• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.114-119
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• 2000

To estimate Agricultural water demand, many factors such as weather, type of crop, soil, cultivation method, crop coefficient and cultivation area, etc. must be considered. But it is not easy to estimate water demand in consideration of these many factors, which are variable according to a period and regional environment. So, this study provides estimation system for agricultural water demand(ESAD) in order to estimate water demand easily and accurately, calculates the present and future agricultural water demand and arranges all factors needed for water demand estimation. This study calibrates the application of estimation system for agricultural water demand with the data observed in the other Studies and analyzes agricultural water demand nationwide.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.1
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• pp.53-65
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• 2001

To estimate agricultural water demand, many factors such as weather, crops, soil, cultivation method, crop coefficient and cultivation area, etc. must be considered. But it is not easy to estimate water demand in consideration of these factors, which are variable according to growth stage and regional environment. This study provides estimation system for agricultural water demand(ESAD) in order to estimate water demand easily and accurately, and arranges all factors needed for water demand estimation. This study identifies the application of estimation system for agricultural water demand with the data observed in the other studies, and analyzes nationwide agricultural water demand. The results are as follows. 1) The practice of different rice cultivation in the paddy field resulted in different water demands. Water depth and infiltration ratio in paddy are the most important factors to estimate water demand. The water depths in paddy simulated by ESAD is very similar to the observed ones. 2) Water demand of upland crops varies with the crops, soil, etc.. Effective rainfall estimated by daily routing of soil moisture varies according to the crops, soil, and effective soil zone(root depth). As crop root become grown, effective rainfall and an amount of irrigation water has been increased. 3) The current unit water demand of upland crops applied as 500mm or 550mm to estimate water demand does not reflect the differences caused by the crops, regional surrounding, weather condition, etc. Results from ESAD for the estimation of water demand of upland crops show that ESAD can simulate the actual field conditions reasonably because it simulates the actual irrigation practices with the daily routing of soil moisture.

• Journal of Korean Society of Water and Wastewater
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• v.23 no.2
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• pp.241-249
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• 2009

It is well known that the trend of water demand in large-size water supply systems has been suddenly changed, and many expansions of water supply facilities become unnecessary. To be cost-effective, thus, politicians as well as many professionals lay stress on the adaptive management of water supply facilities. Failure in adapting to the new trend of demand is sure to be the most critical reason of unnecessary expansions. Hence, we try to develop the model and modeling procedure that do not depend on the old data of demand, and provide engineers with the fast learning process. To forecast water demand of Seoul, the Bayesian parameter estimation was applied, which is a representative method for statistical pattern recognition. It results that we can get a useful time-series model after observing water demand during 6 years, although trend of water demand were suddenly changed.

• Journal of Environmental Science International
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• v.11 no.12
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• pp.1275-1279
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• 2002

This study was performed to improve water demand estimation and analize correlation between generation of domestic sewage and domestic water use. To improve the prediction of water demand estimation, new water demand equation was developed. The results is as follows. $InQ_t = {\beta}_0+{\beta}_1InP_t+{\beta}_2InY_t+{\beta}_3InH_t+{varepsilon}_t$By using the statistical analysis of the "generation of domestic sewage" and "domestic water use", the regression equation between them is formed. The result is as follows. Generation of domestic sewage : 0.8487 $\times$ Domestic water use + 684.57 ($R^2$= 0.972)>$R^2$= 0.972)

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.939-949
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• 2020

Currently, the demand for farmland is steadily decreasing due to changes in the agricultural environment and dietary life. In line with this, the government adopted an integrated water management with the enactment of the Framework Act on Water Management on June 2019. Therefore, it is required to take a closer look at agricultural water demand that accounts for 61% of water use for efficient water resources management. In this study, the overal process was evaluated for estimating agricultural water demand. More specifically, agricultural water demand for paddy field, which comprises 67% to 87% of agricultural water demand, was reviewed in detail. The biggest issue in estimating the paddy field water demand is the selection of the method for potential evapotranspiration. FAO recommends Penman-Monteith, but, currently, our criteria suggest a modified Penman equation that shows over estimation. Also, the crop coefficient, which is the main factor in evaluating evapotranspiration, has an issue that does not consider the current climate and crop varieties because it was developed 23 years ago. Comparing the Modified Penman and Penman-Monteith equations using the data from Jeonju National Weather Service, the modified Penman equation showed a big difference compared to the Penman-Monteith equation. When the crop coefficient was applied, the difference between late May and late August increased, where the amount of evapotranspiration was high. The estimation process was applied to four study reservoirs in Gimje. Comparing the estimated water demand with the supplied water record from reservoirs, the results showed that the estimation accuracy depends on not just the potential evapotranspiration, but also the standard water storing level in paddy fields.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.164-175
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• 2019

