Journal Paper Digests 2023 #8
- Controls on the presence and storage of soil inorganic carbon in a semi-arid watershed
- Regolith or soil? An ongoing debate
- How many bootstrap replications are necessary for estimating remote sensing-assisted, model-based standard errors?
- Interpreting ramped combustion thermograms using 13C NMR spectroscopy to characterize soil organic matter composition
- Methods for reducing the tillage force of subsoiling tools: A review
- Future warming from global food consumption
- Spatially Explicit Linkages Between Redox Potential Cycles and Soil Moisture Fluctuations
- A Novel Physics-Aware Machine Learning-Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy
- Toward Large-Scale Soil Moisture Monitoring Using Rail-Based Cosmic Ray Neutron Sensing
- A multivariate approach for mapping a soil quality index and its uncertainty in southern France
A multivariate approach for mapping a soil quality index and its uncertainty in southern France
Pedometricians have spent a lot of effort on mapping soil types and basic soil properties. However, end-users typically need a more elaborate soil quality index for land management. Soil quality indices are typically derived from multiple individual soil properties by evaluating whether specific criteria are met. If this is based on individually mapped soil properties, then an important consequence is that cross-correlations between soil properties are ignored. This makes it impossible to quantify the uncertainties associated with the mapped indices. The objective of this study was to map a soil potential multifunctionality index for agriculture (Agri-SPMI) over a 12,125 km(2) study region located along the French Mediterranean coast to help urban planners preserve soils of the highest quality. The index considered the ability of soils to fulfil four functions under five land use scenarios. A binary map represented each soil function fulfilment for a given scenario. The final soil quality index map was the sum of the 20 binary maps. A regression cokriging model was developed to map the basic soil properties first individually from legacy soil data and spatial soil covariates using a random forest algorithm, and next, interpolate the residuals using cokriging and the linear model of coregionalisation. The mapping uncertainties of soil properties were propagated by calculating the soil quality index over 300 stochastic simulations of soil properties derived from the linear models of coregionalisation. Results showed a poor prediction accuracy of the quality index, mainly because some soil properties were poorly predicted (notably available water capacity and coarse fragments) and used in combination with extreme thresholds that defined land suitability. Overall, the uncertainty was correctly quantified because the stochastic simulations reproduced the width of the observed distribution well, but the shapes of the distributions differed considerably from those of the observations. We envisage some ways for improvement, such as creating probability maps instead of the mean from simulations, and changing the prediction support from point to area.
Toward Large-Scale Soil Moisture Monitoring Using Rail-Based Cosmic Ray Neutron Sensing
Cosmic ray neutron sensing (CRNS) has become a promising method for soil water content (SWC) monitoring. Stationary CRNS offers hectare-scale average SWC measurements at fixed locations maintenance-free and continuous in time, while car-borne CRNS roving can reveal spatial SWC patterns at medium scales, but only on certain survey days. The novel concept of a permanent mobile CRNS system on rails promises to combine the advantages of both methods, while its technical implementation, data processing and interpretation raised a new level of complexity. This study introduced a fully automatic CRNS rail-borne system as the first of its kind, installed within the locomotive of a cargo train. Data recorded from September 2021 to July 2022 along an similar to 9 km railway segment were analyzed, as repeatedly used by the train, supported by local SWC measurements (soil samples and dielectric methods), car-borne and stationary CRNS. The results revealed consistent spatial SWC patterns and temporary variation along the track at a daily resolution. The observed variability was mostly related to surface features, seasonal dynamics and different responses of the railway segments to wetting and drying periods, while some variations were related to measurement uncertainties. The achieved medium scale of SWC mapping could support large scale hydrological modeling and detection of environmental risks, such as droughts and wildfires. Hence, rail-borne CRNS has the chance to become a central tool of continuous SWC monitoring for larger scales (<= 10-km), with the additional benefit of providing root-zone soil moisture, potentially even in sub-daily resolution.
A Novel Physics-Aware Machine Learning-Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy
Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near-accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow-fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7-day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time.
