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Article

Evaluation of Agricultural Water Supply and Selection of Deficient Districts in Yeongsan River Basin of South Korea Considering Supply Priority

by
Sehoon Kim
1,
Jiwan Lee
1,*,
Jinuk Kim
1,
Yongwon Kim
1,
Hyungjin Shin
2,*,
Inhong Song
3 and
Seongjoon Kim
4
1
Department of Civil, Environmental and Plant Engineering, Graduate School, Konkuk University, Seoul 05029, Korea
2
Rural Research Institute, Korea Rural Community Corporation, Ansan-si 15634, Korea
3
Department of Rural Systems Engineering, Global Smart Farm Convergence Major, Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea
4
Division of Civil and Environmental Engineering, College of Engineering, Konkuk University, Seoul 05029, Korea
*
Authors to whom correspondence should be addressed.
Water 2022, 14(3), 298; https://doi.org/10.3390/w14030298
Submission received: 30 September 2021 / Revised: 27 December 2021 / Accepted: 17 January 2022 / Published: 19 January 2022
(This article belongs to the Section Water Resources Management, Policy and Governance)
Browse Figures
Versions Notes

Abstract

:
Considering the priority of water supply, agricultural water supply capacity downstream of the Yeongsan River Basin was evaluated in this study. The water balance was analyzed using the Please check all author names carefullyMODSIM-decision support system (MODSIM-DSS) to evaluate the agricultural water supply capacity. MODSIM-DSS can also be used to analyze the watershed demand status and agricultural water supply facilities. In this study, the watershed inflow and agricultural reservoir inflow (supply) data for each watershed were obtained using the SWAT outflow data. SWAT was calibrated and validated using 16-years (2005–2020) of daily streamflow data from one water level station and two weirs, by considering water withdrawal and return flows from agricultural, domestic, and industrial water use. The coefficient of determination (R2) was analyzed as 0.50 to 0.80 at three stations. Then, water balance analyses were performed for 41 years (1980–2020) by applying the SWAT outflow results to MODSIM-DSS. The results showed that agricultural water shortages of 517.8 × 106 m3, 520.0 × 106 m3, 579.8 × 106 m3, and 517.5 × 106 m3, occurred in 1992, 1994, 1995, and 2015, respectively. The efficiencies of agricultural water supply for these years were 58.2%, 58.0%, 53.2%, and 58.2%, respectively, which were lower than the 40-year average of 84.5%.

1. Introduction

In most countries, agricultural water accounts for more than 70% of the total water use, which additionally comprises domestic, industrial, and ecological water use. However, agricultural water has a low water use efficiency [1]. In South Korea, agriculture is a water-intensive sector, and accounted for 62% of total water consumption in 2011 [2]. A series of water resource problems have been caused by agricultural developments and climate change [3]. Particularly, the increasing frequency of extreme weather might enhance pressures on global agricultural water supply in the future. In addition, the general increase in water demand has resulted in a shortage of agricultural water [4]. Appropriate agricultural water management plays a crucial role in alleviating water resource stress [5].
Currently, agricultural water in Korea is mainly used for rice paddy farming. A large amount of agricultural water demand is concentrated between April and June, and the demand for water supply reduces in October [6]. However, in recent years, South Korea has experienced severe drought and water scarcity in spring (from April to June) [7]; in fact, some regions have faced severe droughts for more than three years (2013–2015) [8]. Long-term water shortages have led to indiscriminate use of agricultural water, which has led to severe water disputes.
Current water-related laws in South Korea do not specifically stipulate the priority of agricultural water supply [9]. The traditional use of agricultural water in South Korea has been managed by making and operating customary rules to sequentially supply water from areas close to a reservoir to the downstream regions [10]. To solve this agricultural water shortage problem, basin-unit water resource assessment and water balance analysis tools that can maximize water use efficiency during droughts are required; however, this must be preceded by water supply capacity assessments of existing agricultural hydraulic facilities.
In general, many models to assess water supply have been adopted internationally, such as the integrated water allocation model (IWAM), water evaluation and planning (WEAP) [11], RiverWare [12], and the river basin network flow model (MODSIM) [13]. To date, most studies have analyzed the effects of future climate change on agricultural water supply; to solve agricultural water shortages, water supply has been prioritized for the water transfer between basins and reservoirs [14,15,16,17,18]. However, none of these methods can completely solve the water crisis. The South Korean government has always prioritized water issues and has attempted to solve them by means of reservoirs and water transfer between basins. However, these methods have resulted in shortages in reservoir storage, water disputes, and basin conflicts, thus complicating agricultural water supply and causing significant crop damage [19]. In addition, over 17,500 reservoirs in South Korea were constructed before the 1940s and have been in use for over 70 years [20]. These reservoirs were designed to withstand droughts at a frequency of 10 years, and coping with the increasing demand for water due to climate change is difficult [21,22]. Ultimately, it was decided that Korea should address agricultural water shortages using the river management system through methods such as supplying water with optimal efficiency within the basin, pumping, and groundwater facility construction. Therefore, strategies to alleviate agricultural water scarcity in watershed areas and the proper management of vulnerable areas are critical.
Many researchers have suggested that when a reservoir cannot be operated due to drought, pumping facilities should be used as water supply priorities for agricultural water [23,24,25,26]. An et al. [27] evaluated an efficient management system using pumping stations, diversion weirs, and groundwater wells, and suggested that agricultural water supply would be possible; however, these studies suggested the ideal amount of water supply to solve the shortage of agricultural water, regardless of the national development plan. Therefore, there was a gap in the national plan for agricultural water resources.
Given this background, we analyzed water shortages of 41 years, including years with severe drought. In addition, we applied the water supply priority method, which can reduce the dependence on reservoirs and a supplementary water supply scenario according to the river management system regulations. The major objectives of this study were to (1) apply the current agricultural water supply facilities to identify vulnerable areas; and (2) apply a supplementary water supply scenario in case of agricultural water shortage and analyze resulting changes in shortage.

