This page has only limited features, please log in for full access.
Drought is one of the most severe natural disasters. However, many of its characteristic variables have complex nonlinear relationships. Therefore, it is difficult to construct effective drought assessment models. In this study, we analyzed regional drought characteristics in China to identify their relationship with changes in meridional and zonal temperature gradients. Drought duration and severity were extracted according to standardized precipitation evapotranspiration index (SPEI) drought grades. Trends in drought duration and severity were detected by the Mann-Kendall test for the period of 1979–2019; they showed that both parameters had been steadily increasing during that time. Nevertheless, the increasing trend in drought severity was particularly significant for northwest and southwest China. A composite analysis confirmed the relationships between drought characteristics and temperature gradients. The northwest areas were relatively less affected by temperature gradients, as they are landlocked, remote from the ocean, and only slightly influenced by the land–ocean thermal contrast (LOC) and the meridional temperature gradient (MTG). The impacts of LOC and MTG on drought duration and severity were positive in the southwest region of China but negative in the northeast. As there was a strong correlation between drought duration and severity, we constructed a 2D copula function model of these parameters. The Gaussian, HuslerReiss, and Frank copula functions were the most appropriate distributions for the northeast, northwest, and southwest regions, respectively. As drought processes are highly complex, the present study explored the internal connections between drought duration and severity and their responses to meteorological conditions. In this manner, an accurate method of predicting future drought events was developed.
Abudureymjang Otkur; Dian Wu; Yin Zheng; Jong-Suk Kim; Joo-Heon Lee. Copula-Based Drought Monitoring and Assessment According to Zonal and Meridional Temperature Gradients. Atmosphere 2021, 12, 1066 .
AMA StyleAbudureymjang Otkur, Dian Wu, Yin Zheng, Jong-Suk Kim, Joo-Heon Lee. Copula-Based Drought Monitoring and Assessment According to Zonal and Meridional Temperature Gradients. Atmosphere. 2021; 12 (8):1066.
Chicago/Turabian StyleAbudureymjang Otkur; Dian Wu; Yin Zheng; Jong-Suk Kim; Joo-Heon Lee. 2021. "Copula-Based Drought Monitoring and Assessment According to Zonal and Meridional Temperature Gradients." Atmosphere 12, no. 8: 1066.
Considerable attention has recently been focused on the impacts of climate change and human activities on river streamflow conditions. This study explored these effects using three hydrological modeling techniques such as multi-regression, a two-parameter hydrological model, and hydrological sensitivity analysis, followed by trend analysis and change point detection. The non-parametric Mann-Kendall test was used to analyze the trends in hydro-meteorological parameters. The non-parametric Pettitt test and double cumulative curve techniques were used to identify change points in annual streamflow series during 1978–2014. After determining the change point year to be 1997, the series were split into two parts: a pre-change (natural) period (1978–1997) and a post-change (human-induced) period (1998–2014). The hydrological models were calibrated and estimated for the pre-change (natural) period, which provided the relative change in annual streamflow for the post-change (human-induced) period. The contribution of climate variability ranged from 36.3% to 55.9%, and human activities accounted for 44.5% to 63.7% of streamflow variability. These results suggest that human activities are more impactful than climate variability. The outcomes of this study show that streamflow in the basin was influenced by climate variability, but human actions were also major driving forces in altering the streamflow.
Sabab Ali Shah; Muhammad Jehanzaib; Joo-Heon Lee; Tae-Woong Kim. Exploring the Factors Affecting Streamflow Conditions in the Han River Basin from a Regional Perspective. KSCE Journal of Civil Engineering 2021, 1 -11.
AMA StyleSabab Ali Shah, Muhammad Jehanzaib, Joo-Heon Lee, Tae-Woong Kim. Exploring the Factors Affecting Streamflow Conditions in the Han River Basin from a Regional Perspective. KSCE Journal of Civil Engineering. 2021; ():1-11.
Chicago/Turabian StyleSabab Ali Shah; Muhammad Jehanzaib; Joo-Heon Lee; Tae-Woong Kim. 2021. "Exploring the Factors Affecting Streamflow Conditions in the Han River Basin from a Regional Perspective." KSCE Journal of Civil Engineering , no. : 1-11.
Tropical cyclones (TCs) influence extreme rainfall events. However, changes in TCs caused by their poleward migration and subsequent impacts on extreme rainfall are yet to be adequately explored. In this study, the long-term trends of TCs and typological characteristics of TC-induced rainfall were analysed. Daily rainfall data were obtained from 759 Chinese stations and TC data were obtained in the Northwest Pacific (NWP) regions from 1961 to 2017. From May to October of each year, the TC-induced rainfall was separated from the total rainfall data and spatiotemporal distribution patterns of TC-induced rainfall were identified. The amount and frequency of TC-induced rainfall decrease from the coastal inland. The TCs migrate northward, leading to a decrease (increase) in their activities south (north) of 20°N, respectively. In the South China Sea, TC activity is decreasing. The number of TC landfall events is decreasing (increasing) in the Pearl River basin (Southeast River basin). Furthermore, nonstationary increasing trends in the Pearl River and Southeast basins account for 41.7 and 62.9%, respectively, whereas the decreasing trends account for 30.6 and 14.3%, respectively. The Southeast River basin may face more TC-induced extreme rainfall in the future. The results can provide information for water resource management and flood disaster warning systems in China, especially in the southeast.
