Assessment of Climate Change Impacts on the Future Hydrologic Cycle of the Han River Basin in South Korea Using a Grid-Based Distributed Model

2016 ◽  
Vol 65 ◽  
pp. 11-21 ◽  
Author(s):  
So Ra Ahn ◽  
Seong Joon Kim
Keyword(s):  
Climate Change ◽  
South Korea ◽  
River Basin ◽  
Climate Change Impacts ◽  
Hydrologic Cycle ◽  
Distributed Model ◽  
Han River ◽  
The Future ◽  
Han River Basin ◽  
Grid Based

A framework for quantifying the impacts of future climate and land use/cover changes on runoff in the Han River basin, China

2020 ◽  
Author(s):  
Jing Tian ◽  
Shenglian Guo ◽  
Chong-Yu Xu
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Water Resources ◽  
River Basin ◽  
Future Climate ◽  
Annual Rainfall ◽  
Base Period ◽  
Han River ◽  
The Future ◽  
Han River Basin

<p>As a link between the atmosphere and the earth’s surface, the hydrological cycle is impacted by both climate change and land use/cover change (LUCC). For most basins around the world, the co-variation of climate change and LUCC will continue in the future, which highlights the significance to explore the temporal-spatial distribution and variation mechanism of runoff and to improve our ability in water resources planning and management. Therefore, the purpose of this study is to propose a framework to examine the response of runoff to climate change and LUCC under different future scenarios. Firstly, the future climate scenarios under BCC-CSM1.1 and BNU-ESM are both downscaled and bias-corrected by the Daily bias correction (DBC) method, meanwhile, the future LUCC scenarios are predicted by the Cellular Automaton-Markov (CA-Markov) model according to the integrated basin plans of future land use. Then, based on the baseline scenario S0 (meteorological data from 1966 to 2005 and current situation LUCC2010), the following three scenarios are set with different combinations of future climate land-use situations, i.e., S1: only climate change scenario; S2: only the LUCC scenario; S3: climate and LUCC co-variation scenario. Lastly, the Soil and Water Assessment Tool (SWAT) model is used to simulate the hydrological process and quantify the impacts of climate change and LUCC on the runoff yield. The proposed framework is applied to the Han River basin in China. Results show that: (1) compared with the base period (1966-2005), the annual rainfall, daily maximum, and minimum air temperature during 2021-2060 will have an increase of 4.0%, 1.8℃, 1.6℃ in RCP4.5 while 3.7%, 2.5℃, 2.3℃ in RCP8.5, respectively; (2) from 2010 to 2050, the forest land and construction land in the Han River basin will have an increase of 2.8% and 1.2%, respectively, while that of farmland and grassland will have a decrease of 1.5% and 2.5%, respectively; (3) comparing with the single climate change or LUCC scenario, the co-variation scenario possesses the largest uncertainty in runoff projection. Under the two concentration paths, there is a consistent upward change in future runoff (2021-2060) of the studied basin compared with that in the base period, furthermore, the increase rate in RCP4.5 (+5.10%) is higher than that in RCP8.5 (+2.67%). The results of this study provide a useful reference and help for water resources and land use management in the Han River basin.</p>

scholarly journals Optimal Rainfall Estimation by Considering Elevation in the Han River Basin, South Korea

2013 ◽  
Vol 52 (4) ◽  
pp. 802-818 ◽  
Author(s):  
Seong-Sim Yoon ◽  
Deg-Hyo Bae
Keyword(s):  
South Korea ◽  
River Basin ◽  
Flood Control ◽  
Rainfall Data ◽  
Spatiotemporal Variability ◽  
Radar Rainfall ◽  
Mountainous Regions ◽  
Han River ◽  
Orographic Rainfall ◽  
Han River Basin

AbstractMore than 70% of South Korea has mountainous terrain, which leads to significant spatiotemporal variability of rainfall. The country is exposed to the risk of flash floods owing to orographic rainfall. Rainfall observations are important in mountainous regions because flood control measures depend strongly on rainfall data. In particular, radar rainfall data are useful in these regions because of the limitations of rain gauges. However, radar rainfall data include errors despite the development of improved estimation techniques for their calculation. Further, the radar does not provide accurate data during heavy rainfall in mountainous areas. This study presents a radar rainfall adjustment method that considers the elevation in mountainous regions. Gauge rainfall and radar rainfall field data are modified by using standardized ordinary cokriging considering the elevation, and the conditional merging technique is used for combining the two types of data. For evaluating the proposed technique, the Han River basin was selected; a high correlation between rainfall and elevation can be seen in this basin. Further, the proposed technique was compared with the mean field bias and original conditional merging techniques. Comparison with kriged rainfall showed that the proposed method has a lesser tendency to oversmooth the rainfall distribution when compared with the other methods, and the optimal mean areal rainfall is very similar to the value obtained using gauges. It reveals that the proposed method can be applied to an area with significantly varying elevation, such as the Han River basin, to obtain radar rainfall data of high accuracy.

scholarly journals Monthly Precipitation Forecasting in the Han River Basin, South Korea, Using Large-Scale Teleconnections and Multiple Regression Models

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1590
Author(s):  
Chul-Gyum Kim ◽  
Jeongwoo Lee ◽  
Jeong Eun Lee ◽  
Nam Won Kim ◽  
Hyeonjun Kim
Keyword(s):  
South Korea ◽  
River Basin ◽  
Regression Models ◽  
Climate Indices ◽  
Monthly Precipitation ◽  
Precipitation Forecasting ◽  
Han River ◽  
The Past ◽  
Multiple Regression Models ◽  
Han River Basin

In this study, long-term precipitation forecasting models capable of reflecting constantly changing climate characteristics and providing forecasts for up to 12 months in advance were developed using lagged correlations with global and local climate indices. These models were applied to predict monthly precipitation in the Han River basin, South Korea. Based on the lead month of forecast, 10 climate indices with high correlations were selected and combined to construct four-variable multiple regression models for monthly precipitation forecasting. The forecast results for the analytical period (2010–2019) showed that predictability was low for some summer seasons but satisfactory for other seasons and long periods. In the goodness-of-fit test results, the Nash–Sutcliffe efficiency (0.48–0.57) and the ratio of the root mean square error to the standard deviation of the observation (0.66–0.72) were evaluated to be satisfactory while the percent bias (9.4–15.5%) was evaluated to be between very good and good. Due to the nature of the statistical models, however, the predictability is highly likely to be reduced if climate phenomena that are different from the statistical characteristics of the past appear in the forecast targets or predictors. The forecast results were also presented as tercile probability information (below normal, normal, above normal) through a comparison with the observation data of the past 30 years. The results are expected to be utilized as useful forecast information in practice if the predictability for some periods is improved.

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