• Water Engineering Research
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• 제1권3호
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• pp.233-242
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• 2000

A system for regularly appraising the reliability of streamflow data, KORSAS (KOwaco's Regular Streamflow Appraising System) was developed on PC based Windows for hydrological specialists and engineers working in the Korea Water Resources Corporation (KOWACO). The reliability of streamflow rates can be evaluated with KORSAS in various as pects according to the evaluation duration and method. The former being selected as short term (event based) or long term(continus based), and the latter being classified into comparison methods of flow measurement, other stations results, and simulation. Rainfall-runoff models can be used together with KORSAS in order to evaluate the reliability of observed flow data by comparing with simulated flow data. The objective of this study is to develop a systematic methodology in various aspects to evaluate the reliability of streamflow data regularly.

• 한국농공학회지
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• 제42권
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• pp.24-30
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• 2000

A system for evaluating streamflow data (KORSAS) was developed, and is operated using PC based Windows to help the hydrological observation practitioner's working in Korea Water Resources Corporation (KOWACO). This system has modules including; DB access and data management, flow measurement arranging, H-Q relation deriving, area rainfall calculating, flow calculating, and flow evaluating modules. Evaluation of observed streamflow is accomplished through the following processes. First, hourly streamflow data is calculated from water level data stored in a DB server by applying the rating relationship between water level and flow rates derived from the past flow measurements. Second, hourly areal rainfal data is calculated from point data stored in the DB server by applying Thiessen networks. Third, hydrographs are displayed on a daily, weekly, monthly, or seasonal duration basis, and are compared to hydrographs of reservoir inflow, hydrographs at water level observation stations and hydrographs derived from simulated results using models.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2016년도 학술발표회
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• pp.49-49
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• 2016

The impact of land cover changes on streamflow of the Akaki catchment will be assessed using Soil and Water Assessment Tool (SWAT) model. The study will analyze the historical land cover changes (1993 to 2016) that have taken place in the catchment and its effect on the streamflow of the study area. Arc GIS will be used to analysis the satellite images obtained from the United States Geological Survey (USGS). To investigate the impact of land cover change on streamflow the model set up will be done using readily available spatial and temporal data, and calibrated against measured discharge. Two third of the data will be used for model calibration (1993?2000) and the remaining one-third for model validation (2001?2004). Model performance will be evaluated by using Nash and Sutcliff efficiency (NS) and coefficient of determination (R2). The calibrated model will be used to assess two land cover change (2002 and 2016) scenarios and its likely impacts of land use changes on the runoff will be quantified. The evaluation of the model response to these changes on streamflow will be presented properly. The study will contribute a lot to understand land use and land cover change on streamflow. This enhances the ability of stakeholder to implement sound policies to minimize undesirable future impacts and management alternatives which have a significant role in future flood control of the study area.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• 한국물환경학회지
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• 제30권1호
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• pp.60-67
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• 2014

In this study, four methods for calculation of continuous daily flow was suggested using short-term or partial recording station of streamflow including missing data. Using these methods, standard flows at the outlet of unit/small basins for the management of total maximum daily loads (TMDLs) in Namgang dam basin were estimated from full-period flow duration curve (FDC). Four methods of extension are described, and their properties are explored. The methods are regression (REG), regression plus noise (RPN), and maintenance of variance extension types 1 and 2 (MOVE.1, MOVE.2). In these methods, the continuous daily flow was calculated using extension equation based on correlation analysis, after conducting the correlation analysis between historic record of streamflow and long-term recording station (a base station). Finally the best optimal method was selected as the MOVE.2, and the standard flows in the abundant, ordinary, low and drought flow estimated from FDC was evaluated using MOVE.2 in unit/small basins.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.149-149
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• 2022

Accurate quantitative evaluation of baseflow contribution to streamflow is imperative to address seasonal drought vulnerability, flood occurrence and groundwater management concerns for efficient and sustainable water resources management in watersheds. Several baseflow separation algorithms using recursive filters, graphical method and tracer or chemical balance have been developed but resulting baseflow outputs always show wide variations, thereby making it hard to determine best separation technique. Therefore, the current global shift towards implementation of artificial intelligence (AI) in water resources is employed to compare the performance of deep learning models with conventional hydrograph separation techniques to quantify baseflow contribution to streamflow of Piney River watershed, Tennessee from 2001-2021. Streamflow values are obtained from the USGS station 03602500 and modeled to generate values of Baseflow Index (BI) using Web-based Hydrograph Analysis (WHAT) model. Annual and seasonal baseflow outputs from the traditional separation techniques are compared with results of Long Short Term Memory (LSTM) and simple Gated Recurrent Unit (GRU) models. The GRU model gave optimal BFI values during the four seasons with average NSE = 0.98, KGE = 0.97, r = 0.89 and future baseflow volumes are predicted. AI offers easier and more accurate approach to groundwater management and surface runoff modeling to create effective water policy frameworks for disaster management.

