Environmental Engineering / Çevre Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/4321

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Subject: search.filters.subject.GRACEWoS Q: search.filters.wosQuartile.Q2

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  • Article
    Citation - WoS: 19
    Citation - Scopus: 18
    An Appraisal of the Local-Scale Spatio-Temporal Variations of Drought Based on the Integrated Grace/Grace-fo Observations and Fine-Resolution Fldas Model
    (Wiley, 2023) Khorrami, Behnam; Ali, Shoaib; Gündüz, Orhan
    The gravity recovery and climate experiment (GRACE) observations have so far been utilized to detect and trace the variations of hydrological extremes worldwide. However, applying the coarse resolution GRACE estimates for local-scale analysis remains a big challenge. In this study, a new version of the fine resolution (1 km) Famine early warning systems network Land Data Assimilation System (FLDAS) model data was integrated into a machine learning model along with the GRACE data to evaluate the subbasin-scale variations of water storage, and drought. With a correlation of 0.99 and a root mean square error (RMSE) of 3.93mm of its results, the downscaling model turned out to be very successful in modelling the finer resolution variations of TWSA. The water storage deficit (WSD) and Water Storage Deficit Index (WSDI) were used to determine the episodes and severity of drought events. Accordingly, two severe droughts (January 2008 to March 2009 and September 2019 to December 2020) were discerned in the Kizilirmak Basin (KB) located in Central Turkiye. The characterization of droughts was evaluated based on WSDI, scPDSI, and model-based drought indices of the soil moisture storage percentile (SMSP) and groundwater storage percentile (GWSP). The results indicated discrepancies in the drought classes based on different indices. However, the WSDI turned out to be more correlated with GWSP, suggesting its high ability to monitor groundwater droughts as well.
  • Article
    Citation - WoS: 31
    Citation - Scopus: 34
    Model-Coupled Grace-Based Analysis of Hydrological Dynamics of Drying Lake Urmia and Its Basin
    (Wiley, 2023) Khorrami, Behnam; Ali, Shoaib; Şahin, Onur Güngör; Gündüz, Orhan
    Lake Urmia basin (LUB), in northwestern Iran, is under the influence of extreme degradation due to a number of natural and anthropogenic factors. The existence of the Lake is critical for the microclimate of the region as well as the quality of human life and wildlife, which necessitates an up-to-date and holistic analysis of its hydrological dynamics. In this premise, satellite-based terrestrial water storage (TWS) received from the Gravity Recovery and Climate Experiment (GRACE) mission was coupled with hydrometeorological modelling and assessment tools to analyse the hydrological status of the lake and its basin. As a new gap-filling approach, the Seasonal-Trend decomposition using Locally estimated scatterplot smoothing (LOESS) (STL) decomposition technique was proposed in this study to reconstruct the missing TWS data. Integrating satellite precipitation data with the Catchment Land Surface Model (CLSM) and WaterGAP model outputs, the hydrological status of the lake was investigated. The STL-based TWS turned out to concord well with the simulated TWS from the CLSM indicating the acceptable performance of the proposed technique. The findings revealed that the LUB had undergone an alarming hydrological situation from 2003 to 2021 with a total loss of 10 and 7.56km3 from its TWS and groundwater storage (GWS), respectively. The water level time series also indicated that the water level of the lake had diminished with an annual rate of -70 +/- 21cm/year corresponding to a total water level depletion of about 13.35 +/- 3.9m during the 2003-2021 period. The GRACE-derived TWS and GWS also agreed well with the CLSM simulations. Assessment of the extreme events of the LUB suggested that the basin suffered from a severe dry event in 2008 resulting in the depletion of its water storage and water level. It was also found that from 2003 onward, a critical hydrological setting had dominated the LUB with a negative hydrological balance of -0.96km3.
  • Article
    Citation - WoS: 11
    Citation - Scopus: 13
    A Systematic Assessment of Flooding Potential in a Semi-Arid Watershed Using Grace Gravity Estimates and Large-Scale Hydrological Modeling
    (Taylor & Francis, 2022) Khorrami, Behnam; Fıstıkoğlu, Okan; Gündüz, Orhan
    The emergence of the Gravity Recovery And Climate Experiment (GRACE) paved the way for remote tracking of hydrological water cycle components at large scales. With the main motivation of evaluating the feasibility of the coarse resolution GRACE data for small-scale analysis, the GRACE data and large-scale hydrological models were utilized in an integrated manner to monitor the variations of the flood potential index (FPI) over the Western Anatolian Basin (WAB). The results show an ascending trend for monthly and annual FPI over the WAB. The results also suggest that the monthly FPI in 2015, 2003, 2009, and 2016 was the highest, from which the highest potentiality of flood appertains to 2015/07 with an FPI of 0.92. The lowest and highest annual FPI is 0.26 (in 2007) and 0.76 (in 2015), respectively. The validation of the results indicates that variations of FPI coincide with that of the flood incidents, stream discharge, Standardized Precipitation Index (SPI), and the simulated flood risk. The findings accentuate the high feasibility of the GRACE JPL Mascons for better surveillance of floods over local scale areas. Highlights The coarse resolution GRACE JPL mascon functions very well in tracing the spatio-temporal characteristics of flood incidents over local scales. There is an ascending trend in the variations of flood potential over the Western Anatolia Basin (WAB). The WAB has experienced its lowest and highest possibility of flooding in 2007 and 2015 with an average FPI of 0.26 and 0.76, respectively. The variations of the flood potential index (FPI) coincides with that of the reported flood incidents, stream discharge, Standardized Precipitation Index (SPI), and the simulated flood risk.
  • Article
    Citation - WoS: 36
    Citation - Scopus: 38
    Detection and Analysis of Drought Over Turkey With Remote Sensing and Model-Based Drought Indices
    (Taylor & Francis, 2022) Khorrami, Behnam; Gündüz, Orhan
    Under the severe impacts of climate change, drought has become one of the most undesirable and complex natural phenomena with critical consequences for the environment, economy and society. The orthodox drought monitoring approaches use observations of meteorological stations, which are typically restricted in time and space. Remote sensing, conversely, provides continuous global coverage of a variety of hydro-meteorological variables that are influential in drought, and data extracted from remote sensing and modeling missions are now considered more practical and alluring for researchers. In this study, we applied a combination of field data, remotely sensed data and modeled data to detect and quantitatively analyze drought phenomena. To achieve this objective, we utilized Terrestrial Water Storage Anomalies (TWSA) estimations from GRACE mission, Normalized Difference Vegetation Index (NDVI) from MODIS mission, Surface Runoff (R) and Evapotranspiration from ERA5 reanalysis datasets and Soil Moisture (SM) from GLDAS data model to evaluate their feasibility in detecting recent droughts over Turkey. We validated the accuracy of several remote sensing-based indices (GRACE Drought Severity Index, Water Storage Deficit Index [WSDI], Soil Moisture Index, Standardized Runoff Index and NDVI) with the traditional indices (SPI and SPEI) calculated from in situ observations of precipitation. The results revealed that the GRACE-based WSDI gave the best performance with high correlations with the SPI index both temporally and spatially over Turkey. We also found that monthly and annual time series of WSDI agreed well with the SPI index with correlations of 0.69 and 0.73, respectively. The results of drought analysis also indicated that WSDI could be used as a proxy to standard meteorological drought indices over Turkey as it performed well to detect and characterize the recent droughts of Turkey based on its comparisons to SPI results.