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A process-based crop model equipped with seasonal climate forecasts has been typically used to expect crop yields before or in the middle of the growing period. In this work, to consider the farmers’ risk-aversion behavior, we slightly shifted the typical focus to the risk associated with a crucial farming decision in a rainfed paddy rice field, the transplanting timing. Using a simple crop model and a semi-parametric weather generator, we tested hypothetical and real sets of climate forecasts for the rice field under nutrient deficiency in Lao People’s Democratic Republic (PDR). Results showed that the first climatic risk in the study field was an occational dry conditions, and the traditional timing of transplanting seemed to minimize the worst effect of potential dry conditions. It was found that the transplanting timing needs to be 5–10 days earlier than the farming tradition when fertility stress delays foliage development. The tests with hypothetical climate forecasts suggest that duration of a potential dry condition needs to be reliably forecasted to manage the climatic risk by adjusting the transplanting timing. The yield simulations for the 2015 El-Niño event also implcate that adjusting the transplanting timing could effectively reduce the potential impact of dry climate at a cost of slightly reduced yield expectation. This study exemplifies how to explicitly assess the climatic risk associated with the farming decision using ready-to-use climate data and a simple crop model.
Daeha Kim; Jong Ahn Chun; Thavone Inthavong. Managing climate risks in a nutrient-deficient paddy rice field using seasonal climate forecasts and AquaCrop. Agricultural Water Management 2021, 256, 107073 .
AMA StyleDaeha Kim, Jong Ahn Chun, Thavone Inthavong. Managing climate risks in a nutrient-deficient paddy rice field using seasonal climate forecasts and AquaCrop. Agricultural Water Management. 2021; 256 ():107073.
Chicago/Turabian StyleDaeha Kim; Jong Ahn Chun; Thavone Inthavong. 2021. "Managing climate risks in a nutrient-deficient paddy rice field using seasonal climate forecasts and AquaCrop." Agricultural Water Management 256, no. : 107073.
Ather Abbas; Laurie Boithias; Yakov Pachepsky; Kyunghyun Kim; Jong Ahn Chun; Kyung Hwa Cho. Supplementary material to "AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods". 2021, 1 .
AMA StyleAther Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, Kyung Hwa Cho. Supplementary material to "AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods". . 2021; ():1.
Chicago/Turabian StyleAther Abbas; Laurie Boithias; Yakov Pachepsky; Kyunghyun Kim; Jong Ahn Chun; Kyung Hwa Cho. 2021. "Supplementary material to "AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods"." , no. : 1.
Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine learning-based hydrological models requires advanced skills from diverse fields, such as programming and hydrological modeling. Additionally, data pre-processing and post-processing when training and testing machine learning models is a time-intensive process. In this study, we developed a python-based framework that simplifies the process of building and training machine learning-based hydrological models and automates the process of pre-processing of hydrological data and post-processing of model results. Pre-processing utilities assist in incorporating domain knowledge of hydrology in the machine learning model, such as the distribution of weather data into hydrologic response units (HRUs) based on different HRU discretization definitions. The post-processing utilities help in interpreting the model’s results from a hydrological point of view. This framework will help increase the application of machine learning-based modeling approaches in hydrological sciences.
Ather Abbas; Laurie Boithias; Yakov Pachepsky; Kyunghyun Kim; Jong Ahn Chun; Kyung Hwa Cho. AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods. 2021, 2021, 1 -29.
AMA StyleAther Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, Kyung Hwa Cho. AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods. . 2021; 2021 ():1-29.
Chicago/Turabian StyleAther Abbas; Laurie Boithias; Yakov Pachepsky; Kyunghyun Kim; Jong Ahn Chun; Kyung Hwa Cho. 2021. "AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods." 2021, no. : 1-29.
