Heterotrophic Bacteria Associated with Cyanobacteria in Recreational and Drinking Water

Cyanobacterial mass occurrences, also known as water blooms, have been associated with adverse health effects of both humans and animals. They can also be a burden to drinking water treatment facilities. Risk assessments of the blooms have generally focused on the cyanobacteria themselves and their toxins. However, heterotrophic bacteria thriving among cyanobacteria may also be responsible for many of the adverse health effects, but their role as the etiological agents of these health problems is poorly known. In addition, studies on the water purification efficiency of operating water treatment plants during cyanobacterial mass occurrences in their water sources are rare. In the present study, over 600 heterotrophic bacterial strains were isolated from natural freshwater, brackish water or from treated drinking water. The sampling sites were selected as having frequent cyanobacterial occurrences in the water bodies or in the water sources of the drinking water treatment plants. In addition, samples were taken from sites where cyanobacterial water blooms were surmised to have caused human health problems. The isolated strains represented bacteria from 57 different genera of the Gamma-, Alpha- or Betaproteobacteria, Actinobacteria, Flavobacteria, Sphingobacteria, Bacilli and Deinococci classes, based on their partial 16S rRNA sequences. Several isolates had no close relatives among previously isolated bacteria or cloned 16S rRNA genes of uncultivated bacteria. The results show that water blooms are associated with a diverse community of cultivable heterotrophic bacteria. Chosen subsets of the isolated strains were analysed for features such as their virulence gene content and possible effect on cyanobacterial growth. Of the putatively pathogenic haemolytic strains isolated in the study, the majority represented the genus Aeromonas. Therefore, the Aeromonas spp. strains isolated from water samples associated with adverse health effects were screened for the virulence gene types encoding for enterotoxins (ast, alt and act/aerA/hlyA), flagellin subunits (flaA/flaB), lipase (lip/pla/lipH3/alp-1) and elastase (ahyB) by PCR. The majority (90%) of the Aeromonas strains included one or more of the six screened Aeromonas virulence gene types. The most common gene type was act, which was present in 77% of the strains. The fla, ahyB and lip genes were present in 30 37% of the strains. The prevalence of the virulence genes implies that the Aeromonas may be a factor in some of the cyanobacterial associated health problems. Of the 183 isolated bacterial strains that were studied for possible effects on cyanobacterial growth, the majority (60%) either enhanced or inhibited growth of cyanobacteria. In most cases, they enhanced the growth, which implies mutualistic interactions. The results indicate that the heterotrophic bacteria have a role in the rise and fall of the cyanobacterial water blooms. The genetic and phenotypic characteristics and the ability to degrade cyanobacterial hepatotoxins of 13 previously isolated Betaproteobacteria strains, were also studied. The strains originated from Finnish lakes with frequent cyanobacterial occurrence. Tested strains degraded microcystins -LR and -YR and nodularin. The strains could not be assigned to any described bacterial genus or species based on their genetic or phenotypic features. On the basis of their characteristics a new genus and species Paucibacter toxinivorans was proposed for them. The water purification efficiency of the drinking water treatment processes during cyanobacterial water bloom in water source was assessed at an operating surface water treatment plant. Large phytoplankton, cyanobacterial hepatotoxins, endotoxins and cultivable heterotrophic bacteria were efficiently reduced to low concentrations, often below the detection limits. In contrast, small planktonic cells, including also possible bacterial cells, regularly passed though the water treatment. The passing cells may contribute to biofilm formation within the water distribution system, and therefore lower the obtained drinking water quality. The bacterial strains of this study offer a rich source of isolated strains for examining interactions between cyanobacteria and the heterotrophic bacteria associated with them. The degraders of cyanobacterial hepatotoxins could perhaps be utilized to assist the removal of the hepatotoxins during water treatment, whereas inhibitors of cyanobacterial growth might be useful in controlling cyanobacterial water blooms. The putative pathogenicity of the strains suggests that the health risk assessment of the cyanobacterial blooms should also cover the heterotrophic bacteria.

