Journal Paper Digests

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Journal Paper Digests 2020 #12

  • Soil magnetic susceptibility and its relationship with naturally occurring processes and soil attributes in pedosphere, in a tropical environment
  • The challenge for the soil science community to contribute to the implementation of the UN Sustainable Development Goals
  • Evaluating a low-cost portable NIR spectrometer for the prediction of soil organic and total carbon using different calibration models
  • The Dynamics of Soil Microbial Communities on Different Timescales: A Review
  • Debates: Does Information Theory Provide a New Paradigm for Earth Science? Sharper Predictions Using Occam’s Digital Razor
  • Can Continental Models Convey Useful Seasonal Hydrologic Information at the Catchment Scale?
  • Does Information Theory Provide a New Paradigm for Earth Science? Hypothesis Testing
  • Climate Adaptation as a Control Problem: Review and Perspectives on Dynamic Water Resources Planning Under Uncertainty
  • Debates-Does Information Theory Provide a New Paradigm for Earth Science? Causality, Interaction, and Feedback

Soil magnetic susceptibility and its relationship with naturally occurring processes and soil attributes in pedosphere, in a tropical environment

By: de Mello, Danilo Cesar; Dematte, Jose A. M.; Silvero, Nelida E. Q.; et al. GEODERMA Volume:  372 Pages:  14364-14364 Published:  AUG 1 2020

Soil magnetic susceptibility (kappa) has potential to be used as a pedoenvironmental indicator from which mineralogy, pedogeochemical, pedogeomorphological and pedogenic processes can be inferred. It can be used in pedosphere studies, as an auxiliary information for appropriate and sustainable soil use and management. This research aimed to analyze how pedogenesis and geochemical processes affect the kappa and some of its attributes, as well as its potential use in discriminating soil great groups, following the digital soil mapping approach. The study area is located in Sao Paulo State - Brazil. Soil samples were collected for physical-chemical analysis from 79 locations (0-20 cm depth). At these sites, magnetic susceptibility was measured with a portable field instrument and analyzed in terms of geology, relief and soil class. The results showed that geology strongly affects kappa, mainly in diabase derived soils, followed by metamorphosed siltstone and siltstone. In fluvial sediments, the kappa exhibits different behaviors due to different sediments deposited by the Capivari River. In less evolved soils, such as Cambisols, lithology is a more important contributor to kappa than pedogenesis. In more evolved soils, pedogenesis increases kappa, whereas argilluviation/ferralitization reduces it. The kappa values did not decrease significantly or even increase downslope, due to the presence of diabase on the lower parts. Differences in kappa where observed between diabase bedrock located in different parts of the study area, indicating more of an influence by geomorphic processes rather than lithology. With respect to soil attributes, positive correlations between kappa and base saturation, cation exchange capacity, organic matter, and iron and clay content were found, whereas a negative correlation was found between kappa and sand content. The kappa correlates with changes in lithology and soil class demonstrating its application as a potential tool for the discrimination of soil great groups and digital soil mapping.

The challenge for the soil science community to contribute to the implementation of the UN Sustainable Development Goals

By: Bouma, Johan; Montanarella, Luca; Evanylo, Gregory SOIL USE AND MANAGEMENT Volume:  35 Issue:  4 Pages:  538-546 Published:  DEC 2019 Context Sensitive Links Full Text from Publisher Close Abstract Seventeen Sustainable Development Goals (SDGs) were adopted by 193 Governments at the General Assembly of the United Nations in 2015 for achievement by 2030. These SDGs present a roadmap to a sustainable future and a challenge to the science community. To guide activities and check progress, targets and indicators have been and are still being defined. The soil science community has published documents that describe the primary importance of soil for SDGs addressing hunger, water quality, climate mitigation and biodiversity preservation, and secondary relevance of soil for addressing several other SDGs. Soil scientists only marginally participated in the SDG discussions and are currently only peripherally engaged in discussions on targets or indicators. Agreement on several soil-related indicators has still not been achieved. Involvement of soil scientists in SDG–based studies is desirable for both developing solutions and increasing the visibility of the soil profession. Inputs into policy decisions should be improved as SDG committee members are appointed by Governments. Possible contributions of soil science in defining indicators for the SDGs are explored in this paper. We advocate the pragmatic use of soil-water-atmosphere-plant simulation models and available soil surveys and soil databases where “representative” soil profiles for mapping units (genetically defined genoforms) are functionally expressed in terms of several phenoforms reflecting effects of different types of soil use and management that strongly affect functionality.

