A nonparametric method for resampling multiseason hydrologic time series is presented. It is based on the idea of rank matching, for simulating univariate time series with strong and/or long-range dependence. The rank matching rule suggests concatenating with higher likelihood those blocks that match at their ends. In the proposed method, termed 'multiseason matched block bootstrap', nonoverlapping within-year blocks of hydrologic data (formed from the observed time series) are conditionally resampled using the rank matching rule. The effectiveness of the method in recovering various statistical attributes, including the dependence structure from finite samples generated from a known population, is demonstrated through a two-level hypothetical Monte Carlo simulation experiment. The method offers enough flexibility to the modeller and is shown to be appropriate for modelling hydrologic data that display strong dependence, nonlinearity and/or multimodality in the time series depicting the hydrologic process. The method is shown to be more efficient than the nonparametric 'k-nearest neighbor bootstrap' method in simulating the monthly streamflows that exhibit a complex dependence structure and bimodal marginal probability density. Even with short block sizes, this bootstrap method is able to predict the drought characteristics reasonably accurately. Copyright © 2005 John Wiley & Sons, Ltd.

Kasthrirengan Srinivasan

Algorithms

Computer simulation

Drought

Mathematical models

Monte Carlo methods

Probability density function

Statistical methods

Stream flow

time series analysis

DROUGHT ANALYSIS

HYDROLOGIC TIME SERIES

MULTISEASON MATCHED BLOCK BOOTSTRAP

Nonparametric

STREAMFLOW SIMULATION

Hydrology

Bootstrapping

flow modeling

Hydrological modeling

streamflow

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Hydrological Processes

A novel simulation-optimization framework is proposed that enables the automation of the hybrid stochastic modeling process for synthetic generation of multi-season streamflows. This framework aims to minimize the drudgery, judgment and subjectivity involved in the selection of the most appropriate hybrid stochastic model. It consists of a multi-objective optimization model as the driver and the hybrid multi-season stochastic streamflow generation model, hybrid matched block boostrap (HMABB) as the simulation engine. For the estimation of the hybrid model parameters, the proposed framework employs objective functions that aim to minimize the overall errors in the preservation of storage capacities at various demand levels, unlike the traditional approaches that are simulation based. Moreover this framework yields a number of competent hybrid stochastic models in a single run of the simulation-optimization framework. The efficacy of the proposed simulation-optimization framework is brought out through application to two monthly streamflow data sets from USA of varying sample sizes that exhibit multi-modality and a complex dependence structure. The results show that the hybrid models obtained from the proposed framework are able to preserve the statistical characteristics as well as the storage characteristics better than the simulation based HMABB model, while minimizing the manual effort and the subjectivity involved in the modeling process. The proposed framework can be easily extended to model multi-site multi-season streamflow data. © 2011 Elsevier B.V.

Journal of Hydrology

Simulation-optimization framework for multi-season hybrid stochastic models

The post-blackening (PB) approach introduced by the authors for modeling annual streamflows in an earlier work is extended to model periodic streamflows. This is basically a semi-parametric approach that blends a simple low-order, linear periodic parametric model with the moving block resampling scheme. The first part of the paper demonstrates the hybrid character of the PB model through Monte-Carlo simulations performed on hypothetical data sets drawn from a known population. Following this, the PB model is used for stochastic simulation of periodic streamflows of Beaver and Weber rivers in the US. The results show that the PB model is more consistent in reproducing a wide variety of statistics of periodic streamflows, compared to low-order linear periodic parametric models (Box-Jenkins type) and the periodic k-nearest-neighbor bootstrap (nonparametric) method. In addition, the PB model is able to preserve cross-year serial correlations as well as the month-to-year cross-correlations. This hybrid approach seems to offer considerable scope for improvement in hydrologic time series modeling.

Post-blackening approach for modeling periodic streamflows

The post-blackening (PB) approach is introduced for modeling annual streamflows that exhibit significant dependence. This is a hybrid approach that blends a simple low-order, linear parametric model with the moving block resampling scheme. Empirical simulations performed using known hypothetical nonlinear parametric models, show that the hybrid model gains significantly by utilizing the merits of both the parametric model and the moving block resampling scheme (nonparametric). Following this, the performance of the PB model is tested with four annual streamflow records with complex dependence, drawn from different parts of the world. The results from these examples show that the PB approach exhibits a better performance in terms of preservation of summary statistics, dependence structure, marginal distribution, and drought characteristics of historical streamflows, compared to low-order linear parametric models and model based resampling schemes (nonparametric model). Furthermore, it offers flexibility to the modeler and is also simple to implement on a personal computer. This hybrid approach seems to offer considerable scope for improvement in hydrologic time series modeling and its applications to water resources planning. (C) 2000 Elsevier Science B.V.

Journal	Hydrological Processes
ISSN	08856087
Open Access	No