• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• 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.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.11-19
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• 2014

This study is analyze future climate and land cover change affects behaviors for amount of streamflow and sediment discharge within basin. We used the climate forecast data in RCP 4.5 and 8.5 (2011-2100) which is opposite view for each other among RCP scenarios that are discussed for 5th report for IPCC. Land cover map built based on a social economic storyline in RCP 4.5/8.5 using Logistic Regression model. In this study we set three scenarios: one scenario for climate change only, one for land cover change only, one for Last both climate change and land cover change. It simulated amount of streamflow and sediment discharge and the result showed a very definite change in the seasonal variation both of them. For climate change, spring and winter increased the amount of streamflow while summer and fall decreased them. Sediment showed the same pattern of change steamflow. Land cover change increases the amount of streamflow while it decreases the amount of sediment discharge, which is believed to be caused by increase of impervious Surface due to urbanization. Although land cover change less affects the amount of streamflow than climate change, it may maximize problems related to the amount of streamflow caused by climate change. Therefore, it's required to address potential influence from climate change for effective water resource management and prepare suitable measurement for water resource.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.579-587
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• 2010

The mid-range streamflow forecast was performed using NWP(Numerical Weather Prediction) provided by KMA. The NWP consists of RDAPS for 48-hour forecast and GDAPS for 240-hour forecast. To enhance the accuracy of the NWP, QPM to downscale the original NWP and Quantile Mapping to adjust the systematic biases were applied to the original NWP output. The applicability of the suggested streamflow prediction system which was verified in Geum River basin. In the system, the streamflow simulation was computed through the long-term continuous SSARR model with the rainfall prediction input transform to the format required by SSARR. The RQPM of the 2-day rainfall prediction results for the period of Jan. 1~Jun. 20, 2006, showed reasonable predictability that the total RQPM precipitation amounts to 89.7% of the observed precipitation. The streamflow forecast associated with 2-day RQPM followed the observed hydrograph pattern with high accuracy even though there occurred missing forecast and false alarm in some rainfall events. However, predictability decrease in downstream station, e.g. Gyuam was found because of the difficulties in parameter calibration of rainfall-runoff model for controlled streamflow and reliability deduction of rating curve at gauge station with large cross section area. The 10-day precipitation prediction using GQPM shows significantly underestimation for the peak and total amounts, which affects streamflow prediction clearly. The improvement of GDAPS forecast using post-processing seems to have limitation and there needs efforts of stabilization or reform for the original NWP.

• Magazine of the Korean Society of Agricultural Engineers
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• v.28 no.1
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• pp.33-40
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• 1986

Unit-graph model to estimate the daily streamfiow was developed on the basis of distribution graph method. The results of evaluating the application of the model to Nakdong watersheds were generally satisfactory and this model would be the groundwork of the "Unit-graph model for daily streamflow in Korean watersheds".eds".uot;.

• Journal of Korea Water Resources Association
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• v.48 no.7
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• pp.545-552
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• 2015

The objective of this study is to spatially assess the streamflow depletion due to groundwater pumping near the main stream of Juksanchoen watershed. The surface water and groundwater integrated model, SWAT-MODFLOW, in this study, was used to simulate streamflow responses to each groundwater pumping from wells located within 500m from the stream. The simulated results showed that the streamflow depletion rate divided by the pumping rate for each well location ranges from 20% to 96%. In particular, the streamflow depletion exceeds 60% of pumping rate if the distance between stream and well is lower than 100 m, hydraulic diffusivity is higher than $500m^2/d$, and streambed hydraulic conductance is above 25m/d. The simulated results were also presented in the form of spatial distribution maps that indicate the fraction of the well pumping rate in order to show the effect of a single well more comprehensively and easily. From the developed areal distribution of stream depletion, higher and more rapid responses to pumping occur near middle-downstream reach, and the spatially averaged percent depletion is about 66.7% for five years of pumping. The streamflow depletion map can provide objective information for the near-stream groundwater permission and management.

