Assessment of Agricultural Water Supply Capacity Using MODSIM-DSS Coupled with SWAT

This study is to evaluate future agricultural water supply capacity in Geum river basin (9,865 km2) using SWAT and MODSIM-DSS. The MODSIM-DSS was established by dividing the basin into 14 subbasins, and the irrigation facilities of agricultural reservoirs, pumping stations, diversions, culverts and groundwater wells were grouped within each subbasin, and networked between subbasins including municipal and industrial water supplies. The SWAT was calibrated and validated using 11 years (2005-2015) daily streamflow data of two dams (DCD and YDD) and 4 years (August 2012 to December 2015) data of three weirs (SJW, GJW, and BJW) considering water withdrawals and return flows from agricultural, municipal, and industrial water uses. The Nash−Sutcliffe efficiency (NSE) of two dam and three weirs inflows were 0.55∼0.70 and 0.57∼0.77 respectively. Through MODSIM-DSS run for 34 years from 1982 to 2015, the agricultural water shortage had occurred during the drought years of 1982, 1988, 1994, 2001 and 2015. The agricultural water shortage could be calculated as 197.8 × 106 m3, 181.9 × 106 m3, 211.5 × 106 m3, 189.2 × 106 m3 and 182.0 × 106 m3 respectively. The big shortages of agricultural water were shown in water resources unit map number of 3004 (Yeongdongcheon) and 3012 (Geumgang Gongju) areas exceeding 25.1 × 106 m3 and 47.4 × 106 m3. From the estimation of future agricultural water requirement using RCP 8.5 INM-CM4 scenario, the 3004 and 3012 areas showed significant water shortages of 26.1 × 106 m3 (104.1%) and 50.9 × 106 m3 (107.4%) in 2080s (2070∼2099) compared to the present shortages. The water shortages decreased to 23.6 × 106 m3 (94.0%) and 43.3 × 106 m3 (91.4%) below of the present shortages by developing irrigation facilities.

Download Full-text

Study on Calculation Methods of Agricultural Water Supply Cost in the Plain Irrigation Area of the Yellow River

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.212-213.498 ◽

2012 ◽

Vol 212-213 ◽

pp. 498-501

Author(s):

Rui Guo ◽

Sheng Le Cao

Keyword(s):

Water Supply ◽

Yellow River ◽

Water Pricing ◽

Water Price ◽

The Yellow River ◽

Agricultural Water ◽

Calculation Methods ◽

Irrigation Area ◽

Agricultural Water Supply

Scientific and reasonable water price is the foundation of beneficial operation of water supply project, and water pricing is on the basis of per cubic meter water supply cost. According to characteristics of water supply project in the plain irrigation area of the Yellow River, a research on calculation methods of agricultural water supply cost is made. Calculation formulas of project lines are put forward and an example was given.

Download Full-text

Research based on real time monitoring system of digitized agricultural water supply

2011 6th International Conference on Computer Science & Education (ICCSE) ◽

10.1109/iccse.2011.6028659 ◽

2011 ◽

Author(s):

Yunchen Liu ◽

Yunpeng Zhang ◽

Yu Chen ◽

Jun Tong

Keyword(s):

Water Supply ◽

Real Time ◽

Monitoring System ◽

Agricultural Water ◽

Real Time Monitoring ◽

Agricultural Water Supply

Download Full-text

Lessons from Assessing Uncertainty in Agricultural Water Supply Estimation for Sustainable Rice Production

Agronomy ◽

10.3390/agronomy9100662 ◽

2019 ◽

Vol 9 (10) ◽

pp. 662 ◽

Cited By ~ 3

Author(s):

Jung-Hun Song ◽

Younggu Her ◽

Sang Min Jun ◽

Soonho Hwang ◽

Jihoon Park ◽

...

Keyword(s):

Water Supply ◽

Irrigation Efficiency ◽

Agricultural System ◽

Sensitivity Index ◽

Agricultural Water ◽

Data Limitations ◽

Management Plans ◽

Agricultural Water Supply ◽

Parameter Values ◽

The Impact

Agricultural water supply (AWS) estimation is one of the first and fundamental steps of developing agricultural management plans, and its accuracy must have substantial impacts on the following decision-making processes. In modeling the AWS for paddy fields, it is still common to determine parameter values, such as infiltration rates and irrigation efficiency, solely based on literature and rough assumptions due to data limitations; however, the impact of parameter uncertainty on the estimation has not been fully discussed. In this context, a relative sensitivity index and the generalized likelihood uncertainty estimation (GLUE) method were applied to quantify the parameter sensitivity and uncertainty in an AWS simulation. A general continuity equation was employed to mathematically represent the paddy water balance, and its six parameters were investigated. The results show that the AWS estimates are sensitive to the irrigation efficiency, drainage outlet height, minimum ponding depth, and infiltration, with the irrigation efficiency appearing to be the most important parameter; thus, they should be carefully selected. Multiple combinations of parameter values were observed to provide similarly good predictions, and such equifinality produced the substantial amount of uncertainty in AWS estimates regardless of the modeling approaches, indicating that the uncertainty should be counted when developing water management plans. We also found that agricultural system simulations using only literature-based parameter values provided poor accuracy, which can lead to flawed decisions in the water resources planning processes, and then the inefficient use of public investment and resources. The results indicate that modelers’ careful parameter selection is required to improve the accuracy of modeling results and estimates from using not only information from the past studies but also modeling practices enhanced with local knowledge and experience.

