Legacy data-based national-scale digital mapping of key soil properties in India

Reddy, Nagarjuna N.; Chakraborty, Poulamee; Roy, Sourav; Singh, Kanika; Minasny, Budiman; McBratney, Alex B.; Biswas, Asim; Das, Bhabani S.

doi:10.1016/j.geoderma.2020.114684

Cited by 60 publications

(49 citation statements)

References 45 publications

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“…The other two methods are representative of the dominant PSM method as implemented, with some differences in detail, in many countries and for many properties (e.g., Reddy et al, 2021;Liu et al, 2020;Araujo-Carrillo et al, 2021). SG2 uses random forests implemented in the ranger R package, with prior covariate selection by recursive feature elimination and model tuning by cross-validation of model hyperparameters (number of covariates at each tree split, number of trees in the forest).…”

Section: Mapping Methodsmentioning

confidence: 99%

How well does Predictive Soil Mapping represent soil geography? An investigation from the USA

Rossiter

Poggio

Beaudette

et al. 2021

Preprint

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Abstract. We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by Predictive Soil Mapping (PSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales, and compare them to spatial patterns known from detailed field surveys (gSSURGO). The methods are illustrated with an example: topsoil pH for an area in central New York State. A companion report examines other areas, soil properties, and depth slices. A set of R Markdown scripts is referenced so that readers can apply the analysis for areas of their interest. For the test case we discover and discuss substan- tial discrepancies between PSM products, as well as large differences between the PSM products and legacy field surveys. These differences are in whole-map statistics, visually-identifiable landscape features, level of detail, range and strength of spatial autocorrelation, landscape metrics (Shannon diversity and evenness, shape, aggregation, mean fractal dimension, co-occurence vectors), and spatial patterns of property maps classified by histogram equalization. Histograms and variogram analysis revealed the smoothing effect of machine-learning models. Property class maps made by histogram equalization were substantially different, but there was no consistent trend in their landscape metrics. The model using only national points and covariates was not better than the global model, and in some cases introduced artefacts from a lithology covariate. Uncertainty (5–95% confidence intervals) provided by SoilGrids and POLARIS were unrealistically wide compared to gSSURGO low and high estimated values and show substantially different spatial patterns. We discuss the potential use of the PSM products as a (partial) replacement for field-based soil surveys.

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Section: Mapping Methodsmentioning

confidence: 99%

How well does Predictive Soil Mapping represent soil geography? An investigation from the USA

Rossiter

Poggio

Beaudette

et al. 2021

Preprint

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“…Secondly, the main distinguishing feature of this work from the previous studies [10,11,24,25,29,31] is the creation of a geotechnical map that was built for the driven piles. According to the geological structure of the foundations, the optimal variations in the pile length for each city zone were determined.…”

Section: Discussionmentioning

confidence: 99%

Development of a Unified Geotechnical Database and Data Processing on the Example of Nur-Sultan City

et al. 2020

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Development of a geotechnical database for the young capital of Kazakhstan, Nur-Sultan city, became a paramount concern of both the scientific community and industry. The creation of the Kazakhstan model of the geotechnical database was based on a fixed dataset which included a city map with determined X, Y, and H coordinates and the user generated data obtained from the materials of engineering and geological surveys from more than 2000 boreholes. Based on the assessment of the built-up area of the city, six main engineering-geological elements (EGE) were identified. The territory of the city was regrouped into eight zones instead of the previous six based on the geological origin and stratigraphy interposition of the EGE. Engineering properties of the soils were considered for physical and mechanical characteristics, where the plasticity limits demonstrated an accurate correlation to the elasticity modulus E and distribution boundaries between the alluvial and eluvial types of the soil on the graph. So, the increased liquid and plastic limits are replicated by higher values of elasticity modulus E, and hence better strength characteristics of a particular soil layer and vice versa, while the moisture content and soil density did not show any obvious pattern and requires additional verification on the construction site. Finally, a geotechnical map was built for the driven piles and the optimal variations in the pile length for each zone were determined.

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“…Climate and catchment control on hydrological droughts are more or less known [13][14][15][16][17] ; however, no studies have attempted to examine how varying soil conditions influence these controls. With the availability of a national-scale digital soil map 18 , here we explore the climate-catchment-soil control on hydrological droughts and identify key drought drivers (KDD) for drought propagation.…”

Section: Introductionmentioning

confidence: 99%

“…We applied a daily variable threshold approach to derive streamflow droughts by developing 366 (additional for leap year) flow duration curves using continuous streamflow records 22 (Methods). While we obtain meteorological and catchmentspecific geospatial attributes from the archived database [23][24][25][26][27] , the soil attributes are derived from a recently developed digital soil database of India 18 (see Data and Method section). We show the extent to which climate, catchment and soil attributes influences and co-vary with catchment-scale drought characteristics (Methods), such as growth, persistence (duration and frequency or number of events), recovery, and drought termination rate (DTR).…”

Section: Introductionmentioning

confidence: 99%

Climate-Catchment-Soil Control on Hydrological Droughts in Peninsular India

Ganguli¹,

Singh²,

Reddy³

et al. 2022

Preprint

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Most land surface system models and observational assessments ignore detailed soil characteristics while describing the drought attributes such as growth, duration, recovery, and the termination rate of the event. With the national-scale digital soil maps available for India, we assessed the climate-catchment-soil nexus using daily observed streamflow records from 98 sites in tropical rain-dominated catchments of peninsular India (8 - 25° N, 72 - 86° E). Results indicated that climate-catchment-soil properties may control hydrological drought attributes to the tune of 14-70%. While terrain features are dominant drivers for drought growth, contributing around 50% variability, soil attributes contribute ~71.5% variability in drought duration. Finally, soil and climatic factors together control the resilience and termination rate. The most relevant climate characteristics are potential evapotranspiration, soil moisture, rainfall, and temperature; temperature and soil moisture are dominant controls for streamflow drought resilience. Among different soil properties, soil organic carbon (SOC) stock could resist drought propagation, despite low-carbon soils across the Indian subcontinent. The findings highlight the need for accounting feedback among climate, soil, and topographical properties in catchment-scale drought propagations.

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Legacy data-based national-scale digital mapping of key soil properties in India

Cited by 60 publications

References 45 publications

How well does Predictive Soil Mapping represent soil geography? An investigation from the USA

How well does Predictive Soil Mapping represent soil geography? An investigation from the USA

Development of a Unified Geotechnical Database and Data Processing on the Example of Nur-Sultan City

Climate-Catchment-Soil Control on Hydrological Droughts in Peninsular India

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