Search

Search Results

• 1. [Article] Toward More Robust Extreme Flood Prediction by Bayesian Hierarchical and Multimodeling

  Flood information, especially extreme flood, is necessary for any large hydraulic structure design and flood risk management. Flood mitigation also requires a comprehensive assessment of flood risk and ...

  Citation × Citation Citation Abstract Copy Title: Toward More Robust Extreme Flood Prediction by Bayesian Hierarchical and Multimodeling Author: Yan, Hongxiang, Moradkhani, Hamid Year: 2016 Flood information, especially extreme flood, is necessary for any large hydraulic structure design and flood risk management. Flood mitigation also requires a comprehensive assessment of flood risk and an explicit quantification of the flood uncertainty. In the present study, we use a multimodel ensemble approach based on Bayesian model averaging (BMA) method to account for model structure and distribution uncertainties. The usefulness of this approach is assessed by a case study over the Willamette River Basin (WRB) in Pacific Northwest, USA. Besides the standard log-Pearson Type III distribution, we also identified that the generalized extreme value and three-parameter lognormal distributions were both potential distributions in WRB. Three different statistical models, including the Bulletin-17B quantile model, index-flood model, and spatial Bayesian hierarchical model, were considered in the study. The BMA method is then used to assign weights to different models, where better performing model receives higher weights. It was found that the major uncertainty in extreme flood prediction is contributed by model structure, while the choice of distribution plays a lesser important role in quantification of flood uncertainty. The BMA approach provides a more robust extreme flood prediction than any single model. Title: Toward More Robust Extreme Flood Prediction by Bayesian Hierarchical and Multimodeling Author: Yan, Hongxiang, Moradkhani, Hamid Year: 2016 Flood information, especially extreme flood, is necessary for any large hydraulic structure design and flood risk management. Flood mitigation also requires a comprehensive assessment of flood risk and an explicit quantification of the flood uncertainty. In the present study, we use a multimodel ensemble approach based on Bayesian model averaging (BMA) method to account for model structure and distribution uncertainties. The usefulness of this approach is assessed by a case study over the Willamette River Basin (WRB) in Pacific Northwest, USA. Besides the standard log-Pearson Type III distribution, we also identified that the generalized extreme value and three-parameter lognormal distributions were both potential distributions in WRB. Three different statistical models, including the Bulletin-17B quantile model, index-flood model, and spatial Bayesian hierarchical model, were considered in the study. The BMA method is then used to assign weights to different models, where better performing model receives higher weights. It was found that the major uncertainty in extreme flood prediction is contributed by model structure, while the choice of distribution plays a lesser important role in quantification of flood uncertainty. The BMA approach provides a more robust extreme flood prediction than any single model. Close

• 2. [Article] A Regional Bayesian Hierarchical Model for Flood Frequency Analysis

  In this study, we propose a regional Bayesian hierarchical model for flood frequency analysis. The Bayesian method is an alternative to the traditional regional flood frequency analysis. Instead of relying ...

  Citation × Citation Citation Abstract Copy Title: A Regional Bayesian Hierarchical Model for Flood Frequency Analysis Author: Yan, Hongxiang, Moradkhani, Hamid Year: 2015 In this study, we propose a regional Bayesian hierarchical model for flood frequency analysis. The Bayesian method is an alternative to the traditional regional flood frequency analysis. Instead of relying on the delineation of implicit homogeneous regions, the Bayesian hierarchical method describes the spatial dependence in its inner structure. Similar to the classical Bayesian hierarchical model, the process layer of our model presents the spatial variability of the parameters by considering different covariates (e.g., drainage area, elevation, precipitation). Beyond the three classical layers (data, process, and prior) of the Bayesian hierarchical model, we add a new layer referred to as the “L-moments layer”. The L-moments layer uses L-moments theory to select the best-fit probability distribution based on the available data. This new layer can overcome the subjective selection of the distribution based on extreme value theory and determine the distribution from the data instead. By adding this layer, we can combine the merits of regional flood frequency and Bayesian methods. A standard process of covariates selection is also proposed in the Bayesian hierarchical model. The performance of the Bayesian model is assessed by a case study over the Willamette River Basin in the Pacific Northwest, U.S. The uncertainty of different flood percentiles can be quantified from the posterior distributions using the Markov Chain Monte Carlo method. Temporal changes for the 100-year flood percentiles are also examined using a 20- and 30-year moving window method. The calculated shifts in flood risk can aid future water resources planning and management. Title: A Regional Bayesian Hierarchical Model for Flood Frequency Analysis Author: Yan, Hongxiang, Moradkhani, Hamid Year: 2015 In this study, we propose a regional Bayesian hierarchical model for flood frequency analysis. The Bayesian method is an alternative to the traditional regional flood frequency analysis. Instead of relying on the delineation of implicit homogeneous regions, the Bayesian hierarchical method describes the spatial dependence in its inner structure. Similar to the classical Bayesian hierarchical model, the process layer of our model presents the spatial variability of the parameters by considering different covariates (e.g., drainage area, elevation, precipitation). Beyond the three classical layers (data, process, and prior) of the Bayesian hierarchical model, we add a new layer referred to as the “L-moments layer”. The L-moments layer uses L-moments theory to select the best-fit probability distribution based on the available data. This new layer can overcome the subjective selection of the distribution based on extreme value theory and determine the distribution from the data instead. By adding this layer, we can combine the merits of regional flood frequency and Bayesian methods. A standard process of covariates selection is also proposed in the Bayesian hierarchical model. The performance of the Bayesian model is assessed by a case study over the Willamette River Basin in the Pacific Northwest, U.S. The uncertainty of different flood percentiles can be quantified from the posterior distributions using the Markov Chain Monte Carlo method. Temporal changes for the 100-year flood percentiles are also examined using a 20- and 30-year moving window method. The calculated shifts in flood risk can aid future water resources planning and management. Close