Dian Wu; Changhyun Jun; Jong‐Suk Kim; Lihua Xiong; Jie Chen. Poleward Migration of Tropical Cyclones and Its Related Typological Characteristics of Seasonal Maximum Precipitation in China. International Journal of Climatology 2021, 1 .
AMA StyleDian Wu, Changhyun Jun, Jong‐Suk Kim, Lihua Xiong, Jie Chen. Poleward Migration of Tropical Cyclones and Its Related Typological Characteristics of Seasonal Maximum Precipitation in China. International Journal of Climatology. 2021; ():1.
Chicago/Turabian StyleDian Wu; Changhyun Jun; Jong‐Suk Kim; Lihua Xiong; Jie Chen. 2021. "Poleward Migration of Tropical Cyclones and Its Related Typological Characteristics of Seasonal Maximum Precipitation in China." International Journal of Climatology , no. : 1.
High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.
Xin Yan; Hua Chen; Bingru Tian; Sheng Sheng; Jinxing Wang; Jong-Suk Kim. A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging. Remote Sensing 2021, 13, 2040 .
AMA StyleXin Yan, Hua Chen, Bingru Tian, Sheng Sheng, Jinxing Wang, Jong-Suk Kim. A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging. Remote Sensing. 2021; 13 (11):2040.
Chicago/Turabian StyleXin Yan; Hua Chen; Bingru Tian; Sheng Sheng; Jinxing Wang; Jong-Suk Kim. 2021. "A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging." Remote Sensing 13, no. 11: 2040.
Non-linear behavioral links with atmospheric teleconnections were identified between the Indian Ocean Dipole (IOD) mode and seasonal precipitation over East Asia (EA) using statistical models. The analysis showed that the lower the lag time, the higher the correlation; more than a two-fold correlation for non-linear regression with a kernel density estimator than for the linear regression method. When the IOD peaked, a pattern of significant reductions in seasonal precipitation during the negative IOD period occurred throughout the Korean Peninsula (KP). The occurrence of the positive IOD was in line with the El Niño phenomenon and generated greater seasonal precipitation than only the positive IOD, which takes place from March to May. This change occurred more in the cold tongue El Niño than the warm pool El Niño, inducing much higher spring precipitation throughout the KP. When negative IODs and La Niña coincided, there was slightly greater precipitation from March to May compared to the sole occurrence of negative IODs. In positive (negative) IOD years, there was anti-cyclonic (cyclonic) circulation in the South China Sea (SCS), helping to transport moisture to EA. The composite precipitation anomalies in the positive (negative) IOD years show above (below) normal precipitation in southern China. In contrast, other parts of the EA experienced drier (humid) signals than normal years. In positive IOD years, the anti-cyclonic circulation strength of the Bay of Bengal and the SCS continued until autumn and spring of the following year. This shows possible remote connections between climate events related to the tropical Indian Ocean and variations in precipitation over EA.
Jong-Suk Kim; Sun-Kwon Yoon; Sang-Myeong Oh; Hua Chen. Seasonal Precipitation Variability and Non-Stationarity Based on the Evolution Pattern of the Indian Ocean Dipole over the East Asia Region. Remote Sensing 2021, 13, 1806 .
AMA StyleJong-Suk Kim, Sun-Kwon Yoon, Sang-Myeong Oh, Hua Chen. Seasonal Precipitation Variability and Non-Stationarity Based on the Evolution Pattern of the Indian Ocean Dipole over the East Asia Region. Remote Sensing. 2021; 13 (9):1806.
Chicago/Turabian StyleJong-Suk Kim; Sun-Kwon Yoon; Sang-Myeong Oh; Hua Chen. 2021. "Seasonal Precipitation Variability and Non-Stationarity Based on the Evolution Pattern of the Indian Ocean Dipole over the East Asia Region." Remote Sensing 13, no. 9: 1806.
