9(4)-06(이보라).fm

Size: px

Start display at page:

Download "9(4)-06(이보라).fm"

양균 모
6 years ago
Views:

1 w» wz, 9«4y(2007) Korean Journal of Agricultural and Forest Meteorology, Vol. 9, No. 4, (2007), pp. 247~259 k x RHESSys sƒ: x y 1 Á ³ 1 *Á½ 2 Áyk 3 Á y 4 Á½ 5 1 w w w y w, 2 w y w y zw, 3 University of North Carolina, USA, 4 w y k, 5 w y» w ( ; ) Evaluation of a Hydro-ecologic Model, RHESSys (Regional Hydro-Ecologic Simulation System): Parameterization and Application at two Complex Terrain Watersheds Bo-Ra Lee 1, Sinkyu Kang 1 *, Eunsook Kim 2, Taehee Hwang 3, Jong-Hwan Lim 4 and Joon Kim 5 1 Department of Environmental Science, Kangwon National University, Chuncheon 2 Department of Environmental Planning, Graduate School of Environmental Studies, Seoul National University, Seoul 3 Department of Geography, University of North Carolina, Saunders Hall, USA 4 Division of Forest Environment, Korea Forest Research Institute, Seoul 5 Global Environment Laboratory & Department of Atmospheric Sciences, Yonsei University, Seoul (Received November 9, 2007; Accepted December 12, 2007) ABSTRACT In this study, we examined the flux of carbon and water using an eco-hydrological model, Regional Hydro-Ecologic Simulation System (RHESSys). Our purposes were to develop a set of parameters optimized for a well-designed experimental watershed (Gwangneung Research Watershed, GN) and then, to test suitability of the parameters for predicting carbon and water fluxes of other watershed with different regimes of climate, topography, and vegetation structure (i.e Gangseonry Watershed in Mt. Jumbong, GS). Field datasets of stream flow, soil water content (SWC), and wood biomass product (WBP) were utilized for model parameterization and validation. After laborious parameterization processes, RHESSys was validated with the field observations from the GN watershed. The parameter set identified at the GN watershed was then applied to the GS watershed in Mt. Jumbong, which resulted in good agreement for SWC but poor predictability for WBP. Our study showed that RHESSys simulated reliable SWC at the GS by adjusting site-specific porosity only. In contrast, vegetation productivity would require more rigorous site-specific parameterization and hence, further study is necessary to identify primary field ecophysiological variables for enhancing model parameterization and application to multiple watersheds. Key words : RHESSys, Parameterization, Application, Multi-watershed Corresponding Author : kangsk@kangwon.ac.kr

2 248 Korean Journal of Agricultural and Forest Meteorology, Vol. 9, No. 4 I. k y w x d w» k w y y y dw. ùy y ³ k k yw ù (Law et al., 2004),» yƒ k k e w wš(white et al., 2002), m yƒ k y e w sƒw (Csiszar et al., 2004), k y(kang et al., 2006a) y e w sƒ (Ojima et al., 1994)w x w w k ƒ w. k y x Ÿw, y, k y, t y sww k w yw w k yw(ecosystem biogeochemistry) x. BIOMASS(McMurtrie et al., 1990), BIOME-BGC (Running and Coughlan, 1988), CENTURY(Parton et al., 1988), PnET(Aber and Federer, 1992), RHESSys (Band et al., 1996), 3PG(Landsberg and Waring, 1997) x k š., ü k yw x w r ù y ƒ ƒw. Eum et al.(2005) Kang et al.(2006b) BIOME-BGC» Ÿ x w y wwš d sƒw z,» w. k l RHESSys(Regional Hydro-Ecologic Simulation System) k y w x k y mw w k yw x ƒ. RHESSys l (GIS) k 3 œ k š w (Tague and Band, 2004). x ƒ, š l s m w. ù y š w k x ƒ. p ü m s,, m y w œ w, k w» w w. w w k yw wì w ƒ RHESSys k x w w. ù RHESSys k w (Baron et al., 1998;Meetemeyer et al., 2001). Band et al.(1996) eù k ew x»z y, Ÿw, w. Meentemeyer et al.(2001) e s t x y w w w» w RHESSys w. w Zierl et al.(2007) v RHESSys w d w,, d ƒ w. ü Kim et al.(2007) Hwang et al.(2007) RHESSys Ÿ x w, m, k yk y (net ecosystem exchange of CO 2 ) d y ww m œ s dƒ plot ³ œ y w Ÿ ³ y w RHESSys y y w. k dw k w. w sww ³ w k w ³ k dw» w y,, ƒ wš. w ü k k ƒ s Ÿ x, w y. RHESSys y w» w»,»z, x, ƒ

