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Full-Text Articles in Engineering
The Quest For Model Uncertainty Quantification: A Hybrid Ensemble And Variational Data Assimilation Framework, Peyman Abbaszadeh, Hamid Moradkhani, Dacian Daescu
The Quest For Model Uncertainty Quantification: A Hybrid Ensemble And Variational Data Assimilation Framework, Peyman Abbaszadeh, Hamid Moradkhani, Dacian Daescu
Civil and Environmental Engineering Faculty Publications and Presentations
This article presents a novel approach to couple a deterministic four‐dimensional variational (4DVAR) assimilation method with the particle filter (PF) ensemble data assimilation system, to produce a robust approach for dual‐state‐parameter estimation. In our proposed method, the Hybrid Ensemble and Variational Data Assimilation framework for Environmental systems (HEAVEN), we characterize the model structural uncertainty in addition to model parameter and input uncertainties. The sequential PF is formulated within the 4DVAR system to design a computationally efficient feedback mechanism throughout the assimilation period. In this framework, the 4DVAR optimization produces the maximum a posteriori estimate of state variables at the beginning …
On The Assessment Of Reliability In Probabilistic Hydrometeorological Event Forecasting, Caleb Matthew Dechant, Hamid Moradkhani
On The Assessment Of Reliability In Probabilistic Hydrometeorological Event Forecasting, Caleb Matthew Dechant, Hamid Moradkhani
Civil and Environmental Engineering Faculty Publications and Presentations
Probabilistic forecasts are commonly used to communicate uncertainty in the occurrence of hydrometeorological events. Although probabilistic forecasting is common, conventional methods for assessing the reliability of these forecasts are approximate. Among the most common methods for assessing reliability, the decomposed Brier Score and Reliability Diagram treat an observed string of events as samples from multiple Binomial distributions, but this is an approximation of the forecast reliability, leading to unnecessary loss of information. This article suggests testing the hypothesis of reliability via the Poisson-Binomial distribution, which is a generalized solution to the Binomial distribution, providing a more accurate model of the …
Sensitivity Of Columbia Basin Runoff To Long-Term Changes In Multi-Model Cmip5 Precipitation Simulations, Mehmet Demirel, Hamid Moradkhani
Sensitivity Of Columbia Basin Runoff To Long-Term Changes In Multi-Model Cmip5 Precipitation Simulations, Mehmet Demirel, Hamid Moradkhani
Civil and Environmental Engineering Faculty Publications and Presentations
In this study, we used precipitation elasticity index of streamflow, to reflect on the sensitivity of streamflow to changes in future precipitation. We estimated precipitation elasticity of streamflow from: (1) simulated streamflow by the VIC model using observed precipitation for the current climate (1963–2003); (2) simulated streamflow by the VIC model using simulated precipitation from 10 GCM - CMIP5 dataset for the future climate (2010–2099) including two different pathways (RCP4.5 and RCP8.5) and two different downscaled products (BCSD and MACA). The hydrological model was calibrated at 1/16 latitude-longitude resolution and the simulated streamflow was routed to the subbasin outlets of …
Impacts Of Climate Change On The Seasonality Of Extremes In The Columbia River Basin, Mehmet Demirel, Hamid Moradkhani
Impacts Of Climate Change On The Seasonality Of Extremes In The Columbia River Basin, Mehmet Demirel, Hamid Moradkhani
Civil and Environmental Engineering Faculty Publications and Presentations
The impacts of climate change on the seasonality of extremes i.e. both high and low flows in the Columbia River basin were analyzed using three seasonality indices, namely the seasonality ratio (SR), weighted mean occurrence day (WMOD) and weighted persistence (WP). These indices reflect the streamflow regime, timing and variability in timing of extreme events respectively. The three indices were estimated from: (1) observed streamflow; (2) simulated streamflow by the VIC model using simulated inputs from ten combinations of bias corrected and downscaled CMIP5 inputs for the current climate (1979–2005); (3) simulated streamflow using simulated inputs from ten combinations of …
The Effect Of Multi-Model Averaging Of Climate Model Outputs On The Seasonality Of Rainfall Over The Columbia River Basin, Mehmet Demirel, Arun Rana, Hamid Moradkhani
The Effect Of Multi-Model Averaging Of Climate Model Outputs On The Seasonality Of Rainfall Over The Columbia River Basin, Mehmet Demirel, Arun Rana, Hamid Moradkhani
Civil and Environmental Engineering Faculty Publications and Presentations
The rainfall seasonality index is the measure of precipitation distribution throughout the seasonal cycle. The aim of this study is to compare the effect of different multi-model averaging methods on the rainfall seasonality index at each 1/16 latitude-longitude cells covering the Columbia River Basin. In accordance with the same, ten different climate model outputs are selected from 45 available climate models from CMIP5 dataset. The reanalysis precipitation data is used to estimate the errors in rainfall seasonality for the climate model outputs. The inverse variance method and statistical multi criteria analysis (SMCA) method were used to estimate the weights for …
Improving Robustness Of Hydrologic Parameter Estimation By The Use Of Moving Block Bootstrap Resampling, Hamid Moradkhani, Mohammad Ebtehaj, Hoshin V. Gupta
Improving Robustness Of Hydrologic Parameter Estimation By The Use Of Moving Block Bootstrap Resampling, Hamid Moradkhani, Mohammad Ebtehaj, Hoshin V. Gupta
Civil and Environmental Engineering Faculty Publications and Presentations
Modeling of natural systems typically involves conceptualization and parameterization to simplify the representations of the underlying process. Objective methods for estimation of the model parameters then require optimization of a cost function, representing a measure of distance between the observations and the corresponding model predictions, typically by calibration in a static batch mode and/or via some dynamic recursive optimization approach. Recently, there has been a focus on the development of parameter estimation methods that appropriately account for different sources of uncertainty. In this context, we introduce an approach to sample the optimal parameter space that uses nonparametric block bootstrapping coupled …
Investigating The Impact Of Remotely Sensed Precipitation And Hydrologic Model Uncertainties On The Ensemble Streamflow Forecasting, Hamid Moradkhani, K. Hsu, Y. Hong, S. Sorooshian
Investigating The Impact Of Remotely Sensed Precipitation And Hydrologic Model Uncertainties On The Ensemble Streamflow Forecasting, Hamid Moradkhani, K. Hsu, Y. Hong, S. Sorooshian
Civil and Environmental Engineering Faculty Publications and Presentations
In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input …