Integrated operation of hydropower, wind and photovoltaic (PV) power, which exploits the regulation flexibility of hydropower to complement the uncertainty of wind and PV power outputs, is a promising way to enhance resource utilization efficiency. However, the short-term economic operation of the wind-solar-hydro complementary system (WSHCS) has risks such as output shortage, power curtailment and spilled water. To address this issue, we propose three risk indicators to quantify them and then optimize the WSHCS. To begin with, the uncertainties of WSHCS are described, and varied forecast scenarios are produced by the copula method. Then, the three risks of WSHCS, which are quantified based on the output errors of wind-solar and regulation ability of hydropower station, are used as objective functions of the optimization model. Finally, a two-layer nested approach is used to optimize online units of hydropower station for minimizing the risks of WSHCS and water consumption of hydropower station. The Guandi wind-solar-hydro hybrid power plant on China’s Yalong River is selected as a case study. Results from the case study show that the output shortage of WSHCS mainly occurs in the two time periods: 1) when the upper limit of regulation ability of the hydropower station is small, and 2) when the output of wind or PV power is large. The probability of output shortage during WSHCS operation is reduced from 100% to 0%, and the probability of spilled water can be reduced by 46.7%~73.9%, without power curtailment. Thus, this study provides the technical support for safe and economic operation of renewable energy.
Kangdi Huang; Pan Liu; Bo Ming; Jong-Suk Kim; Yu Gong. Economic operation of a wind-solar-hydro complementary system considering risks of output shortage, power curtailment and spilled water. Applied Energy 2021, 290, 116805 .
AMA StyleKangdi Huang, Pan Liu, Bo Ming, Jong-Suk Kim, Yu Gong. Economic operation of a wind-solar-hydro complementary system considering risks of output shortage, power curtailment and spilled water. Applied Energy. 2021; 290 ():116805.
Chicago/Turabian StyleKangdi Huang; Pan Liu; Bo Ming; Jong-Suk Kim; Yu Gong. 2021. "Economic operation of a wind-solar-hydro complementary system considering risks of output shortage, power curtailment and spilled water." Applied Energy 290, no. : 116805.
Vulnerability and hazard are terms that are generally applied to drought risk assessment. Vulnerability can be defined as the capacity of a region to cope with and resist the impacts of natural hazards, while hazard can be defined as the likelihood of a natural or human-induced physical event. In this study, principal component analysis (PCA) was used to generate an aggregate drought vulnerability index (DVI) using multiple socio-economic indicators and copula-based drought frequency analysis was performed to calculate a drought hazard index (DHI) considering meteorological drought occurrence patterns. Finally, regional drought risk was evaluated by combining the DVI and DHI among cities within the Chungcheong province, South Korea. Based on the drought risk index (DRI), Jecheon-si (DRI = 0.50) and Gongju-si (DRI = 0.65) were identified as the most hazardous cities in Chungcheongbuk-do and Chungcheongnam-do, respectively. The overall process of drought risk assessment developed in this study is useful for planning drought management and mitigation at the local level.
Jisoo Yu; Ji Eun Kim; Joo-Heon Lee; Tae-Woong Kim. Development of a PCA-Based Vulnerability and Copula-Based Hazard Analysis for Assessing Regional Drought Risk. KSCE Journal of Civil Engineering 2021, 25, 1901 -1908.
AMA StyleJisoo Yu, Ji Eun Kim, Joo-Heon Lee, Tae-Woong Kim. Development of a PCA-Based Vulnerability and Copula-Based Hazard Analysis for Assessing Regional Drought Risk. KSCE Journal of Civil Engineering. 2021; 25 (5):1901-1908.
Chicago/Turabian StyleJisoo Yu; Ji Eun Kim; Joo-Heon Lee; Tae-Woong Kim. 2021. "Development of a PCA-Based Vulnerability and Copula-Based Hazard Analysis for Assessing Regional Drought Risk." KSCE Journal of Civil Engineering 25, no. 5: 1901-1908.
To proactively respond to changes in droughts, technologies are needed to properly diagnose and predict the magnitude of droughts. Drought monitoring using satellite data is essential when local hydrogeological information is not available. The characteristics of meteorological, agricultural, and hydrological droughts can be monitored with an accurate spatial resolution. In this study, a remote sensing-based integrated drought index was extracted from 849 sub-basins in Korea’s five major river basins using multi-sensor collaborative approaches and multivariate dimensional reduction models that were calculated using monthly satellite data from 2001 to 2019. Droughts that occurred in 2001 and 2014, which are representative years of severe drought since the 2000s, were evaluated using the integrated drought index. The Bayesian principal component analysis (BPCA)-based integrated drought index proposed in this study was analyzed to reflect the timing, severity, and evolutionary pattern of meteorological, agricultural, and hydrological droughts, thereby enabling a comprehensive delivery of drought information.
Jong-Suk Kim; Seo-Yeon Park; Joo-Heon Lee; Jie Chen; Si Chen; Tae-Woong Kim. Integrated Drought Monitoring and Evaluation through Multi-Sensor Satellite-Based Statistical Simulation. Remote Sensing 2021, 13, 272 .
AMA StyleJong-Suk Kim, Seo-Yeon Park, Joo-Heon Lee, Jie Chen, Si Chen, Tae-Woong Kim. Integrated Drought Monitoring and Evaluation through Multi-Sensor Satellite-Based Statistical Simulation. Remote Sensing. 2021; 13 (2):272.
