Geology topics

Xiaofeng Liu

Publications and source records attributed to Xiaofeng Liu.

3 recordsLinked to original sources

USGS2024

Increasing phosphorus loss despite widespread concentration decline in US rivers

The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980–2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate.

conterminous United States↗

USGS2023

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. In this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

Nature Reviews Earth & Environment↗

USGS2012

Sediment mobility and bed armoring in the St Clair River: insights from hydrodynamic modeling

The lake levels in Lake Michigan-Huron have recently fallen to near historical lows, as has the elevation difference between Lake Michigan-Huron compared to Lake Erie. This decline in lake levels has the potential to cause detrimental impacts on the lake ecosystems, together with social and economic impacts on communities in the entire Great Lakes region. Results from past work suggest that morphological changes in the St Clair River, which is the only natural outlet for Lake Michigan-Huron, could be an appreciable factor in the recent trends of lake level decline. A key research question is whether bed erosion within the river has caused an increase in water conveyance, therefore, contributed to the falling lake level. In this paper, a numerical modeling approach with field data is used to investigate the possibility of sediment movement in the St Clair River and assess the likelihood of morphological change under the current flow regime. A two-dimensional numerical model was used to study flow structure, bed shear stress, and sediment mobility/armoring over a range of flow discharges. Boundary conditions for the numerical model were provided by detailed field measurements that included high-resolution bathymetry and three-dimensional flow velocities. The results indicate that, without considering other effects, under the current range of flow conditions, the shear stresses produced by the river flow are too low to transport most of the coarse bed sediment within the reach and are too low to cause substantial bed erosion or bed scour. However, the detailed maps of the bed show mobile bedforms in the upper St Clair River that are indicative of sediment transport. Relatively high shear stresses near a constriction at the upstream end of the river and at channel bends could cause local scour and deposition. Ship-induced propeller wake erosion also is a likely cause of sediment movement in the entire reach. Other factors that may promote sediment movement, such as ice cover and dredging in the lower river, require further investigation.

St. Clair River↗