• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• Journal of Ecology and Environment
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• v.45 no.4
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• pp.313-327
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• 2021

Background: Citizen science is becoming a mainstream approach of baseline data collection to monitor biodiversity and climate change. Dragonflies (Odonata) have been ranked as the highest priority group in biodiversity monitoring for global warming. Ischnura senegalensis Rambur has been designated a biological indicator of climate change and is being monitored by the citizen science project "Korean Biodiversity Observation Network." This study has been performed to understand changes in the distribution range of I. senegalensis in response to climate change using citizen science data in South Korea. Results: We constructed a dataset of 397 distribution records for I. senegalensis, ranging from 1980 to 2020. The number of records sharply increased over time and space, and in particular, citizen science monitoring data accounted for the greatest proportion (58.7%) and covered the widest geographical range. This species was only distributed in the southern provinces until 2010 but was recorded in the higher latitudes such as Gangwon-do, Incheon, Seoul, and Gyeonggi-do (max. Paju-si, 37.70° latitude) by 2020. A species distribution model showed that the annual mean temperature (Bio1; 63.2%) and the maximum temperature of the warmest month (Bio5; 16.7%) were the most critical factors influencing its distribution. Future climate change scenarios have predicted an increase in suitable habitats for this species. Conclusions: This study is the first to show the northward expansion in the distribution range of I. senegalensis in response to climate warming in South Korea over the past 40 years. In particular, citizen science was crucial in supplying critical baseline data to detect the distribution change toward higher latitudes. Our results provide new insights on the value of citizen science as a tool for detecting the impact of climate change on ecosystems in South Korea.

• Journal of Climate Change Research
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• v.7 no.3
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• pp.325-334
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• 2016

We projected the distribution of Hedera rhombea, an evergreen broad-leaved climbing plant, under current climate conditions and predicted its future distributions under global warming. Inaddition, weexplained model uncertainty by employing 9 single Species Distribution model (SDM)s to model the distribution of Hedera rhombea. 9 single SDMs were constructed with 736 presence/absence data and 3 temperature and 3 precipitation data. Uncertainty of each SDM was assessed with TSS (Ture Skill Statistics) and AUC (the Area under the curve) value of ROC (receiver operating characteristic) analyses. To reduce model uncertainty, we combined 9 single SDMs weighted by TSS and resulted in an ensemble forecast, a TSS weighted ensemble. We predicted future distributions of Hedera rhombea under future climate conditions for the period of 2050 (2040~2060), which were estimated with HadGEM2-AO. RF (Random Forest), GBM (Generalized Boosted Model) and TSS weighted ensemble model showed higher prediction accuracies (AUC > 0.95, TSS > 0.80) than other SDMs. Based on the projections of TSS weighted ensemble, potential habitats under current climate conditions showed a discrepancy with actual habitats, especially in the northern distribution limit. The observed northern boundary of Hedera rhombea is Ulsan in the eastern Korean Peninsula, but the projected limit was eastern coast of Gangwon province. Geomorphological conditions and the dispersal limitations mediated by birds, the lack of bird habitats at eastern coast of Gangwon Province, account for such discrepancy. In general, potential habitats of Hedera rhombea expanded under future climate conditions, but the extent of expansions depend on RCP scenarios. Potential Habitat of Hedera rhombea expanded into Jeolla-inland area under RCP 4.5, and into Chungnam and Wonsan under RCP 8.5. Our results would be fundamental information for understanding the potential effects of climate change on the distribution of Hedera rhombea.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1388-1392
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• 2008

Water resources management depends on dealing inherent uncertainties stemming from climatic and hydrological inputs and models. Dealing with these uncertainties remains a challenge. Streamflow forecasts basically contain uncertainties arising from model structure and initial conditions. Recent enhancements in climate forecasting skill and hydrological modeling provide an breakthrough for delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The approach here proposes integration and coupling of global climate models (GCM), multiple regional climate models, and numerous hydrological models to improve streamflow forecasting and characterize system uncertainty through generation of ensemble forecasts.

• Journal of Climate Change Research
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• v.6 no.4
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• pp.389-397
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• 2015

The changes in the teleconnection associated with El Nin?o-Southern Oscillation (ENSO) over the East Asia and North Pacific under greenhouse warming are analyzed herein by comparing the Historical run (1970/1971~1999/2000) and the Representative Concentration Pathway (RCP) 4.5 run with 31 climate models, participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5). It is found that CMIP5 models have diverse systematic errors in simulating the ENSO teleconnection pattern from model to model. Therefore, we select 21 models based on the models' performance in simulating teleconnection pattern in the present climate. It is shown that CMIP5 models tend to project an overall weaker teleconnection pattern associated with ENSO over East Asia in the future climate than that in the present climate. It can be also noted that the cyclonic flow over the North Pacific is weakened and shifted eastward. However, uncertainties for the ENSO teleconnection changes still exist, suggesting that much consistent agreements on this future teleconnections associated with ENSO should be taken in a further study.

