Articles | Volume 18, issue 1
https://doi.org/10.5194/hess-18-257-2014
© Author(s) 2014. 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-18-257-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
21 Jan 2014
Research article |
| 21 Jan 2014
Hydrological appraisal of operational weather radar rainfall estimates in the context of different modelling structures
College of Engineering, Swansea University, Swansea, SA2 8PP, UK
College of Engineering, Swansea University, Swansea, SA2 8PP, UK
I. Cluckie
College of Engineering, Swansea University, Swansea, SA2 8PP, UK
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19 citations as recorded by crossref.
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- Evaluating the use of bias-corrected radar rainfall data in three flood events in Samsun, Turkey A. Ozkaya & Z. Akyurek 10.1007/s11069-019-03723-z
- Coupled hydro-meteorological modelling on a HPC platform for high-resolution extreme weather impact study D. Zhu et al. 10.5194/hess-20-4707-2016
- Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks K. Zhu et al. 10.3389/fenvs.2022.1054235
- Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation X. Chen et al. 10.1155/2016/2710457
- Evaluation of global forcing datasets for hydropower inflow simulation in Nepal B. Bhattarai et al. 10.2166/nh.2020.079
- Hydrological simulation using multi-sources precipitation estimates in the Huaihe River Basin M. Abro et al. 10.1007/s12517-021-08254-1
- Remote Sensing and Ground-Based Weather Forcing Data Analysis for Streamflow Simulation J. Corona et al. 10.3390/hydrology1010089
- A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets H. Wang & Y. Xuan 10.3390/rs14153823
- Applicability of open rainfall data to event-scale urban rainfall-runoff modelling T. Niemi et al. 10.1016/j.jhydrol.2017.01.056
- River Discharge Simulation in the High Andes of Southern Ecuador Using High-Resolution Radar Observations and Meteorological Station Data D. Mejía-Veintimilla et al. 10.3390/rs11232804
- Quantitative Precipitation Estimation and Hydrological Modeling in Japan P. Shakti 10.3178/jjshwr.30.6
- Flood forecasting based on radar precipitation nowcasting using U-net and its improved models J. Li et al. 10.1016/j.jhydrol.2024.130871
- The legacy of STAHY: milestones, achievements, challenges, and open problems in statistical hydrology E. Volpi et al. 10.1080/02626667.2024.2385686
- An evaluation framework for identifying the optimal raingauge network based on spatiotemporal variation in quantitative precipitation estimation C. Chang et al. 10.2166/nh.2016.169
- Demonstrating the value of community-based (‘citizen science’) observations for catchment modelling and characterisation E. Starkey et al. 10.1016/j.jhydrol.2017.03.019
- Hydrological appraisal of rainfall estimates from radar, satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin, China M. Abro et al. 10.1080/02626667.2018.1557335
- Improving radar estimates of rainfall using an input subset of artificial neural networks T. Yang et al. 10.1117/1.JRS.10.026013
19 citations as recorded by crossref.
- Reduced fine sediment flux and channel change in response to the managed diversion of an upland river channel M. Perks & J. Warburton 10.5194/esurf-4-705-2016
- Hydrological evaluation of satellite and reanalysis precipitation products in the glacier-fed river basin (Gilgit) M. Abro et al. 10.1007/s12517-020-05621-2
- Evaluating the use of bias-corrected radar rainfall data in three flood events in Samsun, Turkey A. Ozkaya & Z. Akyurek 10.1007/s11069-019-03723-z
- Coupled hydro-meteorological modelling on a HPC platform for high-resolution extreme weather impact study D. Zhu et al. 10.5194/hess-20-4707-2016
- Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks K. Zhu et al. 10.3389/fenvs.2022.1054235
- Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation X. Chen et al. 10.1155/2016/2710457
- Evaluation of global forcing datasets for hydropower inflow simulation in Nepal B. Bhattarai et al. 10.2166/nh.2020.079
- Hydrological simulation using multi-sources precipitation estimates in the Huaihe River Basin M. Abro et al. 10.1007/s12517-021-08254-1
- Remote Sensing and Ground-Based Weather Forcing Data Analysis for Streamflow Simulation J. Corona et al. 10.3390/hydrology1010089
- A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets H. Wang & Y. Xuan 10.3390/rs14153823
- Applicability of open rainfall data to event-scale urban rainfall-runoff modelling T. Niemi et al. 10.1016/j.jhydrol.2017.01.056
- River Discharge Simulation in the High Andes of Southern Ecuador Using High-Resolution Radar Observations and Meteorological Station Data D. Mejía-Veintimilla et al. 10.3390/rs11232804
- Quantitative Precipitation Estimation and Hydrological Modeling in Japan P. Shakti 10.3178/jjshwr.30.6
- Flood forecasting based on radar precipitation nowcasting using U-net and its improved models J. Li et al. 10.1016/j.jhydrol.2024.130871
- The legacy of STAHY: milestones, achievements, challenges, and open problems in statistical hydrology E. Volpi et al. 10.1080/02626667.2024.2385686
- An evaluation framework for identifying the optimal raingauge network based on spatiotemporal variation in quantitative precipitation estimation C. Chang et al. 10.2166/nh.2016.169
- Demonstrating the value of community-based (‘citizen science’) observations for catchment modelling and characterisation E. Starkey et al. 10.1016/j.jhydrol.2017.03.019
- Hydrological appraisal of rainfall estimates from radar, satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin, China M. Abro et al. 10.1080/02626667.2018.1557335
- Improving radar estimates of rainfall using an input subset of artificial neural networks T. Yang et al. 10.1117/1.JRS.10.026013
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