Chicago/Turabian StyleJong-Suk Kim; Seo-Yeon Park; Joo-Heon Lee; Jie Chen; Si Chen; Tae-Woong Kim. 2021. "Integrated Drought Monitoring and Evaluation through Multi-Sensor Satellite-Based Statistical Simulation." Remote Sensing 13, no. 2: 272.
This study investigated the effects of El Niño events on tropical cyclone (TC) characteristics over the western North Pacific (WNP) region. First, TC characteristics associated with large-scale atmospheric phenomena (i.e., genesis position, frequency, track, intensity, and duration) were investigated in the WNP in relation to various types of El Niño events—moderate central Pacific (MCP), moderate eastern Pacific (MEP), and strong basin-wide (SBW). Subsequently, the seasonal and regional variability of TC-induced rainfall across China was analyzed to compare precipitation patterns under the three El Niño types. When extreme El Niño events of varying degrees occurred, the local rainfall varied during the developmental and decaying years. The development of MEP and SBW was associated with a distinct change in TC-induced rainfall. During MEP development, TC-induced rainfall occurred in eastern and northeastern China, whereas in SBW, TC-induced heavy rainfall occurred in southwest China. During SBW development, the southwestern region was affected by TCs over a long period, with the eastern and northeastern regions being affected significantly fewer days. During El Niño decay, coastal areas were relatively more affected by TCs during MCP events, and the Pearl River basin was more affected during SBW events. This study’s results could help mitigate TC-related disasters and improve water-supply management.
Yuhang Liu; Sun-Kwon Yoon; Jong-Suk Kim; Lihua Xiong; Joo-Heon Lee. Changes in Intensity and Variability of Tropical Cyclones over the Western North Pacific and Their Local Impacts under Different Types of El Niños. Atmosphere 2020, 12, 59 .
AMA StyleYuhang Liu, Sun-Kwon Yoon, Jong-Suk Kim, Lihua Xiong, Joo-Heon Lee. Changes in Intensity and Variability of Tropical Cyclones over the Western North Pacific and Their Local Impacts under Different Types of El Niños. Atmosphere. 2020; 12 (1):59.
Chicago/Turabian StyleYuhang Liu; Sun-Kwon Yoon; Jong-Suk Kim; Lihua Xiong; Joo-Heon Lee. 2020. "Changes in Intensity and Variability of Tropical Cyclones over the Western North Pacific and Their Local Impacts under Different Types of El Niños." Atmosphere 12, no. 1: 59.
In the face of changing water environment due to climate change, the assessment of water demand and water supply capacity by region is needed to prevent and mitigate droughts. Herein, we propose a quantitative approach to identify high drought risk areas in South Korea by applying future climate and socio‐economic change scenarios to calculate the demand and supply of municipal, agricultural, and industrial water. Three Coupled Model Intercomparison Project 5 Global Climate Models were selected to assess future drought risk under different climate change scenarios by combining meteorological and socio‐economic factors. The drought hazard was assessed by calculating the severity and frequency of drought based on a rating method. Drought vulnerability was assessed by calculating water shortages in domestic, industrial, and agricultural waters based on water demand and supply capacity and applying entropy weightings. According to future climate change scenarios, the Youngsan River Basin was more vulnerable to drought than other basins. The results of the IPSL‐CM5‐LR model also suggest that the drought risk in the Youngsan River Basin will increase during the period 2071–2099. By demonstrating the relative sensitivity of drought risk on the Korean Peninsula to various future emission scenarios, our work provides valuable information to update mid‐ to long‐term drought mitigation strategies.
Seo‐Yeon Park; Chanyang Sur; Jong‐Suk Kim; Si‐Jung Choi; Joo‐Heon Lee; Tae‐Woong Kim. Projected drought risk assessment from water balance perspectives in a changing climate. International Journal of Climatology 2020, 41, 2765 -2777.
AMA StyleSeo‐Yeon Park, Chanyang Sur, Jong‐Suk Kim, Si‐Jung Choi, Joo‐Heon Lee, Tae‐Woong Kim. Projected drought risk assessment from water balance perspectives in a changing climate. International Journal of Climatology. 2020; 41 (4):2765-2777.
Chicago/Turabian StyleSeo‐Yeon Park; Chanyang Sur; Jong‐Suk Kim; Si‐Jung Choi; Joo‐Heon Lee; Tae‐Woong Kim. 2020. "Projected drought risk assessment from water balance perspectives in a changing climate." International Journal of Climatology 41, no. 4: 2765-2777.