2. Materials and Methods

2.1. Research Method

The purpose of this study is to evaluate the agricultural water shortages in the Yeongsan River Basin by linking supply priorities with the watershed assessment tool and water network optimization model. Figure 1 shows a flowchart of this study. First, the input data for the conducted SWAT were used and the model was calibrated and validated in terms of the weir inflow and storage; the data of two points in the measurement by K-water, were collected. SWAT setup and parameter estimation were applied to the model based on a previous study. Then, the water balance networks were implemented by grouping the river basin into 8 regions using the MODSIM model, while considering agricultural reservoirs, pumping station, weirs, culverts, and wells. National planning data were used for agricultural irrigation facility and water transfer demand data, and SWAT simulation results were used for the water supply. Finally, the water supply scenario was applied in a total of 7 scenarios (details in Section 2.4) using a combination of 3 facilities (pumping station, weir, and well).

2.2. Study Area

The Yeongsan River Basin (3371.4 km2) is located in the southwestern region of South Korea (Figure 2a). Downstream of the Yeongsan River Basin (YRB) is the most important agricultural production area in South Korea. Since 1970, the cultivated area in the YRB has expanded due to agricultural development projects [29]. Consequently, the YRB has the largest proportion of land dedicated to agriculture in South Korea. The land use proportion in the YRB, as shown in Figure 2b, is as follows: urban (5%), rice paddy (32%), upland crops (18%), forests (34%), grasslands (1%), bare fields (2%), and water areas (6%). The annual average amount of available water resources in the YRB is 3 billion m3, accounting for 3.9% of the total water resources (75.3 billion m3) in South Korea [2]. Water management issues in this basin are related to water scarcity and the high rates of agricultural water demand in comparison with those at other basins.
In the YRB, four agricultural dams (Gwanggan (GWD), Jangsung (JSD), Gwangju (GJD), and Naju (NJD)), one water supply dam (Pyeongrim (PYD)), and two multifunctional weirs (Seungchon (SCW) and Juksan (JKW)) have been installed and are currently in operation. Figure 2a shows the YRB and six agricultural sub-basins located downstream of the YRB.

2.3. MODSIM-DSS

2.3.1. Model Description

The MODSIM-decision support system (MODSIM-DSS) is a river basin simulation and decision support model developed in 1978 by modifying the network model, SIMYLD [13]. This model was developed to simulate the physical operation and water distribution of facilities in a river basin. MODSIM-DSS optimizes water allocation in each simulation time step by using the Lagrangian relaxation algorithm, which is a network optimization technique that utilizes network flow programming. The objective function of the flow network at each time step is to minimize the cost of the flow network, to optimally allocate water among users [30]. In addition to representing the physical components of a river basin system, the links and nodes can be used to represent the artificial and conceptual elements for modeling the complex administrative and legal processes that govern water allocation [31].

