Articles | Volume 21, issue 12
https://doi.org/10.5194/hess-21-5971-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-5971-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
30 Nov 2017
Research article |
| 30 Nov 2017
On the value of water quality data and informative flow states in karst modelling
Andreas Hartmann
CORRESPONDING AUTHOR
Faculty of Environment and Natural Resources, University of Freiburg,
Freiburg, Germany
Department of Civil Engineering, University of Bristol, Bristol, UK
Juan Antonio Barberá
Department of Geology and Centre of Hydrogeology, University of
Malaga (CEHIUMA), Malaga 29071, Spain
Bartolomé Andreo
Department of Geology and Centre of Hydrogeology, University of
Malaga (CEHIUMA), Malaga 29071, Spain
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- Information‐Based Machine Learning for Tracer Signature Prediction in Karstic Environments B. Mewes et al. 10.1029/2018WR024558
- Identifying More Realistic Model Structures by Electrical Conductivity Observations of the Karst Spring Y. Chang et al. 10.1029/2020WR028587
- Using continuous electrical conductivity measurements to derive major solute concentrations in karst systems B. Richieri et al. 10.1002/hyp.14929
- Impact of historic and future climate on spring recharge and discharge based on an integrated numerical modelling approach: Application on a snow-governed semi-arid karst catchment area J. Doummar et al. 10.1016/j.jhydrol.2018.08.062
- Identifying relevant hydrological and catchment properties in active subspaces: An inference study of a lumped karst aquifer model D. Bittner et al. 10.1016/j.advwatres.2019.103472
- Influence of precipitation infiltration recharge on hydrological processes of the karst aquifer system and adjacent river F. Huang et al. 10.1016/j.jhydrol.2024.131656
- What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling T. Wagener & F. Pianosi 10.1016/j.earscirev.2019.04.006
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- Characterization, modeling, and remediation of karst in a changing environment Z. Xu et al. 10.1007/s12665-018-7660-7
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- What is the best time to take stream isotope samples for event-based model calibration? L. Wang et al. 10.1016/j.jhydrol.2019.123950
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- Reducing uncertainty of karst aquifer modeling with complementary hydrological observations for the sustainable management of groundwater resources Y. Cousquer & H. Jourde 10.1016/j.jhydrol.2022.128130
- Ten strategies towards successful calibration of environmental models J. Mai 10.1016/j.jhydrol.2023.129414
- Temporal Scale‐Dependent Sensitivity Analysis for Hydrological Model Parameters Using the Discrete Wavelet Transform and Active Subspaces D. Bittner et al. 10.1029/2020WR028511
- Modeling interbasin groundwater flow in karst areas: Model development, application, and calibration strategy V. Nguyen et al. 10.1016/j.envsoft.2019.104606
- On the value of hydrochemical data for the interpretation of flow and transport processes in the Baget karst system, France B. Richieri et al. 10.1007/s10040-024-02801-2
- Nitrate Pathways, Processes, and Timing in an Agricultural Karst System: Development and Application of a Numerical Model A. Husic et al. 10.1029/2018WR023703
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- Effect of Observation Errors on the Timing of the Most Informative Isotope Samples for Event-Based Model Calibration L. Wang et al. 10.3390/hydrology5010004
27 citations as recorded by crossref.
