In the Eastern Gangetic Plain (EGP) soil hydrology is a major determinant of land use and also governs the ecosystem services derived from cropping systems, particularly greenhouse gas (GHG) emissions from rice fields. To characterize patterns of soil hydrology in these, daily field monitoring of water levels was conducted during the monsoon () season in a comparatively wet (2021) and dry (2022) year with flooding depth and drainage tracked with field water tubes across 47 (2021) and 183 (2022) locations. Fields were clustered into hydrologic response types (HRT) which can then be used for land surface modelling, land use recommendations, and to target agronomic interventions that contribute to sustainable development outcomes. Clusters based on two methods of summarizing a single information source were compared. The information source was a time-series of field water-level observations, and the two methods were (1) the original time-series and their first differences and (2) a set of derived hydrologic descriptors that are conceptually related to greenhouse gas (GHG) emissions. Clustering was (1) by k-means with an optimization of cluster numbers and (2) by hierarchical clustering with the same number of clusters as identified by k-means. Hydrologic behaviour shifted dramatically between growing seasons, and it was not possible to identify consistent HRT's across years. The clusters had only a weak relation with soil properties, almost no relation with farmer perception of relative landscape position, and no relation with rice establishment method. Clusters based on time-series were moderately well predicted in the dry year 2022 by optimized random forest models, with the most important predictors being the number of irrigations, seasonal precipitation, pre-monsoon groundwater levels, seasonal groundwater level change, and pH, this latter as a surrogate for landscape position and other soil properties. In the wet year 2021 clusters were (poorly) predicted by just seasonal precipitation and pre-monsoon groundwater levels. This shows the complex relation of soil hydrology with landscape position and land management, as well as synoptic climate. By contrast, clusters based on the descriptors were not well-matched with those from the time-series, and could not be well predicted by random forest models. This shows that different clustering criteria may result in different interpretations of the landscape hydrology and thus different heuristics for anticipating the hydrology of a given field under different management choices.

Soil organic carbon (SOC) stocks are critical for land management strategies and climate change mitigation. However, understanding SOC distribution in South Africa's arid and semi-arid regions remains a challenge due to data limitations, and the complex spatial and sub-surface variability in SOC stocks driven by desertification and land degradation. Thus, to support soil and land-use management practices as well as advance climate change mitigation efforts, there is an urgent need to provide more precise SOC stock estimates within South Africa's arid and semi-arid regions. Hence, this study adopted remote-sensing approaches to determine the spatial sub-surface distribution of SOC stocks and the influence of environmental co-variates at four soil depths (i.e., 0-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). Using two regression-based algorithms, i.e., Extreme Gradient Boosting (XGBoost) and Random Forest (RF), the study found the former (RMSE values ranging from 7.12 t/ha to 29.55 t/ha) to be a superior predictor of SOC in comparison to the latter (RMSE values ranging from 7.36 t/ha to 31.10 t/ha). Nonetheless, both models achieved satisfactory accuracy (R ≥ 0.52) for regional-scale SOC predictions at the studied soil depths. Thereafter, using a variable importance analysis, the study demonstrated the influence of climatic variables like rainfall and temperature on SOC stocks at different depths. Furthermore, the study revealed significant spatial variability in SOC stocks, and an increase in SOC stocks with soil depth. Overall, these findings enhance the understanding of SOC dynamics in South Africa's arid and semi-arid landscapes and emphasizes the importance of considering site specific topo-climatic characteristics for sustainable land management and climate change mitigation. Furthermore, the study offers valuable insights into sub-surface SOC distribution, crucial for informing carbon sequestration strategies, guiding land management practices, and informing environmental policies within arid and semi-arid environments.