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.     

Oceanic-Atmospheric Variability Influences on Baseflows in the Continental United States

Water Resources Management - Baseflows are one of the important components of streamflows and the influences of climate change and variability on changes in baseflows in space and time can aid in...     

Optimal Selection of Predictor Variables in Statistical Downscaling Models of Precipitation

Two screening methods aimed at selection of predictor variables for use in a statistical downscaling (SD) model developed for precipitation are proposed and evaluated in this study. The SD model developed in this study relies heavily on appropriate predictors chosen and accurate relationships between site-specific predictand (i.e. precipitation) and general circulation model (GCM)-scale predictors for providing future projections at different spatial and temporal scales. Methods to characterize these relationships via rigid and flexible functional forms of relationships using mixed integer nonlinear programming (MINLP) formulation with binary variables, and artificial neural network (ANN) methods respectively are developed and evaluated in this study. The proposed methods and three additional methods based on the correlations between predictors and predictand, stepwise regression (SWR) and principal component analysis (PCA) are evaluated in this study. The screening methods are evaluated by employing them in conjunction with an SD model at 22 rain gauge locations in south Florida, USA. The predictor variables that are selected by different predictor selection methods are used in a statistical downscaling model developed in this study to downscale precipitation at a monthly temporal scale. Results suggest that optimal selection of variables using MINLP and ANN provided improved performance and error measures compared to two other models that did not use these methods for screening the variables. Results from application and evaluations of screening methods indicate improved downscaling of precipitation is possible by SD models when an optimal set of predictors are used and the selection of the predictors is site-specific.     

Incorporating Influences of Shallow Groundwater Conditions in Curve Number-Based Runoff Estimation Methods

Runoff generation process in any watershed is mainly affected by precipitation, land use and land cover, existing soil moisture conditions and losses. Shallow groundwater table conditions that occur in many regions are known to affect the soil moisture retention capacity, infiltration and ultimately the runoff. A methodology that links soil moisture capacity to the shallow groundwater table or High-Water Table (HWT) using a nonlinear functional relationship within a curve number (CN)-based runoff estimation method, is proposed and investigated using single and continuous event simulation models in this study. The relationship is used to obtain an adjusted CN that incorporates the effect of change in soil moisture conditions due to HWT. The CN defined for average conditions is replaced by this adjusted CN and is used for runoff estimation. A single event model that uses Soil Conservation Service (SCS) CN approach is used for evaluation of variations in runoff depths and peak discharges based on different HWT conditions. A real-life case study from central Florida region in the USA was adopted for application and evaluation of the proposed methodology. Results from the case study application of the models indicate that HWT conditions significantly influence the magnitudes of peak discharge by as much as 43% and runoff depth by 48% as the water table height reaches the land surface. The magnitudes of increases in peak discharges are specific to case study region and are dependent on the functional form of the relationship linking HWT and soil storage capacity. Also, for specific values of HWT, an equivalency between HWT-based CN and wet antecedent moisture condition (AMC)-based CN can be established.     

Framework for Assessment of Relative Pollutant Loads in Streams with Limited Data

Abstract

A framework that integrates two data-driven techniques is proposed and developed to assess fecal coliform loadings in natural streams. A relationship between transport medium (streamflow) and non-conservative pollutant (fecal coliform) load is first developed using conventional regression technique. The spatial distribution of the fecal load over watersheds is then captured using artificial neural networks through a disaggregation scheme. Streamflow, as a surrogate for non-conservative fecal load, has been used in the disaggregation process. The framework is applied to an area that encompasses four USGS 8-digit Hydrologic Unit Code (HUC) watersheds in the southeastern region of Kentucky, USA. The study attempts to address two major issues: (i) assessment of relative pollutant loads from watersheds and (ii) evaluation into possible reduction in the number of monitoring stations to meet the budgetary constraints. Preliminary results indicate the potential of this approach in assessing the relative fecal loading contribution from different watersheds with the help of conservative hydrological parameters, especially in data-poor conditions.