Propagating uncertainty from catchment experiments to estimates of streamflow reduction by invasive alien plants in southwestern South Africa

Abstract

Long-term catchment experiments from South Africa have demonstrated that afforestation of grasslands and shrublands significantly reduces surface-water runoff.These results have guided the country's forestry policy and the implementation of a national Invasive Alien Plant (IAP) control programme for the past few decades.Unfortunately, woody IAP densities continue to increase, compounding existing threats to water security from population growth and climatic change. Decision makers need defensible estimates of the impacts of afforestation or invasions on runoff to weigh up alternative land use options, or guide investment of limited resources into ecosystem restoration through IAP clearing versus engineering-based wateraugmentation schemes. Existing attempts to extrapolate the impacts observed in catchment afforestation experiments to broad-scale IAP impacts give no indication of uncertainty. Globally, the uncertainty inherent in the results from paired-catchment experiments is seldom propagated into subsequent analyses making use of these data. We present a fully reproducible Bayesian model that propagates uncertainty from input data to final estimates of changes in streamflow when extrapolating from catchment experiments to broader landscapes. We apply our model to South Africa's catchment experiment data, estimating streamflow losses to plantations and analogous plant invasions in the catchments of southwestern South Africa, including uncertainty. We estimate that regional streamflow is reduced by 304 million m 3 or 4.14% annually as a result of IAPs, with an upper estimate of 408 million m 3 (5.54%) and a lower estimate of 267 million m 3 (3.63%). Our model quantifies uncertainty associated with all parameters and their contribution to overall uncertainty, helping guide future research needs. Acknowledging and quantifying inherent uncertainty enables more defensible decisions regarding water resource management.

The hydrological impacts of restoration: A modelling study of alien tree clearing in four mountain catchments in South Africa

“…Using the same models, but simulating stream ow reductions for total invadable area by pine 7-15 years old at 100% density, yields annual stream ow reductions of 15.4 to 30.1% for the 14-year period from 2005 to 2018, which is within the 50% predictive interval of Moncrieff et al, (2021), and well aligned to the initial curve tted by Scott and Smith 1997 (Figure 10).…”

“…excluding water impoundments, urban areas, roads and agriculture) of the catchment was replaced by pine (7-15 years old at 100% density) and the mean annual reduction in stream ow calculated relative to the baseline (scenario 1). These points were then overlain on the new stream ow reduction curves from the South African paired catchment data, which incorporates uncertainty estimates (Moncrieff et al, 2021).…”

“…According to the theory of limits to evaporation, alien tree invasions may be considered analogous to plantations of similar species (Calder, 1998). Using data from some of the longest and most detailed paired catchment experiments, a series of curves were generated to predict impacts of afforestation on stream ow (Moncrieff et al, 2021;Scott & Smith, 1997). The mean annual stream ow reduction modelled by this study is comparable to that of the Scott & Smith (1997) curves for sub-optimal conditions (i.e.…”

“…This is a signi cant nding that improves our understanding of uncertainties in hydrological modelling of the bene ts of ecological infrastructure investments. In understanding impacts of invasive alien trees on ow reduction, not only tree type, density and age is important (Moncrieff et al, 2021;Scott & Smith, 1997), but also underlying geological characteristics. Likewise, impacts on inter ow augmentation seem to be different for different saturated zone parameters, ranging from 0.4-0.7% for the Berg catchments, and 1-1.7% for the Breede catchments (almost 2.5 times greater).…”

“…Mean predictions are represented by solid lines, and the curves tted by Scott and Smith (1997) for optimal and suboptimal growing conditions are represented by dashed lines. Dark shaded areas indicate 50% predictive intervals, while lightly shaded areas indicate the 95% predictive intervals (Adapted from Moncrieff et al, 2021). Data are from a 14-year period (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) and are for the catchments fully invaded by pine (aged 7-15 years) for all current invadable area (i.e.…”