Long-term hydrological response to forest harvest during seasonal low flow: Potential implications for current forest practices.

Seasonal changes in the magnitude and duration of streamflow can have important implications for aquatic species, drinking water supplies, and water quality. In many regions, including the Pacific Northwest (U.S. and Canada), seasonal low flow is declining, primarily due to a changing climate, but is also influenced by urbanization, agriculture, and forestry. We review the responses of seasonal low flow, catchment storage, and tree-water relations to forest harvest over long timescales and discuss the potential implications of these responses for current forest practices and aquatic biota. We identify three distinct periods of expected low flow responses as regrowth occurs following forest harvest: in the first period an initial increase in low flow can occur as replanted stands regenerate, in the second period low flow is characterized by mixed and variable responses as forests become established, and in the third period, which follows canopy closure, low flow declines may occur over long timescales. Of 25 small catchments with ≥10 years post-harvest data, nine catchments had no change or variable low flow and 16 catchments experienced reduced low flow years after harvest. The retention of riparian buffers, limited size of harvest units, and adherence to reforestation requirements have altered the contemporary forest landscape relative to historical forest practices, but data documenting multi-decadal hydrological responses to current harvest practices is limited. Our review suggests that the magnitude of low flow responses attenuates downstream as a broader mosaic of stand ages occurs and multiple hydrological periods are represented. Declines were not observed in the seven large catchments reviewed. The consequences of low flow declines for aquatic biota are not well understood, but where data do exist aquatic biota have not been adversely affected. We identify priorities for future research that will aid in improving predictions of low flow responses to harvest as forests regenerate.

Wetness index based on landscape position and topography (WILT): Modifying TWI to reflect landscape position.

Water and land resource management planning benefits greatly from accurate prediction and understanding of the spatial distribution of wetness. The topographic wetness index (TWI) was conceived to predict relative surface wetness, and thus hydrologic responsiveness, across a watershed based on the assumption that shallow slope-parallel flow is a major driver of the movement and distribution of soil water. The index has been extensively used in modeling of landscape characteristics responsive to wetness, and some studies have shown the TWI performs well in landscapes where interflow is a dominant process. However, groundwater flow dominates the hydrology of low-slope landscapes with high subsurface conductivities, and the TWI assumptions are not likely to perform well in such environments. For groundwater dominated systems, we propose a hybrid wetness index (Wetness Index based on Landscape position and Topography, WILT) that inversely weights the upslope contributing area by the distance to the nearest surface water feature and the depth to groundwater. When explicit depth to groundwater data are not available, height above and separation from surface water features can act as surrogates for proximity to groundwater. The resulting WILT map provides a more realistic spatial distribution of relative wetness across a low-slope Coastal Plain landscape as demonstrated by improved prediction of hydric soils, depth to groundwater, nitrogen and carbon concentrations in the A horizon of the soil profile, and sensitivity to DEM scale.

Constraining 3-PG with a new δ13C submodel: a test using the δ13C of tree rings.

A semi-mechanistic forest growth model, 3-PG (Physiological Principles Predicting Growth), was extended to calculate δ(13)C in tree rings. The δ(13)C estimates were based on the model's existing description of carbon assimilation and canopy conductance. The model was tested in two ~80-year-old natural stands of Abies grandis (grand fir) in northern Idaho. We used as many independent measurements as possible to parameterize the model. Measured parameters included quantum yield, specific leaf area, soil water content and litterfall rate. Predictions were compared with measurements of transpiration by sap flux, stem biomass, tree diameter growth, leaf area index and δ(13)C. Sensitivity analysis showed that the model's predictions of δ(13)C were sensitive to key parameters controlling carbon assimilation and canopy conductance, which would have allowed it to fail had the model been parameterized or programmed incorrectly. Instead, the simulated δ(13)C of tree rings was no different from measurements (P > 0.05). The δ(13)C submodel provides a convenient means of constraining parameter space and avoiding model artefacts. This δ(13)C test may be applied to any forest growth model that includes realistic simulations of carbon assimilation and transpiration.