Net primary productivity (NPP) is a key component of the global carbon cycle. Accurate estimation... more Net primary productivity (NPP) is a key component of the global carbon cycle. Accurate estimation of terrestrial NPP and its sensitivity to regional temperature and precipitation changes are important for understanding the fluxes and amount of total carbon resulting from climate variations. This is especially true for Turkey where anthropogenically driven climate change can impact the ecosystem productivity within its rich vegetation biodiversity and diverse topography. The aim of this study is to estimate the response of NPP to regional climate changes in Turkey using a biogeochemical modelling approach. The CASA model was utilized to predict annual regional fluxes in terrestrial net primary production for present (2000-2010) and future (2060-2080) climate conditions. A comprehensive data set including percentage of tree cover, land cover map, soil texture, NDVI (Normalized Difference Vegetation Index) and climate variables were used to constitute the model. The multi-temporal metr...
The main objective of this study is to estimate the effects of hydrological quantities on the for... more The main objective of this study is to estimate the effects of hydrological quantities on the forest productivity of Goksu Basin located on the Eastern Mediterranean coast of Turkey. The area is characterised by a rich biodiversity in terms of tree species and this ecosystem is vulnerable to water availability and quality. This has particular importance in managing water resources at semi-arid environments of Mediterranean. In this study, the interaction of hydrological quantities and the forest productivity will be modelled in three phases; i) modelling hydrological quantities using J2000 hydrological modelling system, ii) modelling Net Primary Productivity (NPP) of the forest ecosystem using NASA-CASA (Carnegie Ames Stanford Approach) Model, iii) incorporating the J2000 and NASA-CASA model results to understand the interaction of hydrological dynamics and the forest productivity. The J2000 model developed by Friedrich-Schiller University, Germany will be used to provide spatially ...
Percent tree cover is the percentage of the ground surface area covered by a vertical projection ... more Percent tree cover is the percentage of the ground surface area covered by a vertical projection of the outermost perimeter of the plants. It is an important indicator to reveal the condition of forest systems and has a significant importance for ecosystem models as a main input. The aim of this study is to estimate the percent tree cover of various forest stands in a Mediterranean environment based on an empirical relationship between tree coverage and remotely sensed data in Goksu Watershed located at the Eastern Mediterranean coast of Turkey. A regression tree algorithm was used to simulate spatial fractions of Pinus nigra, Cedrus libani, Pinus brutia, Juniperus excelsa and Quercus cerris using multi-temporal LANDSAT TM/ETM data as predictor variables and land cover information. Two scenes of high resolution GeoEye-1 images were employed for training and testing the model. The predictor variables were incorporated in addition to biophysical variables estimated from the LANDSAT TM...
Net primary productivity (NPP) is defined as the net flux of carbon from the atmosphere into gree... more Net primary productivity (NPP) is defined as the net flux of carbon from the atmosphere into green plants per unit time. It has significant importance for the carbon cycle and an critical indicator of ecosystem sustainability. Accurate estimate of net primary productivity (NPP) is critical to understanding the carbon dynamics within the atmosphere–vegetation–soil continuum and the response of terrestrial ecosystem to future climate warming. The aim of this study to estimate the effects of climate change on forest net primary productivity using a remote sensing based biogeochemical model approach in a Mediterranean sub-catchment located in the Goksu River Basin, Turkey. Three are different types of models have been used to estimate NPP. The models that use remote sensing data provided reasonable results and based on the principle of light use efficiency (LUE). In this study, the CASA model will be utilised to predict present-future annual regional fluxes in forest net primary product...
Soil Organic Carbon (SOC) is one of the primary elements in the functioning of ecosystems. Soil e... more Soil Organic Carbon (SOC) is one of the primary elements in the functioning of ecosystems. Soil erosion is a major mechanism of land degradation removing SOC from its initial place and transferring it to either the hydrosphere or the atmosphere, thus affecting key ecosystem functions and services. The Mediterranean region is particularly prone to erosion as it is subject to long dry periods, followed by heavy burst of erosive rain, falling on fragile soils on steep slopes. It is clear that water erosion is irreversibly degrading the soils in many parts of Turkey. The objectives of this study were i) to estimate the temporal distribution of erosion amount in Seyhan River Basin ii) to assess the spatial distribution of organic carbon in the soils and iii) to estimate the depletion of SOC through soil erosion using Pan-European Soil Erosion Risk Assessment (PESERA) erosion model. PESERA (Pan-European Soil Erosion Risk Assessment) as a physically-based regional-scale soil erosion model ...