Download Full-text

Evaluation of the VIKOR and FOWA Multi-Criteria Decision Making Methods for Climate-Change Adaptation of Agricultural Water Supply

Water Resources Management ◽

10.1007/s11269-019-02274-z ◽

2019 ◽

Vol 33 (8) ◽

pp. 2867-2884 ◽

Cited By ~ 6

Author(s):

Parvin Golfam ◽

Parisa-Sadat Ashofteh ◽

Hugo A. Loáiciga

Keyword(s):

Climate Change ◽

Decision Making ◽

Water Supply ◽

Climate Change Adaptation ◽

Multi Criteria Decision Making ◽

Agricultural Water ◽

Agricultural Water Supply

Download Full-text

Hydraulics

10.12737/1045063 ◽

2020 ◽

Author(s):

Sergey Vol'vak

Keyword(s):

Graduate Students ◽

Water Supply ◽

Hydraulic Drive ◽

Study Guide ◽

Agricultural Water ◽

Field Of Study ◽

Hydraulic Machines ◽

Agricultural Water Supply

Study guide corresponds to the program discipline "Hydraulics" and consists of two parts: "Hydraulics and hydraulic machines" and "the Dredging of agricultural processes". This course focuses on the theory of hydraulics, design and operation of hydraulic machines, fans, compressors and other means of dredging of agricultural processes, provides information about the hydraulic drive, the basics of reclamation and mechanized irrigation and agricultural water supply, data on hydropneumatische in agriculture. For students of all forms of education in field of study 35.03.06 "Agroengineering", as well as for graduate students, teachers and technical workers of agriculture.

Download Full-text

Impacts on the utilization degree of canal water caused by agricultural water reallocation: a case study from China

Water Policy ◽

10.2166/wp.2015.162 ◽

2015 ◽

Vol 17 (5) ◽

pp. 815-830 ◽

Cited By ~ 1

Author(s):

Xiaoping Dai ◽

Yuping Han ◽

Xiaohong Zhang ◽

Daoxi Li ◽

Jing Chen

Keyword(s):

Water Supply ◽

Yellow River ◽

Household Head ◽

Water Transfer ◽

Water Diversion ◽

Agricultural Water ◽

Canal Water ◽

Current Compensation ◽

Compensation Methods ◽

Agricultural Water Supply

Our study area in the People's Victory Canal Irrigation District (PVCID), which is downstream of the Yellow River in China, has been undergoing agricultural water transfer to the city for municipal uses. Water supply condition data from PVCID are used to analyze the impacts of water reallocation on agricultural water supply quality, and field survey data in PVCID are used to examine the impacts of agricultural water supply quality on the utilization degree of canal water. Several issues on the current compensation methods are also discussed. Results show that the amount of irrigation water and the number of days for irrigation are affected by water reallocation, especially when the amount of water diversion significantly decreases. Regression results show that timeliness and adequacy of canal water are important factors affecting the utilization degree of canal water. Several factors reveal significant association with the utilization degree of canal water, such as the water price of canal water, water fee charge methods, soil texture of the largest cropland, precipitation, and age of farm household head. Current compensation methods can hardly compensate for the decrease in canal water reliability. Some recommendations are put forward to compensate for the adverse effects of agricultural water transfer.

Download Full-text

Anomaly Detection in Reservoir Water Level Data Using the LSTM Model Based on Deep Learning

Korean Society of Hazard Mitigation ◽

10.9798/kosham.2021.21.1.71 ◽

2021 ◽

Vol 21 (1) ◽

pp. 71-81

Author(s):

Mi-Hye Yang ◽

Won-Ho Nam ◽

Han-Joong Kim ◽

Taegon Kim ◽

An-Kook Shin ◽

...

Keyword(s):

Anomaly Detection ◽

Water Supply ◽

Water Level ◽

Detection Algorithm ◽

Agricultural Water ◽

Level Measurement ◽

Level Data ◽

Water Level Data ◽

Agricultural Water Supply ◽

Agricultural Reservoir

Weather and hydrological phenomena have been changing due to climate change as evidenced by localized torrential rainfall and precipitation falling by more than 30% compared to the annual average. From 2013 to 2017 the ninety-nine reservoirs reached a water storage rate of 0%, making a secure stable water supply for agriculture uncertain. There is an increased need for information regarding agricultural water management to respond to the changes in the agricultural environment and climate. Therefore, automatic water level measurement facilities have been introduced to determine the real-time reservoir storage capacity and agricultural water supply. According to the Ministry of Agriculture, Food and Rural Affairs' guidelines for the installation and operation of water level measurement instruments, automatic water level facilities are currently installed at 1,734 reservoirs and 1,880 irrigation canals, with water level data generated at 10-minute intervals. The official recognition of hydrological water level data for agricultural reservoirs increased from six in 2016 to forty-nine in 2019. Anomaly detection algorithm methods for data regarding the agricultural reservoir level as well as quality control measures based on agricultural reservoir characteristics are required to minimize data quality degradation and generate reliable hydrological data over time. Though it was practically impossible to analyze the correlation between the water level or run-off and influential factors such as weather and terrain, recently a non-linear hydrological analysis has been possible using models such as Artificial Neural Networks (ANNs). This study aims to present an anomaly detection algorithm for reservoir level data using deep learning based LSTM (Long Short-Term Memory) models, in combination with other neural networks for managing quantitative information of agricultural water supply.

Download Full-text