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1. Examining the competing effects of contemporary land management vs. land cover changes on global air quality

   https://doi.org/10.5194/acp-21-16479-2021  

   Wong, Anthony Y.; Geddes, Jeffrey A. (January 2021, Atmospheric Chemistry and Physics)

   Abstract. Our work explores the impact of two important dimensions of landsystem changes, land use and land cover change (LULCC) as well as directagricultural reactive nitrogen (Nr) emissions from soils, on ozone(O3) and fine particulate matter (PM2.5) in terms of air quality overcontemporary (1992 to 2014) timescales. We account for LULCC andagricultural Nr emissions changes with consistent remote sensingproducts and new global emission inventories respectively estimating theirimpacts on global surface O3 and PM2.5 concentrations as well as Nrdeposition using the GEOS-Chem global chemical transport model. Over thistime period, our model results show that agricultural Nr emissionchanges cause a reduction of annual mean PM2.5 levels over Europe andnorthern Asia (up to −2.1 µg m−3) while increasing PM2.5 levels in India, China and the eastern US (up to +3.5 µg m−3). Land cover changes induce small reductions in PM2.5 (up to −0.7 µg m−3) over Amazonia, China and India due to reduced biogenic volatile organic compound (BVOC) emissions and enhanced deposition of aerosol precursor gases (e.g., NO2, SO2). Agricultural Nr emissionchanges only lead to minor changes (up to ±0.6 ppbv) in annual meansurface O3 levels, mainly over China, India and Myanmar. Meanwhile, ourmodel result suggests a stronger impact of LULCC on surface O3 over the time period across South America; the combination of changes in drydeposition and isoprene emissions results in −0.8 to +1.2 ppbv surfaceozone changes. The enhancement of dry deposition reduces the surface ozone level (up to −1 ppbv) over southern China, the eastern US and central Africa. The enhancement of soil NO emission due to crop expansion also contributes to surface ozone changes (up to +0.6 ppbv) over sub-Saharan Africa. Incertain regions, the combined effects of LULCC and agricultural Nr emission changes on O3 and PM2.5 air quality can be comparable (>20 %) to anthropogenic emission changes over the same time period. Finally, we calculate that the increase in global agricultural Nr emissions leads to a net increase in global land area (+3.67×106km2) that potentially faces exceedance of the critical Nr load (>5 kg N ha−1 yr−1). Our result demonstrates the impacts of contemporary LULCC and agricultural Nr emission changes on PM2.5 and O3 in terms of air quality, as well as the importanceof land system changes for air quality over multidecadal timescales. 

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2. Sustainable management of Great Lakes watersheds dominated by agricultural land use

   https://doi.org/10.1016/j.jglr.2016.10.001  

   Kerr, John M.; DePinto, Joseph V.; McGrath, Dennis; Sowa, Scott P.; Swinton, Scott M. (December 2016, Journal of Great Lakes Research)
3. Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management

   https://doi.org/10.1137/1.9781611978032.68  

   Chatterjee, Somya Sharma; Lindsay, Kelly; Chatterjee, Neel; Patil, Rohan; Altintas, Ilkay; Steinbach, Michael; Giron, Daniel; Nguyen, Mai H; Kumar, Vipin (April 2024, SIAM International Conference on Data Mining (SDM24))

   In recent years, the increasing threat of devastating wildfires has underscored the need for effective prescribed fire management. Process-based computer simulations have traditionally been employed to plan prescribed fires for wildfire prevention. However, even simplified process models are too compute-intensive to be used for real-time decision-making. Traditional ML methods used for fire modeling offer computational speedup but struggle with physically inconsistent predictions, biased predictions due to class imbalance, biased estimates for fire spread metrics (e.g., burned area, rate of spread), and limited generalizability in out-of-distribution wind conditions. This paper introduces a novel machine learning (ML) framework that enables rapid emulation of prescribed fires while addressing these concerns. To overcome these challenges, the framework incorporates domain knowledge in the form of physical constraints, a hierarchical modeling structure to capture the interdependence among variables of interest, and also leverages pre-existing source domain data to augment training data and learn the spread of fire more effectively. Notably, improvement in fire metric (e.g., burned area) estimates offered by our framework makes it useful for fire managers, who often rely on these estimates to make decisions about prescribed burn management. Furthermore, our framework exhibits better generalization capabilities than the other ML-based fire modeling methods across diverse wind conditions and ignition patterns. 

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4. LandPKS Toolbox: Open-source mobile app tools for sustainable land management

   Maynard, Jonathan J.; Maniak, Sabina; Hamrick, Laura; Peacock, George; McCord, Sarah E.; Herrick, Jeffrey E. (November 2023, Journal of soil and water conservation)
5. Land management controls on snow accumulation in a cold-region agricultural watershed

   https://doi.org/10.1016/j.scitotenv.2026.181749  

   Jeannotte, Tyson L; Wainty, Sharhad; Mahmood, Taufique H; Ahmmed, Md Helal (May 2026, Science of The Total Environment)

   Over the past three decades, the Northern Great Plains (NGP) has experienced a novel wet climatic continuum influencing snow processes. Amid this wet condition, land management practices and other watershed properties also exert strong control on snow accumulation. However, the influence of climate, vegetation (crop stubble) and topography on snow processes and streamflow generations are poorly understood due to lack of field-based snow accumulation data. Most studies are limited to remotely sensed snow cover estimates or numerical modeling with very limited snow data. To gain an improved understanding of the hydrological processes to climatic variability and land management practices in the NGP, a series of detailed snow surveys were conducted at distributed locations (ten locations) in the Mauvais Coulee Basin (a headwater basin draining to Devils Lake) during the winters of 2017-2023. We also utilized a field-tested cold region hydrologic model (CRHM). The wettest winter year was 2023, with an average snow water equivalent (SWE) of 99 mm. In contrast, 2021 was dry, with only traces of SWE. The influence of land management practices (via remotely sensed tillage index and CRHM modeling), topography, and climatic parameters on snow accumulation were investigated. The relationship between the tillage index and SWE is stronger during wet winters, and SWE also exhibits systematic variation with elevation along the north–south gradient. Together, these patterns indicate that both land management and physiographic setting shape the spatial distribution of SWE. Consistent with this, air temperature and snowfall emerge as the most influential climatic factors controlling SWE, while a machine learning (ML) model applied to CRHM-simulated data further shows that crop stubble height exerts a stronger control on snow accumulation than topography. 

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