Effects of weed control and fertilization at early establishment on tree nitrogen and water use in an exotic F1 hybrid pine of subtropical Australia

Purpose We investigated the effects of weed control and fertilization at early establishment on foliar stable carbon (δ13C) and nitrogen (N) isotope (δ15N) compositions, foliar N concentration, tree growth and biomass, relative weed cover and other physiological traits in a 2-year old F1 hybrid (Pinus elliottii var. elliottii (Engelm) × Pinus caribaea var. hondurensis (Barr. ex Golf.)) plantation grown on a yellow earth in southeast Queensland of subtropical Australia. Materials and methods Treatments included routine weed control, luxury weed control, intermediate weed control, mechanical weed control, nil weed control, and routine and luxury fertilization in a randomised complete block design. Initial soil nutrition and soil fertility parameters included (hot water extractable organic carbon (C) and total nitrogen (N), total C and N, C/N ratio, labile N pools (nitrate (NO3 −) and ammonium (NH4 +)), extractable potassium (K+)), soil δ15N and δ13C. Relative weed cover, foliar N concentrations, tree growth rate and physiological parameters including photosynthesis, stomatal conductance, photosynthetic nitrogen use efficiency, foliar δ15N and foliar δ13C were also measured at early establishment. Results and discussion Foliar N concentration at 1.25 years was significantly different amongst the weed control treatments and was negatively correlated to the relative weed cover at 1.1 years. Foliar N concentration was also positively correlated to foliar δ15N and foliar δ13C, tree height, height growth rates and tree biomass. Foliar δ15N was negatively correlated to the relative weed cover at 0.8 and 1.1 years. The physiological measurements indicated that luxury fertilization and increasing weed competition on these soils decreased leaf xylem pressure potential (Ψxpp) when compared to the other treatments. Conclusions These results indicate how increasing N resources and weed competition have implications for tree N and water use at establishment in F1 hybrid plantations of southeast Queensland, Australia. These results suggest the desirability of weed control, in the inter-planting row, in the first year to maximise site N and water resources available for seedling growth. It also showed the need to avoid over-fertilisation, which interfered with the balance between available N and water on these soils.

Complementary forages – integration at a whole-farm level

A high proportion of the Australian and New Zealand dairy industry is based on a relatively simple, low input and low cost pasture feedbase. These factors enable this type of production system to remain internationally competitive. However, a key limitation of pasture-based dairy systems is periodic imbalances between herd intake requirements and pasture DM production, caused by strong seasonality and high inter-annual variation in feed supply. This disparity can be moderated to a certain degree through the strategic management of the herd through altering calving dates and stocking rates, and the feedbase by conserving excess forage and irrigating to flatten seasonal forage availability. Australasian dairy systems are experiencing emerging market and environmental challenges, which includes increased competition for land and water resources, decreasing terms of trade, a changing and variable climate, an increasing environmental focus that requires improved nutrient and water-use efficiency and lower greenhouse gas emissions. The integration of complementary forages has long been viewed as a means to manipulate the home-grown feed supply, to improve the nutritive value and DM intake of the diet, and to increase the efficiency of inputs utilised. Only recently has integrating complementary forages at the whole-farm system level received the significant attention and investment required to examine their potential benefit. Recent whole-of-farm research undertaken in both Australia and New Zealand has highlighted the importance of understanding the challenges of the current feedbase and the level of complementarity between forage types required to improve profit, manage risk and/or alleviate/mitigate against adverse outcomes. This paper reviews the most recent systems-level research into complementary forages, discusses approaches to modelling their integration at the whole-farm level and highlights the potential of complementary forages to address the major challenges currently facing pasture-based dairy systems.

Water balance modelling in a tropical watershed under deciduous forest (Mule Hole, India): Regolith matric storage buffers the groundwater recharge process

Accurate estimations of water balance are needed in semi-arid and sub-humid tropical regions, where water resources are scarce compared to water demand. Evapotranspiration plays a major role in this context, and the difficulty to quantify it precisely leads to major uncertainties in the groundwater recharge assessment, especially in forested catchments. In this paper, we propose to assess the importance of deep unsaturated regolith and water uptake by deep tree roots on the groundwater recharge process by using a lumped conceptual model (COMFORT). The model is calibrated using a 5 year hydrological monitoring of an experimental watershed under dry deciduous forest in South India (Mule Hole watershed). The model was able to simulate the stream discharge as well as the contrasted behaviour of groundwater table along the hillslope. Water balance simulated for a 32 year climatic time series displayed a large year-to-year variability, with alternance of dry and wet phases with a time period of approximately 14 years. On an average, input by the rainfall was 1090 mm year(-1) and the evapotranspiration was about 900 mm year(-1) out of which 100 mm year(-1) was uptake from the deep saprolite horizons. The stream flow was 100 mm year(-1) while the groundwater underflow was 80 mm year(-1). The simulation results suggest that (i) deciduous trees can uptake a significant amount of water from the deep regolith, (ii) this uptake, combined with the spatial variability of regolith depth, can account for the variable lag time between drainage events and groundwater rise observed for the different piezometers and (iii) water table response to recharge is buffered due to the long vertical travel time through the deep vadose zone, which constitutes a major water reservoir. This study stresses the importance of long term observations for the understanding of hydrological processes in tropical forested ecosystems. (C) 2009 Elsevier B.V. All rights reserved.

