scholarly journals Large scale climate oscillation impacts on temperature, precipitation and land surface phenology in Central Asia

2018 ◽  
Vol 13 (6) ◽  
pp. 065018 ◽  
Author(s):  
Kirsten M de Beurs ◽  
Geoffrey M Henebry ◽  
Braden C Owsley ◽  
Irina N Sokolik
Keyword(s):  
Central Asia ◽  
Land Surface ◽  
Large Scale ◽  
Land Surface Phenology ◽  
Climate Oscillation

scholarly journals Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

2021 ◽  
Author(s):  
Shawn D Taylor ◽  
Dawn M Browning ◽  
Ruben A Baca ◽  
Feng Gao
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Scale Effects ◽  
Simulated Data ◽  
Area Index ◽  
Land Surface Phenology

Land surface phenology, the tracking of seasonal productivity via satellite remote sensing, enables global scale tracking of ecosystem processes, but its utility is limited in some areas. In dryland ecosystems low vegetation cover can cause the growing season vegetation index (VI) to be indistinguishable from the dormant season VI, making phenology extraction impossible. Here, using simulated data and multi-temporal UAV imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, and VI uncertainty. We found that plants with distinct VI signals, such as deciduous shrubs with a high leaf area index, require at least 30-40\% fractional cover on the landscape to consistently detect pixel level phenology with satellite remote sensing. Evergreen plants, which have lower VI amplitude between dormant and growing seasons, require considerably higher cover and can have undetectable phenology even with 100\% vegetation cover. We also found that even with adequate cover, biases in phenological metrics can still exceed 20 days, and can never be 100\% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. Many dryland areas do not have detectable LSP with the current suite of satellite based sensors. Our results showed the feasibility of dryland LSP studies using high-resolution UAV imagery, and highlighted important scale effects due to within canopy VI variation. Future sensors with sub-meter resolution will allow for identification of individual plants and are the best path forward for studying large scale phenological trends in drylands.

scholarly journals Five years of land surface phenology in a large-scale flooding and draining manipulation in a coastal Arctic ecosystem

2017 ◽  
Author(s):  
Santonu Goswami ◽  
John A. Gamon ◽  
Sergio Vargas ◽  
Craig E. Tweedie
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Large Scale ◽  
Ground Level ◽  
The Arctic ◽  
Surface Hydrology ◽  
Experimental Conditions ◽  
Land Surface Phenology ◽  
Growing Period ◽  
Near Ir

AbstractThis study was motivated by the knowledge gap for observing the complex interplay between surface hydrology and plant phenology in arctic landscapes and was conducted as part of a large scale, multi investigator flooding and draining experiment near Barrow, Alaska (71°17’01” N, 156°35’48” W) during 2005 - 2009. Hyperspectral reflectance data were collected in the visible to near IR region of the spectrum using a robotic tram system that operated along a 300m transects during the snow free growing period between June and August, 2005-09. Interannual patterns of land-surface phenology (NDVI) unexpectedly lacked marked differences under experimental conditions. Measurement of NDVI was, however, compromised for presence of surface water. Land-surface phenology and surface water was negatively correlated, which held when scaled to a 2km by 2km MODIS subset of the study area. This result suggested that published findings of ‘greening of the Arctic’ may relate to a ‘drying of the Arctic’ i.e. reduced surface water in vegetated high-latitude landscapes where surface water is close to ground level.

scholarly journals Simulation of summer climate over Central Asia shows high sensitivity to different land surface schemes in WRF

2021 ◽  
Author(s):  
Sha Lu ◽  
Weidong Guo ◽  
Yongkang Xue ◽  
Fang Huang ◽  
Jun Ge
Keyword(s):  
Central Asia ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Global Analysis ◽  
Regional Climate ◽  
Wrf Model ◽  
Heat Fluxes ◽  
Ensemble Average ◽  
Adjustment Process