Evaluating a low-cost portable NIR spectrometer for the prediction of soil organic and total carbon using different calibration models

By: Sharififar, Amin; Singh, Kanika; Jones, Edward; et al. SOIL USE AND MANAGEMENT Volume:  35 Issue:  4 Pages:  607-616 Published:  DEC 2019 Context Sensitive Links Close Abstract This study aims to assess the performance of a low-cost, micro-electromechanical system-based, near infrared spectrometer for soil organic carbon (OC) and total carbon (TC) estimation. TC was measured on 151 soil profiles up to the depth of 1 m in NSW, Australia, and from which a subset of 24 soil profiles were measured for OC. Two commercial spectrometers including the AgriSpec(TM) (ASD) and NeoSpectra(TM) (Neospectra) with spectral wavelength ranges of 350-2,500 and 1,300-2,500 nm, respectively, were used to scan the soil samples, according to the standard contact probe protocol. Savitzky-Golay smoothing filter and standard normal variate (SNV) transformation were performed on the spectral data for noise reduction and baseline correction. Three calibration models, including Cubist tree model, partial least squares regression (PLSR) and support vector machine (SVM), were assessed for the prediction of soil OC and TC using spectral data. A 10-fold cross-validation analysis was performed for evaluation of the models and devices accuracies. Results showed that Cubist model predicts OC and TC more accurately than PLSR and SVM. For OC prediction, Cubist showed R-2 = 0.89 (RMSE = 0.12%) and R-2 = 0.78 (RMSE = 0.16%) using ASD and NeoSpectra, respectively. For TC prediction, Cubist produced R-2 = 0.75 (RMSE = 0.45%) and R-2 = 0.70 (RMSE = 0.50%) using ASD and NeoSpectra, respectively. ASD performed better than NeoSpectra. However, the low-cost NeoSpectra predictions were comparable to the ASD. These finding can be helpful for more efficient future spectroscopic prediction of soil OC and TC with less costly devices.

The Dynamics of Soil Microbial Communities on Different Timescales: A Review

By: Chernov, T. I.; Zhelezova, A. D. EURASIAN SOIL SCIENCE Volume:  53 Issue:  5 Pages:  643-652 Published:  MAY 2020 Context Sensitive Links Close Abstract Soil microbial communities are subjected to significant changes over time. The most rapid changes caused by temperature and soil moisture alternations or the by the inflow of fresh organic matter occur during the several hours or days. They are mostly related to the soil microbial activity. Seasonal dynamics are caused by annual variations in temperature and precipitation that affect the microbial community directly or indirectly through the regulation of plant life. The microbial biomass and the taxonomic composition of soil microbial communities vary significantly throughout the year, which should be taken into account when sampling for a comparative analysis of different soils. The long-term dynamics of microbial communities during primary or secondary successions lead to an increase in the total microbial biomass and the fungi/bacteria ratio, as well as to changes in the taxonomic composition of microbial communities. The main factors of the long-term dynamics are the accumulation of soil organic matter, plant successions, and changes in pH. The diversity of microbial communities during long-term dynamics can vary in different ways and does not follow a single trend. The longest dynamics of soil microbial communities are associated with changes in bioclimatic conditions. Information about soil microbial communities of the past can be obtained by studying buried and permafrost soils. The study of future changes in soil microbial communities is possible in experiments with artificial changes in climatic parameters. Plants are a significant factor in the dynamics of soil microbial communities on all timescales; in short-term periods, the major role is played by the activity of plants; in the long-term trends, the changes in the vegetation abundance and diversity are the most important factors.

