Geology topics

Adrian Chappell

Publications and source records attributed to Adrian Chappell.

4 recordsLinked to original sources

USGS2024

Synoptic analysis and WRF-Chem model simulation of dust events in the southwestern United States

Dust transported from rangelands of the Southwestern United States (US) to mountain snowpack in the Upper Colorado River Basin during spring (March-May) forces earlier and faster snowmelt, which creates problems for water resources and agriculture. To better understand the drivers of dust events, we investigated large-scale meteorology responsible for organizing two Southwest US dust events from two different dominant geographic locations: (a) the Colorado Plateau and (b) the northern Chihuahuan Desert. High-resolution Weather Research and Forecasting coupled with Chemistry model (WRF-Chem) simulations with the Air Force Weather Agency dust emission scheme incorporating a MODIS albedo-based drag-partition was used to explore land surface-atmosphere interactions driving two dust events. We identified commonalities in their meteorological setups. The meteorological analyses revealed that Polar and Sub-tropical jet stream interaction was a common upper-level meteorological feature before each of the two dust events. When the two jet streams merged, a strong northeast-directed pressure gradient upstream and over the source areas resulted in strong near-surface winds, which lifted available dust into the atmosphere. Concurrently, a strong mid-tropospheric flow developed over the dust source areas, which transported dust to the San Juan Mountains and southern Colorado snowpack. The WRF-Chem simulations reproduced both dust events, indicating that the simulations represented the dust sources that contributed to dust-on-snow events reasonably well. The representativeness of the simulated dust emission and transport in different geographic and meteorological conditions with our use of albedo-based drag partition provides a basis for additional dust-on-snow simulations to assess the hydrologic impact in the Southwest US.

southwestern United States↗

USGS2019

Reducing sampling uncertainty in aeolian research to improve change detection

Measurements of aeolian sediment transport support our understanding of mineral dust impacts on Earth and human systems and assessments of aeolian process sensitivities to global environmental change. However, sample design principles are often overlooked in aeolian research. Here, we use high‐density field measurements of sediment mass flux across land use and land cover types to examine sample size and power effects on detecting change in aeolian transport. Temporal variances were 1.6 to 10.1 times the magnitude of spatial variances in aeolian transport for six study sites. Differences in transport were detectable for >67% of comparisons among sites using ~27 samples. Failure to detect change with smaller sample sizes suggests that aeolian transport measurements and monitoring are much more uncertain than recognized. We show how small and selective sampling, common in aeolian research, gives the false impression that differences in aeolian transport can be detected, potentially undermining inferences about process and impacting reproducibility of aeolian research.

Journal of Geophysical Research F: Earth Surface↗

USGS2017

Enhancing wind erosion monitoring and assessment for U.S. rangelands

On the Ground Wind erosion is a major resource concern for rangeland managers because it can impact soil health, ecosystem structure and function, hydrologic processes, agricultural production, and air quality. Despite its significance, little is known about which landscapes are eroding, by how much, and when. The National Wind Erosion Research Network was established in 2014 to develop tools for monitoring and assessing wind erosion and dust emissions across the United States. The Network, currently consisting of 13 sites, creates opportunities to enhance existing rangeland soil, vegetation, and air quality monitoring programs. Decision-support tools developed by the Network will improve the prediction and management of wind erosion across rangeland ecosystems.

Rangelands↗

USGS2016

Toward more realistic projections of soil carbon dynamics by Earth system models

Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.

Global Biogeochemical Cycles↗