Investigating the impact of land surface characteristics on monsoon dynamics with idealized model simulations and theories

Abstract Representation of the canopy hydrological processes has been challenging in land surface modeling due to the subgrid heterogeneity in both precipitation and surface characteristics. The Shuttleworth dynamic–statistical method is widely used to represent the impact of the precipitation subgrid variability on canopy hydrological processes but shows unwanted sensitivity to temporal resolution when implemented into land surface models. This paper presents a canopy hydrology scheme that is robust at different temporal resolutions. This scheme is devised by applying two physically based treatments to the Shuttleworth scheme: 1) the canopy hydrological processes within the rain-covered area are treated separately from those within the nonrain area, and the scheme tracks the relative rain location between adjacent time steps; and 2) within the rain-covered area, the canopy interception is so determined as to sustain the potential evaporation from the wetted canopy or is equal to precipitation, whichever is less, to maintain somewhat wet canopy during any rainy time step. When applied to the Amazon region, the new scheme establishes interception loss ratios of 0.3 at a 10-min time step and 0.23 at a 2-h time step. Compared to interception loss ratios of 0.45 and 0.09 at the corresponding time steps established by the original Shuttleworth scheme, the new scheme is much more stable under different temporal resolutions.

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Wintertime land surface characteristics in climatic simulations over the western Himalayas

Journal of Earth System Science ◽

10.1007/s12040-012-0166-x ◽

2012 ◽

Vol 121 (2) ◽

pp. 329-344 ◽

Cited By ~ 14

Author(s):

A P Dimri

Keyword(s):

Land Surface ◽

Surface Characteristics ◽

Western Himalayas ◽

Land Surface Characteristics

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Pan-Arctic linkages between snow accumulation and growing-season air temperature, soil moisture and vegetation

Biogeosciences ◽

10.5194/bg-10-7575-2013 ◽

2013 ◽

Vol 10 (11) ◽

pp. 7575-7597 ◽

Cited By ~ 8

Author(s):

K. A. Luus ◽

Y. Gel ◽

J. C. Lin ◽

R. E. J. Kelly ◽

C. R. Duguay

Keyword(s):

Soil Moisture ◽

Air Temperature ◽

Land Surface ◽

Regional Scale ◽

Surface Characteristics ◽

Growing Season ◽

Snow Accumulation ◽

Snow Season ◽

Air Temperatures ◽

Land Surface Characteristics

Abstract. Arctic field studies have indicated that the air temperature, soil moisture and vegetation at a site influence the quantity of snow accumulated, and that snow accumulation can alter growing-season soil moisture and vegetation. Climate change is predicted to bring about warmer air temperatures, greater snow accumulation and northward movements of the shrub and tree lines. Understanding the responses of northern environments to changes in snow and growing-season land surface characteristics requires: (1) insights into the present-day linkages between snow and growing-season land surface characteristics; and (2) the ability to continue to monitor these associations over time across the vast pan-Arctic. The objective of this study was therefore to examine the pan-Arctic (north of 60° N) linkages between two temporally distinct data products created from AMSR-E satellite passive microwave observations: GlobSnow snow water equivalent (SWE), and NTSG growing-season AMSR-E Land Parameters (air temperature, soil moisture and vegetation transmissivity). Due to the complex and interconnected nature of processes determining snow and growing-season land surface characteristics, these associations were analyzed using the modern nonparametric technique of alternating conditional expectations (ACE), as this approach does not impose a predefined analytic form. Findings indicate that regions with lower vegetation transmissivity (more biomass) at the start and end of the growing season tend to accumulate less snow at the start and end of the snow season, possibly due to interception and sublimation. Warmer air temperatures at the start and end of the growing season were associated with diminished snow accumulation at the start and end of the snow season. High latitude sites with warmer mean annual growing-season temperatures tended to accumulate more snow, probably due to the greater availability of water vapor for snow season precipitation at warmer locations. Regions with drier soils preceding snow onset tended to accumulate greater quantities of snow, likely because drier soils freeze faster and more thoroughly than wetter soils. Understanding and continuing to monitor these linkages at the regional scale using the ACE approach can allow insights to be gained into the complex response of Arctic ecosystems to climate-driven shifts in air temperature, vegetation, soil moisture and snow accumulation.

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Simulating the Effects of Land Surface Characteristics on Planetary Boundary Layer Parameters for a Modeled Landfalling Tropical Cyclone

Atmosphere ◽

10.3390/atmos13010138 ◽

2022 ◽

Vol 13 (1) ◽

pp. 138

Author(s):

Yu Wang ◽

Corene J. Matyas

Keyword(s):

Tropical Cyclone ◽

Land Surface ◽

Friction Velocity ◽

Inner Core ◽

Sensible Heat Flux ◽

Surface Characteristics ◽

Near Surface ◽

Land Surfaces ◽

Rain Rates ◽

Land Surface Characteristics

This study examined whether varying moisture availability and roughness length for the land surface under a simulated Tropical Cyclone (TC) could affect its production of precipitation. The TC moved over the heterogeneous land surface of the southeastern U.S. in the control simulation, while the other simulations featured homogeneous land surfaces that were wet rough, wet smooth, dry rough, and dry smooth. Results suggest that the near-surface atmosphere was modified by the changes to the land surface, where the wet cases have higher latent and lower sensible heat flux values, and rough cases exhibit higher values of friction velocity. The analysis of areal-averaged rain rates and the area receiving low and high rain rates shows that simulations having a moist land surface produce higher rain rates and larger areas of low rain rates in the TC’s inner core. The dry and rough land surfaces produced a higher coverage of high rain rates in the outer regions. Key differences among the simulations happened as the TC core moved over land, while the outer rainbands produced more rain when moving over the coastline. These findings support the assertion that the modifications of the land surface can influence precipitation production within a landfalling TC.

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SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-401-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 401-405

Author(s):

M. K. Firozjaei ◽

M. Makki ◽

J. Lentschke ◽

M. Kiavarz ◽

S. K. Alavipanah

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Single Channel ◽

Incident Angle ◽

Surface Characteristics ◽

Slope Aspect ◽

Biophysical Parameters ◽

The Impact

Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.

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