Using a Stochastically Driven Model of Plant Hydraulics to Design More Resilient Rehabilitated Landscapes in Arid Ecosystems

In former mining sites and other degraded lands, engineered soil covers are an increasingly popular rehabilitation tool for containing hazardous wastes and preventing further degradation. These covers are usually vegetated both in order to meet ecosystem and biodiversity targets for restoration or rehabilitation, and in order to limit deep drainage by managing the water balance within the soil cover. The depth of the engineered soil cover is a major design parameter for cover systems. Deeper covers create more storage capacity for incoming precipitation and allow plants access to a potentially larger volume of soil moisture, which may reduce their risk of drought mortality and failed rehabilitation efforts. However, the cost of soil covers is directly related to their depth, suggesting a need to optimize cover depth.

Here we use a case study of two Eucalyptus species grown on engineered soil covers of different depths in Western Australia, a semi-arid environment which also experiences intermittent episodes of high precipitation associated with cyclones. For this case study, we explore the interaction between soil depth, deep drainage, variable precipitation, and plant water use and drought vulnerability. Firstly, we test a hydraulic model of plant water uptake against detailed field measurements linking soil moisture, plant water status, water, and carbon fluxes. Having verified the model performance, we use it to investigate plant water potentials, and leakage rates across a Monte Carlo analysis of multiple precipitation realizations and soil cover depths. The climate of Western Australia's Mid West region is described using a stochastic model of both cyclonic and non-cyclonic precipitation. Drought vulnerability is expressed in terms of the probability of exceeding water potential thresholds that are commonly associated with physiological damage. The modeled outcomes demonstrate the potential to optimize the design of engineered soil covers to maximize vegetation resiliency and minimize deep drainage subject to cost constraints associated with cover depth.