A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUS
Abstract The recent development of the physics‐aware Mass‐Conserving Long Short‐Term Memory network (MC‐LSTM) provides an alternative to other data‐driven Deep Learning (DL) models in hydrology. Mass‐Conserving Long Short‐Term Memory incorporates mass conservation directly into the LSTM architecture...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-08-01
|
| Series: | Water Resources Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024WR039131 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846173982852644864 |
|---|---|
| author | Yihan Wang Lujun Zhang N. Benjamin Erichson Tiantian Yang |
| author_facet | Yihan Wang Lujun Zhang N. Benjamin Erichson Tiantian Yang |
| author_sort | Yihan Wang |
| collection | DOAJ |
| description | Abstract The recent development of the physics‐aware Mass‐Conserving Long Short‐Term Memory network (MC‐LSTM) provides an alternative to other data‐driven Deep Learning (DL) models in hydrology. Mass‐Conserving Long Short‐Term Memory incorporates mass conservation directly into the LSTM architecture. Despite the theoretical advancements, studies have reported a surprisingly limited performance of the MC‐LSTM in streamflow simulation. We hypothesize that such a limitation is due to the unrealistic mass conservation scheme in MC‐LSTM, which overlooks unobserved incoming water fluxes beyond precipitation. As an attempt to verify this hypothesis, we propose a Mass Conservation Relaxed LSTM (MCR‐LSTM), which incorporates a bi‐directional mass relaxation (MR) component to account for potential incoming water fluxes beyond precipitation. We train and test the proposed MCR‐LSTM model across 531 watersheds in the contiguous United States (CONUS) against three baseline models: the Sacramento Soil Moisture Accounting, LSTM, and MC‐LSTM. Our results show that MCR‐LSTM outperforms MC‐LSTM despite its underperformance compared to LSTM. Specifically, MCR‐LSTM's advantage over MC‐LSTM is mainly seen in the Plains and Western U.S., where the newly incorporated MR component better simulates water loss and suggests the likely existence of additional incoming water fluxes beyond precipitation, respectively. The novelty and contribution of this study are twofold: firstly, it introduces an alternative physics‐aware DL tool (i.e., MCR‐LSTM) in hydrology with higher accuracy in specific regions compared to MC‐LSTM. Secondly, it provides a diagnosis of regions where strict, precipitation‐based mass conservation constraints may be unrealistic in streamflow simulation. |
| format | Article |
| id | doaj-art-071863f35a3b492baf3d102ec385b66b |
| 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-071863f35a3b492baf3d102ec385b66b2025-08-26T12:02:54ZengWileyWater Resources Research0043-13971944-79732025-08-01618n/an/a10.1029/2024WR039131A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUSYihan Wang0Lujun Zhang1N. Benjamin Erichson2Tiantian Yang3School of Civil Engineering and Environmental Science University of Oklahoma Norman OK USASchool of Civil Engineering and Environmental Science University of Oklahoma Norman OK USALawrence Berkeley National Laboratory Berkeley CA USASchool of Civil Engineering and Environmental Science University of Oklahoma Norman OK USAAbstract The recent development of the physics‐aware Mass‐Conserving Long Short‐Term Memory network (MC‐LSTM) provides an alternative to other data‐driven Deep Learning (DL) models in hydrology. Mass‐Conserving Long Short‐Term Memory incorporates mass conservation directly into the LSTM architecture. Despite the theoretical advancements, studies have reported a surprisingly limited performance of the MC‐LSTM in streamflow simulation. We hypothesize that such a limitation is due to the unrealistic mass conservation scheme in MC‐LSTM, which overlooks unobserved incoming water fluxes beyond precipitation. As an attempt to verify this hypothesis, we propose a Mass Conservation Relaxed LSTM (MCR‐LSTM), which incorporates a bi‐directional mass relaxation (MR) component to account for potential incoming water fluxes beyond precipitation. We train and test the proposed MCR‐LSTM model across 531 watersheds in the contiguous United States (CONUS) against three baseline models: the Sacramento Soil Moisture Accounting, LSTM, and MC‐LSTM. Our results show that MCR‐LSTM outperforms MC‐LSTM despite its underperformance compared to LSTM. Specifically, MCR‐LSTM's advantage over MC‐LSTM is mainly seen in the Plains and Western U.S., where the newly incorporated MR component better simulates water loss and suggests the likely existence of additional incoming water fluxes beyond precipitation, respectively. The novelty and contribution of this study are twofold: firstly, it introduces an alternative physics‐aware DL tool (i.e., MCR‐LSTM) in hydrology with higher accuracy in specific regions compared to MC‐LSTM. Secondly, it provides a diagnosis of regions where strict, precipitation‐based mass conservation constraints may be unrealistic in streamflow simulation.https://doi.org/10.1029/2024WR039131deep learningstreamflow simulationinterpretable AI |
| spellingShingle | Yihan Wang Lujun Zhang N. Benjamin Erichson Tiantian Yang A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUS Water Resources Research deep learning streamflow simulation interpretable AI |
| title | A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUS |
| title_full | A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUS |
| title_fullStr | A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUS |
| title_full_unstemmed | A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUS |
| title_short | A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUS |
| title_sort | mass conservation relaxed mcr lstm model for streamflow simulation across conus |
| topic | deep learning streamflow simulation interpretable AI |
| url | https://doi.org/10.1029/2024WR039131 |
| work_keys_str_mv | AT yihanwang amassconservationrelaxedmcrlstmmodelforstreamflowsimulationacrossconus AT lujunzhang amassconservationrelaxedmcrlstmmodelforstreamflowsimulationacrossconus AT nbenjaminerichson amassconservationrelaxedmcrlstmmodelforstreamflowsimulationacrossconus AT tiantianyang amassconservationrelaxedmcrlstmmodelforstreamflowsimulationacrossconus AT yihanwang massconservationrelaxedmcrlstmmodelforstreamflowsimulationacrossconus AT lujunzhang massconservationrelaxedmcrlstmmodelforstreamflowsimulationacrossconus AT nbenjaminerichson massconservationrelaxedmcrlstmmodelforstreamflowsimulationacrossconus AT tiantianyang massconservationrelaxedmcrlstmmodelforstreamflowsimulationacrossconus |