Decadal-scale shifts in soil hydraulic properties as induced by altered precipitation

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.Soil hydraulic properties influence the partitioning of rainfall into infiltration versus runoff, determine plant-available water, and constrain evapotranspiration. Although rapid changes in soil hydraulic properties from direct human disturbance are well documented, climate change may also induce such shifts on decadal time scales. Using soils from a 25-year precipitation manipulation experiment, we found that a 35% increase in water inputs substantially reduced infiltration rates and modestly increased water retention. We posit that these shifts were catalyzed by greater pore blockage by plant roots and reduced shrink-swell cycles. Given that precipitation regimes are expected to change at accelerating rates globally, shifts in soil structure could occur over broad regions more rapidly than expected and thus alter water storage and movement in numerous terrestrial ecosystems

Isotopic Composition of the Ogallala-high Plains Aquifer Andvadose Zone

AbstractThe Ogallala-High Plains aquifer is an important resource for irrigated agriculture in a semi-arid region of the United States. Steep declines in groundwater levels are putting increasing strain on the viability of the aquifer for irrigation, necessitating improved estimates of recharge rates and sources to the aquifer. This study uses a combined approach to obtain high resolution geochemical and isotopic composition of the vadose zone and aquifer pore fluids to better understand recharge dynamics to the aquifer. Significant differences between the shallow, intermediate and deep vadose zone and shallow and deep aquifer indicate modern precipitation is not providing a significant source of recharge to the aquifer across a large area (diffuse recharge). Rather, recharge to the aquifer is a result of either focused recharge or long-term, delayed drainage from the portion of the vadose zone which was saturated before irrigation development

Is macroporosity controlled by complexed clay and soil organic carbon?

Multi-scale evidence of rapid, climate-induced soil structural changes occurring at yearly to decadal timescales is mounting. As a result, it has become increasingly important to identify the properties and mechanisms controlling the development and maintenance of soil structure and associated macroporosity. This is especially relevant since macroporosity has disproportionate effects on saturated hydraulic conductivity ( ) which strongly influences water storage and flux, thus, affecting the water cycle. In this study, we use decision trees and piecewise linear regression to assess the influence of soil and climate properties on effective porosity (EP; a proxy of macroporosity) in both surface and subsurface horizons under varying land-use and management practices. Data from 1,491 pedons (3,679 horizons) spanning five ecoregions representing bioclimate (e.g., potential vegetation) across the conterminous US demonstrate that, at a continental scale, EP in surface (A) and subsurface (B) horizons is strongly dependent on the complexed fraction of the total mass of soil organic carbon (SOC) and clay; a combined fraction that we refer to as complexed organic carbon and clay (COCC). EP showed a slight positive response to COCC in A horizons but increased steeply with increasing COCC in B horizons. This is because the smaller values of COCC in B horizons reflect a larger pool of clay that has a greater potential to accommodate and complex additions of SOC promoting stronger organo-mineral bonds and the concomitant development and maintenance of soil structure in these horizons. In contrast, larger values of COCC in A horizons reflect conditions where all or most of the clay fraction is effectively complexed with SOC resulting in a larger pool of non-complexed soil organic matter with varying contrasting effects on macroporosity that ultimately mute the response of EP to increases in COCC. In surface horizons, indirect factors such as mean annual precipitation and land use were important predictors of EP, whereas COCC was more influential in controlling EP within the subsoil. The EP-COCC relationship also holds within ecoregions but its effect is mitigated by soil and climate interactions suggesting that the effect of climate on this relationship is indirect and complex. Plowed surface horizons and horizons underlying plowed layers showed greater homogenization (due to disturbance effects reducing heterogeneity in the soil) as well as a reduction in the magnitude and rate of change of EP as a function of COCC compared to undisturbed horizons. Our findings suggest that the complexed fraction of clay and SOC is important for controlling macroporosity and at ecoregion scales and that the EP-COCC relationship may be an important framework for understanding and predicting future land use- and climate-induced changes in soil hydraulic properties.publishedVersio

Combined effects of polyacrylamide and nanomagnetite amendment on soil and water quality, Khorasan Razavi, Iran

Nanotechnology is increasingly being used to remediate polluted soil and water. However, few studies are available assessing the potential of nanoparticles to bind surface particles, decrease erosion, and minimize the loading of water pollutants from agricultural surface discharge. To investigate this potential, we treated in situ field plots with two practical surface application levels of anionic polyacrylamide (PAM only) with and without nanomagnetite (PAM-NM), examined soil physical properties, and evaluated the impact of this amendment on contaminant sorption and soil erosion control. Polyacrylamide and PAM-NM treatments resulted in 32.2 and 151.9 fold reductions in Mn2+, 1.8 and 2.7 fold for PO43--P, and 2.3 and 1.6 fold for NH4+-N, respectively, compared to the control. Thus, we found that the combination of PAM and NM, had an important inhibitory effect on NH4+-N and PO43--P transport from soil-pollutants which can contribute substantially to the eutrophication of surface water bodies. Additionally, since the treatment, especially at a high concentration of NM, was effective at reducing Mn2+ concentrations in the runoff water, the combination of PAM and NM may be important for mitigating potential risks associated with Mn2+ toxicity. Average sediment contents in the runoff monitored during the rainfall simulation were reduced by 3.6 and 4.2 fold for the low and high concentration PAM-NM treatments when compared to a control. This treatment was only slightly less effective than the PAM-only applications (4.9 and 5.9 fold, respectively). We report similar findings for turbidity of the runoff (2.6-3.3 fold for PAM only and 1.8-2.3 fold for PAM-NM) which was caused by the effects of both PAM and NM on the binding of surface particles corresponding to an increase in aggregate size and stability. Findings from this field-based study show that PAM-modified NM adsorbents can be used to both inhibit erosion and control contaminant transport

