초록 ▼

1. 서론
최근 기후변화와 관련하여 극심한 기상이변으로 홍수와 가뭄으로 인한 사회경제적 피해가 증가하고 있다. 기후변화에 관한 정부간 협의체(Intergovernmental Panel on Climate Change, IPCC)에서는 기후변화에 따른 미래 전망을 통해 전 지구의 평균 온도의 상승을 예측하고 있다. 전지구기후모델(Global Climate Model, GCM)을 이용한 모델링 과정에서 생산된 기상 변수들은 농업 및 수자원을 포함한 다양한 분야에 있어서 기후변화의영향을 평가하는데 활용되고 있으며, 많은 기후변화 관

1. 서론
최근 기후변화와 관련하여 극심한 기상이변으로 홍수와 가뭄으로 인한 사회경제적 피해가 증가하고 있다. 기후변화에 관한 정부간 협의체(Intergovernmental Panel on Climate Change, IPCC)에서는 기후변화에 따른 미래 전망을 통해 전 지구의 평균 온도의 상승을 예측하고 있다. 전지구기후모델(Global Climate Model, GCM)을 이용한 모델링 과정에서 생산된 기상 변수들은 농업 및 수자원을 포함한 다양한 분야에 있어서 기후변화의영향을 평가하는데 활용되고 있으며, 많은 기후변화 관련 연구가 국내외에서 수행되어왔다(강지윤 등, 2013; 김대준 등, 2012; 배덕효 등, 2011; 장재호와 안종호, 2012).
기후변화는 장기간에 걸쳐 진행되고 이에 따라서 미래의 기후 특성이 변화될 수 있는 데, 이는 과거에 관측된 기후 특성을 고려하여 설계된 다양한 시설물들의 용량을 초과하는 극심한 홍수 및 가뭄 등 기상이변으로 인하여 사회에 많은 피해를 끼칠 가능성을 의미한다. 이에 정부는 사회 각 분야의 취약성을 분석하고 적응 방안을 수립하고 있으나(국립환경과학원, 2012) 수립된 적응 방안을 시행하기 위해서는 막대한 예산을 필요로 하기 때문에 정책결정에 있어서 신중을 기해야 한다. 기후변화에 따른 취약성 분석 및 영향 평가를 바탕으로 제시된 적응 방안들이 정책적으로 반영되려면 모든 분석들이 과학적인 근거를 기반으로 생산 제공되어야 하고 정책 결정자들이 이해할 수 있는 언어로 정보를 전달하는 과정이 필요하다. 먼 미래에 대한 특정 시나리오를 기반으로 하는 기후변화 연구의 특성을 고려할때 불확실성 관련 정보가 정책 결정자들을 위하여 제공되는 정보에 반드시 포함되어야 한다. 이에 IPCC에서는 정책결정자들을 위한 요약에서 분석된 결과들에 대한 불확실성 관련 정보를 함께 제시하고 있다.

Abstract ▼

In recent years, the extreme social and economic damages caused by flooding and drought have increased due to extreme weather. Extreme drought can cause great economic damage to agricultural productivity due to the reduction of the agricultural water supply, as well as an increase in water consumpti