The widespread negative correlation between the atmospheric vapor pressure deficit and soil moisture lends strong support to the complementary relationship (CR) of evapotranspiration. While it has showed outstanding performance in predicting actual evapotranspiration (ETa) over land surfaces, the calibration-free CR formulation has not been tested in the Australian continent dominantly under (semi-)arid climates. In this work, we comparatively evaluated its predictive performance with seven physical, machine-learning, and land surface models for the continent at a 0.5° × 0.5° grid resolution. Results showed that the calibration-free CR that forces a single parameter to everywhere produced considerable biases when comparing to water-balance ETa (ETwb). The CR method was unlikely to outperform the other physical, machine-learning, and land surface models, overrating ETa in (semi-)humid coastal areas for 2002–2012 while underestimating in arid inland locations. By calibrating the parameter against water-balance ETa independent of the simulation period, the CR method became able to outperform the other models in reproducing the spatial variation of the mean annual ETwb and the interannual variation of the continental means of ETwb. However, interannual the grid-scale variability and trends were captured unacceptably even after the calibration. The calibrated parameters for the CR method were significantly correlated with the mean net radiation, temperature, and wind speed, implying that (multi-)decadal climatic variability could diversify the optimal parameters for the CR method. The other physical, machine-learning, and land surface models provided a consistent indication with the prior global-scale assessments. We also argued that at least some surface information is necessary for the CR method to describe long-term hydrologic cycles at the grid scale.
Daeha Kim; Minha Choi; Jong Ahn Chun. A continental-scale evaluation of the calibration-free complementary relationship with physical, machine-learning, and land-surface models. 2021, 2021, 1 -29.
AMA StyleDaeha Kim, Minha Choi, Jong Ahn Chun. A continental-scale evaluation of the calibration-free complementary relationship with physical, machine-learning, and land-surface models. . 2021; 2021 ():1-29.
Chicago/Turabian StyleDaeha Kim; Minha Choi; Jong Ahn Chun. 2021. "A continental-scale evaluation of the calibration-free complementary relationship with physical, machine-learning, and land-surface models." 2021, no. : 1-29.
While the Budyko framework has been a simple and convenient tool to assess runoff (Q) responses to climatic and surface changes, it has been unclear how parameters of a Budyko function represent the vertical land-atmosphere interactions. Here, we explicitly derived a two-parameter equation by correcting a boundary condition of the Budyko hypothesis. The correction enabled for the Budyko function to reflect the evaporative demand (Ep) that actively responds to soil moisture deficiency. The derived two-parameter function suggests that four physical variables control surface runoff; namely, precipitation (P), potential evaporation (Ep), wet-environment evaporation (Ew), and the catchment properties (n). We linked the derived Budyko function to a definitive complementary evaporation principle, and assessed the relative elasticities of Q to climatic and land surface changes. Results showed that P is the primary control of runoff changes in most of river basins across the world, but its importance declined with climatological aridity. In arid river basins, the catchment properties play a major role in changing runoff, while changes in Ep and Ew seem to exert minor influences on Q changes. It was also found that the two-parameter Budyko function can capture unusual negative correlation between the mean annual Q and Ep. This work suggests that at least two parameters are required for a Budyko function to properly describe the vertical interactions between the land and the atmosphere.
Daeha Kim; Jong Ahn Chun. Two parameters are required for a Budyko function to describe the land-atmosphere interaction. 2021, 1 .
AMA StyleDaeha Kim, Jong Ahn Chun. Two parameters are required for a Budyko function to describe the land-atmosphere interaction. . 2021; ():1.
Chicago/Turabian StyleDaeha Kim; Jong Ahn Chun. 2021. "Two parameters are required for a Budyko function to describe the land-atmosphere interaction." , no. : 1.
A Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) combined with a deep learning approach was created by combining CNN and LSTM networks simulated water quality including total nitrogen, total phosphorous, and total organic carbon. Water level and water quality data in the Nakdong river basin were collected from the Water Resources Management Information System (WAMIS) and the Real-Time Water Quality Information, respectively. The rainfall radar image and operation information of estuary barrage were also collected from the Korea Meteorological Administration. In this study, CNN was used to simulate the water level and LSTM used for water quality. The entire simulation period was 1 January 2016–16 November 2017 and divided into two parts: (1) calibration (1 January 2016–1 March 2017); and (2) validation (2 March 2017–16 November 2017). This study revealed that the performances of both of the CNN and LSTM models were in the “very good” range with above the Nash–Sutcliffe efficiency value of 0.75 and that those models well represented the temporal variations of the pollutants in Nakdong river basin (NRB). It is concluded that the proposed approach in this study can be useful to accurately simulate the water level and water quality.
Sang-Soo Baek; Jongcheol Pyo; Jong Ahn Chun. Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach. Water 2020, 12, 3399 .
AMA StyleSang-Soo Baek, Jongcheol Pyo, Jong Ahn Chun. Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach. Water. 2020; 12 (12):3399.
