• Journal of Korea Water Resources Association
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• v.50 no.11
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• pp.759-767
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• 2017

Two main parameters of NRCS-CN method are curve numbers and intial loss ratio. They are generally selected according to the guideline of US National Engineering Handbook, however, they might cause errors on estimated runoff in Korea because there are differences between soil types and hydrological characteristics of Korean watersheds and those of United States. In this study, applying asymptotic CN regression method, we suggested eight modified NRCS-CN models to decide optimum runoff estimation model for Korean watersheds. RSR (RMSE-observations standard deviation ratio) and NSE (Nash-Sutcliffe efficiency) were used to evaluate model performance, consequently M6 for gauged basins (Avg. RSR was 0.76, Avg. NSE was 0.39) and M7 for ungauged basins (Avg. RSR was 0.82, Avg. NSE was 0.31) were selected. Furthermore it was observed that initial loss ratios ranging from 0.01 to 0.10 were more adequate than the fixed ${\lambda}=0.20$ in most of basins.

• Journal of Korea Water Resources Association
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• v.39 no.3 s.164
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• pp.227-240
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• 2006

In the several decades, various storage facilities have been developed and operated to supply water resource, flood control or environmental preservation etc. Then, how those man-maid storage facilities affect on the downstream water and environment and how the hydrologists can evaluate those features for water resources problem-solving are high-concentrated problems in this field. Most large watersheds in Korea contain various types of artificial facilities such dams, reservoirs, in-land ponds, wetlands etc. But the study to develop the technology for achieving the effect of the variances and properties of the long term streamflow caused by the artificial storage facilities have been on the simple watershed models and experimental modeling in the real fields. In this paper, we introduce the procedure and methods to consider the above problems based on continuous and semi-distributed featured SWAT model. At the first, we describe the elements and mechanisms of storage facilities in SWAT model to see how we can apply that in proper and appropriate manner for real field problems. Then, we applied the process to a sample watershed, Taewha River basin which covers the most of Ulsan region. Specially, we concentrate on our effort to the effect of upper reservoirs on down stream long term flows based on various scenario basis. The result was described and analysed in spacial and temporal variations on that basin using the precise manner.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.227-227
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• 2015

The Wairarapa Valley occupies a predominantly rural area in the lower North Island of New Zealand. It supports a mix of intensive farming (dairy), dry stock farming (sheep and beef cattle) and horticulture (including wine grapes). The valley floor is traversed by the Ruamahanga River, the largest river in the Wellington region with a total catchment area of 3,430 km2. Environmental, cultural and recreational values associated with this Ruamahanga River are very high. The alluvial gravel and sand aquifers of the Wairarapa Valley, support productive groundwater aquifers at depths of up to 100 metres below ground while the Ruamahanga River and its tributaries present a further source of water for users. Water is allocated to users via resource consents by Greater Wellington Regional Council (GWRC). With intensifying land use, demand from the surface and groundwater resources of the Wairarapa Valley has increased substantially in recent times and careful management is needed to ensure values are maintained. This paper describes the approach being taken to manage water resources in the Wairarapa Valley and redefine appropriate limits of sustainable water use. There are three key parts: Quantifying the groundwater resource. A FEFLOW numerical groundwater flow model was developed by GWRC. This modelling phase provided a much improved understanding of aquifer recharge and abstraction processes. It also began to reveal the extent of hydraulic connection between aquifer and river systems and the importance of moving towards an integrated (conjunctive) approach to allocating water. Development of a conjunctive management framework. The FEFLOW model was used to quantify the stream flow depletion impacts of a range of groundwater abstraction scenarios. From this, three abstraction categories (A, B and C) that describe diminishing degrees of hydraulic connection between ground and surface water resources were mapped in 3 dimensions across the Valley. Interim allocation limits have been defined for each of 17 discrete management units within the valley based on both local scale aquifer recharge and stream flow depletion criteria but also cumulative impacts at the valley-wide scale. These allocation limits are to be further refined into agreed final limits through a community-led decision making process. Community involvement in the limit setting process. Historically in New Zealand, limits for sustainable resource use have been established primarily on the basis of 'hard science' and the decision making process has been driven by regional councils. Community involvement in limit setting processes has been through consultation rather than active participation. Recent legislation in the form of a National Policy Statement on Freshwater Management (2011) is reforming this approach. In particular, collaborative consensus-based decision making with active engagement from stakeholders is now expected. With this in mind, a committee of Wairarapa local people with a wide range of backgrounds was established in 2014. The role of this committee is to make final recommendations about resource use limits (including allocation of water) that reflect the aspirations of the communities they represent. To assist the committee in taking a holistic view it is intended that the existing numerical groundwater flow models will be coupled with with surface flow, contaminant transport, biological and economic models. This will provide the basis for assessing the likely outcomes of a range of future land use and resource limit scenarios.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.985-998
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• 2021

