Modelling groundwater regime acceptable for the forest survival after the building of the hydro-electric power plant

Antonić, Oleg; Hatić, Dalibor; Krian, Josip; Bukovec, Dragan

doi:10.1016/s0304-3800(00)00408-7

Cited by 18 publications

(5 citation statements)

References 11 publications

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“…Common ash growth was measured retrospectively by dendrochronological analysis. On each of the 20 plots, we selected 10 individuals: all were common ash, had a symmetrical crown, and were selected to be dominant or codominant in the canopy in order to limit bias due to competition for light (Ma¨kinen and Vanninen 1999, Smelko and Scheer 2000, Antonic et al 2001, Dittmar et al 2003. A total of 195 trees were cored once (Ma¨kinen and Vanninen 1999) with a standard 5-mm increment borer at 1.3 m. The extracted cores were then returned to the laboratory for cross-dating and ring measurement under a microscope with Velmex measuring equipment (0.01 mm precision; Velmex, Bloomfield, New York, USA) Cores were measured twice for data verification.…”

Section: Ash Growth and Regenerationmentioning

confidence: 99%

GEOMORPHOLOGICAL CONTROLS OFFRAXINUS EXCELSIORGROWTH AND REGENERATION IN FLOODPLAIN FORESTS

Dufour

Piégay

2008

Ecology

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Geomorphological changes can alter river hydrology and thus influence floodplain forest growth and regeneration. In this paper we quantify the effect of changes in channel elevation at the scale of four decades on hydrological conditions, overbank sediment deposits, water availability, and their impacts on common ash (Fraxinus excelsior) growth and recruitment in floodplain forests of the Ain River, France. Ash is a drought-sensitive species, and its regeneration is influenced by flood disturbance. We compared ash growth between 20 sample plots located in two contrasting geomorphological contexts using dendrochronological measurements: 11 along reaches degrading over the last 80 years and nine along stable or slightly aggrading reaches. In each context, half of the plots were located near the channel, and the remainder were within the floodplain. Ash regeneration was also quantified in each plot. This work showed that sites which undergo channel degradation are associated with less frequent overbank flows. Whereas there is no trend in either the climatic or stream flow timeseries over the last four decades, the growth patterns of ashes of these sites are significantly different as opposed to plots located in stable or aggraded reaches (bell shaped curve vs. constant increase in tree ring width). The variance through time due to climatic or stream flow control is masked by variance due to topographical changes. Currently plots located in degraded reaches show a lower ash growth (mean of 0.20 cm/yr vs. 0.34 cm/yr over the last decade) and less frequent ash seedling establishment. We have identified a hydrologic threshold for ash growth response when stand elevation values reach 1.5-2 m above the mean annual flow stage. Our results confirm that, at a many-decade timescale, changes in channel elevation must be considered in addition to other hydrological controls of hardwood species growth and recruitment.

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Section: Ash Growth and Regenerationmentioning

confidence: 99%

GEOMORPHOLOGICAL CONTROLS OFFRAXINUS EXCELSIORGROWTH AND REGENERATION IN FLOODPLAIN FORESTS

Dufour

Piégay

2008

Ecology

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“…Heymans and Baird (2000) have used network analysis to evaluate the carbon flow model built for the northern Benguela upwelling ecosystem in Namibia. Antonic et al (2001) have estimated the forest survival after building the hydroelectric power plant on the Drava River, Croatia by means of GIS-constructed database and a neural network. A three-layer LevenbergMarquardt feedforward neural network was used by Karul et al (2000) to model the eutrophication process in the three water bodies in Turkey.…”

Section: Introductionmentioning

confidence: 99%

3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece

Moustris

Ziomas

Παλιατσός

2009

Water Air Soil Pollut

116

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The difficulty in forecasting concentration trends with a reasonable error is still an open problem. In this paper, an effort has been made to this purpose. Artificial Neural Networks are used in order to forecast the maximum daily value of the European Regional Pollution Index as well as the number of consecutive hours, during the day, with at least one of the pollutants above a threshold concentration, 24 to 72 h ahead. The prediction concerns seven different places within the Greater Athens Area, Greece. The meteorological and air pollution data used in this study have been recorded by the network of the Greek Ministry of the Environment, Physical Planning, and Public Works over a 5-year period, 2001-2005. The hourly values of air pressure and global solar irradiance for the same period have been recorded by the National Observatory of Athens. The results are in a very good agreement with the real-monitored data at a statistical significance level of p<0.01.

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“…A renewal of scientific interest has grown exponentially since the last decade, mainly due to the availability of appropriate hardware that has made them convenient for fast data analysis and information processing (Viotti et al, 2002). Many ANN models have been developed in the last fifteen years for very different environmental purposes (Nunnari et al, 1998;Prybutok et al, 2000;Heymans and Baird, 2000;Karul et al, 2000;Antonic et al, 2001;Kolehmainen et al, 2001;Balaguer Ballester et al, 2002;Schlink et al, 2003;Corani, 2005;Slini et al, 2006;Dutot et al, 2007;Papanastasiou et al, 2007;Moustris et al, 2010a;.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prognosis of maximum daily surface ozone concentration within the greater Athens urban area, Greece

2014

Global NEST Journal

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In recent decades, there has been an increasing interest in the prognosis of maximum surface ozone concentrations due to the adverse effects on human health, animal population, agricultural productivity and forestry. The present study deals with the development and application of Artificial Neural Network (ANN) models in predicting the maximum daily surface ozone concentration in several locations within the greater Athens area (GAA), 24-hours in advance. Meteorological and air pollution data during the period 2001 to 2005 were provided by the network of the Hellenic Ministry of the Environment, Energy and Climate Change. Hourly values of barometric pressure and total solar irradiance for the same period have been recorded by the National Observatory of Athens. A training data set for the ANN prognostic model was generated by employing the superposed epoch analysis.The evaluation of the performance of the developed model, using appropriate statistical indices, clearly indicates that the risk of surface ozone values exceeding the European Union (EU) threshold for human health protection can be successfully predicted. This suggests that the proposed ANN model can be used to issue warnings for the general public and especially certain sensitive groups of the population.

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Modelling groundwater regime acceptable for the forest survival after the building of the hydro-electric power plant

Cited by 18 publications

References 11 publications

GEOMORPHOLOGICAL CONTROLS OFFRAXINUS EXCELSIORGROWTH AND REGENERATION IN FLOODPLAIN FORESTS

GEOMORPHOLOGICAL CONTROLS OFFRAXINUS EXCELSIORGROWTH AND REGENERATION IN FLOODPLAIN FORESTS

3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece

Prognosis of maximum daily surface ozone concentration within the greater Athens urban area, Greece

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