• Journal of the Korean earth science society
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• v.32 no.7
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• pp.770-783
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• 2011

In this study, we performed a downscaling of an ECHAM5 simulated dataset for the current and future climate produced under the Special Report on Emission Scenarios A1B (SRES A1B) by utilizing the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM). The current climate simulation was performed for the period 1980-2000 and the future climate run for the period 2040-2070 for the COordinated Regional climate Downscaling EXperiment (CORDEX)'s East Asia domain. The RSM is properly able to reproduce the climatological fields from the evaluation of the current climate simulation. Future climatological precipitation during the summer season is increased over the tropical Oceans, the maritime-continent, and Japan. In winter, on the other hand, precipitation is increased over the tropical Indian Ocean, the maritime-continents and the Western North Pacific, and decreased over the eastern tropical Indian Ocean. For the East Asia region few significant changes are detected in the precipitation climatological field. However, summer rainfall shows increasing trend after 2050 over the region. The future climate ground temperature shows a clear increasing trend in comparison with the current climate. In response to global warming, atmospheric warming is clearly detected, which strengthens the upper level trough.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.263-274
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• 2013

In this study we evaluated the hydrological applicability of multi-satellite precipitation estimates. Three high-resolution global multi-satellite precipitation products, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), the Global Satellite Mapping of Precipitation (GSMaP), and the Climate Precipitation Center (CPC) Morphing technique (CMORPH), were applied to the Coupled Routing and Excess Storage (CREST) model for the evaluation of their hydrological utility. The CREST model was calibrated from 2002 to 2005 and validated from 2006 to 2009 in the Chungju Dam watershed, including two years of warm-up periods (2002-2003 and 2006-2007). Areal-averaged precipitation time series of the multi-satellite data were compared with those of the ground records. The results indicate that the multi-satellite precipitation can reflect the seasonal variation of precipitation in the Chungju Dam watershed. However, TMPA overestimates the amount of annual and monthly precipitation while GSMaP and CMORPH underestimate the precipitation during the period from 2002 to 2009. These biases of multi-satellite precipitation products induce poor performances in hydrological simulation, although TMPA is better than both of GSMaP and CMORPH. Our results indicate that advanced rainfall algorithms may be required to improve its hydrological applicability in South Korea.

• Journal of Korean Society on Water Environment
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• v.27 no.3
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• pp.273-285
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• 2011

This study describes the modeling of climate change impact on runoff across southeast Korea using a conceptual rainfall-runoff model TANK and assesses the results using the concept of environmental flows developed by International Water Management Institute. The future climate time series is obtained by scaling the historical series, informed by 4 global climate models and 3 greenhouse gas emission scenarios, to reflect a $4.0^{\circ}C$ increase at most in average surface air temperature and 31.7% increase at most in annual precipitation, using the spatio-temporal changing factor method that considers changes in the future mean seasonal rainfall and potential evapotranspiration as well as in the daily rainfall distribution. Although the simulation results from different global circulation models and greenhouse emission scenarios indicate different responses in flows to the climate change, the majority of the modeling results show that there will be more runoff in southeast Korea in the future. However, there is substantial uncertainty, with the results ranging from a 5.82% decrease to a 48.15% increase in the mean annual runoff averaged across the study area according to the corresponding climate change scenarios. We then assess the hydrologic perturbations based on the comparison between present and future flow duration curves suggested by IMWI. As a result, the effect of hydrologic perturbation on aquatic ecosystems may be significant at several locations of the Nakdong river main stream in dry season.

• 한국전산유체공학회:학술대회논문집
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• 2000.05a
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• pp.14-19
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• 2000

A coupled general circulation model (GCM) of the global atmosphere, oceans and lands is used for finding the future climate at times of doubled carbon dioxide concentration (DCDC) of the atmosphere. Two runs of the model were made in order to find the future change. Global changes at times of DCDC may be characterized by a global warmig of $1.4^{\circ}C$, a $3\%$ global precipitation increase, and an increase in the surface available water (SAW) over the global land among others. The estimated increase in SAW over the land implies that river discharge is likely to increase with increased chance of severe weather as a result of the future global warming.

• Proceedings of the Korean Society of Crop Science Conference
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• 2000.11a
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• pp.12-27
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• 2000

During the recent decades, he problem of climate variability and change has been in the forefront of scientific problems. The objective of this study was to assess the impact of climate variability on crop growth and yield. The growth duration was the main impact of climate variability on crop yield. Phyllochronterval was shortened in the global worming situations. A simple model to describe developmental traits was provided from heading data of directly seeded rice cultivars and temperature data. Daily mean development rate could be explained by the average temperature during the growth stage. Simple regression equation between daily mean development rate(x) and the average temperature(y) during the growth period as y = ax + b. It can be simply modified as x = 1/a $\ast$ (y-b). The parameters of the model could depict the thermo sensitivity of the cultivars. On the base of this model, the three doubled CO2 GCM scenarios were assessed. The average of these would suggest a decline in rice production of about 11% if we maintained the current cultivars. Future cultivar's developmental traits could be suggested by the two model parameters.