The influence of macrophyte growth,typical of eutrophic conditions,on river flow velocities and turbulence production

The influence of emergent and submerged macrophytes on flow velocity and turbulence production is demonstrated in a 140 m reach of the River Blackwater in Farnborough, Hampshire, UK. Macrophyte growth occurs in patches and is dominated by Sparganium erectum and Sparganium emersum. In May 2001, patches of S. erectum were already established and occupied 18% of the channel area. The flow adjusted to these (predominantly lateral) patches by being channelled through a narrower cross‐section. The measured velocity profiles showed a logarithmic form, with deviations attributable to topographic control. The channel bed was the main source of turbulence. In September 2001, in‐stream macrophytes occupied 27% of the channel, and overhanging bank vegetation affected 32% of the area. Overall flow resistance, described by Manning's n, showed a threefold increase that could be attributed to the growth of S. emersum in the middle of the channel. Velocity profiles showed different characteristic forms depending on their position relative to plant stems and leaves. The overall velocity field had a three‐dimensional structure. Turbulence intensities were generally higher and turbulence profiles tended to mirror the velocity profiles. Evidence for the generation of coherent eddies was provided by ratios of the root mean square velocities. Spectral analysis identified deviations from the Kolmogorov ?5/3 power law and provided statistical evidence for a spectral short‐cut, indicative of additional turbulence production. This was most marked for the submerged vegetation and, in some instances, the overhanging bank vegetation. The long strap‐like leaves of S. emersum being aligned approximately parallel to the flow and the highly variable velocity field created by the patch arrangement of macrophytes suggest that the dominant mechanism for turbulence production is vortex shedding along shear zones. Wake production around individual stems of S. emersum close to the bed may also be important locally. Copyright © 2006 John Wiley & Sons, Ltd.     

An application of Karmarkar's interior-point linear programming algorithm for multi-reservoir operations optimization

Optimization of multi-reservoir systems operations is typically a very large scale optimization problem. The following are the three types of optimization problems solved using linear programming (LP): (i) deterministic optimization for multiple periods involving fine stage intervals, for example, from an hour to a week (ii) implicit stochastic optimization using multiple years of inflow data, and (iii) explicit stochastic optimization using probability distributions of inflow data. Until recently, the revised simplex method has been the most efficient solution method available for solving large scale LP problems. In this paper, we show that an implementation of the Karmarkar's interior-point LP algorithm with a newly developed stopping criterion solves optimization problems of large multi-reservoir operations more efficiently than the simplex method. For example, using a Micro VAX II minicomputer, a 40 year, monthly stage, two-reservoir system optimization problem is solved 7.8 times faster than the advanced simplex code in MINOS 5.0. The advantage of this method is expected to be greater as the size of the problem grows from two reservoirs to multiples of reservoirs. This paper presents the details of the implementation and testing and in addition, some other features of the Karmarkar's algorithm which makes it a valuable optimization tool are illuminated.     

Predicting cumulative sediment depostition in large reservoirs

Sediment deposition and its accumulation in a large resorvoir depends on the inflow and reservoir storage content, respectively. Because of this fact it is possible to model the cumulative deposition of sediment as an additive process defined on a bivariate Markov chain. Using the bivariate Markov chain model the mean and variance of the cumulative deposition of John Martin Reservoir, Colorado, U.S.A. are estimated and compared with observed sedimentation data.     

Tsunami Travel Time Atlas for the Atlantic Ocean

Compared to the Pacific Ocean, tsunamis are rare both in the Atlantic and Indian Oceans. However, the December 26, 2004, tsunami demonstrated that, no matter how rare they may be, when a major tsunami occurs, it could be very disastrous. The most basic information in tsunami warning center requires are charts showing tsunami travel times to various locations around the rim of the ocean. With this in mind, a tsunami travel time atlas for the Atlantic Ocean is in preparation. The Caribbean Sea is also included in this Atlas, as it is more or less a part of the Atlantic Basin.     

A new formulation for the stochastic control of systems with bounded state variables: An application to a single reservoir system

A new formulation is presented for the analysis of reservoir systems synthesizing concepts from the traditional stochastic theory of reservoir storage, moments analysis and reliability programming. The analysis is based on the development of the first and second moments for the stochastic storage state variable. These expressions include terms for the failure probabilities (probabilities of spill or deficit) and consider the storage bounds explicitly. Using this analysis, expected values of the storage state, variances of storage, optimal release policies and failure probabilities — useful information in the context of reservoir operations and design, can be obtained from a nonlinear programming solution. The solutions developed from studies of single reservoir operations on both an annual and monthly basis, compare favorably with those obtained from simulation. The presentation herein is directed to both traditional reservoir storage theorists who are interested in the design of a reservoir and modern reservoir analysts who are interested in the long term operation of reservoirs.     

