Policy makes and other stakeholders working to improve the sustainability of agriculture need access to information on soil spatial variation, and the impacts of management strategies that policy might promote. Models and data can provide such information but typically such products are developed in isolation and so do not allow for an integrated trade-off analysis. To overcome this limitation, we have developed a GIS- modelling framework (GIS-MF) that allows users to interrogate integrated models and data layers. This GIS-MF prototype has been developed for the Tensift watershed which is in the region of Marakesh-Safi, Morocco. The alpha version of the software contains four main components: (i) a field to watershed-scale Digital Soil Mapping viewer, (ii) a watershed scale Ecosystems Services Report viewer (iii) an Interactive Ecosystem Service and Environmental Impacts viewer that allows trade-offs to be explored and (iv) a field-scale yield prediction tool.  For each component it is essential to communicate the uncertainties associated with predictions in a way that is both informative and intuitive to the end users. We have explored several ways for communicating uncertainty that we will present. For the DSM viewer we consider both methods that show uncertainty distributions as well as the probability of exceeding agronomically relevant thresholds. We take a similar approach for the Ecosystems Services Report viewer, and the yield prediction tool. For the uncertainties related to our integrated trade-offs we consider approaches to communicate the changes in multiple objectives. We draw on previous analyses to make conclusions and we will ask the EGU audience their views on each of the methods. 

How to cite: Milne, A., Oulaid, B., el fartassi, I., Zawadzka, J., el Alami, R., Tabiti, K., Metcalfe, H., Diarra, A., Alonso chavez, V., Wayne, T., and Corstanje, R.: Communicating the Uncertainty in Predictions from a GIS-Modelling Framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15802, https://doi.org/10.5194/egusphere-egu23-15802, 2023.

Climate change adaptation decisions often require the consideration of risk rather than the environmental hazard alone. One approach for quantifying risk is to use a risk assessment framework which combines information about hazard, exposure and vulnerability to estimate risk in a spatially consistent way. In recent years, publicly available, open-source risk assessment frameworks have been made available, including the CLIMADA tool. Such tools are increasingly being used in combination with ensembles of climate model projections to quantify risk on climate timescales, presenting the ensemble spread as a measure of climate uncertainty. As climate models are computationally expensive to run, this quantification of uncertainty derived from the ensemble of projections is often limited by the number of members available.

We present a novel framework involving the application and extension of the CLIMADA open-source climate risk assessment tool, demonstrating an approach for providing a richer quantification of uncertainty. We show how a statistical Generalised Additive Model, involving an `ensemble member' random effect term, can be used to statistically represent the climate model ensemble summary of risk and be used to simulate many more realisations of risk, representative of a larger collection of plausible ensemble members. We present an application of the framework to an idealised example related to heat-stress and the associated risk of reduced outdoor physical working capacity in the UK.

How to cite: Dawkins, L.: A framework for uncertainty quantification in climate risk assessments (POSTER), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2353, https://doi.org/10.5194/egusphere-egu23-2353, 2023.

As the measurement of soil hydraulic properties is time-consuming and expensive, they are often computed from easily measurable soil properties via pedotransfer functions (PTFs). There are plenty of existing PTFs which were mainly derived for specific regions or catchments.

In our study, new PTF’s for Austrian soils were developed to estimate soil hydraulic properties such as field capacity or permanent wilting point. The PTFs were built by applying the random forest method, including prediction of uncertainty measures.

The used database includes soil physical data from different project and monitoring campaigns all over Austria and represents many different landscapes and soil types within the country. It consists of 2300 samples from 518 agricultural sampling locations.

For the derivation of new PTF’s, several inputs were available. Since the model is to be applied to standard data from official Austrian soil mapping, we focused on the therein existing data as input variables. We tested four possible combinations for each target property, with particle size distribution and depth as fixed variables. Bulk density and soil organic matter were added in different compositions.