Lee : Evaluation of a Hydro-ecologic Model, RHESSys (Regional Hydro-Ecologic Simulation System)... 249 et al. 이한 두 소유역에 RHESSys를 적용 및 평가함으로써 RHESSys의 다중소유역 확장가능성과 문제점을 평가 하였다.

3 Lee : Evaluation of a Hydro-ecologic Model, RHESSys (Regional Hydro-Ecologic Simulation System) et al. 이한 두 소유역에 RHESSys를 적용 및 평가함으로써 RHESSys의 다중소유역 확장가능성과 문제점을 평가 하였다. 이를 위해 실측자료가 비교적 풍부한 광릉시 험림 유역에서 모형을 모수화한 후, 강원도 점봉산 강 선리 유역에 모형을 적용하였고 그 예측력을 한정된 실측자료를 바탕으로 평가하였다. 다유역에 동일모수 화를 적용하였을 때의 예측력을 분석함으로써 향후 중 대유역으로의 확장시 RHESSys의 모수화 방안과 한계 점을 제안하였다. II. 재료 및 방법 연구지역 본 연구의 대상유역은 경기도 포천시 소흘읍에 위치 한 광릉시험림 유역 (N37 45', E127 9', Fig. 1a)과 강원도 인제군 강선리에 위치한 점봉산 강선리 유역 (N38 02', E128 26', Fig. 1b)이다. 광릉시험림 유역 의 면적은 약 2.2 km, 해발고도는 90~530 m이다. 우 점종은 졸참나무(Quercus serrata), 당단풍(Acer pseudosieboldianum), 까치박달(Carpinus cordata), 서어나무 (Carpinus laxiflora) 등이다. 수령은 년이고 수고 는 m이다(oh et al., 2000). 연평균 기온은 2.1. o o o 2 Fig. 1. o 11.3 C이고 연평균 강수량은 1,482 mm이다. 점봉산 강 선리 유역의 면적은 약 7.5 km 이며 해발고도 7001,425 m 이다. 우점종은 신갈나무(Quercus mongolica), 당단풍(Acer pseudo-sieboldianum), 피나무(Tilia amurensis) 등이다. 수령은 년이고(Go, 1999), 수고는 광릉시험림 유역과 비슷한 m, 연평균 기 온과 강수량은 5.9 C와 1,958 mm이다. o 2 o 생태수문모형 RHESSys는 생태계 생지화학모형인 BIOME-BGC 와 유역수문모형인 TOPMODEL, 그리고 산악 미기상 모형인 MTCLIM이 결합된 군집(community) 모형이 다(Tague and Band, 2004). 수문과정들을 모사하는 TOPMODEL은 지형 지수와 토양 수평전달계수를 사 용하여 유역 내 각 격자의 표면유출과 지표면하유출의 기여면적을 계산한다. RHESSys는 임상도, 토양도, 지 형도 등의 GIS 공간자료를 바탕으로 환경적으로 균일 한 속성을 가진 모형구동의 최소단위를 설정한다(Zierl et al., 2007). 모형은 Basin, Hillslope, Zone, Patch, Strata 등의 위계적 공간구조를 가진다. 여기서 Basin 은 전체 유역, Hillslope는 Basin 내의 소유역, Zone 은 Hillslope 내에서 미세지형에 의해 결정되는 동일 2.2. RHESSys Location of study sites: a) Gangseonry watershed in Mt. Jumbong (GS) and b) Gwangneung Research Watershed (GN).

