Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources
- 1Swiss Soil Monitoring Network (NABO), Agroscope, 8046 Zurich, Switzerland
- 2School of Agricultural, Forest and Food Science (HAFL), Bern University of Applied Sciences (BFH), 3052 Zollikofen, Switzerland
- 3Institute of Biogeochemistry and Pollutant Dynamics, Swiss Federal Institute of Technology (ETH), 8092 Zurich, Switzerland
- 4Forest Soils and Biogeochemistry, Soil Functions and Soil Protection, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), 8903 Birmensdorf, Switzerland
- 5Planning of Landscape and Urban Systems, Swiss Federal Institute of Technology (ETH), 8093 Zurich, Switzerland
Abstract. Spatial information on soil function fulfillment (SFF) is increasingly being used to inform decision-making in spatial planning programs to support sustainable use of soil resources. Soil function maps visualize soils abilities to fulfill their functions, e.g., regulating water and nutrient flows, providing habitats, and supporting biomass production based on soil properties. Such information must be reliable for informed and transparent decision-making in spatial planning programs. In this study, we add to the transparency of soil function maps by (1) indicating uncertainties arising from the prediction of soil properties generated by digital soil mapping (DSM) that are used for soil function assessment (SFA) and (2) showing the response of different SFA methods to the propagation of uncertainties through the assessment. For a study area of 170 km2 in the Swiss Plateau, we map 10 static soil sub-functions for agricultural soils for a spatial resolution of 20 × 20 m together with their uncertainties. Mapping the 10 soil sub-functions using simple ordinal assessment scales reveals pronounced spatial patterns with a high variability of SFF scores across the region, linked to the inherent properties of the soils and terrain attributes and climate conditions. Uncertainties in soil properties propagated through SFA methods generally lead to substantial uncertainty in the mapped soil sub-functions. We propose two types of uncertainty maps that can be readily understood by stakeholders. Cumulative distribution functions of SFF scores indicate that SFA methods respond differently to the propagated uncertainty of soil properties. Even where methods are comparable on the level of complexity and assessment scale, their comparability in view of uncertainty propagation might be different. We conclude that comparable uncertainty indications in soil function maps are relevant to enable informed and transparent decisions on the sustainable use of soil resources.