Preprints
https://doi.org/10.5194/hess-2021-263
https://doi.org/10.5194/hess-2021-263

  20 May 2021

20 May 2021

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

Towards Effective Drought Monitoring in the Middle East and North Africa (MENA) Region: Implications from Assimilating Leaf Area Index and Soil Moisture into the Noah-MP Land Surface Model for Morocco

Wanshu Nie1,2, Sujay V. Kumar3, Kristi R. Arsenault3,4, Christa D. Peters-Lidard5, Iliana E. Mladenova6, Karim Bergaoui7, Abheera Hazra3,8, Benjamin F. Zaitchik1, Sarith P. Mahanama9,10, Rachael McDonnell11, David M. Mocko3,4, and Mahdi Navari3,8 Wanshu Nie et al.
  • 1Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
  • 2NASA Goddard Earth and Sciences Technology and Research (GESTAR), Greenbelt, Maryland, USA
  • 3Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
  • 4Science Applications International Corporation, McLean, Virginia, USA
  • 5Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
  • 6USDA Foreign Agricultural Service, Washington, DC, USA
  • 7ACQUATEC Solutions, Dubai Technology Entrepreneur Campus (DTEC), Dubai Silicon Oasis, Dubai, UAE
  • 8Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
  • 9Science Systems and Applications Inc., Lanham, Maryland, USA
  • 10Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
  • 11Water, Climate Change & Resilience Program, International Water Management Institute-Rome Office, Rome, Italy

Abstract. The Middle East and North Africa (MENA) region has experienced more frequent and severe drought events in recent decades, leading to increasingly pressing concerns over already strained food and water security. An effective drought monitoring and early warning system is thus critical to support risk mitigation and management by countries in the region. Here we investigate the potential for assimilation of leaf area index (LAI) and soil moisture observations to improve representation of the overall hydrological and carbon cycles and drought by an advanced land surface model. The results reveal that assimilating soil moisture does not meaningfully improve model representation of the hydrological and biospheric processes for this region, but rather it degrades simulation of interannual variation of evapotranspiration (ET) and carbon fluxes, mainly due to model weaknesses in representing dynamic phenology. However, assimilating LAI leads to greater improvement, especially for transpiration and carbon fluxes, by constraining the timing of simulated vegetation growth response to evolving climate conditions. LAI assimilation also helps to correct for the erroneous interaction between the dynamic phenology and irrigation during summertime, effectively reducing a large positive bias in ET and carbon fluxes. Independently assimilating LAI or soil moisture alters the categorization of drought, with the differences being greater for more severe drought categories. We highlight the vegetation representation in response to changing land use and hydroclimate as one of the key processes to be captured for building a successful drought early warning system for the MENA region.

Wanshu Nie et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-263', Anonymous Referee #1, 16 Jun 2021
  • RC2: 'Comment on hess-2021-263', Claire Michailovsky, 30 Jun 2021

Wanshu Nie et al.

Wanshu Nie et al.

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Short summary
The Middle East and North Africa region faces significant food/water insecurity and hydrological hazards. Here we investigate the value of assimilating remote sensing datasets into an earth system model to help build an effective drought monitoring system, supporting risk mitigation and management by countries in the region. We highlight incorporating satellite-informed vegetation condition into the model as one of the key processes to be captured for a successful application for the region.