2.3.2. Network Design

In MODSIM-DSS, the river system of the YRB was constructed by connecting a node that inputs data and a link indicating the direction of the river flow. Four types of nodes (NonStorage, Reservoir, Demand, and Flowthru) were applied separately for each purpose. The NonStorage node was used for watershed inflow, river maintenance water, and supply of agricultural facilities (pumping stations, diversion weirs, underground culverts, and groundwater wells). The Reservoir node implemented the operation of the reservoir and dam water storage. The Demand node was adopted for water supply by each basin, and the Flowthru node was used for return flow after water transfer and water supply from the facilities in the basin (Figure 3a).
Figure 3b illustrates the detailed water balance network of sub-basin 5004, which represents an agricultural area. The inflow node of sub-basin 5004 was divided into two types: domestic and industrial water, and agricultural water. The link was used to connect the demand and inflow nodes for the water supply. In addition to the inflow, the agricultural demand nodes utilized the water supply facilities gradually by considering the water supply priority. The steps in the water supply operation were as follows: (1) inflow; (2) river intake facilities (pumping station, diversion weir); (3) groundwater pumping facilities (underground culverts and groundwater wells); and (4) reservoir discharge. In particular, the water from groundwater pumping facilities and water remaining after supply were designed to return to the river maintenance water.

2.3.3. Datasets

Table 1 summarizes the datasets, associated sources, and measurement methods used to input the MODSIM-DSS node. As described above, the nodes in the model are of four types. For the NonStorage node, we considered the inflow, river maintenance water, and four types of agricultural facilities (pumping station, diversion weir, underground culvert, and groundwater well). SWAT modeling outputs for the YRB, including those for the upstream, were used as the natural inflow input data for MODSIM-DSS. For the river maintenance node, we obtained monthly reservoir release monitoring data from the Korea Ministry of Environment (KME). River intake monitoring data for pumping stations and diversion weirs were obtained from the Yeongsan River Flood Control Center (YRFCC). The underground culvert, groundwater well, agricultural reservoir locations, and specification information for YRB were obtained from the Korean Rural Community Corporation (KRC). Water demand scenarios for domestic, industrial, and agricultural water were obtained from the Ministry of Land, Infrastructure, and Transport (MOLIT) as input for the Demand node. In this study, the Flowthru node was considered for the return flow, and we applied the ratio of return flow as defined in Table 1.
MODSIM-DSS has the limitation that it considers all agricultural facilities located in the basin; therefore, each quantity was summed and one representative facility was selected for each sub-basin and applied to the model. The reservoirs were assigned as representative reservoirs, which had storage equivalent to the effective storage of all reservoirs located in each sub-basin. Subsequently, the storage of each reservoir was calculated by multiplying the highest and lowest reservoir rates of the representative reservoirs by the effective storage of all reservoirs located in the five sub-basins (Table 2). The pumping station and diversion weir supply water to the rice paddy area using river intake. In this study, actual river intake monitoring data were used. The majority of river intakes occurs during the summer irrigation period (June–September). The underground culvert and groundwater well use groundwater pumping to supply water to the upland crop area. The operation of these irrigation facilities is closely related to the crop management practices in the basin. Based on the current agricultural operations [29], the total operating time of the irrigation facilities for one year were 270 days (February–October) for culverts and wells. The specifications of the facility were used as input data for the MODSIM-DSS, the agricultural facility storage data was applied the cumulate of the available water supplied during the month (Table 2).

2.3.4. SWAT Modeling

Inflow records from 1980 to 2020 for five sub-basins were obtained from the SWAT modeling results. SWAT, which has been used as the MODSIM-DSS input data in many studies [14,15,16,30,31], is a long-term rainfall runoff watershed model developed by the Agricultural Research Service of the USDA-ARS to estimate the long-term runoff [32,33,34,35,36,37].
The parameters proposed by Kim et al. [28] were applied to the inflow results of SWAT for the YRB. As shown in Table 3, calibration and validation were conducted for Mareuk (MR), Seungchon weir (SCW), and Juksan weir (JSW), shown in Figure 2a. The applicability of SWAT was evaluated by using the coefficient of determination (R2), Nash–Sutcliffe model efficiency (NSE), root-mean-square error (RMSE), and percent bias (PBIAS). The R2 values ranging from 0.50 to 0.80 were analyzed at the 3 stations. The RMSE was 0.95–11.71 mm/day and the PBIAS ranged from −14.1% to 11.71%. Thus, the results were statistically significant at every calibration and validation station.

