Taesam Lee

Long‐term prediction of precipitation and hydrologic extremes with nonstationary oscillation processes

[1] Nonstationary oscillations in climatic variables and indices have been the focus of many studies. Since climate indices or their associated hydrometeorological variables might contain nonstationary oscillation processes, it would be useful to be able to divide the intrinsic nonstationary oscillation into a finite number of components. Those components can then be used to predict the future system evolution. In the current study nonstationary oscillations of certain time series are extracted using a decomposition analysis called the empirical mode decomposition (EMD). In EMD the most important components are modeled with a nonstationary oscillation resampling (NSOR) technique. To predict a long-term oscillation pattern, a time series with a long record is required. The normalized regional precipitation of eastern Canada is one such series. In a second example, the future evolution of extreme streamflows at two stations from the province of Quebec, Canada, is studied by using the long-term patterns of climatic indices. Results indicate that the future long-term patterns are well-modeled with the NSOR and EMD. However, the indirect approach to finding the interconnection sometimes gives rise to a high prediction uncertainty.

Nonparametric Simulation of Single-Site Seasonal Streamflows

Various parametric and nonparametric models have been suggested in literature for stochastic generation of seasonal streamflows. State-of-the-art nonparametric models are reviewed herein and their drawbacks identified. We developed a simple model that employs the k-nearest neighbor resampling algorithm with gamma kernel perturbation (denoted as KGK model), which enables generation of data that are not the same as the historical data. For preserving the annual variability two approaches are developed. The first one employs the aggregate variable concept (KGKA model), and the second one uses a pilot variable that leads the generation of the seasonal data (KGKP model). The pilot variable refers to the annual data that has been previously generated, but its role is not for disaggregation but rather for conditioning the state that guides the generation of seasonal flows. The proposed models have been compared with a currently available nonparametric model that considers the reproduction of the interannual vari...

Meta-heuristic maximum likelihood parameter estimation of the mixture normal distribution for hydro-meteorological variables

In the water resources field, there are emerging problems such as temporal changes of data and new additions of water sources. Non-mixture models are not efficient in analyzing these data because these models are developed under the assumption that data do not change and come from one source. Mixture models could successfully analyze these data because mixture models contain more than one modal. The expectation maximization (EM) algorithm has been widely used to estimate parameters of the mixture normal distribution for describing the statistical characteristics of hydro meteorological data. Unfortunately, the EM algorithm has some disadvantages, such as divergence, derivation of information matrices, local maximization, and poor accuracy. To overcome these disadvantages, this study proposes a new parameter estimation approach for the mixture normal distribution. The developed model estimates parameters of the mixture normal distribution by maximizing the log likelihood function using a meta-heuristic algorithm—genetic algorithm (GA). To verify the performance of the developed model, simulation experiments and practical applications are implemented. From the results of experiments and practical applications, the developed model presents some advantages, such as (1) the proposed model more accurately estimates the parameters even with small sample sizes compared to the EM algorithm; (2) not diverging in all application; and (3) showing smaller root mean squared error and larger log likelihood than those of the EM algorithm. We conclude that the proposed model is a good alternative in estimating the parameters of the mixture normal distribution for kutotic and bimodal hydrometeorological data.

Error influence of radar rainfall estimate on rainfall-runoff simulation

Radar systems have been widely employed to measure precipitation and predict flood risks. However, radar as a rainfall measuring device and the produced rainfall estimate contain uncertainties and errors resulting from sources such as mis-calibration, beam blockage, anomalous propagation, and ground clutter. Previously, these radar errors have been individually studied. However, in practical applications, separating and estimating these errors are not possible. In the current study, to analyze the effects of radar rainfall errors, especially for their effect on the peak discharge, through a synthetic runoff simulation, a spatial error model based on univariate Gaussian random numbers was employed. Furthermore, a Monte Carlo simulation, one of the most widely used techniques for intensive simulation toward obtaining practical results, was performed. The results indicated that the variability of the peak discharge increases as the assumed true rainfall increases. In addition, the higher standard deviation of the tested radar rainfall error leads to a higher peak discharge bias. To investigate the cause of this bias, an additional simulation was performed. This simulation revealed that the regression line for the peak discharge corresponding to rainfall amount increases quadratically. The results show that the higher bias is a result of the higher deviation of peak discharges in the cells, with a greater than mean rainfall, even with the same number of cells for lower and higher rainfall amounts.

Heterogeneous Mixture Distributions for Modeling Multisource Extreme Rainfalls

AbstractFrequency analysis has been widely applied to investigate the behavior and characteristics of hydrometeorological variables. Hydrometeorological variables occasionally show mixture distributions when multiple generating phenomena cause the extreme events to occur. In such cases, a mixture distribution should be applied. Past studies on mixture distributions assumed that they are drawn from the same probability density functions. In fact, many hydrometeorological variables can consist of different types of probability density functions. Research on heterogeneous mixture distributions can lead to improvements in understanding the behavior and characteristics of hydrometeorological variables and in the capacity to model them properly. In the present study heterogeneous mixture distributions are developed to model extreme hydrometeorological events. To fit heterogeneous mixture distributions, the authors present an extension of the metaheuristic maximum likelihood approach. The performance of the para...

