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The relationships between a variety of hydro-meteorological variables and irrigation water use rates (WUR) were investigated in this study. Standardized Precipitation Index (SPI), Potential Evapotranspiration (PET), and Normalized Difference Vegetation Index (NDVI) were explored to identify the relationship with the WUR. The Yeongsan river basin, the agricultural land of which is mostly occupied by well-irrigated paddy, was used for the pilot study. Four different temporal scales of SPI-3, 6, 9, and 12 were tested, and PET was calculated using the Thornthwaite method. To calculate NDVI, the surface spectral reflectance data, which was acquired by Moderate Resolution Imaging Spectroradiometer (MODIS) equipped on the Terra satellite, were used. As a result, there was a statistically significant relationship between SPI9 and the WUR during drought periods in which negative values of SPI9 were obtained. The WUR was strongly correlated with both PET and NDVI. Compared with SPI, the variability of WUR in this study area was more sensitively affected by PET and NDVI, which can cause a potential lack of agricultural water supply. The finding of this study implies that SPI9, PET, and NDVI are the critical factors for predicting water withdrawal during drought conditions so that they can be used for irrigational water use management. Although a part of the findings of this study has been discussed by a few previous studies, this study is novel in that it quantifies the relationship between these factors using actual field observations of streamflow withdrawal for irrigation.
Jang Sung; Donghae Baek; Young Ryu; Seung Seo; Kee-Won Seong. Effects of Hydro-Meteorological Factors on Streamflow Withdrawal for Irrigation in Yeongsan River Basin. Sustainability 2021, 13, 4969 .
AMA StyleJang Sung, Donghae Baek, Young Ryu, Seung Seo, Kee-Won Seong. Effects of Hydro-Meteorological Factors on Streamflow Withdrawal for Irrigation in Yeongsan River Basin. Sustainability. 2021; 13 (9):4969.
Chicago/Turabian StyleJang Sung; Donghae Baek; Young Ryu; Seung Seo; Kee-Won Seong. 2021. "Effects of Hydro-Meteorological Factors on Streamflow Withdrawal for Irrigation in Yeongsan River Basin." Sustainability 13, no. 9: 4969.
To minimize the damage from contaminant accidents in rivers, early identification of the contaminant source is crucial. Thus, in this study, a framework combining Machine Learning (ML) and the Transient Storage zone Model (TSM) was developed to predict the spill location and mass of a contaminant source. The TSM model was employed to simulate non-Fickian Breakthrough Curves (BTCs), which entails relevant information of the contaminant source. Then, the ML models were used to identify the BTC features, characterized by 21 variables, to predict the spill location and mass. The proposed framework was applied to the Gam Creek, South Korea, in which two tracer tests were conducted. In this study, six ML methods were applied for the prediction of spill location and mass, while the most relevant BTC features were selected by Recursive Feature Elimination Cross-Validation (RFECV). Model applications to field data showed that the ensemble Decision tree models, Random Forest (RF) and Xgboost (XGB), were the most efficient and feasible in predicting the contaminant source.
Siyoon Kwon; Hyoseob Noh; Il Won Seo; Sung Hyun Jung; Donghae Baek. Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis. International Journal of Environmental Research and Public Health 2021, 18, 1023 .
AMA StyleSiyoon Kwon, Hyoseob Noh, Il Won Seo, Sung Hyun Jung, Donghae Baek. Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis. International Journal of Environmental Research and Public Health. 2021; 18 (3):1023.
Chicago/Turabian StyleSiyoon Kwon; Hyoseob Noh; Il Won Seo; Sung Hyun Jung; Donghae Baek. 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis." International Journal of Environmental Research and Public Health 18, no. 3: 1023.
