Journal Paper Digests

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Journal Paper Digests 2017 #31

  • Shifts in pore connectivity from precipitation versus groundwater rewetting increases soil carbon loss after drought
  • A novel earth observation based ecological indicator for cyanobacterial blooms
  • Wavelength feature mapping as a proxy to mineral chemistry for investigating geologic systems: An example from the Rodalquilar epithermal system
  • Dynamics of Agricultural Soil Erosion in European Russia
  • Towards process-informed bias correction of climate change simulations
  • Higher climatological temperature sensitivity of soil carbon in cold than warm climates
  • Aeolian sediment fingerprinting using a Bayesian mixing model
  • Near real time de-noising of satellite-based soil moisture retrievals: An intercomparison among three different techniques
  • Landsat images and crop model for evaluating water stress of rainfed soybean

Shifts in pore connectivity from precipitation versus groundwater rewetting increases soil carbon loss after drought

Authors: Smith, AP; Bond-Lamberty, B; Benscoter, BW; Tfaily, MM; Hinkle, CR; Liu, CX; Bailey, VL

Source: NATURE COMMUNICATIONS, 8 1335-1335; NOV 6 2017

Abstract: Droughts and other extreme precipitation events are predicted to increase in intensity, duration, and extent, with uncertain implications for terrestrial carbon (C) sequestration. Soil wetting from above (precipitation) results in a characteristically different pattern of pore-filling than wetting from below (groundwater), with larger, well-connected pores filling before finer pore spaces, unlike groundwater rise in which capillary forces saturate the finest pores first. Here we demonstrate that pore-scale wetting patterns interact with antecedent soil moisture conditions to alter pore-scale, core-scale, and field-scale C dynamics. Drought legacy and wetting direction are perhaps more important determinants of short-term C mineralization than current soil moisture content in these soils. Our results highlight that microbial access to C is not solely limited by physical protection, but also by drought or wetting-induced shifts in hydrologic connectivity. We argue that models should treat soil moisture within a three-dimensional framework emphasizing hydrologic conduits for C and resource diffusion.

A novel earth observation based ecological indicator for cyanobacterial blooms

Authors: Anttila, S; Fleming-Lehtinen, V; Attila, J; Junttila, S; Alasalmi, H; Hallfors, H; Kervinen, M; Koponen, S

Source: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 64 145-155; FEB 2018

Abstract: Cyanobacteria form spectacular mass occurrences almost annually in the Baltic Sea. These harmful algal blooms are the most visible consequences of marine eutrophication, driven by a surplus of nutrients from anthropogenic sources and internal processes of the ecosystem. We present a novel Cyanobacterial Bloom Indicator (CyaBI) targeted for the ecosystem assessment of eutrophication in marine areas. The method measures the current cyanobacterial bloom situation (an average condition of recent 5 years) and compares this to the estimated target level for ‘good environmental status’ (GES). The current status is derived with an index combining indicative bloom event variables. As such we used seasonal information from the duration, volume and severity of algal blooms derived from earth observation (50) data. The target level for GES was set by using a remote sensing based data set named Fraction with Cyanobacterial Accumulations (FCA; Kahru & Elmgren, 2014) covering years 1979-2014. Here a shift-detection algorithm for time series was applied to detect time-periods in the FCA data where the level of blooms remained low several consecutive years. The average conditions from these time periods were transformed into respective CyaBI target values to represent target level for GES. The indicator is shown to pass the three critical factors set for marine indicator development, namely it measures the current status accurately, the target setting can be scientifically proven and it can be connected to the ecosystem management goal. An advantage of the CyaBI method is that it’s not restricted to the data used in the development work, but can be complemented, or fully applied, by using different types of data sources providing information on cyanobacterial accumulations.

Wavelength feature mapping as a proxy to mineral chemistry for investigating geologic systems: An example from the Rodalquilar epithermal system

Authors: van der Meer, F; Kopackova, V; Koucka, L; van der Werff, HMA; van Ruitenbeek, FJA; Bakker, WH

Source: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 64 237-248; FEB 2018

Abstract: The final product of a geologic remote sensing data analysis using multi spectral and hyperspectral images is a mineral (abundance) map. Multispectral data, such as ASTER, Landsat, SPOT, Sentinel-2, typically allow to determine qualitative estimates of what minerals are in a pixel, while hyperspectral data allow to quantify this. As input to most image classification or spectral processing approach, endmembers are required. An alternative approach to classification is to derive absorption feature characteristics such as the wavelength position of the deepest absorption, depth of the absorption and symmetry of the absorption feature from hyperspectral data. Two approaches are presented, tested and compared in this paper: the ‘Wavelength Mapper’ and the ‘QuanTools’. Although these algorithms use a different mathematical solution to derive absorption feature wavelength and depth, and use different image post-processing, the results are consistent, comparable and reproducible. The wavelength images can be directly linked to mineral type and abundance, but more importantly also to mineral chemical composition and subtle changes thereof. This in turn allows to interpret hyperspectral data in terms of mineral chemistry changes which is a proxy to pressure-temperature of formation of minerals. We show the case of the Rodalquilar epithermal system of the southern Spanish Gabo de Gata volcanic area using HyMAP airborne hyperspectral images.

