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: 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: 36Citation - Scopus: 40Land Deformation and Sinkhole Occurrence in Response To the Fluctuations of Groundwater Storage: an Integrated Assessment of Grace Gravity Measurements, Icesat/Icesat-2 Altimetry Data, and Hydrologic Models(Taylor & Francis, 2021) Khorrami, Behnam; Arık, Fetullah; Gündüz, OrhanUncontrolled extraction of water from groundwater aquifers causes groundwater depletion, which in turn triggers the formation of sinkholes in many parts of the world. Monitoring and detection of these geomorphologic features are of utmost importance and priority for the decision-makers to minimize significant environmental as well as socio-economic implications of land deformation. In this study, a systematic approach is proposed to investigate the spatio-temporal associations of groundwater storage changes with sinkhole evolution and land deformation by using a number of remotely sensed and modeled data as well as in-situ observations. The proposed approach is implemented and tested in Konya Closed Basin (KCB), Turkey, which is one of the most critical areas in central Turkey concerning sinkhole formation. The results of GRACE (Gravity Recovery and Climate Experiment) estimates suggest that there is a descending trend in the temporal variations of TWSA (Terrestrial Water Storage Anomalies) and GWSA (Groundwater Storage Anomalies) over KCB with an average storage depletion of 4.12 ± 0.34 cm/yr and 3.40 ± 0.61 cm/yr, respectively. The analysis of land deformation from ICESat/ICESat-2 (Ice, Cloud, and Land Elevation Satellite) altimetry data also indicates a descending trend with an estimated average vertical displacement of 5 cm/yr for the study area, which seems to be in rational accord with the sinkhole evolution over KCB. The results further suggest that the sinkhole evolution over KCB has an acceptable association with the variations of groundwater storage, groundwater use, and precipitation.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.Article Citation - WoS: 2Citation - Scopus: 2A Deterministic and Stochastic Assessment for Exposure and Risk of Arsenic Via Ingestion of Edible Crops(Springer Verlag, 2019) Can Terzi, Begüm; Gündüz, Orhan; Sofuoğlu, Sait CemilNatural arsenic contamination is a critical problem for various places around the world. Simav Plain (Kutahya, Turkey) is one such area that was shown to have natural arsenic contamination in its waters and soils. Arsenic exposure through ingestion of edible crops cultivated in Simav Plain and associated health risks were assessed in this study. To achieve this objective, arsenic levels in 18 crop species were estimated based on measured soil arsenic concentrations. Individual and aggregate non-carcinogenic and carcinogenic risks associated with ingestion of arsenic-contaminated crops were then assessed with scenario-based deterministic point estimates and stochastic population estimates. Monte Carlo simulation was used for the estimation of population health risks. Accordingly, wheat was found as the highest-ranked crop specie for the both types of health risks, followed by tomato and potato, which are three of the most consumed crops in the region. The risk levels estimated in this study were relatively high, indicating consumption of crops grown in the plain may be posing significant health risks even at lower-bound estimates. Consuming wheat, tomato, potato, and their products from uncontaminated sources was found to reduce the aggregate risks up to 88% implicating the importance of proposing suitable management measures for similar risk-prone areas.