Groundwater recharge algorithm for forest management models

Fabian Schwaiger, Werner Poschenrieder, Thomas Rötzer, Peter Biber and Hans Pretzsch

Ecological Modelling, 2018, vol. 385, issue C, 154-164

Abstract: Multifunctionality is a critical objective in forest management planning. Water related ecosystem services are only sparsely implemented in Forest Management Models (FMM) although water scarcity is highly relevant. This study proposes an approach to integrate groundwater recharge into a FMM. The approach is based on knowledge transfer between two different forest growth models. For site-specific simulations on the landscape level, observation-based models require functions that describe groundwater recharge in a non-mechanistic way. However, groundwater recharge is difficult to measure and strongly depends on environmental conditions. Thus, we calibrated the observation-based FMM site-specific for two different case study areas, using a process-based forest growth model and substitute empiricism. Relations between forest structure and groundwater recharge were derived with multiple linear regressions and included in a FMM. The groundwater recharge was remarkably influenced by tree species and stand structure at both sites. The approach simulates groundwater recharge plausibly depending on site conditions and stand management on landscape level. Groundwater recharge was between 30–50% of the occurring precipitation and higher within broadleaved stands. Exemplary simulation of a European beech - Norway spruce mixed forest stand reveals a trade-off between groundwater recharge and stand volume growth depending on forest management.

Keywords: Groundwater recharge; Forest management model; Landscape simulation; Ecosystem service; Sustainable forest management planning (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:385:y:2018:i:c:p:154-164

DOI: 10.1016/j.ecolmodel.2018.07.006

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