2.4. Scenario Definition

Recently, there has been an increase in the use of river water for agricultural purposes due to the deteriorating hydrometeorological conditions and droughts [38]. The water requirement has increased because of the lack of soil moisture, and the shortage of water due to the drop in water level in the groundwater wells and reservoirs is supplied by river water [38]. Dams are the main hydraulic structure in South Korea; however, it has become increasingly difficult to find suitable places to build dams in Korea due to geographical and environmental reasons. Therefore, in this study, water supply scenarios were developed to address agricultural water shortages in the YRB. In scenario, the development storage was defined as the cumulative maximum of a facility operation during the month.
Scenarios 1 and 2 focused on the increase in intake from rivers by using pumping stations and diversion weirs. The use of water from the YRB is controlled as part of water management and permission is required to use the river water; the maximum river intake plan data of the river information system of the YRFCC were applied.
Scenario 3 was designed for groundwater development using groundwater wells, and the groundwater well development plan data of the Ministry of Agriculture, Food and Rural Affairs (MAFRA) were applied. Subsequently, scenarios 4–7 combined two or more of the previous scenarios to analyze the facilities that contributed greatly to the mitigation of the agricultural water shortage. Scenarios 1, 4, 5, and 7, with common pumping stations, had a high water supply (Table 4).

3. Results and Discussion

3.1. Actual Water Supply Analysis Results of MODSIM-DSS

Prior to implementing the water supply scenarios, the land use and agricultural facilities installed for water supply were analyzed, and the data for the actual water supply and water shortages were derived using MODSIM-DSS. Table 5 summarizes the land-use ratio and the five types of agricultural water supply facilities (reservoirs, pumping stations, diversions weirs, underground culverts, and groundwater wells) in each sub-basin. In addition, the capacity of agricultural facilities (CAF) was expressed as the installed storage of agricultural facilities per agricultural area, including paddy and crop areas. A CAF of 1 indicates a significant reliance on the facility. Regarding the dependence on agricultural water supply in the YRB, the reservoir had the highest CAF (0.285), and the pumping station had the second highest CAF (0.133).
The results of the CAF showed that the ratio of agricultural area in each sub-basin and the storage of agricultural water facilities were proportional. The rice paddy and upland crop area of the sub-basin 5007 accounted for 38.29% and 17.74% of the total agricultural area in the YRB, respectively. The CAF of the reservoir in the 5007 sub-basin was 0.080, which was the highest in the YRB. In addition, pumping stations, underground culverts, and groundwater wells were utilized to supply agricultural water. Sub-basins 5004 and 5006 were also agricultural areas, where upland crop areas occupied more than 56% of each sub-basin, and the water supply was mainly dependent on the reservoirs. The CAF of groundwater wells in the sub-basin 5006 was 0.009, indicating the highest reliance on groundwater in the YRB (Table 5). Figure 4 illustrates the results of the water supply analysis according to water demand and potential water supply from 1980 to 2020. The supply capacity ratio of agricultural water was 53.2–84.5% for the 41-year period. The agricultural water shortage exceeded 40% in 1988, 1994, 1995, and 2015, and South Korea was reported to have experienced a severe drought [39,40,41]. In 1988, the annual average precipitation and total runoff were 898.3 mm and 396.5 mm, respectively, and the agricultural water shortage was calculated as 512.5 × 106 m3. In addition, the agricultural water shortages in 1994, 1995, and 2015 were 520.0 × 106 m3, 579.8 × 106 m3, and 517.5 × 106 m3, respectively, and the agricultural water supply efficiencies were 58.0%, 53.2%, and 58.2%, respectively, which were lower than the 41-year average of 84.5%.
The monthly agricultural water demand pattern based on the year of agricultural water shortage is illustrated in Figure 5. Paddy constitutes 88% of the total crop production in South Korea and is mostly cultivated in the summer; consequently, irrigation demand is high in summer, and precipitation is also high [42]. The rice paddy growing season begins in mid-May and continues until mid-June; it is harvested from mid-September to the end of October [43]. Song et al. [44] found that the demand for irrigation was relatively small in July due to heavy rain (flood period), and the reservoir water level increased as the reservoir inflow increased. Therefore, the demand and supply trends of South Korea show that high demand and supply occur in June and August. However, July and September comprise the harvesting season, and are marked by heavy rains; therefore, supply and demand tend to be low.
The agricultural water supply capacity during the irrigation period differs depending on the status of the agricultural facilities located in each watershed. As shown in Figure 3b, the nodes were designed to be supplied in steps by prioritizing each source of water supply. In addition, nodes were used to examine the supply amount at each step, and the factors were derived for agricultural water shortage. Table 6 shows the analysis of the water supply capacity in the YRB; the average agricultural water supply was 55.9%. The agricultural water shortage at the sub-basins 5004, 5006, and 5007 was over 78.7 × 106 m3. According to land use data, the ratio of paddy to field crops in the area is 56% or higher, creating a high demand for agricultural water (Table 5).
Next, we calculated the factors affecting water shortage during the irrigation period. The water supply from natural inflow was the highest, accounting for 36.1 × 106 m3. Agricultural water, which is highly dependent on natural inflow, may increase the threat of agricultural drought with growing demand for water supply [45]. The pumping station had the second highest water supply at 25.4 × 106 m3. Wei et al. [46] described that water resources for the joint operation of a reservoir and pumping station under insufficient irrigation conditions were established considering regional water rights. To maintain a suitable water volume in the reservoir and meet the demand, Zhuan and Xia [47] established an optimal operating schedule using a pumping station. Im et al. [48] evaluated the contribution of agricultural facilities (reservoirs, pumping stations, diversion weirs, and groundwater wells) to the water supply. The pumping station and reservoir showed high water supply rates of 55.7% and 28.2%, respectively; the pumping station had the advantage of controlling the water intake and did not cause environmental problems such as submerged areas that occur in the case of the reservoir. In this study, identical results showed that, after natural inflow, the next highest proportion of agricultural water supply was at the reservoir and water pumping facilities. From the results of applying the operating rules recommended by KRC to the agricultural reservoirs (R.D), it was calculated that 6.9 × 106 m3 of the water was supplied for agricultural use, and groundwater wells, underground culvers, and diversion weirs had water supplies of 3.6, 2.1, and 1.7 × 106 m3, respectively.

