Implementation costs of Agricultural Management Practices for Water Quality Improvement in the Great Barrier Reef Catchments. CSIRO: Water for Healthy Country National Research Flagship

Executive summary. In this report we analyse implementation costs and benefits for agricultural management practices, grouped into farming systems. In order to do so, we compare plot scale gross margins for the dominant agricultural production systems (sugarcane, grazing and banana cultivation) in the NRM regions Wet Tropics, Burdekin Dry Tropics and Mackay Whitsundays. Furthermore, where available, we present investment requirements for changing to improved farming systems. It must be noted that transaction costs are not captured within this project. For sugarcane, this economic analysis shows that there are expected benefits to sugarcane growers in the different regions through transitions to C and B class farming systems. Further transition to A-class farming systems can come at a cost, depending on the capital investment required and the length of the investment period. Obviously, the costs and benefits will vary for each individual grower and will depend on their starting point and individual property scenario therefore each circumstance needs to be carefully considered before making a change in management practice. In grazing, overall, reducing stocking rates comes at a cost (reduced benefits). However, when operating at low utilisation rates in wetter country, lowering stocking rates can potentially come at a benefit. With win-win potential, extension is preferred to assist farmer in changing management practices to improve their land condition. When reducing stocking rates comes at a cost, incentives may be applicable to support change among farmers. For banana cultivation, the results indicate that the transition to C and B class management practices is a worthwhile proposition from an economic perspective. For a change from B to A class farming systems however, it is not worthwhile from a financial perspective. This is largely due to the large capital investment associated with the change in irrigation system and negative impact in whole of farm gross margin. Overall, benefits will vary for each individual grower depending on their starting point and their individual property scenario. The results presented in this report are one possible set of figures to show the changes in profitability of a grower operating in different management classes. The results in this report are not prescriptive of every landholder. Landholders will have different costs and benefits from transitioning to improved practices, even if similar operations are practiced, hence it is recommended that landholders that are willing to change management undertake their own research and analysis into the expected costs and benefits for their own soil types and property circumstances.

Effect of water quality and season on the population dynamics of Cabomba caroliniana in subtropical Queensland, Australia

Cabomba caroliniana is a submersed aquatic macrophyte that originates from the Americas and is currently invading temperate, subtropical, and tropical freshwater habitats around the world. Despite being a nuisance in many countries, little is known about its ecology. We monitored C. caroliniana populations in three reservoirs in subtropical Queensland, Australia, over 5.5 years. Although biomass, stem length, and plant density of the C. caroliniana stands fluctuated over time, they did not exhibit clear seasonal patterns. Water depth was the most important environmental factor explaining C. caroliniana abundance. Plant biomass was greatest at depths from 2–4 m and rooted plants were not found beyond 5 m. Plant density was greatest in shallow water and decreased with depth, most likely as a function of decreasing light and increasing physical stress. We tested the effect of a range of water physico-chemical parameters. The concentration of phosphorus in the water column was the variable that explained most of the variation in C. caroliniana population parameters. We found that in subtropical Australia, C. caroliniana abundance does not appear to be affected by seasonal conditions but is influenced by other environmental variables such as water depth and nutrient loading. Therefore, further spread will more likely be governed by local habitat rather than climatic conditions.

Effect of water quality and season on the population dynamics of Cabomba caroliniana in subtropical Queensland, Australia

Cabomba caroliniana is a submersed aquatic macrophyte that originates from the Americas and is currently invading temperate, subtropical, and tropical freshwater habitats around the world. Despite being a nuisance in many countries, little is known about its ecology. We monitored C. caroliniana populations in three reservoirs in subtropical Queensland, Australia, over 5.5 years. Although biomass, stem length, and plant density of the C. caroliniana stands fluctuated over time, they did not exhibit clear seasonal patterns. Water depth was the most important environmental factor explaining C. caroliniana abundance. Plant biomass was greatest at depths from 2–4 m and rooted plants were not found beyond 5 m. Plant density was greatest in shallow water and decreased with depth, most likely as a function of decreasing light and increasing physical stress. We tested the effect of a range of water physico-chemical parameters. The concentration of phosphorus in the water column was the variable that explained most of the variation in C. caroliniana population parameters. We found that in subtropical Australia, C. caroliniana abundance does not appear to be affected by seasonal conditions but is influenced by other environmental variables such as water depth and nutrient loading. Therefore, further spread will more likely be governed by local habitat rather than climatic conditions.

