Articles | Volume 24, issue 8
https://doi.org/10.5194/hess-24-3967-2020
© Author(s) 2020. 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-24-3967-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Aug 2020
Research article |
| 12 Aug 2020
| 12 Aug 2020
Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach
Research Applications Laboratory, National Center for Atmospheric Research, Boulder CO, USA
Eric Gilleland
Research Applications Laboratory, National Center for Atmospheric Research, Boulder CO, USA
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Cited
18 citations as recorded by crossref.
- An open workflow to gain insights about low‐likelihood high‐impact weather events from initialized predictions T. Kelder et al. 10.1002/met.2065
- Sailing synthetic seas: Stochastic simulation of benchmark sea state time series F. Serinaldi et al. 10.1016/j.coastaleng.2022.104164
- Temperature change-informed future multisite streamflow generation to support water supply vulnerability assessments under climate change S. Ji & K. Ahn 10.1016/j.jhydrol.2023.129928
- Extreme floods in Europe: going beyond observations using reforecast ensemble pooling M. Brunner & L. Slater 10.5194/hess-26-469-2022
- How Probable Is Widespread Flooding in the United States? M. Brunner et al. 10.1029/2020WR028096
- Space–time dependence of compound hot–dry events in the United States: assessment using a multi-site multi-variable weather generator M. Brunner et al. 10.5194/esd-12-621-2021
- Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management L. Slater et al. 10.5194/hess-25-3897-2021
- Generalized divergences for statistical evaluation of uncertainty in long-memory processes H. Yoshioka & Y. Yoshioka 10.1016/j.chaos.2024.114627
- An unbiased estimator of coefficient of variation of streamflow L. Ye et al. 10.1016/j.jhydrol.2021.125954
- Correcting Systematic Bias in Climate Model Simulations in the Time‐Frequency Domain C. Kusumastuti et al. 10.1029/2022GL100550
- Streamflow forecasting method with a hybrid physical process-mathematical statistic S. Wang et al. 10.1007/s00477-023-02542-w
- Floods and droughts: a multivariate perspective M. Brunner 10.5194/hess-27-2479-2023
- Hybrid modified continuous time Markov chain model for daily streamflow generation L. Shilpa & K. Srinivasan 10.1016/j.jhydrol.2022.128206
- Interpreting extreme climate impacts from large ensemble simulations—are they unseen or unrealistic? T. Kelder et al. 10.1088/1748-9326/ac5cf4
- Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land‐Surface Processes M. Brunner et al. 10.1029/2020GL088000
- Dynamic long-term streamflow probabilistic forecasting model for a multisite system considering real-time forecast updating through spatio-temporal dependent error correction R. Mo et al. 10.1016/j.jhydrol.2021.126666
- Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
- Temporal Scale‐Dependent Sensitivity Analysis for Hydrological Model Parameters Using the Discrete Wavelet Transform and Active Subspaces D. Bittner et al. 10.1029/2020WR028511
18 citations as recorded by crossref.
- An open workflow to gain insights about low‐likelihood high‐impact weather events from initialized predictions T. Kelder et al. 10.1002/met.2065
- Sailing synthetic seas: Stochastic simulation of benchmark sea state time series F. Serinaldi et al. 10.1016/j.coastaleng.2022.104164
- Temperature change-informed future multisite streamflow generation to support water supply vulnerability assessments under climate change S. Ji & K. Ahn 10.1016/j.jhydrol.2023.129928
- Extreme floods in Europe: going beyond observations using reforecast ensemble pooling M. Brunner & L. Slater 10.5194/hess-26-469-2022
- How Probable Is Widespread Flooding in the United States? M. Brunner et al. 10.1029/2020WR028096
- Space–time dependence of compound hot–dry events in the United States: assessment using a multi-site multi-variable weather generator M. Brunner et al. 10.5194/esd-12-621-2021
- Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management L. Slater et al. 10.5194/hess-25-3897-2021
- Generalized divergences for statistical evaluation of uncertainty in long-memory processes H. Yoshioka & Y. Yoshioka 10.1016/j.chaos.2024.114627
- An unbiased estimator of coefficient of variation of streamflow L. Ye et al. 10.1016/j.jhydrol.2021.125954
- Correcting Systematic Bias in Climate Model Simulations in the Time‐Frequency Domain C. Kusumastuti et al. 10.1029/2022GL100550
- Streamflow forecasting method with a hybrid physical process-mathematical statistic S. Wang et al. 10.1007/s00477-023-02542-w
- Floods and droughts: a multivariate perspective M. Brunner 10.5194/hess-27-2479-2023
- Hybrid modified continuous time Markov chain model for daily streamflow generation L. Shilpa & K. Srinivasan 10.1016/j.jhydrol.2022.128206
- Interpreting extreme climate impacts from large ensemble simulations—are they unseen or unrealistic? T. Kelder et al. 10.1088/1748-9326/ac5cf4
- Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land‐Surface Processes M. Brunner et al. 10.1029/2020GL088000
- Dynamic long-term streamflow probabilistic forecasting model for a multisite system considering real-time forecast updating through spatio-temporal dependent error correction R. Mo et al. 10.1016/j.jhydrol.2021.126666
- Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
- Temporal Scale‐Dependent Sensitivity Analysis for Hydrological Model Parameters Using the Discrete Wavelet Transform and Active Subspaces D. Bittner et al. 10.1029/2020WR028511
Latest update: 19 Apr 2024
Short summary
Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but yet unobserved streamflow time series with the same characteristics as the observed data. We propose a stochastic simulation approach in the frequency domain instead of the time domain. Our evaluation results suggest that the flexible, continuous simulation approach is valuable for a diverse range of water management applications.
Stochastically generated streamflow time series are used for various water management and hazard...
