scholarly journals Subsurface lateral flow from hillslope and its contribution to nitrate loading in streams through an agricultural catchment during subtropical rainstorm events

2011 ◽  
Vol 15 (10) ◽  
pp. 3153-3170 ◽  
Author(s):  
B. Zhang ◽  
J. L. Tang ◽  
Ch. Gao ◽  
H. Zepp
Keyword(s):  
Water Potential ◽  
Soil Water ◽  
Stream Flow ◽  
Soil Water Potential ◽  
Lateral Flow ◽  
Source Surface ◽  
N Loss ◽  
Particulate Nitrogen ◽  
Total N ◽  
Subsurface Lateral Flow

Abstract. Subsurface lateral flow from agricultural hillslopes is often overlooked compared with overland flow and tile drain flow, partly due to the difficulties in monitoring and quantifying. The objectives of this study were to examine how subsurface lateral flow generated through soil pedons from cropped hillslopes and to quantify its contribution to nitrate loading in the streams through an agricultural catchment in the subtropical region of China. Profiles of soil water potential along hillslopes and stream hydro-chemographs in a trenched stream below a cropped hillslope and at the catchment outlet were simultaneously recorded during two rainstorm events. The dynamics of soil water potential showed positive matrix soil water potential over impermeable soil layer at 0.6 to 1.50 m depths during and after the storms, indicating soil water saturation and drainage processes along the hillslopes irrespective of land uses. The hydro-chemographs in the streams, one trenched below a cropped hillslope and one at the catchment outlet, showed that the concentrations of particulate nitrogen and phosphorus corresponded well to stream flow during the storm, while the nitrate concentration increased on the recession limbs of the hydrographs after the end of the storm. All the synchronous data revealed that nitrate was delivered from the cropped hillslope through subsurface lateral flow to the streams during and after the end of the rainstorms. A chemical mixing model based on electricity conductivity (EC) and H+ concentration was successfully established, particularly for the trenched stream. The results showed that the subsurface lateral flow accounted for 29% to 45% of total stream flow in the trenched stream, responsible for 86% of total NO3−-N loss (or 26% of total N loss), and for 5.7% to 7.3% of total stream flow at the catchment outlet, responsible for about 69% of total NO3−-N loss (or 28% of total N loss). The results suggest that subsurface lateral flow through hydraulically stratified soil pedons have to be paid more attention for controlling non-point source surface water pollution from intensive agricultural catchment particularly in the subtropical areas with great soil infiltration.

scholarly journals Subsurface lateral flow from hillslope and its contribution to nitrate loading in the streams during typical storm events in an agricultural catchment

2011 ◽  
Vol 8 (2) ◽  
pp. 4151-4193 ◽  
Author(s):  
J. Tang ◽  
B. Zhang ◽  
C. Gao ◽  
H. Zepp
Keyword(s):  
Surface Water ◽  
Soil Water ◽  
Stream Flow ◽  
Mixing Model ◽  
Lateral Flow ◽  
Storm Events ◽  
N Loss ◽  
Total N ◽  
Subsurface Lateral Flow ◽  
Chemical Mixing

Abstract. Compared with overland flow from agricultural hillslopes, subsurface lateral flow is often overlooked partly due to monitoring difficulties and the lack of quantitative identification its role in nutrient delivery to surface water. The objectives of this study were to examine how subsurface lateral flow generates from hillslopes to streams and to quantify its contribution to nutrient loading in streams. Hillslope hydrology and stream hydrology were simultaneously monitored during two typical storms and subsurface flow was separated by chemical mixing model. Positive soil water potential at the soil depths from 0.60 to 1.50 m was observed at the middle course of the storm events, suggesting soil water was saturated following the storms and the drained after the end of the storms. The hydro-chemographs in the stream in a trench below a hillslope showed that suspended sediment, particulate N and P were dominant in the stream during the storms, while after the end of the rainstorms the nitrate concentration and electricity conductivity (EC) in the stream increased with time on the recession limbs of the hydrographs. Meanwhile, a rebound or delayed curve appeared on the recession limbs for several hours immediately after the end of rainstorms. All the synchronous data confirmed nitrate was delivered from the hillslope through subsurface lateral flow to the streams even after the end of rainstorms. A chemical mixing model based on EC and pH showed that the subsurface lateral flow during the rainstorm events accounted for 29% to 45% of the stream flow and about 86% of total NO3−-N loss (or 26% of total N loss) from the peanut hillslope and for 5.7% to 7.3% of the stream flow about 69% of total NO3−-N loss (or 28% of total N loss) from the catchment outlet. The results suggest that subsurface lateral flow generated within a shallow soil profile have to be paid more attention for controlling non-point source surface water pollution from intensive agricultural catchment.