In recent years, the extreme social and economic damages caused by flooding and drought have increased due to extreme weather. Extreme drought can cause great economic damage to agricultural productivity due to the reduction of the agricultural water supply, as well as an increase in water consumption by crops. Agricultural facilities for water supply include reservoirs, pumping stations, weirs, and infiltration galleries. According to a survey in 2011, there are 17,505 reservoirs in Korea, accounting for 25% of the total agricultural water facilities. However, 772,108 ha are irrigated by reservoirs, which accounts for 58% of the entire irrigated area. Therefore, as a major supplier of agricultural water in Korea, it is important to look into the behavior of reservoirsin relation to climate change. This requires estimation of the changes in inflow amounts from upstream areas, water consumption by cropsin irrigated areas, and water storagelevel of reservoirs. Finally, agricultural drought vulnerability should be analyzed by consideringwater demand and available water supply viaagricultural facilities for each crop growth stage, taking into account temporal and spatial changes. With this information, an adaptation plan for agricultural reservoir drought can be provided. Therefore, the objective of this study is to evaluate the impacts of climate change on agricultural water resources in Korea by considering the agricultural water supply through reservoirs, water demand from irrigatedcrop land, and changes in storage level and related agricultural drought according to the characteristics of the reservoirs.
104 reservoirs were selected based onthe maximum available data. The selected reservoirs were classified into five clusters based on storage ratio (lowest storage / max. storage) and time of minimum storage level. Among the 104 study reservoirs, 5 reservoirs which have stream flow gauge stations down stream of the reservoir, were selected to evaluate the applicability of SWAT on ungauged watersheds.
This study consisted of 3 sub‐components: 1) generating climate change scenario data through bias correction and statistical downscaling of multiple Global Climate Models (GCMs), 2) reservoir modeling using SWAT and HOMWRS, and 3) estimating the changes in watershed characteristics, including cluster analysis of study reservoirs and spatial analysis of the reservoir‐irrigated paddy area. The reservoir modeling included 4 sub‐items, including 1) evaluating the impacts of upstream inflow on available water supply, 2) evaluating the impact of ET changes on water demand within an irrigation district, 3) estimating the daily storage level of reservoirs based on the estimated demand and supply, and 4) providing an agricultural drought index based on the estimated storage level. This reservoir modeling approach was applied within the selected 5 representative reservoirs and then extended to all the remaining 99 study reservoirs.
Among 34 available GCMs, the KMA 12.5km resolution RCM and eight and ten GCMs for the RCP4.5 and 8.5 scenarios, respectively, were selected because the selected GCMs provided the 6 weather variables required for SWAT application. The selected GCMs were bias corrected and downscaled for historical (1976‐2005), future 2020s (1911‐2040), 2050s (2041‐2070), and 2080s (2071‐2100) periods using the non‐parametric quantile mapping method based on observed data from 76 Korea Meteorological Administration (KMA) stations. Compared to the observed data, the bias corrected data appropriately reflected the temporaltrends of the selected weather variables, precipitation characteristic index, and the three reservoir modeling outputs, namely inflow, water demand, and storage level. It was decided that the quantile mapping method is suitable for agricultural reservoir analysis in which reproducing temporal trends is important for management purposes.
The bias‐corrected climate data for future periods showed the highest uncertainties in the precipitation variable according to the selection of GCMs by showing different (increasing or decreasing) trends compared to the historical period. However, both the minimum and maximum temperature variables increased in all GCMs, regardless of RCP scenarios. The future scenario data of other weather variables showed a tendency to converge closely to the past observations.
In the case of the RCP8.5 scenario, most of the eleven GCM data, including the KMA 12.5km RCM data, showed a tendency to increase during most months. As a result, the 30‐year monthly mean of the multi‐model ensemble (MME) showed a 5.6% increase in total precipitation.
When the MME was used, inflow to reservoirsin the future period (2011~2040) increased by 7.8% and 9.3% for the RCP4.5 and 8.5 scenarios, respectively, mainly due to the increase in precipitation. Similarly, irrigation water demands in 2020s increased 0.7% and 0.5% for RCP4.5 and 8.5, respectively, due to the increase in temperature. As a result, the water storage level increased by 2.3% and 1.6%, respectively, for RCP4.5 and 8.5 due to the combined influence of the increase in inflow and in water demand.
Clustering reservoirs based on characteristics such as storage capacity and ratio of watershed area to benefitted area cannot explain the responses of reservoirs within each cluster which show a wide range of variations in storage levels. When storage rate and time with minimum storage level were used for reservoir clustering, variations in each cluster decreased and it has been considered that clusters with the lowest storage rate can be most vulnerable to severe drought under climate change. However, inflow and water demand showed similar temporal patterns among clusters by showing an increasing trend, regardless of clustering methods.
As far as the evaluation of the applicability of SWAT for integrated watershed management by including agricultural reservoirs in the watershed modeling frame, SWAT showed limitations in representing the processes of ponded paddy fields and linking required water demands in benefitted areas to reservoir storage. However, SWAT showed reasonable performance for the ungauged watershed conditions and the difference in inflow amounts between SWAT and HOMWRS showed a range from‐11.8% and 15.5% uncertainty envelope.
As an adaptation plan, structural, non‐structural (management‐based), and institutional measures are available. However, it was suggested that a no‐regret or low‐regret approach, which is based on non‐structural and institutional measures will be appropriate, considering the high uncertainty in climate change impact analysis results. Non‐structural measures may include efficient water management to minimize the loss of agricultural water supplies by adjusting the amount and time of irrigation and increasing the use of effective rainfall. This may require the development of a drought forecasting system, decision support system for effective irrigation, and an automatic water management system and monitoring system. Institutional solutions may include the inclusion of a governing body within the integrated watershed management concept for maximizing the use of surplus water and return‐flow by linking water resources‐related facilities. Also, the quality of agricultural water, environmental water, and the ecosys em should be considered in the concept of integrated water resources management.

목차 Contents

• 표지 ... 1
• ABSTRACT ... 2
• 1. 서론 ... 4
• 2. 연구 자료 및 방법 ... 7
• 2.1 연구 재료 ... 8
• 2.2 연구 방법 ... 15
• 3. 연구결과 ... 24
• 3.1 기후변화 시나리오 분석 결과 ... 24
• 3.2 SWAT의 적용성 평가 및 유출 모델링에 따른 불확실성 평가 ... 31
• 3.3 저수지 가뭄지표 분석 ... 33
• 4. 결론 및 토론 ... 50
• 부록 1 대상 저수지 제원 및 대표 저수지 유역 구분 ... 54
• 부록 2 CMIP5 토지이용 변화 분석 ... 58
• 부록 3 주요 관측소에서의 편의보정 후 기상변수의 월변화 ... 60
• 부록 4 한반도 대표 관측소에 대한 강우 특성 분석 결과 ... 72
• 부록 5 대표 저수지 유역에서의 SWAT 모델링 결과 ... 78
• REFERENCES ... 84
• 끝페이지 ... 85