3.2. Scenario Analysis

3.2.1. Changes in Water Shortage Due to Application of Water Supply Scenarios

We summarized the changes in agricultural water shortage by applying the scenarios in Table 3 to the vulnerable sub-basins analyzed in Table 6. Figure 6 shows the changes in agricultural water shortage according to the application of the scenarios. The results were estimated for the sub-basins 5004, 5006, and 5007, where the average agricultural water shortage was 78.7 × 106 m3 or more. Scenario 1 showed a high reduction in the shortage at a single facility, where the shortage reduced to 61.7 × 106 m3, 6.7 × 106 m3, and 7.6 × 106 m3, respectively, at each of the three sub-basins, and the water shortage in the sub-basin 5008 was resolved. However, scenarios 2 and 3 currently lack water supply to the sub-basins 5007 and 5008 (Table 5), and there are no plans for providing additional water supply in the future. Scenarios 2 and 3 were found to have low water supply efficiency downstream of the YRB. Dadar et al. [49] noted that pumping stations and wastewater processing units are the vital centers and main arteries of water transmission. They are major sources of water generation in water distribution networks. In this study, when a single facility was considered, the application of scenario 1 to a pumping station showed the highest contribution to water supply. In Figure 6, scenarios 4 and 7, which showed high rate of decrease in water shortage, had average sub-basin shortages of 14.6 × 106 m3 and 13.0 × 106 m3, respectively. The application of the scenarios to the watershed 5004, which is the most vulnerable area, resulted in average shortages of 33.2 × 106 m3 and 29.2 × 106 m3, respectively. The water shortage problem was largely resolved by applying scenario 7.
In addition, we summarized the changes in agricultural water shortage for the drought-struck years, derived from Section 3.1, as shown in Table 7. The change in water shortage was based on the amount of shortage in Table 6, and the percentage difference in water shortage under each scenario is summarized. In scenarios 1, 2, and 3, which were applied to a single facility, the monthly average water supply was 111.8 × 106 m3, 8.0 × 106 m3, and 3.6 × 106 m3, respectively, indicating that scenario 1 had the largest additional supply (Table 4). As a result, the average annual water shortage decreased to 68%, 26%, and 6%, respectively, showing the same result as in Section 3.2.1. In scenarios 4, 5, and 6, which combined two facilities, the annual average water shortage decreased by 75%, 71%, and 29%, respectively. The resolution of water shortage in the drought-struck years was highly efficient in scenario 4, which combined scenario 1 and scenario 2. The result of scenario 7, which was applied to three facilities, was compared with that of scenario 4; the average decrease in annual shortage and efficiency were higher by 11.3 × 106 m3 and 2%, respectively. The difference in the results was considered to be the effect of the added groundwater wells. However, groundwater wells have high development costs and are constructed in specific areas where surface water is not available; therefore, it is difficult to solve the overall water shortage in the watershed [48]. Perrone and Jasechko [50] suggested that excessive groundwater pumping and reduction in groundwater recharge can cause loss of groundwater storage capacity, streamflow depletion, land subsidence, and ecosystem damage. Therefore, in this study, scenario 4, using the existing river intake facility, is considered suitable for solving the water shortage.

