A HydroLSTM‐Based Machine‐Learning Approach to Discovering Regionalized Representations of Catchment Dynamics
Abstract Finding similarities between model parameters across different catchments has proved to be challenging. Existing approaches struggle due to catchment heterogeneity and non‐linear dynamics. In particular, attempts to correlate catchment attributes with hydrological responses have failed due...
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR039008 |
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| author | Luis A. De la Fuente Andrew Bennett Hoshin V. Gupta Laura E. Condon |
| author_facet | Luis A. De la Fuente Andrew Bennett Hoshin V. Gupta Laura E. Condon |
| author_sort | Luis A. De la Fuente |
| collection | DOAJ |
| description | Abstract Finding similarities between model parameters across different catchments has proved to be challenging. Existing approaches struggle due to catchment heterogeneity and non‐linear dynamics. In particular, attempts to correlate catchment attributes with hydrological responses have failed due to interdependencies among variables and consequent equifinality. Machine Learning (ML), particularly the Long Short‐Term Memory (LSTM) approach, has demonstrated strong predictive and spatial regionalization performance. However, understanding the nature of the regionalization relationships remains difficult. This study proposes a novel approach to partially decouple learning the representation of (a) catchment dynamics by using the HydroLSTM architecture and (b) spatial regionalization relationships by using a Random Forest (RF) clustering approach to learn the relationships between the catchment attributes and dynamics. This coupled approach, called Regional HydroLSTM, learns a representation of “potential streamflow” using a single cell‐state, while the output gate corrects it to correspond to the temporal context of the current hydrologic regime. RF clusters mediate the relationship between catchment attributes and dynamics, allowing identification of spatially consistent hydrological regions, thereby providing insight into the factors driving spatial and temporal hydrological variability. Results suggest that by combining complementary architectures, we can enhance the interpretability of regional machine learning models in hydrology, offering a new perspective on the “catchment classification” problem. We conclude that an improved understanding of the underlying nature of hydrologic systems can be achieved by careful design of ML architectures to target the specific things we are seeking to learn from the data. |
| format | Article |
| id | doaj-art-710d87be0a1f4c119f981ab40b96fbc3 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-710d87be0a1f4c119f981ab40b96fbc32025-08-26T12:02:53ZengWileyWater Resources Research0043-13971944-79732025-08-01618n/an/a10.1029/2024WR039008A HydroLSTM‐Based Machine‐Learning Approach to Discovering Regionalized Representations of Catchment DynamicsLuis A. De la Fuente0Andrew Bennett1Hoshin V. Gupta2Laura E. Condon3Department of Hydrology and Atmospheric Sciences The University of Arizona Tucson AZ USADepartment of Hydrology and Atmospheric Sciences The University of Arizona Tucson AZ USADepartment of Hydrology and Atmospheric Sciences The University of Arizona Tucson AZ USADepartment of Hydrology and Atmospheric Sciences The University of Arizona Tucson AZ USAAbstract Finding similarities between model parameters across different catchments has proved to be challenging. Existing approaches struggle due to catchment heterogeneity and non‐linear dynamics. In particular, attempts to correlate catchment attributes with hydrological responses have failed due to interdependencies among variables and consequent equifinality. Machine Learning (ML), particularly the Long Short‐Term Memory (LSTM) approach, has demonstrated strong predictive and spatial regionalization performance. However, understanding the nature of the regionalization relationships remains difficult. This study proposes a novel approach to partially decouple learning the representation of (a) catchment dynamics by using the HydroLSTM architecture and (b) spatial regionalization relationships by using a Random Forest (RF) clustering approach to learn the relationships between the catchment attributes and dynamics. This coupled approach, called Regional HydroLSTM, learns a representation of “potential streamflow” using a single cell‐state, while the output gate corrects it to correspond to the temporal context of the current hydrologic regime. RF clusters mediate the relationship between catchment attributes and dynamics, allowing identification of spatially consistent hydrological regions, thereby providing insight into the factors driving spatial and temporal hydrological variability. Results suggest that by combining complementary architectures, we can enhance the interpretability of regional machine learning models in hydrology, offering a new perspective on the “catchment classification” problem. We conclude that an improved understanding of the underlying nature of hydrologic systems can be achieved by careful design of ML architectures to target the specific things we are seeking to learn from the data.https://doi.org/10.1029/2024WR039008regionalizationcatchment attributesstreamflowsimilarityclusteringHydroLSTM |
| spellingShingle | Luis A. De la Fuente Andrew Bennett Hoshin V. Gupta Laura E. Condon A HydroLSTM‐Based Machine‐Learning Approach to Discovering Regionalized Representations of Catchment Dynamics Water Resources Research regionalization catchment attributes streamflow similarity clustering HydroLSTM |
| title | A HydroLSTM‐Based Machine‐Learning Approach to Discovering Regionalized Representations of Catchment Dynamics |
| title_full | A HydroLSTM‐Based Machine‐Learning Approach to Discovering Regionalized Representations of Catchment Dynamics |
| title_fullStr | A HydroLSTM‐Based Machine‐Learning Approach to Discovering Regionalized Representations of Catchment Dynamics |
| title_full_unstemmed | A HydroLSTM‐Based Machine‐Learning Approach to Discovering Regionalized Representations of Catchment Dynamics |
| title_short | A HydroLSTM‐Based Machine‐Learning Approach to Discovering Regionalized Representations of Catchment Dynamics |
| title_sort | hydrolstm based machine learning approach to discovering regionalized representations of catchment dynamics |
| topic | regionalization catchment attributes streamflow similarity clustering HydroLSTM |
| url | https://doi.org/10.1029/2024WR039008 |
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