Journal of the Korean Society of Agricultural Engineers ISSN 1738-3692 Vol. 56, No. 4, pp. 59~68, July 2014 eissn 2093-7709 DOI:http://dx.doi.org/10.5389/KSAE.2014.56.4.059 Projection of Future Water Supply Sustainability in Agricultural Reservoirs under RCP Climate Change Scenarios 남원호 * 홍은미 ** 김태곤 *** 최진용 ****, Nam, Won-Ho Hong, Eun-Mi Kim, Taegon Choi, Jin-Yong ABSTRACT Climate change influences multiple environmental aspects, certain of which are specifically related to agricultural water resources such as water supply, water management, droughts and floods. Understanding the impact of climate change on reservoirs in relation to the passage of time is an important component of water resource management for stable water supply maintenance. Changes on rainfall and hydrologic patterns due to climate change can increases the occurrence of reservoir water shortage and affect the future availability of agricultural water resources. It is a main concern for sustainable development in agricultural water resources management to evaluate adaptation capability of water supply under the future climate conditions. The purpose of this study is to predict the sustainability of agricultural water demand and supply under future climate change by applying an irrigation vulnerability assessment model to investigate evidence of climate change occurrences at a local scale with respect to potential water supply capacity and irrigation water requirement. Thus, it is a recommended practice in the development of water supply management strategies on reservoir operation under climate change. Keywords: Agricultural reservoir; climate change, irrigation vulnerability assessment model, RCP scenarios, sustainability I. 서론 * 기후변화는극한수문사상의규모, 빈도의증가, 기온및강수량패턴의변화, 증발산량및유출의시공간적변동을초래함으로써미래의기상, 수문사상의불확실성을증가시킨다 (Sung et al., 2012; Hwang et al., 2013; Jun et al., 2013). 이러한불확실성은기상학적, 수문학적가뭄의발생빈도및심도를증가시키고 (Sohn et al., 2014; Yoo et al., 2014), 수자원관리및계획수립에어려움을야기한다. 미래기후변화에대한농업분야의연구는초기에는논벼의미래기준증발산량산정에관한연구 (Hong et al., 2009; Chung, 2010) 및농업용수수요량추정 (Chung, 2009; Yun et al., 2011; * School of Natural Resources, University of Nebraska-Lincoln ** 서울대학교농업생명과학연구원선임연구원 *** 서울대학교조경 지역시스템공학부 **** 서울대학교조경 지역시스템공학부교수, 농업생명과학연구원 Corresponding author Tel.: +82-2-880-4583 Fax: +82-2-873-2087 E-mail: iamchoi@snu.ac.kr 2014 년 1 월 21 일투고 2014 년 7 월 22 일심사완료 2014 년 7 월 22 일게재확정 Jee et al., 2012) 에대한연구들이진행되어왔으며, 이후기후변화에따른유역의유출량추정및수문평가연구 (Kim et al., 2013b; Kim et al., 2013c; Ahn et al., 2014), 유입량의변화에따른저수지적응능력평가 (Chung et al., 2011) 에관한연구들이진행되었다. 미래기후변화에따른이상기후현상에대비하기위한수자원의전망에관한연구는수자원관리및계획의수립에필요한수문자료의경향성분석에대한연구 (Shin et al., 2010; Hwang, 2012; Lee et al., 2012; Jeung et al., 2013; Kim et al., 2014), 및가뭄의주기성및경향성, 발생빈도등의통계학적특성을분석하는연구 (Lee et al., 2013; Park et al., 2013) 가진행되었지만, 정책결정자와이해당사자들을위한기후변화적응능력및대응대책의정성적, 정량적평가지표가부재한상황이다. 미래기후변화로인한기상, 수문사상의불확실성 (Nkomozepi and Chung, 2014) 및기후변화에따른영향평가의불확실성 (Hwang et al, 2013; Nam et al., 2014) 은농업용저수지운영및관리측면에서용수공급체계의취약성이증가할가능성이있다. 기후변화전망에따르면강수량증가가예상되는동시에강수강도의증가및연강수일수의감소, 기온상승으로인한대기중수분요구량증가와이에따른증발산량증가등이전 59