3.2.2. Evaluation of Water Supply Scenario according to Flow Regime Analysis

Water supply through river intake and development of groundwater facilities can affect the natural streamflow and groundwater level. Kim et al. [51] evaluated the effects of groundwater abstraction on streamflow in the Sinduncheon watershed, and Kim et al. [52] performed a flow regime analysis on the river intake point downstream of a multi-purpose dam. Therefore, we applied the scenario evaluated in MODSIM-DSS to SWAT and performed a river flow regime analysis to determine a scenario that can solve the water shortage while minimizing the influence of natural streamflow. We conducted a flow regime analysis for 41 years in the sub-basin 5004, which is the most vulnerable area. The river flow regime was evaluated as flood flow (Q10), low flow (Q275), and drought flow (Q355); the changes according to the water supply scenarios are shown in Figure 7. The overall change in the flow regime gradually increased from Q10 to Q355. To analyze the rate of change in streamflow, Kim et al. [53] calculated the coefficient of flow regime (CFR), which compares the variability of flow rate using a value obtained by dividing the flow rate corresponding to Q10 by that corresponding to Q355. In this study, the same method was applied to analyze the change in flow regime according to each water supply scenario. Based on the actual water supply, it was shown that Q275 in each water supply scenario decreased by 12.4% to 23.6% and Q355 decreased by 47.3% to 55.2%; the change in flow regime by using the CFR also decreased from 68.5% to 96.2%. Scenarios 4 and 7, in which the water shortage was effectively reduced by more than 75%, were analyzed to have 21.4% and 21.7% reductions in Q275, respectively, in addition to flow regime changes of 54.7% and 55.1%, respectively, in Q355. In the flow regime change using CRF, scenario 4 showed a change of 94.3%, but scenario 7 showed the maximum difference in flow regime fluctuation at 96.2%. In all water supply scenarios applied in this study, the low flow and drought flow decreased, and the flow regime of the river changed. However, Jung et al. [54] evaluated the land use changes and effects of groundwater use through hydrological models, and reported that most of the drought flow occurred in spring and winter. Figure 6 shows that there is no significant difference in low flow during the summer period. Finally, scenario 4 was considered the most appropriate when the cost of additional facility development, rate of decrease of water shortage, and change in river flow regime are comprehensively evaluated.

4. Conclusions

In this study, we analyzed the agricultural water shortage and proposed a water supply scenario to address it. MODSIM-DSS was used to analyze the agricultural water shortage, and the SWAT simulation results were used as inputs for the natural inflow. The input specifications and operation rules for agricultural facilities were determined based on the actual operation monitoring data, and the current Yeongsan River Basin agricultural water supply status was reliably established. Specific scenarios involving additional agricultural water supply facilities were applied to address the water shortage.
In scenarios 1, 2, and 3, which were applied to a single facility, the monthly average water supply was 111.8 × 106 m3, 8.0 × 106 m3, and 3.6 × 106 m3, respectively, indicating that scenario 1 had the largest additional supply. As a result, the average annual water shortage decreased to 68%, 26%, and 6%, respectively. In scenarios 4, 5, and 6, which combined two facilities, the annual average water shortage decreased by 75%, 71%, and 29%, respectively. The resolution of water shortage in the drought-struck years was highly efficient in scenario 4, which combined scenario 1 and scenario 2.
The result of scenario 7, which was applied to three facilities, was compared with that of scenario 4; the average decrease in annual shortage and efficiency were higher by 11.3 × 106 m3 and 2%, respectively. The difference in the results was considered to be the effect of the added groundwater wells. However, groundwater wells have high development costs and are constructed in specific areas where surface water is not available; therefore, it is difficult to solve the overall water shortage in the watershed. Therefore, in this study, scenario 4, using the existing river intake facility, is considered suitable for solving the water shortage.
The results of this study recognize the problems of supplying agricultural water through reservoirs constructed in the past, and provide data to solve the water shortage problem in the basin itself by applying the method of water intake from the river and the groundwater development plan.

Author Contributions

Conceptualization, S.K. (Seongjoon Kim) and I.S.; methodology, S.K. (Sehoon Kim); validation, Y.K. and S.K. (Sehoon Kim); investigation, H.S. and J.K.; writing, S.K. (Sehoon Kim) and J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture Foundation and Disaster Response Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (320051-3 and 321070-4).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available upon reasonable request to the corresponding author.

