Interest in future rainfall extremes is increasing, but the lack of consistency in the future rainfall extremes outputs simulated in climate models increases the difficulty of establishing climate change adaptation measures for floods. In this study, a methodology is proposed to investigate future rainfall extremes using future surface air temperature (SAT) or dew point temperature (DPT). The non-stationarity of rainfall extremes is reflected through non-stationary frequency analysis using SAT or DPT as a co-variate. Among the parameters of generalized extreme value (GEV) distribution, the scale parameter is applied as a function of co-variate. Future daily rainfall extremes are projected from 16 future SAT and DPT ensembles obtained from two global climate models, four regional climate models, and two representative concentration pathway climate change scenarios. Compared with using only future rainfall data, it turns out that the proposed method using future temperature data can reduce the uncertainty of future rainfall extremes outputs if the value of the reference co-variate is properly set. In addition, the confidence interval of the rate of change of future rainfall extremes is quantified using the posterior distribution of the parameters of the GEV distribution sampled using Bayesian inference.

The effect of mountainous regions with high elevation on hourly timescale rainfall presents great difficulties in flood forecasting and warning in mountainous areas. In this study, the hourly rainfall–elevation relationship of the regional scale is investigated using the hourly rainfall fields of three storm events simulated by Weather Research and Forecasting (WRF) model. From this relationship, a parameterized model that can estimate the spatial rainfall field in real time using the hourly rainfall observation data of the ground observation network is proposed. The parameters of the proposed model are estimated using eight representative pixel pairs in valleys and mountains. The proposed model was applied to the Namgang Dam watershed, a representative mountainous region in the Korea, and it was found that as elevation increased in eight selected pixel pairs, rainfall intensity also increased. The increase in rainfall due to the mountain effect was clearly observed with more rainfall in high mountainous areas, and the rainfall distribution was more realistically represented using an algorithm that tracked elevation along the terrain. The proposed model was validated using leave-one-out cross-validation with seven rainfall observation sites in mountainous areas, and it demonstrated clear advantages in estimating a spatial rainfall field that reflects the mountain effect. These results are expected to be helpful for flood forecasting and warning, which need to be calculated quickly, in mountainous areas. Considering the importance of orographic effects on rainfall spatial distribution in mountainous areas, more storm events and physical analysis of environmental factors (wind direction, thermal cycles, and mountain slope angle) should be continuously studied.

As global surface air temperature (SAT) rises, the highest daily SAT is also rising. The rise in the highest daily SAT leads to an increase in heat waves, which is turn increases the physical and ecological damage being caused by the heat waves. In Korea, the highest daily SAT is used to determine whether a heat wave has occurred or not. In this study, H-index using the highest daily SAT was calculated to quantify the heat waves at 60 Automated Surface Observing System (ASOS) sites operated by the Korea Meteorological Agency (KMA). The changes in H-index were also investigated using projected future SAT data gathered from KMA's national standard climate change scenarios. As a result, it was possible to investigate the temporal and spatial changes in heat wave behavior by quantifying the heat wave using the H-index. The H-index is expected to increase faster in future than its current upward trend.

Understanding the temporal and spatial variability of water quality is important in order to establish effective customized management strategies for polluted aquatic ecosystems. Although various water quality management methods have been proposed based on insights into river water pollution factors through physically based modeling or statistical techniques, it is difficult to find studies that analyze the relative importance of these water pollution factors in a relatively large watershed using a step-by-step methodology. In this study, the spatial variability of river water quality is analyzed using time-averaged river water quality data collected from 40 sites in the Nakdong river basin, located on the Korean Peninsula. We focused on biological oxygen demand, total suspended solids, total nitrogen, and total organic carbon. A two-step exhaustive search approach was used to find a linear model that best links the various factors of the watershed with the average river water quality. The optimal model was selected by applying cross-correlation analysis and Bayesian inference. Through the process of finding the optimal statistical model, the major factors that have the most influence on river water quality were identified by analyzing the factors affecting river water quality, their levels of influence, and their levels of uncertainty. Identifying a set of processes provides insight into the key factors influencing spatial variability in average stream water quality conditions. We were able to identify the relative influences and uncertainties of the hydrological, climatic, topographical, and geological characteristics of the watershed on the spatial variability of river water quality. The proposed spatial variability model of average river water quality can be used to predict river water quality responses to future climate change, land use pattern change, and soil management strategy change.

One of challenges to hydrologists is to estimate runoff from ungauged watershed. Hydrologic estimation through modelling is a reasonable, economical and useful approach to quantity and quality management of watershed. The model framework has been comprehensive and complex to reproduce natural phenomena more realistically with the development of computer hardware. However, driving a complex model requires a lot of effort and time, and the use of many parameters reduces the accessibility of end users and the applicability to the ungauged watershed. In this study, we developed a distributed hydrologic model based on soil moisture simulation using simple composition and fewer parameters. Instead of minimizing the number of parameters, GIS data were used to reflect the watershed characteristics into the model. The proposed model was applied to the four dam watersheds in Korea to assess its performance. As a result, it is confirmed that reasonable hydrologic components simulation is possible through the simulation of soil moisture, even though it was a simple model with only three input parameters. If spatial data such as satellite data is additionally applied, the performance of the model is expected to improve further.Acknowledgment: This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Public Technology Program based on Environmental Policy Project, funded by Korea Ministry of Environment(MOE)(2016000200002).Keywords: Distributed hydrological model; Hydrologic components simulation; Soil moisture; Simple hydrological model.

Wetland ecosystems have complex interactions of physical and biogeochemical processes, but the first step toward restoring the health of wetland ecosystems is an accurate understanding of the water cycle in wetland ecosystems. In addition, a quantitative understanding of the wetland water cycle is essential to utilize wetlands in regional water balance and ecosystem conservation. However, observational data essential for understanding the wetland water cycle are difficult to obtain through field measurements or are difficult to observe due to cost issues. Therefore, this study proposes a procedure for estimating wetland inflow using Sentinel-2 satellite data. To this end, a classification-based artificial intelligence model using data from major multi-purpose dams located on the Nakdong River in the southeastern part of the Korean Peninsula is designed. Input data for artificial intelligence learning is created by the following procedure. 1) Derivation of the water level-water surface area relationship curve using the water level-water volume relationship of the multi-purpose dam. 2) Using the water level-water surface area relationship curve and DEM, derive an identifier that distinguishes water and land areas. 3) Design a random forest model that compares Sentinel-2 satellite information and water-land identifiers. 4) Derivation of identifiers that can identify water and land in unmeasured wetland areas from water-land information of satellite information. By combining the water surface area of the wetland estimated through this process and the DEM of the wetland area, the wetland water level-water surface area-water volume relationship curve is calculated, and finally the wetland inflow is simulated. The simulated wetland inflow can be used to estimate the parameters of various hydrologic models, and it is expected that the understanding of the wetland water cycle can be improved by using the verified hydrological model. Acknowledgement This work was supported by Korea Environmental Industry&Technology Institute (KEITI) through Wetland Ecosystem Value Evaluation and Carbon Absorption Value Promotion Technology Development Project, funded by Korea Ministry of Environment (MOE). (2022003640001)