• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.2
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• pp.11-23
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• 2014

The impact and adaption on agricultural water resources considering climate change is significant for reservoirs. The change in rainfall patterns and hydrologic factors due to climate change increases the uncertainty of agricultural water supply and demand. The quantitative evaluation method of uncertainty based on agricultural water resource management under future climate conditions is a major concern. Therefore, it is necessary to improve the vulnerability management technique for agricultural water supply based on a probabilistic and stochastic risk evaluation theory. The objective of this study was to analyse the uncertainty of water resources under future climate change using probability distribution function of water supply in agricultural reservoir and demand in irrigation district. The uncertainty of future water resources in agricultural reservoirs was estimated using the time-specific analysis of histograms and probability distributions parameter, for example the location and the scale parameter. According to the uncertainty analysis, the future agricultural water supply and demand in reservoir tends to increase the uncertainty by the low consistency of the results. Thus, it is recommended to prepare a resonable decision making on water supply strategies in terms of using climate change scenarios that reflect different future development conditions.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.246-247
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• 2005

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.6
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• pp.39-44
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• 2012

Water supply capacity and operational capability in agricultural reservoirs are expressed differently in the limited storage due to seasonal and local variation of precipitation. Since agricultural water supply and demand basically assumes the uncertainty of hydrological phenomena, it is necessary to improve probabilistic approach for potential risk assessment of water supply capacity in reservoir for enhanced operational storage management. Here, it was introduced the irrigation vulnerability characteristic curves to represent the water supply capacity corresponding to probability distribution of the water demand from the paddy field and water supply in agricultural reservoir. Irrigation vulnerability probability was formulated using reliability analysis method based on water supply and demand probability distribution. The lower duration of irrigation vulnerability probability defined as the time period requiring intensive water management, and it will be considered to assessment tools as a risk mitigated water supply planning in decision making with a limited reservoir storage.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.89-102
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• 2010

Future uncertainty on water demand caused by future climate condition and water consumption leads a difficulty to determine the reservoir operation rule for supplying sufficient water to users. It is, thus, important to operate reservoirs not only for distributing enough water to users using the limited water resources but also for preventing floods and drought under the unknown future condition. In this study, the reservoir storage is determined in the first stage when future condition is unknown, and then, water distribution to users and river stream is optimized using the available water resources from the first stage decision using 2-stage stochastic linear programming (2-SLP). The objective function is to minimize the difference between target and actual water storage in reservoirs and the water shortage in users and river stream. Hedging rule defined by a precaution against severe drought by restricting outflow when reservoir storage decreases below a target, is also applied in the reservoir operation rule for improving the model applicability to the real system. The developed model is applied in a system with five reservoirs in the Han River basin, Korea to optimize the multi-reservoir system under various future water demand scenarios. Three multi-purposed dams - Chungju, Hoengseong, and Soyanggang - are considered in the model. Gwangdong and Hwacheon dams are also considered in the system due to the large capacity of the reservoirs, but they are primarily for water supply and power generation, respectively. As a result, the water demand of users and river stream are satisfied in most cases. The reservoirs are operated successfully to store enough water during the wet season for preparing the coming drought and also for reducing downstream flood risk. The developed model can provide an effective guideline of multi-reservoir operation rules in the basin.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.5
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• pp.123-128
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• 2012

For optimal reservoir operation and management, there are essential elements including water supply in agricultural reservoir and demand in irrigation district. To estimate agricultural water demand and supply, many factors such as weather, crops, soil, growing conditions cultivation method and the watershed/irrigation area should be considered, however, there are occurred water supply impossible duration under the influence of the variability and uncertainty of meteorological and hydrological phenomenon. Focusing on agricultural reservoir, amount and tendency of agricultural water supply and demand shows seasonally/regionally different patterns. Through the analysis of deviation and changes in the timing of the two elements, duration in excess of water supply can be identified quantitatively. Here, we introduce an approach to assessment of irrigation vulnerable duration for effective management of agricultural reservoir using time dependent change analysis of residual water supply and irrigation water requirements. Irrigation vulnerable duration has been determined through the comparison of water supply in agricultural reservoir and demand in irrigation district based on the water budget analysis, therefore can be used as an improved and basis data for the effective and intensive water management.

