Towards better agricultural drought assessment and irrigation management: improving the simulation and understanding of plant water stress for crops in Noah-MP land surface model


Short Summary:

Two new methods for modeling plant water stress will be implemented into the Noah-MP land surface model to improve the simulation of plant water stress for crops. The new and existing methods will be evaluated and compared within the same modeling framework to improve our mechanistic understanding of plant water stress and the modeling of it. The revised Noah-MP models will also be useful for assessing crops’ response to dry weather conditions and guiding irrigation management, and thus have practical uses in agricultural production in Illinois and the U.S. Corn Belt.

Statement of Expected Benefits: 

Accurately modeling plant water stress is key to evaluation of drought impacts on agro-ecosystem productivity and irrigation management. Plant water stress can be triggered by insufficient soil water content (SWC; water supply) and high atmospheric vapor pressure deficit (VPD; water demand), either independently or collectively (Katul et al., 2012). For crops, plant water stress is one of the major hazards in dry weather conditions, especially when extreme weather is becoming more and more frequent under climate change (Rosenzweig et al., 2001).

Statement of Expected Results:

We plan to implement and test a plant hydraulic model and a water supply-demand based model in the Noah-MP LSM and compare the results with the existing Ball-Berry stomatal conductance scheme with an empirical soil water stress function. We hypothesize that both newly implemented models can improve the performance of soil water stress representation and vegetation water use dynamics. We further expect that the water supply-demand based approach would be advantageous when fewer plant hydraulic measurements are available because its parsimonious representation could be more easily constrained.


 The objective of this study is threefold:  (1) implement the two new methods in Noah-MP;  (2) compare the results of the two new methods and existing method in Noah-MP;  (3) compare the performance of the two new methods under different scenarios and evaluate their pros and cons that may help us understand
their applicability in different situations.

Tags: No tags