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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  24 Jul 2020

24 Jul 2020

Review status
A revised version of this preprint is currently under review for the journal HESS.

Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

Noemi Vergopolan1, Sitian Xiong2, Lyndon Estes2, Niko Wanders3, Nathaniel W. Chaney4, Eric F. Wood1, Megan Konar5, Kelly Caylor6,7, Hylke E. Beck1, Nicolas Gatti8, Tom Evans9, and Justin Sheffield10 Noemi Vergopolan et al.
  • 1Civil and Environmental Engineering Department, Princeton University, USA
  • 2School of Geography, Clark University, USA
  • 3Department of Physical Geography, Faculty of Geosciences, Utrecht University, The Netherlands
  • 4Department of Civil and Environmental Engineering, Duke University, USA
  • 5Civil and Environmental Engineering Department, University of Illinois at Urbana-Champaign, USA
  • 6Department of Geography, University of California, Santa Barbara, USA
  • 7Bren School of Environmental Science and Management, University of California, Santa Barbara, USA
  • 8Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, USA
  • 9School of Geography, Development and Environment, University of Arizona, USA
  • 10School of Geography and Environmental Science, University of Southampton, Southampton, UK

Abstract. Soil moisture is highly variable in space, and its deficits (i.e. droughts) plays an important role in modulating crop yields and its variability across landscapes. Limited hydroclimate and yield data, however, hampers drought impact monitoring and assessment at the farmer field-scale. This study demonstrates the potential of field-scale soil moisture simulations to advance high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field-scale. We present a multi-scale modeling approach that combines HydroBlocks, a physically-based hyper-resolution Land Surface Model (LSM), and machine learning. We applied HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3-hourly 30-m resolution. These simulations along with remotely sensed vegetation indices, meteorological conditions, and data describing the physical properties of the landscape (topography, land cover, soil properties) were combined with district-level maize data to train a random forest model (RF) to predict maize yields at the district- and field-scale (250-m) levels. Our model predicted yields with a coefficient of variation (R2) of 0.61, Mean Absolute Error (MAE) of 349 kg ha−1, and mean normalized error of 22 %. We captured maize losses due to the 2015/2016 El Niño drought at similar levels to losses reported by the Food and Agriculture Organization (FAO). Our results revealed that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Consequently, soil moisture was also the most effective indicator of drought impacts in crops when compared with precipitation, soil and air temperatures, and remotely-sensed NDVI-based drought indices. By combining field-scale root zone soil moisture estimates with observed maize yield data, this research demonstrates how field-scale modeling can help bridge the spatial scale discontinuity gap between drought monitoring and agricultural impacts.

Noemi Vergopolan et al.

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Noemi Vergopolan et al.

Noemi Vergopolan et al.


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Publications Copernicus
Short summary
Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems at data-sparse developing countries, and it shows how field-scale soil moisture leverages and improves crop yield prediction and drought impact assessments.
Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or...