- A GIS-based model for simulating the hydrological effects of land use changes on karst systems – The integration of the LuKARS model into FREEWAT D. Bittner et al. 10.1016/j.envsoft.2020.104682
- Disparate Seasonal Nitrate Export From Nested Heterogeneous Subcatchments Revealed With StorAge Selection Functions T. Nguyen et al. 10.1029/2021WR030797
- Storage dynamics, hydrological connectivity and flux ages in a karst catchment: conceptual modelling using stable isotopes Z. Zhang et al. 10.5194/hess-23-51-2019
- Bayesian Calibration and Sensitivity Analysis for a Karst Aquifer Model Using Active Subspaces M. Teixeira Parente et al. 10.1029/2019WR024739
- Hydrogeology of the Sterkfontein Cave System, Cradle of Humankind, South Africa P. Hobbs & N. de Meillon 10.25131/gssajg.120.3.403
- Incorporating experimentally derived streamflow contributions into model parameterization to improve discharge prediction A. Hartmann et al. 10.5194/hess-27-1325-2023
- Revealing the positive influence of young water fractions derived from stable isotopes on the robustness of karst water resources predictions K. Çallı et al. 10.1016/j.jhydrol.2023.129549
- Information‐Based Machine Learning for Tracer Signature Prediction in Karstic Environments B. Mewes et al. 10.1029/2018WR024558
- Identifying More Realistic Model Structures by Electrical Conductivity Observations of the Karst Spring Y. Chang et al. 10.1029/2020WR028587
- Using continuous electrical conductivity measurements to derive major solute concentrations in karst systems B. Richieri et al. 10.1002/hyp.14929
- Impact of historic and future climate on spring recharge and discharge based on an integrated numerical modelling approach: Application on a snow-governed semi-arid karst catchment area J. Doummar et al. 10.1016/j.jhydrol.2018.08.062
- Identifying relevant hydrological and catchment properties in active subspaces: An inference study of a lumped karst aquifer model D. Bittner et al. 10.1016/j.advwatres.2019.103472
- Influence of precipitation infiltration recharge on hydrological processes of the karst aquifer system and adjacent river F. Huang et al. 10.1016/j.jhydrol.2024.131656
- What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling T. Wagener & F. Pianosi 10.1016/j.earscirev.2019.04.006
- Hydrological Modeling of the Effect of the Transition From Flood to Drip Irrigation on Groundwater Recharge Using Multi‐Objective Calibration S. Pool et al. 10.1029/2021WR029677
- Characterization, modeling, and remediation of karst in a changing environment Z. Xu et al. 10.1007/s12665-018-7660-7
- Characterizing Catchment‐Scale Nitrogen Legacies and Constraining Their Uncertainties F. Sarrazin et al. 10.1029/2021WR031587
- What is the best time to take stream isotope samples for event-based model calibration? L. Wang et al. 10.1016/j.jhydrol.2019.123950
- A soil moisture monitoring network to characterize karstic recharge and evapotranspiration at five representative sites across the globe R. Berthelin et al. 10.5194/gi-9-11-2020
- A robust objective function for calibration of groundwater models in light of deficiencies of model structure and observations R. Schneider et al. 10.1016/j.jhydrol.2022.128339
- Reducing uncertainty of karst aquifer modeling with complementary hydrological observations for the sustainable management of groundwater resources Y. Cousquer & H. Jourde 10.1016/j.jhydrol.2022.128130
- Ten strategies towards successful calibration of environmental models J. Mai 10.1016/j.jhydrol.2023.129414
- Temporal Scale‐Dependent Sensitivity Analysis for Hydrological Model Parameters Using the Discrete Wavelet Transform and Active Subspaces D. Bittner et al. 10.1029/2020WR028511
- Modeling interbasin groundwater flow in karst areas: Model development, application, and calibration strategy V. Nguyen et al. 10.1016/j.envsoft.2019.104606
- On the value of hydrochemical data for the interpretation of flow and transport processes in the Baget karst system, France B. Richieri et al. 10.1007/s10040-024-02801-2
- Nitrate Pathways, Processes, and Timing in an Agricultural Karst System: Development and Application of a Numerical Model A. Husic et al. 10.1029/2018WR023703
- Pitfalls and a feasible solution for using KGE as an informal likelihood function in MCMC methods: DREAM(ZS) as an example Y. Liu et al. 10.5194/hess-26-5341-2022
Latest update: 26 Jul 2024
Short summary
In karst modeling, there is often an imbalance between the complexity of model structures and the data availability for parameterization. We present a new approach to quantify the value of water quality data for improved karst model parameterization. We show that focusing on “informative” time periods, which are time periods with decreased observation uncertainty, allows for further reduction of simulation uncertainty. Our approach is transferable to other sites with limited data availability.
In karst modeling, there is often an imbalance between the complexity of model structures and...
