• Journal of Ecology and Environment
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• v.42 no.4
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• pp.265-271
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• 2018

Background: Temperature-driven variation in pollinator assemblage and activity are important information, especially at high altitudes, where rising temperature trends exceed global levels. Temporal patterns of pollinators in a flowering season can be used as a proxy to predict the changes of high-altitude plants' mutualistic relationships. We observed a spring temperature change in one population of a high-altitude endemic species, Megaleranthis saniculifolia on Mt. Sobaeksan, and related it to pollinator assemblage and activity changes. Methods: This study was conducted at two sites, each facing different slopes (NE and NW), for two times in the spring of 2013 (early-flowering, April 27-28, vs. mid-flowering, May 7-8, 2013). We confirmed that the two sites were comparable in snowmelt regime, composition of flowering plants, and flower density, which could affect pollinator assemblage and activity. Pollinator assemblage and activity were investigated at three quadrats ($1m^2$ with 5-m distance) for each site, covering a total of 840 min observation for each site. We analyzed correlations between the temperature and visitation frequency. Results: Twelve pollinator species belonging to four orders were observed for M. saniculifolia at both sites during early- and mid-flowering times. Diptera (five species) and hymenopteran species (four species) were the most abundant pollinators. Pollinator richness increased at both sites toward the mid-flowering time [early vs. mid = 7 (NE) and 3 (NW) vs. 9 (NE) and 5 (NW)]. Compared to the early-flowering time, visitation frequency showed a fourfold increase in the mid-flowering time. With the progression of spring, major pollinators changed from flies to bees. Upon using data pooled over both sites and flowering times, hourly visitation frequency was strongly positively correlated with hourly mean air temperature. Conclusions: The spring temperature change over a relatively brief flowering period of M. saniculifolia at high altitudes can alter pollinator assemblages through pollinator dominance and visitation frequency changes. Thus, this study emphasizes information on intra- and inter-annual variations in the mutualistic relationship between pollinators and M. saniculifolia to further assess the warming impacts on M. saniculifolia's reproductive fitness.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.137-137
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• 2021

In this study we aimed to enhance streamflow prediction skill of a land-surface hydrological model, WRF-Hydro, over one of the snow dominated catchments lies in Himalayan mountainous range, Astore. To assess the response of the Himalayan river flows to climate change is complex due to multiple contributors: precipitation, snow, and glacier melt. WRF-Hydro model with default glacier module lacks generating streamflow in summer period but recently developed WRF-Hydro-CROCUS model overcomes this issue by melting snow/ice from the glaciers. We showed that by implementing WRF-Hydro-CROCUS model over Astore the results were significantly improved in comparison to WRF-Hydro with default glacier module. To constraint the model with the observed streamflow we chose 17 sensitive parameters of WRF-Hydro, which include groundwater parameters, surface runoff parameters, channel parameters, soil parameters, vegetation parameters and snowmelt parameters. We used Dynamically Dimensioned Search (DDS) method to calibrate the daily streamflow with the Nash-Sutcliffe efficiency (NSE) being greater than 0.7 both in calibration (2009-2010) and validation (2011-2013) period. Based on the number of iterations per parameter, we found that the parameters related to channel and runoff process are most sensitive to streamflow. The attempts to address the responses of the streamflows to climate change are still very weak and vague especially northwest Himalayan Part of Pakistan and this study is one of a few successful applications of process-based land-surface hydrologic model over this mountainous region of UIB that can be utilized to have an in-depth understanding of hydrological responses of climate change.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.194-199
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• 2006