To ensure the applicability of the newly derived PTF’s, the available data set was randomly divided into two subsets. A fraction of 80 % of the samples were contained in training data set for the derivation of the prediction models, the validation set includes 20 % of the data for determining statistical parameters and for comparison with other PTFs. This comparison showed good applicability of the new PTFs in relation to the previous models with R² ranging from 0,73 to 0,83 per target variable.

Due to the fact that each prediction contains uncertainties, uncertainty maps for all agricultural areas in Austria were created. Therefore the 25% and 75% percentile and the respective interquartile range (IQR) of our predicted properties were determined. The results show mean values for the IQR between 6,8 % for field capacity at 60 hPa and 5,7 % for permanent wilting point.

As main outcome, new models for hydraulic properties of Austrian soils were produced. The comparison with established PTFs showed higher accuracy for the prediction of soil properties in Austrian soils than other established PTFs. With the implemented prediction of uncertainty, areas with good predictions can be identified as well as areas with high uncertainties.

How to cite: Darmann, F., Kumpan, M., Winkler, I., Strauss, P., and Weninger, T.: Progressing pedotransfer functions for nation-wide mapping of soil hydraulic properties, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11306, https://doi.org/10.5194/egusphere-egu23-11306, 2023.

The long-term application of inorganic fertilizer and animal manure to agricultural fields has resulted in soil test P (STP) concentrations that greatly exceed the agronomic critical level in many areas of the world. This build-up of soil P, often referred to as legacy P, can increase the risk of P being mobilized during runoff and leaching events, even years or decades after P inputs have decreased or ceased. If this mobilized P reaches P-limited surface waters, eutrophication can result, leading to serious water quality problems that can adversely impact the environment, human health, and recreational activities. Due to the cost and effort involved in soil, manure, and runoff sampling and testing, as well as implementation of best management practices, nutrient management policies and strategies are often guided by computer model predictions. However, computer model predictions are inherently uncertain, thus it is important to account for this uncertainty when interpreting modeling data and using modeling results to guide decision making

In this study we conducted a sensitivity and uncertainty analysis using the Annual P Loss Estimator (APLE) model focusing on model predictions of STP. We calculated and evaluated the sensitivity coefficients of predicted STP and changes in STP using 1- and 10-yr simulations with and without P application. We also compared two methods for estimating prediction uncertainties: first-order variance approximation (FOVA) and Monte Carlo simulation (MCS). Finally, we compared uncertainties in APLE-predicted STP to uncertainties in measured STP collected from multiple sites in Maryland under different manuring and cropping treatments. Results from our sensitivity analysis showed that predicted STP and changes in STP for 1-yr simulations without P inputs were most sensitive to initial STP whereas model STP predictions were most sensitive to manure and fertilizer application rates when sensitivity analyses included P inputs. For the 10-yr simulations without P application inputs, the range in sensitivity coefficients for crop uptake and precipitation were much greater than for the 1-yr simulations. Prediction uncertainties from FOVA were comparable to those from MCS for model input uncertainties up to 50%. Using FOVA to calculate APLE STP prediction uncertainties using the Maryland data set, the mean measured STP for nearly all site years fell within the 95% confidence intervals of the STP prediction uncertainties. Our results provide users of APLE insight into what model inputs require the most careful measurement when using the model to predict changes in STP under conditions of P drawdown (i.e., no P application) or P buildup. Our results also demonstrate the importance of including model prediction uncertainties when estimating long-term drawdown of STP in agricultural fields.

How to cite: Bolster, C. and Vadas, P.: Sensitivity and uncertainty analysis for predicted soil test phosphorus using the APLE model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7145, https://doi.org/10.5194/egusphere-egu23-7145, 2023.

Detailed soil maps are crucial to balance interests of urban planning and soil protection. In Switzerland, high-quality arable land is protected by an inventory and surfaces inside the inventory can only exceptionally be developed. Recently, legal frameworks were modified to require detailed soil maps to change the inventory. So far, conventional soil maps at scale of 1:5'000 – directly linked to a prescribed sampling density – are acknowledged to be sufficiently accurate. As conventional mapping is very time-consuming digital soil mapping is currently considered by regional governments to accelerate data collection.

