Abstract:
Accurate flood forecasting is essential for mitigating flood damage and addressing operational flood scenarios. In recent years, data assimilation methods have drawn atte...Show MoreMetadata
Abstract:
Accurate flood forecasting is essential for mitigating flood damage and addressing operational flood scenarios. In recent years, data assimilation methods have drawn attention due to their potentials to handle explicitly the various sources of uncertainty in hydrologic models. In this study, we implement sequential data assimilation for short-term flood forecasting and parameter uncertainty assessment using grid-based spatially distributed hydrologic models. The lag-time window is introduced to consider the response times of internal hydrologic processes. Results show improvement of flood predictions via particle filtering. For uncertainty assessment, parameters in both radar rainfall estimates and hydrologic models are estimated using kernel smoothing and a lag-time window via particle filtering. Results show that the proposed DA method can be used as a framework to estimate parameters and their predictive uncertainty in an integrative way.
Date of Conference: 09-12 July 2013
Date Added to IEEE Xplore: 21 October 2013
ISBN Information:
Conference Location: Istanbul, Turkey
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Data Assimilation ,
- Hydrological Model ,
- Uncertainty Assessment ,
- Flood Forecasting ,
- Sequential Data Assimilation ,
- Smoothing ,
- Model Parameters ,
- Parameter Estimates ,
- Sources Of Uncertainty ,
- Prediction Uncertainty ,
- Particle Filter ,
- Hydrological Processes ,
- Short-term Forecasting ,
- Integrative Way ,
- Weather Radar ,
- Data Assimilation Methods ,
- Various Sources Of Uncertainty ,
- Root Mean Square Error ,
- Time Step ,
- State Variables ,
- Posterior Probability Density Function ,
- Kinematic Wave ,
- Soil Moisture Content ,
- Model Setup ,
- Natural Distribution ,
- Current Time Step ,
- Bayesian Filtering ,
- Negligible Weight ,
- Soil Layer ,
- Unit Width
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Data Assimilation ,
- Hydrological Model ,
- Uncertainty Assessment ,
- Flood Forecasting ,
- Sequential Data Assimilation ,
- Smoothing ,
- Model Parameters ,
- Parameter Estimates ,
- Sources Of Uncertainty ,
- Prediction Uncertainty ,
- Particle Filter ,
- Hydrological Processes ,
- Short-term Forecasting ,
- Integrative Way ,
- Weather Radar ,
- Data Assimilation Methods ,
- Various Sources Of Uncertainty ,
- Root Mean Square Error ,
- Time Step ,
- State Variables ,
- Posterior Probability Density Function ,
- Kinematic Wave ,
- Soil Moisture Content ,
- Model Setup ,
- Natural Distribution ,
- Current Time Step ,
- Bayesian Filtering ,
- Negligible Weight ,
- Soil Layer ,
- Unit Width
- Author Keywords