Effect of Soil Water Potential of Two Soils on Soybean Emergence 1

1979 ◽  
Vol 71 (6) ◽  
pp. 980-982 ◽  
Author(s):  
L. G. Heatherly ◽  
W. J. Russell
Keyword(s):  
Water Potential ◽  
Soil Water ◽  
Soil Water Potential

scholarly journals A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1208
Author(s):  
Massimiliano Bordoni ◽  
Fabrizio Inzaghi ◽  
Valerio Vivaldi ◽  
Roberto Valentino ◽  
Marco Bittelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Potential ◽  
Soil Water ◽  
Temporal Trends ◽  
Soil Water Potential ◽  
Shallow Landslides ◽  
Water Dynamics ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Physically Based

Soil water potential is a key factor to study water dynamics in soil and for estimating the occurrence of natural hazards, as landslides. This parameter can be measured in field or estimated through physically-based models, limited by the availability of effective input soil properties and preliminary calibrations. Data-driven models, based on machine learning techniques, could overcome these gaps. The aim of this paper is then to develop an innovative machine learning methodology to assess soil water potential trends and to implement them in models to predict shallow landslides. Monitoring data since 2012 from test-sites slopes in Oltrepò Pavese (northern Italy) were used to build the models. Within the tested techniques, Random Forest models allowed an outstanding reconstruction of measured soil water potential temporal trends. Each model is sensitive to meteorological and hydrological characteristics according to soil depths and features. Reliability of the proposed models was confirmed by correct estimation of days when shallow landslides were triggered in the study areas in December 2020, after implementing the modeled trends on a slope stability model, and by the correct choice of physically-based rainfall thresholds. These results confirm the potential application of the developed methodology to estimate hydrological scenarios that could be used for decision-making purposes.

NITROGEN TRANSFORMATIONS NEAR UREA IN SOIL WITH DIFFERENT WATER POTENTIALS

1988 ◽  
Vol 68 (3) ◽  
pp. 569-576 ◽  
Author(s):  
YADVINDER SINGH ◽  
E. G. BEAUCHAMP
Keyword(s):  
Water Potential ◽  
Soil Water ◽  
Incubation Period ◽  
Relative Rate ◽  
Soil Water Potential ◽  
Nitrification Inhibitor ◽  
Silt Loam ◽  
Nitrogen Transformations ◽  
Water Potentials ◽  
Initial Soil

Two laboratory incubation experiments were conducted to determine the effect of initial soil water potential on the transformation of urea in large granules to nitrite and nitrate. In the first experiment two soils varying in initial soil water potentials (− 70 and − 140 kPa) were incubated with 2 g urea granules with and without a nitrification inhibitor (dicyandiamide) at 15 °C for 35 d. Only a trace of [Formula: see text] accumulated in a Brookston clay (pH 6.0) during the transformation of urea in 2 g granules. Accumulation of [Formula: see text] was also small (4–6 μg N g−1) in Conestogo silt loam (pH 7.6). Incorporation of dicyandiamide (DCD) into the urea granule at 50 g kg−1 urea significantly reduced the accumulation of [Formula: see text] in this soil. The relative rate of nitrification in the absence of DCD at −140 kPa water potential was 63.5% of that at −70 kPa (average of two soils). DCD reduced the nitrification of urea in 2 g granules by 85% during the 35-d period. In the second experiment a uniform layer of 2 g urea was placed in the center of 20-cm-long cores of Conestogo silt loam with three initial water potentials (−35, −60 and −120 kPa) and the soil was incubated at 15 °C for 45 d. The rate of urea hydrolysis was lowest at −120 kPa and greatest at −35 kPa. Soil pH in the vicinity of the urea layer increased from 7.6 to 9.1 and [Formula: see text] concentration was greater than 3000 μg g−1 soil. There were no significant differences in pH or [Formula: see text] concentration with the three soil water potential treatments at the 10th day of the incubation period. But, in the latter part of the incubation period, pH and [Formula: see text] concentration decreased with increasing soil water potential due to a higher rate of nitrification. Diffusion of various N species including [Formula: see text] was probably greater with the highest water potential treatment. Only small quantities of [Formula: see text] accumulated during nitrification of urea – N. Nitrification of urea increased with increasing water potential. After 35 d of incubation, 19.3, 15.4 and 8.9% of the applied urea had apparently nitrified at −35, −60 and −120 kPa, respectively. Nitrifier activity was completely inhibited in the 0- to 2-cm zone near the urea layer for 35 days. Nitrifier activity increased from an initial level of 8.5 to 73 μg [Formula: see text] in the 3- to 7-cm zone over the 35-d period. Nitrifier activity also increased with increasing soil water potential. Key words: Urea transformation, nitrification, water potential, large granules, nitrifier activity, [Formula: see text] production

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