Investigation of new approaches for the determination of the annual maximum rainfall depths of different durations.

Assessing soil organic carbon (SOC) at the field scale is crucial for effective environmental and18 agronomic management, especially within the precision agriculture framework. This enables the19 implementation of strategies to enhance soil quality, increase carbon sequestration, and improve crop20 yields. However, the process of sampling and assessing SOC is resource-intensive, demanding both21 in time and labour. Methods for minimizing information loss while reducing the sampling scheme,22 such as spatial simulated annealing (SSI), are particularly valuable in the scope of SOC assessment,23 especially when faced with budget constraints. Within the structure of the SSI method, two critical24 components have been identified: i) the selection of highly informative covariates for the primary25 variable (SOC); ii) the selection of an appropriate variogram model for spatial variability assessment.26 Covariates strongly correlated with SOC, along with a well-performing model, can significantly27 enhance the efficiency of the sampling scheme reduction process. The selected covariates leverage28 indirect information obtained from ground penetrating radar (GPR) that can be collected at a higher29 spatial density compared to SOC data. We conducted this study using data from a field experiment,30 2 which included 71 georeferenced sampling locations. Through a stepwise approach, we progressively31 reduced the number of sampling points to 10, 15, and 20 observations. Utilizing SSI, we refined the32 sampling scheme according to two distinct variogram models: the spherical and Gaussian-Matérn33 models. This process allowed us to identify the optimal variogram model, which has a key role in34 maximizing the reduction of redundant points while preserving those with valuable information. Our35 findings underlined also the significance of covariates in regression-kriging, particularly when36 employing the spherical model, to predict the points excluded from the SOC sampling scheme.