• 3. [Article] Quantifying Uncertainty in Urban Flooding Analysis Considering Hydro-Climatic Projection and Urban Development Effects

  How will the combined impacts of land use change, climate change, and hydrologic modeling influence changes in urban flood frequency and what is the main uncertainty source of the results? Will such changes ...

  Citation × Citation Citation Abstract Copy Title: Quantifying Uncertainty in Urban Flooding Analysis Considering Hydro-Climatic Projection and Urban Development Effects Author: Jung, Il-Won, Chang, Heejun, Moradkhani, Hamid Year: 2011 How will the combined impacts of land use change, climate change, and hydrologic modeling influence changes in urban flood frequency and what is the main uncertainty source of the results? Will such changes differ by catchment with different degrees of current and future urban development? We attempt to answer these questions in two catchments with different degrees of urbanization, the Fanno catchment with 84% urban land use and the Johnson catchment with 36% urban land use, both located in the Pacific Northwest of the US. Five uncertainty sources – general circulation model (GCM) structures, future greenhouse gas (GHG) emission scenarios, land use change scenarios, natural variability, and hydrologic model parameters – are considered to compare the relative source of uncertainty in flood frequency projections. Two land use change scenarios, conservation and development, representing possible future land use changes are used for analysis. Results show the highest increase in flood frequency under the combination of medium high GHG emission (A1B) and development scenarios, and the lowest increase under the combination of low GHG emission (B1) and conservation scenarios. Although the combined impact is more significant to flood frequency change than individual scenarios, it does not linearly Title: Quantifying Uncertainty in Urban Flooding Analysis Considering Hydro-Climatic Projection and Urban Development Effects Author: Jung, Il-Won, Chang, Heejun, Moradkhani, Hamid Year: 2011 How will the combined impacts of land use change, climate change, and hydrologic modeling influence changes in urban flood frequency and what is the main uncertainty source of the results? Will such changes differ by catchment with different degrees of current and future urban development? We attempt to answer these questions in two catchments with different degrees of urbanization, the Fanno catchment with 84% urban land use and the Johnson catchment with 36% urban land use, both located in the Pacific Northwest of the US. Five uncertainty sources – general circulation model (GCM) structures, future greenhouse gas (GHG) emission scenarios, land use change scenarios, natural variability, and hydrologic model parameters – are considered to compare the relative source of uncertainty in flood frequency projections. Two land use change scenarios, conservation and development, representing possible future land use changes are used for analysis. Results show the highest increase in flood frequency under the combination of medium high GHG emission (A1B) and development scenarios, and the lowest increase under the combination of low GHG emission (B1) and conservation scenarios. Although the combined impact is more significant to flood frequency change than individual scenarios, it does not linearly Close

• 4. [Article] Hydrologic Remote Sensing and Land Surface Data Assimilation

  Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications ...

  Citation × Citation Citation Abstract Copy Title: Hydrologic Remote Sensing and Land Surface Data Assimilation Author: Moradkhani, Hamid Year: 2008 Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface?atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation. Title: Hydrologic Remote Sensing and Land Surface Data Assimilation Author: Moradkhani, Hamid Year: 2008 Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface?atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation. Close