Spatially Explicit Linkages Between Redox Potential Cycles and Soil Moisture Fluctuations
Reduction-oxidation cycles measured through soil redox potential (E-h) are associated with dynamic soil microbial activity. Understanding changes in the composition of, and resource use by, soil microbial communities requires E-h predictability under shifting hydrologic drivers. Here, 50-cm soil column installations are manipulated to vary hydrologic and geochemical conditions, and are extensively monitored by a dense instrumental deployment to record the depth-time variation of physical and biogeochemical conditions. We contrast measurements of E-h, soil saturation and key compounds in water samples (probing the majority of soil microbial metabolisms) with computations of the relevant state variables, to investigate the interplay between soil moisture and redox potential dynamics. Our results highlight the importance of joint spatially resolved hydrologic flow/transport and redox processes, the worth of contrasting experiments and computations for a sufficient understanding of the E-h dynamics, and the minimum amount of biogeochemistry needed to characterize the dynamics of electron donors/acceptors that are responsible for the patterns of E-h not directly explained by physical oxic/anoxic transitions. As an example, measured concentrations of sulfate, ammonium and iron II suggest coexistence of both oxic and anoxic conditions. We find that the local saturation velocity (a threshold value of the time derivative of soil saturation) exerts a significant hysteretic control on oxygen intrusion and on the cycling of redox potentials, in contrast with approaches using a single threshold saturation level as the determinant of anoxic conditions. Our findings improve our ability to target how and where hotspots of activity develop within soil microbial communities.
Future warming from global food consumption
Food consumption is a major source of greenhouse gas (GHG) emissions, and evaluating its future warming impact is crucial for guiding climate mitigation action. However, the lack of granularity in reporting food item emissions and the widespread use of oversimplified metrics such as CO2 equivalents have complicated interpretation. We resolve these challenges by developing a global food consumption GHG emissions inventory separated by individual gas species and employing a reduced-complexity climate model, evaluating the associated future warming contribution and potential benefits from certain mitigation measures. We find that global food consumption alone could add nearly 1 degrees C to warming by 2100. Seventy five percent of this warming is driven by foods that are high sources of methane (ruminant meat, dairy and rice). However, over 55% of anticipated warming can be avoided from simultaneous improvements to production practices, the universal adoption of a healthy diet and consumer- and retail-level food waste reductions.
Although the role of the human diet in climate change has been widely acknowledged, current practices fail to capture its realistic effect on warming. In this Analysis, Ivanovich et al. develop a global food consumption emission inventory and estimate the associated future climate impact using a reduced-complexity climate model.
Methods for reducing the tillage force of subsoiling tools: A review
Conservation tillage technology (CTT) is a promising and efficient method for farmland protection and utiliza-tion. As one of the core technologies of CTT, mechanical subsoiling has been identified as an effective practice to disrupt hardpans and reduce soil bulk density to promote better water infiltration and crop root development. The tillage force during subsoiling (TFDS) is extremely high and methods for reducing the TFDS have been developed. A comprehensive summary about typical subsoiling tools and corresponding tool characteristics and current methods for TFDS reduction were reported in this review. The TFDS reduction methods were introduced in five aspects: biomimetic, oscillatory, lubrication, experimental, and others (i.e., line element design, layered subsoiling, crop straw and root anti-blocking, high-pressure gas splitting, rotary subsoiling, and selection of working depth and time). The mechanisms and limitations of current methods were analysed. The effect of various methods on draught forces of subsoiling tools with similar working parameters were quantitatively investigated and compared using statistical analyses. This review also recommends four future research di-rections about further reduction of TFDS to promote the development of TFDS reduction technology and CTT.
Interpreting ramped combustion thermograms using 13C NMR spectroscopy to characterize soil organic matter composition
While many advanced analytical methods have been applied to soil organic matter (SOM), its highly complex and heterogeneous chemical composition still eludes complete characterization. Analytical thermal analysis has been proposed as a relatively rapid, inexpensive method for SOM characterization that requires no pre-treatment, but is challenging due to a lack of direct information about chemical composition. The goal of this study was to inform the interpretation of coupled differential scanning calorimetry and evolved gas analyses (DSC, CO2-EGA) using spectral correlations with solid phase 13C NMR data. We used a subset of soils collected as part of the Australian National Soil Carbon Research Program (SCaRP), which were physically fractionated and charac-terized using conventional analytical methods. Correlating the well-understood NMR spectra with the less -understood DSC and CO2-EGA thermograms provided some indications of which chemical compounds combust at which temperatures. Overall, the EGA data generated stronger correlations compared to correlations with DSC data, which was attributable to greater variability in DSC data due thermal reactions associated with minerals. Direct comparison of NMR and thermal data for the mineral associated organic matter in the fine (<50 mu m) fraction was not possible due to the need to demineralize samples prior to NMR analyses. Thermal analyses showed substantial differences in samples pre-and post-HF pretreatment, and the NMR data for HF treated samples showed scattered and weak correlation patterns with DSC and EGA data for untreated samples. While precise chemical compositions cannot be gleaned directly from thermal analyses results, thermal approaches provide an avenue of investigation into SOM reactivity based on bioenergetics that may be a quantitative rep-resentation of SOM persistence.
