scholarly journals A FIELD-SCALE MODELING STUDY AND CHARACTERIZING HYDRAULIC PROPERTIES FOR WATER, AIR AND HEAT FLOW IN THE UNSATURATED ZONE OF YUCCA MOUNTAIN, NEVADA

1998 ◽  
Author(s):  
S. FINSTERLE, AND G.S. BODVARSSON Y.S. WU
Keyword(s):  
Heat Flow ◽  
Unsaturated Zone ◽  
Hydraulic Properties ◽  
Yucca Mountain ◽  
Field Scale ◽  
Scale Modeling ◽  
Modeling Study

A site-scale model for fluid and heat flow in the unsaturated zone of Yucca Mountain, Nevada

1999 ◽  
Vol 38 (1-3) ◽  
pp. 185-215 ◽  
Author(s):  
Yu-Shu Wu ◽  
Charles Haukwa ◽  
G.S Bodvarsson
Keyword(s):  
Heat Flow ◽  
Unsaturated Zone ◽  
Yucca Mountain ◽  
Scale Model ◽  
A Site

scholarly journals Experimental investigation to characterize simple versus multi scaling analysis of hydraulic conductivity at a mesoscale

2021 ◽  
Author(s):  
Guglielmo Federico Antonio Brunetti ◽  
Samuele De Bartolo ◽  
Carmine Fallico ◽  
Ferdinando Frega ◽  
Maria Fernanda Rivera Velásquez ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Spatial Variability ◽  
Hydraulic Properties ◽  
Scaling Laws ◽  
Scaling Analysis ◽  
Field Scale ◽  
Porous Formation ◽  
Proper Design ◽  
First Time

AbstractThe spatial variability of the aquifers' hydraulic properties can be satisfactorily described by means of scaling laws. The latter enable one to relate the small (typically laboratory) scale to the larger (typically formation/regional) ones, therefore leading de facto to an upscaling procedure. In the present study, we are concerned with the spatial variability of the hydraulic conductivity K into a strongly heterogeneous porous formation. A strategy, allowing one to identify correctly the single/multiple scaling of K, is applied for the first time to a large caisson, where the medium was packed. In particular, we show how to identify the various scaling ranges with special emphasis on the determination of the related cut-off limits. Finally, we illustrate how the heterogeneity enhances with the increasing scale of observation, by identifying the proper law accounting for the transition from the laboratory to the field scale. Results of the present study are of paramount utility for the proper design of pumping tests in formations where the degree of spatial variability of the hydraulic conductivity does not allow regarding them as “weakly heterogeneous”, as well as for the study of dispersion mechanisms.

scholarly journals Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

2020 ◽  
Author(s):  
Noemi Vergopolan ◽  
Sitian Xiong ◽  
Lyndon Estes ◽  
Niko Wanders ◽  
Nathaniel W. Chaney ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Scale ◽  
Root Zone ◽  
Maize Yield ◽  
Remotely Sensed ◽  
Drought Monitoring ◽  
Surface Model ◽  
Field Scale ◽  
Yield Data ◽  
Scale Modeling

Abstract. Soil moisture is highly variable in space, and its deficits (i.e. droughts) plays an important role in modulating crop yields and its variability across landscapes. Limited hydroclimate and yield data, however, hampers drought impact monitoring and assessment at the farmer field-scale. This study demonstrates the potential of field-scale soil moisture simulations to advance high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field-scale. We present a multi-scale modeling approach that combines HydroBlocks, a physically-based hyper-resolution Land Surface Model (LSM), and machine learning. We applied HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3-hourly 30-m resolution. These simulations along with remotely sensed vegetation indices, meteorological conditions, and data describing the physical properties of the landscape (topography, land cover, soil properties) were combined with district-level maize data to train a random forest model (RF) to predict maize yields at the district- and field-scale (250-m) levels. Our model predicted yields with a coefficient of variation (R2) of 0.61, Mean Absolute Error (MAE) of 349 kg ha−1, and mean normalized error of 22 %. We captured maize losses due to the 2015/2016 El Niño drought at similar levels to losses reported by the Food and Agriculture Organization (FAO). Our results revealed that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Consequently, soil moisture was also the most effective indicator of drought impacts in crops when compared with precipitation, soil and air temperatures, and remotely-sensed NDVI-based drought indices. By combining field-scale root zone soil moisture estimates with observed maize yield data, this research demonstrates how field-scale modeling can help bridge the spatial scale discontinuity gap between drought monitoring and agricultural impacts.

Field-Scale Modeling of Benzene, Toluene, Ethylbenzene, and Xylenes (BTEX) Released from Multiple Source Zones

2012 ◽  
Vol 16 (3) ◽  
pp. 156-176 ◽  
Author(s):  
Abdorreza Vaezihir ◽  
Mohammad Zare ◽  
Ezzat Raeisi ◽  
John Molson ◽  
James Barker
Keyword(s):  
Multiple Source ◽  
Field Scale ◽  
Scale Modeling ◽  
Source Zones ◽  
Benzene Toluene
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