Subsurface tile drainage speeds water removal from agricultural fields that are historically prone to flooding. While managed drainage systems improve crop yields, they can also contribute tothe eutrophication of downstream ecosystems, as tile-drained systems are conduits for nutrients to adjacent waterways. The changing climate of the Midwestern US has already altered precipitation regimes which will likely continue into the future, with unknown effects on tile drain water and nutrient loss to waterways. Adding vegetative cover (i.e., as winter cover crops) is one approach that can retain water and nutrients on fields to minimize export via tile drains. In the current study, we evaluate the effect of cover crops on tile drain discharge and soluble reactive phosphorus (SRP) loads using bi-monthly measurements from 43 unique tile outlets draining fields with or without cover crops in two watersheds in northern Indiana. Using four water years of data (n = 844 measurements), we examined the role of short-term antecedent precipitation conditions and variation in soil biogeochemistry in mediating the effect of cover crops on tile drain flow and SRP loads. We observed significant effects of cover crops on both tile drain discharge and SRP loads, but these results were season and watershed specific. Cover crop effects were identified only in spring, where their presence reduced tile drain discharge in both watersheds and SRP loads in one watershed. Varying effects on SRP loads between watersheds were attributed to different soil biogeochemical characteristics, where soils with lower bioavailable P and higher P sorption capacity were less likely to have a cover crop effect. Antecedent precipitation was important in spring, and cover crop differences were still evident during periods of wet and dry antecedent precipitation conditions. Overall, we show that cover crops have the potential to significantly decrease spring tile drain P export, and these effects are resilient to a wide range of precipitation conditions. 相似文献
Subsurface deformation is a driver for river path selection when deformation rates become comparable to the autogenic mobility rate of rivers. Here we combine geomorphology, soil and sediment facies analyses, and geophysical data of the Late Quaternary sediments of the central Garo-Rajmahal Gap in Northwest Bengal to link subsurface deformation with surface processes. We show variable sedimentation characteristics, from slow rates (<0.8 mm/year) in the Tista megafan at the foot of the Himalaya to nondeposition at the exposed surface of the Barind Tract to the south, enabling the development of mature soils. Combined subsidence in the Tista fan and uplift of the Barind Tract are consistent with a N-S flexural response of the Indian plate to loading of the Himalaya Mountains given a low value of elastic thickness (15–25 km). Provenance analysis based on bulk strontium concentration suggests a dispersal of sediment consistent with this flexural deformation—in particular the abandonment of the Barind Tract by a Pleistocene Brahmaputra River and the current extents of the Tista megafan lobes. Overall, these results highlight the control by deeply rooted deformation patterns on the routing of sediment by large rivers in foreland settings. 相似文献
Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.