AbstractLand surface processes are vital to the performance of regional climate models in dynamic downscaling application. In this study, we investigate the sensitivity of the simulation by using the weather research and forecasting (WRF) model at 10-km resolution to the land surface schemes over Central Asia. The WRF model was run for 19 summers from 2000 to 2018 configured with four different land surface schemes including CLM4, Noah-MP, Pleim-Xiu and SSiB, hereafter referred as Exp-CLM4, Exp-Noah-MP, Exp-PX and Exp-SSiB respectively. The initial and boundary conditions for the WRF model simulations were provided by the National Centers for Environmental Prediction Final (NCEP-FNL) Operational Global Analysis data. The ERA-Interim reanalysis (ERAI), the GHCN-CAMS and the CRU gridded data were used to comprehensively evaluate the WRF simulations. Compared with the reanalysis and observational data, the WRF model can reasonably reproduce the spatial patterns of summer mean 2-m temperature, precipitation, and large- scale atmospheric circulation. The simulations, however, are sensitive to the option of land surface scheme. The performance of Exp-CLM4 and Exp-SSiB are better than that of Exp-Noah-MP and Exp-PX assessed by Multivariable Integrated Evaluation (MVIE) method. To comprehensively understand the dynamic and physical mechanisms for the WRF model’s sensitivity to land surface schemes, the differences in the surface energy balance between Ave-CLM4-SSiB (the ensemble average of Exp-CLM4 and Exp-SSiB) and Ave-NoanMP-PX (the ensemble average of Exp-Noah-MP and Exp-PX) are analyzed in detail. The results demonstrate that the sensible and latent heat fluxes are respectively lower by 30.42 W·m−2 and higher by 14.86 W·m−2 in Ave-CLM4-SSiB than that in Ave-NoahMP-PX. As a result, large differences in geopotential height occur over the simulation domain. The simulated wind fields are subsequently influenced by the geostrophic adjustment process, thus the simulations of 2-m temperature, surface skin temperature and precipitation are respectively lower by about 2.08 ℃, 2.23 ℃ and 18.56 mm·month−1 in Ave-CLM4-SSiB than that in Ave-NoahMP-PX over Central Asia continent.

scholarly journals Associations between large-scale climate oscillations and land surface phenology in Iran

2019 ◽  
Vol 278 ◽  
pp. 107682 ◽  
Author(s):  
Alireza Araghi ◽  
Christopher J. Martinez ◽  
Jan Adamowski ◽  
Jørgen Eivind Olesen
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Oscillations ◽  
Land Surface Phenology

scholarly journals The response of African land surface phenology to large scale climate oscillations

2010 ◽  
Vol 114 (10) ◽  
pp. 2286-2296 ◽  
Author(s):  
Molly E. Brown ◽  
Kirsten de Beurs ◽  
Anton Vrieling
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Oscillations ◽  
Land Surface Phenology

scholarly journals ChinaCropPhen1km: A high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on LAI products

2019 ◽  
Author(s):  
Yuchuan Luo ◽  
Zhao Zhang ◽  
Yi Chen ◽  
Ziyue Li ◽  
Fulu Tao
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Agricultural System ◽  
System Modelling ◽  
Crop Phenology ◽  
China Meteorological Administration ◽  
Essential Information ◽  
Land Surface Phenology ◽  
Staple Crops

Abstract. Crop phenology provides essential information for land surface phenology dynamics monitoring and modelling, and crop management and production. Most previous studies mainly investigated crop phenology at site scale, however, land surface phenology dynamics monitoring and modelling at a large-scale need a high-resolution spatially explicit information on crop phenology dynamics. In this study, we proposed a method to retrieve 1km-grid crop phenological dataset for three main crops from 2000 to 2015 based on GLASS LAI products. First, we compared three common smoothing methods and chose the most suitable methods for different crops and regions. Then, we developed an optimal filter-based phenology detection (OFP) approach which combined both inflexion- and threshold-based method and detected the key phenological stages of three staple crops at 1km spatial resolution across China. Finally, we established a high resolution gridded-phenology product for three staple crops in China during 2000–2015, named as ChinaCropPhen1km. Compared with the intensive phenological observations from the Agricultural Meteorological Stations of China Meteorological Administration, the dataset had a high accuracy with errors of retrieved phenological date less than 10 days, and represented the spatiotemporal patterns of the observed phenological dynamics at site scale fairly well. The well-validated dataset can be applied for many purposes including improving agricultural system or earth system modelling over a large area. DOI of the referenced dataset: https://doi.org/10.6084/m9.figshare.8313530 (Luo et al., 2019).

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