Conflicts of Interest

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Water 14 00298 g001 550
Figure 1. Flowchart of the study [28].
Figure 1. Flowchart of the study [28].
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Water 14 00298 g002 550
Figure 2. Study area: (a) the river intake stations of YRB and (b) land use.
Figure 2. Study area: (a) the river intake stations of YRB and (b) land use.
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Figure 3. Network design of (a) YRB and (b) sub-basin 5004.
Figure 3. Network design of (a) YRB and (b) sub-basin 5004.
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Figure 4. Demand, supply and shortage of YRB.
Figure 4. Demand, supply and shortage of YRB.
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Figure 5. Monthly demand, supply, and shortage of agricultural water for vulnerable years.
Figure 5. Monthly demand, supply, and shortage of agricultural water for vulnerable years.
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Figure 6. Change in agricultural water shortage by applying the water supply scenarios during the irrigation period (June to September).
Figure 6. Change in agricultural water shortage by applying the water supply scenarios during the irrigation period (June to September).
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Figure 7. Flow regime analysis applying a water supply scenario to 5004 sub-basin.
Figure 7. Flow regime analysis applying a water supply scenario to 5004 sub-basin.
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Table
Table 1. Node types for network design in MODSIM-DSS.
Table 1. Node types for network design in MODSIM-DSS.
Node TypeDescriptionSource
NonStorageInflowNatural inflow of watershed and reservoirSWAT modeling data (1980–2020)
River maintenance waterMonthly flow data for river maintenance in reservoirsKME
Pumping stationRiver intake monitoring data for rice paddy areaYRFCC (2010–2020)
Diversion weirRiver intake monitoring data for rice paddy areaYRFCC (2010–2020)
Underground culvertTotal operating hours of the irrigation facilities for 1 year were 270 days (February–October)KRC
Groundwater wellTotal operating hours of the irrigation facilities for 1 year were 270 days (February–October)KRC
ReservoirMaximum, minimum, and target storage of the reservoirKRC
DemandProjected water demand scenario for domestic, industrial, and agricultural MOLIT
FlowthruReturn flowRatio of return flow (sum of residential, industrial, and agricultural) to runoffKME
Table
Table 2. Agricultural facility storage of YRB (unit: 106 m3/month *).
Table 2. Agricultural facility storage of YRB (unit: 106 m3/month *).
Sub-BasinReservoirUnderground CulvertPumping StationDiversion WeirGroundwater Well
No.R.RE.SMax.Min.No.A.O.SNo.A.O.S.M.S.No.A.O.S.M.S.No.A.O.S.M.S.
5004101Suyang11.915.51.870.3161.36.770.74.41000.61.7
500539Geumjeon4.86.51.0-0.030.20.810.00.1840.40.7
5006108Suyang11.917.32.0-0.0214.114.720.20.24692.34.6
500764Suyang11.914.01.7112.485.047.340.43.31561.01.0
500828Yulchi3.64.31.050.054.642.3---480.20.2
E.S.: effective storage, R.R.: representative reservoir, No.: number of stations, A.O.S.: actual operation storage, M.S.: maximum storage, and * agricultural facility storage is cumulate of the storage during the month.
Table
Table 3. Calibration and validation results for streamflow (MR) and weir inflow (SCW and JSW).
Table 3. Calibration and validation results for streamflow (MR) and weir inflow (SCW and JSW).
Evaluation CriteriaMRSCWJSW
Cal.Val.Cal.Val.Cal.Val.
R20.740.700.500.800.580.78
NSE0.520.500.660.660.560.67
RMSE (mm/day)11.718.5711.162.742.270.95
PBIAS (%)+5.5+9.4−2.4+16.4−14.1−0.7
Cal.: calibration (2005–2010), and Val.: validation (2011–2020).
Table
Table 4. Model simulation scenarios (unit: 106 m3/month *).
Table 4. Model simulation scenarios (unit: 106 m3/month *).
ScenariosDescriptionDevelop Storage
1Maximum river intake using pumping station111.8
2Maximum river intake using diversion weir8.0
3Ground water development by groundwater well3.6
4Maximum river intake using pumping station and diversion weir (scenario 1 + scenario 2)119.8
