp>Rainfall‐Runoff</scp> process
On strictly enforced mass conservation constraints for modelling the <span>Rainfall‐Runoff</span> process Open
It has been proposed that conservation laws might not be beneficial for accurate hydrological modelling due to errors in input (precipitation) and target (streamflow) data (particularly at the event time scale), and this might explain why …
View article: Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks
Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks Open
Ingesting near-real-time observation data is a critical component of many operational hydrological forecasting systems. In this paper, we compare two strategies for ingesting near-real-time streamflow observations into long short-term memo…
View article: Deep learning rainfall–runoff predictions of extreme events
Deep learning rainfall–runoff predictions of extreme events Open
The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that the predictive accuracy of data-driven models based on deep learning may not be reliable in extrapolation or for…
View article: Hydrology Research Articles Are Becoming More Interdisciplinary
Hydrology Research Articles Are Becoming More Interdisciplinary Open
<p>We used Natural Language Processing (NLP) to assess topic diversity in the abstracts of all&#160;research articles (75,000) from eighteen water science and hydrology journals published&#160;between 1991 and 2019 -- these a…