In general, the upstream dam changes downstream flow regime dramatically, i.e., from natural flow regime to hydropeaking flows. This study investigates the impact of the natural flow pattern on downstream fish habitat in a regulated river in Korea using the physical habitat simulation. The study area is a 13.4 km long reach of the Geum-gang River, located downstream from the Yongdam Dam, Korea. A field monitoring revealed that three fish species are dominant, namely Zacco platypus, Coreoleuciscus splendidus, and Opsariichthys bidens, and they account for 70% of the total fish community. Specially, Opsariichthys bidens is an indigenous species in the Geum-gang River. The three fish species are selected as target fish species for the physical habitat simulation. The Nays2D model, a 2D shallow water equation solver, and the HSI (Habitat Suitability Index) model are used for hydraulic and habitat simulations, respectively. To assess the impact of the natural flow pattern, this study uses the annual natural flow regime and hydropeaking flows from the dam. It is found that the natural flow regime increases significantly the Composite Suitability Index (CSI) in the study reach. Then, using the Building Block Approach (BBA), the scenarios for the modifying dam operations are presented in the study reach. Both Scenario 1 and scenario 2 are proposed by using the hydrological method considering both magnitude and duration of the inflow and averaging the inflow over each month, respectively. It is revealed that the natural flow regime embodied in scenario 1 and scenario 2 increases the Weighted Usable Area (WUA) significantly, compared to the hydropeaking flows. In conclusion, the modifying the dam operations by restoring to the natural flow pattern is advantageous to fish community.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.969-983
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• 2021

Evapotranspiration, one of the hydrometeorological components, is considered an important variable for water resource planning and management and is primarily used as input data for hydrological models such as water balance models. The FAO56 PM method has been recommended as a standard approach to estimate the reference evapotranspiration with relatively high accuracy. However, the FAO56 PM method is often challenging to apply because it requires considerable hydrometeorological variables. In this perspective, the Hargreaves equation has been widely adopted to estimate the reference evapotranspiration. In this study, a set of parameters of the Hargreaves equation was calibrated with relatively long-term data within a Bayesian framework. Statistical index (CC, RMSE, IoA) is used to validate the model. RMSE for monthly results reduced from 7.94 ~ 24.91 mm/month to 7.94 ~ 24.91 mm/month for the validation period. The results confirmed that the accuracy was significantly improved compared to the existing Hargreaves equation. Further, the evaporative demand drought index (EDDI) based on the evaporative demand (E₀) was proposed. To confirm the effectiveness of the EDDI, this study evaluated the estimated EDDI for the recent drought events from 2014 to 2015 and 2018, along with precipitation and SPI. As a result of the evaluation of the Han-river watershed in 2018, the weekly EDDI increased to more than 2 and it was confirmed that EDDI more effectively detects the onset of drought caused by heatwaves. EDDI can be used as a drought index, particularly for heatwave-driven flash drought monitoring and along with SPI.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.707-721
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• 2022

In this study, a state-of-the-art review on urban inundation simulation technology was presented summarizing major achievements and limitations, and future research recommendations and challenges. More than 160 papers published in major domestic academic journals since the 2000s were analyzed. After analyzing the core themes and contents of the papers, the status of technological development was reviewed according to simulation methodologies such as physically-based and data-driven approaches. In addition, research trends for application purposes and advances in overseas and related fields were analyzed. Since more than 60% of urban inundation research used Storm Water Management Model (SWMM), developing new modeling techniques for detailed physical processes of dual drainage was encouraged. Data-based approaches have become a new status quo in urban inundation modeling. However, given that hydrological extreme data is rare, balanced research development of data and physically-based approaches was recommended. Urban inundation analysis technology, actively combined with new technologies in other fields such as artificial intelligence, IoT, and metaverse, would require continuous support from society and holistic approaches to solve challenges from climate risk and reduce disaster damage.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.877-886
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• 2022

This study was carried out to analyze how the flow and water quality of the Sangsa Lake (juam control basin) change according to future climate change and what countermeasures are needed. Aquatic Ecosystem Model) was used in conjunction. As climate change scenarios, RCP (Representative Concentration Pathways) 4.5 and RCP 8.5 scenarios of AR5 (5th Assessment Report) according to the Intergovernmental Panel on Climate Change (IPCC) were used. For the climate change scenario, detailed data on the Sangsa Lake basin were used by the Korea Meteorological Administration, and after being evaluated as a correction and verification process for the 10-year period from 2012 to 2021, the present, 2025-2036, 2045- The summer period from June to August and the winter period from December to February were analyzed separately for each year by dividing it into 2056 and 2075-2086. RCP 8.5 was higher than RCP 4.5 as an arithmetic mean for the flow rate of the watershed of the superior lake for the entire simulation period, and TN and TP also showed a tendency to be higher at RCP 4.5. However, in RCP 8.5, the outflow of pollutants decreased during the dry season and the outflow of pollutants increased during the summer, indicating that the annual pollutant outflow was concentrated during the flood season, and it is analyzed that countermeasures are needed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.321-335
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• 2023

Monitoring sediment loads in natural rivers is the key process in river engineering, but it is costly and dangerous. In practice, suspended loads are directly measured, and total loads, which is a summation of suspended loads and bed loads, are estimated. This study proposes a real-time sediment discharge monitoring system using the horizontal acoustic Doppler current profiler (H-ADCP) and support vector regression (SVR). The proposed system is comprised of the SVR model for suspended sediment concentration (SVR-SSC) and for total loads (SVR-Q_TL), respectively. SVR-SSC estimates SSC and SVR-Q_TL mimics the modified Einstein procedure. The grid search with K-fold cross validation (Grid-CV) and the recursive feature elimination (RFE) were employed to determine SVR's hyperparameters and input variables. The two SVR models showed reasonable cross-validation scores (R²) with 0.885 (SVR-SSC) and 0.860 (SVR-Q_TL). During the time-series sediment load monitoring period, we successfully detected various sediment transport phenomena in natural streams, such as hysteresis loops and sensitive sediment fluctuations. The newly proposed sediment monitoring system depends only on the gauged features by H-ADCP without additional assumptions in hydraulic variables (e.g., friction slope and suspended sediment size distribution). This method can be applied to any ADCP-installed discharge monitoring station economically and is expected to enhance temporal resolution in sediment monitoring.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.