Chicago/Turabian StyleSang-Soo Baek; Jongcheol Pyo; Jong Ahn Chun. 2020. "Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach." Water 12, no. 12: 3399.
Cell classification and cell counting are essential for the detection, monitoring, forecasting, and management of harmful algae populations. Conventional methods of algae classification and cell counting are known to be time-consuming, labor-intensive, and subjective, depending on the expertise of the observers. The objectives of this study were to classify and quantify five cyanobacteria using the deep learning techniques of a fast regional convolutional neural network (R-CNN) and convolutional neural network (CNN). Water samples taken from the Haman weir of Nakdong River and Baekje weir of the Geum River were observed under the optical microscope. The images captured by the microscope were used to classify cyanobacteria species using the fast R-CNN model. Post-processing of the classified images generated by the model reduced the noises of the cell features, thereby improving the accuracy of the CNN model in quantifying cyanobacteria cells. The distinctive morphological features of the five species were extracted by the fast R-CNN model. This model was able to achieve a reasonable agreement with the manual classification results, yielding average precision (AP) values of 0.929, 0.973, 0.829, 0.890, and 0.890 for Microcystis aeruginosa, Microcystis wesenbergii, Dolichospermum, Oscillatoria, and Aphanizomenon, respectively. The CNN model for the Microcystis species obtained an R2 value of 0.775 and RMSE value of 26 cells for training, and an R2 of 0.854 and RMSE of 23 cells for validation. A minor underestimation and overestimation for a population with 250 cells were observed, respectively, which are due to the overlapping of cells and the presence of blurry regions in the input images. In conclusion, this study was able to demonstrate the reliable performance of cyanobacteria classification and cell counting using deep learning approaches.
Sang-Soo Baek; Jongcheol Pyo; Yakov Pachepsky; Yongeun Park; Mayzonee Ligaray; Chi-Yong Ahn; Young-Hyo Kim; Jong Ahn Chun; Kyung Hwa Cho. Identification and enumeration of cyanobacteria species using a deep neural network. Ecological Indicators 2020, 115, 106395 .
AMA StyleSang-Soo Baek, Jongcheol Pyo, Yakov Pachepsky, Yongeun Park, Mayzonee Ligaray, Chi-Yong Ahn, Young-Hyo Kim, Jong Ahn Chun, Kyung Hwa Cho. Identification and enumeration of cyanobacteria species using a deep neural network. Ecological Indicators. 2020; 115 ():106395.
Chicago/Turabian StyleSang-Soo Baek; Jongcheol Pyo; Yakov Pachepsky; Yongeun Park; Mayzonee Ligaray; Chi-Yong Ahn; Young-Hyo Kim; Jong Ahn Chun; Kyung Hwa Cho. 2020. "Identification and enumeration of cyanobacteria species using a deep neural network." Ecological Indicators 115, no. : 106395.
The rapid increase of impervious area and climate change greatly affect the hydrological, environmental, and ecological system at the local, regional, and global scales. These phenomena can increase the peak flow and surface runoff carrying anthropogenic pollutants, thereby severely deteriorating the water quality of the surface waters. Low-impact development (LID) practices have been proposed as a promising urban management methodology to mitigate these environmental issues. Numerical models have been increasingly utilized as an analyzing tool for evaluating the LID performance. However, LID-associated numerical models are oversimplified in terms of water quality simulation by only considering the dilution effect by rainfall in LIDs. This study resolved this challenge by modifying the water quality module of LID in the stormwater management model (SWMM). We evaluated the module performance for simulating total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) of the LID facilities. Using the developed model, we conducted the LID scenario analysis under the climate change scenarios. The scenario analysis was applied in the urban area which focused on flow rate and TSS. The modified module provided accurate results for pollutant simulations, yielding an average value of Root mean square error-observation Standard deviation Ratio (RSR) of 0.52, while the original module showed an unacceptable performance, with an RSR value of 1.11. Scenario analysis showed that the hydrological outputs were sensitive to the volume of rainfall while the water quality results were sensitive to the temporal distribution of rainfall. Based on these statements, the modified water quality module in LID developed in this study will be useful in designing LID facilities and in formulating guidelines for LID installation.
Sang-Soo Baek; Mayzonee Ligaray; Jongcheol Pyo; Jong-Pyo Park; Joo-Hyon Kang; Yakov Pachepsky; Jong Ahn Chun; Kyung Hwa Cho. A novel water quality module of the SWMM model for assessing low impact development (LID) in urban watersheds. Journal of Hydrology 2020, 586, 124886 .
