Abstract
Recent advances in measuring and modeling root water uptake along with refined electrical petrophysical models may help fill the existing gap in hydrological root model parametrization. In this paper, we discuss the choices to be made to combine root-zone hydrology and geoelectrical data with the aim of characterizing the active root zone. For each model and observation type we discuss sources of uncertainty and how they are commonly addressed in a stochastic inversion framework. We point out different degrees of integration in the existing hydrogeophysical approaches to parametrize models of root-zone hydrology. This paper aims at giving emphasis to stochastic approaches, in particular to Data Assimilation (DA) schemes, that are generally identified as the best way to combine geoelectrical data with Root Water Uptake (RWU) models. In addition, the study points out a more suitable objective function taken from the optimal transport theory that better captures complex geometry of root systems. Another pathway for improvement of geoelectrical data integration into RWU models using DA relies on the use of stem based methods as a leverage to introduce more extensive root knowledge into RWU macroscopic hydrological models.
Highlights
Root Water Uptake ModelsMany authors underlined the importance of identifying the best approach to describe RWU
Benjamin Mary 1,2*, Luca Peruzzo 2, Veronika Iván 1, Enrico Facca 3, Gabriele Manoli 4, Mario Putti 3, Matteo Camporese 5, Yuxin Wu 2 and Giorgio Cassiani 1
This paper aims at giving emphasis to stochastic approaches, in particular to Data Assimilation (DA) schemes, that are generally identified as the best way to combine geoelectrical data with Root Water Uptake (RWU) models
Summary
Many authors underlined the importance of identifying the best approach to describe RWU. Given the resolution of geoelectrical methods, macroscopic models (e.g., Muma et al, 2013; Vanderborght et al, 2021) describing RWU are often preferred to microscopic (or functional structural) ones (e.g., Javaux et al, 2008) These two types of models are usually based on solving a modified form of the 3D Richards equation to account for a certain root distribution and root/trunk xylem/stomatal hydraulic conductance (Volpe et al, 2013). The root parameterization employed in hydrological models defines the “state model” which is commonly described by a state vector xSW(t)(e.g., soil water content) with a Prior PDF function p(xSW) This prior PDF quantifies what we know about the state of the system given the prior distribution of the parameters and before considering the observed data. What about the microscopic models? There isn’t a lot mentioned about them
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