The Hybrid approach introduced by the authors for at-site modeling of annual and periodic streamflows in earlier works is extended to simulate multi-site multi-season streamflows. It bears significance in integrated river basin planning studies. This hybrid model involves: (i) partial pre-whitening of standardized multi-season streamflows at each site using a parsimonious linear periodic model; (ii) contemporaneous resampling of the resulting residuals with an appropriate block size, using moving block bootstrap (non-parametric, NP) technique; and (iii) post-blackening the bootstrapped innovation series at each site, by adding the corresponding parametric model component for the site, to obtain generated streamflows at each of the sites. It gains significantly by effectively utilizing the merits of both parametric and NP models. It is able to reproduce various statistics, including the dependence relationships at both spatial and temporal levels without using any normalizing transformations and/or adjustment procedures. The potential of the hybrid model in reproducing a wide variety of statistics including the run characteristics, is demonstrated through an application for multi-site streamflow generation in the Upper Cauvery river basin, Southern India. © 2004 Elsevier B.V. All rights reserved.

Kasthrirengan Srinivasan

MOVING BLOCK BOOTSTRAP TECHNIQUE

Stochastic simulation

Computer simulation

Mathematical models

Mathematical transformations

Random processes

River basin projects

Stream flow

BASIN MANAGEMENT

Bootstrapping

flow modeling

Hydrological modeling

streamflow

Asia

Cauvery Basin

Eastern Hemisphere

Eurasia

India

South Asia

World

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

Journal of Hydrology

A simulation-optimization (S-O) framework is developed for the hybrid stochastic modeling of multi-site multi-season streamflows. The multi-objective optimization model formulated is the driver and the multi-site, multi-season hybrid matched block bootstrap model (MHMABB) is the simulation engine within this framework. The multi-site multi-season simulation model is the extension of the existing single-site multi-season simulation model. A robust and efficient evolutionary search based technique, namely, non-dominated sorting based genetic algorithm (NSGA - II) is employed as the solution technique for the multi-objective optimization within the S-O framework. The objective functions employed are related to the preservation of the multi-site critical deficit run sum and the constraints introduced are concerned with the hybrid model parameter space, and the preservation of certain statistics (such as inter-annual dependence and/or skewness of aggregated annual flows). The efficacy of the proposed S-O framework is brought out through a case example from the Colorado River basin. The proposed multi-site multi-season model AMHMABB (whose parameters are obtained from the proposed S-O framework) preserves the temporal as well as the spatial statistics of the historical flows. Also, the other multi-site deficit run characteristics namely, the number of runs, the maximum run length, the mean run sum and the mean run length are well preserved by the AMHMABB model. Overall, the proposed AMHMABB model is able to show better streamflow modeling performance when compared with the simulation based SMHMABB model, plausibly due to the significant role played by: (i) the objective functions related to the preservation of multi-site critical deficit run sum; (ii) the huge hybrid model parameter space available for the evolutionary search and (iii) the constraint on the preservation of the inter-annual dependence. Split-sample validation results indicate that the AMHMABB model is able to predict the characteristics of the multi-site multi-season streamflows under uncertain future. Also, the AMHMABB model is found to perform better than the linear multi-site disaggregation model (MDM) in preserving the statistical as well as the multi-site critical deficit run characteristics of the observed flows. However, a major drawback of the hybrid models persists in case of the AMHMABB model as well, of not being able to synthetically generate enough number of flows beyond the observed extreme flows, and not being able to generate values that are quite different from the observed flows. © 2016

Simulation-optimization framework for multi-site multi-season hybrid stochastic streamflow modeling

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.

Simulation-optimization framework for multi-season hybrid stochastic models

Adequate knowledge of the complexity of streamflows in river basins is important for proper assessment, planning, and management of our water resources. However, identification of the complexity of streamflows is very challenging, due to the various mechanisms governing the streamflow process both in space and in time. The present study examines the complexity of daily streamflows in the west-flowing rivers of India. The false nearest neighbor (FNN) algorithm, a nonlinear dynamic-based method, is employed to determine the dimensionality of streamflow time series and, hence, to examine the complexity. The FNN method uses reconstruction of the single-variable streamflow time series in a multi-dimensional phase space, and identifies the FNNs in the reconstructed phase space. Daily streamflow time series from 13 gaging stations in the Tapi to Tadri basin are analyzed. The effect of delay time on the FNN dimension estimation of the 13 streamflow series is also investigated, by considering four different delay time values to represent different kinds of separation of elements in the phase space reconstruction vector (daily, annual, autocorrelation, mutual information). Collectively considering the different delay time values, the FNN dimensions for the 13 streamflow series are found to have a wide range of variability (with dimensions ranging from 5 to 20), indicating a wide range of complexity in the dynamics of streamflow in the Tapi to Tadri basin. The results generally reveal that: (1) for any given station, the FNN dimensions change with changes in delay time; and (2) for any given delay time, the FNN dimensions change for the different stations. There does not seem to be any clear pattern in the spatial variability of streamflow across the entire basin, although there are small pockets of local similarity. Based on the FNN dimension results, potential issues in the application of the FNN method to the streamflow series in the Tapi to Tadri basin (and streamflow, more broadly) are also discussed. The outcomes of the present study have important implications for streamflow data monitoring, streamflow modeling and forecasting, flood frequency analysis, environmental flow requirements, and water resource assessment, among others. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Stochastic Environmental Research and Risk Assessment

Complexity of streamflows in the west-flowing rivers of India

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

A hybrid model is presented for stochastic simulation of multiseason streamflows. This involves partial prewhitening of the streamflows using a parsimonious linear periodic parametric model, followed by resampling the resulting residuals using moving block bootstrap to obtain innovations and subsequently postblackening these innovations to generate synthetic replicates. This model is simple and is efficient in reproducing both linear and nonlinear dependence inherent in the observed streamflows. The first part of this paper demonstrates the hybrid character of the model through stochastic simulations performed using monthly streamflows of Weber River (Utah) that exhibit a complex dependence structure. In the latter part of the paper the hybrid model is shown to be efficient in simulating multiseason streamflows, through an example of the San Juan River (New Mexico). This model ensures annual-to-monthly consistency without the need for any adjustment procedures. Furthermore, the hybrid model is able to preserve both within-year and cross-year monthly serial correlations for multiple lags. Also, it is seen to be consistent in predicting the reservoir storage (validation) statistic at low as well as high demand levels.

Fulltext

Water Resources Research

A hybrid stochastic model for multiseason streamflow simulation

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.

Post-blackening approach for modeling dependent annual streamflows

ICPSR Data Holdings

New York Times/Cornell University/NY1 News New York State Poll, May 2009

User&#39;s Guide for TOUGH2-MP - A Massively Parallel Version of the TOUGH2 Code

Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows

Journal	Journal of Hydrology
ISSN	00221694
Open Access	No