망되므로 (Chung, 2009; Kim et al., 2013a), 강수량, 유역유입량, 증발산량등이주요변인으로작용하는농업수자원및농업용저수지용수공급능력의안정성평가및취약성기준이요구된다. 기후변화영향에대한농업용저수지의취약성및적응능력의정량적분석은기후변화적응대책의우선순위수립을위한필수적인요소로써기후변화에능동적으로대응하기위하여효율적인적응대책수립을위한과학적, 정책적도구가필요하다. 본연구에서는미래기후변화에의한기상및수문현상변화에따른농업수자원이수측면에서의용수공급능력을분석하고, 농업용저수지의용수공급에대한기후변화대응능력을평가하고자한다. 이를위해농업용저수지공급계획량과수요예측량의확률론및신뢰성해석기법을이용하여농업용수공급취약성을평가한선행연구 (Nam et al., 2012; Nam and Choi, 2014) 를기초로미래기후변화대응을위한용수공급취약성및지속가능성의개념을정의하고, 기후변화영향및취약성평가방법론의적용가능성을검토하였다. 용수공급취약성평가모형을미래기후변화시나리오에적용하여공급량과수요량의변화를분석하고용수공급취약성확률의변화를산정함으로써미래기후변화대응을위한용수공급지속가능성을평가하였다. II. 재료및방법 1. 농업용저수지의설계한발빈도 농업생산기반정비통계연보 (KRC, 2012) 에의하면농업용저수지는총 17,531개소이며, 한국농어촌공사 (Korea Rural Community Corporation, KRC) 관리저수지는 3,356개소 (19 %), 시 군관리저수지는 14,175개소 (81 %) 로구분된다. 농업용저수지중수리답면적 100 ha 미만의저수지는약 94 % 를차지하고있으며, 한국농어촌공사관리저수지 62 %, 시 군관리저수지 64 % 는설치경과년수가 50년이상으로노후화가심화되어저수지본연의용수공급기능을상실하거나농업용수공급및유지관리측면에서비효율성이지속되고있다. 농업용저수지의관리기관별설계한발빈도 (design frequency of drought) 현황을살펴보면, 한국농어촌공사관리저수지의약 78 % 는 10년빈도가뭄에도안정적으로농업용수를공급할수있도록설계된반면, 시 군관리저수지의경우약 63 % 는평년한발빈도로설계되어관리기관별상이한특징을보였다. 10년미만의설계한발빈도로축조된소규모저수지는약한가뭄에도안전한용수공급이어려울뿐만아니라, 최근기후변화로인한가을부터이듬해봄까지정례적인이상가뭄발생및극한강우의증가로인한동일강우대비유효강우량이감소됨으로써가뭄에대한재해위험도가상대적으로가중되고있다. 이 러한상황에서농업수자원시스템의운영방안수립에필수적인용수공급능력의평가척도로서보편적으로사용되는설계빈도는기후변화로인한수문사상의변화및수요의다변화에대응하지못하는단점이있다 (Nam et al., 2012). 따라서이수측면에서안정적이고지속가능한농업용수공급에관한재평가및전망이요구된다. 2. 용수공급취약성의정의본연구에서는기후변화에따른미래용수공급의지속가능성을평가하기위하여신뢰도 (reliability) 기반의관개취약성평가모델 (Nam et al., 2012; Nam, 2013) 을활용하였다. 관개취약성평가모델은이수측면에서관개시작시점의저수량및관개기간의누적공급계획량, 누적수요예측량을활용하여용수공급가능여부를판단할수있는모형 (Nam and Choi, 2014) 으로본연구에서는기후변화시나리오를사용하여용수공급능력을재평가하고취약성지표의변화를분석하였다. 농업용저수지용수공급시스템을구성하는주요요소로써유역에서저수지로부터공급가능한가용용수량 (potential water supply capacity, ) 과관개지역의필요수량 (irrigation water requirement, ) 을식 (1), 식 (2) 와같이공급계획량과수요예측량으로정의한다 (Nam et al., 2012). (1) (2) 는공급가능용수량, (reservoir capacity) 는저수지의유효저수량, (reservoir watershed inflow) 는유역의유입량, (reservoir overflow) 는월류량, 는관개시작시점, 은관개종료시점이다. 수요예측량 ( ) 은논의필요수량, (paddy water requirement) 로정의하였다. 저수지유역의유입량은강우-유출모형인 Tank 모형을사용하였고, 관개지구의필요수량은 FAO (Food and Agricultural Organization) 의 Modified-Penman (Doorenbos and Pruitt, 1977) 식과작물계수를적용하여작물의소비수량을산정하고침투량, 수로손실및관리손실을반영하여수요량을산정하였다. 저수지의월류량은저수지물수지모의를통하여저수지유입량으로부터수위가만수위시자연월류하는양으로산정하였다 (Nam et al., 2014). 관개지역의잔여수요예측량이유역으로부터저수지의공급계획량을초과하는경우를용수공급의실패상태 (failure) 로정의하였으며, 관개취약성지표 (irrigation vulnerability probability, 60 한국농공학회논문집제 56 권제 4 호, 2014
남원호 홍은미 김태곤 최진용 ) 는용수공급의실패상태를식 (3) 과같이확률로서표현된다. 즉관개취약성모형은관개시작시점에서관개종료시점까지의유입량과필요수량의누적값을활용하여용수공급실패여부를판단할수있다. (3) 3. 연구대상지역및기후변화시나리오본연구에서는 Table 1에도시한바와같이중부지역과남부지역으로구분하여다양한유역면적, 관개면적, 유효저수량특성을갖는 8개의소규모농업용저수지를선정하였다. 용수공급취약성및지속가능성평가의기준이되는저수지설계한발빈도는 Res. B와 Res. C를제외하고 10년설계한발빈도를가지며, 지배측후소의경우중부지방의수원측후소와남부지방의남원측후소의기후자료를활용하였다. Fig. 1은대상저수지및기상관측소위치를도시한것이다. 기상청 (Korea Meteorological Administration, KMA) 에서는적응대책수립을위한기후변화시나리오생산의일환으로기후변화에관한정부간협의체 (Intergovernmental Panel on Climate Change, IPCC) 5차평가보고서 (Fifth Assessment Report, AR5) 에적합한주요대표경로농도 (Representative Concentration Pathways, RCP) 에대하여전지구및지역기후모델을이용하여기후변화시나리오를생산, 제공하고있다. 현재기상청에서제공하고있는지역기후모델의해상도는한반도 (12.5 km), 남한상세 (1 km) 의두가지종류의기후변화시나리오를제공하고있다. 남한상세기후변화시나리오는지역기후모델 (HadGEM3-RA) 을통해생산된한반도기후변화시나리오를바탕으로통계학적상세화과정 (Kim, 2005; Kim et al., 2012) 을통해생산되며, PRIDE 모델 (PRISM based Downscaling Estimation Model) 에적용하여 1 km 해상도의관측격자자료를생산한다 (Kim et al., 2013d). 본연구에서는남원, 수원기상관측지점의위경도에근접한 격자점을기준으로남한상세시나리오의기온, 강수량과한반도시나리오의상대습도, 평균풍속, 일사량자료를사용하였다. Table 2와같이기후변화시나리오의분석기간을과거 (observed) 기준기간 30년 (1981~2010년) 과미래의경우단기 (2025s, 2011~2030년 ), 중기 (2055s, 2031~2070년 ), 장기 (2085s, 2071~2100년 ) 로분류하였다. 