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Full-Text Articles in Engineering
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 …
Improved Bayesian Multi-Modeling: Integration Of Copulas And Bayesian Model Averaging, Shahrbanou Madadgar, Hamid Moradkhani
Improved Bayesian Multi-Modeling: Integration Of Copulas And Bayesian Model Averaging, Shahrbanou Madadgar, Hamid Moradkhani
Civil and Environmental Engineering Faculty Publications and Presentations
Bayesian Model Averaging (BMA) is a popular approach to combine hydrologic forecasts from individual models, and characterize the uncertainty induced by model structure. In the original form of BMA, the conditional probability density function (PDF) of each model is assumed to be a particular probability distribution (e.g. Gaussian, gamma, etc.). If the predictions of any hydrologic model do not follow certain distribution, a data transformation procedure is required prior to model averaging. Moreover, it is strongly recommended to apply BMA on unbiased forecasts, whereas it is sometimes difficult to effectively remove bias from the predictions of complex hydrologic models. To …
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 …
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 …
Toward A Reliable Prediction Of Seasonal Forecast Uncertainty: Addressing Model And Initial Condition Uncertainty With Ensemble Data Assimilation And Sequential Bayesian Combination, Caleb Matthew Dechant, Hamid Moradkhani
Toward A Reliable Prediction Of Seasonal Forecast Uncertainty: Addressing Model And Initial Condition Uncertainty With Ensemble Data Assimilation And Sequential Bayesian Combination, Caleb Matthew Dechant, Hamid Moradkhani
Civil and Environmental Engineering Faculty Publications and Presentations
Uncertainties are an unfortunate yet inevitable part of any forecasting system. Within the context of seasonal hydrologic predictions, these uncertainties can be attributed to three causes: imperfect characterization of initial conditions, an incomplete knowledge of future climate and errors within computational models. This study proposes a method to account for all threes sources of uncertainty, providing a framework to reduce uncertainty and accurately convey persistent predictive uncertainty. In currently available forecast products, only a partial accounting of uncertainty is performed, with the focus primarily on meteorological forcing. For example, the Ensemble Streamflow Prediction (ESP) technique uses meteorological climatology to estimate …
Examining The Effectiveness And Robustness Of Sequential Data Assimilation Methods For Quantification Of Uncertainty In Hydrologic Forecasting, Caleb Matthew Dechant, Hamid Moradkhani
Examining The Effectiveness And Robustness Of Sequential Data Assimilation Methods For Quantification Of Uncertainty In Hydrologic Forecasting, Caleb Matthew Dechant, Hamid Moradkhani
Civil and Environmental Engineering Faculty Publications and Presentations
In hydrologic modeling, state-parameter estimation using data assimilation techniques is increasing in popularity. Several studies, using both the ensemble Kalman filter (EnKF) and the particle filter (PF) to estimate both model states and parameters have been published in recent years. Though there is increasing interest and a growing literature in this area, relatively little research has been presented to examine the effectiveness and robustness of these methods to estimate uncertainty. This study suggests that state-parameter estimation studies need to provide a more rigorous testing of these techniques than has previously been presented. With this in mind, this paper presents a …
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 …
A Sequential Bayesian Approach For Hydrologic Model Selection And Prediction, Kuo-Lin Hsu, Hamid Moradkhani, Soroosh Sorooshian
A Sequential Bayesian Approach For Hydrologic Model Selection And Prediction, Kuo-Lin Hsu, Hamid Moradkhani, Soroosh Sorooshian
Civil and Environmental Engineering Faculty Publications and Presentations
When a single model is used for hydrologic prediction, it must be capable of estimating system behavior accurately at all times. Multiple-model approaches integrate several model behaviors and, when effective, they can provide better estimates than that of any single model alone. This paper discusses a sequential model fusion strategy that uses the Bayes rule. This approach calculates each model's transient posterior distribution at each time when a new observation is available and merges all model estimates on the basis of each model's posterior probability. This paper demonstrates the feasibility of this approach through case studies that fuse three hydrologic …
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 …
Uncertainty Assessment Of Hydrologic Model States And Parameters: Sequential Data Assimilation Using The Particle Filter, Hamid Moradkhani, Kuo-Lin Hsu, Hoshin V. Gupta, Soroosh Sorooshian
Uncertainty Assessment Of Hydrologic Model States And Parameters: Sequential Data Assimilation Using The Particle Filter, Hamid Moradkhani, Kuo-Lin Hsu, Hoshin V. Gupta, Soroosh Sorooshian
Civil and Environmental Engineering Faculty Publications and Presentations
Two elementary issues in contemporary Earth system science and engineering are (1) the specification of model parameter values which characterize a system and (2) the estimation of state variables which express the system dynamic. This paper explores a novel sequential hydrologic data assimilation approach for estimating model parameters and state variables using particle filters (PFs). PFs have their origin in Bayesian estimation. Methods for batch calibration, despite major recent advances, appear to lack the flexibility required to treat uncertainties in the current system as new information is received. Methods based on sequential Bayesian estimation seem better able to take advantage …