Environmental Engineering / Çevre Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/4321
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Article Citation - WoS: 6Citation - Scopus: 6A Novel Land Surface Temperature Reconstruction Method and Its Application for Downscaling Surface Soil Moisture With Machine Learning(Elsevier, 2024) Güngör, Şahin; Gündüz, OrhanDownscaling of soil moisture data is important for high resolution hydrological modeling. Most downscaling studies in the literature have used spatially discontinuous land surface temperature (LST) maps as the main auxiliary parameter, which limits the creation of continuous soil moisture maps. The number of studies on soil moisture downscaling with machine learning that use gapless LST maps is limited. With this motivation, a hybrid reconstruction method has been proposed in this study to practically obtain continuous LST maps, which are then used to produce high resolution surface soil moisture (SSM) datasets. The proposed method is shown to have high mean performance with R2 and RMSE values of 0.94 and 1.84°K, respectively, for the period between 2019 and 2022. The developed reconstructed LST maps were then used to downscale original 9 km spatial resolution soil moisture datasets of SMAP L3 and SMAP L4 with Random Forest (RF) machine learning algorithm. The RF model were run with four different rainfall datasets, and the MSWEP rainfall dataset was found to produce the best results. The use of antecedent rainfall values as input variables in machine learning models has been shown to improve the performance of the models R2 0.76 to 0.93. The accuracy of the downscaled data was later evaluated for Western Anatolia Basins (WAB) in Türkiye with 31 in-situ stations. The downscaled SMAP L4 had good average statistical indicators R (0.815 ± 0.1), RMSE (0.09 ± 0.047 cm3/cm3), and ubRMSE (0.058 ± 0.025 cm3/cm3). Downscaled SMAP L3 was also validated with in-situ observations with satisfactory R (0.79 ± 0.074), RMSE (0.09 ± 0.043 cm3/cm3), and ubRMSE (0.06 ± 0.026 cm3/cm3) statistics. Furthermore, the performance of the downscaled SMAP L3 was also cross validated with SMAP + Sentinel 1 (L2) dataset between 2019 and 2022. The mean statistics of R (0.761 ± 0.11) and Root Mean Squared Difference (RMSD) (0.05 ± 0.014 cm3/cm3) between downscaled SMAP L3 and L2 data revealed that the new reconstruction method of LST used in the RF model for downscaling of soil moisture performed well to obtain high resolution soil moisture datasets. The proposed technique also overcame the difficulties associated with coastal regions where data was masked for quality considerations, by not only enhancing overall spatial resolution but also filling these data gaps and giving a complete SSM coverage. © 2024 Elsevier B.V.Article Citation - WoS: 16Citation - Scopus: 20Development of an Emission Estimation Method With Satellite Observations for Significant Forest Fires and Comparison With Global Fire Emission Inventories: Application To Catastrophic Fires of Summer 2021 Over the Eastern Mediterranean(Elsevier, 2023) Bilgiç, Efem; Tuna Tuygun, Gizem; Gündüz, OrhanIn the past few decades, forest fires have increased in number and severity, especially in the Mediterranean regions of Turkiye and Greece, where significant fires caused damage to thousands of hectares of land as well as wildlife. The main objective of the present study is to develop an emission estimation method with satellite-based burned area data from significant forest fire events in the Eastern Mediterranean in July-August 2021. In the first stage of this study, pre-fire and post-fire images of the study area acquired by the Sentinel-2 satellite were processed to calculate the normalized burn rate difference index (dNBR). Then, CORINE Land Cover (CLC) data were used for detecting land cover classes in the burned areas. Atmospheric emissions of NOx, CO, SO2, total suspended particulate matter (TSP), particulate matter with diameters that are equal to or smaller than 2.5 & mu;m (PM2.5), and black carbon (BC) were estimated using the EMEP/EEA Tier 2 -technology-specific approach method, in which burned area maps were retrieved using Sentinel-2 imageries and later combined with land cover type and burning efficiency to estimate the quantity of burning biomass emissions. Emission factors were then used to estimate the fires' trace gas and aerosol emissions. The results showed that the highest burned areas were found in the western Mediterranean region in Turkiye and Central Greece (⁓50,000 ha). The atmospheric