Debates: Does Information Theory Provide a New Paradigm for Earth Science? Sharper Predictions Using Occam’s Digital Razor

By: Weijs, Steven. V.; Ruddell, Benjamin. L. WATER RESOURCES RESEARCH Volume:  56 Issue:  2 Pages:  26471-26471 Published:  FEB 2020 Context Sensitive Links Free Full Text from Publisher Close Abstract Occam’s Razor is a bedrock principle of science philosophy, stating that the simplest hypothesis (or model) is preferred, at any given level of model predictive performance. A modern restatement often attributed to Einstein explains, “Everything should be made as simple as possible, but not simpler.” Using principles from (algorithmic) information theory, both model descriptive performance and model complexity can be quantified in bits. This quantification yields a Pareto-style trade-off between model complexity (length of the model program in bits) and model performance (information loss in bits, or the missing information, needed to describe the original observations). Model complexity and performance can be collapsed to one single measure of lossless model size, which, when minimized, leads to optimal model complexity versus loss trade-off for generalization and prediction. Our view puts both simple data-driven and complex physical-process-based models on a continuum, in the sense that both describe patterns in observed data in compressed form, with different degrees of generality, model complexity, and descriptive performance. Information theory-based assessment of compression performance with fair and meaningful accounting for model complexity will enable us to best compare and combine the strengths of physics knowledge and data-driven modeling for a given problem, given the availability of data. “Suppose we draw a set of points on paper in a totally random manner” …“I am saying it is possible to find a geometric line whose notation is constant and uniform, following a certain law, that will pass through all points, and in the sameorder they were drawn.” … “But if that law is strongly composed,the thing that conforms to it should be seen as irregular”Gottfried Wilhelm Leibniz, 1686: Discours de metaphysique V, VI (from French)

Can Continental Models Convey Useful Seasonal Hydrologic Information at the Catchment Scale?

By: Crochemore, L.; Ramos, M. -H.; Pechlivanidis, I. G. WATER RESOURCES RESEARCH Volume:  56 Issue:  2 Pages:  25700-25700 Published:  FEB 2020 Context Sensitive Links Free Full Text from Publisher Close Abstract The development and availability of climate forecasting systems have allowed the implementation of seasonal hydroclimatic services at the continental scale. User guidance and quality of the forecast information are key components to ensure user engagement and service uptake, yet forecast quality depends on the hydrologic model setup. Here, we address how seasonal forecasts from continental services can be used to address user needs at the catchment scale. We compare a continentally calibrated process-based model (E-HYPE) and a catchment-specific parsimonious model (GR6J) to forecast streamflow in a set of French catchments. Results show that despite expected high performance from the catchment setup against observed streamflow, the continental setup can, in some catchments, match or even outperform the catchment-specific setup for 3-month aggregations and threshold exceedance. Forecast systems can become comparable when looking at statistics relative to model climatology, such as anomalies, and adequate initial conditions are the main source of skill in both systems. We highlight the need for consistency in data used in modeling chains and in tailoring service outputs for use at the catchment scale. Finally, we show that the spread in internal model states varies largely between the two systems, reflecting the differences in their setups and calibration strategies, and highlighting that caution is needed before extracting hydrologic variables other than streamflow. We overall argue that continental hydroclimatic services show potential on addressing needs at the catchment scale, yet guidance is needed to extract, tailor and use the information provided. Plain Language Summary Climatic variations can have a significant impact on a number of water-related sectors. Managing such variations through accurate predictions is thus crucial. Continental hydroclimate services have recently received attention to address various user needs. However, predictions for months ahead can be limited at catchment scale, highlighting the need for data tailoring. Here, we compare the predictions from two hydrologic setups at catchment scale. One setup (E-HYPE) is used in a European hydroclimate service, whereas the other (GR6J) is used for local water-related risk assessment. Our results show that predictions from the continental setup can be as accurate as the predictions from the local model when predicting streamflow averaged over several months and when looking at changes in streamflow rather than absolute values. A good estimation of the hydrologic states, such as soil moisture or lake levels, prior to the prediction is the most important factor in obtaining accurate streamflow predictions in both setups. However, the differences in the setups can result in different uncertainties for variables other than streamflow, like in the case of soil water content. We argue that useful information is provided by continental services, yet guidance for information extraction can result into tailored information for regional needs.