A Transient Storage Model (TSM) which considers the storage exchange process that induces an abnormal mixing phenomenon has been widely used to analyze solute transport in natural rivers. The primary step in applying TSM is calibration of four key parameters: flow zone dispersion coefficient (Kf), main flow zone area (Af), storage zone area (As), and storage exchange rate (α); by fitting the measured Breakthrough Curves (BTCs). In this study, to overcome the costly tracer tests that are necessary for parameter calibration, two dimensionless empirical models were derived, to estimate TSM parameters, using multi-gene genetic programming (MGGP) and principal components regression (PCR). A total of 128 datasets with complete variables from 14 published papers were chosen from an extensive meta-analysis and were applied to derivations. The performance comparison revealed that the MGGP-based equations yielded the superior prediction results. According to TSM analysis of field experiment data from Cheongmi Creek, South Korea, although all assessed empirical equations produced acceptable BTCs in monotonous reach, the MGGP model was superior to the other models. The predicted BTCs obtained by the empirical models in some highly complicated reaches were biased due to misprediction of As. Sensitivity analyses of MGGP models showed that the sinuosity is the most influential factor in Kf, As, and α, whlie Af is more sensitive to U/U*. This study proves that the MGGP-based model can be used for economic TSM analysis, thus providing an alternative option to direct calibration and the inverse modeling initial parameters.
Hyoseob Noh; Siyoon Kwon; Il Won Seo; Donghae Baek; Sung Hyun Jung. Multi-Gene Genetic Programming Regression Model for Prediction of Transient Storage Model Parameters in Natural Rivers. Water 2020, 13, 76 .
AMA StyleHyoseob Noh, Siyoon Kwon, Il Won Seo, Donghae Baek, Sung Hyun Jung. Multi-Gene Genetic Programming Regression Model for Prediction of Transient Storage Model Parameters in Natural Rivers. Water. 2020; 13 (1):76.
Chicago/Turabian StyleHyoseob Noh; Siyoon Kwon; Il Won Seo; Donghae Baek; Sung Hyun Jung. 2020. "Multi-Gene Genetic Programming Regression Model for Prediction of Transient Storage Model Parameters in Natural Rivers." Water 13, no. 1: 76.
Channel meanders in rivers induce complex three-dimensional (3D) flow characteristics such as secondary flows and flow recirculation. Helical secondary flows promote transverse dispersion, and flow recirculation zones trap tracers. As a result, these meander-driven flows may cause anomalous transport manifested by unusually elevated levels of tracer concentration at early and late times of breakthrough curves (BTCs). In this study, we perform 3D numerical simulations in meandering channels across a wide range of channel sinuosity to investigate the impact of meander geometry on flow and transport. We solve 3D Reynolds-averaged Navier-Stoke equations blended with a SST k − ω turbulence model to obtain velocity and turbulence fields. We then incorporate the obtained flow fields into a Lagrangian particle tracking model to simulate solute transport. Sinuosity higher than 1.5 leads to the onset of horizontal recirculating flows along the apex outer banks, and these recirculation zones expand as sinuosity increases. The analysis of the transport simulations elucidates that the interplay between the secondary flows and recirculation zones induces anomalous transport. The helical secondary flows bring particles into the recirculation zones by promoting transverse dispersion, and the recirculating flows delay particle transport by the trapping effect. We show that the tail power-law slope and truncated time of BTCs as well as Lagrangian tortuosity distributions change dramatically with the emergence of recirculation zones. These analyses demonstrate that the recirculation zones are acting as the primary driver of anomalous transport. Finally, we successfully predict the observed anomalous transport with a Spatial Markov Model (SMM), which is an upscaled transport model that incorporates Lagrangian velocity distribution and spatial velocity correlation. The successful predictions show that the Lagrangian velocity statistics effectively capture the underlying mechanisms of anomalous transport in meandering open-channel flows.
Jun Song Kim; Il Won Seo; Donghae Baek; Peter K. Kang. Recirculating flow-induced anomalous transport in meandering open-channel flows. Advances in Water Resources 2020, 141, 103603 .