Frequency Analysis of Nonidentically Distributed Hydrometeorological Extremes Associated with Large-Scale Climate Variability Applied to South Korea

AbstractIn the frequency analyses of extreme hydrometeorological events, the restriction of statistical independence and identical distribution (iid) from year to year ensures that all observations are from the same population. In recent decades, the iid assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Nino–Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, the objective of the current study is to propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observati...

Application of Harmony Search to Design Storm Estimation from Probability Distribution Models

The precision of design storm estimation depends on the selection of an appropriate probability distribution model (PDM) and parameter estimation techniques. Generally, estimated parameters for PDMs are provided based on the method of moments, probability weighted moments, and maximum likelihood (ML). The results using ML are more reliable than the other methods. However, the ML is more laborious than the other methods because an iterative numerical solution must be used. In the meantime, metaheuristic approaches have been developed to solve various engineering problems. A number of studies focus on using metaheuristic approaches for estimation of hydrometeorological variables. Applied metaheuristic approaches offer reliable solutions but use more computation time than derivative-based methods. Therefore, the purpose of the current study is to enhance parameter estimation of PDMs for design storms using a recently developed metaheuristic approach known as a harmony search (HS). The HS is compared to the genetic algorithm (GA) and ML via simulation and case study. The results of this study suggested that the performance of the GA and HS was similar and showed more accurate results than that of the ML. Furthermore, the HS required less computation time than the GA.

Basin rotation method for analyzing the directional influence of moving storms on basin response

Hydrologic responses to variations in storm direction provide useful information for the analysis and prediction of floods and the development of watershed management strategies. However, the prediction of hydrologic responses to changes in storm direction is a difficult task that requires meteorological simulations and extensive computation. It is also difficult to identify the center of rotation of a storm affecting a basin of interest. Therefore, we propose a simple approach of rotating the basin position relative to the storm within the rainfall–runoff simulation model instead of changing the pathway of the storm, which we term the basin rotation method (BRM). The proposed BRM was tested on four major typhoon events in South Korea. The results illustrated that the original basin orientation (i.e., before it was rotated) exhibits earlier and higher peak discharge and earlier recession compared to the basin after rotation. We conclude that the proposed method (BRM) is a viable alternative for use in assessing the directional influence of moving storms on floods caused by historical rather than hypothetical storm events.

KNN-based local linear regression for the analysis and simulation of low flow extremes under climatic influence

Climate change frequently causes highly nonlinear and irregular behaviors in hydroclimatic systems. The stochastic simulation of hydroclimatic variables reproduces such irregular behaviors and is beneficial for assessing their impact on other regimes. The objective of the current study is to propose a novel method, a k-nearest neighbor (KNN) based on the local linear regression method (KLR), to reproduce nonlinear and heteroscedastic relations in hydroclimatic variables. The proposed model was validated with a nonlinear, heteroscedastic, lag-1 time dependent test function. The validation results of the test function show that the key statistics, nonlinear dependence, and heteroscedascity of the test data are reproduced well by the KLR model. In contrast, a traditional resampling technique, KNN resampling (KNNR), shows some biases with respect to key statistics, such as the variance and lag-1 correlation. Furthermore, the proposed KLR model was used to simulate the annual minimum of the consecutive 7-day average daily mean flow (Min7D) of the Romaine River, Quebec. The observed and extended North Atlantic Oscillation (NAO) index is incorporated into the model. The case study results of the observed period illustrate that the KLR model sufficiently reproduced key statistics and the nonlinear heteroscedasticity relation. For the future period, a lower mean is observed, which indicates that drier conditions other than normal might be expected in the next decade in the Romaine River. Overall, it is concluded that the KLR model can be a good alternative for simulating irregular and nonlinear behaviors in hydroclimatic variables.

Multisite stochastic simulation of daily precipitation from copula modeling with a gamma marginal distribution

Multisite stochastic simulations of daily precipitation have been widely employed in hydrologic analyses for climate change assessment and agricultural model inputs. Recently, a copula model with a gamma marginal distribution has become one of the common approaches for simulating precipitation at multiple sites. Here, we tested the correlation structure of the copula modeling. The results indicate that there is a significant underestimation of the correlation in the simulated data compared to the observed data. Therefore, we proposed an indirect method for estimating the cross-correlations when simulating precipitation at multiple stations. We used the full relationship between the correlation of the observed data and the normally transformed data. Although this indirect method offers certain improvements in preserving the cross-correlations between sites in the original domain, the method was not reliable in application. Therefore, we further improved a simulation-based method (SBM) that was developed to model the multisite precipitation occurrence. The SBM preserved well the cross-correlations of the original domain. The SBM method provides around 0.2 better cross-correlation than the direct method and around 0.1 degree better than the indirect method. The three models were applied to the stations in the Nakdong River basin, and the SBM was the best alternative for reproducing the historical cross-correlation. The direct method significantly underestimates the correlations among the observed data, and the indirect method appeared to be unreliable.