Typhoons or mature tropical cyclones (TCs) can affect inland areas of up to hundreds of kilometers with heavy rains and strong winds, along with landslides causing numerous casualties and property damage due to concentrated precipitation over short time periods. To reduce these damages, it is necessary to accurately predict the rainfall induced by TCs in the western North Pacific Region. However, despite dramatic advances in observation and numerical modeling, the accuracy of prediction of typhoon-induced rainfall and spatial distribution remains limited. The present study offers a statistical approach to predicting the accumulated rainfall associated with typhoons based on a historical storm track and intensity data along with observed rainfall data for 55 typhoons affecting the southeastern coastal areas of China from 1961 to 2017. This approach is shown to provide an average root mean square error of 51.2 mm across 75 meteorological stations in the southeast coastal area of China (ranging from 15.8 to 87.3 mm). Moreover, the error is less than 70 mm for most stations, and significantly lower in the three verification cases, thus demonstrating the feasibility of this approach. Furthermore, the use of fuzzy C-means clustering, ensemble averaging, and corrections to typhoon intensities, can provide more accurate rainfall predictions from the method applied herein, thus allowing for improvements to disaster preparedness and emergency response.
Jong-Suk Kim; Anxiang Chen; Junghwan Lee; Il-Ju Moon; Young-Il Moon. Statistical Prediction of Typhoon-Induced Rainfall over China Using Historical Rainfall, Tracks, and Intensity of Typhoon in the Western North Pacific. Remote Sensing 2020, 12, 4133 .
AMA StyleJong-Suk Kim, Anxiang Chen, Junghwan Lee, Il-Ju Moon, Young-Il Moon. Statistical Prediction of Typhoon-Induced Rainfall over China Using Historical Rainfall, Tracks, and Intensity of Typhoon in the Western North Pacific. Remote Sensing. 2020; 12 (24):4133.
Chicago/Turabian StyleJong-Suk Kim; Anxiang Chen; Junghwan Lee; Il-Ju Moon; Young-Il Moon. 2020. "Statistical Prediction of Typhoon-Induced Rainfall over China Using Historical Rainfall, Tracks, and Intensity of Typhoon in the Western North Pacific." Remote Sensing 12, no. 24: 4133.
This study comprehensively evaluates eight satellite-based precipitation datasets in streamflow simulations on a monsoon-climate watershed in China. Two mutually independent datasets—one dense-gauge and one gauge-interpolated dataset—are used as references because commonly used gauge-interpolated datasets may be biased and unable to reflect the real performance of satellite-based precipitation due to sparse networks. The dense-gauge dataset includes a substantial number of gauges, which can better represent the spatial variability of precipitation. Eight satellite-based precipitation datasets include two raw satellite datasets, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Climate Prediction Center MORPHing raw satellite dataset (CMORPH RAW); four satellite-gauge datasets, Tropical Rainfall Measuring Mission 3B42 (TRMM), PERSIANN Climate Data Record (PERSIANN CDR), CMORPH bias-corrected (CMORPH CRT), and gauge blended datasets (CMORPH BLD); and two satellite-reanalysis-gauge datasets, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS). The uncertainty related to hydrologic model physics is investigated using two different hydrological models. A set of statistical indices is utilized to comprehensively evaluate the precipitation datasets from different perspectives, including detection, systematic, random errors, and precision for simulating extreme precipitation. Results show that CMORPH BLD and MSWEP generally perform better than other datasets. In terms of hydrological simulations, all satellite-based datasets show significant dampening effects for the random error during the transformation process from precipitation to runoff; however, these effects cannot hold for the systematic error. Even though different hydrological models indeed introduce uncertainties to the simulated hydrological processes, the relative hydrological performance of the satellite-based datasets is consistent in both models. Namely, CMORPH BLD performs the best, which is followed by MSWEP, CMORPH CRT, and TRMM. PERSIANN CDR and CHIRPS perform moderately well, and two raw satellite datasets are not recommended as proxies of gauged observations for their worse performances.
Jie Chen; Ziyi Li; Lu Li; Jialing Wang; Wenyan Qi; Chong-Yu Xu; Jong-Suk Kim. Evaluation of Multi-Satellite Precipitation Datasets and Their Error Propagation in Hydrological Modeling in a Monsoon-Prone Region. Remote Sensing 2020, 12, 3550 .
AMA StyleJie Chen, Ziyi Li, Lu Li, Jialing Wang, Wenyan Qi, Chong-Yu Xu, Jong-Suk Kim. Evaluation of Multi-Satellite Precipitation Datasets and Their Error Propagation in Hydrological Modeling in a Monsoon-Prone Region. Remote Sensing. 2020; 12 (21):3550.
Chicago/Turabian StyleJie Chen; Ziyi Li; Lu Li; Jialing Wang; Wenyan Qi; Chong-Yu Xu; Jong-Suk Kim. 2020. "Evaluation of Multi-Satellite Precipitation Datasets and Their Error Propagation in Hydrological Modeling in a Monsoon-Prone Region." Remote Sensing 12, no. 21: 3550.