250 Korean Journal of Agricultural and Forest Meteorology, Vol. 9, No. 4 w»z, Patch Zone ü k m y, Strata ƒ Patch ü x y (Tague and Band, 2004; Zierl et al., 2007). RHESSys yw» w,, m,,, m y, yw w y dw.

4 250 Korean Journal of Agricultural and Forest Meteorology, Vol. 9, No. 4 w»z, Patch Zone ü k m y, Strata ƒ Patch ü x y (Tague and Band, 2004; Zierl et al., 2007). RHESSys yw» w,, m,,, m y, yw w y dw. Hwang et al.(2007) w RHESSys w. Hwang et al. (2007) m d syd syd w» RHESSys x, w ¾ «m y y w w w š, w» w «d w. w» RHESSys syd w, w ¾ ƒ ¾ œ w» w p w»ƒ. «d w w «ew syd «d yd w š, «d m w w x » x w Ÿ x š», w. Ÿ x Kim et al. (2007) w » w. Kang et al.(2006b) ü AWS d» d z w ù l ƒ w 1978 l 2006 ¾» w AWS d w»» ( š» r 2 =0.89,» r 2 =0.92),» (r =0.84)ƒ ƒƒ ƒ GIS Ÿ x 1:5000 e x l 30 m eš x(digital Elevation Model, DEM), 1:25,000 e x l 60 m DEM (Fig. 2). ArcInfo GIS vp w DEM l w w š, 1 w» w x (Topographical Wetness Index) w. W a = ln index a A L = tanβ (1) (2)» W index, a š ¼ w», β, A w» w, L w w š ¼. A ArcInfo w w. Ÿ x Landsat ETM+ w ù y e Fig. 2. GIS input data: a) the GN and b) the GSU

5 Lee et al.: G Evaluation of a Hydro-ecologic Model, RHESSys (Regional Hydro-Ecologic Simulation System)UUU 251 w 30 m w w š(hwang et al., 2007), y ƒ w ù y ƒ w x RHESSys d sƒw» w x Ÿ x m š (Wood Biomass Product, WBP), m ƒƒ w. Jung et al.(2002) Ÿ x ( 0.22 km ) d w 1982 l ¾ w. WBP ww ù l Lim(1998) ( 3) w w. ù l l w ƒ {š (D) 3 w (B) w š, w WBP w. B = αd β (3) α β z. Ÿ x Kim (2006) šw ¾ WBP w. Kim(2006) 10 plot w WBP Ÿ x WBPƒ ¾ gcm 2 y ¾ y 1 š s³ WBP gcm 2 y 1 šw. WBP Go(1999)ƒ ww ù l w Lim(1998) w. WBP ¾ gcm 2 y ¾ y s³ WBP gcm 2 y 1. m ew Time Domain Reflectometry(TDR) d w. w m Ÿ x v k d w 2002, AWS e d w 2005 m w. l m d w m s³w z x d w. LI-COR 2000 d» w d w. Ÿ x Kim(2006) 2005 v k d w 4.98 w š, 2005 š 4œ d w 5.58 x d w RHESSys y sƒ REHSSys w m k,, w w» v w. ù d w» w»ƒ» RHESSys k k ƒ y x» w» œw, z ƒ(spin-up) w. z ƒ w x»yw š, y» 200.»y RHESSys Ÿ x ¾, ¾ w z, ƒ x w. Ÿ x x y ww š, w sƒw. wr m p» ƒ w. m (sand:silt:clay) Ÿ 55:28:17, 64:27:9 w š, œ ƒƒ d w (Kang et al., 2006; Hwang et al., 2007; Table 1). Kim et al.(2007) RHESSys x Table 1. Values of soil parameters used in RHESSys modeling at the Gwangneung research watershed (GN) and the Gangseonry watershed in Mt. Jumbong (GS) Values Parameters Reference 0.48 GN 0.53 GS Porosity Measured pore_size_index Dingman (1994) psi_air_entry Dingman (1994) 55 GN 64 GS 28 GN 27 GS 17 GN 9 GS sand(%) clay(%) silt(%) Measured Measured Measured

