• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.467-467
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• 2015

Sustainable water resource management requires the assessment of hydrological variability in response to climate fluctuations and anthropogenic activities. Determining quantitative estimates of water balance and total basin discharge are of utmost importance to understand the variations within a basin. Hard-to-reach areas with few infrastructures, coupled with lengthy administrative procedures makes in-situ data collection and water management processes very difficult and unreliable. In this study, the hydrological behavior of Lake Chad whose extent, extreme climatic and environmental conditions make it difficult to collect field observations was examined. During a 10 year period [January 2003 to December 2013], dataset from space-borne and global hydrological models observations were analyzed. Terrestial water storage (TWS) data retrieved from Gravity Recovery and Climate Experiment (GRACE), lake level variations from Satellite altimetry, water fluxes and soil moisture from Global Land Data Assimilation System (GLDAS) were used for this study. Furthermore, we combined altimetry lake volume with TWS over the lake drainage basin to estimate groundwater and soil moisture variations. This will be validated with groundwater estimates from WaterGAP Global Hydrology Model (WGHM) outputs. TWS showed similar variation patterns Lake water level as expected. The TWS in the basin area is governed by the lake's surface water. As expected, rainfall from GLDAS precedes GRACE TWS with a phase lag of about 1 month. Estimates of groundwater and soil moisture content volume changes derived by combining altimetric Lake Volume with TWS over the drainage basin are ongoing. Results obtained shall be compared with WaterGap Hydrology Model (WGHM) groundwater estimate outputs.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.3
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• pp.57-67
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• 2023

A flash drought is a rapid-onset drought that develops over a short period of time as weather and environmental factors change rapidly, unlike general droughts, due to meteorological abnormalities. Abnormally high evapotranspiration rates and rapid declines in soil moisture increase vegetation stress. In addition, crop yields may decrease due to flash droughts during crop growth and may damage agricultural and economic ecosystems. In this study, Flash Drought Intensity Index (FDII) based on soil moisture data from Gravity Recovery Climate Experiment (GRACE) was used to analyze flash drought. FDII, which is calculated using soil moisture percentile, is expressed by multiplying two factors: the rate of intensification and the drought severity. FDII was developed for domestic flash drought events from 2014 to 2018. The flash drought that occurred in 2018, Chungcheongbuk-do showed the highest FDII. FDII was higher in heat wave flash drought than in precipitation deficit flash drought. The results of this study show that FDII is reliable flash drought analysis tool and can be applied to quantitatively analyze the characteristics of flash drought in South Korea.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.927-938
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• 2021

A similar magnitude of non-gravitational perturbations are act on the formation flying low earth orbit satellites with a certain time difference. Using this temporal correlation, the non-gravity acceleration of the low earth orbiting satellites can be transferred for the othersatellites. There is a period in which the accelerometer data of one satellite is unavailable for GRACE and GRACE-FO satellites. In this case, the accelerometer data transplant method described above is officially used to recover the accelerometer data at the Jet Propulsion Laboratory (JPL). In this paper, we proposed a model for predicting accelerometer data of formation flying low earth orbit satellites using a neural network (NN) model to improve the estimation accuracy of the transplant method. Although the transplant method cannot reflect the satellite's position and space environmental factors, the NN model can use them as model inputs to increase the prediction accuracy. A prediction test of an accelerometer data using NN model was performed for one month, and the prediction accuracy was compared with the transplant method. The NN model outperformsthe transplant method with 55.0% and 40.1% error reduction in the along-track and radial directions, respectively.