RÜZGÂR EROZYONUNUN KONUMSAL DAĞILIMI VE TOPRAK YÜZEYİ BAĞLANTISALLIĞI ARASINDAKİ ETKİLEŞİMLERİN DOĞRUDAN ÖLÇÜMLER VE TAHMİN MODELLERİ İLE ARAŞTIRILMASI

Tez Türü: Doktora

Tezin Yürütüldüğü Kurum: Ankara Üniversitesi, Ziraat Fakültesi, Toprak Bilimi Ve Bitki Besleme Bölümü, Türkiye

Tez Danışmanı: Günay Erpul

Tezin Onay Tarihi: 2021

Tezin Dili: Türkçe

Desteklendiği Program: Diğer

Özet:

There is a need for development of sustainable land use practices and management strategies and assessment of potential current and future trends in arid and semi-arid regions. This thesis was conducted to determine wind erosion risks for 11 different wind cases experienced over wheat-planted and fallow lands under semi-arid arid conditions with the use of direct measurements and different estimation models. BEST® sediment traps were used to determine severity of wind erosion with direct measurements in field. Two different wind erosion estimation models were used to estimate soil loss through wind cases throughout the experiments. The semi-variogram model parameters obtained through geostatistical analyses were used to determine the factors influencing maximum likelihood distance for sediment flow rate of both plots. Efficiency of some artificial intelligence algorithms were also tested to determine the factors effecting soil losses and to estimate soil loss ratios. According to present findings, soil loss was encountered in both wheat-planted and fallow plots (kg/m²). Wheat-planted plot was found to be quite sensitive to wind erosion in autumn, in which winter cereals are sown and fallow plot was found to be sensitive to wind erosion in spring season. Among the estimation models, calibrated RWEQ model yielded the best outcomes. Model results revealed that models should be calibrated for local conditions, otherwise they may yield lower or greater estimations. According to geostatistical analyses results, cover was identified as the most significant factor influencing maximum likelihood distance for sediment flow rates in fallow plot and wind velocity and cover were identified as the most significant parameters in wheat-planted plot. Promising outcomes were achieved for estimation of wind erosion-induced soil losses with the use of artificial intelligence algorithms.

February 2021, 144 pages

Key Words: Soil loss, soil wind erosion, BEST sediment trap, predictive models, spatial analysis, land use, artificial intelligence