Il-Won Jung

Potential Impacts of Climate Change on Flood-Induced Travel Disruptions: A Case Study of Portland, Oregon, USA

This study investigated potential impacts of climate change on travel disruption resulting from road closures in two urban watersheds in the Portland, Oregon, metropolitan area. We used ensemble climate change scenarios, a hydrologic model, a stream channel survey, a hydraulic model, and a travel forecast model to develop an integrated impact assessment method. High-resolution climate change scenarios are based on the combinations of two emission scenarios and eight general circulation models. The Precipitation-Runoff Modeling System was calibrated and validated for the historical period of 1988 and 2006 and simulated for determining the probability of floods for 2020 through 2049. We surveyed stream cross-sections at five road crossings for stream channel geometry and determined flood water surface elevations using the Hydrologic Engineering Centers River Analysis System (HEC-RAS) model. Four of the surveyed bridges and roadways were lower in elevation than the current 100-year flood water surface elevation, leading to relatively frequent nuisance flooding. These roadway flooding events will become more frequent under some climate change scenarios in the future, but climate change impacts will depend on local geomorphic conditions. Whereas vehicle miles traveled was not significantly affected by road closure, vehicle hours delay demonstrated a greater impact from road closures, increasing by 10 percent in the Fanno Creek area. Our research demonstrated the usefulness of the integration of top-down and bottom-up approaches in climate change impact assessment and the need for spatially explicit modeling and participatory planning in flood management and transportation planning under increasing climate uncertainty.

Future Korean Water Resources Projection Considering Uncertainty of GCMs and Hydrological Models

The objective of this study is to examine the climate change impact assessment on Korean water resources considering the uncertainties of Global Climate Models (GCMs) and hydrological models. The 3 different emission scenarios (A2, A1B, B1) and 13 GCMs` results are used to consider the uncertainties of the emission scenario and GCM, while PRMS, SWAT, and SLURP models are employed to consider the effects of hydrological model structures and potential evapotranspiration (PET) computation methods. The 312 ensemble results are provided to 109 mid-size sub-basins over South Korean and Gaussian kernel density functions obtained from their ensemble results are suggested with the ensemble mean and their variabilities of the results. It shows that the summer and winter runoffs are expected to be increased and spring runoff to be decreased for the future 3 periods relative to past 30-year reference period. It also provides that annual average runoff increased over all sub-basins, but the increases in the northern basins including Han River basin are greater than those in the southern basins. Due to the reason that the increase in annual average runoff is mainly caused by the increase in summer runoff and consequently the seasonal runoff variations according to climate change would be severe, the climate change impact on Korean water resources could intensify the difficulties to water resources conservation and management. On the other hand, as regards to the uncertainties, the highest and lowest ones are in winter and summer seasons, respectively.

Generation of High Resolution Scenarios for Climate Change Impacts on Water Resources (I): Climate Scenarios on Each Sub-basins

To evaluate the climate change impacts on water resources, this study generates and analyzes the climate change scenarios for 139 sub-basins in Korea using high resolution () SHES A2 scenario and LARS-WG. The high resolution NCAR/PSU MM5 scenario is downscaled from 350km horizontal resolution ECHO-G data. The A2 scenario relatively well reproduced Korean spatial precipitation characteristics, but it underestimated the precipitation over the Han River and the Gum River basins. The LARS-WG was selected and evaluated to overcome the limitation of climate model and to create a highly reliable climate scenario. The results show that the monthly mean minimum and maximum temperature and monthly mean precipitation are within from the observed mean, and from the standard deviation that represents the generated data are highly reliable. Moreover, the comparison results between observed data and generated data from LARS-WG show that the latter can reflect the regional climate characteristic very well that can not be simulated from the former.

Korean Flood Vulnerability Assessment on Climate Change

The purposes of this study are to suggest flood vulnerability assessment method on climate change with evaluation of this method over the 5 river basins and to present the uncertainty range of assessment using multi-model ensemble scenarios. In this study, the data related to past historical flood events were collected and flood vulnerability index was calculated. The vulnerability assessment were also performed under current climate system. For future climate change scenario, the 39 climate scenarios are obtained from 3 different emission scenarios and 13 GCMs provided by IPCC DDC and 312 hydrology scenarios from 3 hydrological models and 2~3 potential evapotranspiration computation methods for the climate scenarios. Finally, the spatial and temporal changes of flood vulnerability and the range of uncertainty were performed for future S1 (2010~2039), S2 (2040~2069), S3 (2070~2099) period compared to reference S0 (1971~2000) period. The results of this study shows that vulnerable region`s were Han and Sumjin, Youngsan river basins under current climate system. Considering the climate scenarios, variability in Nakdong, Gum and Han river basins are large, but Sumjin river basin had little variability due to low basic-stream ability to adaptation.

Parameter Regionalization of Semi-Distributed Runoff Model Using Multivariate Statistical Analysis

The objective of this study is to suggest parameter regionalization scheme which is integrated two multivariate statistical methods: principal components analysis(PCA) and hierarchical cluster analysis(HCA). This technique is to apply semi-distributed rainfall-runoff model on ungauged catchments. 7 catchment characteristics (area, mean altitude, mean slope, ratio of forest, water content at saturation, field capacity and wilting point) are estimated for 109 mid-sized sub-basins. The first two components from PCA results account for 82.11% of the total variance in the dataset. Component 1 is related to the location of the catchments relevant to the altitude and Component 2 is connected with the area of these. 103 ungauged catchments are clustered using HCA as the following 6 groups: Goesan 23, Andong 6, Imha 5, Hapcheon 21, Yongdam 4, Seomjin 44. SWAT model is used to simulate runoff and the parameters of the model on the 6 gauged basins are estimated. The model parameters were regionalized for Soyang, Chungju and Daecheong dam basins which are assumed as ungauged ones. The model efficiency coefficients of the simulated inflows for these three dams were at least 0.8. These results also mean that goodness of fit is high to the observed inflows. This research will contribute to estimate and analyze hydrologic components on the ungauged catchments.