과거기준기간대비미래기간의기후인자및유역유입량, 용수공급취약성의변화분석을통해각농업용저수지에대한용수공급의지속가능성을전망하고자한다. Fig. 1 Locations of the agricultural reservoirs and the meteorological stations used in this study Table 1 Characteristics for agricultural reservoir in the study area Region Central region Southern region Meteorological station Suwon Namwon Reservoir name Symbol Effective storage capacity Watershed area Irrigated area Frequency of drought ( ) ( ) ( ) (year) Heungbu Res. A 1,840 1,320 652 10 Myeoku Res. B 600 830 246 5 Botong Res. C 1,071 716 364 3 Samindong Res. D 302 328 121 10 Dongma Res. E 99 127 45 10 Dochon Res. F 365 500 134 10 Nangye Res. G 177 118 58 10 Paldeok Res. H 1,034 1,172 510 10 Journal of the Korean Society of Agricultural Engineers, 56(4), 2014. 7 61
Table 2 Classification of climate change data based on period Classification Period Source Climate model CASE 1 (Baseline) 1981-2010 KMA (Korea Meteorological Administration) Observed data CASE 2 (2025s) 2011-2040 CASE 3 (2055s) 2041-2070 CASE 4 (2085s) 2071-2100 RCP scenarios (RCP 4.5, 8.5, source of KMA) HadGEM3-RA 였으며, 2055s의경우원기호, 2085s의경우 X 기호로표시하였다. 과거기준기간대비미래기간 (2011년~2100년 ) 의연별기후요소의분포를도시하였으며, 미래 3기간에대한최소 / 최대평균기온증감량및최소 / 최대총강수량변화율에해당되는 III. 적용및고찰 1. 기후요소인자의변화분석 본연구에서는 RCP 기후변화시나리오에따른기후요소 ( 평균기온, 총강수량 ) 및기준증발산량대한과거 (Observed) 대비미래 3기간 (2025s, 2055s, 2085s) 의변화율을산정하였다. Table 3에도시한바와같이평균기온의경우미래 3기간모두상승하였으며, RCP 8.5 시나리오의 2085s 기간상승폭이수원과남원측후소모두 2.1 C로상승폭이가장크게나타났다. 강수량의변화는남원측후소의 2085s 기간을제외하고미래 3기간모두상승하였으며, RCP 8.5 시나리오의 2055s 기간의상승폭이측후소별로각각 11.2 %, 14.1 % 로분석되었다. 기준증발산량은 RCP 4.5 시나리오의경우 0.7~3.2 %, RCP 8.5 시나리오의경우 3.3~6.9 % 의증가율을보였으며, RCP 8.5 시나리오의 2085s 기간의증가율이측후소별로각각 6.7 %, 6.9 % 로전망되었다. Fig. 2와 Fig. 3은측후소별 RCP 시나리오에대한미래단기, 중기, 장기기간의연평균기온과연총강수량을변화를비교한것이다. X축은과거기준기간대비미래기간의평균기온에대한증감량 (degree) 이고, Y축은과거기준기간대비미래기간별연총강수량에대한변화비율 (change ratio) 이다 (Nkomozepi and Chung, 2014). 미래 2025s에해당되는년도의각년도별평균기온증감량과총강수량의변화율을삼각형기호로표시하 (a) RCP 4.5 (b) RCP 8.5 Fig. 2 Projected yearly relative annual precipitation change and absolute temperature change in Suwon Table 3 Comparisons of meteorological factors between the baseline period (KMA) and climate change period (RCP scenarios) Meteorological station Suwon Namwon Factors Observed RCP 4.5 RCP 8.5 2025s 2055s 2085s 2025s 2055s 2085s PREC 1) 1,312 1,610 1,673 1,819 1,604 1,806 1,823 TEMP 2) 12.1 13.2 14.1 14.7 13.4 15.2 17.3 RE 3) 856 911 941 948 931 964 1,035 PREC 1,360 1,778 1,864 1,932 1,677 1,952 1,916 TEMP 12.3 13.1 14.1 14.7 13.4 15.1 17.2 RE 913 942 972 980 968 1,001 1,073 1) PREC: annual precipitation (mm), 2) TEMP: average temperature (degree), 3) RE: reference evapotranspiration (mm) 62 한국농공학회논문집제 56 권제 4 호, 2014
남원호 홍은미 김태곤 최진용 (a) RCP 4.5 Table 4 Comparisons of runoff in each reservoirs between the baseline period (KMA) and climate change period (RCP scenarios) Symbol Res. A Res. B Res. C Res. D Res. E Res. F Res. G Res. H Observed 10,772 (2,490) 6,627 (1,535) 5,681 (1,318) 2,536 (589) 995 (327) 4,037 (1,324) 924 (304) 9,806 (3,196) RCP 4.5 RCP 8.5 2025s 2055s 2085s 2025s 2055s 2085s 13,167 (2,563) 7,985 (1,454) 6,932 (1,386) 3,089 (644) 1,317 (243) 5,392 (978) 1,223 (225) 13,170 (2,353) 14,116 (3,299) 8,665 (2,044) 7,424 (1,756) 3,302 (790) 1,399 (242) 5,720 (973) 1,299 (225) 13,962 (2,345) 16,135 (4,274) 9,786 (2,355) 8,339 (1,963) 3,760 (962) 1,459 (265) 5,903 (1,131) 1,350 (249) 15,210 (3,276) 13,375 (2,933) 8,204 (1,817) 7,028 (1,561) 3,121 (702) 1,216 (257) 4,985 (1,029) 1,129 (239) 12,194 (2,471) 15,443 (4,435) 9,482 (2,743) 8,125 (2,355) 3,614 (1,057) 1,479 (330) 6,050 (1,336) 1,373 (307) 14,762 (3,222) 15,658 (4,169) 9,617 (2,576) 8,241 (2,210) 3,667 (990) 1,346 (287) 5,510 (1,159) 1,250 (266) 13,460 (2,802) Units: average (10 3 m 3 ), standard deviation in parentheses (b) RCP 8.5 Fig. 3 Projected yearly relative annual precipitation change and absolute temperature change in Namwon 지점을연결하여, 미래기간별변화범위를표시하였다. RCP 4.5 시나리오에대비하여 RCP 8.5 시나리오에대한기후요소인평균기온및연강수량의평균및분산이크게산정됨에따라변화율폭이크게산정되었으며, 평균기온의경우 6.0 C 이상, 총강수량의경우 2배이상의변화율을갖는기간이분석되었다. 2. 용수공급인자의변화분석 기후변화에따른용수공급요소중저수지별유역유입량의분석기간별평균과분산을 Table 4와같이정리하였다. 과거기준기간의유역유입량을기준으로미래 90년간유역유입량의변화율이 RCP 4.5 시나리오의경우평균 20.5 (Res. B-2025s)~ 55.1 % (Res. H-2085s), RCP 8.5 시나리오의경우 22.2 (Res. G-2025s)~50.5 % (Res. H-2085s) 로증가할것으로전망되었다. 유역유입량의증가율은강수량의변화율과유사하며, 미래 Table 5 Comparisons of paddy water requirement in each reservoirs between the baseline period (KMA) and climate change period (RCP scenarios) Symbol Res. A Res. B Res. C Res. D Res. E Res. F Res. G Res. H Observed 4,830 (727) 1,822 (274) 2,697 (406) 898 (135) 316 (71) 940 (212) 410 (92) 3,580 (806) RCP 4.5 RCP 8.5 2025s 2055s 2085s 2025s 2055s 2085s 3,994 (724) 1,506 (273) 2,230 (404) 743 (135) 216 (52) 643 (154) 280 (67) 2,447 (586) 4,074 (823) 1,537 (311) 2,275 (460) 758 (153) 230 (54) 686 (161) 299 (70) 2,612 (614) 3,994 (818) 1,506 (308) 2,230 (457) 743 (152) 217 (53) 646 (158) 282 (69) 2,460 (603) 4,168 (814) 1,572 (307) 2,327 (455) 775 (151) 258 (61) 767 (182) 334 (80) 2,920 (694) Units: average (10 3 m 3 ), standard deviation in parentheses 3,832 (895) 1,445 (338) 2,140 (500) 713 (167) 241 (52) 719 (155) 313 (68) 2,735 (590) 4,456 (1,034) 1,681 (390) 2,488 (577) 829 (192) 293 (74) 874 (219) 381 (96) 3,327 (835) 평가기간에따라유역유입량의평균및분산이증가함으로써장기미래로갈수록불확실성이증가하고, 전망결과의일관성이낮아지는현상을보인다 (Bae et al., 2011; No et al., 2013). Table 5는저수지별관개지역의필요수량의분석기간별평균 Journal of the Korean Society of Agricultural Engineers, 56(4), 2014. 7 63
과분산을나타낸것이다. 기온의증가로인해기준증발산량은증가하였지만, 강우량의증가로인한유효우량의증가로관개지역의필요수량은감소하였으며, 특히 RCP 8.5 시나리오의 2085s기간에가장높은평균값과분산을나타내었다. 유역유입량의분포변화를살펴보기위하여, Fig. 4와 Fig. 5 와같이중부지역의 Res. D와남부지역의 Res. F의대표저수지를선정하여유역유입량변화를상자그림 (box plot) 으로도시하였다. 전체적으로기후변화에따라미래유역유입량의일분위값은현재의삼분위수준까지상승하는경향을보이며, 현재의평균유역유입량수준에서미래최저유역유입량이나타났다. 최대유역유입량은크게증가하는데, 특히 RCP 8.5 시나리오에서중기구간 (2055s) 에서는현재평균대비 2.5배수준의유역유입량이예상된다. 3. 미래기후변화를고려한용수공급취약성평가 Table 6과 Table 7은 Res. D와 Res. F 대상저수지의기후변화에따른용수공급취약성평가의분석결과를나타낸것이다. Res. D와 Res. F 저수지는 10년설계한발빈도를갖는유효저수량, 유역면적, 관개면적이유사한저수지로서, 관개취약성평가모델의공급계획량, 수요예측량, 관개취약성지표결과를정리하였다. 미래공급계획량및수요예측량은과거기준기간보다평균값은낮게산정이되었지만, 장기미래로갈수록 Table 6 Water supply vulnerability assessment based on RCP scenarios in Res. D Factors Observed A. PWS 1) 1,030 (108) Runoff 2,202 (591) 1,473 Overflow (615) B. IWR 2) 898 (135) C. WSF 3) 131 (173) RCP 4.5 RCP 8.5 2025s 2055s 2085s 2025s 2055s 2085s 915 (65) 2,760 (898) 2,147 (919) 743 (135) 172 (150) 922 (98) 2,845 (787) 2,225 (821) 758 (153) 164 (182) 928 (144) 3,404 (1,272) 2,779 (1,354) 743 (152) 185 (209) 936 (107) 2,710 (727) 2,077 (773) 775 (151) 161 (186) 925 (118) 3,101 (1,047) 2,478 (1,106) 713 (167) 212 (204) 982 (144) 3,003 (1,075) 2,323 (1,147) 829 (192) 153 (240) D. Z-index 0.758 1.151 0.904 0.884 0.866 1.041 0.637 E. IVP 4) 0.224 0.125 0.183 0.188 0.193 0.149 0.262 1) PWS: potential water supply, 2) IWR: irrigation water requirement, 3) WSF: water supply failure, 4) IVP: irrigation vulnerability probability Units: average (10 3 m 3 ), standard deviation in parentheses Fig. 4 Comparison of runoff based on RCP scenarios in Res. D Fig. 5 Comparison of runoff based on RCP scenarios in Res. F Table 7 Water supply vulnerability assessment based on RCP scenarios in Res. F Factors Observed A. PWS 1) 1,176 (118) Runoff 3,430 (1,191) 2,614 Overflow (1,227) B. IWR 2) 940 (212) C. WSF 3) 236 (243) RCP 4.5 RCP 8.5 2025s 2055s 2085s 2025s 2055s 2085s 990 (141) 4,451 (941) 3,827 (1,022) 643 (154) 347 (209) 1,033 (147) 4,660 (820) 3,992 (872) 686 (161) 347 (218) 994 (150) 5,133 (1,231) 4,504 (1,308) 646 (158) 348 (218) 1,123 (180) 3,975 (940) 3,217 (1,064) 767 (182) 356 (256) 1,073 (140) 4,877 (1,396) 4,169 (1,481) 719 (155) 354 (209) 1,207 (197) 4,097 (1,047) 3,256 (1,174) 874 (219) 333 (295) D. Z-index 0.973 1.663 1.590 1.592 1.390 1.696 1.131 E. IVP 4) 0.165 0.048 0.056 0.056 0.082 0.045 0.129 1) PWS: potential water supply, 2) IWR: irrigation water requirement, 3) WSF: water supply failure, 4) IVP: irrigation vulnerability probability Units: average (10 3 m 3 ), standard deviation in parentheses 64 한국농공학회논문집제 56 권제 4 호, 2014
남원호 홍은미 김태곤 최진용 분산은증가하였으며, RCP 8.5 시나리오의 2085s 기간에서최댓값이나타났다. 공급계획량이줄어드는결과는앞서유역유입량이증가하는현상과배치되어보이지만, 저수지가만수일경우에는유역유입량이모두방류되어공급계획량에포함되지않기때문에실제가용할수있는용수량은감소하는것으로분석되었다. 과거기준기간의용수공급취약성확률의경우 Res. D는 0.224, Res. F는 0.165로나타났으며, 이는설계한발빈도의 10년빈도 (1/10 = 0.100) 보다관개취약성이높은것으로산정된결과로해당저수지의설계시한발대응능력보다과거기간운영된한발대응능력이감소한것으로평가할수있다. 미래시나리오중단기기간 (2025s) 의경우, 관개취약성확률은 Res. D의경우 RCP 4.5, RCP 8.5 시나리오에따라각각 0.125, 0.193, Res. F의경우 0.048, 0.082으로산정되어과거기준기간에비하여취약성이낮아지는결과가나타났다. 이는과거기준기간대비미래공급계획량의감소량보다수요예측량의감소량이크게산정되었기때문이다. 두요소의차이로산정되는용수공급실패기준값 (z-index) 이증가하여, Res. D의미래시나리오 RCP 8.5의장기 (2085s) 를제외하고는 Res. D와 Res. F에대한모든구간에서관개취약성확률이감소하는경향을확인하였다. 공급계획량과수요예측량의변화에따라관개취약성이변화하므로, 저수지의특징에따라공급계획량과수요예측량을분석해야만관개취약성을구할수있다. 따라서일반화된결론을내리기에는한계가있지만, RCP 4.5 시나리오는단기구간 (2025s) 에서는관개취약성이크게개선되는반면, 중기, 장기구간에서는개선효과가적은경향은확인할수있었다. 또한, RCP 8.5 시나리오는중기구간 (2055s) 에서관개취약성개선효과가가장컸으며, 단기, 장기순으로개선효과가차등적으로발생하였다. Res. B, 남부지역의 Res. F, Res. G의경우과거기준기간의용수공급취약성확률은설계한발빈도와유사하게산정되었다. 하지만중부지역의 Res. D, 남부지역의 Res. E, Res. H의경우설계한발빈도보다높게산정되었으며, Res. C의경우과거설계한발빈도는 3년빈도였지만과거기준기간의취약성확률은 10년빈도로산정되었다. 저수지별, RCP 시나리오별로다양한결과를보였으며, 전반적으로미래로갈수록용수공급취약성확률은증가하였으며, 특히 RCP 8.5 시나리오의 2085s 기간의취약성확률이가장높은값을나타냈다. 용수공급취약성확률변화로부터전망된용수공급실패확률은단기적 (2025s) 으로는관개취약성이개선되나, 미래로갈수록관개수부족현상의발생빈도가잦아질것으로전망되었다. Fig. 6 Assessment of water supply sustainability based on RCP 4.5 scenarios in the central region 4. 미래용수공급지속가능성평가 미래기후변화에의한기상및수문현상의변동성은불확실성을내포하고있기때문에기후변화영향평가및대응정책수립과정의혼란을야기한다. 따라서수자원관리자에게불확실성을갖는정보를효과적으로전달하는것은주요한과정이다. 수자원시스템의지속가능성은생태환경및수문현상의변화에대한현재의목표를미래에도유지하는것으로정의되며 (Kay, 2000; Loucks, 2000; Fry et al., 2012), 본연구에서는용수공급취약성확률의과거, 현재, 미래의변화를분석함으로써미래용수공급의지속가능성을정량적으로평가하였다. Fig. 6 Fig. 9는과거기상자료및 RCP 시나리오에따른용수공급계획시점의취약성확률변화를도시한것으로, 저수지별, 미래기간별로상이한결과를보여주었다. 중부지역의 Res. A와 Fig. 7 Assessment of water supply sustainability based on RCP 8.5 scenarios in the central region Journal of the Korean Society of Agricultural Engineers, 56(4), 2014. 7 65