Regolith or soil? An ongoing debate
Definitions of soil and regolith abound, the traditional view seeing soil as the top part of the regolith. Leighton (1958) claimed that soil and regolith are the same thing. Definitions of soil arising from research on soil and regolith in extraterrestrial, Precambrian, and some human-made settings tends to reinforce this view. Over the last two decades, the work of some researchers, including whole-regolith pedologists, has reinforced the case for Leighton’s claim, suggesting that soil and regolith are indeed the same and form pedo-weathering profiles. This view partly reflects a growing realization that the substratum plays an important role in some soil-forming processes, and should be included in soil classification schemes. The regolith-or-soil debate also applies when looking at soil in the contexts of the Earth System and the Critical Zone, both of which stress the connections between unweathered rock, weathered rock, fluid flows, and biological activity at local, regional, and global scales. It is unlikely that the debate will end: realistically, research on the whole-regolith pedological studies, including those that tackle wider links with the Earth System, will accompany pedological studies that focus on the solum and soil-forming processes within it, although they will assuredly inform each other; and, even though a case is made for regolith and soil being the same thing, it is will doubtless spark disagreement.
How many bootstrap replications are necessary for estimating remote sensing-assisted, model-based standard errors?
When probability samples are not available, the model-based framework may be the only option for constructing inferences in the form of prediction intervals for population means. Further, for machine learning and some non-parametric and nonlinear regression prediction techniques, resampling methods such as the bootstrap may be the only option for obtaining the standard errors necessary for constructing those prediction intervals.All bootstrap approaches entail repeatedly sampling from the original sample, estimating the parameter of interest for each replication, and estimating the standard error of the estimate of the parameter as the standard deviation of the bootstrap estimates over replications. The objective of the study was to develop a procedure for terminating resampling such that the resulting number of replications assures, at least in probability, that the estimate of the standard error stabilizes to the standard error corresponding to one million replications. The analyses used a variety of datasets: five forest inventory datasets with either volume or aboveground biomass as the dependent variable and metrics from either airborne laser scanning or Landsat as independent variables, three from Europe, one from Southwest Asia, and one from Africa; and two forest/non-forest versus Landsat datasets, one from Minnesota and one from Wisconsin, both in the USA. The primary contribution of the study was development and demonstration of a procedure that specifies criteria for terminating resampling that assure in probability that the bootstrap estimate of the standard error stabilizes to the estimate obtained with one million replications.
Controls on the presence and storage of soil inorganic carbon in a semi-arid watershed
Soil inorganic carbon (SIC) constitutes-40-50% of the terrestrial soil carbon and is an integral part of the global carbon cycle. Rainfall is a primary factor controlling SIC accumulation; however, the distribution and hierarchy of controls on SIC development in arid and semi-arid regions is poorly understood. The Reynolds Creek Experimental Watershed (RCEW) in southwestern Idaho is an ideal location to study factors influencing SIC because it spans a wide mean annual precipitation range (235 mm to 900 mm) along a 1,425 to 2,111 m elevation gradient and has soils derived from a wide variety of parent materials (granite, basalt, dust, and al-luvium). We collected soil samples along this elevational gradient to understand local controls on SIC distri-butions. SIC content was quantified at 71 soil pits and/or augered cores collected between approximately 0-1 m depth or until refusal. Consistent with previous studies, we found variations in precipitation governed the presence or absence of SIC; field measurements of the top 1 m of soils confirm little or no SIC in soils receiving > 500 mm in mean annual precipitation. Below this 500 mm threshold, SIC pools varied substantially and significantly between sites. Results showed that 90% of sites (64 sites) contained less than 10 kg m- 2 SIC, 7% (5 sites) contained 10-20 kg m- 2, and 3% (2 sites) contain between 24 and 29 kg m- 2 SIC. The total SIC within RCEW was estimated at-5.17 x 105 Mg. After precipitation, slope consistently ranked as the second most important predictor of SIC accumulation in random forest analysis. Wind-blown dust likely contributed to SIC accumulation; prior work indicates an average dust flux rate in RCEW of about 11 +/- 4.9 g m- 2 year -1. This study provides an initial model predicting SIC distribution and accumulation in a shrub-dominated dryland watershed.