Weather forecasting, especially that of extreme climatic events, has gained considerable attention among researchers due to their impacts on natural ecosystems and human life. The applicability of artificial neural networks (ANNs) in non-linear process forecasting has significantly contributed to hydro-climatology. The efficiency of neural network functions depends on the network structure and parameters. This study proposed a new approach to forecasting a one-day-ahead maximum temperature time series for South Korea to discuss the relationship between network specifications and performance by employing various scenarios for the number of parameters and hidden layers in the ANN model. Specifically, a different number of trainable parameters (i.e., the total number of weights and bias) and distinctive numbers of hidden layers were compared for system-performance effects. If the parameter sizes were too large, the root mean square error (RMSE) would be generally increased, and the model’s ability was impaired. Besides, too many hidden layers would reduce the system prediction if the number of parameters was high. The number of parameters and hidden layers affected the performance of ANN models for time series forecasting competitively. The result showed that the five-hidden layer model with 49 parameters produced the smallest RMSE at most South Korean stations.
Trang Tran; Taesam Lee; Jong-Suk Kim. Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series? Atmosphere 2020, 11, 1072 .
AMA StyleTrang Tran, Taesam Lee, Jong-Suk Kim. Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series? Atmosphere. 2020; 11 (10):1072.
Chicago/Turabian StyleTrang Tran; Taesam Lee; Jong-Suk Kim. 2020. "Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?" Atmosphere 11, no. 10: 1072.
This study developed a hydrological drought forecasting framework linked to the meteorological model and land surface model (LSM) considering hydrologic facilities and evaluated the feasibility of the Modified Surface Water Supply Index (MSWSI) for drought forecasts in South Korea. The Global Seasonal Forecast System version 5 (GloSea5) and variable infiltration capacity (VIC) models were adapted for meteorological and hydrological models for ensemble weather forecasts and corresponding hydrologic river and dam inflow forecasts, respectively. Instead of direct use for weather and runoff forecasts, the anomaly between the ensemble forecast and hindcast data for each month was computed. Then, the monthly forecasted weather and runoff were obtained by adding this anomaly and the statistical nominal values obtained from the average monthly runoff during the last 30 years. For the selection of drought index duration, past historical observation data and drought records were used, and the 3-month period of the MSWSI outperformed any other durations in the study area. In addition, the simulated monthly river and dam inflows agreed well with the observed inflows; therefore, the model-driven runoff data from the VIC model were usable for hydrological drought forecasts. A case study result for the 2015–2016 drought event demonstrated that the hydrological drought forecasting framework suggested in this study is reliable for drought forecasting up to a 2-month forecast lead time. It is therefore concluded that the proposed framework linked with GloSea5, the VIC model and MSWSI(3) provides useful information for supporting decision-making related to water supply and management.
Jae-Min So; Joo-Heon Lee; Deg-Hyo Bae. Development of a Hydrological Drought Forecasting Model Using Weather Forecasting Data from GloSea5. Water 2020, 12, 2785 .
AMA StyleJae-Min So, Joo-Heon Lee, Deg-Hyo Bae. Development of a Hydrological Drought Forecasting Model Using Weather Forecasting Data from GloSea5. Water. 2020; 12 (10):2785.
Chicago/Turabian StyleJae-Min So; Joo-Heon Lee; Deg-Hyo Bae. 2020. "Development of a Hydrological Drought Forecasting Model Using Weather Forecasting Data from GloSea5." Water 12, no. 10: 2785.
In this study, we used statistical models to analyze nonlinear behavior links with atmospheric teleconnections between hydrometeorological variables and Indian Ocean Dipole (IOD) mode over the East Asia (EA) region. The analysis of atmospheric teleconnections was conducted using principal component analysis and singular spectrum analysis techniques. Moreover, the nonlinear lag-time correlations between climate indices and hydrological variables were calculated using mutual information (MI) techniques. The teleconnection-based nonlinear correlation coefficients (CCs) were higher than the linear CCs in each lag time. Additionally, we documented that the IOD has a direct influence on hydro-meteorological variables, such as precipitation within the Korean Peninsula (KP). Moreover, during the warm season (June to September) the variation of hydro-meteorological variables in the KP demonstrated significantly decreasing patterns during positive IOD years and they have neutral conditions during negative IOD years in comparison with long-term normal conditions. Finally, the revealed relationship between climate indices and hydro-meteorological variables and their possible changes will allow better understanding of stakeholder decision-making regarding to manage of freshwater management over the EA region. It can also provide useful data for long-range water resources prediction, to minimize hydrological uncertainties in a changing climate.
Jong-Suk Kim; Sun-Kwon Yoon; Sang-Myeong Oh. Hydrometeorological Variability and Its Nonstationarity According to the Evolution Pattern of Indian Ocean Dipole over the East Asia Region. 2020, 1 .
