Articles | Volume 26, issue 9
https://doi.org/10.5194/hess-26-2405-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-2405-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 May 2022
Research article |
| 09 May 2022
| 09 May 2022
Karst spring discharge modeling based on deep learning using spatially distributed input data
Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences, Hydrogeology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Tanja Liesch
Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences, Hydrogeology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Guillaume Cinkus
HydroSciences Montpellier (HSM), Université de Montpellier, CNRS, IRD, 34090 Montpellier, France
Nataša Ravbar
ZRC SAZU, Karst Research Institute, Titov trg 2, 6230 Postojna, Slovenia
Zhao Chen
Institute of Groundwater Management, Technical University of Dresden, 01062 Dresden
Naomi Mazzilli
UMR 1114 EMMAH (AU-INRAE), Université d'Avignon, 84000 Avignon, France
Hervé Jourde
HydroSciences Montpellier (HSM), Université de Montpellier, CNRS, IRD, 34090 Montpellier, France
Nico Goldscheider
Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences, Hydrogeology, Kaiserstr. 12, 76131 Karlsruhe, Germany
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27 citations as recorded by crossref.
- Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić 10.3389/frwa.2022.934709
- Hybrid modeling of karstic springs: Error correction of conceptual reservoir models with machine learning N. Bouhafa et al. 10.2166/ws.2024.092
- Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning Ž. Brkić & M. Kuhta 10.3390/su141610447
- A parsimonious model for springs discharge reconstruction and forecast for drought management: Lessons from a case study in Central Italy N. Guyennon et al. 10.1016/j.ejrh.2024.102021
- Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al. 10.1016/j.ejrh.2024.101744
- Effects of passive-storage conceptualization on modeling hydrological function and isotope dynamics in the flow system of a cockpit karst landscape G. Li et al. 10.5194/hess-26-5515-2022
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- Assessment of drought conditions and prediction by machine learning algorithms using Standardized Precipitation Index and Standardized Water-Level Index (case study: Yazd province, Iran) R. Shakeri et al. 10.1007/s11356-023-29522-5
- Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
- Evaluating the effects of water exchange between surface rivers and karst aquifers on surface flood simulations at different watershed scales J. Li et al. 10.1016/j.jhydrol.2023.129851
- Multiorder hydrologic Position for Europe — a Set of Features for Machine Learning and Analysis in Hydrology M. Nölscher et al. 10.1038/s41597-022-01787-4
- Hydrological Functioning and Water Availability in a Himalayan Karst Basin under Climate Change S. Sarker et al. 10.3390/su15118666
- A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
- Karst aquifer discharge response to rainfall interpreted as anomalous transport D. Elhanati et al. 10.5194/hess-28-4239-2024
- Evaluating climate change impacts on snow cover and karst spring discharge in a data-scarce region: a case study of Iran N. Zeydalinejad et al. 10.1007/s11600-024-01400-9
- A Fully Connected Neural Network (FCNN) Model to Simulate Karst Spring Flowrates in the Umbria Region (Central Italy) F. De Filippi et al. 10.3390/w16182580
- EStreams: An integrated dataset and catalogue of streamflow, hydro-climatic and landscape variables for Europe T. do Nascimento et al. 10.1038/s41597-024-03706-1
- Estimation of hydraulic conductivity functions in karst regions by particle swarm optimization with application to Lake Vrana, Croatia V. Travaš et al. 10.5194/hess-27-1343-2023
- A hybrid self-adaptive DWT-WaveNet-LSTM deep learning architecture for karst spring forecasting R. Zhou et al. 10.1016/j.jhydrol.2024.131128
- Use of deep learning to identify optimal meteorological inputs to forecast seasonal precipitation S. Zenkoji et al. 10.3178/hrl.16.67
- Advanced streamflow forecasting for Central European Rivers: The Cutting-Edge Kolmogorov-Arnold networks compared to Transformers F. Granata et al. 10.1016/j.jhydrol.2024.132175
- Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions G. Cinkus et al. 10.5194/hess-27-1961-2023
- Climate change impacts on the Nahavand karstic springs using the data mining techniques R. Fasihi et al. 10.1007/s00704-023-04810-9
- Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
- Managing climate change impacts on the Western Mountain Aquifer: Implications for Mediterranean karst groundwater resources L. Bresinsky et al. 10.1016/j.hydroa.2023.100153
1 citations as recorded by crossref.
Latest update: 08 Nov 2024
Short summary
Modeling complex karst water resources is difficult enough, but often there are no or too few climate stations available within or close to the catchment to deliver input data for modeling purposes. We apply image recognition algorithms to time-distributed, spatially gridded meteorological data to simulate karst spring discharge. Our models can also learn the approximate catchment location of a spring independently.
Modeling complex karst water resources is difficult enough, but often there are no or too few...