AMA StyleJun Song Kim, Il Won Seo, Donghae Baek, Peter K. Kang. Recirculating flow-induced anomalous transport in meandering open-channel flows. Advances in Water Resources. 2020; 141 ():103603.
Chicago/Turabian StyleJun Song Kim; Il Won Seo; Donghae Baek; Peter K. Kang. 2020. "Recirculating flow-induced anomalous transport in meandering open-channel flows." Advances in Water Resources 141, no. : 103603.
River meanders form complex 3D flow patterns, including secondary flows and flow separation. In particular, the flow separation traps solutes and delays their transport via storage effects associated with recirculating flows. The simulation of the separated flows highly relies in the performance of turbulence models. Thus, these closure schemes can control dispersion behaviors simulated in rivers. This study performs 3D simulations to quantify the impact of the turbulence models on solute transport simulations in channels under different sinuosity conditions. The 3D Reynolds-averaged Navier-Stokes equations coupled with the k − ε , k − ω and SST k − ω models are adopted for flow simulations. The 3D Lagrangian particle-tracking model simulates solute transport. An increase in sinuosity causes strong transverse gradients of mean velocity, thereby driving the onset of the separated flow recirculation along the outer bank. Here, the onset and extent of the flow separation are strongly influenced by the turbulence models. The k − ε model fails to reproduce the flow separation or underestimates its size. As a result, the k − ε model yields residence times shorter than those of other models. In contrast, the SST k − ω model exhibits a strong tailing of breakthrough curves by generating more pronounced flow separation.
Jun Song Kim; Donghae Baek; Inhwan Park. Evaluating the Impact of Turbulence Closure Models on Solute Transport Simulations in Meandering Open Channels. Applied Sciences 2020, 10, 2769 .
AMA StyleJun Song Kim, Donghae Baek, Inhwan Park. Evaluating the Impact of Turbulence Closure Models on Solute Transport Simulations in Meandering Open Channels. Applied Sciences. 2020; 10 (8):2769.
Chicago/Turabian StyleJun Song Kim; Donghae Baek; Inhwan Park. 2020. "Evaluating the Impact of Turbulence Closure Models on Solute Transport Simulations in Meandering Open Channels." Applied Sciences 10, no. 8: 2769.
Carl J. Legleiter; Richard R. McDonald; Jonathan M. Nelson; Paul J. Kinzel; Ryan L. Perroy; Donghae Baek; Il Won Seo. Remote sensing of tracer dye concentrations to support dispersion studies in river channels. Journal of Ecohydraulics 2019, 4, 131 -146.
AMA StyleCarl J. Legleiter, Richard R. McDonald, Jonathan M. Nelson, Paul J. Kinzel, Ryan L. Perroy, Donghae Baek, Il Won Seo. Remote sensing of tracer dye concentrations to support dispersion studies in river channels. Journal of Ecohydraulics. 2019; 4 (2):131-146.
Chicago/Turabian StyleCarl J. Legleiter; Richard R. McDonald; Jonathan M. Nelson; Paul J. Kinzel; Ryan L. Perroy; Donghae Baek; Il Won Seo. 2019. "Remote sensing of tracer dye concentrations to support dispersion studies in river channels." Journal of Ecohydraulics 4, no. 2: 131-146.
Bathymetric mapping is a prerequisite procedure to conduct assessments of water quality, habitat and environmental flow for riverine ecosystems using hydraulic modelling. This study evaluates the capability of a geographically weighted regression (GWR) model, which can capture a spatially heterogeneous relationship between inputs and an output, to retrieve bathymetry of a shallow stream, of which water depth is less than about 1 m from simple RGB imagery. A field experiment was performed for measuring water depth and simultaneously for acquiring remotely-sensed data with RGB digital numbers (DN) using a digital camera mounted on an unmanned aerial vehicle (UAV). A 2D shallow water model, which was validated by comparison with the field-surveyed data, was used to simulate the water depth of unmeasured regions. Band ratios of ln(DNG/DNR) was selected as an optimal spectral input of bathymetric inversion models through the principal component analysis (PCA). Results showed that global inversion models based on multiple linear regression (MLR) and artificial neural network (ANN) resulted in large discrepancy between estimation and observation due to the spatially varying response of the PCA-selected band ratio to water depth over the experimental channel. In contrast, the GWR model successfully alleviated the biases of the conventional models as R2 increased to 0.85 from 0.60 by accurately modelling the effect of spatial heterogeneity, which arose from variable bottom types attributed to submerged vegetation, on the remote-sensing radiance-water depth relationship.
