William Booty

Pollution remediation planning in developing countries : Conventional modelling versus knowledge-based prediction

Increasing water scarcity in many developing countries is forcing investments into remediation of water quality at the basin or sub-basin scale in order to increase water availability. Remediation decisions involving complex aquatic environments are often made in data-poor and knowledge-poor situations. Remediation objectives are often poorly articulated, raise unrealistic expectations, and cannot be evaluated in cost-benefit terms. Mathematical modelling, as a means of determining remediation options, is the usual method of choice in data-rich developed countries and requires substantial investment in reliable data, scientific capacity and a sophisticated management culture that generally are not found in developing countries. Modelling is expensive, has numerous other technical problems in developing countries, requires a high degree of input by foreign experts, and rarely leaves residual capacity in the developing country. In contrast, new techniques in knowledge-based (K-B) prediction focus on use of local and domain knowledge to establish meaningful program objectives. K-B-based decision support systems (DSS) allow the client to game with alternative remediation options with outputs expressed in degree of uncertainty in the assumptions and analytical processes included in the DSS system. The K-B approach builds local capacity and, by providing access to domain knowledge, reliance on foreign experts diminishes as local experts assume similar tasks elsewhere in the country.

Modelling Changes in Stream Water Quality Due to Climate Change in a Southern Ontario Watershed

This research represents a pilot project to establish a methodology for assessing the sensitivity of watershed stream water quality to changes in water quantity caused by climate change. The pilot watershed is the Duffins Creek watershed, located 20 km east of the City of Toronto, Canada. Scenarios of climate change analyzed in this project were drawn from two internationally recognized climate models: the Canadian Centre for Climate Modelling and Analysis (CCCma) CGCM1 and the Hadley Centre HadCM2. The AGNPS (Agricultural Non-Point Source) model was used to predict changes in stream water chemistry. The results are compared to baseline conditions as well as future conditions based on 2020 land use scenarios. It was determined that 2020 land use scenarios typically result in much smaller changes in peak flows than are predicted for the climate change scenarios, especially the wet climate change scenarios. Understanding climate change responses is critical for the development of watershed plans and drinking water source protection studies. Currently, watershed studies are completed using climate information based on relatively short-term monitoring databases that reflect past weather patterns. It is widely understood that management actions advocated in watershed studies could be improved if consideration were given to the implication of climate changes.

Land use change impacts on water quality in three lake winnipeg watersheds.

Lake Winnipeg eutrophication results from excess nutrient loading due to agricultural activities across the watershed. Estimating nonpoint-source pollution and the mitigation effects of beneficial management practices (BMPs) is an important step in protecting the water quality of streams and receiving waters. The use of computer models to systematically compare different landscapes and agricultural systems across the Red-Assiniboine basin has not been attempted at watersheds of this size in Manitoba. In this study, the Soil and Water Assessment Tool was applied and calibrated for three pilot watersheds of the Lake Winnipeg basin. Monthly flow calibration yielded overall satisfactory Nash-Sutcliffe efficiency (NSE), with values above 0.7 for all simulations. Total phosphorus (TP) calibration NSE ranged from 0.64 to 0.76, total N (TN) ranged from 0.22 to 0.75, and total suspended solids (TSS) ranged from 0.29 to 0.68. Based on the assessment of the TP exceedance levels from 1993 to 2007, annual loads were above proposed objectives for the three watersheds more than half of the time. Four BMP scenarios based on land use changes were studied in the watersheds: annual cropland to hay land (ACHL), wetland restoration (WR), marginal annual cropland conversion to hay land (MACHL), and wetland restoration on marginal cropland (WRMAC). Of these land use change scenarios, ACHL had the greatest impact: TSS loads were reduced by 33 to 65%, TN by 58 to 82%, and TP by 38 to 72% over the simulation period. By analyzing unit area and percentage of load reduction, the results indicate that the WR and WRMAC scenarios had a significant impact on water quality in high loading zones in the three watersheds. Such reductions of sediment, N, and P are possible through land use change scenarios, suggesting that land conservation should be a key component of any Lake Winnipeg restoration strategy.

Case Studies of Canadian Environmental Decision Support Systems

This chapter will discuss two different decision support systems that we have developed for Canadian environmental applications. We will first discuss how these systems utilize data and models to solve domain-specific problems and focus on effectiveness rather than efficiency in the decision making processes. In particular we will discuss how they are useful in better understanding the complex interaction between land and water and how they also provide a method to make informed resource management decisions and that they require the integration of scientific data, information, models and knowledge across multimedia (air, land and water), multi-disciplines and diverse landscapes. We will discuss how modelling is an important asset of any environmental decision support system (EDSS), particularly considering the high cost of full scale field work. Modelling presents a cost effective approach to assess the impact on the environment. We will discuss how a typical EDSS needs to be developed to address the issues of linking multi-media models at different geospatial scales, how it provides interfaces that can accept, select, link and recalibrate discipline-specific component models, and how it can seek optimal solutions for a given domain problem. We will also discuss that very often the EDSS is built around the concept of a management user interface to assist policy makers in their decision making. The technical users employ other tools to build model inputs, execute, and calibrate and validate the models while the management or policy makers view the inputs and outputs of the system that the technical users have built. This will allow management to investigate the analytical results based on robust science built by the researchers. Key functionality includes mapping and visualization of the results, scenario gaming and key statistical analyses of the results. The first example we will discuss is the Environmental Effects Modelling Statistical Assessment Tools Decision Support System. We will discuss how it provides a user-friendly data analysis, display and decision support tool for Canada’s federal environmental effects monitoring program for pulp and paper and mining industries. We will describe how the tool allows the assessment of the effects of effluent from industrial or other sources on fish and benthic populations. We will explain that in many of our EDSS systems, it is coupled with artificial intelligence such as expert systems to guide the users in the right direction. We will explain how the results are used in assessing the adequacy of existing regulations for protecting aquatic environments. We will explain how the design of such an EDSS has benefited from significant input from scientists, researchers, other end-users, system

Integrated Assessments Of River Health Using Decision Support Software

A prototype decision support system has been developed to assess the environmental outcomes of proposed flow regimes for the regulated, lowland floodplain rivers of the Murray-Darling Basin in Australia. The DSS assesses the environmental outcomes of proposed flow regimes primarily by providing integrated assessments of river health at different spatial scales based on the outputs from riverine habitat condition and riverine nuisance species models. These models rely on data from river hydrology simulation models — both within and external to the DSS. The current version of the software includes three models of riverine habitat condition: native fish, floodplain vegetation, and waterbird breeding. The only riverine nuisance species model currently included is a model to predict the severity of blue-green algal blooms. In this paper a brief description of the DSS framework and the nature of the underlying models is provided. The paper focuses on describing the methods used to integrate the individual model outputs into overall river health assessments.