5Maximum river intake using pumping station and groundwater development by groundwater well (scenario 1 + scenario 3)115.4
6Maximum river intake using diversion weir and groundwater development by groundwater well (scenario 2 + scenario 3)11.6
7Maximum river intake using pumping station, diversion weir, and groundwater development by groundwater well
(scenario 1 + scenario 2 + scenario 3) 		123.4
* Agricultural facility storage is cumulate of the storage during the month.
Table
Table 5. Agricultural water shortage according to comparison of land use, facilities, and unit area in downstream of YRB.
Table 5. Agricultural water shortage according to comparison of land use, facilities, and unit area in downstream of YRB.
Sub-BasinLand Use (km2)Agricultural Facilities Storage (106 m3/Month *)Capacity of Agricultural Facilities (106 m3/1 km2) *
TotalUrbanPaddyCropForestWaterRESP.SD.WU.CG.WRESP.SD.WU.CG.W
5004408.1026.7128.9100.4125.726.311.931.270.730.300.600.0520.0060.0030.0010.003
5005219.0012.266.935.691.812.54.820.180.00-0.420.0470.002 0.004
5006483.6026.4161.4100.1154.841.011.904.130.17-2.320.0460.0160.001 0.009
5007264.3011.5101.246.984.620.111.905.030.422.350.990.0800.0340.0030.0160.007
5008150.707.343.217.051.931.23.604.59--0.150.0600.076 0.002
Total1525.784.1501.6300.0508.9131.044.2151.322.74.50.2850.1330.0070.0170.025
RES: reservoir, P.S: pumping station, D.W: diversion weir, U.C: underground culvert, G.W: groundwater well, agricultural facility storage is cumulate of the storage during the month, and * agricultural facilities storage per paddy and crop area.
Table
Table 6. Annual agricultural water demand, supply, and shortage during the irrigation period (unit: 106 m3/year).
Table 6. Annual agricultural water demand, supply, and shortage during the irrigation period (unit: 106 m3/year).
Sub-BasinDemandSupplyP.W.S.R
(%)		ShortageS.R
(%)		Water Supply Steps Based on PriorityR.F
1st2nd3rd
InflowR.DP.SD.WU.CG.W
5004192.973.538.1119.461.950.74.79.55.11.22.40.0
500559.625.342.534.357.519.62.51.80.00.01.70.3
5006218.1102.847.1115.352.946.512.234.80.20.09.30.2
5007197.1118.460.1 78.739.951.412.238.73.19.44.00.3
500859.254.491.94.88.112.33.042.40.00.00.64.0
Average145.474.955.970.544.136.16.925.41.72.13.61.0
P.W.S.R: potential water supply rate, S.R: shortage rate, R.D: reservoir discharge, P.S: pumping station, D.W: diversion weir, U.C: underground culvert, G.W: groundwater well, and R.F: remaining flow.
Table
Table 7. The water shortage change applying the water supply scenarios for the vulnerable years (unit: 106 m3, (%)).
Table 7. The water shortage change applying the water supply scenarios for the vulnerable years (unit: 106 m3, (%)).
YearsAgricultural Water Shortage during the Irrigation Period (June to September)
Scenario 1Scenario 2Scenario 3Scenario 4Scenario 5Scenario 6Scenario 7
1988339.4 (66)118.8 (23)37.2 (7)370.3 (72)349.6 (68)134.2 (26)380.4 (74)
1994380.3 (73)149.5 (29)24.0 (5)411.8 (79)391.1 (75)164.8 (32)422.6 (81)
1995378.9 (65)156.5 (27)44.0 (8)436.7 (75)404.7 (70)186.4 (32)451.0 (78)
2015349.2 (67)127.3 (25)23.5 (5)380.1 (73)359.3 (69)142.6 (28)390.1 (75)
Avg.361.9 (68)138.0 (26)32.2 (6)399.7 (75)376.2 (71)157.0 (29)411.0 (77)
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Kim, S.; Lee, J.; Kim, J.; Kim, Y.; Shin, H.; Song, I.; Kim, S. Evaluation of Agricultural Water Supply and Selection of Deficient Districts in Yeongsan River Basin of South Korea Considering Supply Priority. Water 2022, 14, 298. https://doi.org/10.3390/w14030298

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Kim S, Lee J, Kim J, Kim Y, Shin H, Song I, Kim S. Evaluation of Agricultural Water Supply and Selection of Deficient Districts in Yeongsan River Basin of South Korea Considering Supply Priority. Water. 2022; 14(3):298. https://doi.org/10.3390/w14030298

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Kim, Sehoon, Jiwan Lee, Jinuk Kim, Yongwon Kim, Hyungjin Shin, Inhong Song, and Seongjoon Kim. 2022. "Evaluation of Agricultural Water Supply and Selection of Deficient Districts in Yeongsan River Basin of South Korea Considering Supply Priority" Water 14, no. 3: 298. https://doi.org/10.3390/w14030298

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