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Potential Impacts of Climate Change on Vegetative Ecosystem Services of Soil Retention [US-IALE 04/08/18]
Citation:
Cada, P., M. Mehaffey, AND A. Neale. Potential Impacts of Climate Change on Vegetative Ecosystem Services of Soil Retention [US-IALE 04/08/18]. US-IALE 2018 Annual Meeting, Chicago, IL, April 08 - 12, 2018.
Impact/Purpose:
The approach was designed to derive a quantitative approximation of the ecological services provided by vegetative cover, management practices, and other surface features with respect to sediment runoff from current and future extreme weather events across the continental US for incorporation into the EnviroAtlas. Understanding how shifts in storm event intensities are expected to change sediment runoff responses are valuable for local, regional, and landscape planning.
Description:
Planning for a sustainable future should include an accounting of services currently provided by ecosystems such as erosion control. Retention of soil not only maintains but improves soil fertility, improves water retention, and decreases sedimentation in streams and rivers thereby reducing the need for costly flood control, dredging of reservoirs and waterways, while maintaining habitat for fish and other species important to recreational and tourism industries. Previous efforts leveraged landscape-scale geospatial data available for the CONterminous United States (CONUS) to estimate sediment erosion (RUSLE-based, Renard, et al. 1997) employing recent geospatial techniques of sediment delivery ratio (SDR) estimation (Cavalli, et al. 2013). These efforts led to a quantitative approximation of the ecological services provided by vegetative cover type with respect to protecting soils from the erosion processes of detachment, transport, and deposition. Recent efforts have examined the potential impacts that changes in climate may have on sediment pollution by altering rainfall erosivity (R factor in RUSLE method). These alterations were developed using the late-21st century using Global Climate Model (GCM) data. GCM data from the aCMIP5 was statistically downscaled to the same temporal and spatial resolution as PRISM (4 km, daily) using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown, 2012). Daily precipitation and temperature data were retrieved for the CONUS from 5 GCMs with relatively low seasonal and regional biases (Mehran et al, 2014; Toreti et al, 2013; Zhu et al, 2014). Monthly hindcast (1990 – 2005) and futurecast (2070 – 2099) R factors were computed for each GCM. Relative change rasters were created for each GCM and were then used to generate future seasonal R rasters for each GCM, and subsequently seasonal sediment yields. Final products are HUC-12 scale outputs of estimated sediment loads associated with each future climate scenario for the CONUS.
