Ramesh S. V. Teegavarapu

Optimal Operation of Reservoir Systems using Simulated Annealing

A stochastic search technique, simulated annealing (SA), is used to optimize the operation of multiple reservoirs. Seminal application of annealing technique in general to multi-period, multiple-reservoir systems, along with problem representation and selection of different parameter values used in the annealing algorithm for specific cases is discussed. The search technique is improved with the help of heuristic rules, problem-specific information and concepts from the field of evolutionary algorithms. The technique is tested for application to a benchmark problem of four-reservoir system previously solved using a linear programming formulation and its ability to replicate the global optimum solution is examined. The technique is also applied to a system of four hydropower generating reservoirs in Manitoba, Canada, to derive optimal operating rules. A limited version of this problem is solved using a mixed integer nonlinear programming and results are compared with those obtained using SA. A better objective function value is obtained using simulated annealing than the value from a mixed integer non-linear programming model developed for the same problem. Results obtained from these applications suggest that simulated annealing can be used for obtaining near-optimal solutions for multi-period reservoir operation problems that are computationally intractable.

Modeling uncertainty in reservoir loss functions using fuzzy sets

Imprecision involved in the definition of reservoir loss functions is addressed using fuzzy set theory concepts. A reservoir operation problem is solved using the concepts of fuzzy mathematical programming. Membership functions from fuzzy set theory are used to represent the decision makers preferences in the definition of shape of loss curves. These functions are assumed to be known and are used to model the uncertainties. Linear and nonlinear optimization models are developed under fuzzy environment. A new approach is presented that involves development of compromise reservoir operating policies based on the rules from the traditional optimization models and their fuzzy equivalents while considering the preferences of the decision maker. The imprecision associated with the definition of penalty and storage zones and uncertainty in the penalty coefficients are the main issues addressed through this study. The models developed are applied to the Green Reservoir, Kentucky. Simulations are performed to evaluate the operating rules generated by the models considering the uncertainties in the loss functions. Results indicate that the reservoir operating policies are sensitive to change in the shapes of loss functions.

Short communication: Modeling climate change uncertainties in water resources management models

The impact of climate change on hydrologic design and management of hydrosystems could be one of the important challenges faced by future practicing hydrologists and water resources managers. Many water resources managers currently rely on the historical hydrological data and adaptive real-time operations without consideration of the impact of climate change on major inputs influencing the behavior of hydrologic systems and the operating rules. Issues such as risk, reliability and robustness of water resources systems under different climate change scenarios were addressed in the past. However, water resources management with the decision makers preferences attached to climate change has never been dealt with. This short paper discusses issues related to impacts of climate change on water resources management and application of a soft-computing approach, fuzzy set theory, for climate-sensitive management of hydrosystems. A real-life case study example is presented to illustrate the applicability of a soft-computing approach for handling the decision makers preferences in accepting or rejecting the magnitude and direction of climate change.

Geo-spatial grid-based transformations of precipitation estimates using spatial interpolation methods

Geo-spatial interpolation methods are often necessary in instances where the precipitation estimates available from multisensor source data on a specific spatial grid need to be transformed to another grid with a different spatial grid or orientation. The study involves development and evaluation of spatial interpolation or weighting methods for transforming hourly multisensor precipitation estimates (MPE) available in the form of 4x4km^2 HRAP (hydrologic rainfall analysis project) grid to a Cartesian 2x2km^2 radar (NEXt generation RADar:NEXRAD) grid. Six spatial interpolation weighting methods are developed and evaluated to assess their suitability for transformation of precipitation estimates in space and time. The methods use distances and areal extents of intersection segments of the grids as weights in the interpolation schemes. These methods were applied to transform precipitation estimates from HRAP to NEXRAD grids in the South Florida Water Management District (SFWMD) region in South Florida, United States. A total of 192 rain gauges are used as ground truth to assess the quality of precipitation estimates obtained from these interpolation methods. The rain gauge data in the SFWMD region were also used for radar data bias correction procedures. To help in the assessment, several error measures are calculated and appropriate weighting functions are developed to select the most accurate method for the transformation. Three local interpolation methods out of six methods were found to be competitive and inverse distance based on four nearest neighbors (grids) was found to be the best for the transformation of data.

