Hydrology and Watershed Management Studies

Neueste Paper

• A Multi-Timestep LSTM Ensemble regressor for Enhanced Short-Term Runoff Prediction

  Hamid Saadatfar, AmirHossein Eshghi, Behnaz Behdani · 23. September 2026

  Accurately forecasting river runoff is key to managing water resources, controlling floods, and planning agriculture. This study examines the Ajichay River in northwest Iran, a major tributary of Lake Urmia that has experienced increasing water-related stress in recent years. We introduce a daily ru…

• A Responsible Artificial Intelligence Framework for Groundwater Modeling

  Chong Chen, Yulu Zhang, Qingxi Guo, Yihan Liu · 18. August 2026

  The rapid development and widespread application of artificial intelligence (AI) have sparked intense discussions on how to deploy responsible AI systems in a manner aligned with human values and ethical standards. Compared to fields like healthcare, energy, or finance, the application of AI in grou…

• From Conceptual Hydrologic Models to Conceptually Interpretable Neural Networks: A Snow-Water Mass-Conserving-Perceptron Framework for Discovering Catchment-Scale Precipitation-Storage-Runoff Representations

  Yuan-Heng Wang, Hoshin V. Gupta · 30. Juli 2026

  The Mass-Conserving Perceptron (MCP) establishes a modeling paradigm in which conceptual hydrologic models can be reformulated as physically constrained, conceptually interpretable neural networks. Here, we develop a snow-water MCP network framework and evaluate it across 513 CAMELS-US basins. We fi…

• Transformer-based Diffusion models for Hydrological Time Series Probabilistic Imputation and Forecasting

  Ferdinand Bhavsar (INRAE), Lionel Benoit (INRAE), Maxime Savatier (ANDRA), Edith Gabriel (INRAE) · 24. Juli 2026

  The modeling of hydrometeorological time series with limited observations is a key challenge in the monitoring of hydro-systems and water resources, as well as for flood or drought risk assessment. Due to the high variability of the underlying processes and the sparsity of available measurements, tr…

• Joint distribution of upstream runoff governs downstream river-discharge prediction uncertainty in distributed ML models

  Karan Ruparell, Tristan Hascoet, Takemasa Miyoshi, Kieran M. R. Hunt, Hannah L. Cloke, Christel Prudhomme, Florian Pappenberger · 7. Juli 2026

  Uncertainty quantification of hydrological predictions is necessary to inform operational decisions. Recent generative machine-learning methods have advanced probabilistic streamflow prediction, but have remained confined to lumped models that predict a basin outlet directly. At the same time, deter…

• Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics

  Yuan-Heng Wang, Yang Yang, Fabio Ciulla, Hoshin V. Gupta, Charuleka Varadharajan · 16. Juni 2026

  While many modern studies are dedicated to ML-based large-sample hydrologic modeling, these efforts have not necessarily translated into predictive improvements that are grounded in enhanced physical-conceptual understanding. Here, we report on a CONUS-wide large-sample study (spanning diverse hydro…

• Evaluating Transformer and LSTM Frameworks for Prediction in Ungauged Basins

  Taye Akinrele, James Halgren, Noorbakhsh Amiri Golilarz, Sudip Mittal, Shahram Rahimi · 3. Juni 2026

  Watershed networks exhibit convergent topologies in which multiple tributaries merge into downstream channels,integrating diverse upstream hydrological processes. In ungauged basins, the absence of direct observations increases uncertainty and limits the ability to anticipate extreme events. This st…

• Improving Model Performance by Adapting the KGE Metric to Account for System Non-Stationarity

  M Jawad, HV Gupta, YH Wang, MA Farmani, A Behrangi, GY Niu · 7. April 2026

  Geoscientific systems tend to be characterized by pronounced temporal non-stationarity, arising from seasonal and climatic variability in hydrometeorological drivers, and from natural and anthropogenic changes to land use and cover. As has been pointed out, such variability renders "the assumption o…

• Process-Aware AI for Rainfall-Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraints

  Mohammad A. Farmani, Hoshin V. Gupta, Ali Behrangi, Muhammad Jawad, Sadaf Moghisi, Guo-Yue Niu · 27. März 2026

  Machine learning models can achieve high predictive accuracy in hydrological applications but often lack physical interpretability. The Mass-Conserving Perceptron (MCP) provides a physics-aware artificial intelligence (AI) framework that enforces conservation principles while allowing hydrological p…

• Selection of CMIP6 Models for Regional Precipitation Projection and Climate Change Assessment in the Jhelum and Chenab River Basins

  Saad Ahmed Jamal, Ammara Nusrat, Muhammad Azmat, Muhammad Osama Nusrat · 16. Februar 2026

  Effective water resource management depends on accurate projections of flows in water channels. For projected climate data, use of different General Circulation Models (GCM) simulates contrasting results. This study shows selection of GCM for the latest generation CMIP6 for hydroclimate change impac…

• Reclaiming First Principles: A Differentiable Framework for Conceptual Hydrologic Models

  Jasper A. Vrugt, Jonathan M. Frame, Ethan Bollman · 9. Februar 2026

  Conceptual hydrologic models remain the cornerstone of rainfall-runoff modeling, yet their calibration is often slow and numerically fragile. Most gradient-based parameter estimation methods rely on finite-difference approximations or automatic differentiation frameworks (e.g., JAX, PyTorch and Tens…

• Towards Operational Streamflow Forecasting in the Limpopo River Basin using Long Short-Term Memory Networks

  James Tlhomole, Edoardo Borgomeo, Karthikeyan Matheswaran, Mariangel Garcia Andarcia · 13. Januar 2026

  Robust hydrological simulation is key for sustainable development, water management strategies, and climate change adaptation. In recent years, deep learning methods have been demonstrated to outperform mechanistic models at the task of hydrological discharge simulation. Adoption of these methods ha…

• Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings

  Pengfei Qu, Wenyu Ouyang, Chi Zhang, Yikai Chai, Shuolong Xu, Lei Ye, Yongri Piao, Miao Zhang, Huchuan Lu · 6. Januar 2026

  Predicting river flow in places without streamflow records is challenging because basins respond differently to climate, terrain, vegetation, and soils. Traditional basin attributes describe some of these differences, but they cannot fully represent the complexity of natural environments. This study…

• Physics Guided Machine Learning Methods for Hydrology

  Ankush Khandelwal, Shaoming Xu, Xiang Li, Xiaowei Jia, Michael Stienbach, Christopher Duffy, John Nieber, Vipin Kumar · 12. November 2025

  Streamflow prediction is one of the key challenges in the field of hydrology due to the complex interplay between multiple non-linear physical mechanisms behind streamflow generation. While physics based models are rooted in rich understanding of the physical processes, a significant performance gap…

• Using Machine Learning to Discover Parsimonious and Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff Dynamics

  Yuan-Heng Wang, Hoshin V. Gupta · 11. November 2025

  Due largely to challenges associated with physical interpretability of machine learning (ML) methods, and because model interpretability is key to credibility in management applications, many scientists and practitioners are hesitant to discard traditional physical-conceptual (PC) modeling approache…

• RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting

  Mohamad Hakam Shams Eddin, Yikui Zhang, Stefan Kollet, Juergen Gall · 27. Oktober 2025