DELIVERED AT THE 14 TH INTERNATIONAL CONFERENCE ON QUANTITATIVE METHODS FOR ENVIRONMENTAL SCIENCES 3-7 NOVEMBER 2003 Challenges in Developing an Information Management System for Lake Erie – a case study

S OF PAPERS DELIVERED AT THE14TH INTERNATIONAL CONFERENCE ON QUANTITATIVE METHODS FOR ENVIRONMENTAL SCIENCES 3-7 NOVEMBER 2003 Challenges in Developing an Information Management System for Lake Erie –a case study Neela M. Akhouri, Jignesh M. Patel and Stephen L. Goldman Lake Erie Research Center, University of Toledo, Oregon, Ohio, USA Neela.Akhouri@Utoledo.edu Lake Erie, one of the largest fresh water lakes in the world was moribund in the early 1970’s. With the enactment of the Clean Water Act of 1972 and the Great Lakes Water Quality Agreement between Canada and the United States, water quality management agencies have been successful to a large extent in controlling nutrient and contaminant levels in the ecosystem and a resurgence of fish and wildlife has been evident. The present state of Lake Erie reported by US Environment Protection Agency (2002 LaMP, USEPA) indicates land-use, nutrient enrichment, habitat degradation and loss, pathogen and toxic chemical contamination, and human health still continue to be priority management issues. To understand the various factors influencing sustainability of the lake ecosystem at a larger spatial and temporal scale (e.g. entire length of the river or creek) and to mitigate any detrimental changes, management goals and objectives should be based on the current state of its ecosystem components. To establish these management goals, it is necessary to form a baseline understanding of change over time in the watershed ecosystems. To develop baseline indices; analyze trends, changes, adaptations, and to establish relationships between factors influencing ecological changes, massive sets of data and a tool to “re-purpose” previously-existing data are required. However, historically, ecological data have been collected by a single or small group of investigators in plots of ≤ 1 m over relatively short periods of time (Kareiva and Anderson, 1988; Brown and Roughgarden, 1990). Therefore, it is not possible to accrue data at large spatial and temporal scales by any one project or program. For the past several decades, many disjointed programs and policies have existed at the federal, state, and local levels to record and analyze the processes occurring at different spatial and temporal scales in Lake Erie and its drainage basin. Even though the need for availability of extensive data sets (Meyer et al., 1999), and integrated analysis framework (Hardy, 1998) has been established, the daunting task of integration and synthesis of voluminous and complex data (Great Lakes Commission, 2003) has been stymied due to its (1) spatial dispersion, (2) structural and semantic heterogeneity, and (3) lack of detailed information regarding available data and availability of data. Details of these problems and our approach to finding a solution using technology tools will be presented in this paper. Climate and the multi-decadal properties of rainfall and river flow W.J.R. Alexander Department of Civil and Biosystems Engineering, University of Pretoria, South Africa. alexwjr@iafrica.com A comprehensive database consisting of 11 804 years of South African annual hydrometeorological data from 183 sites was assembled and analysed. The principal conclusion was that there has been a beneficial increase in the mean annual rainfall over South Africa by at least 9% during the 78-year period of record. There was some indication of an acceleration of the increase during the past two decades. It was also established that there was a corresponding increase in open water surface evaporation during this period. It is postulated that the concurrent increase in these two processes is due to naturally occurring global warming. There is also a statistically significant 18 to 22-year periodicity in many South African rainfall and river flow records. It is characterised by sudden reversals from periods of below average conditions to conditions of high rainfall and floods. It is probably related to solar activity. The analyses produced no evidence to support the views that climate change will result in a meaningful reduction in rainfall, or an increase in the frequency or severity of floods and droughts within the next 50 years. All evidence was to the contrary. Statistical analysis of extreme floods W.J.R. Alexander Department of Civil and Biosystems Engineering, University of Pretoria, South Africa alexwjr@iafrica.com In this presentation it is demonstrated that widespread, severe floods are caused by infrequent, but not rare, meteorological phenomena, including tropical cyclones and cut-off low pressure systems. The magnitude of the severe floods relative to the series of annual maximum floods at any one site can be readily determined, but all direct statistical analysis methods seriously under-estimate their frequency of occurrence. This is demonstrated by the statistical analyses of wide area, severe