STIR Proposal For Research Area 2.1.2 Surface Energy Balance: Transient Soil Density Impacts Land Surface Characteristics and Characterization

2015 ◽  
Author(s):  
Joshua L. Heitman ◽  
Y. Kojima ◽  
R. Horton
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Surface Characteristics ◽  
Research Area ◽  
Soil Density ◽  
Land Surface Characteristics

scholarly journals Impact of Increasing Urban Density on Local Climate: Spatial and Temporal Variations in the Surface Energy Balance in Melbourne, Australia

2007 ◽  
Vol 46 (4) ◽  
pp. 477-493 ◽  
Author(s):  
Andrew M. Coutts ◽  
Jason Beringer ◽  
Nigel J. Tapper
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Heat Storage ◽  
Surface Characteristics ◽  
Housing Density ◽  
Local Climate ◽  
Urban Sites ◽  
Land Surface Characteristics

Abstract Variations in urban surface characteristics are known to alter the local climate through modification of land surface processes that influence the surface energy balance and boundary layer and lead to distinct urban climates. In Melbourne, Australia, urban densities are planned to increase under a new strategic urban plan. Using the eddy covariance technique, this study aimed to determine the impact of increasing housing density on the surface energy balance and to investigate the relationship to Melbourne’s local climate. Across four sites of increasing housing density and varying land surface characteristics (three urban and one rural), it was found that the partitioning of available energy was similar at all three urban sites. Bowen ratios were consistently greater than 1 throughout the year at the urban sites (often as high as 5) and were higher than the rural site (less than 1) because of reduced evapotranspiration. The greatest difference among sites was seen in urban heat storage, which was influenced by urban canopy complexity, albedo, and thermal admittance. Resulting daily surface temperatures were therefore different among the urban sites, yet differences in above-canopy daytime air temperatures were small because of similar energy partitioning and efficient mixing. However, greater nocturnal temperatures were observed with increasing density as a result of variations in heat storage release that are in part due to urban canyon morphology. Knowledge of the surface energy balance is imperative for urban planning schemes because there is a possibility for manipulation of land surface characteristics for improved urban climates.

Estimation of Evapotranspiration using Remote Sensing and Surface Energy Balance Algorithm for Land (SEBAL) in Canal Command

2021 ◽  
Vol 58 (03) ◽  
pp. 274-285
Author(s):  
H. V. Parmar ◽  
N. K. Gontia
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Surface Energy ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Crop Growth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Surface Temperatures ◽  
Canal Command

Remote sensing based various land surface and bio-physical variables like Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), surface albedo, transmittance and surface emissivity are useful for the estimation of spatio-temporal variations in evapotranspiration (ET) using Surface Energy Balance Algorithm for Land (SEBAL) method. These variables were estimated under the present study for Ozat-II canal command in Junagadh district, Gujarat, India, using Landsat-7 and Landsat-8 images of summer season of years 2014 and 2015. The derived parameters were used in SEBAL to estimate the Actual Evapotranspiration (AET) of groundnut and sesame crops. The lower values NDVI observed during initial (March) and end (May) stages of crop growth indicated low vegetation cover during these periods. With full canopy coverage of the crops, higher value of NDVI (0.90) was observed during the mid-crop growth stage. The remote sensing-based LST was lower for agricultural areas and the area near banks of the canal and Ozat River, while higher surface temperatures were observed for rural settlements, road and areas with exposed dry soil. The maximum surface temperatures in the cropland were observed as 311.0 K during March 25, 2014 and 315.8 K during May 31, 2015. The AET of summer groundnut increased from 3.75 to 7.38 mm.day-1, and then decreased to 3.99 mm.day-1 towards the end stage of crop growth. The daily AET of summer sesame ranged from 1.06 to 7.72 mm.day-1 over different crop growth stages. The seasonal AET of groundnut and sesame worked out to 358.19 mm and 346.31 mm, respectively. The estimated AET would be helpful to schedule irrigation in the large canal command.

scholarly journals Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

2013 ◽  
Vol 7 (3) ◽  
pp. 961-975 ◽  
Author(s):  
A. Roy ◽  
A. Royer ◽  
B. Montpetit ◽  
P. A. Bartlett ◽  
A. Langlois
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Liquid Water ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Liquid Water Content ◽  
Canadian Land Surface Scheme ◽  
Snow Model ◽  
Surface Scheme ◽  
The One