AMA StyleJong-Suk Kim, Sun-Kwon Yoon, Sang-Myeong Oh. Hydrometeorological Variability and Its Nonstationarity According to the Evolution Pattern of Indian Ocean Dipole over the East Asia Region. . 2020; ():1.
Chicago/Turabian StyleJong-Suk Kim; Sun-Kwon Yoon; Sang-Myeong Oh. 2020. "Hydrometeorological Variability and Its Nonstationarity According to the Evolution Pattern of Indian Ocean Dipole over the East Asia Region." , no. : 1.
Long-term meteorological drought can lead to hydrological drought by restricting water resources required by humans, such as streamflow and reservoir storage. We developed two new indices for monitoring hydrologic drought-based satellite-derived evapotranspiration, a major factor in hydrological drought occurrence. The Water Budget-based Drought Index (WBDI) estimates potential runoff by the differences between precipitation and evapotranspiration using water budget analysis while the Energy-based Water Deficit Index (EWDI) is an index of available water capacity through evapotranspiration and soil moisture based on solar radiation using energy budget analysis. We used these, along with the existing Standardized Precipitation-Evapotranspiration Index (SPEI, based on precipitation, atmospheric temperature, and evapotranspiration), to map the spatiotemporal patterns of drought on the Korean Peninsula from 2001 to 2014. For validation against actual drought conditions, we compared the results with streamflow data from five gauging stations in South Korea. EWDI—the diagnostic approach—performed best when assessing current hydrological drought conditions, while WBDI and SPEI—prognostic approach—best captured drought conditions after 2–3 months of lag time. Our results confirmed that evapotranspiration is a major factor affecting hydrological drought; the new methods demonstrated here make it possible to evaluate drought through diagnostic and prognostic perspectives depending on the situation, thereby improving scientific drought evaluation capacity.
Chanyang Sur; Seo-Yeon Park; Jong-Suk Kim; Joo-Heon Lee. Prognostic and diagnostic assessment of hydrological drought using water and energy budget-based indices. Journal of Hydrology 2020, 591, 125549 .
AMA StyleChanyang Sur, Seo-Yeon Park, Jong-Suk Kim, Joo-Heon Lee. Prognostic and diagnostic assessment of hydrological drought using water and energy budget-based indices. Journal of Hydrology. 2020; 591 ():125549.
Chicago/Turabian StyleChanyang Sur; Seo-Yeon Park; Jong-Suk Kim; Joo-Heon Lee. 2020. "Prognostic and diagnostic assessment of hydrological drought using water and energy budget-based indices." Journal of Hydrology 591, no. : 125549.
Xini Zha; Lihua Xiong; Shenglian Guo; Jong-Suk Kim; Dedi Liu. AR-GARCH with Exogenous Variables as a Postprocessing Model for Improving Streamflow Forecasts. Journal of Hydrologic Engineering 2020, 25, 04020036 .
AMA StyleXini Zha, Lihua Xiong, Shenglian Guo, Jong-Suk Kim, Dedi Liu. AR-GARCH with Exogenous Variables as a Postprocessing Model for Improving Streamflow Forecasts. Journal of Hydrologic Engineering. 2020; 25 (8):04020036.
Chicago/Turabian StyleXini Zha; Lihua Xiong; Shenglian Guo; Jong-Suk Kim; Dedi Liu. 2020. "AR-GARCH with Exogenous Variables as a Postprocessing Model for Improving Streamflow Forecasts." Journal of Hydrologic Engineering 25, no. 8: 04020036.
Merging gauge observation with a single original satellite-based precipitation product (SPP) is a common approach to generate spatial precipitation estimates. For the generation of high-quality precipitation maps, however, this common method has two drawbacks: (1) the spatial resolutions of original SPPs are still too coarse; and (2) a single SPP can’t capture the spatial pattern of precipitation well. To overcome these drawbacks, a two-step scheme consisting of downscaling and fusion was proposed to merge gauge observation with multiple SPPs. In both downscaling and fusion steps, the geographically weighted ridge regression (GWRR) method, which is a combination of the geographically weighted regression (GWR) method and the ridge regression method, is proposed and implemented to generate improved spatial precipitation estimates by overcoming the collinearity problem of the pure GWR method. The proposed two-step merging scheme was applied to Xijiang Basin of China for deriving daily precipitation estimates from the data of both gauge observation and four near real-time SPPs (i.e., TMPA-3B42RT, CMORPH, PERSIANN and GSMaP_NRT) during the period of 2010–2017. The results showed that: (1) the collinearity problem caused by GWR was not serious in downscaling but serious enough to prevent GWR from being directly used in the fusion; and (2) the proposed two-step merging scheme significantly improved the spatial resolution and accuracy of precipitation estimates over the original SPPs. Comparisons also showed that, in the second step (fusion) of the merging scheme, the use of multiple SPPs provided more reliable spatial precipitation estimates than using a single SPP.