A pilot study of the Social Water Assessment Protocol in a mining region of Ghana

The Social Water Assessment Protocol (SWAP) is a tool consisting of a series of questions on fourteen themes designed to capture the social context of water around a mine site. A pilot study of the SWAP, conducted in Prestea-Huni Valley, Ghana, showed that some communities were concerned about whether the groundwater was potable. The mining company’s concern was that there was a cycle of dependency amongst communities that received treated water from the mining company. The pilot identified potential data sources and stakeholder groups for each theme, gaps in themes and suggested refinements to questions to improve the SWAP.

A Fair Contract for Managing Water Scarcity

In public utilities, under supply constraints, fairness considerations lead to a market failure. This paper characterizes a two-period principal-agent contract for demand management, that mitigates this market failure in urban water systems. The contract is designed as an extensive form mechanism using subgame perfect Nash equilibrium (SPNE) as the solution concept. The contract is fair; and is shown to be economically efficient if, in case of deviation by the agent, the gain to the agent and the loss to the principal are small. It is shown that the assumption can be avoided in an infinite horizon contract.

Nonlinear ensemble prediction of chaotic daily rainfall

The significance of treating rainfall as a chaotic system instead of a stochastic system for a better understanding of the underlying dynamics has been taken up by various studies recently. However, an important limitation of all these approaches is the dependence on a single method for identifying the chaotic nature and the parameters involved. Many of these approaches aim at only analyzing the chaotic nature and not its prediction. In the present study, an attempt is made to identify chaos using various techniques and prediction is also done by generating ensembles in order to quantify the uncertainty involved. Daily rainfall data of three regions with contrasting characteristics (mainly in the spatial area covered), Malaprabha, Mahanadi and All-India for the period 1955-2000 are used for the study. Auto-correlation and mutual information methods are used to determine the delay time for the phase space reconstruction. Optimum embedding dimension is determined using correlation dimension, false nearest neighbour algorithm and also nonlinear prediction methods. The low embedding dimensions obtained from these methods indicate the existence of low dimensional chaos in the three rainfall series. Correlation dimension method is done on th phase randomized and first derivative of the data series to check whether the saturation of the dimension is due to the inherent linear correlation structure or due to low dimensional dynamics. Positive Lyapunov exponents obtained prove the exponential divergence of the trajectories and hence the unpredictability. Surrogate data test is also done to further confirm the nonlinear structure of the rainfall series. A range of plausible parameters is used for generating an ensemble of predictions of rainfall for each year separately for the period 1996-2000 using the data till the preceding year. For analyzing the sensitiveness to initial conditions, predictions are done from two different months in a year viz., from the beginning of January and June. The reasonably good predictions obtained indicate the efficiency of the nonlinear prediction method for predicting the rainfall series. Also, the rank probability skill score and the rank histograms show that the ensembles generated are reliable with a good spread and skill. A comparison of results of the three regions indicates that although they are chaotic in nature, the spatial averaging over a large area can increase the dimension and improve the predictability, thus destroying the chaotic nature. (C) 2010 Elsevier Ltd. All rights reserved.

Constraining uncertainty in regional hydrologic impacts of climate change: Nonstationarity in downscaling

Regional impacts of climate change remain subject to large uncertainties accumulating from various sources, including those due to choice of general circulation models (GCMs), scenarios, and downscaling methods. Objective constraints to reduce the uncertainty in regional predictions have proven elusive. In most studies to date the nature of the downscaling relationship (DSR) used for such regional predictions has been assumed to remain unchanged in a future climate. However,studies have shown that climate change may manifest in terms of changes in frequencies of occurrence of the leading modes of variability, and hence, stationarity of DSRs is not really a valid assumption in regional climate impact assessment. This work presents an uncertainty modeling framework where, in addition to GCM and scenario uncertainty, uncertainty in the nature of the DSR is explored by linking downscaling with changes in frequencies of such modes of natural variability. Future projections of the regional hydrologic variable obtained by training a conditional random field (CRF) model on each natural cluster are combined using the weighted Dempster-Shafer (D-S) theory of evidence combination. Each projection is weighted with the future projected frequency of occurrence of that cluster (''cluster linking'') and scaled by the GCM performance with respect to the associated cluster for the present period (''frequency scaling''). The D-S theory was chosen for its ability to express beliefs in some hypotheses, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The methodology is tested for predicting monsoon streamflow of the Mahanadi River at Hirakud Reservoir in Orissa, India. The results show an increasing probability of extreme, severe, and moderate droughts due to limate change. Significantly improved agreement between GCM predictions owing to cluster linking and frequency scaling is seen, suggesting that by linking regional impacts to natural regime frequencies, uncertainty in regional predictions can be realistically quantified. Additionally, by using a measure of GCM performance in simulating natural regimes, this uncertainty can be effectively constrained.

