BaHys—A Bayesian Modeling Framework for Long‐Term Concentration‐Discharge Hysteresis: A Case Study on Chloride
Abstract Improving river water quality requires a thorough understanding of the relationship between constituent concentration and water discharge during runoff events (i.e., C‐Q hysteresis), which may be strongly non‐linear. Analysis of C‐Q hysteresis on large temporal scales provides unprecedented...
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| Format: | Article |
| Language: | English |
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Wiley
2024-06-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR035427 |
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| author | Maria Cairoli Francisco Souza Gerard Stroomberg Geert Postma Lutgarde Buydens Jeroen Jansen |
| author_facet | Maria Cairoli Francisco Souza Gerard Stroomberg Geert Postma Lutgarde Buydens Jeroen Jansen |
| author_sort | Maria Cairoli |
| collection | DOAJ |
| description | Abstract Improving river water quality requires a thorough understanding of the relationship between constituent concentration and water discharge during runoff events (i.e., C‐Q hysteresis), which may be strongly non‐linear. Analysis of C‐Q hysteresis on large temporal scales provides unprecedented insights into event dynamics and long‐term concentration trends in surface and groundwater. Despite the increasing availability of time series data on water quality, there are still limited quantitative modeling frameworks that enable this analysis. Here, we combine Bayesian modeling and an existing mass balance to model long‐term C‐Q hysteresis dynamics in multi‐decade constituent concentration and water discharge time series. We focus on the case study of chloride and demonstrate that our model can simultaneously characterize the size and rotation of C‐Q hysteresis, and diffuse and low‐flow inputs to constituent concentration using only time‐series data from the river Rhine. Over 28 years of monitoring, we find that chloride exhibits a dominant clockwise dilution behavior that does not vary considerably under different hydro‐climatic conditions, hinting to similar mobilization mechanisms over time. We also show decreasing chloride concentrations in surface and groundwater, due to the cessation of mining activities in the Rhine. Our approach uses uncertainty estimates to show the range within which model parameter values lie, aiding decision‐makers in a robust assessment of river water quality. We conclude that Bayesian modeling of C‐Q hysteresis provides a powerful framework for investigating long‐term contamination dynamics that can be extended to several constituents to find factors controlling their export, ultimately suggesting mitigation measures for river contamination. |
| format | Article |
| id | doaj-art-879284d60a104e09a25c4e29274876f8 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-879284d60a104e09a25c4e29274876f82025-08-20T03:30:57ZengWileyWater Resources Research0043-13971944-79732024-06-01606n/an/a10.1029/2023WR035427BaHys—A Bayesian Modeling Framework for Long‐Term Concentration‐Discharge Hysteresis: A Case Study on ChlorideMaria Cairoli0Francisco Souza1Gerard Stroomberg2Geert Postma3Lutgarde Buydens4Jeroen Jansen5Radboud University Institute for Molecules and Materials Nijmegen The NetherlandsRadboud University Institute for Molecules and Materials Nijmegen The NetherlandsRadboud University Institute for Molecules and Materials Nijmegen The NetherlandsRadboud University Institute for Molecules and Materials Nijmegen The NetherlandsRadboud University Institute for Molecules and Materials Nijmegen The NetherlandsRadboud University Institute for Molecules and Materials Nijmegen The NetherlandsAbstract Improving river water quality requires a thorough understanding of the relationship between constituent concentration and water discharge during runoff events (i.e., C‐Q hysteresis), which may be strongly non‐linear. Analysis of C‐Q hysteresis on large temporal scales provides unprecedented insights into event dynamics and long‐term concentration trends in surface and groundwater. Despite the increasing availability of time series data on water quality, there are still limited quantitative modeling frameworks that enable this analysis. Here, we combine Bayesian modeling and an existing mass balance to model long‐term C‐Q hysteresis dynamics in multi‐decade constituent concentration and water discharge time series. We focus on the case study of chloride and demonstrate that our model can simultaneously characterize the size and rotation of C‐Q hysteresis, and diffuse and low‐flow inputs to constituent concentration using only time‐series data from the river Rhine. Over 28 years of monitoring, we find that chloride exhibits a dominant clockwise dilution behavior that does not vary considerably under different hydro‐climatic conditions, hinting to similar mobilization mechanisms over time. We also show decreasing chloride concentrations in surface and groundwater, due to the cessation of mining activities in the Rhine. Our approach uses uncertainty estimates to show the range within which model parameter values lie, aiding decision‐makers in a robust assessment of river water quality. We conclude that Bayesian modeling of C‐Q hysteresis provides a powerful framework for investigating long‐term contamination dynamics that can be extended to several constituents to find factors controlling their export, ultimately suggesting mitigation measures for river contamination.https://doi.org/10.1029/2023WR035427Bayesian modelinghysteresischloridemass balanceuncertaintyC‐Q relationship |
| spellingShingle | Maria Cairoli Francisco Souza Gerard Stroomberg Geert Postma Lutgarde Buydens Jeroen Jansen BaHys—A Bayesian Modeling Framework for Long‐Term Concentration‐Discharge Hysteresis: A Case Study on Chloride Water Resources Research Bayesian modeling hysteresis chloride mass balance uncertainty C‐Q relationship |
| title | BaHys—A Bayesian Modeling Framework for Long‐Term Concentration‐Discharge Hysteresis: A Case Study on Chloride |
| title_full | BaHys—A Bayesian Modeling Framework for Long‐Term Concentration‐Discharge Hysteresis: A Case Study on Chloride |
| title_fullStr | BaHys—A Bayesian Modeling Framework for Long‐Term Concentration‐Discharge Hysteresis: A Case Study on Chloride |
| title_full_unstemmed | BaHys—A Bayesian Modeling Framework for Long‐Term Concentration‐Discharge Hysteresis: A Case Study on Chloride |
| title_short | BaHys—A Bayesian Modeling Framework for Long‐Term Concentration‐Discharge Hysteresis: A Case Study on Chloride |
| title_sort | bahys a bayesian modeling framework for long term concentration discharge hysteresis a case study on chloride |
| topic | Bayesian modeling hysteresis chloride mass balance uncertainty C‐Q relationship |
| url | https://doi.org/10.1029/2023WR035427 |
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