Integrated Response of Grassland Biomass Along Co-varying Gradients of Climate and Grazing Management Using an Eco-hydrologic Model

Abstract:

Grasses in rangeland ecosystems cover a large portion of the contiguous United States and are used to support the production of livestock. These grasslands experience a wide range of precipitation and temperature regimes, as well as management activities like grazing. Assessing the coupled response of biomass to both climatic change and human activities is important to decision makers to ensure the sustainable management of their lands. The objective of this study is to examine the sensitivity of biomass under co-varying conditions of climate and grazing management.

For this, we used the Regional Hydro-ecologic Simulation System (RHESSys), a physically-based model that simulates coupled water and biogeochemical processes. We selected representative grassland sites using the Köppen-Geiger climate classification system and information on major grass species. Historical data on precipitation, temperature, and grazing patterns (intensity, frequency, duration) were incrementally perturbed to simulate climatic change and possible changes in management. To visualize this multi-dimensional parameter space, we created surface response plots of varying climate and grazing factors for the mean and variance of both aboveground and belowground biomass, as well as the ratio between the two.

Mean biomass generally increased with warmer temperatures and decreased with more intense grazing. The sensitivity of biomass (i.e. variance) increased with more extreme perturbations in climate and intense types of grazing management. However, co-varying climate conditions with either grazing intensity, frequency, or duration revealed different biomass responses and tradeoffs. For example, some changes in grazing duration could be reversed by changes in climate. Effects of high intensity grazing could be buffered depending on the timing of grazing (i.e. start/end date). Using simple perturbations with process-based modeling provides useful information for land managers for future planning.