Farshid Rahmani
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View article: Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning
Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning Open
To track rapid changes within our water sector, Global Water Models (GWMs) need to realistically represent hydrologic systems' response patterns - such as the baseflow fraction of streamflow - but are hindered by their limited ability to l…
View article: Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning
Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning Open
To track rapid changes within our water sector, Global Water Models (GWMs) need to realistically represent hydrologic systems' response patterns - such as baseflow fraction - but are hindered by their limited ability to learn from data. He…
View article: High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning
High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning Open
The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel version…
View article: High-Resolution Differentiable Models for Operational National and Global Water Modeling and Assessment
High-Resolution Differentiable Models for Operational National and Global Water Modeling and Assessment Open
Continental and global water models have long been trapped in slow growth and inadequate predictive power, as they are not able to effectively assimilate information from big data. While Artificial Intelligence (AI) models greatly improve …
View article: Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models
Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models Open
Data assimilation (DA) enables hydrologic models to update their internal states using near-real-time observations for more accurate forecasts. With deep neural networks like long short-term memory (LSTM), using either lagged observations …
View article: Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions
Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions Open
In recent years, Visual Question Answering (VQA) has made significant strides, particularly with the advent of multimodal models that integrate vision and language understanding. However, existing VQA datasets often overlook the complexiti…
View article: Differentiable modeling for global water resources under global change
Differentiable modeling for global water resources under global change Open
Process-based modeling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills …
View article: Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning
Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning Open
Recently, rainfall‐runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics‐NN models—particularly, a genre called differentiable modeling that intermi…
View article: Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling
Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling Open
Although deep learning models for stream temperature ( T s ) have recently shown exceptional accuracy, they have limited interpretability and cannot output untrained variables. With hybrid differentiable models, neural networks (NNs) can b…
View article: Improving large-basin river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning
Improving large-basin river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning Open
Recently, rainfall-runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics-NN models — particularly, a genre called differentiable modeling that inter…
View article: Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats Open
Climate change threatens our ability to grow food for an ever-increasing population. There is a need for high-quality soil moisture predictions in under-monitored regions like Africa. However, it is unclear if soil moisture processes are g…
View article: Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing
Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing Open
Differentiable modeling has been introduced recently as a method to learn relationships from a combination of data and structural priors. This method uses end-to-end gradient tracking inside a process-based model to tune internal states an…
View article: Evaluating a Global Soil Moisture dataset from a Multitask Model (GSM3 v1.0) for current and emerging threats to crops
Evaluating a Global Soil Moisture dataset from a Multitask Model (GSM3 v1.0) for current and emerging threats to crops Open
Climate change threatens our ability to grow food for a growing population. There are concurrent droughts and floods happening globally, with the greatest impacts felt in Africa. There is a need for high-quality soil moisture predictions i…
View article: Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing
Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing Open
Recently, runoff simulations in small, headwater basins have been improved by methodological advances such as deep learning (DL). Hydrologic routing modules are typically needed to simulate flows in stem rivers downstream of large, heterog…
View article: Assessment of Transfer Learning Techniques to Improve Streamflow Predictions in Data-Sparse Regions
Assessment of Transfer Learning Techniques to Improve Streamflow Predictions in Data-Sparse Regions Open
<p>Reliable streamflow predictions are critical for managing water resources for flood warning, agricultural irrigation apportionment, hydroelectric production, to name a few. However, there are geographical heterogeneities in availa…
View article: A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data
A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data Open
Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as limited coverage of in situ data or low resolution/accuracy of satellite data. H…
View article: A multiscale deep learning model for soil moisture integrating satellite and in-situ data
A multiscale deep learning model for soil moisture integrating satellite and in-situ data Open
Model codes for the article: "A multiscale deep learning model for soil moisture integrating satellite and in-situ data"
View article: Deep Learning Stream Temperature Model: Recommendations for Modeling Gauged and Ungauged Basins
Deep Learning Stream Temperature Model: Recommendations for Modeling Gauged and Ungauged Basins Open
<p>Stream water temperature (T<sub>s</sub>) is a variable that plays a pivotal role in managing water resources. We used the long short-term memory (LSTM) deep learning architecture to develop a basin centric single T<…
View article: On the data synergy effect of large-sample multi-physics catchment modeling with machine learning
On the data synergy effect of large-sample multi-physics catchment modeling with machine learning Open
<p>Watersheds in the world are often perceived as being unique from each other, requiring customized study for each basin. Models uniquely built for each watershed, in general, cannot be leveraged for other watersheds. It is also a c…
View article: Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data Open
Stream water temperature ( T _s ) is a variable of critical importance for aquatic ecosystem health. T _s is strongly affected by groundwater-surface water interactions which can be learned from streamflow records, but previously such info…
View article: Developing and Testing a Long Short-Term Memory Stream Temperature Model in Daily and Continental Scale
Developing and Testing a Long Short-Term Memory Stream Temperature Model in Daily and Continental Scale Open
Earth and Space Science Open Archive posterOpen AccessYou are viewing the latest version by default [v1]Developing and Testing a Long Short-Term Memory Stream Temperature Model in Daily and Continental ScaleAuthorsFarshidRahmaniiDSamanthaO…