Zhang et al. (2025) Moisture from US Corn Belt fuels more intense convective storms

Identification

• Journal: Communications Earth & Environment
• Year: 2025
• Date: 2025-12-16
• Authors: Zhe Zhang, Andreas Franz Prein, Changhai Liu, Fei Chen, Ronnie Abolafia‐Rosenzweig, Tzu‐Shun Lin, Roy Rasmussen
• DOI: 10.1038/s43247-025-03089-0

Research Groups

• NSF National Center for Atmospheric Research (Advanced Study Program, Research Applications Laboratory, Mesoscale and Microscale Meteorology), Boulder, CO, USA
• ETH Zürich, Institute for Atmospheric and Climate Science, Zürich, Switzerland
• Universidade de Santiago de Compostela, Nonlinear Physics Group, Faculty of Physics, Galicia, Spain
• Hong Kong University of Science and Technology, Division of Environment and Sustainability, Hong Kong, China

Short Summary

This study quantifies how integrated groundwater-crop-irrigation interactions in the US Corn Belt intensify convective storms, finding they significantly increase storm frequency, lifetime, and movement speed by enhancing warm-moist inflows.

Objective

• To quantify the integrated effects of shallow groundwater, dynamic crops, and irrigation on convective storms over the US Corn Belt, specifically tracking moisture from Corn Belt evapotranspiration into individual storms.

Study Configuration

• Spatial Scale: US Corn Belt, using a regional climate model with 4-kilometer (km) horizontal grid spacing.
• Temporal Scale: Three growing seasons (May-August) for the years 2010 (wet), 2011 (normal), and 2012 (dry), with April used as a spin-up period.

Methodology and Data

• Models used:
  • Weather Research and Forecasting (WRF) model, Version 4.3.3 (convection-permitting).
  • Noah-MP land surface model (LSM) incorporating shallow groundwater, dynamic crop growth, and irrigation schemes.
  • WRF-WVT (Water Vapor Tracer) algorithm for moisture tracking.
  • MOAAP (Multi Object Analysis of Atmospheric Phenomenon) tracking algorithm for Mesoscale Convective Systems (MCSs).
• Data sources:
  • Initial and boundary conditions: European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5).
  • Validation: StageIV precipitation product, Global Groundwater Monitoring Network (GGMN), International Soil Moisture Network (ISMN), MODIS ET product (MOD16A), METAR observational network (2-meter dewpoint temperature).

Main Results

• Integrated groundwater-crop-irrigation (GW-Crop-Irr) interactions amplify Mesoscale Convective System (MCS) frequency by 24-35%, extend storm lifetime by up to 10%, and accelerate storm movement, significantly improving model performance compared to a baseline simulation.
• Moisture from Corn Belt evapotranspiration (ET) contributes 17-20% to MCS precipitation in the GW-Crop-Irr simulation, an increase from 12-17% in the baseline, thereby boosting the proportion of MCS precipitation in total precipitation from 20-36% to 28-40%.
• GW-Crop-Irr interactions enhance rainfall intensity near the MCS maximum precipitation center (Pmax) by 2.7 millimeters per hour (mm/h) (a 6% rise), with Corn Belt ET moisture contributing 3.5 mm/h (a 46% increase) to this intensification, particularly in stronger storms.
• GW-Crop-Irr interactions modify the MCS environment by increasing boundary layer equivalent potential temperature (𝜃𝑒) by 5 Kelvin (K), elevating convective available potential energy (CAPE), reducing convective inhibition (CIN), and lowering the lifting condensation level (LCL), creating a more unstable environment.
• The moisture contribution from the Corn Belt to the entire storm column increases from less than 14% to more than 22% (a 57% enhancement) in the GW-Crop-Irr simulation, fueling stronger warm-moist updrafts.
• Enhanced surface temperature contrasts and faster propagation speeds in GW-Crop-Irr suggest strengthened cold pool dynamics, consistent with faster storm movement.

Contributions

• Provides the first direct quantification of the integrated effects of shallow groundwater, dynamic crop growth, and irrigation on intense convective storms in the US Corn Belt using a high-resolution, convection-permitting climate model.
• Uniquely applies an advanced Eulerian water vapor tracer technique to offer an "anatomic perspective" of moisture sourcing from local agricultural activities to individual severe convective storms.
• Offers a realistic process-based understanding of the underlying physical mechanisms by which groundwater-crop-irrigation interactions enhance convective storms through modifying the thermodynamic environment.
• Highlights the critical need for accurate representations of these pivotal land surface processes to advance understanding of complex land-atmosphere interactions and improve subseasonal-to-seasonal (S2S) predictability.

Funding

• National Science Foundation (NSF)
• NSF NCAR Advanced Study Program
• NCAR’s Water System program

Citation

@article{Zhang2025Moisture,
  author = {Zhang, Zhe and Prein, Andreas F. and He, Cenlin and Liu, Changhai and Miguez-Macho, Gonzalo and Chen, Fei and Abolafia‐Rosenzweig, Ronnie and Lin, Tzu‐Shun and Rasmussen, Roy},
  title = {Moisture from US Corn Belt fuels more intense convective storms},
  journal = {Communications Earth & Environment},
  year = {2025},
  doi = {10.1038/s43247-025-03089-0},
  url = {https://doi.org/10.1038/s43247-025-03089-0}
}