The complexity and interrelations of hydrological systems

The gap

Evidence profile

Sourced from the stated research gap and future-work section and abstract and limitations section of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 3 journals. Those papers have been cited 71 times in total.

Supporting evidence — 4 representative gaps

• Integrating Physical-Based Xinanjiang Model and Deep Learning for Interpretable Streamflow Simulation: A Multi-Source Data Fusion Approach across Diverse Chinese Basins (2026) · Hydrology and earth system sciences · doi

  The complexity and interrelations of hydrological systems inevitably introduce uncertainties, particularly concerning model parameter estimation and structure selection. Existing single models struggle to accurately capture the stochastic, non-stationary, and nonlinear dynamics of basin streamflow in changing environments.

  generalstated research gap
  Keywords: complexity interrelations hydrological systems inevitably introduce uncertainties particularly
• Integrating Physical-Based Xinanjiang Model and Deep Learning for Interpretable Streamflow Simulation: A Multi-Source Data Fusion Approach across Diverse Chinese Basins (2026) · Hydrology and earth system sciences · doi

  Exploring high-precision and reliable streamflow simulation models has become an urgent and practical issue in the field of hydrology. Further analysis of the core role of the XAJ model in the ensemble is needed. The study suggests that further research is needed to improve the simulation accuracy and reduce the risk of overfitting.

  generalfuture-work section
  Keywords: exploring high-precision reliable streamflow simulation models has become
• Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon (2024) · Water Resources Research · cited 71× · doi

  In this study, a novel framework that seamlessly integrates a process‐based hydrological model encoded as a neural network (NN), an additional NN for mapping spatially distributed and physically meaningful parameters from watershed attributes, and NN‐based replacement models representing inadequately understood processes is developed.

  generalabstractevidence 5/5
  Keywords: based novel framework seamlessly integrates process hydrological model encoded neural network additional mapping spatially distributed
• Diagnosing the Roles of Meteorological Forcing, Storage Memory, and Network Connectivity in Cold-Region Streamflow Prediction (2026) · Water Resources Management · doi

  data-driven streamflow models consistently underestimated peak magnitudes, - the largest underestimation occurred during the April 2022 flood, - assumptions of spatial homogeneity within hydrologic response units often fail to represent the effects of heterogeneous land cover, soils, and topography, - physics-based hydrologic models remain limited by computationally intensive calibration and difficulties in representing complex watershed processes

  generallimitations sectionevidence 5/5
  Keywords: data-driven streamflow models consistently underestimated peak magnitudes largest

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