Scale and scaling in soils

Pachepsky, Yakov; Hill, Robert L.

doi:10.1016/j.geoderma.2016.08.017

Cited by 77 publications

(57 citation statements)

References 132 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Data were collected over a 50-y period, which is likely smoothing out some of the SOC loss in the model. In addition, the mismatch in scale between a soil pedon (0.5 m on a side) and the pixel size of the HYDE v3.2 land-use data (10 km) can create situations where the dominant soil properties of a pixel are not represented by the particular soil pedons that were sampled within that pixel (34). Relatedly, regions with low sampling density may be overly influenced by a few data points that may not be representative of that region as a whole.…”

Section: Resultsmentioning

confidence: 99%

Soil carbon debt of 12,000 years of human land use

Sanderman

Hengl

Fiske

2017

Proc. Natl. Acad. Sci. U.S.A.

938

495

View full text Add to dashboard Cite

Human appropriation of land for agriculture has greatly altered the terrestrial carbon balance, creating a large but uncertain carbon debt in soils. Estimating the size and spatial distribution of soil organic carbon (SOC) loss due to land use and land cover change has been difficult but is a critical step in understanding whether SOC sequestration can be an effective climate mitigation strategy. In this study, a machine learning-based model was fitted using a global compilation of SOC data and the History Database of the Global Environment (HYDE) land use data in combination with climatic, landform and lithology covariates. Model results compared favorably with a global compilation of paired plot studies. Projection of this model onto a world without agriculture indicated a global carbon debt due to agriculture of 133 Pg C for the top 2 m of soil, with the rate of loss increasing dramatically in the past 200 years. The HYDE classes "grazing" and "cropland" contributed nearly equally to the loss of SOC. There were higher percent SOC losses on cropland but since more than twice as much land is grazed, slightly higher total losses were found from grazing land. Important spatial patterns of SOC loss were found: Hotspots of SOC loss coincided with some major cropping regions as well as semiarid grazing regions, while other major agricultural zones showed small losses and even net gains in SOC. This analysis has demonstrated that there are identifiable regions which can be targeted for SOC restoration efforts.agriculture | soil organic matter | climate change | soil degradation T he incredible rise of human civilizations and the continuing sustainability of current and future human societies are inextricably linked to soils and the wide array of services soils provide (1-3). Human population and economic growth has led to an exponential rise in use of soil resources. Roughly 50 million km 2 of soils are currently being managed to some degree by humans for food, fiber, and livestock production (4), leading to the declaration that we live on a "used planet" (5). The consequences of human domination of soil resources are far ranging (6, 7): accelerated erosion, desertification, salinization, acidification, compaction, biodiversity loss, nutrient depletion, and loss of soil organic matter (SOM).Of these soil threats, loss of SOM has received the most attention, due to the critical role SOM plays in the contemporary carbon cycle (8, 9) and as a key component of sustaining food production (10, 11). Despite the intense research interest in SOM and soil organic carbon (SOC) as the dominant component of SOM, there remain many unknowns (12) that impede progress in implementing sound land management strategies to rebuild SOC stocks (13).Conversion of native soil to agricultural uses typically leads to a decline in SOC levels (14-16). The rate and extent of decline in SOC stocks should vary greatly across the globe, due to differences in soil properties, climate, type of land-use conversion, and, importantly, the specific manage...

show abstract

Section: Resultsmentioning

confidence: 99%

Soil carbon debt of 12,000 years of human land use

Sanderman

Hengl

Fiske

2017

Proc. Natl. Acad. Sci. U.S.A.

938

495

View full text Add to dashboard Cite

show abstract

“…To our knowledge, the impact of uncertainty in PTFs and the different types of PTFs used to predict fluxes and states have not yet been evaluated in global climate models. Also, the influence of the different aggregation/upscaling approaches—Pachepsky and Hill () overviewed concepts of scaling methods in soil science—in global climate models needs clarification. The upscaling of local‐scale soil properties used in the various PTFs to predict soil water and energy‐related fluxes at the global scale has not been fully clarified especially in view of the newly available high‐quality global soil maps.…”

Section: Discussionmentioning

confidence: 99%

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

Looy

Bouma

Herbst

et al. 2017

Reviews of Geophysics

Self Cite

414

297

View full text Add to dashboard Cite

Soil, through its various functions, plays a vital role in the Earth's ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. In this paper, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscaling techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration, and organic carbon content, root density, and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale.

show abstract

“…The observation operators were then used to predict profile soil moisture content from surface measurements by adjusting for the systematic differences between these two layers. It is important to note that the CDFs used in this study were built from soil moisture time series and assume that all soil moisture values are equally probable (Pachepsky and Hill, 2016).…”

Section: The Cdf Matching Methodsmentioning

confidence: 99%

Depth scaling of soil moisture content from surface to profile: multistation testing of observation operators

Gao¹,

Brocca²,

Zhao³

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract. The accurate assessment of profile soil moisture for spatial domains is usually difficult due to the associated costs, strong spatial-temporal variability, and nonlinear relationship between surface and profile moisture. Here we attempted to use observation operators built by Cumulative Distribution Frequency (CDF) matching method to directly predict profile soil moisture from surface measurements based on multi-station in situ observations from the Soil and Climate Analysis Network (SCAN). We first analyzed the effects of temporal resolution (hourly, daily and weekly) and data length (half year in nongrowing season, half year in growing season, one year, two years and four years) on the performance of observation operators. The results showed that temporal resolution had a negligible influence on the performance of observation operators. However, data length significantly changed the prediction accuracy of observation operators, and a two-year interval was identified as the optimal data length in building observation operators. A dataset with a two-year duration was therefore used to test the robustness of observation operators in three primary climates (humid continental, humid subtropical and semiarid) of the continental USA, with the popular exponential filter employed as a reference approach. The results indicated that observation operators reliably predicted profile soil moisture for the majority of stations in both calibration and validation periods and performed almost equally well with the exponential filter method. This suggests that observation operators are a feasible statistical tool for depth scaling of soil moisture. The findings here have the potential to be applied in profile soil moisture prediction from surface measurements at a range of environments if the target site has long enough (two years) soil moisture observations even with coarse temporal resolutions. Continuous and accurate measurement of profile soil moisture, however, is difficult because of expensive field Hydrol. Earth Syst. Sci. Discuss., https://doi

show abstract

Scale and scaling in soils

Cited by 77 publications

References 132 publications

Soil carbon debt of 12,000 years of human land use

Soil carbon debt of 12,000 years of human land use

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

Depth scaling of soil moisture content from surface to profile: multistation testing of observation operators

Contact Info

Product

Resources

About