Spatial and temporal variations of airborne dust fallout in Khorasan Razavi Province, Northeastern Iran

Dust deposition rates depend mainly on the rate of dust supply, climatic conditions, and topography in the source and sink areas. The objective of this study was to investigate the role of these variables in the spatial and temporal variation of airborne dust fallout in Khorasan Razavi Province, Northeast Iran. Airborne dust samples were collected monthly from May 2014 to April 2015. Dust fallout rate was modelled as a function of air temperature, precipitation, relative humidity, wind velocity and distance from source regions. The lowest and highest rates of atmospheric dust fallout occurred in December and June, with average amounts of 9.97 gm(-2) and 20.96 gm(-2), respectively. The strongest winds were observed in June immediately following a relatively humid period (i.e., March-May) with considerably higher precipitation and lower evaporation. Spatial distributions showed that the highest dust fallout rates occurred in the southern and western parts of the province-areas adjoining the vast playas. During the spring and summer season, the distance from the nearest playa was a key factor that explained more of the variation in dust flux than climatic parameters. Both runoff by fresh sediment moved onto the surface of the playa and the formation of loose sediment on the surfaces of wet playas are mechanisms that can increase dust emissions. The lowest deposition rates were observed in the mountainous region in the north of the province likely due to higher precipitation, atmospheric humidity, and soil moisture. This work represents the first baseline dust data for Khorasan Razavi Province and may be useful in evaluating the effects of future land use and climate change on aeolian land surface processes

Geogenic and anthropogenic sources of potentially toxic elements in airborne dust in northeastern Iran

Little attention has been given to the nature and sources of airborne dust affecting northeastern Iran. The objectives of this study were to examine the concentrations of selected potentially toxic elements (i.e., Cr, Cu, Fe, Mn, Ni, Pb, and Zn), distinguish geogenic from anthropogenic sources, and assess the pollution intensity. A total of 600 samples were collected at 50 locations 12 times between May 2014 and April 2015 for fallout rate; 250 of these samples were selected for geochemical analysis. Mean dust concentrations of Cu, Pb, and Zn were found to be higher in autumn compared to spring as well as higher in the most populous cities. Results suggested that Ni, Cr, Mn, and Fe have come from mainly natural geologic sources, while concentrations of Cu, Pb, and Zn in the dust were associated with anthropogenic sources. Enrichment factors showed minimal to significant enrichment for Cu and Pb and moderate to very high enrichment for Cr, Ni, and Zn. The mean geo-accumulation index revealed that the contamination levels for Cu, Pb, and Zn peaked during autumn. In addition to industrial and traffic sources, seasonal differences in meteorological conditions can create frequent and persistent thermal inversions that at ground level can result in increases in Cu, Ni, Pb, and Zn concentrations during autumn. Because of the diversity of geology and terrain in combination with significant seasonal shifts in winds over this region, this study highlights the need to consider both geogenic and anthropogenic sources in evaluating pollution risks in northeastern Iran

Consequences of spatial structure in soil–geomorphic data on the results of machine learning models

In this paper, we examined the degree to which inherent spatial structure in soil properties influences the outcomes of machine learning (ML) approaches to predicting soil spatial variability. We compared the performances of four ML algorithms (support vector machine, artificial neural network, random forest, and random forest for spatial data) against two non-ML algorithms (ordinary least squares regression and spatial filtering regression). None of the ML algorithms produced residuals that had lower mean values or were less autocorrelated over space compared with the non-ML approaches. We recommend the use of random forest when a soil variable of interest is weakly autocorrelated (Moran’s I  0.4). Overall, this work opens the door to a more consistent selection of model algorithms through the establishment of threshold criteria for spatial autocorrelation of input variables

Predicting the Influence of Multi-Scale Spatial Autocorrelation on Soil–Landform Modeling

Although numerous soil–landform modeling investigations have documented the effects and importance of spatial autocorrelation (SAC), little is known about how to predict the magnitude of those effects from the degree of SAC in the model variables. In this study, we quantified the SAC inherent in soil and landform variables of four widely divergent pedogeomorphological systems around the world to examine general relationships between SAC and spatial regression model results. Spatial regressions were performed by incorporating spatial filters, extracted by spatial eigenvector mapping, into non-spatial models as additional predictor variables. Results indicated that incorporation of spatial filters improved the performance of the non-spatial regressions—increases in R2 and decreases in both Akaike Information Criterion (AIC) and residual SAC were observed. More remarkable was that the degree of improvement was strongly and linearly related (i.e., proportional) to the level of SAC inherently possessed by each soil variable. Our findings show that spatial modeling outcomes are sensitive to the degree of SAC possessed by a soil property when treated as a response variable. Thus, the level of SAC present in a soil variable can serve as a direct indicator for how much improvement a non-spatial model will undergo if that SAC is appropriately taken into account