6 252 Korean Journal of Agricultural and Forest Meteorology, Vol. 9, No. 4 Table 2. Values of eco-physiological parameters used in RHESSys modeling Values Parameters Reference Values Parameters Reference 30.8 proj_sla Measured in GN (E. Kim) 0.88 PLNR kfrag_base From Biome-BGC psi_open 51 froot_cn Measured in GN -2.2 epc.psi_close 100 day_leafon Measured in GN 900 epc.vpd_open 275 day_leafoff Measured in GN 2500 epc.vpd_close 50 ndays_expand Measured in GN epc.gl_smax 40 ndays_litfall Measured in GN epc.gl_c leaf_cn Measured in GN 2.0 lai_ratio 56.6 leaflitr_cn Measured in GN 1.0 leaf_turnover 1.0 proj_swa Waring and Running (1998) 0.7 livewood_turnover 1.8 alloc_stemc_leafc calibration 50 livewood_cn 0.4 alloc_livewoodc_woodc calibration 0.38 leaflitr_flab 0.77 deadwood_fcel 0.44 leaflitr_fcel 0.23 deadwood_flig 0.18 leaflitr_flig 1.20 alloc_frootc_leafc White et al. (2000) 0.34 frootlitr_flab 0.22 alloc_crootc_stem 0.44 frootlitr_fcel 0.2 alloc_prop_day_growth 0.22 frootlitr_flig White et al (2000) s w w 6 w y w. ƒ sƒw sy (saturated hydraulic conductivity, K sat ) ¾ w m sen1 ƒ j» w. sen1 wì» w, K sat sen2 y w š, Kim et al.(2007) w w. Ÿ x w d w sen1 sen2 yw. y Tague and Band(2004) w sen ¾, sen ¾ w Monte-Carlo x 1000 ww z w. ƒ Zierl et al.(2007) Nash-Sutcliffe modeling efficiency coefficient (ME) w w. N ( Si Oi 2 ) i = 1 ME = N ( Si Oi 2 ) i = 1 (4)» Si RHESSys d, Oi d, Oi d s³. w e (Specific Leaf Area, SLA) k (C:N ratio) x d w (Kim et al., 2007, Table 2). ü g w (Percent of Leaf Nitrogen in Rubisco, PLNR) Ÿw f1, f2, f3, f4 yw. f1 š, f2», f3», f4 k. y ww PLNR White et al.(2000) w, f3 f4ƒ f1 f2 w ƒ ùkû. f3 f4 w y ww š, PLNR f1, f2 White et al.(2000) w w. Table 3 White et al.(2000) w ü f3 f4 y k z, WBP d d ƒ ƒ f3, f4 w. m 2 (Root mean square error, RMSE)( 5) s³rw (Mean bias error, MBE)( 6) w. RMSE = N ( Si Oi 2 ) i = N (5)