IV. 결 론 Fig. 8 Assessment of water supply sustainability based on RCP 4.5 scenarios in the southern region Fig. 9 Assessment of water supply sustainability based on RCP 8.5 scenarios in the southern region 미래기후변화시나리오의경우미래후기로갈수록온도, 강수량이증가하였지만, 기후변화는비선형적인현상으로온실가스감축대책을적용한 RCP 4.5 시나리오임에도불구하고저수지의용수공급취약성은증가하는경향을보였다. 저수지의저수용량, 즉용수공급능력은한정되어있으며, 강수의절대적양이아닌시기적인편차에의해용수공급능력이산정되기때문에 (Nam et al., 2014), RCP 시나리오별 / 저수지별로용수공급취약성확률은상이한결과가도출되었다. 기후변화대응을위해서는미래용수공급취약성의변화를평가기준으로서활용하여지속적인모니터링이가능하고기후변화에대응할수있는맞춤형관리전략을구분할수있을것으로판단된다. 본연구에서는미래기후변화에의한기상및수문현상변화에따른농업수자원이수측면에서의용수공급취약성을분석하고, 농업용저수지용수공급의기후변화대응능력을평가하였다. 신뢰성해석기법을바탕으로개발된용수공급취약성평가모형을미래기후변화시나리오에적용하여공급량과수요량의변화를분석하고용수공급취약성확률의변화를산정하였고, 미래기후변화대응을위한용수공급취약성및지속가능성의개념을정의하였다. RCP 시나리오를활용하여미래기후변화에따른연별기후요소의변화분석결과, 1) RCP 4.5 시나리오에대비하여 RCP 8.5 시나리오에대한기후요소인평균기온및연강수량의평균및분산이크게산정됨에따라변화율폭이크게산정되었다. 2) 유역유입량의증가율은강수량의변화율과유사하며, 미래평가기간에따라유역유입량의평균및분산이증가함으로써장기미래로갈수록전망결과의일관성이낮아짐으로써불확실성이증가하는경향을보였다. 3) 저수지별관개지역의필요수량의분석결과기온의증가로인해기준증발산량은증가하였지만, 강우량의증가로인한유효우량의증가로관개지역의필요수량은감소하였다. 4) 용수공급취약성평가는저수지별, RCP 시나리오별로상이하였으며, 특징적으로유역유입량과는달리공급계획량이줄어드는것으로분석되었다. 5) 용수공급취약성확률변화로부터전망된용수공급실패확률은단기적으로취약성확률이감소하는반면, 미래로갈수록용수공급취약성확률은증가하였으며, 특히 RCP 8.5 시나리오의 2085s 기간의취약성확률이가장높은값을나타냈다. 본연구에서제시한영향평가에따른농업용저수지의용수공급에대한취약성및지속가능성의정량적분석은효율적인적응대책수립을위한의사결정지원도구로써활용가능할것으로판단된다. 저수지별로미래기후변화에따른영향이다르므로, 향후저수지특성을분석하여비슷한패턴을갖는저수지로유형화할수있기를기대한다. 이논문은 2013년도정부 ( 교육부 ) 의재원으로한국연구재단의지원을받아수행된기초연구사업임 (2013R1A6A3A03019009). REFERENCES 1. Ahn, J.M., T.H. Im, I.J. Lee, and S.U. Cheon, 2014. Assessment of future river environment considering climate change and basin runoff characteristics. Journal 66 한국농공학회논문집제 56 권제 4 호, 2014
남원호 홍은미 김태곤 최진용 of the Korean Water Resources Association 47(3): 269-283 (in Korean). 2. Bae, D.H., I.W. Jung, B.J. Lee, and M.H. Lee, 2011. Future Korean water resources projection considering uncertainty of GCMs and hydrological models. Journal of the Korean Water Resources Association 44(5): 389-406 (in Korean). 3. Chung, G.H., M.H. Jeon, H.S. Kim, and T.W. Kim, 2011. Adaptation capability of reservoirs considering climate change in the Han river basin, South Korea. Journal of the Korean Society of Civil Engineers 31(5B): 439-447 (in Korean). 4. Chung, S.O., 2009. Climate change impacts on paddy irrigation requirement in the Nakdong river basin. Journal of the Korean Society of Agricultural Engineers 51(2): 35-41 (in Korean). 5. Chung, S.O., 2010. Simulating evapotranspiration and yield response of rice to climate change using FAO- AquaCrop. Journal of the Korean Society of Agricultural Engineers 52(3): 57-64 (in Korean). 6. Doorenbos, J., and W.O. Pruitt, 1977. Guidelines for Predicting Crop Water Requirements. FAO Irrigation and Drainage Paper 24, FAO, Rome. 7. Fry, L.M., D.W. Watkins, N. Reents, M.D. Rowe, and J.R. Mihelcic, 2012. Climate change and development impacts on the sustainability of spring-fed water supply systems in the Alto Beni region of Bolivia. Journal of Hydrology 468-469: 120-129. 8. Hong, E.M., J.Y. Choi, S.H. Lee, S.H. Yoo, and M.S. Kang, 2009. Estimation of paddy rice evapotranspiration considering climate change using LARS-WG. Journal of the Korean Society of Agricultural Engineers 51(3): 25-35 (in Korean). 