Predicting and understanding the spatial variability of snow-related quantities plays an importan... more Predicting and understanding the spatial variability of snow-related quantities plays an important role in catchment hydrology. Modelling the spatial variability of these quantities is complicated by the interrelated nature of the processes involved. The snow cover is also critical in the ecosystem via its effect on the surface energy and water balance. Therefore, its accurate representation is essential to a better understanding of climate effects on the hydrological cycle. The aim of the study is to estimate the spatial variability of seasonal snow cover using process-based modelling and remote sensing techniques in Seyhan Watershed, Turkey. Digital Elevation Model (DEM), land cover, geology, soil, Hydrological Response Units (HRUs) maps and meteorological time series will be utilized as main modelling entities. The model outcomes then will be validated using snow measurements and snow cover maps derived from LANDSAT and MODIS images acquired between 2000 and 2012. A quantitative ...
Fire weather indices (FWIs) are among the most effective techniques to define real time or long t... more Fire weather indices (FWIs) are among the most effective techniques to define real time or long term forest fire risk using meteorological data. In this research, long term forest fire risk of Turkey was modelled using a fire weather index called F index for present (1990 – 2010) and future (2061 – 2080) periods. Dry bulb temperature, relative humidity and maximum wind speed were mapped using 945 meteorological stations in Turkey, with a spatial resolution of 250 m. Long term mean F index values (from 1990 to 2010 and from 2061 to 2080) were calculated for 7 months representing fire seasons from April to October. Average fire occurrence of each month and monthly mean F index values of the forestlands were correlated using Pearson correlation statistic and determination coefficiency (R 2) was 0.82. Additionally, projected annual mean temperature and humidity based on HadGEM2-ES model RCP 4.5 scenario were used to derive future F index. Mean F index values of the forestlands were shown that forest fire risk of Turkey will have an increase of 21.1% in 2070s.
Net primary productivity (NPP) is a key component of the global carbon cycle. Accurate estimation... more Net primary productivity (NPP) is a key component of the global carbon cycle. Accurate estimation of terrestrial NPP and its sensitivity to regional temperature and precipitation changes are important for understanding the fluxes and amount of total carbon resulting from climate variations. This is especially true for Turkey where anthropogenically driven climate change can impact the ecosystem productivity within its rich vegetation biodiversity and diverse topography. The aim of this study is to estimate the response of NPP to regional climate changes in Turkey using a biogeochemical modelling approach. The CASA model was utilized to predict annual regional fluxes in terrestrial net primary production for present (2000-2010) and future (2060-2080) climate conditions. A comprehensive data set including percentage of tree cover, land cover map, soil texture, NDVI (Normalized Difference Vegetation Index) and climate variables were used to constitute the model. The multi-temporal metr...
The main objective of this study is to estimate the effects of hydrological quantities on the for... more The main objective of this study is to estimate the effects of hydrological quantities on the forest productivity of Goksu Basin located on the Eastern Mediterranean coast of Turkey. The area is characterised by a rich biodiversity in terms of tree species and this ecosystem is vulnerable to water availability and quality. This has particular importance in managing water resources at semi-arid environments of Mediterranean. In this study, the interaction of hydrological quantities and the forest productivity will be modelled in three phases; i) modelling hydrological quantities using J2000 hydrological modelling system, ii) modelling Net Primary Productivity (NPP) of the forest ecosystem using NASA-CASA (Carnegie Ames Stanford Approach) Model, iii) incorporating the J2000 and NASA-CASA model results to understand the interaction of hydrological dynamics and the forest productivity. The J2000 model developed by Friedrich-Schiller University, Germany will be used to provide spatially ...