• 한국농공학회논문집
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• 제56권3호
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• pp.19-29
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• 2014

This study was to evaluate the potential climate change impact on watershed hydrological components of evapotranspiration, surface runoff, lateral flow, return flow, and streamflow using Soil and Water Assessment Tool (SWAT). For Yongdam dam watershed (930 $km^2$), the SWAT model was calibrated for five years (2002-2006) and validated for three years (2004-2006) using daily streamflow data at three locations and daily soil moisture data at five locations. The Nash-Sutcliffe model efficiency (NSE) and coefficient of determination ($R^2$) were 0.43-0.67 and 0.48-0.70 for streamflow, and 0.16-0.65 and 0.27-0.76 for soil moisture, respectively. For future evaluation, the HadGEM3-RA climate data by Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios were adopted. The biased future data were corrected using 30 years (1982-2011, baseline period) of ground weather data. The HadGEM3-RA 2080s (2060-2099) temperature and precipitation showed increase of $+4.7^{\circ}C$ and +22.5 %, respectively based on the baseline data. The impacts of future climate change on the evapotranspiration, surface runoff, baseflow, and streamflow showed changes of +11.8 %, +36.8 %, +20.5 %, and +29.2 %, respectively. Overall, the future hydrologic results by RCP emission scenarios showed increase patterns due to the overall increase of future temperature and precipitation.

• 한국농공학회논문집
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• 제59권3호
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• pp.41-50
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• 2017

The purpose of this study were to evaluate the effect of best management practices (BMPs) of Haean highland agricultural catchment ($62.8km^2$) under future climate change using SWAT (Soil and Water Assessment Tool). Before future evaluation, the SWAT was setup using 3 years (2009~2011) of observed daily streamflow, suspended solid (SS), total nitrogen (T-N), and total phosphorus (T-P) data at three locations of the catchment. The SWAT was calibrated with average 0.74 Nash and Sutcliffe model efficiency for streamflow, and 0.78, 0.63, and 0.79 determination coefficient ($R^2$) for SS, T-N, and T-P respectively. Under the HadGEM-RA RCP (Representative Concentration Pathway) 4.5 and 8.5 scenarios, the future precipitation and maximum temperature showed maximum increases of 8.3 % and $4.2^{\circ}C$ respectively based on the baseline (1981~2005). The future 2040s and 2080s hydrological components of evapotranspiration, soil moisture, and streamflow showed changes of +3.2~+17.2 %, -0.1~-0.7 %, and -9.1~+8.1 % respectively. The future stream water quality of suspended solid (SS), total nitrogen (T-N), and total phosphorus (T-P) showed changes of -5.8~+29.0 %, -4.5~+2.3 %, and +3.7~+17.4 % respectively. The future SS showed wide range according to streamflow from minus to plus range. We can infer that this was from the increase of long-term rainfall variability in 2040s less rainfalls and 2080s much rainfalls. However, the results showed that the T-P was the future target to manage stream water quality even in 2040s period.

• 대한원격탐사학회지
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• 제24권1호
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• pp.45-55
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• 2008

The impact on streamflow and groundwater recharge considering future potential climate and land use change was assessed using SLURP (Semi-distributed Land-Use Runoff Process) continuous hydrologic model. The model was calibrated and verified using 4 years (1999-2002) daily observed streamflow data for a $260.4km^2$ which has been continuously urbanized during the past couple of decades. The model was calibrated and validated with the coefficient of determination and Nash-Sutcliffe efficiency ranging from 0.8 to 0.7 and 0.7 to 0.5, respectively. The CCCma CGCM2 data by two SRES (Special Report on Emissions Scenarios) climate change scenarios (A2 and B2) of the IPCC (Intergovemmental Panel on Climate Change) were adopted and the future weather data was downscaled by Delta Change Method using 30 years (1977 - 2006, baseline period) weather data. The future land uses were predicted by CA (Cellular Automata)-Markov technique using the time series land use data of Landsat images. The future land uses showed that the forest and paddy area decreased 10.8 % and 6.2 % respectively while the urban area increased 14.2 %. For the future vegetation cover information, a linear regression between monthly NDVI (Normalized Difference Vegetation Index) from NOAA/AVHRR images and monthly mean temperature using five years (1998 - 2002) data was derived for each land use class. The future highest NDVI value was 0.61 while the current highest NDVI value was 0.52. The model results showed that the future predicted runoff ratio ranged from 46 % to 48 % while the present runoff ratio was 59 %. On the other hand, the impact on runoff ratio by land use change showed about 3 % increase comparing with the present land use condition. The streamflow and groundwater recharge was big decrease in the future.

• 지질공학
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• 제34권2호
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• pp.329-338
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• 2024

본 연구에서는 인제 지역에 대해 표준강수지수(SPI), 수문학적 가뭄지수(SDI)를 이용한 가뭄 평가를 수행하였다. 가뭄 분석을 위한 기초자료(강우, 유량) 자료 등을 통해서 월별 유량 비율 등을 검토하였고, 인제군 유역 인근의 강수 및 수위/유량 관측소를 활용하여 기상학적 가뭄 및 수문학적 가뭄분석을 진행한 결과 모든 가뭄지수(SPI, SDI)에서 공통적으로 2014년에 발생한 가뭄이 2017년까지 지속되었던 것으로 분석되었다. 지속기간 12개월의 수문기상자료를 활용하여 가뭄지수를 산정하여 분석한 경우, 심한가뭄 지속기간이 24개월 정도 지속되었던 것으로 확인되었으며 따라서 가뭄으로 인한 피해가 극심했을 것으로 평가된다.