Optimization of Tank Model Parameters Using Multi-Objective Genetic Algorithm (I): Methodology and Model Formulation

The objective of this study is to evaluate the applicability of multi-objective genetic algorithm(MOGA) in order to calibrate the parameters of conceptual rainfall-runoff model, Tank model. NSGA-II, one of the most imitating MOGA implementations, is combined with Tank model and four multi-objective functions such as to minimize volume error, root mean square error (RMSE), high flow RMSE, and low flow RMSE are used. When NSGA-II is employed with more than three multi-objective functions, a number of Pareto-optimal solutions usually becomes too large. Therefore, selecting several preferred Pareto-optimal solutions is essential for stakeholder, and preference-ordering approach is used in this study for the sake of getting the best preferred Pareto-optimal solutions. Sensitivity analysis is performed to examine the effect of initial genetic parameters, which are generation number and Population size, to the performance of NSGA-II for searching the proper paramters for Tank model, and the result suggests that the generation number is 900 and the population size is 1000 for this study.

A Study on PRMS Applicability for Korean River Basin

The objective of this study is to evaluate the applicability and simulation capability of PRMS, developed by U.S. Geological Survey, over the seven multi-purpose dam watersheds in Korea. The basic concepts of model components and their parameters are investigated for the evaluation of model applicability and the possibility of model parameter estimation is suggested based on the data availibility. For model parameter estimation, some parameters are directly estimated from measurable basin characteristics, but the others are estimated by Rosenbrock`s automatic optimization scheme. The results show that the simulated flows from the model were very close to the observed ones. Although the default values for snowmelt model parameter are used, the results from snowmelt simulation is also acceptable. The model shows that the simulation capability is not sensitive to the basin size, however, according to increasing basin area, simulation characteristics are close to those for lumped model rather than semi-distributed model.

Climate elasticity of Korean streamflows.

We investigated the sensitivity of Korean streamflows to climate variation. Historical dam inflows and climate data for eight multi-purpose dam sites were collected and examined to determine key factors affecting streamflow change. The results show that annual streamflow primarily responds to change in precipitation rather than temperature. However, the combination of less precipitation and high temperature induces a more serious decrease in streamflow than does similar precipitation and with low temperature. This result indicates that Korean water resources could be more vulnerable to drought due to increasing temperature caused by global warming. To estimate spatial differences in climate sensitivity, we also calculated climate elasticity for 109 mid-size watersheds using streamflow simulated by the Precipitation Runoff Modeling System (PRMS). Climate elasticity ranges over 1.5~1.9, indicating that a +20% increase in annual precipitation leads to a +30~+38% increase in annual streamflow.

Optimization of Tank Model Parameters Using Multi-Objective Genetic Algorithm (II): Application of Preference Ordering

Preference ordering approach is applied to optimize the parameters of Tank model using multi-objective genetic algorithm (MOGA). As more than three multi-objective functions are used in MOGA, too many non-dominated optimal solutions would be obtained thus the stakeholder hardly find the best optimal solution. In order to overcome this shortcomings of MOGA, preference ordering method is employed. The number of multi-objective functions in this study is 4 and a single Pareto-optimal solution, which is 2nd order efficiency and 3 degrees preference ordering, is chosen as the most preferred optimal solution. The comparison results among those from Powell method and SGA (simple genetic algorithm), which are single-objective function optimization, and NSGA-II, multi-objective optimization, show that the result from NSGA-II could be reasonalby accepted since the performance of NSGA-II is not deteriorated even though it is applied to the verification period which is totally different from the calibration period for parameter estimation.

Spatially distinct response of rice yield to autonomous adaptation under the CMIP5 multi-model projections

Rice (Oryza sativa L.) is a very important staple crop, as it feeds more than half of the world’s population. Numerous studies have focused on the negative impacts of climate change on rice production. However, there is little debate on which region of the world is more vulnerable to climate change and how adaptation to this change can mitigate the negative impacts on rice production. We investigated the impacts of climate change on rice yield, based on simulations combining a global crop model, M-GAZE, and Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model projections. Our focus was the impact of mitigating emission forcings (representative concentration pathway RCP 4.5 vs. RCP 8.5) and autonomous adaptation (i.e., changing crop variety and planting date) on rice yield. In general, our results showed that climate change due to anthropogenic warming leads to a significant reduction in rice yield. However, autonomous adaptation provides the potential to reduce the negative impact of global warming on rice yields in a spatially distinct manner. The adaptation was less beneficial for countries located at a low latitude (e.g., Cambodia, Thailand, Brazil) compared to mid-latitude countries (e.g., USA, China, Pakistan), as regional climates at the lower latitudes are already near the upper temperature thresholds for acceptable rice growth. These findings suggest that the socioeconomic effects from rice production in lowlatitude countries can be highly vulnerable to anthropogenic global warming. Therefore, these countries need to be accountable to develop transformative adaptation strategies, such as adopting (or developing) heat-tolerant varieties, and/or improve irrigation systems and fertilizer use efficiency.