Isolation and characterization of phosphate-solubilizing bacteria from seagrass rhizosphere soil

Phosphate-solubilizing bacterial strains(6 Nos.) were isolated from the rhizosphere soils of two seagrasses(Halophila ovalis(R.Br.) Hook and Halodule pinifolia(Miki) Hartog) in the Vellar estuary.Experimental studies found that the strain PSSG6 was effective in phosphate solubilization with Phosphate Solubilization efficiency index E = 375 ± 8.54,followed by the strain PSSG5 with Phosphate Solubilization efficiency index E = 275 ± 27.3.Of the 6 strains isolated,the strains PSSG4 and PSSG5 be-longed to the genus Bacillus,and PSSG1,PSSG2 and PSSG3 were identified as Citrobacter sp.,Shigella sp.,and Klebsiella sp.,respectively,by conventional method,and PSSG6 was identified as Bacillus circulans using conventional and molecular methods.     

Operations optimization of multireservoir systems using storage moments equations

Fletcher–Ponnambalam presented a new model for considering the balance equation of the storage volume of the reservoir using indicator functions. For stochastic inflows, the two storage moments of this balance equation, namely, the mean and variance, calculated using a random release policy were found to be quite accurate unlike any known models. Significantly, this model required no discretization of storage volumes or releases. In this paper, this work has been extended to two new cases: for multireservoir systems which require further consideration of the stochastic releases and for arbitrary distribution of inflows using the Beta-equivalent Kumaraswamy distribution which has a simpler form than Beta. The randomized release policies are easy to use even in a multireservoir problem. The Parambikulm-Aliyar Project from India is used as a case study. Results show accurate predictions of mean of storages in the multireservoir case but show the need for further improvement in the standard deviations of storages. The optimal benefits and the policies obtained are shown to be at least as good as obtained using Monte Carlo-based methods.     

Flow modelling in gravel‐bed rivers: rethinking the bottom boundary condition

A key problem in computational fluid dynamics (CFD) modelling of gravel‐bed rivers is the representation of multi‐scale roughness, which spans the range from grain size, through bedforms, to channel topography. These different elements of roughness do not clearly map onto a model mesh and use of simple grain‐scale roughness parameters may create numerical problems. This paper presents CFD simulations for three cases: a plane bed of fine gravel, a plane bed of fine gravel including large, widely‐spaced pebble clusters, and a plane gravel bed with smaller, more frequent, protruding elements. The plane bed of fine gravel is modelled using the conventional wall function approach. The plane bed of fine gravel including large, widely‐spaced pebble clusters is modelled using the wall function coupled with an explicit high‐resolution topographic representation of the pebble clusters. In these cases, the three‐dimensional Reynolds‐averaged continuity and Navier–Stokes equations are solved using the standard k ? ε turbulence model, and model performance is assessed by comparing predicted results with experimental data. For gravel‐bed rivers in the field, it is generally impractical to map the bed topography in sufficient detail to enable the use of an explicit high‐resolution topography. Accordingly, an alternative model based on double‐averaging is developed. Here, the flow calculations are performed by solving the three‐dimensional double‐averaged continuity and Navier‐Stokes equations with the spatially‐averaged 〈k ? ε〉 turbulence model. For the plane bed of fine gravel including large, widely‐spaced pebble clusters, the model performance is assessed by comparing the spatially‐averaged velocity with the experimental data. The case of a plane gravel bed with smaller, more frequent, protruding elements is represented by a series of idealized hypothetical cases. Here, the spatially‐averaged velocity and eddy viscosity are used to investigate the applicability of the model, compared with using the explicit high‐resolution topography. The results show the ability of the model to capture the spatially‐averaged flow field and, thus, illustrate its potential for representing flow processes in natural gravel‐bed rivers. Finally, practical data requirements for implementing such a model for a field example are given. Copyright © 2011 John Wiley & Sons, Ltd.     

Storage-yield analysis of surface water reservoirs: the role of reliability constraints and operating policies

Stochastic optimization methods are used for optimal design and operation of surface water reservoir systems under uncertainty. Chance-constrained (CC) optimization with linear decision rules (LDRs) is an old approach for determining the minimum reservoir capacity required to meet a specific yield at a target level of reliability. However, this approach has been found to overestimate the reservoir capacity. In this paper, we propose the reason for this overestimation to be the fact that the reliability constraints considered in standard CC LDR models do not have the same meaning as in other models such as reservoir operation simulation models. The simulation models have fulfilled a target reliability level in an average sense (i.e., annually), whereas the standard CC LDR models have met the target reliability level every season of the year. Mixed integer nonlinear programs are presented to clarify the distinction between the two types of reliability constraints and demonstrate that the use of seasonal reliability constraints, rather than an average reliability constraint, leads to 80–150 % and 0–32 % excess capacity for SQ-type and S-type CC LDR models, respectively. Additionally, a modified CC LDR model with an average reliability constraint is proposed to overcome the reservoir capacity overestimation problem. In the second stage, we evaluate different operating policies and show that for the seasonal (average) reliability constraints, open-loop, S-type, standard operating policy, SQ-type, and general SQ-type policies compared to a model not using any operation rule lead to 190–460 % (200–550 %), 100–200 % (80–300 %), 0–90 % (0–60 %), 30–90 % (0–20 %), and 10–90 % (0–10 %) excess capacity, respectively.