9. Hwang, S.W., 2012. Utility of gridded observations for statistical bias-correction of climate model outputs and its hydrologic implication over west central Florida. Journal of the Korean Society of Agricultural Engineers 54(5): 91-102. 10. Hwang, S.W., Y.G. Her, and S.W. Chang, 2013. Uncertainty in regional climate change impact assessment using bias-correction technique for future climate scenarios. Journal of the Korean Society of Agricultural Engineers 55(4): 95-106 (in Korean). 11. Jee, Y.K., J.H. Lee, and S.D. Kim, 2012. Climate change impacts on agricultural water in Nakdongriver watershed. Journal of the Korean Society of Agricultural Engineers 54(3): 149-157 (in Korean). 12. Jeung, S.J., J.H. Sung, and B.S. Kim, 2013. Change projection of extreme indices using RCP climate change scenario. Journal of the Korean Water Resources Association 46(11): 1089-1101 (in Korean). 13. Jun, S.M., E.S. Chung, S.H. Lee, and Y. Kim, 2013. Development and application of robust decision making technique considering uncertainty of climatic change scenarios. Journal of the Korean Water Resources Association 46(9): 897-907 (in Korean). 14. Kay, P.A., 2000. Measuring sustainability in Israel s water system. International Water Resources Association 25(4): 617-623. 15. Kim, B.S., J.H. Sung, B.H. Lee, and D.J. Kim, 2013a. Evaluation on the impact of extreme droughts in South Korea using the SPEI and RCP 8.5 climate change scenario. Journal of the Korean Society of Hazard Mitigation 13(2): 97-109 (in Korean). 16. Kim, C.R., Y.O. Kim, S.B. Seo, and S.W. Choi, 2013b. Water balance projection using climate change scenarios in the Korean Peninsula. Journal of the Korean Water Resources Association 46(8): 807-819 (in Korean). 17. Kim, M.H., H.S. Kang, J.H. Lee, H.J. Baek, and C.H. Cho, 2013c. Estimates of the water cycle and river discharge change over the global land at the end of 21 st century based on RCP scenarios of HadGEM2-AO climate model. Atmosphere. Korean Meteorological Society 23(4): 425-441 (in Korean). 18. Kim, M.K., 2005. The applicability of the statistical downscaling method for climate change scenario in Korea. Asia-Pacific Journal of Atmospheric Sciences 41: 217-227 (in Korean). 19. Kim, M.K., M.S. Han, D.H. Jang, S.G. Baek, W.S. Lee, Y.H. Kim, and S. Kim, 2012. Production technique of observation grid data of 1km resolution. Journal of Climate Research 7(1): 55-68 (in Korean). 20. Kim, M.K., D.H. Lee, and J. Kim, 2013d. Production and validation of daily grid data with 1km resolution Journal of the Korean Society of Agricultural Engineers, 56(4), 2014. 7 67
in South Korea. Journal of Climate Research 8(1): 13-25 (in Korean). 21. Kim, S.U., Y.S. Lee, and C.E. Lee, 2014. The application of various non-parametric trend tests to observed and future rainfall data in the Nakdong River basin. Journal of the Korean Water Resources Association 47(3): 223-235 (in Korean). 22. Korean Rural Community and Agriculture Corporation (KRC), 2012. Statistical yearbook of land and water development for agriculture. Ministry for food, Agriculture, Forestry and Fisheries, Republic of South Korea (in Korean). 