Predicting long‐term streamflow variability in moist eucalypt forests using forest growth models and a sapwood area index

Abstract

A major challenge in surface hydrology involves predicting streamflow in ungauged catchments with heterogeneous vegetation and spatiotemporally varying evapotranspiration (ET) rates. We present a top-down approach for quantifying the influence of broad-scale changes in forest structure on ET and hence streamflow. Across three catchments between 18 and 100 km 2 in size and with regenerating Eucalyptus regnans and E. delegatensis forest, we demonstrate how variation in ET can be mapped in space and over time using LiDAR data and commonly available forest inventory data. The model scales plot-level sapwood area (SA) to the catchment-level using basal area (BA) and tree stocking density (N) estimates in forest growth models. The SA estimates over a 69 year regeneration period are used in a relationship between SA and vegetation induced streamflow loss (L) to predict annual streamflow (Q) with annual rainfall (P) estimates. Without calibrating P, BA, N, SA, and L to Q data, we predict annual Q with R 2 between 0.68 and 0.75 and Nash Sutcliffe efficiency (NSE) between 0.44 and 0.48. To remove bias, the model was extended to allow for runoff carry-over into the following year as well as minor correction to rainfall bias, which produced R 2 values between 0.72 and 0.79, and NSE between 0.70 and 0.79. The model under-predicts streamflow during drought periods as it lacks representation of ecohydrological processes that reduce L with either reduced growth rates or rainfall interception during drought. Refining the relationship between sapwood thickness and forest inventory variables is likely to further improve results.

Modelling water yields in response to logging and Representative Climate Futures

“…However, it is challenging to apply the Macaque model because it contains over 70 parameters and most are default values (Jaskierniak, 2011). Many of these parameters require calibration, but data for them can be difficult to obtain for remote catchments (Jaskierniak et al, 2016), as well as obtaining all of the GCM variables for the respective climate inputs.…”

“…A simpler approach has been developed by Jaskierniak et al (2016) and Benyon et al (2015), which uses measures of sapwood area derived from basal area and tree stocking density estimates. These inputs provide greater certainty in modelling with strong correlations between predicted and observed streamflow.…”

Disturbance Hydrology: Preparing for an Increasingly Disturbed Future

“…Paradoxically, natural landscape disturbances also play an integral role in ecohydrological functioning and biodiversity (Seidl et al, 2014a;Thom & Siedl, 2016). Land-use and land-management decisions can certainly influence the frequency and magnitude of such disturbances (Loheide et al, 2009;Nyman et al, 2013;Robichaud et al, 2010;Thomson et al, 2005), so it is important to distinguish disturbance hydrology from the The majority of the contributions to this special issue examine the hydrologic impacts of distinct vegetation changes, such as bark-beetle infestation (Biederman et al, 2015;Knowles et al, 2017;Penn et al, 2016), wildfire (Ebel et al, 2016;Rengers et al, 2016), forest structure change (Jaskierniak et al, 2016), and peatland degradation (Menberu et al, 2016). However, several others illustrate the importance of geologic hazards, including earthquakes (Rutter et al, 2016), landslides (Mirus et al, 2017), and volcanic eruptions (Major et al, 2016).…”

“…Comparisons to observed debris-flow timing were accurate to within minutes for both approaches, indicating that even the simplified approach could be applied to predicting debris-flow timing in burned watersheds. Jaskierniak et al (2016) explore the utility of a ''top down'' modeling approach for predicting streamflow in southern Victoria, Australia, where mixed eucalyptus forests have undergone an unprecedented scale of disturbance due to wildfire as well as timber harvesting. Building on previous observations that sapwood area correlates with evapotranspiration losses, Jaskierniak et al (2016) use a growth model constrained with lidar data to quantify recovery of a mixed forest structure in response to episodic wildfires.…”

“…Jaskierniak et al (2016) explore the utility of a ''top down'' modeling approach for predicting streamflow in southern Victoria, Australia, where mixed eucalyptus forests have undergone an unprecedented scale of disturbance due to wildfire as well as timber harvesting. Building on previous observations that sapwood area correlates with evapotranspiration losses, Jaskierniak et al (2016) use a growth model constrained with lidar data to quantify recovery of a mixed forest structure in response to episodic wildfires. With minimal calibration, their parsimonious approach for scaling mean annual evapotranspiration losses using the growth model provides reasonable predictions of annual expected catchment streamflow in ungauged basins, which is useful for water resources management.…”