Gullies contribute high sediment loads to receiving waters and significantly degrade landscapes. In drylands, low annual rainfall and resultant poor ground cover, coupled with high-intensity storms and dispersive soils, predispose these landscapes to gully erosion. Land management, such as grazing, exacerbates gully-forming processes by degrading ground cover and compacting soils, thereby increasing and concentrating overland flow. Current surface erosion models do not adequately represent sediment export from gullied terrain due to lack of distributed data and complex hydrogeomorphic processes, such as overland flow concentration, waterfall erosion, soil pipe collapse, and mass wasting. Here, we outline the strengths and weaknesses of past modelling approaches in erodible terrain and focus on how gully erosion processes can be better simulated at appropriate scales using newly available remote-sensing techniques and databases, coupled with improved understanding of relevant hydrogeomorphic processes. We also discuss and present examples of challenges related to assessing land management practices in drylands that affect gully erosion.

The North Sea sedimentary basin is characterized by geological complexity, encompassing a wide range of rock types and structures, including multiple reservoirs (carbonates and siliciclastic) with variations in reservoir quality and heterogeneity. These phenomena pose significant challenges for accurately predicting reservoir properties using traditional well log analysis. Moreover, these challenges are further compounded by complex geological conditions and scarcity of available data. Hence, the aim of this study was to address these challenges by applying advanced machine learning techniques within this basin. This study delves into both supervised and unsupervised machine learning approaches to forecast the essential petrophysical properties that are crucial for assessing reservoir quality. These properties encompass total porosity, effective porosity, and shale volume, all derived from well log data originating from the North Sea sedimentary basin. The models were trained using data from four wells consisting of 32,215 data points (80% for training, 10% for testing, and 10% for validation). Furthermore, our study introduced pioneering data-driven preprocessing workflow, encompassing exploratory data analysis, missing data imputation, and outlier detection to improve the performance of the machine learning models. ANN and RF models achieved the best results among the algorithms evaluated, with an average MAE of 0.01, RMSE of 0.01, and R-squared of 0.95 for total porosity, effective porosity, and volume of shale, respectively. These metrics demonstrate that the model can accurately predict reservoir properties in a challenging sedimentary basin, even with limited data availability, enabling reservoir characteristics and field development optimization, particularly in areas where core data are scarce.