Jun Song Kim; Donghae Baek; Il Won Seo; Jaehyun Shin. Retrieving shallow stream bathymetry from UAV-assisted RGB imagery using a geospatial regression method. Geomorphology 2019, 341, 102 -114.
AMA StyleJun Song Kim, Donghae Baek, Il Won Seo, Jaehyun Shin. Retrieving shallow stream bathymetry from UAV-assisted RGB imagery using a geospatial regression method. Geomorphology. 2019; 341 ():102-114.
Chicago/Turabian StyleJun Song Kim; Donghae Baek; Il Won Seo; Jaehyun Shin. 2019. "Retrieving shallow stream bathymetry from UAV-assisted RGB imagery using a geospatial regression method." Geomorphology 341, no. : 102-114.
A new method of unmanned aerial vehicle (UAV)-based tracer tests using RGB images was developed in order to acquire the spatio-temporal concentration distribution of tracer clouds in open channel flows. Tracer tests using Rhodamine WT were conducted to collect the RGB images using the commercial digital camera mounted on a UAV, and the concentration of Rhodamine WT using in-situ fluorometric probes. The correlation analysis showed that the in-situ measured concentrations of Rhodamine WT were strongly correlated with the digital number (DN) of the RGB images, even though the response of DN to the concentration was spatially heterogeneous. The empirical relationship between the DN values and the Rhodamine WT concentration data was estimated using artificial neural network (ANN) models. The trained ANN models, which consider the effect of water depth and river bed, accurately retrieved the detailed spatio-temporal concentration distributions of all study areas that had an R2 higher than 0.9. The acquired spatio-temporal concentration distributions by the proposed method based on the UAV images gave general as well as detailed views of the tracer cloud moving dynamically in open channel flows that cannot be easily observed using conventional in-situ measurements.
Donghae Baek; Il Won Seo; Jun Song Kim; Jonathan M. Nelson. UAV-based measurements of spatio-temporal concentration distributions of fluorescent tracers in open channel flows. Advances in Water Resources 2019, 127, 76 -88.
AMA StyleDonghae Baek, Il Won Seo, Jun Song Kim, Jonathan M. Nelson. UAV-based measurements of spatio-temporal concentration distributions of fluorescent tracers in open channel flows. Advances in Water Resources. 2019; 127 ():76-88.
Chicago/Turabian StyleDonghae Baek; Il Won Seo; Jun Song Kim; Jonathan M. Nelson. 2019. "UAV-based measurements of spatio-temporal concentration distributions of fluorescent tracers in open channel flows." Advances in Water Resources 127, no. : 76-88.
Jun Song Kim; Il. Won Seo; Donghae Baek. Modeling spatial variability of harmful algal bloom in regulated rivers using a depth-averaged 2D numerical model. Journal of Hydro-environment Research 2018, 20, 63 -76.
AMA StyleJun Song Kim, Il. Won Seo, Donghae Baek. Modeling spatial variability of harmful algal bloom in regulated rivers using a depth-averaged 2D numerical model. Journal of Hydro-environment Research. 2018; 20 ():63-76.
Chicago/Turabian StyleJun Song Kim; Il. Won Seo; Donghae Baek. 2018. "Modeling spatial variability of harmful algal bloom in regulated rivers using a depth-averaged 2D numerical model." Journal of Hydro-environment Research 20, no. : 63-76.