flood-producing rainfall. The statistical analyses confirm that the widely used log Pearson type 3 distribution using conventional moment estimators, remains the preferred method for the statistical analysis of hydrological and meteorological data, but that no direct statistical analysis methods can be used with confidence for return periods exceeding 50 years. It is also demonstrated that there is no justification for making any allowances for future climate change in design flood estimation procedures, as any increases, should they occur, will be undistinguishable against the background of the large natural variability. Stochastic rainfall generation for rainfall-runoff modelling Enrica Bellone University College London enrica@stts.ucl. ac.uk Rainfall and evaporation are important inputs to the rainfall-runoff models used for flood design. We overview the stochastic simulation methods being studied at IC/UCL. For single-site rainfall, a wide range of Poisson-based models have been investigated. Simulations from these models may be combined with synthetic evaporation series, derived using a Generalized Linear Model (GLM)based approach, to obtain the necessary fine scale inputs for runoff generation. A spatial extension of the Poisson-based models (fitted to UK radar data) provides a continuous space continuous time approach to the rainfall simulation problem. Alternatively, GLMs have been applied to daily raingauge records, where radar data are not available or the underlying hypotheses of the point process models are not satisfied. While the Poisson-based models require both spatial and temporal stationarity, the GLM approach does not involve a stationarity assumption. Combining the two approaches, for regions where both radar and gauge data are available, may be a way to allow spatial and temporal non-stationarities, including climate change scenarios, in continuous simulations. A Statistician's Take on the Benthic Index of Biotic Integrity Grace Chiu Pacific Institute for the Mathematical Sciences (PIMS) and Department of Statistics, University of Washington Stream health is often measured by the multimetric benthic index of biotic integrity''(B-IBI) developed by Kerans and Karr (1994). B-IBI metrics quantify the well-being of benthic inhabitants of the stream. For the Puget Sound Lowland streams, such metrics include the total number of taxa, the proportion of predatory taxa, etc. Each metric is converted into a score of 1, 3, or 5, where a higher value indicates a healthier stream with respect to the metric. Summing the metric scores yields the B-IBI. Conventional scoring requires subjective and space/time-dependent input on the cutoff points for the metric-to-score conversion. To a statistician, simple standardization (centering and division by the standard deviation) may be a more natural, non-study-specific conversion. Our more statistically oriented B-IBI (SOBIBI) is the sum of all scores computed this way. Currently, we are comparing the performance of our SOBIBI to the reported properties of the B-IBI via plots and bootstrap simulations. In this talk, I will introduce the background of the project, discuss our current progress, and invite ideas from the audience. A Quantitative Decision Support Instrument for Water Quality Management Marius Claassen, Alta de Waal, Xolani Hadebe and Thabo Sekonyela CSIR, South Africa mclaassen@csir.co.za, adewaal@csir.co.za, xhadebe@csir.co.za, and tsekonyela@csir.co.za The promulgation of the National Water Act (NWA), 1998 (Act No. 36 of 1998) and various other acts, as well as the publication of various policies and White Papers (such as Water Supply and Sanitation, 1994, etc.) have given a new direction to water resources management, and specifically Water Quality Management (WQM), in South Africa. The purpose of the NWA is to ensure that the South Africa’s water resources are protected, used, developed, conserved, managed and controlled in an equitable, efficient and sustainable manner. This necessitates a change in the approach of WQM to an integrated source, resource and remediation focused management approach. Currently no quantitative technique exists to assist licence officers in making decisions regarding the allocation of water licences. Licence officers are frustrated because of the lack of structure and support that currently exists to alleviate their tasks. To make this integrated approach operational, management instruments are needed for regional offices to include resource-directed water quality issues into licence allocations. This calls for a decision support tool that will guide regional offices in making important licence allocation decisions. The decision support tool must be able to facilitate a decision despite incomplete, imprecise, and highly variable data. Also, the decision needs to be based on multiple criteria such as socio-economic factors, race and gender considerations, alignment with catchment strategy, etc. We suggest that Bayesian networks provide a possible solution to this problem. Bayesian networks have proven to be an extrem