To test digital soil mapping to generate such detailed maps we selected a typical area on the Swiss Plateau of 800 hectares. We sampled 1'120 locations by feature space coverage design. Spatial predictions were computed for soil properties and soil suitability classes were derived. Additional independent validation data was sampled by a stratified random design at 120 locations and used to evaluate overall prediction accuracy. For rootable soil depth, the main soil attribute decisions are based on, model uncertainty was quantified by quantile regression forest.  

Various representations of uncertainty at selected point locations and for a map excerpt were prepared. Following one of the recently formulated ten Pedometrics challenges, we evaluated (mis)understanding thereof by in-depth guided expert interviews with six inventory decision makers. Level of acceptable uncertainty for the inventory and whether end users rather trusted certain sampling densities as opposed to statistical accuracy measures was further discussed in the interviews.

How to cite: Burgos, S., Tanner, S., and Nussbaum, M.: What is the accepted level of erroneous decision for the Swiss arable land inventory? Uncertainty perception of digital soil maps., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17408, https://doi.org/10.5194/egusphere-egu23-17408, 2023.

There is an implicit quality associated with all soil maps which depends on a range of factors including the mapping algorithm, the extent and quality of the calibration data, and the quality and relevance of the co-variates. Spatially explicit prediction uncertainty information is increasingly provided when digital soil mapping methods are used. This may take the form of a 90% prediction interval such as that supplied by ISRIC in their global SoilGrids product. In this study we used independent data sets of particle size measurements from New Zealand and the Netherlands to investigate the accuracy of the prediction interval information in the SoilGrids clay, sand and silt layers. While prediction intervals were wide for both countries, we had expected that these would be narrower for the Netherlands as SoilGrids has more calibration points in the Netherlands than in New Zealand. Spatially, there was much more variation in the prediction interval width in the Netherlands than in New Zealand, with some areas being less uncertain and some highly uncertain. New Zealand had uncertain predictions for the entire country, although the prediction intervals were not as wide as in the most uncertain areas in the Netherlands.

Independent validation showed that the clay prediction intervals were too wide: for New Zealand between 95 and 98% of the validation data were within the 90% prediction interval, for the Netherlands this was between 95 and 97%. For sand we found the opposite, with only between 60 and 70% of the data falling in the 90% prediction interval for New Zealand and between 77 and 88% for the Netherlands. Comparison of prediction errors with prediction interval widths showed that the prediction errors tended to be larger at locations with wide prediction intervals, although this relationship was clearer for the Netherlands than for New Zealand. Estimates that fell outside the sand prediction interval were associated with narrow as well as wide prediction interval widths. Our analyses highlight the importance of users considering soil uncertainty information before using the soil data. It is also important that producers of soil information document the accuracy limitations to help guide potential users and that they evaluate the validity of the uncertainty information prior to release.

How to cite: Lilburne, L., Heuvelink, G., and Helfenstein, A.: Interpreting and evaluating digital soil mapping prediction uncertainties, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17024, https://doi.org/10.5194/egusphere-egu23-17024, 2023.

Grasslands are one of the most important and complex ecological systems due to their characteristic dynamics influenced by meteorological and climate patterns. In this sense, drought is one of the most challenging obstacles to overcome. Especially in semi-arid areas, where biomass production is greatly limited by the amount of precipitation. In this line, remote sensing methods have been demonstrated to be a valuable instrument for monitoring vegetation in wide areas, and vegetation indices (VIs) have shown a high sensitivity to vegetation variations. In this line, the soil water content has been shown to be a key factor in vegetation growth. In this work, we compare the temporal dynamics of two semi-arid grassland areas based on a soil water content index estimated in each area.

We selected two semi-arid areas in Spain and time series of VIs are built based on multispectral images of MODIS TERRA product with a temporal resolution of 8 days in each area. Red (620-670 nm) and Near Infrared (841-876 nm) reflectance channels were extracted and filtered by the quality of the pixel. Then, a soil water content index (WCI) is calculated based on the water balance of the soil over time. Recurrence plots (RP) and recurrence quantification analysis (RQA) were calculated to characterize the influence of soil water content on vegetation index dynamics. The characterisation was based on various RQA complexity measurements, including Determinism (DET), among others.