23.Lee, K., H.J. Baek, S. Park, H.S. Kang, and C.H. Cho, 2012. Future projection of changes in extreme temperatures using high resolution regional climate change scenario in the Republic of Korea. Journal of the Korean Geographical Society 47(2): 208-225 (in Korean). 24. Lee, Y.H., Y.J. Oh, C.S. Na, M.H. Kim, K.K. Kang, and S.T. Yoon, 2013. Vulnerability assessment on spring drought in the field of agriculture. Climate Change Research 4(4): 397-407 (in Korean). 25. Loucks, D.P., 2000. Sustainable water resources management. International Water Resources Association 25(1): 3-10. 26. Nam, W.H., T.G. Kim, J.Y. Choi, and J.J. Lee, 2012. Vulnerability assessment of water supply in agricultural reservoir utilizing probability distribution and reliability analysis methods. Journal of the Korean Society of Agricultural Engineers 54(2): 37-46 (in Korean). 27. Nam, W.H., 2013. Sustainability and operations evaluation of agricultural reservoirs based on probability theory. Ph.D. diss., Seoul National University. 28. Nam, W.H., E.M. Hong, and J.Y. Choi, 2014. Uncertainty of water supply in agricultural reservoirs considering the climate change. Journal of the Korean Society of Agricultural Engineers 56(2): 11-23 (in Korean). 29. Nam, W.H., and J.Y. Choi, 2014. Development of an irrigation vulnerability assessment model in agricultural reservoirs utilizing probability theory and reliability analysis. Agricultural Water Management 142: 115-126. 30. Nkomozepi, T., and S.O. Chung, 2014. Uncertainty of hydro-meteorological predictions due to climate change in the Republic of Korea. Journal of the Korean Water Resources Association 47(3): 257-267. 31. No, S.H., K.S. Jung, J.H. Park, and K.S. Ryoo, 2013. Water supply change outlook for Geum River basin considering RCP climate change scenario. Journal of the Korean Water Resources Association 46(5): 505-517 (in Korean). 32. Park, B.S., J.H. Lee, C.J. Kim, and H.W. Jang, 2013. Projection of future drought of Korea based on probabilistic approach using multi-model and multi climate change scenarios. Journal of the Korean Society of Civil Engineers 33(5): 1871-1885 (in Korean). 33. Shin, H.J., M.J. Park, H.K. Joh, G.A. Park, and S.J. Kim, 2010. Projection and analysis of future temperature and precipitation using LARS-WG downscaling technique for 8 meteorological stations of South Korea. Journal of the Korean Society of Agricultural Engineers 52(4): 83-91 (in Korean). 34. Sohn, K.H., D.H. Bae, and J.H. Ahn, 2014. Projection and analysis of drought according to future climate and hydrological information in Korea. Journal of the Korean Water Resources Association 47(1): 71-82 (in Korean). 35. Sung, J.H., H.S. Kang, S.H. Park, C.H. Cho, D.H. Bae, and Y.O. Kim, 2012. Projection of extreme precipitation at the end of 21st century over South Korea based Representative Concentration Pathways (RCP). Atmosphere. Korean Meteorological Society 22(2): 221-231 (in Korean). 36. Yoo, J.Y., H.H. Kwon, T.W. Kim, and S.O. Lee, 2014. Probabilistic assessment of drought characteristics based on homogeneous hidden markov model. Journal of the Korean Society of Civil Engineers 34(1): 145-153 (in Korean). 37. Yun, D.K., S.O. Chung, and S.J. Kim, 2011. Climate change impacts on paddy water requirement. Journal of the Korean Society of Agricultural Engineers 53(4): 39-47 (in Korean). 68 한국농공학회논문집제 56 권제 4 호, 2014