7 Lee et al.: G Evaluation of a Hydro-ecologic Model, RHESSys (Regional Hydro-Ecologic Simulation System)UUU 253 Fig. 3. Daily streamflow and daily rainfall: a) Daily streamflow at the GN from 1982 to 1999 and simulated daily streamflow from RHESSys; b) Daily rainfall at the same period at the GN. MBE N i = 1 ( Si Oi) = N (6) WBP w y w w z, y w m w RHESSys x d sƒw. RHESSys m dw», x dw «(root zone) w «¾ ù m (%) w z d w ( 7). «¾ Ÿ v k d 0.75 m w. d x d e w Ÿ x m d w w x d w w. Soil Water Content (SWC in % volume) root zone_storage = root depth III. (7) 3.1. y d sƒ d RHESSys d w sen1 0.57, sen ƒ ME(=0.79). Kim et (sen1=0.80; sen2=1.00; ME=0.75) ƒ ù, x d w. w w RHESSys Hwang et al.(2007) w «d z» w. w sen1, sen2 w d d Fig. 5 s, 0.89 d d w w (p=0.01). ù Kim et al.(2007) šw w» w ƒw ú w d. w 1990 w d w w. x d w ƒ ù, x dw w w v ƒ, z x d d ³ w. RHESSsy ƒ» y w ƒ z sƒw v ƒ. ww w y x d w. f3 f4 y WBP ƒƒ -16~20% al.(2007)

8 254 Korean Journal of Agricultural and Forest Meteorology, Vol. 9, No. 4 Parameterization results at the carbon allocation parameters, f3, f4. We selected the point (simbol:+) with the minimum average of RMSE, percent RMSE, MB, percent MB. Fig. 4. Parameterization of carbon allocation parameters. Statistical criteria for comparison of observed and simulated wood biomass product (WBP, gcm 2yr 1) and Leaf area index (LAI) at the GN. Observed Simulated Parameter RMSE %RMSE MBE Value %MBE 2 2 Mean Max Mean Max (range) (gcm ) (gcm ) WBP LAI WBP LAI f3 ( ) f4 ( ) 0.4 Table ~8%의 변화를 보였다. 임목의 탄소분배비율 f3, f4 의 모수화 결과, f3은 1.8 그리고 f4는 0.4 에서 가 장 낮은 RMSE(=0.024 gcm 2)와 MBE(=0.0 gcm 2)를 보였다(Fig. 4). 이 때 실측값에 대한 백분율 RMSE 와 MBE는 각각 8.84%와 -0.17%였고, RHESSys가 예측한 최대 LAI 값은 실측 최대 LAI 값(=4.98)과 유사한 값(=4.56)을 보였다(Table 3). 1991년부터 2004년까지 광릉시험림 유역의 WBP 연변화 경향에서 2001년의 WBP가 다른 해에 비해 급격히 감소되는 것을 볼 수 있다. Kim(2006)은 이 러한 WBP의 급격한 감소를 2001년 봄의 건조한 기 후와 연관하여 설명하였다. 본 연구에서 결정된 f3과 f4를 사용하여 모형을 구동시킨 결과, 광릉시험림 유역 에서 추정된 WBP 변화경향과 유사하게 RHESSys의 2001년 WBP도 급격히 감소하였고, 절대값 역시 상당 히 유사하게 모사하였다(Fig. 5). 광릉시험림 유역의 광릉 플럭스 타워 부근에 설치된 TDR센서의 일 단위 토양수분 값과 RHESSys의 일 단위 토양수분 예측값을 비교해 본 결과, 상관계수는 0.80으로 실측값과 예측값 사이에 유의한 상관관계를

