2012
DOI: 10.1002/hyp.9362
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Towards a comprehensive approach to parameter estimation in land surface parameterization schemes

Abstract: In climate models, the land–atmosphere interactions are described numerically by land surface parameterization (LSP) schemes. The continuing improvement in realism in these schemes comes at the expense of the need to specify a large number of parameters that are either directly measured or estimated. Also, an emerging problem is whether the relationships used in LSPs are universal and globally applicable. One plausible approach to evaluate this is to first minimize uncertainty in model parameters by calibratio… Show more

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Cited by 48 publications
(51 citation statements)
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References 116 publications
(219 reference statements)
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“…This is supported by a number of other studies [Xia et al, 2002;Rosolem et al, 2013;Gong et al, 2015]. However parameter tuning at PFT level does not reduce model errors uniformly across all land points within a PFT, as found in this study.…”
Section: Journal Of Advances In Modeling Earth Systems 101002/2015mssupporting
confidence: 89%
“…This is supported by a number of other studies [Xia et al, 2002;Rosolem et al, 2013;Gong et al, 2015]. However parameter tuning at PFT level does not reduce model errors uniformly across all land points within a PFT, as found in this study.…”
Section: Journal Of Advances In Modeling Earth Systems 101002/2015mssupporting
confidence: 89%
“…Such methods often include parameter sensitivity analyses for effective optimizations (Gupta et al, 2000;Jackson et al, 2003;Mo et al, 2008;Nasonova et al, 2011;Rosero et al, 2010;Williams and Maxwell, 2011). To make model runs more reliable, previous studies have calibrated several uncertain parameters in only one or two schemes related to their targeted variables (Cretat and Phol, 2012;Essery et al, 2013;MiguezMacho and Fan, 2012), sometimes multiple parameters in many schemes (Moriasi et al, 2007;Rosolem et al, 2013). However, this type of optimization tends to be limited to only a few sites due to the tremendous computing resources and time.…”
Section: Introductionmentioning
confidence: 99%
“…It is worth noting that the sensitivity analysis approach applied here is not a fully multiobjective sensitivity analysis approach such as proposed by Rosolem et al (2012Rosolem et al ( , 2013, which applies sensitivity analysis to all objectives in an integrated way, and which is objective. However, compared to the fully multiobjctive sensitivity analysis approach (as proposed in Rosolem et al, 2012), which easily requires over 10 000 model runs, our approach is very computationally efficient, as both the Morris method and the SDP method only need several hundred model runs, which is highly appreciable for physically based and distributed hydrologic models.…”
Section: State-dependent Parameter (Sdp) Methodsmentioning
confidence: 99%