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Abstract The determination of a proper minimum environmental flow in a river basin is one of the most critical components of sustainable water resources management, with hydraulic and habitat modelling being the most advance methods for that analysis. The present study illustrates the ecohydraulic modelling of Nestos River Delta at the deltaic zone of a heavily modified watershed under various low flow scenarios, using the RIVER2D Model. RIVER2D is a two-dimensional, depth averaged hydrodynamic and fish habitat model, widely used in environmental flow assessment studies. A detailed digital model of the river channel and its surrounding area was developed, including all the morphological characteristics of the river channel, as well as its various sandy islets. Field surveys in combination with Digital Elevation Model data were used for the development of the digital terrain used as input to RIVER2D model. Several different steady state hydraulic simulations were conducted under typical low flow conditions, producing water depth and water velocity (direction and magnitude) maps for each discharge scenario, while results were verified with the use of field measurements. In the next step, RIVER2D was used for the fish habitat modelling of the study area, with the application of fish preference curves developed specifically for the study area. Finally, the fish habitat modelling was conducted for four different fish species, divided into two age groups, forced under the same five typical low flow conditions. Results reveal that the ecohydraulic modelling of river basins should be considered as an indispensable component in sustainable water resources management. [ABSTRACT FROM AUTHOR]

Abstract: Fuzzy logic-based methods have been recently applied to environmental problems, demonstrating appropriateness to address imprecise data. A methodology, based on fuzzy inference systems (FIS) is presented, in an effort to assess organic matter removal efficiency in free-water surface (FWS) constructed wetlands (CWs). A fuzzy model was developed and is validated using experimental data collected in a two-year period in five pilot-scale FWS CW units. The CWs differed in plan view shape, soil type of the substrate material and plant species, and operated under four different hydraulic residence times (HRTs) at various ambient conditions. Model predictions showed good agreement with the experimental data. An effort was also made to collect data from the literature and verify the model using independent data from various other CWs of different settings and operating conditions. The proposed methodology seems to be a satisfactory modeling tool to be used in predicting FWS CW performance. [Copyright &y& Elsevier]

Abstract: Forecasting, using historic time-series data, has become an important tool for fisheries management. ARIMA modeling, Modeling for Optimal Forecasting techniques and Decision Support Systems based on fuzzy mathematics may be used to predict the general trend of a given fish landings time-series with increased reliability and accuracy. The present paper applies these three modeling methods to forecast anchovy fish catches landed in a given port (Thessaloniki, Greece) during 1979–2000 and hake and bonito total fish catches during 1982–2000. The paper attempts to assess the model''s accuracy by comparing model results to the actual monthly fish catches of the year 2000. According to the measures of forecasting accuracy established, the best forecasting performance for anchovy was shown by the DSS model (MAPE=28.06%, RMSE=76.56, U-statistic=0.67 and R 2 =0.69). The optimal forecasting technique of genetic modeling improved significantly the forecasting values obtained by the selected ARIMA model. Similarly, the DSS model showed a noteworthy forecasting efficiency for the prediction of hake landings, during the year 2000 (MAPE=2.88%, RMSE=13.75, U-statistic=0.19 and R 2 =0.98), as compared to the other two modeling techniques. Optimal forecasting produced by combined modeling scored better than application of the simple ARIMA model. Overall, DSS results showed that the Fuzzy Expected Intervals methodology could be used as a very reliable tool for short-term predictions of fishery landings. [Copyright &y& Elsevier]