Determining the Green-Ampt Effective Hydraulic Conductivity from Rainfall-runoff Data for the WEPP Model

1994 ◽  
Vol 37 (2) ◽  
pp. 411-418 ◽  
Author(s):  
L. M. Risse ◽  
M. A. Nearing ◽  
M. R. Savabi
2019 ◽  
Vol 55 (9) ◽  
pp. 7902-7915
Author(s):  
Abhishek Goyal ◽  
Renato Morbidelli ◽  
Alessia Flammini ◽  
Corrado Corradini ◽  
Rao S. Govindaraju

1997 ◽  
Vol 50 (3) ◽  
pp. 290 ◽  
Author(s):  
Mary R. Kidwell ◽  
Mark A. Weltz ◽  
D. Phillip Guertin

Author(s):  
Edwaldo D. Bocuti ◽  
Ricardo S. S. Amorim ◽  
Luis A. Di L. Di Raimo ◽  
Wellington de A. Magalhães ◽  
Emílio C. de Azevedo

ABSTRACT The objective of this study was to determine the effective hydraulic conductivity of six areas located in the Cerrado region of Mato Grosso, Brazil, and to identify physical attributes of soils with potential for predicting effective hydraulic conductivity. The tests to determine the effective hydraulic conductivity were carried out in six areas, covering the textural classes sand, sandy loam and clay, and the following uses: pasture, Cerrado and agriculture. Particle size, sand fractionation, total carbon content, degree of clay flocculation, bulk density, macroporosity, microporosity, mean weight diameter, mean geometric diameter and aggregate stability index were determined. From the data, statistical analyses of contrasts were performed by the Kruskal - Wallis test, and simple Pearson’s correlation coefficient was determined between variables. The average values of effective hydraulic conductivity for the pasture, agriculture and Cerrado areas were 95.73, 27.83 and 48.31 mm h-1, respectively. Higher value of effective hydraulic conductivity was observed in the Pasture area point 2 when compared to the Agriculture area point 2, because the amount of clay determined in Agriculture area was approximately 16 times greater than that of the area Pasture point 2, conditioning lower water infiltration in the soil profile of the area Agriculture point 2. Among the physical attributes analyzed, those with the highest potential for Ke prediction were: clay, silt, sand (coarse, medium and fine), total carbon and aggregate stability index.


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