• Title/Summary/Keyword: Rainfall time series

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Trend Analysis of Extreme Precipitation Using Quantile Regression (Quantile 회귀분석을 이용한 극대강수량 자료의 경향성 분석)

  • So, Byung-Jin;Kwon, Hyun-Han;An, Jung-Hee
    • Journal of Korea Water Resources Association
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    • v.45 no.8
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    • pp.815-826
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    • 2012
  • The underestimating trend using existing ordinary regression (OR) based trend analysis has been a well-known problem. The existing OR method based on least squares approximate the conditional mean of the response variable given certain values of the time t, and the usual assumption of the OR method is normality, that is the distribution of data are not dissimilar form a normal distribution. In this regard, this study proposed a quantile regression that aims at estimating either the conditional median or other quantiles of the response variable. This study assess trend in annual daily maximum rainfall series over 64 weather stations through both in OR and QR approach. The QR method indicates that 47 stations out of 67 weather stations are a strong upward trend at 5% significance level while OR method identifies a significant trend only at 13 stations. This is mainly because the OR method is estimating the condition mean of the response variable. Unlike the OR method, the QR method allows us flexibly to detect the trends since the OR is designed to estimate conditional quantiles of the response variable. The proposed QR method can be effectively applied to estimate hydrologic trend for either non-normal data or skewed data.

Understanding of Surface Water-Groundwater Connectivity in an Alluvial Plain using Statistical Methods (통계기법을 활용한 충적층내 지하수-지표수 연계 특성 해석)

  • Kim, Gyoo-Bum;Son, Young-Chul;Lee, Seung-Hyun;Jeong, An-Chul;Cha, Eun-Jee;Ko, Min-Jeong
    • The Journal of Engineering Geology
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    • v.22 no.2
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    • pp.207-221
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    • 2012
  • A statistical analysis of time series of water level at 27 groundwater monitoring wells was conducted to analyze the surface water-groundwater connectivity in the wide alluvial plains surrounding the Nakdong River, Korea. Change in groundwater level is strongly related to river water level, yielding an average cross-correlation coefficient of 0.601, which is much higher than that between rainfall and groundwater level (0.125). Principal component analysis of groundwater level indicates that wells in the study area can be classified into two groups: wells in Group A are located close to a river, have water levels closely related to river level, and generally show a large increase in groundwater level during heavy rainfall. On the other hand, wells in Group B located far from a river are relatively less related to river level. Including hydrologic and statistical analyses, geochemical analysis and temperature monitoring are additionally required to reveal the relationship between surface water level and groundwater level, and to assess the possibility of groundwater flooding.

A study of applying soil moisture for improving false alarm rates in monitoring landslides (산사태 모니터링 오탐지율 개선을 위한 토양수분자료 활용에 관한 연구)

  • Oh, Seungcheol;Jeong, Jaehwan;Choi, Minha;Yoon, Hongsik
    • Journal of Korea Water Resources Association
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    • v.54 no.12
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    • pp.1205-1214
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    • 2021
  • Precipitation is one of a major causes of landslides by rising of pore water pressure, which leads to fluctuations of soil strength and stress. For this reason, precipitation is the most frequently used to determine the landslide thresholds. However, using only precipitation has limitations in predicting and estimating slope stability quantitatively for reducing false alarm events. On the other hand, Soil Moisture (SM) has been used for calculating slope stability in many studies since it is directly related to pore water pressure than precipitation. Therefore, this study attempted to evaluate the appropriateness of applying soil moisture in determining the landslide threshold. First, the reactivity of soil saturation level to precipitation was identified through time-series analysis. The precipitation threshold was calculated using daily precipitation (Pdaily) and the Antecedent Precipitation Index (API), and the hydrological threshold was calculated using daily precipitation and soil saturation level. Using a contingency table, these two thresholds were assessed qualitatively. In results, compared to Pdaily only threshold, Goesan showed an improvement of 75% (Pdaily + API) and 42% (Pdaily + SM) and Changsu showed an improvement of 33% (Pdaily + API) and 44% (Pdaily + SM), respectively. Both API and SM effectively enhanced the Critical Success Index (CSI) and reduced the False Alarm Rate (FAR). In the future, studies such as calculating rainfall intensity required to cause/trigger landslides through soil saturation level or estimating rainfall resistance according to the soil saturation level are expected to contribute to improving landslide prediction accuracy.

