Environmental Engineering / Çevre Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/4321
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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: 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.