Evaluating the role of extension in helping to improve water quality in the Great Barrier Reef

From 2012-2014 the Queensland Government delivered an extension project to help sugarcane growers adopt best management practices to reduce pollutant loss to the Great Barrier Reef. Coutts J&R were engaged to measure progress towards the project's engagement, capacity gain and practice change targets. The monitoring and evaluation program comprised a database, post-workshop evaluations and grower and advisor surveys. Coutts J&R conducted an independent phone survey with 97 growers, a subset of the 900 growers engaged in extension activities. Of those surveyed 64% stated they had made practice changes. There was higher (74%) adoption by growers engaged in one-on-one extension than those growers only involved in group-based activities (36%). Overall, the project reported 41% (+/-10%, 95% confidence) of growers engaged made a practice change. The structured monitoring and evaluation program, including independent surveys, was essential to quantify practice change and demonstrate the effectiveness of extension in contributing to water quality improvement.

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA)problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max-min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga-Bhadra river system in India.

Sediment resuspension as a water quality regulator in lakes

Sediment resuspension, the return of the bottom material into the water column, is an important process that can have various effects on a lake ecosystem. Resuspension caused by wind-induced wave disturbance, currents, turbulent fluctuations and bioturbation affects water quality characteristics such as turbidity, light conditions, and concentrations of suspended solids (SS) and nutrients. Resuspension-mediated increase in turbidity may favour the dominance of phytoplankton over macrophytes. The predator-prey interactions contributing to the trophic state of a lake may also be influenced by increasing turbidity. Directly, the trophic state of a lake can be influenced by the effect of sediment resuspension on nutrient cycling. Resuspension enhances especially the cycling of phosphorus by bringing the sedimentary nutrients back into the water column and may thereby induce switches between phosphorus and nitrogen limitation. The contribution of sediment resuspension to gross sedimentation, turbidity, and concentration of SS and nutrients was studied in a small, deep lake as well as in a multibasin lake with deep and shallow areas. The effect of ice cover on sediment resuspension and thereby on phosphorus concentrations was also studied. The rates of gross sedimentation and resuspen¬sion were estimated with sediment traps and the associations between SS and nutrients were considered. Sediment resuspension, caused by wind activity, comprised most of the gross sedimenta¬tion and strongly contributed to the concentration of SS and turbidity in the lakes studied. Additionally, via the influence on SS, resuspension affected the concentration of total phosphorus (TP) and soluble reactive phosphorus (SRP), as well as the total nitrogen to total phosphorus (TN:TP) ratio. Although contrasting results concerning the dependence between the SS and SRP concentrations were observed, it could be concluded that sediment resuspension during strong algal blooms (pH > 9) led to aerobic release of P. The main findings of this thesis were that in the course of the growing season, sediment resuspension coupled with phytoplankton succession led to liberation of P from resuspended particles, which in turn resulted in high TP concentrations and low TN:TP ratios. This development was likely a cause of strong cyanobacterial blooms in midsummer.

Water quality: Indicators, human impact and environmental health

This book provides an overview of state of the art assessments of water quality; with an understanding how water quality is affected, and improving water quality for irrigation, drinking and recreation activities.

Statistical tools for analyzing water quality data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm.

Comparison of ANN models for predicting water quality in distribution systems

Deterministic models have been widely used to predict water quality in distribution systems, but their calibration requires extensive and accurate data sets for numerous parameters. In this study, alternative data-driven modeling approaches based on artificial neural networks (ANNs) were used to predict temporal variations of two important characteristics of water quality chlorine residual and biomass concentrations. The authors considered three types of ANN algorithms. Of these, the Levenberg-Marquardt algorithm provided the best results in predicting residual chlorine and biomass with error-free and ``noisy'' data. The ANN models developed here can generate water quality scenarios of piped systems in real time to help utilities determine weak points of low chlorine residual and high biomass concentration and select optimum remedial strategies.