Impact Assessment of Climate Change on Disaster Risk in North Korea based on RCP8.5 Climate Change Scenario (RCP8.5 기후변화시나리오를 이용한 기후변화가 북한의 재해위험에 미치는 영향 평가)

  • Jeung, Se-Jin;Kim, Byung-Sik;Chae, Soo Kwon
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.38 no.6
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    • pp.809-818
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    • 2018
  • In this paper, in order to evaluate the impact of future climate change in North Korea, we collected the climate data of each station in North Korea provided by WMO and expanded the lack of time series data. Using the RCP climate change scenario, And the impact of climate change on disasters using local vulnerability to disasters in the event of a disaster. In order to evaluate this, the 11 cities in North Korea were evaluated for Design Rainfall Load, human risk index (HRI), and disaster impact index (DII) at each stage. As a result, Jaffe increased from C grade to B grade in the Future 1 period. At Future 2, North Hwanghae proved to be dangerous as it was, and Gangwon-do and Hwanghae-do provincial grade rose to C grade. In the case of Future 3, Pyongyang City dropped from C grade to D grade, Hamgyong and Gyeongsang City descend from B grade to C grade, Gangwon-do and Jagangdo descend from C grade to D grade and Pyongyang city descend from C grade to D grade. Respectively.

Characteristics of Groundwater Levels Fluctuation and Quality in Ddan-sum Area (낙동강 하중도 딴섬의 지하수위 변동 및 수질 특성)

  • Kim, Gyoobum;Choi, Doohoung;Shin, Seonho
    • Journal of the Korean GEO-environmental Society
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    • v.12 no.2
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    • pp.35-43
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    • 2011
  • Confined aquifer, which is separated with upper clayey or silty materials, is partially distributed at the depths of the sediments in Ddan-sum area on the lower Nakdong river. Measurements of groundwater levels at 13 sites explain that groundwater flow shows seasonally various due to seasonal rainfall and agricultural water use. From 9 long-term monitoring data of groundwater levels at 7 sites, 3 types of groundwater levels time series can be classified using principal component analysis. The first type is seen in the center of Ddan-sum and has a round-shape graph due to a weak response to stream water levels. The second type exists in the outer part of Ddan-sum and shows sharply peak-shape graph due to a rapid and strong response to stream water levels and rainfall. The last type, which is seen in a deep layer, has a periodicity by tital effect. From geochemical analysis at each monitoring sites, [$Ca-HCO_3$] type happens in the center of Ddan-sum far from Nakdong river, and [$Na-HCO_3$] and [$Ca-SO_4(Cl)$] types exist in the outer of Ddan-sum affected by river quality.

An analysis of land displacements in terms of hydrologic aspect: satellite-based precipitation and groundwater levels (수문학적 관점에서의 지반 변위 분석: 인공위성 강우데이터와 지하수위 연계)

  • Oh, Seungcheol;Kim, Wanyub;Kang, Minsun;Yoon, Hongsic;Yang, Jungsuk;Choi, Minha
    • Journal of Korea Water Resources Association
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    • v.55 no.12
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    • pp.1031-1039
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    • 2022
  • As one of the hydrological factors closely related to landslides, precipitation indirectly affects slope stability by generating external forces. Groundwater level fluctuations have attracted more attention lately as factors that directly affect slope stability have become more prominent. Therefore, this study attempted to analyze the relationship between variables through changes in precipitation, groundwater levels, and land displacement. A time series-based analysis was conducted using satellite-based precipitation and point-based groundwater levels in conjunction with the PSInSAR technique to simulate land displacement in urban and mountainous areas. There was a sharp rise in groundwater levels in both urban and mountain areas during heavy rainfall, and a continuous decrease in urban areas when rainfall was low. 6 mm of displacements was observed in the mountainous area as a results of soil outflow from the topsoil layer, which was accompanied by an increased groundwater level. Meanwhile, different results were found in urban area. In response to the rise in groundwater level, the land displacement increases due to the expansion of soil skeletons, while the decrease seems to be attributed to anthropogenic influences. Overall, there was no consistent relationship between groundwater levels and land displacement, which appears to be caused by factors other than hydrological factors. Additional consideration of environmental factors could contribute to a deeper understanding of the relationship between the two factors.