AMA StyleSang-Soo Baek, Mayzonee Ligaray, Jongcheol Pyo, Jong-Pyo Park, Joo-Hyon Kang, Yakov Pachepsky, Jong Ahn Chun, Kyung Hwa Cho. A novel water quality module of the SWMM model for assessing low impact development (LID) in urban watersheds. Journal of Hydrology. 2020; 586 ():124886.
Chicago/Turabian StyleSang-Soo Baek; Mayzonee Ligaray; Jongcheol Pyo; Jong-Pyo Park; Joo-Hyon Kang; Yakov Pachepsky; Jong Ahn Chun; Kyung Hwa Cho. 2020. "A novel water quality module of the SWMM model for assessing low impact development (LID) in urban watersheds." Journal of Hydrology 586, no. : 124886.
Green roof can mitigate urban stormwater and improve environmental, economic, and social conditions. Various modeling approaches have been effectively employed to implement a green roof, but previous models employed simplifications to simulate water movement in green roof systems. To address this issue, we developed a new modeling tool (SWMM-H) by coupling the stormwater management and HYDRUS-1D models to improve simulations of hydrological processes. We selected green roof systems to evaluate the coupled model. Rainfall-runoff experiments were conducted for a pilot-scale green roof and urban subbasin. Soil moisture in the green roof and runoff volume in the subbasin were simulated more accurately by using SWMM-H instead of SWMM. The scenario analysis showed that SWMM-H selected sandy loam for controlling runoff whereas SWMM recommended sand. In conclusion, SWMM-H could be a useful tool for accurately understanding hydrological processes in green roofs.
Sangsoo Baek; Mayzonee Ligaray; Yakov Pachepsky; Jong Ahn Chun; Kwang-Sik Yoon; Yongeun Park; Kyung Hwa Cho. Assessment of a green roof practice using the coupled SWMM and HYDRUS models. Journal of Environmental Management 2020, 261, 109920 .
AMA StyleSangsoo Baek, Mayzonee Ligaray, Yakov Pachepsky, Jong Ahn Chun, Kwang-Sik Yoon, Yongeun Park, Kyung Hwa Cho. Assessment of a green roof practice using the coupled SWMM and HYDRUS models. Journal of Environmental Management. 2020; 261 ():109920.
Chicago/Turabian StyleSangsoo Baek; Mayzonee Ligaray; Yakov Pachepsky; Jong Ahn Chun; Kwang-Sik Yoon; Yongeun Park; Kyung Hwa Cho. 2020. "Assessment of a green roof practice using the coupled SWMM and HYDRUS models." Journal of Environmental Management 261, no. : 109920.
A higher drought risk in Java Island is generally known than the other regions in Indonesia. Tracking soil moisture can be an alternative way to monitor drought rather than precipitation-based drought indices. The objective of this study was to assess root-zone water storage (defined by root-zone soil moisture contents) based on a linked approach between the generalized complementary relationship (GCR) and a single bucket model in Java Island. Since it does not require precipitation for estimating actual evapotranspiration (ETa), the GCR allowed implementation of a simple single bucket model. The ETa and root-zone soil moisture estimated in this study were compared against the Global Land Evaporation Amsterdam Model (GLEAM) and the root-zone water storage additionally compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis data products. Overall, the GCR ETa estimates were higher than those from GLEAM, and similar patterns of the root-zone water storage were found in the comparisons of both GLEAM and ERA5. The comparative evaluation suggests a further study on the adjustment of Priestley-Taylor coefficient value in Java for better application of the GCR. The soil moisture estimated by the single bucket model and the root-zone soil moisture products of GLEAM were highly correlated (0.8 or greater Pearson correlation coefficients). Low root-zone water storage and high ETa rates were found in eastern Java relative to the other areas, indicating high water shortage risks in dry season. This study found that El Niño clearly contributed to the variability of the root-zone water storage in Java especially in wet seasons (December to February). It is also suggested that the proposed approach can be useful to operationally provide soil water availability in Java from readily available meteorological observations.
Nastiti Andini; Daeha Kim; Jong Ahn Chun. Operational soil moisture modeling using a multi-stage approach based on the generalized complementary principle. Agricultural Water Management 2020, 231, 106026 .
AMA StyleNastiti Andini, Daeha Kim, Jong Ahn Chun. Operational soil moisture modeling using a multi-stage approach based on the generalized complementary principle. Agricultural Water Management. 2020; 231 ():106026.