Dynamics of Agricultural Soil Erosion in European Russia

Authors: Litvin, LF; Kiryukhina, ZP; Krasnov, SF; Dobrovol’skaya, NG

Source: EURASIAN SOIL SCIENCE, 50 (11):1344-1353; NOV 2017

Abstract: Socioeconomic transformation together with climate change in recent decades significantly affected the geography of agricultural erosion in European Russia. Calculations of erosion rate and soil loss from slopes using logical-mathematical erosion models within different landscape zones and administrative regions revealed spatial-temporal regularities in the dynamics of these parameters and made it possible to assess the role of changes in the main natural and anthropogenic factors of erosion. A universal significant reduction in the mass of soil material washed from tilled slopes is revealed on the background of multidirectional changes in erosion rate.

Towards process-informed bias correction of climate change simulations

Authors: Maraun, D; Shepherd, TG; Widmann, M; Zappa, G; Walton, D; Gutierrez, JM; Hagemann, S; Richter, I; Soares, PMM; Hall, A; Mearns, LO

Source: NATURE CLIMATE CHANGE, 7 (11):764-773; NOV 2017

Abstract: Biases in climate model simulations introduce biases in subsequent impact simulations. Therefore, bias correction methods are operationally used to post-process regional climate projections. However, many problems have been identified, and some researchers question the very basis of the approach. Here we demonstrate that a typical cross-validation is unable to identify improper use of bias correction. Several examples show the limited ability of bias correction to correct and to downscale variability, and demonstrate that bias correction can cause implausible climate change signals. Bias correction cannot overcome major model errors, and naive application might result in ill-informed adaptation decisions. We conclude with a list of recommendations and suggestions for future research to reduce, post-process, and cope with climate model biases.

Higher climatological temperature sensitivity of soil carbon in cold than warm climates

Authors: Koven, CD; Hugelius, G; Lawrence, DM; Wieder, WR

Source: NATURE CLIMATE CHANGE, 7 (11):817-+; NOV 2017

Abstract: The projected loss of soil carbon to the atmosphere resulting from climate change is a potentially large but highly uncertain feedback to warming. The magnitude of this feedback is poorly constrained by observations and theory, and is disparately represented in Earth system models (ESMs)(1-3). To assess the climatological temperature sensitivity of soil carbon, we calculate apparent soil carbon turnover times(4) that reflect long-term and broad-scale rates of decomposition. Here, we show that the climatological temperature control on carbon turnover in the top metre of global soils is more sensitive in cold climates than in warm climates and argue that it is critical to capture this emergent ecosystem property in global-scale models. We present a simplified model that explains the observed high cold-climate sensitivity using only the physical scaling of soil freeze-thaw state across climate gradients. Current ESMs fail to capture this pattern, except in anESMthat explicitly resolves vertical gradients in soil climate and carbon turnover. An observed weak tropical temperature sensitivity emerges in a different model that explicitly resolves mineralogical control on decomposition. These results support projections of strong carbon- climate feedbacks from northern soils(5,6) and demonstrate a method for ESMs to capture this emergent behaviour.