Biodegradable Check Dam and Synthetic Polymer, its Experimental Evaluation for Turbidity Control of Agricultural Drainage Water

  • Kim, Minyoung;Kim, Seounghee;Kim, Jinoh;Lee, Sangbong;Kim, Youngjin;Cho, Yongho
    • Korean Journal of Soil Science and Fertilizer
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    • v.46 no.6
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    • pp.458-462
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    • 2013
  • A drainage ditch is normally a component of drainage networks in farming systems to remove surplus water, but at the same time, it may act as a major conduit of agricultural nonpoint source pollutions such as sediment, nitrogen, phosphorus, and so on. The hybrid turbidity reduction system using biodegradable check dam and synthetic polymer was developed in this study to manage pollutant discharge from agricultural farmlands during rainfall events and/or irrigation periods. The performance of this hybrid system was assessed using a laboratory open channel sized in 10m-length and 0.2m-width. Various check dams using agricultural byproducts (e.g., rice straw, rice husks, coconut fiber and a mixture of rice husks and coconut fiber) were tested and additional physical factors (e.g., channel slope, flowrate, PAM dosage, turbidity level, etc.) affecting on turbidity reduction were applied to assess their performance. A series of lab experiments clearly showed that the hybrid turbidity reduction system could play a significant role as a supplementary of Best Management Practice (BMP). Moreover, the findings of this study could facilitate to develop an advanced BMP for minimizing nonpoint source pollution from agricultural farmlands and ultimately to achieve the sustainable agriculture.

Characteristics of Drainage Pervious Block Considering Urban Rainfall (도심지 강우 특성을 고려한 투수성 보도블록의 배수 특성)

  • Seo, Da-Wa;Yun, Tae-Sup;Youm, Kwang-Soo;Jeong, Sang-Seom;Mun, Sung-Ho
    • Journal of the Korean Geotechnical Society
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    • v.31 no.1
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    • pp.53-64
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    • 2015
  • This study presents the experimental results of pervious blocks subjected to a series of unique inflow conditions in urban area. The measured properties include the strength, permeability, drainage capacity and runoff, and evaporation for blocks made of two different size of aggregates. Results revealed that the strength satisfies the Korean Standard regardless of aggregate size whereas the immediate runoff occurred for the block with small size aggregate. On the other hand, the block with large aggregates allowed the drainage upon the initial inflow condition, which became hampered to induce the runoff by subsequent inflow. It was attributed to the fact that the capillary water often served as the hydraulic barrier in partially saturated condition. The salient observation indicated that the runoff highly depended on the evaporation and pre-wetting condition as well as the porosity and pore connectivity. The bilinear evaporate rate that makes the degree of saturation vary also had great influence on deterining the time-dependent runoff.

Impact of Trend Estimates on Predictive Performance in Model Evaluation for Spatial Downscaling of Satellite-based Precipitation Data

  • Kim, Yeseul;Park, No-Wook
    • Korean Journal of Remote Sensing
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    • v.33 no.1
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    • pp.25-35
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    • 2017
  • Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

Prediction of pollution loads in agricultural reservoirs using LSTM algorithm: case study of reservoirs in Nonsan City

  • Heesung Lim;Hyunuk An;Gyeongsuk Choi;Jaenam Lee;Jongwon Do
    • Korean Journal of Agricultural Science
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    • v.49 no.2
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    • pp.193-202
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    • 2022
  • The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R2 that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.