• Journal of Korea Water Resources Association
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• v.39 no.9 s.170
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• pp.755-766
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• 2006

Since the accuracy of climate forecast information has improved from better understanding of the climatic system, particularly, from the better understanding of ENSO and the improvement in meteorological models, the forecasted climate information is becoming the important clue for streamflow prediction. This study investigated the available climate forecast information to improve the extended streamflow prediction in Korea, such as MIMI(Monthly Industrial Meteorological Information) and GDAPS(Global Data Assimilation and Prediction) and measured their accuracies. Both MIMI and the 10-day forecast of GDAPS were superior to a naive forecasts and peformed better for the flood season than for the dry season, thus it was proved that such climate forecasts would be valuable for the flood season. This study then forecasted the monthly inflows to Chungju Dam by using MIMI and GDAPS. For MIMI, we compared three cases: All, Intersection, Union. The accuracies of all three cases are better than the naive forecast and especially, Extended Streamflow Predictions(ESPs) with the Intersection and with Union scenarios were superior to that with the All scenarios for the flood season. For GDAPS, the 10-day ahead streamflow prediction also has the better accuracy for the flood season than for the dry season. Therefore, this study proved that using the climate information such as MIMI and GDAPS to reduce the meteorologic uncertainty can improve the accuracy of the extended streamflow prediction for the flood season.

• Water for future
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• v.9 no.2
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• pp.87-100
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• 1976

These studies are aimed at the analysis of systematic variation pattern of water resources in Korean river catchments and the development of their simulation models from the stochastic analysis of monthly and annual hydrologic data as main elements of water resources, i.e. rainfall and streamflow. In the analysis, monthly & annual rainfall records in Soul, Taegu, Pusan and Kwangju and streamflow records at the main gauging stations in Han, Nakdong and Geum river were used. Firstly, the systematic variation pattern of annual streamflow was found by the exponential function relationship between their standard deviations and mean values of log-annual runoff. Secondly, stochastic characteristics of annual rainfall & streamflow series were studied by the correlogram Monte Carlo method and a single season model of 1st-order Markov type were applied and compared in the simulation of annual hydrologic series. In the simulation, single season model of Markov type showed better results than LN-model and the simulated data were fit well with historical data. But it was noticed that LN-model gave quite better results in the simulation of annual rainfall. Thirdly, stochastic characteristics of monthly rainfall & streamflow series were also studied by the correlogram and spectrum analysis, and then the Model-C, which was developed and applied for the synthesis of monthly perennial streamflow by lst author and is a Markov type model with transformed skewed random number, was used in the simulation of monthly hydrologic series. In the simulation, it was proved that Model-C was fit well for extended area in Korea and also applicable for menthly rainfall as well as monthly streamflow.

• Journal of Korea Water Resources Association
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• v.48 no.9
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• pp.769-779
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• 2015

The streamflow depletion due to groundwater pumping from deep aquifer near the Juksan stream has been simulated, in this study, by using the surface water and groundwater integrated model, SWAT-ODFLOW in order to analyze the relationship between the stream depletion and hydraulic properties of aquifer and streambed, and to spatially assess the streamflow depletion. The simulated results showed that the streamflow depletion rate divided by the pumping rate for each well location ranges from 10% to 90% with reflecting the various well-stream distance, transmissivity, storativity, and streambed hydraulic conductance. In particular, the streamflow depletion exceeds about 50% of pumping rate for conditions with transmissivity higher than $10m^2/day$ or storage coefficient lower than 0.1. The simulated results in the form of spatial maps indicated that the spatially averaged percent depletion of streamflow is about 53.6% for five years of pumping which is lower than that for shallow aquifer pumping by 12.9%. From the spatially distributed stream depletion, it was found that higher and more rapid stream depletion to pumping occurs near middle-downstream reach.