Multisite evaluation of an improved SWAT irrigation scheduling algorithm for corn (Zea mays L.) production in the U.S. Southern Great Plains

2019 ◽  
Vol 118 ◽  
pp. 23-34 ◽  
Author(s):  
Y. Chen ◽  
G.W. Marek ◽  
T.H. Marek ◽  
P.H. Gowda ◽  
Q. Xue ◽  
...  
Keyword(s):  
Zea Mays ◽  
Great Plains ◽  
Zea Mays L ◽  
Scheduling Algorithm ◽  
Irrigation Scheduling ◽  
Southern Great Plains ◽  
The U.S

Seasonal and interannual variability in13C composition of ecosystem carbon fluxes in the U.S. Southern Great Plains

Tellus B ◽  
2011 ◽  
Vol 63 (2) ◽  
Author(s):  
Margaret S. Torn ◽  
Sebastien C. Biraud ◽  
Christopher J. Still ◽  
William J. Riley ◽  
Joe A. Berry
Keyword(s):  
Interannual Variability ◽  
Great Plains ◽  
Carbon Fluxes ◽  
Southern Great Plains ◽  
Seasonal And Interannual Variability ◽  
Ecosystem Carbon ◽  
Ecosystem Carbon Fluxes ◽  
The U.S

scholarly journals Vulnerability of crops and native grasses to summer drying in the U.S. Southern Great Plains

2015 ◽  
Vol 213 ◽  
pp. 209-218 ◽  
Author(s):  
Naama Raz-Yaseef ◽  
Dave P. Billesbach ◽  
Marc L. Fischer ◽  
Sebastien C. Biraud ◽  
Stacey A. Gunter ◽  
...  
Keyword(s):  
Great Plains ◽  
Native Grasses ◽  
Southern Great Plains ◽  
The U.S

Carbon dioxide and water vapor fluxes of multi-purpose winter wheat production systems in the U.S. Southern Great Plains

2021 ◽  
Vol 310 ◽  
pp. 108631
Author(s):  
Pradeep Wagle ◽  
Prasanna H. Gowda ◽  
Brian K. Northup ◽  
James P.S. Neel ◽  
Patrick J. Starks ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Water Vapor ◽  
Winter Wheat ◽  
Great Plains ◽  
Production Systems ◽  
Wheat Production ◽  
Southern Great Plains ◽  
The U.S

scholarly journals Quantifying the Land–Atmosphere Coupling Behavior in Modern Reanalysis Products over the U.S. Southern Great Plains

2015 ◽  
Vol 28 (14) ◽  
pp. 5813-5829 ◽  
Author(s):  
Joseph A. Santanello ◽  
Joshua Roundy ◽  
Paul A. Dirmeyer
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
Southern Great Plains ◽  
Diurnal Cycles ◽  
The North ◽  
Energy Cycle ◽  
Coupling Behavior ◽  
Regional Reanalysis ◽  
Modern Era ◽  
The U.S

Abstract The coupling of the land with the planetary boundary layer (PBL) on diurnal time scales is critical to regulating the strength of the connection between soil moisture and precipitation. To improve understanding of land–atmosphere (L–A) interactions, recent studies have focused on the development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, the authors apply a suite of local land–atmosphere coupling (LoCo) metrics to modern reanalysis (RA) products and observations during a 17-yr period over the U.S. southern Great Plains. Specifically, a range of diagnostics exploring the links between soil moisture, evaporation, PBL height, temperature, humidity, and precipitation is applied to the summertime monthly mean diurnal cycles of the North American Regional Reanalysis (NARR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR). Results show that CFSR is the driest and MERRA the wettest of the three RAs in terms of overall surface–PBL coupling. When compared against observations, CFSR has a significant dry bias that impacts all components of the land–PBL system. CFSR and NARR are more similar in terms of PBL dynamics and response to dry and wet extremes, while MERRA is more constrained in terms of evaporation and PBL variability. Each RA has a unique land–PBL coupling that has implications for downstream impacts on the diurnal cycle of PBL evolution, clouds, convection, and precipitation as well as representation of extremes and drought. As a result, caution should be used when treating RAs as truth in terms of their water and energy cycle processes.

scholarly journals Using ARM Observations to Evaluate Climate Model Simulations of Land-Atmosphere Coupling on the U.S. Southern Great Plains

2017 ◽  
Vol 122 (21) ◽  
pp. 11,524-11,548 ◽  
Author(s):  
Thomas J. Phillips ◽  
Stephen A. Klein ◽  
Hsi-Yen Ma ◽  
Qi Tang ◽  
Shaocheng Xie ◽  
...  
Keyword(s):  
Great Plains ◽  
Climate Model ◽  
Model Simulations ◽  
Southern Great Plains ◽  
Climate Model Simulations ◽  
The U.S

Variation in phytolith assemblages within a population of corn (cv. Mandan Yellow Flour)

1990 ◽  
Vol 68 (8) ◽  
pp. 1638-1645 ◽  
Author(s):  
Susan C. Mulholland ◽  
George Rapp Jr. ◽  
Amy L. Ollendorf ◽  
Ronald Regal
Keyword(s):  
Zea Mays ◽  
Statistical Analysis ◽  
Great Plains ◽  
Zea Mays L ◽  
Morphological Characters ◽  
Individual Plant ◽  
Leaf Position ◽  
Plant Group ◽  
Two Samples ◽  
Multiple Samples

This project investigates the effects of leaf side, leaf position, individual plant, and hill (or plant group) on phytolith assemblages from Zea mays L. cultivar Mandan Yellow Flour. Thirty-two samples were examined, and 200 phytoliths were classified from each sample. Statistical analysis indicates that leaf side is significant at the 0.05 level above random counting variation; leaf position, individual plant, and hill are not generally significant beyond the effect of leaf side. Analysis of a single sample, either a half or entire leaf, is not sufficient for compilation of representative phytolith assemblages from a plant population. Phytolith reference collections should not be based on single samples of each species. Multiple samples from a population need to be analyzed to obtain information on assemblage variation within the population. The variation of corn phytolith assemblages in this study overlaps those of many Panicoid grasses, indicating that additional morphological characters need to be identified to distinguish corn from wild grasses in the Great Plains. Key words: phytoliths, corn, frequencies, variation.

scholarly journals Diagnosis of the summertime warm and dry bias over the U.S. Southern Great Plains in the GFDL climate model using a weather forecasting approach

2006 ◽  
Vol 33 (18) ◽  
pp. n/a-n/a ◽  
Author(s):  
Stephen A. Klein ◽  
Xianan Jiang ◽  
Jim Boyle ◽  
Sergey Malyshev ◽  
Shaocheng Xie
Keyword(s):  
Great Plains ◽  
Climate Model ◽  
Weather Forecasting ◽  
Southern Great Plains ◽  
The U.S ◽  
Dry Bias
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