scholarly journals The divergence between potential and actual evapotranspiration: An insight from climate, water, and vegetation change

2022 ◽  
Vol 807 ◽  
pp. 150648
Author(s):  
Yuan Liu ◽  
Qi Jiang ◽  
Qianyang Wang ◽  
Yongliang Jin ◽  
Qimeng Yue ◽  
...  
2000 ◽  
pp. 26-31
Author(s):  
E. I. Parfenova ◽  
N. M. Chebakova

Global climate warming is expected to be a new factor influencing vegetation redistribution and productivity in the XXI century. In this paper possible vegetation change in Mountain Altai under global warming is evaluated. The attention is focused on forest vegetation being one of the most important natural resources for the regional economy. A bioclimatic model of correlation between vegetation and climate is used to predict vegetation change (Parfenova, Tchebakova 1998). In the model, a vegetation class — an altitudinal vegetation belt (mountain tundra, dark- coniferous subalpine open woodland, light-coniferous subgolets open woodland, dark-coniferous mountain taiga, light-coniferous mountain taiga, chern taiga, subtaiga and forest-steppe, mountain steppe) is predicted from a combination of July Temperature (JT) and Complex Moisture Index (CMI). Borders between vegetation classes are determined by certain values of these two climatic indices. Some bioclimatic regularities of vegetation distribution in Mountain Altai have been found: 1. Tundra is separated from taiga by the JT value of 8.5°C; 2. Dark- coniferous taiga is separated from light-coniferous taiga by the CMI value of 2.25; 3. Mountain steppe is separated from the forests by the CMI value of 4.0. 4. Within both dark-coniferous and light-coniferous taiga, vegetation classes are separated by the temperature factor. For the spatially model of vegetation distribution in Mountain Altai within the window 84 E — 90 E and 48 N — 52 N, the DEM (Digital Elevation Model) was used with a pixel of 1 km resolution. In a GIS Package IDRISI for Windows 2.0, climatic layers were developed based on DEM and multiple regressions relating climatic indices to physiography (elevation and latitude). Coupling the map of climatic indices with the authors' bioclimatic model resulted into a vegetation map for the region of interest. Visual comparison of the modelled vegetation map with the observed geobotanical map (Kuminova, 1960; Ogureeva, 1980) showed a good similarity between them. The new climatic indices map was developed under the climate change scenario with summer temperature increase 2°C and annual precipitation increase 20% (Menzhulin, 1998). For most mountains under such climate change scenario vegetation belts would rise 300—400 m on average. Under current climate, the dark-coniferous and light-coniferous mountain taiga forests dominate throughout Mountain Altai. The chern forests are the most productive and floristically rich and are also widely distributed. Under climate warming, light-coniferous mountain taiga may be expected to transform into subtaiga and forest-steppe and dark-coniferous taiga may be expected to transform partly into chern taiga. Other consequences of warming may happen such as the increase of forest productivity within the territories with sufficient rainfall and the increase of forest fire occurrence over territories with insufficient rainfall.


HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 512c-512
Author(s):  
R.C. Beeson

The objective of this study was to determine crop coefficients (KC) for Ligustrum japonica growing in three container sizes using the Penman equation to calculate reference evapotranspiration (ETR). Rooted cuttings were transplanted into 3-liter containers and upcanned as needed into 10- and 23-L containers. Production was scheduled such that a series of plants in each container size were about 2 months from commercial marketable size every 4 months. Beginning 1 Jan. 1995 until 31 Dec. 1996, three uniform plants of each size were suspended in weighing lysimeters and surrounded by similar size plants filling an area 3.7 by 4.9 m. Plants within each area were overhead irrigated at 2000 h as needed, based on a 30% moisture allowed deficit. Plants were exchanged every 4 months such that the annual mean size was that of a marketable plant. Actual evapotranspiration (ETA) was calculated from half-hour measurements of each plant's weight and adjusted for rainfall. From these and daily calculated ETR, KC were determined for each size of container. KCs ranged from 1.06 to 1.50 when ETA was converted to mm/day based on allocated bed space. Comparisons of volumes of supplemental irrigation to ETA and effects of assumptions required in converting ETA to mm/day will be discussed.


2002 ◽  
Vol 39 (2) ◽  
pp. 279-293 ◽  
Author(s):  
R.S. Smith ◽  
R.S. Shiel ◽  
D. Millward ◽  
P. Corkhill ◽  
R.A. Sanderson

Author(s):  
Yuanhe Yu ◽  
Yuzhen Shen ◽  
Jinliang Wang ◽  
Yuchun Wei ◽  
Lanping Nong ◽  
...  

2021 ◽  
Vol 13 (7) ◽  
pp. 1230
Author(s):  
Simeng Wang ◽  
Qihang Liu ◽  
Chang Huang

Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial evolution of vegetation cover, and its responses to climate extremes in the arid region of northwest China (ARNC). Mann-Kendall test, Anomaly analysis, Pearson correlation analysis, Time lag cross-correlation method, and Least absolute shrinkage and selection operator logistic regression (Lasso) were conducted to quantitatively analyze the response characteristics between Normalized Difference Vegetation Index (NDVI) and climate extremes from 2000 to 2018. The results showed that: (1) The vegetation in the ARNC had a fluctuating upward trend, with vegetation significantly increasing in Xinjiang Tianshan, Altai Mountain, and Tarim Basin, and decreasing in the central inland desert. (2) Temperature extremes showed an increasing trend, with extremely high-temperature events increasing and extremely low-temperature events decreasing. Precipitation extremes events also exhibited a slightly increasing trend. (3) NDVI was overall positively correlated with the climate extremes indices (CEIs), although both positive and negative correlations spatially coexisted. (4) The responses of NDVI and climate extremes showed time lag effects and spatial differences in the growing period. (5) Precipitation extremes were closely related to NDVI than temperature extremes according to Lasso modeling results. This study provides a reference for understanding vegetation variations and their response to climate extremes in arid regions.


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