• Journal of Korea Water Resources Association
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• v.38 no.4 s.153
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• pp.281-292
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• 2005

The water balance analysis for the long-term water resources plan is a simple calculation that compares water demands with possible water supplies. For a watershed being considered the reports on the performance of the water balance analysis, however, have shown inconsistent results and thus have not earned credibility due to the uncertainty of the data acquired and models used. In this research, uncertainties in the water scarcity estimate were assessed through probability representation based on the Monte Carlo simulation using Latin Hypercube Sampling (LHS). The natural flow, municipal demand, industrial demand, agricultural demand, and return flow rate were selected as representative input variables for the water balance analysis, and their distributions were set based on the linear regression and the entropy theory. The statistical properties of the output variable samples were analyzed in comparison with a deterministic estimate of the water scarcity of an existing study. Application of LHS to three sub-basins of the Geum river basin showed the deterministic estimate could be overestimated or underestimated. The sensitivity analysis as well as the uncertainty analysis found that the return flow rate of the agricultural water is the most uncertain but is rarely sensitive to the output of the water balance analysis.

• Journal of Korean Society on Water Environment
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• v.29 no.5
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• pp.681-690
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• 2013

Watershed models have been increasingly used to support an integrated management of land and water, non-point source pollutants, and implement total daily maximum load policy. However, these models demand a great amount of input data, process parameters, a proper calibration, and sometimes result in significant uncertainty in the simulation results. For this reason, uncertainty analysis is necessary to minimize the risk in the use of the models for an important decision making. The objectives of this study were to evaluate three different uncertainty analysis algorithms (SUFI-2: Sequential Uncertainty Fitting-Ver.2, GLUE: Generalized Likelihood Uncertainty Estimation, ParaSol: Parameter Solution) that used to analyze the sensitivity of the SWAT(Soil and Water Assessment Tool) parameters and auto-calibration in a watershed, evaluate the uncertainties on the simulations of runoff and sediment load, and suggest alternatives to reduce the uncertainty. The results confirmed that the parameters which are most sensitive to runoff and sediment simulations were consistent in three algorithms although the order of importance is slightly different. In addition, there was no significant difference in the performance of auto-calibration results for runoff simulations. On the other hand, sediment calibration results showed less modeling efficiency compared to runoff simulations, which is probably due to the lack of measurement data. It is obvious that the parameter uncertainty in the sediment simulation is much grater than that in the runoff simulation. To decrease the uncertainty of SWAT simulations, it is recommended to estimate feasible ranges of model parameters, and obtain sufficient and reliable measurement data for the study site.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.3
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• pp.293-305
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• 2011

A special concern is paid to the risks with which small-sized water resources systems are confronted in supplying water in the far future. Taking the Gwangdong dam reservoir as a case study, the authors seek to understand demand-side and supply-side disturbances of a reservoir, which, respectively, corresponds to effects of water demand changes on the intake amount and those of climate changes on the inflow amount. In result, it is demonstrated that both disturbances in the next 50 years are almost unpredictable. Yet the projection ranges, thought of as relatively reliable information that models offer, reveal that severity and period of water shortage is very likely to change. It is therefore concluded that water resources management requires more rigorous approaches to overcoming high uncertainties. The methods and models for projecting those disturbances are selected, based on practicality and applicability. Nevertheless, they show a large usefulness, especially in dealing with data shortage and reducing the needs for expensive modeling resources.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.439-449
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• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.131-138
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• 2010

Due to the increased water demand and severe drought as an effect of the global warming, the effluent from wastewater treatment plants becomes considered as an alternative water source to supply agricultural, industrial, and public (gardening) water demand. The effluent from the wastewater treatment plant is a sustainable water source because of its good quality and stable amount of water discharge. In this study, the water reuse system was developed to minimize total construction cost to cope with the uncertain water demand in future using two-stage stochastic linear programming with binary variables. The pipes in the water reuse network were constructed in two stages of which in the first stage, the water demands of users are assumed to be known, while the water demands in the second stage have uncertainty in the predicted value. However, the water reuse system has to be designed now when the future water demands are not known precisely. Therefore, the construction of a pipe parallel with the existing one was allowed to meet the increased water demands in the second stage. As a result, the trade-off of construction costs between a pipe with large diameter and two pipes having small diameters was evaluated and the optimal solution was found. Three scenarios for the future water demand were selected and a hypothetical water reuse network considering the uncertainties was optimized. The results provide the information about the economies of scale in the water reuse network and the long range water supply plan.