9 Lee et al.: G Evaluation of a Hydro-ecologic Model, RHESSys (Regional Hydro-Ecologic Simulation System)UUU 255 Fig. 5. Observed wood biomass product (WBP) and simulated WBP at the GN. WBP measured by tree-ring data from 1991 to 2004 and simulated WBP from RHESSys. (p<0.001; Fig. 6a). ù RHESSys d sƒ š. p d m 30% ƒ x e wš x d še dw d w ùkþ (Fig. 6a) Ÿ x y w, sen1, sen2, f3, f4 RHESSys g l 2005 ¾ x k z TDR w d 2005 m RHESSys d m w (Fig. 6b). Ÿ x œ (=0.48) w 0.91 d d ù (p<0.001), dm sƒw. wr, m» d ¾š (bulk density)ƒ û œ ƒ (Kang et al., 2003). Kang et al.(2003) w œ (=0.53) w x ww, Ÿ x œ 0.48 w w d (R=0.94, p<0.001; Fig. 6b) l 1997 ¾ RHESSys WBP y d WBP w d WBPƒ d WBP w w Fig. 6. Observed soil water content (SWC) and simulated SWC: a) SWC measured by TDR (2002) at the GN Flux Tower and simulated soil water content from RHESSys at the same period Simulated SWC, SWC_sim(a) is the value of one certain cell, whereas SWC_sim(b) is the value of 9 contiguous cells around a certain point, and b) SWC measured by TDR (2005) at the GS and simulated SWC from RHESSys at the same period. The porosity of SWC_sim(a) is 0.48 which is the value of GN and that of SWC_sim(b) is 0.53, an average value of 9 contiguous cells. Fig. 7. Measured wood biomass product (WBP) and simulated WBP at the GS. WBP measured by tree-ring data from 1978 to 1999 and simulated WBP from RHESSys.

10 256 Korean Journal of Agricultural and Forest Meteorology, Vol. 9, No. 4 w š. ù y w w, w d w 28% sƒw. Ÿ x WBP d WBP y w w (Fig. 7). IV. š Ÿ x d WBP RHESSys x d w y ww š m w y x d sƒw. w Ÿ x yw x x p ƒ g. Ÿ x d x w ME» w š(sen1=0.574, sen2=9.8; ME=0.79), d WBP x WBP w Ÿw w (f3=1.8, f4=0.4; RMSE=0.024 gcm 2, MBE =0.0 gcm 2 ). Ÿ x d w m RHESSys m w y sƒ w w (R=0.80, p<0.001). m w d š (R=0.94, p<0.001), œ m d w. ù Ÿ x RHESSys d WBP d WBP w d. Ÿ x x m d 30%¾ sƒw. w RHESSys d m y sƒ w, RHESSys w e. RHESSys en,, w z «. y w ó m d ƒ w» d m d sƒ ù y w. ù RHESSys w m yƒ j m w w. p, d œ (0.53) m s(soil texture) w wš, ƒ s³ -3.5% w š, w m y w (R=0.94, p<0.001). wz w Ÿ w sƒ ww v ƒ ù, RHESSys œ m s ƒ w m p š w w m y w. Ÿ x Ÿw y ƒ û RMSE MBE ƒ x ww, dw WBP w d WBP 2001 j w w w (R=0.60, p=0.28). dw WBP x d WBP w y w w (R=0.06, p<0.001).» y, x d p w y v w.», 1999 d» ƒ»» (» )» ( ) w» w. ù»» l» xw w ƒ» y x w w q. w Ÿ x w x d ƒ w p w w, yw x d w w w. Ÿ w SLA k p w w. yw» p w x d d x d w v w. wz ƒ j w x w w v x d w w v ƒ. Ÿ x

11 Lee et al.: G Evaluation of a Hydro-ecologic Model, RHESSys (Regional Hydro-Ecologic Simulation System)UUU 257»z w f3, f4 Ÿw w p ƒ ù, Ÿ x w w w sƒw. d d WBP, p w p ƒ w y w d (Fig. 7). d ¾, x y mw w. y k w w y»w. wz x d w w w x yw w v ƒ š., yw k x RHESSys k w x w w, d w w. p m d ƒ œ m sƒ w w e y w š, y y w ƒ p w d w w. ù x w w e / y w/ w w sƒƒ w š, w y k y w w y y w. ƒ p w y v w e w w x d w w z ƒ v w. k l k w. k y w x d w» w k y y y dw. k x RHESSys(Regional Hydro Ecological Simulation System) Ÿ x yw z, x»z ƒ w RHESSys y y ƒ w. RHESSys Geographic Information System(GIS) k œ k w k x. y Ÿ x ù l w w. s w Monte-Carlo w y w. x Ÿ d m w RHESSys m sƒ w w ù, y w w, y w. Ÿ w w, Ÿ ƒ RHESSys m sƒwù w w w w. w d œ x m d w y w. w x d. w y k y w w y y w. ƒ p w y v w ew w x d w w z ƒ v w. 21» v p y» (1-8-3), w y y w» k,, w w Brain Korea 21- w y» w w. w Ÿ ù l œw š ½ Ì. REFERENCES Aber, J. A., and C. A. Federer, 1992: A generalized, lumpedparameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystem. Oecologia 92,

