The component ecosystem models used in this analysis have been previously validated individually. The PnET-CN model was validated for water and nutrient balance, (Ollinger et al. 2002, 2008, Aber et al. 2005), whereas FrAMES was validated for river discharge using a simple water balance model (Vörösmarty et al. 1998, Wisser et al. 2010), water temperature (Stewart et al. 2013), chloride (Zuidema, Wollheim, Mineau, et al., unpublished manuscript), and riverine DIN (Wollheim et al. 2008a, b, Stewart et al. 2011). In this study, terrestrial vegetation processes in PnET–FrAMES drive regional runoff, nutrient, chloride, and water temperature loading that is then routed through the network.
Changes in the land indicators (Table 2; Agricultural Cover per person and Forest Cover) directly reflect the land-cover scenarios (Thorn et al. 2017) and have important direct ecosystem service implications. Briefly, in the Backyard scenario, watershed forest cover declines from 80% in 2010 to 60% in 2100, to accommodate increased rural and suburban development (Fig. 4). In the Small Community Food/Low scenario, forest declines to 64% in 2100, replaced by agricultural lands, primary for pasture and hay. By 2100, population increases by 170% in the Backyard/High scenario and declines by 5% in the Small Community Food/Low scenario. The increase in agricultural land relative to population under the Small Community Food/Low scenario (~1.0 acres per person in 2100 compared with 0.2 in 2010) increases the potential for per capita local food production compared with the contemporary period (Thorn et al. 2017). The Maple Presence indicator, which primarily represents the geographic distribution of suitable habitat for Acer saccharum (Iverson et al. 2008), declines from 49% of total forest cover in the present day to 27% and 31% in the Backyard/High emission and Small Community Food/Low emission scenarios, respectively. The declines result from shifts in climate associated with each land-cover scenario.
Coupled terrestrial–aquatic models express the understanding of processes relevant to specific land-cover decisions, permit exploration of relevant tradeoffs (Antle and Capalbo 2001, Bennett et al. 2009), and are essential in understanding the response of ecosystem dynamics to changes in both climate and land cover. Results from PnET–FrAMES provide a measure of the potential impact of each land-cover scenario and each climate scenario on different ecosystem service indicators. As regional managers have little influence over climate change and because we are currently on a higher emission trajectory (Pachauri et al. 2014), managers should consider potential land cover in conjunction with the higher emissions climate scenario to guide future planning. Projections from PnET–FrAMES suggest that infilled development (Community) scenarios exhibit two types of paradigms with respect to specific aquatic environmental indicators under a high emission future (Fig. 7). The three land-cover scenarios with limited expansion of the residential footprint (Constant, Small Community Food, Large Community Wild) show smaller levels of Fish Habitat Loss and Water Shortfalls than the dispersed buildout (Backyard). The Large Community Wildlands scenario represents a doubling of population similar to Backyard scenario, but has much lower environmental detriment based on these two indicators. However, the Large Community Wildlands scenario does show increased impact on the Nitrogen Export indicator (more point sources) and Flood Risk indicator (more people along large river corridors) relative to Backyard, suggesting that there are tradeoffs that must be further managed under an infilled paradigm.
Despite the predominant influence of climate on the environmental indicators in the UMRW, land-cover management can mitigate degradation to certain attributes of the ecosystem (e.g., Fig. 8). Build-out scenarios directly influence land-based environmental indicators such as Forest and Agricultural Cover. Scenarios that maintain greater forest land have slightly greater C sequestration (Fig. A4.4e), whereas scenarios with greater agriculture increase food supply (Thorn et al. 2017) and resiliency (Tilman et al. 2002). In addition, these land covers affect numerous sociocultural resources, including water-related ecosystem services. Depending on the relative value placed on different indicators (Murphy et al. 2017), the landscape can be managed to maximize ecosystem services that are most important.
This book presents the most comprehensive model yet for describing the structure and functioning of running freshwater ecosystems. (RES) is a result of combining several theories published in recent decades, dealing with aquatic and terrestrial systems. New analyses are fused with a variety of new perspectives on how river network ecosystems are structured and function, and how they change along longitudinal, lateral, and temporal dimensions. Among these novel perspectives is a dramatically new view of the role of hydrogeomorphic forces in forming functional process zones from headwaters to the mouths of great rivers.
Process-based terrestrial–aquatic models are essential for quantifying potential future environmental impacts due to spatially distributed changes in land cover and climate. Here, we simulated dynamic interactions across land and water domains at a daily temporal resolution and a spatial resolution that accounts for the heterogeneity and associated processes of the UMRW in New Hampshire. Our approach linked models of intermediate complexity to connect terrestrial and aquatic domains and allowed us to explore the impacts of changes in climate and land cover on aquatic ecosystems through the use of different scenario combinations. Process-based models enable understanding of macroscale ecosystem responses based on underlying ecosystem processes.
