CauSTream: Causal Spatio-Temporal Representation Learning for Streamflow Forecasting

• URL: http://arxiv.org/abs/2512.16046v1
• Date: Thu, 18 Dec 2025 00:07:23 GMT
• Title: CauSTream: Causal Spatio-Temporal Representation Learning for Streamflow Forecasting
• Authors: Shu Wan, Reepal Shah, John Sabo, Huan Liu, K. Selçuk Candan,
• Abstract summary: We propose a unified causal learning framework for streamflow forecasting called CauSTream.<n>CauSTream learns a runoff causal graph among meteorological forcings and a routing graph capturing dynamic dependencies across stations.<n>We evaluate CauSTream on three major U.S. river basins across three forecasting horizons.
• Score: 7.693401294814023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Streamflow forecasting is crucial for water resource management and risk mitigation. While deep learning models have achieved strong predictive performance, they often overlook underlying physical processes, limiting interpretability and generalization. Recent causal learning approaches address these issues by integrating domain knowledge, yet they typically rely on fixed causal graphs that fail to adapt to data. We propose CauStream, a unified framework for causal spatiotemporal streamflow forecasting. CauSTream jointly learns (i) a runoff causal graph among meteorological forcings and (ii) a routing graph capturing dynamic dependencies across stations. We further establish identifiability conditions for these causal structures under a nonparametric setting. We evaluate CauSTream on three major U.S. river basins across three forecasting horizons. The model consistently outperforms prior state-of-the-art methods, with performance gaps widening at longer forecast windows, indicating stronger generalization to unseen conditions. Beyond forecasting, CauSTream also learns causal graphs that capture relationships among hydrological factors and stations. The inferred structures align closely with established domain knowledge, offering interpretable insights into watershed dynamics. CauSTream offers a principled foundation for causal spatiotemporal modeling, with the potential to extend to a wide range of scientific and environmental applications.

Related papers

• Use What You Know: Causal Foundation Models with Partial Graphs [97.91863420927866]
  Recently proposed Causal Foundation Models (CFMs) promise a more unified approach by amortising causal discovery and inference in a single step.<n>We bridge this gap by introducing methods to condition CFMs on causal information, such as the causal graph or more readily available ancestral information.
  arXiv  Detail & Related papers  (2026-02-16T17:56:37Z)
• DoFlow: Causal Generative Flows for Interventional and Counterfactual Time-Series Prediction [16.417858983587248]
  DoFlow is a flow based generative model defined over a causal DAG that delivers coherent observational and interventional predictions.<n>DoFlow provides explicit likelihoods of future trajectories, enabling principled anomaly detection.
  arXiv  Detail & Related papers  (2025-11-04T00:01:25Z)
• Solving Inverse Problems with FLAIR [68.87167940623318]
  We present FLAIR, a training-free variational framework that leverages flow-based generative models as prior for inverse problems.<n>Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.
  arXiv  Detail & Related papers  (2025-06-03T09:29:47Z)
• Unveiling the Basin-Like Loss Landscape in Large Language Models [64.07900377968143]
  We observe that pre-training creates a textitbasic capability basin, and subsequent alignment fine-tuning forms textitspecific capability basins.<n>We find that adversarial fine-tuning moves along the nearly worst-case directions, thus rapidly degrading model capabilities.
  arXiv  Detail & Related papers  (2025-05-23T09:06:40Z)
• Topology-Aware Conformal Prediction for Stream Networks [68.02503121089633]
  We propose Spatio-Temporal Adaptive Conformal Inference (textttCISTA), a novel framework that integrates network topology and temporal dynamics into the conformal prediction framework.<n>Our results show that textttCISTA effectively balances prediction efficiency and coverage, outperforming existing conformal prediction methods for stream networks.
  arXiv  Detail & Related papers  (2025-03-06T21:21:15Z)
• Spatio-temporal Causal Learning for Streamflow Forecasting [11.820781497467898]
  Causal Streamflow Forecasting (CSF) is tested in a real-world study in the Brazos River basin in Texas.<n>Our results demonstrate that our method outperforms regular computational-temporal graph neural networks.<n>This research offers a novel approach to streamflow prediction, showcasing the potential of combining advanced neural network techniques with domain-specific knowledge.
  arXiv  Detail & Related papers  (2024-11-26T23:19:56Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Streaming Motion Forecasting for Autonomous Driving [71.7468645504988]
  We introduce a benchmark that queries future trajectories on streaming data and we refer to it as "streaming forecasting" Our benchmark inherently captures the disappearance and re-appearance of agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks. We propose a plug-and-play meta-algorithm called "Predictive Streamer" that can adapt any snapshot-based forecaster into a streaming forecaster.
  arXiv  Detail & Related papers  (2023-10-02T17:13:16Z)
• Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting [22.434845478979604]
  We propose a mechanism for mining causal structures from continuous-time models. We train models to capture the causal structure by enforcing sparsity in the weights of the input layers of the dynamics models. We apply our method to a real-world problem, namely tsunami forecasting, where the exact causal-structures are difficult to characterize.
  arXiv  Detail & Related papers  (2022-10-10T18:53:13Z)
• Causal Autoregressive Flows [4.731404257629232]
  We highlight an intrinsic correspondence between a simple family of autoregressive normalizing flows and identifiable causal models. We exploit the fact that autoregressive flow architectures define an ordering over variables, analogous to a causal ordering, to show that they are well-suited to performing a range of causal inference tasks.
  arXiv  Detail & Related papers  (2020-11-04T13:17:35Z)
• Autoregressive flow-based causal discovery and inference [4.83420384410068]
  Autoregressive flow models are well-suited to performing a range of causal inference tasks. We exploit the fact that autoregressive architectures define an ordering over variables, analogous to a causal ordering. We present examples over synthetic data where autoregressive flows, when trained under the correct causal ordering, are able to make accurate interventional and counterfactual predictions.
  arXiv  Detail & Related papers  (2020-07-18T10:02:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.