Chicago/Turabian StyleNastiti Andini; Daeha Kim; Jong Ahn Chun. 2020. "Operational soil moisture modeling using a multi-stage approach based on the generalized complementary principle." Agricultural Water Management 231, no. : 106026.
Daeha Kim; Jong Ahn Chun; Jonghan Ko. A hybrid approach combining the FAO-56 method and the complementary principle for predicting daily evapotranspiration on a rainfed crop field. Journal of Hydrology 2019, 577, 1 .
AMA StyleDaeha Kim, Jong Ahn Chun, Jonghan Ko. A hybrid approach combining the FAO-56 method and the complementary principle for predicting daily evapotranspiration on a rainfed crop field. Journal of Hydrology. 2019; 577 ():1.
Chicago/Turabian StyleDaeha Kim; Jong Ahn Chun; Jonghan Ko. 2019. "A hybrid approach combining the FAO-56 method and the complementary principle for predicting daily evapotranspiration on a rainfed crop field." Journal of Hydrology 577, no. : 1.
Daeha Kim; Woo-Seop Lee; Seon Tae Kim; Jong Ahn Chun. Historical Drought Assessment Over the Contiguous United States Using the Generalized Complementary Principle of Evapotranspiration. Water Resources Research 2019, 55, 6244 -6267.
AMA StyleDaeha Kim, Woo-Seop Lee, Seon Tae Kim, Jong Ahn Chun. Historical Drought Assessment Over the Contiguous United States Using the Generalized Complementary Principle of Evapotranspiration. Water Resources Research. 2019; 55 (7):6244-6267.
Chicago/Turabian StyleDaeha Kim; Woo-Seop Lee; Seon Tae Kim; Jong Ahn Chun. 2019. "Historical Drought Assessment Over the Contiguous United States Using the Generalized Complementary Principle of Evapotranspiration." Water Resources Research 55, no. 7: 6244-6267.
Hydrological changes attributable to global warming increase the severity and frequency of droughts, which in turn affect agriculture. Hence, we proposed the Standardized Agricultural Drought Index (SADI), which is a new drought index specialized for agriculture and crops, and evaluated current and expected droughts in the Korean Peninsula. The SADI applies crop phenology to the hydrological cycle, which is a basic element that assesses drought. The SADI of rice and maize was calculated using representative hydrological variables (precipitation, evapotranspiration, and runoff) of the crop growing season. In order to evaluate the effectiveness of SADI, the three-month Standardized Precipitation Index, which is a representative drought index, and rainfed crop yield were estimated together. The performance evaluation of SADI showed that the correlation between rainfed crop yield and SADI was very high compared with that of existing drought index. The results of the assessment of drought over the past three decades provided a good indication of a major drought period and differentiated the results for crops and regions. The results of two future scenarios showed common drought risks in the western plains of North Korea. Successfully validated SADIs could be effectively applied to agricultural drought assessments in light of future climate change, and would be a good example of the water-food nexus approach.
Chul-Hee Lim; Seung Hee Kim; Jong Ahn Chun; Menas C. Kafatos; Woo-Kyun Lee. Assessment of Agricultural Drought Considering the Hydrological Cycle and Crop Phenology in the Korean Peninsula. Water 2019, 11, 1105 .
AMA StyleChul-Hee Lim, Seung Hee Kim, Jong Ahn Chun, Menas C. Kafatos, Woo-Kyun Lee. Assessment of Agricultural Drought Considering the Hydrological Cycle and Crop Phenology in the Korean Peninsula. Water. 2019; 11 (5):1105.
Chicago/Turabian StyleChul-Hee Lim; Seung Hee Kim; Jong Ahn Chun; Menas C. Kafatos; Woo-Kyun Lee. 2019. "Assessment of Agricultural Drought Considering the Hydrological Cycle and Crop Phenology in the Korean Peninsula." Water 11, no. 5: 1105.