Abstract. Snow grain size is a key parameter for modeling microwave snow emission properties and the surface energy balance because of its influence on the snow albedo, thermal conductivity and diffusivity. A model of the specific surface area (SSA) of snow was implemented in the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS) version 3.4. This offline multilayer model (CLASS-SSA) simulates the decrease of SSA based on snow age, snow temperature and the temperature gradient under dry snow conditions, while it considers the liquid water content of the snowpack for wet snow metamorphism. We compare the model with ground-based measurements from several sites (alpine, arctic and subarctic) with different types of snow. The model provides simulated SSA in good agreement with measurements with an overall point-to-point comparison RMSE of 8.0 m2 kg–1, and a root mean square error (RMSE) of 5.1 m2 kg–1 for the snowpack average SSA. The model, however, is limited under wet conditions due to the single-layer nature of the CLASS model, leading to a single liquid water content value for the whole snowpack. The SSA simulations are of great interest for satellite passive microwave brightness temperature assimilations, snow mass balance retrievals and surface energy balance calculations with associated climate feedbacks.

scholarly journals Regional evapotranspiration from an image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous US

2018 ◽  
Vol 22 (4) ◽  
pp. 2311-2341 ◽  
Author(s):  
Nishan Bhattarai ◽  
Kaniska Mallick ◽  
Nathaniel A. Brunsell ◽  
Ge Sun ◽  
Meha Jain
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Temperature ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Regional Scale ◽  
Absolute Error ◽  
Error Statistics ◽  
Aridity Gradient ◽  
Balance System

Abstract. Recent studies have highlighted the need for improved characterizations of aerodynamic conductance and temperature (gA and T0) in thermal remote-sensing-based surface energy balance (SEB) models to reduce uncertainties in regional-scale evapotranspiration (ET) mapping. By integrating radiometric surface temperature (TR) into the Penman–Monteith (PM) equation and finding analytical solutions of gA and T0, this need was recently addressed by the Surface Temperature Initiated Closure (STIC) model. However, previous implementations of STIC were confined to the ecosystem-scale using flux tower observations of infrared temperature. This study demonstrates the first regional-scale implementation of the most recent version of the STIC model (STIC1.2) that integrates the Moderate Resolution Imaging Spectroradiometer (MODIS) derived TR and ancillary land surface variables in conjunction with NLDAS (North American Land Data Assimilation System) atmospheric variables into a combined structure of the PM and Shuttleworth–Wallace (SW) framework for estimating ET at 1 km × 1 km spatial resolution. Evaluation of STIC1.2 at 13 core AmeriFlux sites covering a broad spectrum of climates and biomes across an aridity gradient in the conterminous US suggests that STIC1.2 can provide spatially explicit ET maps with reliable accuracies from dry to wet extremes. When observed ET from one wet, one dry, and one normal precipitation year from all sites were combined, STIC1.2 explained 66 % of the variability in observed 8-day cumulative ET with a root mean square error (RMSE) of 7.4 mm/8-day, mean absolute error (MAE) of 5 mm/8-day, and percent bias (PBIAS) of −4 %. These error statistics showed relatively better accuracies than a widely used but previous version of the SEB-based Surface Energy Balance System (SEBS) model, which utilized a simple NDVI-based parameterization of surface roughness (zOM), and the PM-based MOD16 ET. SEBS was found to overestimate (PBIAS = 28 %) and MOD16 was found to underestimate ET (PBIAS = −26 %). The performance of STIC1.2 was better in forest and grassland ecosystems as compared to cropland (20 % underestimation) and woody savanna (40 % overestimation). Model inter-comparison suggested that ET differences between the models are robustly correlated with gA and associated roughness length estimation uncertainties which are intrinsically connected to TR uncertainties, vapor pressure deficit (DA), and vegetation cover. A consistent performance of STIC1.2 in a broad range of hydrological and biome categories, as well as the capacity to capture spatio-temporal ET signatures across an aridity gradient, points to the potential for this simplified analytical model for near-real-time ET mapping from regional to continental scales.

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