The Tor Tong Daeng Irrigation Project with the irrigation area of 61,400 hectares is located in the Ping Basin of the Upper Central Plain of Thailand where farmers depended on both surface water and groundwater. In the drought year, water storage in the Bhumipol Dam is inadequate to allocate water for agriculture, and caused water deficit in many irrigation projects. Farmers need to find extra sources of water such as water from farm pond or groundwater as a supplement. The operation of Bhumipol Dam and irrigation demand estimation are vital for irrigation water allocation to help solve water shortage issue in the irrigation project. The study aims to determine the smart dam operation system to mitigate water shortage in this irrigation project via introduction of machine learning to improve dam operation and irrigation demand estimation via soil moisture estimation from satellite images. Via ANN technique application, the inflows to the dam are generated from the upstream rain gauge stations using past 10 years daily rainfall data. The input vectors for ANN model are identified base on regression and principal component analysis. The structure of ANN (length of training data, the type of activation functions, the number of hidden nodes and training methods) is determined from the statistics performance between measurements and ANN outputs. On the other hands, the irrigation demand will be estimated by using satellite images, LANDSAT. The Enhanced Vegetation Index (EVI) and Temperature Vegetation Dryness Index (TVDI) values are estimated from the plant growth stage and soil moisture. The values are calibrated and verified with the field plant growth stages and soil moisture data in the year 2017-2018. The irrigation demand in the irrigation project is then estimated from the plant growth stage and soil moisture in the area. With the estimated dam inflow and irrigation demand, the dam operation will manage the water release in the better manner compared with the past operational data. The results show how smart system concept was applied and improve dam operation by using inflow estimation from ANN technique combining with irrigation demand estimation from satellite images when compared with the past operation data which is an initial step to develop the smart dam operation system in Thailand.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.37-40
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• 2002

It is to show the problems of the existing techniques to estimate agricultural water demand and to suggest the new methods considering the water demand for non-irrigated area and decrease of water loss in canal. It is to suggest the methods to improve the techniques for estimating agricultural water demand and to analyze the water demand and supply according to the facilities capacity. Until now, the concept of per the unit used to estimate agriculture water demand is useful to estimate demand, but is insufficient to cope with the variations of conditions in future. And the paddy area of government is not realistic against a trend of decrease. Water demand decrease is caused by constructions of irrigation facilities as constructing of irrigation canal, but application loss ratio is fixed. Increase of the water demand owing to the increase of the yield per the unit area is also the actual condition which is not considered. The guide-line must contain these contents for a demand estimate.

• KCID journal
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• v.14 no.2
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• pp.236-244
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• 2007

Agricultural water demand in North Korea must be considered for the near-future investment in agricultural consolidation projects and to prepare for the future unification. Thus, the objective of this study is to estimate the agricultural water demand of paddy fieldss in North Korea. GIS data including land cover classification map, Thiessen network and administration maps of North Korea, and meteorological data were synthesized. In order to estimate paddy water demand for a 10-year return period, the FAO Blaney-Criddle method and the fixed effective rainfall ratio method were used. The results showed that 4.77 billion $\beta$(c)/year paddy water demand is required for the 512,400 ha of paddy fieldss. Paddy water demand in the three major regions - Hwanghaedo, Pyeongando, Hamgyeongnamdo - was estimated chargong 81.7 percent of total paddy water demand in North Korea.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.3
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• pp.75-85
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• 2021

This study evaluated variations in the paddy rice water demand based on the continuous changing in rice transplanting period and ponding depth at the four study paddy fields, which represent typical rice producing regions in Korea. Total 7 scenarios on rice transplanting periods were applied while minimum ponding depth of 0 and 20 mm were applied in accordance with maximum ponding depth ranging from 40 mm to 100 mm with 20 mm interval. The weather data from 2013 to 2019 was also considered. The results indicated that the highest rice water demand occurred at high temperature and low rainfall region. Increased rice transplanting periods showed higher rice water demand. The rice water demand for 51 transplanting days closely matched with the actual irrigation water supply. In case of ponding depth, the results showed that the minimum ponding depth had a proportional relationship with rice water demand, while maximum ponding depth showed the contrary results. Minimum ponding depth had a greater impact on rice water demand than the maximum ponding depth. Therefore, these results suggest that increasing the rice transplanting period, which reflects the current practice is desirable for a reliable estimation of rice water demand.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.4
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• pp.527-534
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• 2006

Demand side management(DSM) is the newly raised issues in the water resources management in recent. Many of the policy tools among demand management, the most important measures might be a pricing system. Furthermore, the responses of consumers on the price for water consumption level is the key factor for policy making. Here, we estimated panel data for 167 regions and over 7 years periods in Korea. Compare to other previous studies the price elasticities were somewhat low. The estimated price elasticity was -0.05. It was because the short term estimated period may derive lower elasticities. However, it might be a recent trend after the continuous increment of water pricing and consumers not willing to decrease their residential water consumption with increasing water pricing. According to this results, water saving effect might be much smaller than we expect with pricing policy. However, It does not imply there is no price effects on water consumption and it's still meaningful as a tool of water management.