Optimal and Adaptive Operation of a Hydropower System with Unit Commitment and Water Quality Constraints

Management of water resources has become more complex in recent years as a result of changing attitudes towards sustainability and the attribution of greater attention to environmental issues, especially under a scenario of water scarcity risk introduced by climate changes and anthropogenic pressures. This study addresses the optimal short-term operation of a multi-purpose hydropower system under an environment where objectives are conflicting. New optimization models using mixed integer nonlinear programming (MINLP) with binary variables adopted for incorporating unit commitment constraints and adaptive real-time operations are developed and applied to a real life hydropower reservoir in Brazil, utilizing evolutionary algorithms. These formulations address water quality concerns downstream of the reservoir and optimal operations for power generation in an integrated manner and deal with uncertain future flows due to climate change. Results obtained using genetic algorithm (GA) solvers were superior to gradient based methods, converging to superior optimal solutions especially due to computational intractability problems associated with combinatorial domain of integer variables in the unit commitment formulation. The adaptive operation formulation in conjunction with the solution of turbine unit commitment problem yielded more reliable solutions, reducing forecasting uncertainty and providing more flexible operational rules.

Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records

Abstract New mathematical programming models are proposed, developed and evaluated in this study for estimating missing precipitation data. These models use nonlinear and mixed integer nonlinear mathematical programming (MINLP) formulations with binary variables. They overcome the limitations associated with spatial interpolation methods relevant to the arbitrary selection of weighting parameters, the number of control points within a neighbourhood, and the size of the neighbourhood itself. The formulations are solved using genetic algorithms. Daily precipitation data obtained from 15 rain gauging stations in a temperate climatic region are used to test and derive conclusions about the efficacy of these methods. The developed methods are compared with some naïve approaches, multiple linear regression, nonlinear least-square optimization, kriging, and global and local trend surface and thin-plate spline models. The results suggest that the proposed new mathematical programming formulations are superior to those obtained from all the other spatial interpolation methods tested in this study. Editor D. Koutsoyiannis; Associate editor S. Grimaldi Citation Teegavarapu, R.S.V., 2012. Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records. Hydrological Sciences Journal, 57 (3), 383–406.

Individual and coupled influences of AMO and ENSO on regional precipitation characteristics and extremes

Understanding the influences of Atlantic multidecadal oscillation (AMO) and El Nino southern oscillation (ENSO) on regional precipitation extremes and characteristics in the state of Florida is the focus of this study. Exhaustive evaluations of individual and combined influences of these oscillations using, descriptive indices-based assessment of statistically significant changes in rainfall characteristics, identification of spatially varying influences of oscillations on dry and wet spell transition states, antecedent precipitation prior to extreme events, intraevent temporal distribution of precipitation and changes in temporal occurrences of extremes including dry/wet cycles are carried out. Rain gage and gridded precipitation data analysis using parametric hypothesis tests confirm statistically significant changes in the precipitation characteristics from one phase to another of each oscillation and also in coupled phases. Spatially nonuniform and uniform influences of AMO and ENSO, respectively, on precipitation are evident. AMO influences vary in peninsular and continental parts of Florida and the warm (cool) phase of AMO contributes to increased precipitation extremes during wet (dry) season. The influence of ENSO is confined to dry season with El Nino (La Nina) contributing to increase (decrease) in extremes and total precipitation. Wetter antecedent conditions preceding daily extremes are dominant in AMO warm phase compared to the cool and are likely to impact design floods in the region. AMO influence on dry season precipitation extremes is noted for ENSO neutral years. The two oscillations in different phases modulate each other with seasonal and spatially varying impacts and implications on flood control and water supply in the region.

A short-term reservoir operation model for multicrop irrigation

An integrated model is developed for short-term yearly reservoir operation for irrigation of multiple crops. The model optimizes a measure of annual crop production, starting from the current period in real time. Reservoir storage at the begining of a period, inflow during the previous period, crop soil moisture values and crop production already achieved up to the beginning of the period are used as inputs to the model. The solution specifies the reservoir release and optimal irrigation allocations to individual crops during an intra-seasonal period. The model overcomes some of the limitations of an earlier model developed by Mujumdar & Ramesh (1997) by replacing the two dynamic programming (DP) formulations with a single linear programming (LP) formulation. Application of the model is studied through a case study in India.

Missing precipitation data estimation using optimal proximity metric-based imputation, nearest-neighbour classification and cluster-based interpolation methods

Abstract New optimal proximity-based imputation, K-nearest neighbour (K-NN) classification and K-means clustering methods are proposed and developed for estimation of missing daily precipitation records. Mathematical programming formulations are developed to optimize the weighting, classification and clustering schemes used in these methods. Ten different binary and real-valued distance metrics are used as proximity measures. Two climatic regions, Kentucky and Florida, (temperate and tropical) in the USA, with different gauge density and network structure, are used as case studies to evaluate the new methods. A comprehensive exercise is undertaken to compare the performances of the new methods with those of several deterministic and stochastic spatial interpolation methods. The results from these comparisons indicate that the proposed methods performed better than existing methods. Use of optimal proximity metrics as weights, spatial clustering of observation sites and classification of precipitation data resulted in improvement of missing data estimates. Editor D. Koutsoyiannis; Associate editor C. Onof

Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

AbstractSeveral statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, ...