Risk minimization in water quality control problems of a river system

Methodologies are presented for minimization of risk in a river water quality management problem. A risk minimization model is developed to minimize the risk of low water quality along a river in the face of conflict among various stake holders. The model consists of three parts: a water quality simulation model, a risk evaluation model with uncertainty analysis and an optimization model. Sensitivity analysis, First Order Reliability Analysis (FORA) and Monte-Carlo simulations are performed to evaluate the fuzzy risk of low water quality. Fuzzy multiobjective programming is used to formulate the multiobjective model. Probabilistic Global Search Laussane (PGSL), a global search algorithm developed recently, is used for solving the resulting non-linear optimization problem. The algorithm is based on the assumption that better sets of points are more likely to be found in the neighborhood of good sets of points, therefore intensifying the search in the regions that contain good solutions. Another model is developed for risk minimization, which deals with only the moments of the generated probability density functions of the water quality indicators. Suitable skewness values of water quality indicators, which lead to low fuzzy risk are identified. Results of the models are compared with the results of a deterministic fuzzy waste load allocation model (FWLAM), when methodologies are applied to the case study of Tunga-Bhadra river system in southern India, with a steady state BOD-DO model. The fractional removal levels resulting from the risk minimization model are slightly higher, but result in a significant reduction in risk of low water quality. (c) 2005 Elsevier Ltd. All rights reserved.

Water quality assessment in water protection planning

Yhteenveto: Veden laadun arviointi vesiensuojelun suunnittelussa

Influence of sampling frequency on the estimates of runoff water quality

Tiivistelmä: Havaintotiheyden vaikutus valumavesien laatuarvioihin

Water quality estimation by optical remote sensing in boreal lakes

In lake-rich regions, the gathering of information about water quality is challenging because only a small proportion of the lakes can be assessed each year by conventional methods. One of the techniques for improving the spatial and temporal representativeness of lake monitoring is remote sensing from satellites and aircrafts. The experimental material included detailed optical measurements in 11 lakes, air- and spaceborne remote sensing measurements with concurrent field sampling, automatic raft measurements and a national dataset of routine water quality measurements from over 1100 lakes. The analyses of the spatially high-resolution airborne remote sensing data from eutrophic and mesotrophic lakes showed that one or a few discrete water quality observations using conventional monitoring can yield a clear over- or underestimation of the overall water quality in a lake. The use of TM-type satellite instruments in addition to routine monitoring results substantially increases the number of lakes for which water quality information can be obtained. The preliminary results indicated that coloured dissolved organic matter (CDOM) can be estimated with TM-type satellite instruments, which could possibly be utilised as an aid in estimating the role of lakes in global carbon budgets. Based on the results of reflectance modelling and experimental data, MERIS satellite instrument has optimal or near-optimal channels for the estimation of turbidity, chlorophyll a and CDOM in Finnish lakes. MERIS images with a 300 m spatial resolution can provide water quality information in different parts of large and medium-sized lakes, and in filling in the gaps resulting from conventional monitoring. Algorithms that would not require simultaneous field data for algorithm training would increase the amount of remote sensing-based information available for lake monitoring. The MERIS Boreal Lakes processor, trained with the optical data and concentration ranges provided by this study, enabled turbidity estimations with good accuracy without the need for algorithm correction with field measurements, while chlorophyll a and CDOM estimations require further development of the processor. The accuracy of interpreting chlorophyll a via semi empirical algorithms can be improved by classifying lakes prior to interpretation according to their CDOM level and trophic status. Optical modelling indicated that the spectral diffuse attenuation coefficient can be estimated with reasonable accuracy from the measured water quality concentrations. This provides more detailed information on light attenuation from routine monitoring measurements than is available through the Secchi disk transparency. The results of this study improve the interpretation of lake water quality by remote sensing and encourage the use of remote sensing in lake monitoring.