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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #369377

Research Project: Uncertainty of Future Water Availability Due to Climate Change and Impacts on the Long Term Sustainability and Resilience of Agricultural Lands in the Southern Great Plains

Location: Agroclimate and Natural Resources Research

Title: Dynamic depth distribution of cesium-133 near soil surfaces in packed soils under multiple simulated rains

Author
item Zhang, Xunchang

Submitted to: Catena
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/25/2020
Publication Date: 5/30/2020
Citation: Zhang, X.J. 2020. Dynamic depth distribution of cesium-133 near soil surfaces in packed soils under multiple simulated rains. Catena. 194:104710. https://doi.org/10.1016/j.catena.2020.104710.
DOI: https://doi.org/10.1016/j.catena.2020.104710

Interpretive Summary: Various mathematical models of cesium (Cs) erosion conversion have been used to predict soil erosion. A knowledge of the initial depth distribution of Cs near the soil surfaces is vital to improve soil loss prediction. The objectives were to 1) characterize the dynamic distributions of Cs near the soil surfaces in multiple rains spiked with Cs using a rainfall simulator and 2) further estimate the depth distribution parameter H for use in an improved Cs model. Three silt loam soils were packed to soil boxes (1m long, 0.5m wide, 0.1m deep), and rained on at 30 mm h-1 and 10% slope in five consecutive rains. Soil cores (62.5 mm i.d.) were taken following each rain and were sectioned in the 1- to 10-mm intervals. Event runoff and sediment were collected. The results showed the initial Cs distribution near the soil surfaces was approximately exponential (meaning that Cs concentration decreases with depth very rapidly) and the H parameter could adequately characterize the initial Cs depth distribution. The averaged H values were 0.338, 0.304, and 0.354 g/cm2 for the three soils, equivalent to the soil depths of 2.76, 2.20, and 2.58 mm, indicating that majority of Cs was retained within the top 3-mm depth. Cs was strongly adsorbed by the soils, showing the limited mobility of the freshly deposited Cs+ in the soils. The better H estimation should lead to more accurate soil loss prediction using the improved model. The results would be useful to erosion scientists to predict spatial pattern of soil erosion using the Cs model, which in turn can be used by soil conservationists to lay out precision-conservation practices to control soil erosion.

Technical Abstract: Various Cs-137 conversion models have been used to predict soil erosion. A knowledge of the initial distribution of Cs-137 is vital to improve soil loss prediction. The objectives were to 1) characterize the dynamic distributions of Cs-133 near the soil surfaces in multiple rains spiked with Cs-133 using a rainfall simulator and 2) further estimate the relaxation mass depth (H) for use in an improved Cs-137 mass balance model. Three silt loam soils were packed to soil boxes (1x0.5x0.1 m), and rained on at 30 mm/h and 10% slope in five consecutive rains. Soil cores (62.5 mm i.d.) were taken following each rain and were sectioned in the 1- to 10-mm intervals. Event runoff and sediment were collected. Cs+ was extracted with an NH4OAc method using a centrifuge procedure, and analyzed with Inductively Coupled Plasma-Mass Spectrometer. The results showed the initial Cs distribution near the soil surfaces was approximately exponential and the H parameter could adequately characterize the initial Cs distribution. The averaged H values were 0.338, 0.304, and 0.354 g/cm2 for the three soils, equivalent to the soil depths of 2.76, 2.20, and 2.58 mm, indicating that majority of Cs was distributed within the top 3-mm depth. Cs was strongly adsorbed by the soils as indicated by the large Kd values of >32.6 L/kg, showing the limited mobility of the freshly deposited Cs in the soils. The better H estimation should lead to more accurate soil loss prediction using the improved model.