12 258 Korean Journal of Agricultural and Forest Meteorology, Vol. 9, No. 4 Band, L. E., D. S. Mackay, I. F. Creed, R. Semkin, and D. Jeffries, 1996: Ecosystem process at the watershed scale: sensitivity to potential climate change. Limnology and Oceanography 41(5), Baron, J. S., M. D. Hartman, T. G. F. Kittel, L. E. Band, D. S. Ojima, and R. B. Lammers, 1998: Effects of land cover, water redistribution, and temperature on ecosystem processes in the South Platte basin. Ecological Applications 8(4), Csiszar, I., C. O. Justice, A. D. McGuire, M. A. Cochrane, D. P. Roy, F. Brown, S. G. Conard, P. G. H. Frost, L. Giglio, C. Elvidge, M. D. Flannigan, E. S. Kasischke, D. J. McRae,GT. S. Rupp, B. J. Stocks, D. L. Verbyla, 2004: Land Use and Fires, Land Change Science: Observing, Monitoring, and Understanding Trajectories of Change on the Earth's Surface. Gutman, G. Janetos, A.GC. Justice, C.GO. Moran, E.GF. Mustard, J.GF. Rindfuss, R.GR. Skole, D. Turner II, B.G L. Cochrane, M. A. (Eds.), Dordrecht, Netherlands, Kluwer, Chapter 19. Eum, S., S. Kang, and D. Lee, 2005: Climatic controls on net primary production (NPP) of a rugged forested landscape in the mid-western Korean Peninsula. Korean Journal of Agricultural and Forest Meteorology 7(1), Famiglietti, J. S., and E. F. Wood, 1994: Multiscale modeling of spatially variable water and energy balance processes. Water Resources Research 30(11), Go, D., 1999: Disturbance regime of a temperate hardwood forest as deduced from treeing patterns: Mt. Jumbong forest in Korea. M. D. Thesis, Seoul National University, Seoul, Korea. Hwang, T., S. Kang, Y. Kim, J. Kim, D. Lee, and L. E. Band, 2007: Evaluatingdrought effect on MODIS Gross Primary Production (GPP) with an eco-hydrological model in the Mountainous Forest, East Asia. Global Change Biology (in press) Jung, Y. H., K. H. Kim, C. Y. Lee, C. G. Jung, J. Y. Yu, and J. H. Jeun, 2002: Document of Long-term monitoring for forest hydrology. Korea Forest Research Institute, Seoul, Korea. Kang, S., S. Doh, D. Lee, D. Lee, L. J. Virginia, and J. S. Kimball, 2003: Topographic and climate controls on soil respiration in six temperate mixed-hardwood forest slopes, Korea. Global Change Biology 9, Kang, S., J. S. Kimball, and S. W. Running, 2006a: Simulating effects of fire disturbance and climate change on regional forest productivity and evapotranspiration in boreal ecosystems. Science of The Total Environment 362, Kang, S., D. Lee, J.G Lee, and S.G W.G Running, 2006b: Topographic and climatic controls on soil environments and net primary production in a rugged temperate hardwood forest in Korea. Ecological Research 21(1), Kim, E., S. Kang, B. R. Lee, K. Kim, and J. Kim, 2007: Parameterization and application of Regional Hydro- Ecologic Simulation System (RHESSys) for integrating the eco-hydrological processes in the Gwangneung headwater catchment. Korean Journal of Agricultural and Forest Meteorology 9(2), Kim, S., 2005: Spatial analysis of evapotranspiration in a forested watershed using energy balance model and RHESSys. M. D. Thesis, Seoul National University, Seoul, Korea. Kim, Y., 2006: Annual growth of standing biomass estimated with tree-ring data and biomass equation at a Gwangneung deciduous forest, Korea. M. D. Thesis, Seoul National University, Seoul, Korea. Landsberg, J. J., and R. H. Waring, 1997: A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest Ecology and Management 95, Lee, H. Y., 2003: GIS: Geographic Information System, Bum Moon Sa, 606pp. Lim, J. H., 1998: A forest dynamics model based on topographically-induced heterogeneity in solar radiation and soil moisture on the Kwangneung Experimental Forest. Ph. D. Thesis, Seoul National University, Seoul, Korea. Lim, J. H., J. H. Shin, G. Z. Jin, J. H. Chun, and J. S. Oh, 2003: Forest stand structure, site characteristics and carbon budget of Kwangneung natural forest in Korea. Korean Journal of Agricultural and Forest Meteorology 5(2), Law, B. E., D. Turner, J. Campbell, O. J. Sun, S. Vantuyl, W. D. Ritts, and W. B. Cohen, 2004: Disturbance and climate effects in carbon stocks and fluxes across Western Oregon USA. Global Change Biology 10, Lee, G. Y., S. H. Kim, K. H. Kim, and H. S. Lee, 2005: Analysis of soil moisture recession characteristics on hillslope through the intensive monitoring using TDR. Korean Journal of Agricultural and Forest Meteorology 7(1), (in Korean with English Abstract) McMurtrie, R. E., D. A. Rook, and F. M. Kelliher, 1990: Modelling the yield of Pinus radiata on a site limited by water and nutrition. Forest Ecology and Management 30, Meentemeyer, R. K., A. Moody, and J. Franklin, 2001: Landscape-scale patterns of shrub-species abundance in California chaparral. Plant Ecology 156, Ojima, D. S., D. S. Schimel, W. J. Parton, and C. E. Owensby, 1994: Long- and short-term effects of fire on nitrogen cycling in tallgrass prairie. Biogeochemistry 24, Oh, J. S., J. H. Shim, and J. H. Lim, 2000: Long-term ecological research programme in Forestry Research Institute, Korea. Korea Journal of Ecology 23,