Optimal reservoir operation for irrigation of multiple crops using genetic algorithms

This paper presents a genetic algorithm (GA) model for obtaining an optimal operating policy and optimal crop water allocations from an irrigation reservoir. The objective is to maximize the sum of the relative yields from all crops in the irrigated area. The model takes into account reservoir inflow, rainfall on the irrigated area, intraseasonal competition for water among multiple crops, the soil moisture dynamics in each cropped area, the heterogeneous nature of soils. and crop response to the level of irrigation applied. The model is applied to the Malaprabha single-purpose irrigation reservoir in Karnataka State, India. The optimal operating policy obtained using the GA is similar to that obtained by linear programming. This model can be used for optimal utilization of the available water resources of any reservoir system to obtain maximum benefits.

Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster-Shafer and Bayesian approaches

Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster-Shafer (D-S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D-S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D-S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D-S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster-Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D-S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D-S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change. (C) 2010 Elsevier Ltd. All rights reserved.

An integrated model for optimal reservoir operation for irrigation of multiple crops

An integrated model is developed, based on seasonal inputs of reservoir inflow and rainfall in the irrigated area, to determine the optimal reservoir release policies and irrigation allocations to multiple crops. The model is conceptually made up of two modules, Module 1 is an intraseasonal allocation model to maximize the sum of relative yields of all crops, for a given state of the system, using linear programming (LP). The module takes into account reservoir storage continuity, soil moisture balance, and crop root growth with time. Module 2 is a seasonal allocation model to derive the steady state reservoir operating policy using stochastic dynamic programming (SDP). Reservoir storage, seasonal inflow, and seasonal rainfall are the state variables in the SDP. The objective in SDP is to maximize the expected sum of relative yields of all crops in a year. The results of module 1 and the transition probabilities of seasonal inflow and rainfall form the input for module 2. The use of seasonal inputs coupled with the LP-SDP solution strategy in the present formulation facilitates in relaxing the limitations of an earlier study, while affecting additional improvements. The model is applied to an existing reservoir in Karnataka State, India.

Potential-distance relationships of clay-water systems considering the Stern theory

This paper deals with the use of Stem theory as applied to a clay-water electrolyte system, which is more realistic to understand the force system at micro level man the Gouy-Chapman theory. The influence of the Stern layer on potential-distance relationship has been presented quantitatively for certain specified clay-water systems and the results are compared with the Gouy-Chapman model. A detailed parametric study concerning the number of adsorption spots on the clay platelet, the thickness of the Stern layer, specific adsorption potential and the value of dielectric constant of the pore fluid in the Stern layer, was carried out. This study investigates that the potential obtained at any distance using the Stern theory is higher than that obtained by the Gouy-Chapman theory. The hydrated size of the ion is found to have a significant influence on the potential-distance relationship for a given clay, pore fluid characteristics and valence of the exchangeable ion.

WLS method for parameter estimation in water distribution networks

The weighted-least-squares method based on the Gauss-Newton minimization technique is used for parameter estimation in water distribution networks. The parameters considered are: element resistances (single and/or group resistances, Hazen-Williams coefficients, pump specifications) and consumptions (for single or multiple loading conditions). The measurements considered are: nodal pressure heads, pipe flows, head loss in pipes, and consumptions/inflows. An important feature of the study is a detailed consideration of the influence of different choice of weights on parameter estimation, for error-free data, noisy data, and noisy data which include bad data. The method is applied to three different networks including a real-life problem.

Integrated Reservoir Operation Modeling for Irrigation Systems - A Multi-Objective Approach

An integrated reservoir operation model is presented for developing effective operational policies for irrigation water management. In arid and semi-arid climates, owing to dynamic changes in the hydroclimatic conditions within a season, the fixed cropping pattern with conventional operating policies, may have considerable impact on the performance of the irrigation system and may affect the economics of the farming community. For optimal allocation of irrigation water in a season, development of effective mathematical models may guide the water managers in proper decision making and consequently help in reducing the adverse effects of water shortage and crop failure problems. This paper presents a multi-objective integrated reservoir operation model for multi-crop irrigation system. To solve the multi-objective model, a recent swarm intelligence technique, namely elitist-mutated multi-objective particle swarm optimisation (EM-MOPSO) has been used and applied to a case study in India. The method evolves effective strategies for irrigation crop planning and operation policies for a reservoir system, and thereby helps farming community in improving crop benefits and water resource usage in the reservoir command area.