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Öğe EVALUATION OF THE EFFECTS OF SOME WATERSHED CHARACTERISTICS ON WATER AND SUSPENDED SEDIMENT YIELD IN AGRICULTURAL AND FOREST DOMINATED WATERSHEDS(Croatian Forestry Soc, 2021) Citgez, Tarik; Karagul, Refik; Ozcan, MehmetTopography, geological structure and land use play a determinative role in the streamflow and total suspended sediment yield of watersheds having similar climate, soil and vegetation characteristics. In order to facilitate sustainable water resource management and effective land use planning, there is an increasing need for research investigating the effects of these factors. This study was carried out in forested and agricultural dominated sub-watersheds of the Big Melen watershed in the Western Black Sea Region of Turkey. Hazelnut plantations are grown on most of the agricultural areas in both watersheds. The forested watershed has a steep topography and its geological structure consists of sandstone-mudstone and sedimentary rock. The agricultural watershed area is larger and unlike the forested watershed, there is argillaceous limestone in its geological structure. The precipitation, streamflow and total suspended sediment yield in the watersheds were measured for two years. The total precipitation of the study area over the two years was 2217.3 mm. The water yield of the forested watershed was 867.6 mm, while that of the agricultural watershed was 654.9 mm. In the two years, the total suspended sediment transported from the forested watershed was 19.51 t ha(-1) and from the agricultural watershed 7.70 t ha(-1). However, except for the high values measured after an extreme rainfall event, the unit surface suspended sediment yield of the agricultural watershed was found to be higher than that of the forested watershed. These findings showed that watershed characteristics such as slope, geological structure and rainfall intensity may be more effective on the streamflow and total suspended sediment yield of the watersheds than land use.Öğe Investigation of changes in leaf area ındex in different forest stands(Springer, 2025) Degermenci, Ahmet Salih; Zengin, Hayati; Ozcan, Mehmet; Citgez, TarikLeaf area & imath;ndex (LAI) is a fundamental metric of forest canopy structure, driving photosynthetic capacity, carbon sequestration, and ecosystem productivity. In this study, we quantified LAI variation across six forest stand types (pure fir, pure beech, pure Scots pine, mixed coniferous, mixed deciduous, and mixed deciduous-coniferous) and six developmental stages in the D & uuml;zce region of Turkey, an area characterized by mixed, heterogeneous woodlands. Field measurements from 260 systematically distributed sample plots collected in 2015 employed hemispherical photography for LAI, the N-tree method for basal area (BA) and diameter at breast height (DBH), and atmospherically corrected Landsat 8 OLI imagery for NDVI. Descriptive analyses revealed LAI values ranging from 0.36 m(2)/m(2) in pure Scots pine to 6.30 m(2)/m(2) in mixed deciduous stands. One-way ANOVA and Duncan's multiple-range tests confirmed significant differences among stand types (p < 0.01), with mixed and vertically stratified stands exhibiting the highest LAI. Developmental stages showed increasing mean LAI trends from juvenile (ab) to mature (d) classes, though stage-only ANOVA was not significant (p = 0.378) due to high within-stage variability (CV approximate to 31%). Pearson correlations indicated moderate positive relationships between LAI and both DBH (r = 0.49) and BA (r = 0.53), whereas NDVI displayed the strongest association (r = 0.75 overall; up to r = 0.80 in mixed stands). A multiple linear regression model integrating NDVI, DBH, and BA explained 60.6% of LAI variance (F = 129.7, p < 0.001; adjusted R-2 = 0.601), with NDVI emerging as the dominant predictor (standardized beta = 0.683), followed by DBH (beta = 0.326) and BA (beta = 0.187). These findings underscore the complementary value of integrating spectral indices and structural parameters in the estimation of LAI, particularly in heterogeneous forest stands. The structural complexity of mixed stands appears to play a critical role in enhancing canopy development. To improve estimation accuracy in conifer-dominated or high-LAI forests, future studies should consider incorporating alternative vegetation indices and LiDAR-derived structural metrics to overcome limitations such as spectral saturation and insufficient vertical resolution. Such integrated approaches can significantly enhance the scalability and cost-effectiveness of forest health and productivity monitoring efforts.Öğe Multiple linear regression models for the estimation of water flows for forest management and planning in Turkiye(Water Research Commission, 2023) Zengin, Hayati; Ozcan, Mehmet; Degermenci, Ahmet Salih; Citgez, TarikWhile there are many factors, including climatology, geography, topography, vegetation and soil, that affect hydrologic processes, understanding the role of forests seems most essential, due to their manageable nature. In this study, a holistic approach was taken, and possible factors affecting streamflow, including tree, sapling, shrub, herb and soil strata, were measured for 29 small catchments/stream basins located in Turkey. Linear regression models were developed in order to estimate water flow (m3 & BULL;ha-1). Several models were suggested for use in practice. These models were based on the data on hand and displayed a sufficient level of explained variance in the dependent variable. Model 5, based on the variables of catchment area (ha), drainage density, ratio of coniferous stand areas in the catchment (%), tree volume (m3 & BULL;ha-1), leaf area index, number of short saplings (number & BULL;ha-1), and topsoil sand rate (%), was recommended for flow estimation, achieving a 0.73 adjR2 value for test data. These variables can be obtained as part of a survey and water managers can use them to estimate water flow of the catchment. The generated models can be used in multiple-use planning of forests, e.g. in adjusting the volume of stands to get optimum benefit from wood and water production. One of the most interesting results and one that was opposite to that documented in the general literature, was the positive correlation between tree volume and flow per hectare, which suggests a strategy of growing older tree stands to enable greater water production.Öğe Spatio-temporal analysis of snow depth and snow water equivalent in a mountainous catchment: Insights from in-situ observations and statistical modelling(Wiley, 2024) Citgez, Tarik; Eker, Remzi; Aydin, AbdurrahimThis research, conducted in the mountainous catchment near Abant Lake in the Western Black Sea region of T & uuml;rkiye, aimed to investigate the spatiotemporal variations of snow depth (SD) and snow water equivalent (SWE) throughout the snow season from December 2019 to March 2020, encompassing both accumulation and melting periods. In total, 14 snow surveys were conducted, covering 58 permanent snow measurement points (PSMP) marked with snow poles. The classification and regression tree (CART) method was employed to statistically analyse their relationships with eight variables: snow period, forest canopy, aspect, slope, elevation, slope position, plan and profile curvature. The root mean square error (RMSE) for SD and SWE was determined to be 0.15 m and 46 mm, respectively. The study findings revealed that mean SD and SWE values were higher in forest gaps compared with under-forest and open areas. Although the snow cover disappeared earliest in under-forest areas, the melting rate was observed to be 43% and 17% slower compared with forest gaps and open areas, respectively. Wind redistribution resulted in minimum snow accumulation on western aspects, upper slope positions and ridges, while maximum accumulation was observed on southern aspects, valleys and lower slope positions. Higher elevations (>1580 meters) experienced faster snow melting rates, leading to earlier disappearance of snow cover. PSMPs located on slopes with lower degrees (<15 degrees) exhibited lesser accumulation and earlier snow disappearance. The CART model identified the snow period as the most significant factor in predicting SD and SWE, based on variations in snowfall and air temperature. Other significant variables included forest canopy, aspect and elevation. The study suggests that the CART method is well-suited for modelling complex snow dynamics, providing valuable insights into spatiotemporal variations in SD and SWE in mountainous regions.