In general, our results revealed that WCI was able to distinguish between areas. RPs revealed a different temporal pattern in each area using WCI and VIs. Furthermore, RQA measurements revealed that the dry area presented a different dynamic in contrast to the wetter area. In general, WCI was shown to be a useful index in characterizing soil water content, and recurrence plots were able to describe and characterise the dynamics of each area.

Acknowledgements: The authors acknowledge the support of Clasificación de Pastizales Mediante Métodos Supervisados - SANTO, from Universidad Politécnica de Madrid (project number: RP220220C024).

References

Almeida-Ñauñay, A.F., Benito, R.M., Quemada, M., Losada, J.C., Tarquis, A.M., 2022. Recurrence plots for quantifying the vegetation indices dynamics in a semi-arid grassland. Geoderma 406, 115488. https://doi.org/10.1016/j.geoderma.2021.115488

Almeida-Ñauñay, A.F., Benito, R.M., Quemada, M., Losada, J.C., Tarquis, A.M., 2021. The Vegetation–Climate System Complexity through Recurrence Analysis. Entropy 23, 559. https://doi.org/10.3390/e23050559

Martín-Sotoca, J.J., Saa-Requejo, A., Moratiel, R., Dalezios, N., Faraslis, I., Tarquis, A.M., 2019. Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds. Nat. Hazards Earth Syst. Sci. 19, 1685–1702. https://doi.org/10.5194/nhess-19-1685-2019

Sanz, E., Saa-Requejo, A., Díaz-Ambrona, C.H., Ruiz-Ramos, M., Rodríguez, A., Iglesias, E., Esteve, P., Soriano, B., Tarquis, A.M., 2021. Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands. Remote Sens. 13, 840. https://doi.org/10.3390/rs13050840

How to cite: Almeida-Ñauñay, A., Sanz, E., Quemada, M., Martin-Sotoca, J. J., and Tarquis, A. M.: Soil water content in vegetation indices dynamics through a recurrence plots approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8081, https://doi.org/10.5194/egusphere-egu23-8081, 2023.

Machine learning models have been widely used in digital soil mapping to predict the spatial distribution of soil properties across the landscape. However, due to the unpredictable behavior of soil properties, insufficient training sample size, input data error, uncertainty in model parameters and structure, uncertainty inherently exists in machine learning predictions. Therefore, knowledge of prediction uncertainty is vital for decision-makers and end-users. This study quantifies the overall uncertainty of digital soil maps of Germany for soil acidity, organic carbon, cation exchange capacity, and clay using quantile regression forest (QRF) and artificial neural network (ANN) models. Here, we propose the use of a novel ANN model that directly estimates the lower and upper bounds of a prediction interval by using an architecture with two output neurons. A multi-objective evolutionary algorithm (i.e., non-dominated sorting genetic algorithm II) was employed to parameterize the ANN weights. The results of the modeling indicated that ANN performed better than the QRF for predicting soil properties. Additionally, the ANNs produced narrower prediction intervals in comparison to the QRF. Most importantly, ANN yielded prediction interval coverage probabilities that were more closely aligned to their associated confidence levels in comparison to the QRF. In general, the ANNs were not only effective in predicting soil properties, but they were also effective in constructing reasonable prediction intervals for soil characteristics; and therefore, it is recommended to be used for predicting soil properties and quantifying their uncertainty in digital soil mapping.

Keywords: uncertainty estimation, prediction interval, multi-objective optimization, artificial neural networks, machine learning, Germany

How to cite: Taghizadeh-Mehrjardi, R., B.M. Heuvelink, G., and Scholten, T.: Quantification of uncertainty using artificial neural networks for mapping of soil properties in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15495, https://doi.org/10.5194/egusphere-egu23-15495, 2023.