Environmental indicators in the water domain reflect the combined influence of projected climate and land-cover change, as well as terrestrial and aquatic processes. In general, all water indicators reflect increasing degradation of aquatic ecosystem services for the Backyard/High scenario, but remain relatively unchanged for the Small Community Food/Low scenario (Fig. 6). The Fish Habitat Loss indicator is projected to rise substantially in the Backyard/High scenario to average about 40% of total stream and river length after 2075. Only slight increases, and fewer extreme events, occur under the Small Community Food/Low scenario. The Nitrogen Export indicator increases steadily over the 100-yr period for the Backyard/High scenario to 995 tons N yr-1 up 3100% from current conditions. Under the Community/Low scenario, however, N export changes relatively less, increasing only 70% to an average of 52 tons N yr-1. The steady increase in the Backyard/High scenario is due to elevated N loading associated with widespread land-cover change and increased population. The pattern in Community/Low occurs because in this scenario, agricultural management activities were assumed to reflect the greater concern for the environment (optimal fertilizer application rates, improved waste water treatment plant N removal), and because it reflects a lower human population (Table 1).
The Large Community Wildlands scenario projects greater N export than the Backyard scenario (Fig. 7b), despite the former conserving more forest, and upgrading WWTPs to higher treatment levels with lower per capita human waste N inputs. The logistic loading function that is used to parameterize nonpoint N inputs to rivers as a function of land cover (Appendix 3) assumes relatively low N increases for low to moderate density urban and agricultural development (average increases to about ~21% urban + Ag land cover for Backyard and 9% for Large Community Wildlands). The mechanistic explanation for this pattern is that N removal by terrestrial ecosystems remains high up to certain thresholds of natural ecosystem loss, consistent with a number of previous studies (Groffman et al. 2004, Wollheim et. 2005). The Backyard scenario assumes a low density of future land-cover change that is often below the threshold. Thus, human waste associated with population growth, which is managed through distributed septic systems and not WWTP, is mostly retained, and there is relatively little change in N inputs. In contrast, the Large Community Wildlands scenario assumes domestic waste N is transferred to WWTP with Total Nitrogen (TN) removal efficiencies of 90%. The assumption of low response to land-cover change embedded in the loading function is derived from one location with a certain social and biogeophysical context (e.g., rural/suburban septic infrastructure, wetland abundance, etc. Wollheim et al. 2008b). Although the generality of this loading function to the entire domain should be tested more thoroughly, the validation results suggest it is reasonable and therefore useful for exploring potential future responses.
The iterative and collaborative process of environmental indicator selection identified ten indicators, from an initial set of 43 (Table A2.1), to represent a comprehensive suite of environmental conditions for the UMRW. The experts group used the criteria that indicators should hold perceived relevance to the general public, should equitably represent the three domains, and should be constrained by existing modeling capability. The selected indicators represented three indicators for each of land and climate domains, and four indicators for the water domain. The indicators are defined in Table 2 and explained in detail in the supplementary material (Appendix 2). The ten selected indicators are similar to several included in comprehensive lists generated previously (de Groot et al. 2010, Burkhard et al. 2012). However, our final indicators differed from previous lists to be more relevant to local residents. For instance, we used total forest cover as a percentage of the watershed instead of wood biomass stock in units of mass per area (de Groot e al. 2010) based on lack of relevance of wood biomass to the general public living in the watershed. The water indicators emphasized basin scale and subannual estimates of water conditions, requiring space- and time-varying aquatic modeling. The choice of indicators in representing specific ecosystem services is discussed more thoroughly elsewhere in this special issue (Mavrommati et al. 2017).
The PnET-CN model accounts for the influence of photosynthesis on evapotranspiration and nutrient uptake, forest age, and plant physiological responses (stomatal conductance) to changing CO2 (Ollinger et al. 2008). Together, these factors control C sequestration, nutrient export, and runoff generation immediately relevant to several ecosystem services. In the coupled model (Fig. 2), PnET-CN calculates daily runoff and dissolved inorganic nitrogen (DIN) flux from forest rooting zones. These outputs are then partitioned into shallow groundwater or surface (quick flow) flow paths, with different characteristic travel times. In urban regions, precipitation and snowmelt on hydrologically connected impervious areas run directly to the stream network, with the remainder infiltrating to lawn areas. Chloride, a potential stressor of aquatic biota, from snowmelt on the road-salt-treated fraction of impervious areas is transported conservatively following the soil and groundwater flow paths (Zuidema, Wollheim, Mineau, et al., unpublished manuscript). To link with the aquatic network, we also incorporated the role of terrestrial flow paths and riparian zones in regulating DIN loads. The PnET-CN model predicts leaching from the forest rooting zone. To account for retention along terrestrial–riparian flow paths, we applied a constant retention factor of 70% to all leachate, consistent with reactivity of riparian zones (Green et al. 2009) or buffers that average about 25 m (Mayer et al. 2005). We account for DIN loading from urban and agricultural areas using an empirical relationship between DIN, land use, and flow found in other New England watersheds (Wollheim et al. 2008a, b, Stewart et al. 2011, Mineau et al. 2015). Water temperature in terrestrial runoff is modeled as described in Stewart et al. (2013). Water temperature and DIN inputs from land are further modified by instream processes as water flow through the river network.