Understanding the DayCent model: Calibration,sensitivity, and identifiability through inverse modeling期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

Understanding the DayCent model: Calibration,sensitivity, and identifiability through inverse modeling

Affiliation:	1. Department of Biological System Engineering, University of Wisconsin–Madison, Madison, WI, USA;2. United States Geological Survey, Wisconsin Water Science Center, Middleton, WI, USA;3. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA;4. Dept. of Agronomy, Iowa State University, Ames, IA, USA

Abstract:

Keywords:	DayCent model Inverse modeling PEST Sensitivity analysis Parameter identifiability Parameter correlations ANPP"} {"#name":"keyword" "$":{"id":"kwrd0015"} "$$":[{"#name":"text" "_":"aboveground net primary productivity ARS"} {"#name":"keyword" "$":{"id":"kwrd0025"} "$$":[{"#name":"text" "_":"Agricultural Research Service C"} {"#name":"keyword" "$":{"id":"kwrd0035"} "$$":[{"#name":"text" "_":"carbon CEC"} {"#name":"keyword" "$":{"id":"kwrd0045"} "$$":[{"#name":"text" "_":"cation-exchange capacity methane C/N ratio"} {"#name":"keyword" "$":{"id":"kwrd0065"} "$$":[{"#name":"text" "_":"carbon to nitrogen ratio d"} {"#name":"keyword" "$":{"id":"kwrd0075"} "$$":[{"#name":"text" "_":"index of agreement DEFAC"} {"#name":"keyword" "$":{"id":"kwrd0085"} "$$":[{"#name":"text" "_":"decomposition factor DNDC"} {"#name":"keyword" "$":{"id":"kwrd0095"} "$$":[{"#name":"text" "_":"denitrification decomposition model EPA"} {"#name":"keyword" "$":{"id":"kwrd0105"} "$$":[{"#name":"text" "_":"Environmental Protection Agency GHG"} {"#name":"keyword" "$":{"id":"kwrd0115"} "$$":[{"#name":"text" "_":"greenhouse gas GML"} {"#name":"keyword" "$":{"id":"kwrd0125"} "$$":[{"#name":"text" "_":"Gauss–Marquardt–Levenberg ammonium cation J"} {"#name":"keyword" "$":{"id":"kwrd0145"} "$$":[{"#name":"text" "_":"Jacobian matrix N"} {"#name":"keyword" "$":{"id":"kwrd0155"} "$$":[{"#name":"text" "_":"nitrogen nitrous oxide NPP"} {"#name":"keyword" "$":{"id":"kwrd0175"} "$$":[{"#name":"text" "_":"net primary productivity nitrate anion PEST"} {"#name":"keyword" "$":{"id":"kwrd0195"} "$$":[{"#name":"text" "_":"parameter estimation software MB"} {"#name":"keyword" "$":{"id":"kwrd0205"} "$$":[{"#name":"text" "_":"mean bias RMSE"} {"#name":"keyword" "$":{"id":"kwrd0215"} "$$":[{"#name":"text" "_":"root mean square error rRMSE"} {"#name":"keyword" "$":{"id":"kwrd0225"} "$$":[{"#name":"text" "_":"relative root mean square error SOC"} {"#name":"keyword" "$":{"id":"kwrd0235"} "$$":[{"#name":"text" "_":"soil organic carbon SOM"} {"#name":"keyword" "$":{"id":"kwrd0245"} "$$":[{"#name":"text" "_":"soil organic matter SVD"} {"#name":"keyword" "$":{"id":"kwrd0255"} "$$":[{"#name":"text" "_":"singular value decomposition SWSR"} {"#name":"keyword" "$":{"id":"kwrd0265"} "$$":[{"#name":"text" "_":"sum of weighted squared residuals VSWC"} {"#name":"keyword" "$":{"id":"kwrd0275"} "$$":[{"#name":"text" "_":"volumetric soil water content UAN"} {"#name":"keyword" "$":{"id":"kwrd0285"} "$$":[{"#name":"text" "_":"urea ammonium nitrate
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