쌍천유역의 강수량과 지하수위의 관계를 분석한 결과 융설효과를 확인하였고 갈수기에 지하수위가 현저히 저하됨을 확인하였다. 쌍천유역의 지하수위와 GOI의 상관관계를 분석한 결과 70일 이동평균값을 이용한 GOI가 가장 높은 상관관계를 보여주었다. 융설효과를 고려하기 위해서 먼저 유역의 DEM 자료를 이용하여 100m 간격으로 고도별 면적분포를 구하고 기온이 100m당 $0.5^{\circ}C$씩 감소하는 것을 고려하여 강수사상이 발생하면 $0^{\circ}C$ 이하가 되는 고도에서는 강설사상이 발생하는 것으로 가정하였다. 이 때 고도별 면적분포에서 구해지는 면적비를 고려하여 강수사상을 강우와 강설로 나누었다. 이후에 고도를 고려한 기온이 $0^{\circ}C$ 이상인 날에 그 고도의 설적이 모두 녹는 것으로 가정하였고 강우가 발생한 것으로 처리하였다. 유역평균 일최대침투량을 알아내기 위하여 강수량자료를 일정값 이상은 고정하여 수정된 강수량자료로 70일 이동평균값을 구하고 이 값들과 지하수위와의 상관관계를 분석해 본 결과 40mm가 일최대침투량으로 가정하였을 때 가장 높은 상관관계를 보여주었다. 쌍천유역의 경우 40mm가 한계침투량이다. 이렇게 수정된 강수자료를 이용하여 이동평균을 구하여 지하수위와의 상관관계를 구해본 결과 쌍천유역의 2003년부터 2005년까지 2개년 자료에 대해서 융설을 고려했을 때 상관관계가 더 높아짐을 알 수 있고 한계침투량을 고려했을 때도 상관관계가 더 높아짐을 알 수 있으며 융설효과와 한계침투량을 동시에 고려했을 경우에 가장 높은 상관관계를 얻을 수 있었다.$2.8g/cm^3$로 가정했을 때, 경상분지의 화강암류의 압력평균값이 약 $0.73{\sim}3.16kbar$의 범위를 가졌고, 경상분지내 백악기 화강암류의 정치 깊이는 $2.6{\sim}11.4km$범위를 가졌다. 이는 경상분지 화강암류에 대해 유추된 기존의 정성적인 생각과 일치한다는 것을 알 수 있었고, 각섬석의 $Al^T$함량을 이용한 여러 경험적, 실험적인 압력계가 많은 제한점이 있지만 경상분지의 백악기 불국사화강암류에는 정성적으로 유효함을 알 수 있었다. 우리는 최종적으로 경상분지내 백악기 화강암류는 천부관입 암체이고 노출된 화강암류가 천부지각이라는 것을 알 수 있었다. 것이 아니라 낙관적 예측을 수행하는 경향이 있음을 발견할 수 있었다.원밭, 화산회밭으로 6개 유형으로 분류할 경우 각각의 분포면적은 41.9%, 23.3%, 17.5%, 13.9%, 1.1. 2.2% 이었다. 도시화 및 도로확대 등 다양한 토지이용 및 지형개변으로 과거의 토양정보가 많이 변경되었다. 그래서, 앞으로는 인공위성자료 및 항공사진을 이용하여 빠르고 쉽게 활용할 수 있는 토양조사 방법개발과 기 구축된 토양도의 수정, 보완 작업이 필요한 절실히 요구되고 있는 현실이다.브로 출시에 따른 마케팅 및 고객관리와 관련된 시사점을 논의한다.는 교합면에서 2, 3, 4군이 1군에 비해 변연적합도가 높았으며 (p < 0.05), 인접면과 치은면에서는 군간 유의차를 보이지 않았다 이번 연구를 통하여 복합레진을 간헐적 광중합시킴으로써 변연적합도가 향상될 수 있음을 알 수 있었다.시장에 비해 주가가 비교적 안정적인

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.3
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• pp.151-165
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• 2008

Ground slope and aspect are important parameters for physical deterministic water balance models like BROOK90 or hydrological models which attempt to calculate evapotranspiration, snowmelt, and net radiation. This study constructs a Digital Elevation Model(DEM) and examines how DEM resolution can change the average ground slope and aspect of a river basin and attempts to evaluate the effects on simulation results of BROOK90, a physical deterministic water balance model. The study area is Byungcheon river basin in Korea. DEM has been constructed using a 1:25,000 digital map with the methods of TIN and Topo To Raster. The total of 20 DEMs with 10m~100m resolution have been constructed, with a 10m interval. It was found that the higher the DEM resolution, the steeper the average ground slope value of the Byungcheon river basin. In turn, the direct solar radiation of a hilly area in the model increased the evapotranspiration and reduced the stream runoff in the Byungcheon river basin. On the other hand, a lower DEM resolution tends to move the average aspect from southeast to south in the Byungcheon river basin. Accordingly, it was found that stream runoff was reduced and evapotranspiration increased.

