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.Conference Object 3d Modeling of a Historical Mine Waste Site Using Uav Images: Estimation of Stockpile Volumes(Springer, 2023) Önal, Okan; Gündüz, OrhanIn recent decades, the use of Unmanned Aerial Vehicles (UAV) for land surveying became very popular because of their simplicity and low cost. Aerial images of the site can be used for the reconstruction of the site’s 3D digital model. Once proper calibrations are made, these digital models can be used for several purposes including stockpile volume estimation, stability analyses, forensic engineering and archiving, etc. In this study, the 3D model of an abandoned historical mine waste disposal site located in Balıkesir-Turkey was reconstructed for the estimation of the waste stockpile volumes. The historical mine site is a facility that was abandoned more than 80 years ago. Mine wastes of different quality were disposed of in and around the site along the hydrologically intermittent creek that passes through the site. No engineered precautions were taken at the site to reduce the environmental impacts and all waste piles were exposed to the natural eroding effect of precipitation and wind. The total amount of the waste volume is not known accurately, which prevents researchers to quantify the potential impacts associated with different waste stockpiles. Thus, a 3D digital model of the site was created by using UAV data obtained from a quadcopter and later processed to obtain a digital topography of the site with an improved accuracy value of ± 2 cm. The stockpiles were later analyzed with geographic information systems to characterize the magnitude of mine wastes and to propose alternative engineering solutions for environmental mitigation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.Article Citation - WoS: 21Citation - Scopus: 21Remote Sensing-Based Monitoring and Evaluation of the Basin-Wise Dynamics of Terrestrial Water and Groundwater Storage Fluctuations(Springer, 2023) Khorrami, Behnam; Gündüz, OrhanThe recent dynamics of terrestrial water storage (TWS) and groundwater storage (GWS) fluctuations were investigated based on the Gravity Recovery And Climate Experiment (GRACE) observations over 25 basins of Türkiye. Coarse-resolution GRACE estimates were downscaled based on the Random Forest algorithm. The impacts of precipitation (P) and evapotranspiration (ET) on the variations of water storage were also assessed. The findings demonstrated good performance for the RF model in simulating finer resolution estimates of TWS. The results indicated a diminishing trend of TWS and its hydrologic components over all the basins from 2003 to 2020. The Doğu Akdeniz Basin with the annually decreasing TWS and GWS of 1.15cm/yr and 1.10cm/yr was the most critical basin of Türkiye. The least storage loss was observed in the Batı Karadeniz Basin with the annual TWS and GWS loss of 0.38cm/yr and 0.45cm/yr , respectively. Based on the results, Türkiye has lost, on average, an estimated 5.16km3/yr and 4.09km3/yr of its TWS and GWS, respectively, which are equivalent to the total storage loss of 92.88km3 and 73.62km3 of TWS and GWS during the last 18 years. The results also indicated that P and ET interact differently with the variations of TWS and GWS. The net water flux was revealed to be partially correlated with the total water storage fluctuations, suggesting the governing role of other deriving forces particularly the anthropogenic factors in the spatiotemporal variations of Türkiye’s water storage; therefore, a sector-specific analysis of the water storage variations is crucial for the country, particularly by concentrating more on the dynamics of GWS. Graphical Abstract: [Figure not available: see fulltext.]. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.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: 40Citation - Scopus: 38Investigating the Local-Scale Fluctuations of Groundwater Storage by Using Downscaled Grace/Grace-fo Jpl Mascon Product Based on Machine Learning (ml) Algorithm(Springer, 2023) Khorrami, Behnam; Ali, Shoaib; Gündüz, OrhanGroundwater storage is of grave significance for humanity by sustaining the required water for agricultural irrigation, industry, and domestic use. Notwithstanding the impressive contribution of the state-of-the-art Gravity Recovery and Climate Experiment (GRACE) to detecting the groundwater storage anomaly (GWSA), its feasibility for the characterization of GWSA variation hotspots over small scales is still a major challenge due to its coarse resolution. In this study, a spatial water balance approach is proposed to enhance the spatial depiction of groundwater storage and depletion changes that can detect the hotspots of GWSA variation. In this study, Random Forest Machine Learning (RFML) model was utilized to simulate fine-resolution (10 km) groundwater storage based on the coarse resolution (50 km) of GRACE observations. To this end, parameters including soil moisture, snow water, evapotranspiration, precipitation, surface runoff, surface elevation, and GRACE data were integrated into the RFML model. The results show that with a correlation of above 0.98, the RFML model is very successful in simulating the fine-resolution groundwater storage over the Western Anatolian Basin (WAB), Turkiye. The results indicate an estimated annual depletion rate of 0.14 km(3)/year for the groundwater storage of the WAB, which is equivalent to about 2.57 km(3) of total groundwater depletion from 2003 to 2020. The findings also suggest that the downscaled GWSA is in harmony with the original GWSA in terms of temporal variations. The validation of the results demonstrates that the correlation is increased from 0.56 (for the GRACE-derived GWSA) to 0.60 (for the downscaled GWSA) over the WAB.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.