Characterizing soil hydrology in the Indo-Gangetic plain of Bihar, India: Methods and preliminary results - Rossiter, D.G.; Arenas-Calle, Laura; Urfels, Anton; Nayak, Harishankar; Sherpa, Sonam; McDonald, Andrew (Elsevier, 2024)
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 (kharif) 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.