Aerodynamic and canopy resistance of short-rotation forest in relation to leaf area index and climate

1993 ◽  
Vol 66 (3) ◽  
pp. 265-279 ◽  
Author(s):  
Anders Lindroth
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Resistance ◽  
Rotation Forest ◽  
Area Index ◽  
Short Rotation ◽  
Short Rotation Forest

scholarly journals A comparison of different methods for assessing leaf area index in four canopy types

2019 ◽  
Vol 65 (2) ◽  
pp. 67-80 ◽  
Author(s):  
Cristina Ariza-Carricondo ◽  
Francesca Di Mauro ◽  
Maarten Op de Beeck ◽  
Marilyn Roland ◽  
Bert Gielen ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Direct Methods ◽  
Leaf Angle ◽  
Pedunculate Oak ◽  
Angle Distribution ◽  
Indirect Methods ◽  
Area Index ◽  
Short Rotation ◽  
Maize Field

Abstract The agreement of Leaf Area Index (LAI) assessments from three indirect methods, i.e. the LAI–2200 Plant Canopy Analyzer, the SS1 SunScan Canopy Analysis System and Digital Hemispherical Photography (DHP) was evaluated for four canopy types, i.e. a short rotation coppice plantation (SRC) with poplar, a Scots pine stand, a Pedunculate oak stand and a maize field. In the SRC and in the maize field, the indirect measurements were compared with direct measurements (litter fall and harvesting). In the low LAI range (0 to 2) the discrepancies of the SS1 were partly explained by the inability to properly account for clumping and the uncertainty of the ellipsoidal leaf angle distribution parameter. The higher values for SS1 in the medium (2 to 6) to high (6 to 8) ranges might be explained by gap fraction saturation for LAI–2200 and DHP above certain values. Wood area index –understood as the woody light-blocking elements from the canopy with respect to diameter growth– accounted for overestimation by all indirect methods when compared to direct methods in the SRC. The inter-comparison of the three indirect methods in the four canopy types showed a general agreement for all methods in the medium LAI range (2 to 6). LAI–2200 and DHP revealed the best agreement among the indirect methods along the entire range of LAI (0 to 8) in all canopy types. SS1 showed some discrepancies with the LAI–2200 and DHP at low (0 to 2) and high ranges of LAI (6 to 8).

Assessing Impacts of Integrating MODIS Vegetation Data in the Weather Research and Forecasting (WRF) Model Coupled to Two Different Canopy-Resistance Approaches

2014 ◽  
Vol 53 (6) ◽  
pp. 1362-1380 ◽  
Author(s):  
Anil Kumar ◽  
Fei Chen ◽  
Michael Barlage ◽  
Michael B. Ek ◽  
Dev Niyogi
Keyword(s):  
Boundary Layer ◽  
Land Use ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Canopy Resistance ◽  
Area Index ◽  
The Impact

AbstractThe impact of 8-day-averaged data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor—namely, the 1-km leaf area index, absorbed photosynthetic radiation, and land-use data—is investigated for use in the Weather Research and Forecasting (WRF) model for regional weather prediction. These high-resolution, near-real-time MODIS data are hypothesized to enhance the representation of land–atmosphere interactions and to potentially improve the WRF model forecast skill for temperature, surface moisture, surface fluxes, and soil temperature. To test this hypothesis, the impact of using MODIS-based land surface data on surface energy and water budgets was assessed within the “Noah” land surface model with two different canopy-resistance schemes. An ensemble of six model experiments was conducted using the WRF model for a typical summertime episode over the U.S. southern Great Plains that occurred during the International H2O Project (IHOP_2002) field experiment. The six model experiments were statistically analyzed and showed some degree of improvement in surface latent heat flux and sensible heat flux, as well as surface temperature and moisture, after land use, leaf area index, and green vegetation fraction data were replaced by remotely sensed data. There was also an improvement in the WRF-simulated temperature and boundary layer moisture with MODIS data in comparison with the default U.S. Geological Survey land-use and leaf area index inputs. Overall, analysis suggests that recalibration and improvements to both the input data and the land model help to improve estimation of surface and soil parameters and boundary layer moisture and led to improvement in simulating convection in WRF runs. Incorporating updated land conditions provided the most notable improvements, and the mesoscale model performance could be further enhanced when improved land surface schemes become available.

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