Shilei Chen; Lihua Xiong; Qiumei Ma; Jong-Suk Kim; Jie Chen; Chong-Yu Xu. Improving daily spatial precipitation estimates by merging gauge observation with multiple satellite-based precipitation products based on the geographically weighted ridge regression method. Journal of Hydrology 2020, 589, 125156 .
AMA StyleShilei Chen, Lihua Xiong, Qiumei Ma, Jong-Suk Kim, Jie Chen, Chong-Yu Xu. Improving daily spatial precipitation estimates by merging gauge observation with multiple satellite-based precipitation products based on the geographically weighted ridge regression method. Journal of Hydrology. 2020; 589 ():125156.
Chicago/Turabian StyleShilei Chen; Lihua Xiong; Qiumei Ma; Jong-Suk Kim; Jie Chen; Chong-Yu Xu. 2020. "Improving daily spatial precipitation estimates by merging gauge observation with multiple satellite-based precipitation products based on the geographically weighted ridge regression method." Journal of Hydrology 589, no. : 125156.
Time series forecasting of meteorological variables such as daily temperature has recently drawn considerable attention from researchers to address the limitations of traditional forecasting models. However, a middle-range (e.g., 5–20 days) forecasting is an extremely challenging task to get reliable forecasting results from a dynamical weather model. Nevertheless, it is challenging to develop and select an accurate time-series prediction model because it involves training various distinct models to find the best among them. In addition, selecting an optimum topology for the selected models is important too. The accurate forecasting of maximum temperature plays a vital role in human life as well as many sectors such as agriculture and industry. The increase in temperature will deteriorate the highland urban heat, especially in summer, and have a significant influence on people’s health. We applied meta-learning principles to optimize the deep learning network structure for hyperparameter optimization. In particular, the genetic algorithm (GA) for meta-learning was used to select the optimum architecture for the network used. The dataset was used to train and test three different models, namely the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Our results demonstrate that the hybrid model of an LSTM network and GA outperforms other models for the long lead time forecasting. Specifically, LSTM forecasts have superiority over RNN and ANN for 15-day-ahead in summer with the root mean square error (RMSE) value of 2.719 (°C).
Trang Thi Kieu Tran; Taesam Lee; Ju-Young Shin; Jong-Suk Kim; Mohamad Kamruzzaman. Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization. Atmosphere 2020, 11, 487 .
AMA StyleTrang Thi Kieu Tran, Taesam Lee, Ju-Young Shin, Jong-Suk Kim, Mohamad Kamruzzaman. Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization. Atmosphere. 2020; 11 (5):487.
Chicago/Turabian StyleTrang Thi Kieu Tran; Taesam Lee; Ju-Young Shin; Jong-Suk Kim; Mohamad Kamruzzaman. 2020. "Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization." Atmosphere 11, no. 5: 487.
This study analyzed the sensitivity of rainfall patterns in South China and the Indochina Peninsula (ICP) using statistical simulations of observational data. Quantitative changes in rainfall patterns over the ICP were examined for both wet and dry seasons to identify hotspots sensitive to ocean warming in the Indo-Pacific sector. The rainfall variability was amplified by combined and/or independent effects of the El Niño–Southern Oscillation and the Indian Ocean Dipole (IOD). During the years of El Niño and a positive phase of the IOD, rainfall is less than usual in Thailand, Cambodia, southern Laos, and Vietnam. Conversely, during the years of La Niña and a negative phase of the IOD, rainfall throughout the ICP is above normal, except in parts of central Laos, northern Vietnam, and South China. This study also simulated the change of ICP rainfall in the wet and dry seasons with intentional IOD changes and verified IOD-sensitive hotspots through quantitative analysis. The results of this study provide a clear understanding both of the sensitivity of regional precipitation to the IOD and of the potential future impact of statistical changes regarding the IOD in terms of understanding regional impacts associated with precipitation in changing climates.
Jong-Suk Kim; Phetlamphanh Xaiyaseng; Lihua Xiong; Sun-Kwon Yoon; Taesam Lee. Remote Sensing-Based Rainfall Variability for Warming and Cooling in Indo-Pacific Ocean with Intentional Statistical Simulations. Remote Sensing 2020, 12, 1458 .
AMA StyleJong-Suk Kim, Phetlamphanh Xaiyaseng, Lihua Xiong, Sun-Kwon Yoon, Taesam Lee. Remote Sensing-Based Rainfall Variability for Warming and Cooling in Indo-Pacific Ocean with Intentional Statistical Simulations. Remote Sensing. 2020; 12 (9):1458.
Chicago/Turabian StyleJong-Suk Kim; Phetlamphanh Xaiyaseng; Lihua Xiong; Sun-Kwon Yoon; Taesam Lee. 2020. "Remote Sensing-Based Rainfall Variability for Warming and Cooling in Indo-Pacific Ocean with Intentional Statistical Simulations." Remote Sensing 12, no. 9: 1458.