How Can We Make Progress with Decision Support Systems in Landscape and River Basin Management? Lessons Learned from a Comparative Analysis of Four Different Decision Support Systems

This article analyses the benefits and shortcomings of the recently developed decision support systems (DSS) FLUMAGIS, Elbe-DSS, CatchMODS, and MedAction. The analysis elaborates on the following aspects: (i) application area/decision problem, (ii) stakeholder interaction/users involved, (iii) structure of DSS/model structure, (iv) usage of the DSS, and finally (v) most important shortcomings. On the basis of this analysis, we formulate four criteria that we consider essential for the successful use of DSS in landscape and river basin management. The criteria relate to (i) system quality, (ii) user support and user training, (iii) perceived usefulness and (iv) user satisfaction. We can show that the availability of tools and technologies for DSS in landscape and river basin management is good to excellent. However, our investigations indicate that several problems have to be tackled. First of all, data availability and homogenisation, uncertainty analysis and uncertainty propagation and problems with model integration require further attention. Furthermore, the appropriate and methodological stakeholder interaction and the definition of ‘what end-users really need and want’ have been documented as general shortcomings of all four examples of DSS. Thus, we propose an iterative development process that enables social learning of the different groups involved in the development process, because it is easier to design a DSS for a group of stakeholders who actively participate in an iterative process. We also identify two important lines of further development in DSS: the use of interactive visualization tools and the methodology of optimization to inform scenario elaboration and evaluate trade-offs among environmental measures and management alternatives.     

Suspended sediment, nitrogen and phosphorus concentrations and exports during storm-events to the Tuross estuary, Australia

This paper presents a process for estimating pollutant loads from water quality data, to improve catchment-scale modelling in the region for resource management purposes. It describes a program to estimate suspended sediment, total and dissolved nitrogen and phosphorus loads to the Tuross estuary from the Tuross River catchment (1810 km(2)) of coastal southeast Australia. Event-based water quality sampling results obtained during storm events in 2005 are presented. Event 1, during July 2005 was the largest storm event in terms of peak flow for 3.5 years. Other events monitored were also in July, November and December 2005. The early July 2005 event had a flow-weighted mean suspended sediment (SS) concentration during the first 4 days of 63 mg L(-1). Of the events monitored, this was unusual as it was preceded by drought and had the largest SS concentrations (peaking at 180 mg L(-1)) during the rising-stage. In contrast, the November event had a much lower flow-weighted SS mean (28 mg L(-1)), even though peak flow magnitudes were similar. The July and November 2005 events had peak flows of 12,360 and 11,330 ML day(-1). Low-cost rising-stage siphon samplers were used to collect samples during the rapidly rising phase of these events. The use of such samplers and consideration of time-lead/lag flow adjustments, quantified using cross-correlation analysis to account for hysteresis effects, were incorporated into the load estimation techniques. The technique is a potentially useful approach for understanding relationships between water quality concentrations and flow for modelling catchment source strengths and transport processes.     

Evaluation of municipal solid waste composting kinetics

Most modern municipal solid waste (MSW) composting operations have emphasized the enhancement of decomposition of the organic fraction of the waste to comply with strict environmental regulations. This can be achieved once the composting process kinetics are well understood. This study examined process kinetics through experimentation with bench-scale reactors under controlled composting conditions to show the interdependence between biological, chemical and physical factors during composting of MSW. The effects of temperature, moisture content, waste particle size and carbon to nitrogen (C/N) ratio on process kinetics were evaluated. The results obtained revealed that these factors should be carefully controlled in order to achieve optimum process performance. It has been found that the organic matter degradation during composting follows a first-order kinetic model.     

Index models to evaluate the risk of phosphorus and nitrogen loss at catchment scales

This paper investigates index models as a tool to estimate the risk of N and P source strengths and loss at the catchment scale. The index models assist managers in improving the focus of remediation actions that reduce nutrient delivery to waterbodies. N and P source risk factors (e.g. soil nutrient concentrations) and transport risk factors (e.g. distance-to-streams) are used to determine the overall risk of nutrient loss for a case study in the Tuross River catchment of coastal southeast Australia. In the development of the N index model for Tuross, particulate N was considered important based on the observed event water quality data. In contrast to previous N index models, erosion and contributing distance were therefore included in the Tuross River catchment N index. Event-based water quality monitoring, and soil information, or in data-poor catchments conceptual understanding, are essential to represent catchment-scale processes. The techniques have high applicability in other catchments, and are complementary to other modelling techniques such as process-based semi-distributed modelling. Index models generally provide much more detailed spatial resolution than fully- or semi-distributed conceptual modelling approaches. Semi-distributed models can be used to quantify nutrient loads and provide overall direction to set the broad focus of management. Index models can then be used to refine on-the-ground investigations and investment priorities. In this way semi-distributed models can be combined with index models to provide a set of powerful tools to influence management decisions and outcomes.