Since the Cambodian economy is largely dependent on agricultural production, it is important to understand the effects of climate change on rice production, the primary staple crop of Cambodia. This study assessed the economic impacts of climate change in Cambodia to provide an appropriate set of policy suggestions that could lead to sustainable agricultural productivity and economic growth. The results from the GLAM-Rice crop model and various climate models indicate that Cambodia will be severely affected by climate change, which will lead to lower rice production and economic growth. The changes in rice yield under the RCP 8.5 and RCP 4.5 baseline scenarios reduced the GDP by 8.16% and 10.57%, respectively. By employing an investment model based on a real options framework, the economic effects and feasibility of adaptation strategies such as irrigation and adjustment of planting dates are identified. The analysis indicates that irrigation is a feasible option and the most efficacious strategy to reduce the negative impacts of climate change for the agricultural sector. The index of economic feasibility for irrigation, defined by the ratio of the current realized agriculture value-added to the identified threshold, is 0.6343 and 0.8803 under the RCP 8.5 and RCP 4.5 baseline scenarios, respectively. The results suggest that the priority choice for adaptation measure be in order of irrigation, 20-day later adjustment, and 20-day earlier adjustment.
Jeonghyun Kim; Hojeong Park; Jong Ahn Chun; Sanai Li. Adaptation Strategies under Climate Change for Sustainable Agricultural Productivity in Cambodia. Sustainability 2018, 10, 4537 .
AMA StyleJeonghyun Kim, Hojeong Park, Jong Ahn Chun, Sanai Li. Adaptation Strategies under Climate Change for Sustainable Agricultural Productivity in Cambodia. Sustainability. 2018; 10 (12):4537.
Chicago/Turabian StyleJeonghyun Kim; Hojeong Park; Jong Ahn Chun; Sanai Li. 2018. "Adaptation Strategies under Climate Change for Sustainable Agricultural Productivity in Cambodia." Sustainability 10, no. 12: 4537.
Reliable assessments of flood risks are essential to formulate robust adaptation policies to climate change. Although the scenario‐neutral flood assessments have reduced the dependency on uncertain climate predictions, coarse temporal resolutions of rainfall–runoff modelling adopted for the stress tests may introduce appreciable bias to flood risks driven by climatic stresses. To refine the temporal scale of flow estimates, this study proposes to incorporate a multiplicative random cascade (MRC) scheme and a simple catchment model in the bottom‐up flood risk assessment. Results showed that use of a daily flow indicator for the stress tests could considerably underestimate the impact of climatic change on flood risks. The non‐linearity between daily and hourly peak flows could increasingly amplify flood risks as the mean and variance of daily precipitation become greater in the study catchment. The first‐order catchment model combined with the MRC could acceptably estimate hourly peak flows and the catchment recession behaviours at high flows. This study suggests that subdaily flood indicators should be used in the scenario‐neutral assessments for small or mesoscale catchments to prevent underprediction of flood risks. To expand the applicability of the bottom‐up framework, we also suggest developing efficient tools that can perturb high‐resolution weather time series for the stress tests.
Daeha Kim; Jong Ahn Chun; Christianne M. Aikins. An hourly-scale scenario-neutral flood risk assessment in a mesoscale catchment under climate change. Hydrological Processes 2018, 32, 3416 -3430.
AMA StyleDaeha Kim, Jong Ahn Chun, Christianne M. Aikins. An hourly-scale scenario-neutral flood risk assessment in a mesoscale catchment under climate change. Hydrological Processes. 2018; 32 (22):3416-3430.
Chicago/Turabian StyleDaeha Kim; Jong Ahn Chun; Christianne M. Aikins. 2018. "An hourly-scale scenario-neutral flood risk assessment in a mesoscale catchment under climate change." Hydrological Processes 32, no. 22: 3416-3430.
The objectives of this study were to assess the climate change impacts on sea-level rise (SLR) and freshwater recharge rates and to investigate these SLR and freshwater recharge rates on seawater intrusion in coastal groundwater systems through the Saturated-Unsaturated Transport (SUTRA) model. The Gunsan tide gauge station data were used to project SLR based on polynomial regressions. Freshwater recharge rates were assumed as 10% of the projected annual precipitation under climate change. The Byeonsan2 groundwater monitoring well for seawater intrusion was selected for the study. A total of 15 scenarios, including the baseline period (2005–2015), were made based on SLR projections and estimated freshwater recharge rates. The changes in salinity relative to the baseline at the monitoring well for each scenario were investigated through the SUTRA model. From the scenario of 0.57 m SLR with a freshwater recharge rate of 0.0058 kg s−1, the largest salinity increase (40.3%) was simulated. We concluded that this study may provide a better understanding of the climate change impacts on seawater intrusion by considering both SLR and freshwater recharge rates.