Percent tree cover is the percentage of the ground surface area covered by a vertical projection ... more Percent tree cover is the percentage of the ground surface area covered by a vertical projection of the outermost perimeter of the plants. It is an important indicator to reveal the condition of forest systems and has a significant importance for ecosystem models as a main input. The aim of this study is to estimate the percent tree cover of various forest stands in a Mediterranean environment based on an empirical relationship between tree coverage and remotely sensed data in Goksu Watershed located at the Eastern Mediterranean coast of Turkey. A regression tree algorithm was used to simulate spatial fractions of Pinus nigra, Cedrus libani, Pinus brutia, Juniperus excelsa and Quercus cerris using multi-temporal LANDSAT TM/ETM data as predictor variables and land cover information. Two scenes of high resolution GeoEye-1 images were employed for training and testing the model. The predictor variables were incorporated in addition to biophysical variables estimated from the LANDSAT TM...
Net primary productivity (NPP) is defined as the net flux of carbon from the atmosphere into gree... more Net primary productivity (NPP) is defined as the net flux of carbon from the atmosphere into green plants per unit time. It has significant importance for the carbon cycle and an critical indicator of ecosystem sustainability. Accurate estimate of net primary productivity (NPP) is critical to understanding the carbon dynamics within the atmosphere–vegetation–soil continuum and the response of terrestrial ecosystem to future climate warming. The aim of this study to estimate the effects of climate change on forest net primary productivity using a remote sensing based biogeochemical model approach in a Mediterranean sub-catchment located in the Goksu River Basin, Turkey. Three are different types of models have been used to estimate NPP. The models that use remote sensing data provided reasonable results and based on the principle of light use efficiency (LUE). In this study, the CASA model will be utilised to predict present-future annual regional fluxes in forest net primary product...
Soil Organic Carbon (SOC) is one of the primary elements in the functioning of ecosystems. Soil e... more Soil Organic Carbon (SOC) is one of the primary elements in the functioning of ecosystems. Soil erosion is a major mechanism of land degradation removing SOC from its initial place and transferring it to either the hydrosphere or the atmosphere, thus affecting key ecosystem functions and services. The Mediterranean region is particularly prone to erosion as it is subject to long dry periods, followed by heavy burst of erosive rain, falling on fragile soils on steep slopes. It is clear that water erosion is irreversibly degrading the soils in many parts of Turkey. The objectives of this study were i) to estimate the temporal distribution of erosion amount in Seyhan River Basin ii) to assess the spatial distribution of organic carbon in the soils and iii) to estimate the depletion of SOC through soil erosion using Pan-European Soil Erosion Risk Assessment (PESERA) erosion model. PESERA (Pan-European Soil Erosion Risk Assessment) as a physically-based regional-scale soil erosion model ...
Predicting and understanding the spatial variability of snow-related quantities plays an importan... more Predicting and understanding the spatial variability of snow-related quantities plays an important role in catchment hydrology. Modelling the spatial variability of these quantities is complicated by the interrelated nature of the processes involved. The snow cover is also critical in the ecosystem via its effect on the surface energy and water balance. Therefore, its accurate representation is essential to a better understanding of climate effects on the hydrological cycle. The aim of the study is to estimate the spatial variability of seasonal snow cover using process-based modelling and remote sensing techniques in Seyhan Watershed, Turkey. Digital Elevation Model (DEM), land cover, geology, soil, Hydrological Response Units (HRUs) maps and meteorological time series will be utilized as main modelling entities. The model outcomes then will be validated using snow measurements and snow cover maps derived from LANDSAT and MODIS images acquired between 2000 and 2012. A quantitative ...
Fire weather indices (FWIs) are among the most effective techniques to define real time or long t... more Fire weather indices (FWIs) are among the most effective techniques to define real time or long term forest fire risk using meteorological data. In this research, long term forest fire risk of Turkey was modelled using a fire weather index called F index for present (1990 – 2010) and future (2061 – 2080) periods. Dry bulb temperature, relative humidity and maximum wind speed were mapped using 945 meteorological stations in Turkey, with a spatial resolution of 250 m. Long term mean F index values (from 1990 to 2010 and from 2061 to 2080) were calculated for 7 months representing fire seasons from April to October. Average fire occurrence of each month and monthly mean F index values of the forestlands were correlated using Pearson correlation statistic and determination coefficiency (R 2) was 0.82. Additionally, projected annual mean temperature and humidity based on HadGEM2-ES model RCP 4.5 scenario were used to derive future F index. Mean F index values of the forestlands were shown that forest fire risk of Turkey will have an increase of 21.1% in 2070s.
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Papers by Ahmet Cilek