Aeolian sediment fingerprinting using a Bayesian mixing model

Authors: Gholami, H; Telfer, MW; Blake, WH; Fathabadi, A

Source: EARTH SURFACE PROCESSES AND LANDFORMS, 42 (14):2365-2376; NOV 2017

Abstract: Identifying sand provenance in depositional aeolian environments (e.g. dunefields) can elucidate sediment pathways and fluxes, and inform potential land management strategies where windblown sand and dust is a hazard to health and infrastructure. However, the complexity of these pathways typically makes this a challenging proposition, and uncertainties on the composition of mixed-source sediments are often not reported. This study demonstrates that a quantitative fingerprinting method within the Bayesian Markov Chain Monte Carlo (MCMC) framework offers great potential for exploring the provenance and uncertainties associated with aeolian sands. Eight samples were taken from dunes of the small (similar to 58km(2)) Ashkzar erg, central Iran, and 49 from three distinct potential sediment sources in the surrounding area. These were analyzed for 61 tracers including 53 geochemical elements (trace, major and rare earth elements (REE)) and eight REE ratios. Kruskal-Wallis H-tests and stepwise discriminant function analysis (DFA) allowed the identification of an optimum composite fingerprint based on six tracers (Rb, Sr, Sr-87, (La/Yb)(n), Ga and Ce), and a Bayesian mixing model was applied to derive the source apportionment estimates within an uncertainty framework. There is substantial variation in the uncertainties in the fingerprinting results, with some samples yielding clear discrimination of components, and some with less clear fingerprints. Quaternary terraces and fans contribute the largest component to the dunes, but they are also the most extensive surrounding unit; clay flats and marls, however, contribute out of proportion to their small outcrop extent. The successful application of these methods to aeolian sediment deposits demonstrates their potential for providing quantitative estimates of aeolian sediment provenances in other mixed-source arid settings, and may prove especially beneficial where sediment is derived from multiple sources, or where other methods of provenance (e.g. detrital zircon U-Pb dating) are not possible due to mineralogical constraints

Near real time de-noising of satellite-based soil moisture retrievals: An intercomparison among three different techniques

Authors: Massari, C; Su, CH; Brocca, L; Sang, YF; Ciabatta, L; Ryu, D; Wagner, W

Source: REMOTE SENSING OF ENVIRONMENT, 198 17-29; SEP 1 2017

Abstract: Real-time de-noising of satellite-derived soil moisture observations presents opportunities to deliver more accurate and timely satellite data for direct satellite users. So far, the most commonly used techniques for reducing the impact of noise in the retrieved satellite soil moisture observations have been based on moving average filters and Fourier based methods. This paper introduces a new alternative wavelet based approach called Wiener-Wavelet-Based Filter (WiW), which uses an entropy based de-noising method to design a causal version of the filter. WiW is used as a post-retrieval processing tool to enhance the quality of observations derived from one active (the Advanced Scatterometer, ASCAT) and one passive (the Advanced Microwave Scanning Radiometer for Earth Observing System, AMSRE) satellite sensors. The filter is then compared with two candidate de-noising techniques, namely: i) a Wiener causal filter introduced by Su et al. (2013) and ii) a conventional moving average filter. The validation is carried out globally at 173 (for AMSRE) and 243 (for ASCAT) soil moisture stations. Results show that all the three de-noising techniques can increase the agreement between satellite and in situ measurements in terms of correlation and signal-to-noise ratio. The Wiener-based methods show least signal distortion and demonstrate to be conservative in retaining the signal information in de-noised data. Importantly, the Wiener filters can be calibrated with the data at hand, without the need for auxiliary data.

Landsat images and crop model for evaluating water stress of rainfed soybean

Authors: Sayago, S; Ovando, G; Bocco, M

Source: REMOTE SENSING OF ENVIRONMENT, 198 30-39; SEP 1 2017

Abstract: Soil water content is a vital resource that plays a central role in agricultural areas. In Argentina the soybean (Glycine max (L) Merrill) is the most important crop, considering the economic yield and the sown area. Actually, remote sensing allows continuous monitoring of crops and to evaluate the impact of water stress in their development. The combination of Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI) is an indicator that provides information about the condition of the vegetation and surface soil moisture content. In this study we evaluate relationships between indicators of crop water stress and the Temperature Vegetation Dryness Index (TVDI) determined from Landsat, for sites with rainfed soybean in the agricultural central zone of Cordoba (Argentina).Field data were acquired continuously throughout the whole growing season. For each sample date and plot, data of percent green vegetation cover, soil moisture content and phenology were registered. The use of a simulation crop model allowed obtaining indicators of crop water stress indices: (1) soil water deficit, (2) crop water use vs. reference crop evapotranspiration, and (3) fraction of the available water capacity that is readily available.The NDVI/LST spaces presented a trapezoidal form, which indicated that TVDI will have similar sensitivity for the full range of NDVI and showed temporal changes of wet and dry edges.When soybean cover exceeds 60%, the combination of TVDI with (2) and (3) can enhance the ability of detecting crop water stress conditions (R-2 = 0.62 and 0.82, respectively). However, the relationship between TVDI and (1) showed low correlation values (R-2 = 0.21).

Sydney Institute of Agriculture World Soil Day SOILinar

The world was in massive celebration on December 5. If you were wondering what all of that was about, it was because it was World Soil Da...… Continue reading

Journal Paper Digests

Published on November 12, 2017