• Journal of Soil and Groundwater Environment
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• v.9 no.4
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• pp.15-23
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• 2004

Deicer operations provide traffic safety during winter driving conditions in urban areas. Using large quantities of de-icing chemicals (i.e., $CaCl_2$ and NaCl) can cause serious environmental problems and may change behaviors of heavy metals in roadside sediments, resulting in an increase in mobilization of heavy metals due to complexation of heavy metals with chloride ions. To examine effect of de-icing salt concentration on the leaching behaviors and mobility of heavy metals (cadmium, zinc, copper, lead, arsenic, nickel, chromium, cobalt, manganese, and iron), leaching experiments were conducted on roadside sediments collected from Seoul city using de-icing salt solutions having various concentrations (0.01-5.0M). Results indicate that zinc, copper, and manganese in roadside sediments were easily mobilized, whereas chromium and cobalt remain strongly fixed. The zinc, copper and manganese concentrations measured in the leaching experiments were relatively high. De-icing salts can cause a decrease in partitioning between adsorbed (or precipitated) and dissolved metals, resulting in an increase in concentrations of dissolved metals in salt laden snowmelt. As a result, run-off water quality can be degraded. The de-icing salt applied on the road surface also lead to infiltration and contamination of heavy metal to groundwater.

• 한국해양학회지
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• v.7 no.1
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• pp.24-45
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• 1972

The Han River is an important water source in Seoul and neighbouring districts, for public and industrial supply, and for agriculture and fishery. Nowadays, more than six million inhabitants are supplied withe water from this river. The total length of the river is 470km, and has 17 10$\^$9/㎥ an average annual flow. The hydrographic characteristics at Seoul are 653㎥/sec in an average flow, 4,608㎥/sec in the maximum average flow, and 201㎥/sec in the minimum average flow. These are influenced in some degree by snowmelt in early spring, and greatly by the flood during summer. For the pollution problems, the periods of low flow are critical ones. As a rule they occur around the months November through June. Nowadays, most of the sewage from towns and industries is discharged untreated. Apart from domestic and industrial sewages, there are some discharges of mineral matter by mines in the upriver region. In general, water quality of the Han River is kept very clean and healthy until Kwangnaru of the upper region of Seoul. A large pollution, however, is received in the downstream by the domestic and industrial sewages of Seoul. It can be seen that dissolved oxygen, COD and BOD$\sub$5/ diminish markedly, and the intensity of almost every water parameter of the river continues to increase. Comparison of the figures for 1971 derived from a sampling point 40km downstream of Kwangnaru leads to the conclusion that hardness, Ca and Mg were no changed; alkalinity, Si and soluble- Fe were slightly increased; CO$\sub$2/, acidity, Cl, NO$\sub$2/-N, Cu, Zn and Al were increased in 2 and 3 times; total residue, total ignitious residue, COD, BOD$\sub$5/, NH$\sub$4/-N, PO$\sub$4/-P, Mn, Pb and total-Fe were increased in 4 to 7 times; and SO$\sub$4/, particulate-Fe and Cd were increased in 10 to 11 times. On the other hand, coliforms were increased in 650 times; fecal coliforms in 365 times; enterococci and total plate counts in 30 times, respectively. In view points of water quality standards, the down Han River water is now leveling out in Cd, coliforms and fecal coliforms for the agricultural use; in dissolved oxygen and some trace elements (Cu, Zn, Pb and Cd) for the fishery use; in ammonia, COD, BOD$\sub$5/, and Cd for the drinking use.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.