Articles | Volume 26, issue 14
https://doi.org/10.5194/hess-26-3863-2022
© 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-26-3863-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Analysis of high streamflow extremes in climate change studies: how do we calibrate hydrological models?
Department of Civil, Environmental and Mechanical Engineering,
University of Trento, 38123 Trento, Italy
Diego Avesani
Department of Civil, Environmental and Mechanical Engineering,
University of Trento, 38123 Trento, Italy
Patrick Zulian
Department of Civil, Environmental and Mechanical Engineering,
University of Trento, 38123 Trento, Italy
Aldo Fiori
Department of Engineering, Roma Tre University, 00154 Rome, Italy
Alberto Bellin
Department of Civil, Environmental and Mechanical Engineering,
University of Trento, 38123 Trento, Italy
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- Using Multi-Source Data to Assess the Hydrologic Alteration and Extremes under a Changing Environment in the Yalong River Basin Y. He et al. 10.3390/w15071357
- A Digital Twin Dam and Watershed Management Platform D. Park & H. You 10.3390/w15112106
- Exploring Climate Sensitivity in Hydrological Model Calibration J. Lee et al. 10.3390/w15234094
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31 citations as recorded by crossref.
- A Screening Procedure for Identifying Drought Hot-Spots in a Changing Climate A. Galletti et al. 10.3390/w15091731
- Severe floods predictive ability: A proxy based probabilistic assessment of the Italian early warning system F. Silvestro et al. 10.1111/jfr3.12970
- Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction S. López-Chacón et al. 10.3390/w15112020
- Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian‐based integrated approach X. Yan et al. 10.1002/hyp.15040
- Projected Future Flooding Pattern of Wabash River in Indiana and Fountain Creek in Colorado: An Assessment Utilizing Bias-Corrected CMIP6 Climate Data S. Paudel et al. 10.3390/forecast5020022
- Quantifying the climate change impacts on the magnitude and timing of hydrological extremes in the Baro River Basin, Ethiopia S. Kassaye et al. 10.1186/s40068-023-00328-1
- Evaluation of Extreme Hydroclimatic Trends in River Basins Located in the Northeast and South Regions of Brazil P. Esposte Coutinho & M. Cataldi 10.3390/atmos14091388
- Streamflow Variation under Climate Conditions Based on a Soil and Water Assessment Tool Model: A Case Study of the Bailong River Basin S. Li et al. 10.3390/su16103901
- Hydrological Evaluation of CRA40 and ERA5 Reanalysis Precipitation Products over Ganjiang River Basin in Humid Southeastern China Z. Li et al. 10.3390/w16192774
- Assessment of Climate Change Impact on Hydropower Generation: A Case Study for Três Marias Power Plant in Brazil B. da Silva et al. 10.3390/cli11100201
- In Search of Climate Crisis in Greece Using Hydrological Data: 404 Not Found D. Koutsoyiannis et al. 10.3390/w15091711
- Flood Inundation and Streamflow Changes in the Kabul River Basin under Climate Change S. Baig & S. Hasson 10.3390/su16010116
- Projection and Analysis of Floods in the Upper Heihe River Basin under Climate Change Y. Ye et al. 10.3390/atmos14071083
- Identification of a Function to Fit the Flow Duration Curve and Parameterization of a Semi-Arid Region in North China L. Ma et al. 10.3390/atmos14010116
- Effects of Climate Change on Streamflow in the Godavari Basin Simulated Using a Conceptual Model including CMIP6 Dataset N. Reddy et al. 10.3390/w15091701
- Spring Runoff Simulation of Snow-Dominant Catchment in Steppe Regions: A Comparison Study of Lumped Conceptual Models S. Eroshenko et al. 10.3390/inventions9050109
- Assessing the Impact of Climate Change on an Ungauged Watershed in the Congo River Basin S. Masamba et al. 10.3390/w16192825
- Hybrid physically based and machine learning model to enhance high streamflow prediction S. López-Chacón et al. 10.1080/02626667.2024.2426720
- Application of Different Weighting Schemes and Stochastic Simulations to Parameterization Processes Considering Observation Error: Implications for Climate Change Impact Analysis of Integrated Watershed Models E. Lee et al. 10.3390/w15101880
- Evaluating Climate Change Effects on a Snow-Dominant Watershed: A Multi-Model Hydrological Investigation A. Sharifinejad & E. Hassanzadeh 10.3390/w15183281
- Glaciers determine the sensitivity of hydrological processes to perturbed climate in a large mountainous basin on the Tibetan Plateau Y. Nan & F. Tian 10.5194/hess-28-669-2024
- Impact of climate change on the water resources of the Atbara River using novel hydrological models M. El-Mahdy et al. 10.2166/wst.2024.070
- Stochastic Rational Method for Estimation of Flood Peak Uncertainty in Arid Basins: Comparison between Monte Carlo and First Order Second Moment Methods with a Case Study in Southwest Saudi Arabia N. Al-Amri et al. 10.3390/su15064719
- The Strategic Random Search (SRS) – A new global optimizer for calibrating hydrological models H. Wei et al. 10.1016/j.envsoft.2023.105914
- Impact of Climate Change on the Water Balance of the Akaki Catchment A. Guyasa et al. 10.3390/w16010054
- Mitigating water pollution in a Portuguese river basin under climate change through agricultural sustainable practices J. Ramião et al. 10.1007/s11027-024-10121-9
- Simulating Changes in Hydrological Extremes—Future Scenarios for Morocco L. Giustarini et al. 10.3390/w15152722
- Using Multi-Source Data to Assess the Hydrologic Alteration and Extremes under a Changing Environment in the Yalong River Basin Y. He et al. 10.3390/w15071357
- A Digital Twin Dam and Watershed Management Platform D. Park & H. You 10.3390/w15112106
- Exploring Climate Sensitivity in Hydrological Model Calibration J. Lee et al. 10.3390/w15234094
- Streamflow Response to Climate and Land-Use Changes in a Tropical Island Basin C. Cao et al. 10.3390/su151813941
Latest update: 20 Nov 2024
Short summary
In this work, we introduce a methodology for devising reliable future high streamflow scenarios from climate change simulations. The calibration of a hydrological model is carried out to maximize the probability that the modeled and observed high flow extremes belong to the same statistical population. Application to the Adige River catchment (southeastern Alps, Italy) showed that this procedure produces reliable quantiles of the annual maximum streamflow for use in assessment studies.
In this work, we introduce a methodology for devising reliable future high streamflow scenarios...