13 Lee et al.: G Evaluation of a Hydro-ecologic Model, RHESSys (Regional Hydro-Ecologic Simulation System)UUU 259 Parton, W. J., W. B. Stewart, and C. V. Cole, 1988: Dynamics of C, N, P and S in grassland soils: A model. Biogeochemistry 5, Running, S. W., and J. C. Coughlan, 1988: A general model of forest ecosystem processes for regional applications: I. hydrologic balance, canopy gas exchange and primary production processes. Ecological Modelling 42, Tague, C. L., and L. E. Band, 2004: RHESSys: Regional Hydro-Ecologic Simulation System-An object oriented approach to spatially distributed modeling of Carbon, Water, and Nutrient cycling. Earth Interactions 8, White, M. A., P. E. Thornton, S. W. Running, and R. R. Nemani, 2000: Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model: Net primary production controls. Earth Interactions 4, White, M. A., R. R. Nemani, P. E. Thornton, and S. W. Running, 2002: Satellite evidence of phonological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest. Ecosystems 5, Zierl, B., H. Bugmann, and C. L. Tague, 2007: Water and carbon fluxes of European ecosystem: An evaluation of the ecohydrological model RHESSys. Hydrological Processes 21(25),

10(3)-12.fm

10(3)-12.fm w y wz 10«3y 273~280 (2010.12.) Journal of Korean Society of Urban Environment p yá xá½k w y œw (2010 9 15, 2010 12 2 k) Analysis of Characteristics of Delivered Nonpoint Source Pollution at Forested Watershed