Jong Ahn Chun; Changmook Lim; Daeha Kim; Jin Sung Kim. Assessing Impacts of Climate Change and Sea-Level Rise on Seawater Intrusion in a Coastal Aquifer. Water 2018, 10, 357 .
AMA StyleJong Ahn Chun, Changmook Lim, Daeha Kim, Jin Sung Kim. Assessing Impacts of Climate Change and Sea-Level Rise on Seawater Intrusion in a Coastal Aquifer. Water. 2018; 10 (4):357.
Chicago/Turabian StyleJong Ahn Chun; Changmook Lim; Daeha Kim; Jin Sung Kim. 2018. "Assessing Impacts of Climate Change and Sea-Level Rise on Seawater Intrusion in a Coastal Aquifer." Water 10, no. 4: 357.
Rainfall–runoff modelling has long been a special subject in hydrological sciences, but identifying behavioural parameters in ungauged catchments is still challenging. In this study, we comparatively evaluated the performance of the local calibration of a rainfall–runoff model against regional flow duration curves (FDCs), which is a seemingly alternative method of classical parameter regionalisation for ungauged catchments. We used a parsimonious rainfall–runoff model over 45 South Korean catchments under semi-humid climate. The calibration against regional FDCs was compared with the simple proximity-based parameter regionalisation. Results show that transferring behavioural parameters from gauged to ungauged catchments significantly outperformed the local calibration against regional FDCs due to the absence of flow timing information in the regional FDCs. The behavioural parameters gained from observed hydrographs were likely to contain intangible flow timing information affecting predictability in ungauged catchments. Additional constraining with the rising limb density appreciably improved the FDC calibrations, implying that flow signatures in temporal dimensions would supplement the FDCs. As an alternative approach in data-rich regions, we suggest calibrating a rainfall–runoff model against regionalised hydrographs to preserve flow timing information. We also suggest use of flow signatures that can supplement hydrographs for calibrating rainfall–runoff models in gauged and ungauged catchments.
Daeha Kim; Il Won Jung; Jong Ahn Chun. A comparative assessment of rainfall–runoff modelling against regional flow duration curves for ungauged catchments. Hydrology and Earth System Sciences 2017, 21, 5647 -5661.
AMA StyleDaeha Kim, Il Won Jung, Jong Ahn Chun. A comparative assessment of rainfall–runoff modelling against regional flow duration curves for ungauged catchments. Hydrology and Earth System Sciences. 2017; 21 (11):5647-5661.
Chicago/Turabian StyleDaeha Kim; Il Won Jung; Jong Ahn Chun. 2017. "A comparative assessment of rainfall–runoff modelling against regional flow duration curves for ungauged catchments." Hydrology and Earth System Sciences 21, no. 11: 5647-5661.
The Farquhar—von Caemmerer—Berry (FvCB) biochemical model of photosynthesis, commonly used to estimate CO2 assimilation at various spatial scales from leaf to global, has been used to assess the impacts of climate change on crop and ecosystem productivities. However, it is widely known that the parameters in the FvCB model are difficult to accurately estimate. The objective of this study was to assess the methods of Sharkey et al. and Gu et al., which are often used to estimate the parameters of the FvCB model. We generated An/Cidatasets with different data accuracies, numbers of data points, and data point distributions. The results showed that neither method accurately estimated the parameters; however, Gu et al.’s approach provided slightly better estimates. Using Gu et al.’s approach and datasets with measurement errors and the same accuracy as a typical open gas exchange system (i.e., Li-6400), the majority of the estimated parameters—Vcmax (maximal Rubisco carboxylation rate), Kco (effective Michaelis-Menten coefficient for CO2), gm (internal (mesophyll) conductance to CO2 transport) and Γ* (chloroplastic CO2 photocompensation point)—were underestimated, while the majority of Rd (day respiration) and α (the non-returned fraction of the glycolate carbon recycled in the photorespiratory cycle) were overestimated. The distributions of Tp (the rate of triose phosphate export from the chloroplast) were evenly dispersed around the 1:1 line using both approaches. This study revealed that a high accuracy of leaf gas exchange measurements and sufficient data points are required to correctly estimate the parameters for the biochemical model. The accurate estimation of these parameters can contribute to the enhancement of food security under climate change through accurate predictions of crop and ecosystem productivities. A further study is recommended to address the question of how the measurement accuracies can be improved.