emissions from these fires were calculated to be similar in both countries. Furthermore, emission amounts were compared with three different global fire emission inventories including GFAS, GFED, and FINN. The emissions obtained from the GFAS database were the highest emissions of the four emission estimation approaches and our estimated emissions were close to the GFAS. Emissions calculated from the other two databases (FINN and GFED) mostly provided underestimated emissions. The emission uncertainties in this study mainly originated from assumptions regarding the inclusion of burned area efficiency in emission calculations, the landcover dataset, and the emission factors used. Overall, this study is considered a new approach to emission calculations using Sentinel-2 data. This research provides further insight into the use of Sentinel-2 data in emission calculation applications at the local to regional scales.Article Citation - WoS: 4Citation - Scopus: 5Radiological Modeling of the Impacts of the Chernobyl Nuclear Power Plant Accident on Turkey and Southwest Asia(Elsevier, 2022) Bilgiç, Efem; Gündüz, OrhanMany studies investigated the impacts of the Chernobyl Nuclear Power Plant accident on Europe. However, majority of these have spatially excluded the highly populated southeast region of Chernobyl, including countries such as Turkey, Armenia, Georgia and Iran. In this study, a comprehensive environmental and radiological analysis were conducted particularly for this region. For this purpose, atmospheric dispersion and ground deposition of radionuclides were estimated using a Lagrangian particle dispersion model, FLEXPART. Totally, six simulations were conducted and model results were validated with measurements from Europe and Turkey. Furthermore, total effective dose equivalent (TEDE) values were estimated for adults and infants using the most current dose conversion factors of ICRP. Highest deposition of 137Cs were found in around Eastern Black Sea areas (10–40 kBq/m2). Similar values were found in some locations of Armenia and Azerbaijan under some scenarios, but country averages of 137Cs deposition were lower than 10 kBq/m2 for both countries. No significant depositions were found in southwest Iran, but relatively higher depositions (2–10 kBq/m2) of 137Cs were estimated along the Turkish border. Although there were slightly higher values in northern areas of Syria, Iraq, Lebanon and Cyprus, 137Cs depositions were mostly less than 2 kBq/m2. The 1-year TEDE value was calculated less than 1 mSv throughout the model domain except for some regions of eastern Black Sea. Highest values in lifetime dose values were calculated along the Black Sea coasts of Turkey and Georgia. Overall, infants were affected more from ionizing radiation compared to adults in this region.Article Citation - WoS: 44Citation - Scopus: 45An Enhanced Water Storage Deficit Index (ewsdi) for Drought Detection Using Grace Gravity Estimates(Elsevier, 2021) Khorrami, Behnam; Gündüz, OrhanAccurate detection and monitoring of drought events are important particularly in arid and semi-arid regions of the world. Gravity Recovery and Climate Experiment (GRACE) gravity estimates have been used widely for this purpose and a number of indices have been developed using the GRACE Terrestrial Water Storage Anomalies (TWSA) values. In the current study, a new approach is proposed to enhance the performance of the GRACE-based Water Storage Deficit Index (WSDI). The proposed Enhanced Water Storage Deficit Index (EWSDI) was developed based on the grid-based standardization of the Water Storage Deficit (WSD) values. The decomposed time series of the TWSA were computed in an attempt to evaluate the performance of the approach based on different components of the TWSA time series. Standardized Precipitation Index (SPI) and modelled Soil Moisture Storage (SMS) were also used to validate the functionality of this new GRACE-derived index. The applicability of the EWSDI index was tested in the semi-arid climatic conditions of Turkey and the results showed that the detrended EWSDI better correlated with SPI-09 and annual SPI with correlation coefficient values of 0.70 and 0.76, respectively. The findings also suggested an approximate enhancement of 13% over the existing WSDI when applied on the detrended TWSA. The findings of this study reveal that the proposed approach is effective in improving the performance of the existing WSDI to detect drought events in terms of monthly and annual correlation coefficients achieved. © 2021 Elsevier B.V.