Does Information Theory Provide a New Paradigm for Earth Science? Hypothesis Testing

By: Nearing, Grey S.; Ruddell, Benjamin L.; Bennett, Andrew R.; et al. WATER RESOURCES RESEARCH Volume:  56 Issue:  2 Pages:  24918-24918 Published:  FEB 2020 Context Sensitive Links Free Full Text from Publisher Close Abstract Model evaluation and hypothesis testing are fundamental to any field of science. We propose here that by changing slightly the way we think and communicate about inference-from being fundamentally a problem of uncertainty quantification to being a problem of information quantification-allows us to avoid certain problems related to testing models as hypotheses. We propose that scientists are typically interested in assessing the information provided by models, not the truth value or likelihood of a model. Information theory allows us to formalize this perspective.

Climate Adaptation as a Control Problem: Review and Perspectives on Dynamic Water Resources Planning Under Uncertainty

By: Herman, Jonathan D.; Quinn, Julianne D.; Steinschneider, Scott; et al. WATER RESOURCES RESEARCH Volume:  56 Issue:  2 Pages:  24389-24389 Published:  FEB 2020 Context Sensitive Links Free Full Text from Publisher Close Abstract Climate change introduces substantial uncertainty to water resources planning and raises the key question: when, or under what conditions, should adaptation occur? A number of recent studies aim to identify policies mapping future observations to actions-in other words, framing climate adaptation as an optimal control problem. This paper uses the control paradigm to review and classify recent dynamic planning studies according to their approaches to uncertainty characterization, policy structure, and solution methods. We propose a set of research gaps and opportunities in this area centered on the challenge of characterizing uncertainty, which prevents the unambiguous application of control methods to this problem. These include exogenous uncertainty in forcing, model structure, and parameters propagated through a chain of climate and hydrologic models; endogenous uncertainty in human-environmental system dynamics across multiple scales; and sampling uncertainty due to the finite length of historical observations and future projections. Recognizing these challenges, several opportunities exist to improve the use of control methods for climate adaptation, namely, how problem context and understanding of climate processes might assist with uncertainty quantification and experimental design, out-of-sample validation and robustness of optimized adaptation policies, and monitoring and data assimilation, including trend detection, Bayesian inference, and indicator variable selection. We conclude with a summary of recommendations for dynamic water resources planning under climate change through the lens of optimal control.

Debates-Does Information Theory Provide a New Paradigm for Earth Science? Causality, Interaction, and Feedback

By: Goodwell, Allison E.; Jiang, Peishi; Ruddell, Benjamin L.; et al. WATER RESOURCES RESEARCH Volume:  56 Issue:  2 Pages:  24940-24940 Published:  FEB 2020 Context Sensitive Links Free Full Text from Publisher Close Abstract The concept of causal interactions between components is an integral part of hydrology and Earth system sciences. Modelers, decision makers, scientists, and other water resources stakeholders all utilize some notion of cause-and-effect to understand processes, make decisions, and infer how systems react to change. However, there are different perspectives on the meaning of causality in complex systems and, further, different frameworks and methodologies with which to detect causal interactions. We propose here that information theory (IT) provides a compelling framework for the detection of causality and discuss approaches for several levels of analyses that capture interactions that range from pairwise to multivariate in nature. We illustrate these types of analyses with an example based on weather station time series variables, in which variables may interact pairwise or jointly and on short to long time scales. In general, many unsolved or even unanticipated questions in the hydrologic sciences could benefit from this perspective.

Journal Paper Digests

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Journal Paper Digests

Published on June 08, 2020

Journal Paper Digests

Published on June 02, 2020