Qingguo Wang; Jong Ahn Chun; David Fleisher; Vangimalla Reddy; Dennis Timlin; Jonathan Resop. Parameter Estimation of the Farquhar—von Caemmerer—Berry Biochemical Model from Photosynthetic Carbon Dioxide Response Curves. Sustainability 2017, 9, 1288 .
AMA StyleQingguo Wang, Jong Ahn Chun, David Fleisher, Vangimalla Reddy, Dennis Timlin, Jonathan Resop. Parameter Estimation of the Farquhar—von Caemmerer—Berry Biochemical Model from Photosynthetic Carbon Dioxide Response Curves. Sustainability. 2017; 9 (7):1288.
Chicago/Turabian StyleQingguo Wang; Jong Ahn Chun; David Fleisher; Vangimalla Reddy; Dennis Timlin; Jonathan Resop. 2017. "Parameter Estimation of the Farquhar—von Caemmerer—Berry Biochemical Model from Photosynthetic Carbon Dioxide Response Curves." Sustainability 9, no. 7: 1288.
Exposure to highly toxic pesticides could potentially cause cancer and disrupt the development of vital systems. Monitoring activities were performed to assess the level of contamination; however, these were costly, laborious, and short-term leading to insufficient monitoring data. However, the performance of the existing Soil and Water Assessment Tool (SWAT model) can be restricted by its two-phase partitioning approach, which is inadequate when it comes to simulating pesticides with limited dataset. This study developed a modified SWAT pesticide model to address these challenges. The modified model considered the three-phase partitioning model that classifies the pesticide into three forms: dissolved, particle-bound, and dissolved organic carbon (DOC)-associated pesticide. The addition of DOC-associated pesticide particles increases the scope of the pesticide model by also considering the adherence of pesticides to the organic carbon in the soil. The modified SWAT and original SWAT pesticide model was applied to the Pagsanjan-Lumban (PL) basin, a highly agricultural region. Malathion was chosen as the target pesticide since it is commonly used in the basin. The pesticide models simulated the fate and transport of malathion in the PL basin and showed the temporal pattern of selected subbasins. The sensitivity analyses revealed that application efficiency and settling velocity were the most sensitive parameters for the original and modified SWAT model, respectively. Degradation of particulate-phase malathion were also significant to both models. The rate of determination (R2) and Nash-Sutcliffe efficiency (NSE) values showed that the modified model (R2 = 0.52; NSE = 0.36) gave a slightly better performance compared to the original (R2 = 0.39; NSE = 0.18). Results from this study will be able to aid the government and private agriculture sectors to have an in-depth understanding in managing pesticide usage in agricultural watersheds.
Mayzonee Ligaray; Minjeong Kim; Sangsoo Baek; Jin-Sung Ra; Jong Ahn Chun; Yongeun Park; Laurie Boithias; Olivier Ribolzi; Kangmin Chon; Kyung Hwa Cho. Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines. Water 2017, 9, 451 .
AMA StyleMayzonee Ligaray, Minjeong Kim, Sangsoo Baek, Jin-Sung Ra, Jong Ahn Chun, Yongeun Park, Laurie Boithias, Olivier Ribolzi, Kangmin Chon, Kyung Hwa Cho. Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines. Water. 2017; 9 (7):451.
Chicago/Turabian StyleMayzonee Ligaray; Minjeong Kim; Sangsoo Baek; Jin-Sung Ra; Jong Ahn Chun; Yongeun Park; Laurie Boithias; Olivier Ribolzi; Kangmin Chon; Kyung Hwa Cho. 2017. "Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines." Water 9, no. 7: 451.
Jong Ahn Chun; Kwangmin Kang; Daeha Kim; Hyun-Hee Han; In-Chang Son. Prediction of full blooming dates of five peach cultivars ( Prunus persica ) using temperature-based models. Scientia Horticulturae 2017, 220, 250 -258.
AMA StyleJong Ahn Chun, Kwangmin Kang, Daeha Kim, Hyun-Hee Han, In-Chang Son. Prediction of full blooming dates of five peach cultivars ( Prunus persica ) using temperature-based models. Scientia Horticulturae. 2017; 220 ():250-258.
Chicago/Turabian StyleJong Ahn Chun; Kwangmin Kang; Daeha Kim; Hyun-Hee Han; In-Chang Son. 2017. "Prediction of full blooming dates of five peach cultivars ( Prunus persica ) using temperature-based models." Scientia Horticulturae 220, no. : 250-258.