Atmospheric nitrogen (N) deposition and climate change are transforming the way N moves through dryland watersheds. For example, N deposition is increasing N export to streams, which may be exacerbated by changes in the magnitude, timing, and intensity of precipitation (i.e., the precipitation regime). While deposition controls the amount of N entering a watershed, the precipitation regime influences rates of internal cycling; when and where soil N, plant roots, and microbes are hydrologically connected; how quickly plants and microbes assimilate N; and rates of denitrification, runoff, and leaching. We used the ecohydrological model RHESSys to investigate (1) how N dynamics differ between N-limited and N-saturated conditions in a dryland watershed, and (2) how total precipitation and its intra-annual intermittency (i.e., the time between storms in a year), interannual intermittency (i.e., the duration of dry months across multiple years), and interannual variability (i.e., variance in the amount of precipitation among years) modify N dynamics. Streamflow N export was more sensitive to increasing intermittency and variability in N-limited vs. N-saturated model scenarios, particularly when total precipitation was lower—the opposite was true for denitrification. N export and denitrification increased or decreased the most with increasing interannual intermittency compared to other changes in precipitation timing. This suggests that under future climate change, prolonged droughts that are followed by more intense storms may pose a major threat to water quality in dryland watersheds.

Climate change and nitrogen (N) pollution are altering biogeochemical and ecohydrological processes in dryland watersheds, increasing N export, and threatening water quality. While simulation models are useful for projecting how N export will change in the future, most models ignore biogeochemical “hotspots” that develop in drylands as moist microsites become hydrologically disconnected from plant roots when soils dry out. These hotspots enable N to accumulate over dry periods and rapidly flush to streams when soils wet up. To better project future N export, we developed a framework for representing hotspots using the ecohydrological model RHESSys. We then conducted a series of virtual experiments to understand how uncertainties in model structure and parameters influence N export. Modeled export was sensitive to the abundance of hotspots in a watershed, increasing linearly and then reaching an asymptote with increasing hotspot abundance. Peak streamflow N was also sensitive to a soil moisture threshold at which subsurface flow from hotspots reestablished, allowing N to be transferred to streams; it increased and then decreased with an increasing threshold value. Finally, N export was generally higher when water diffused out of hotspots slowly. In a case study, we found that when hotspots were modeled explicitly, peak streamflow nitrate export increased by 29%, enabling us to better capture the timing and magnitude of N losses observed in the field. This modeling framework can improve projections of N export in watersheds where hotspots play an increasingly important role in water quality.

Climate change and nitrogen (N) pollution are altering biogeochemical and ecohydrological processes in dryland watersheds, increasing N export, and threatening water quality. While simulation models are useful for projecting how N export will change in the future, most models ignore biogeochemical “hotspots” that develop in drylands as moist microsites become hydrologically disconnected from plant roots when soils dry out. These hotspots enable N to accumulate over dry periods and rapidly flush to streams when soils wet up. To better project future N export, we developed a framework for representing hotspots using the ecohydrological model RHESSys. We then conducted a series of virtual experiments to understand how uncertainties in model structure and parameters influence N export. Modeled export was sensitive to the abundance of hotspots in a watershed, increasing linearly and then reaching an asymptote with increasing hotspot abundance. Peak streamflow N was also sensitive to a soil moisture threshold at which subsurface flow from hotspots reestablished, allowing N to be transferred to streams; it increased and then decreased with an increasing threshold value. Finally, N export was generally higher when water diffused out of hotspots slowly. In a case study, we found that when hotspots were modeled explicitly, peak streamflow nitrate export increased by 29%, enabling us to better capture the timing and magnitude of N losses observed in the field. This modeling framework can improve projections of N export in watersheds where hotspots play an increasingly important role in water quality.

Fire regimes are influenced by both exogenous drivers (e.g., increases in atmospheric CO2; and climate change) and endogenous drivers (e.g., vegetation and soil/litter moisture), which constrain fuel loads and fuel aridity. Herein, we identified how exogenous and endogenous drivers can interact to affect fuels and fire regimes in a semiarid watershed in the inland northwestern U.S. throughout the 21st century. We used a coupled ecohydrologic and fire regime model to examine how climate change and CO2 scenarios influence fire regimes over space and time. In this semiarid watershed we found that, in the mid-21st century (2040s), the CO2 fertilization effect on vegetation productivity outstripped the effects of climate change-induced fuel decreases, resulting in greater fuel loading and, thus, a net increase in fire size and burn probability; however, by the late-21st century (2070s), climatic warming dominated over CO2 fertilization, thus reducing fuel loading and fire activity. We also found that, under future climate change scenarios, fire regimes will shift progressively from being flammability to fuel-limited, and we identified a metric to quantify this shift: the ratio of the change in fuel loading to the change in its aridity. The threshold value for which this metric indicates a flammability versus fuel-limited regime differed between grasses and woody species but remained stationary over time. Our results suggest that identifying these thresholds in other systems requires narrowing uncertainty in exogenous drivers, such as future precipitation patterns and CO2 effects on vegetation.

Although natural disturbances such as wildfire, extreme weather events, and insect outbreaks play a key role in structuring ecosystems and watersheds worldwide, climate change has intensified many disturbance regimes, which can have compounding negative effects on ecosystem processes and services. Recent studies have highlighted the need to understand whether wildfire increases or decreases after large-scale beetle outbreaks. However, observational studies have produced mixed results. To address this, we applied a coupled ecohydrological-fire regime-beetle effects model (RHESSys-WMFire-Beetle) in a semiarid watershed in the western US. We found that surface fire probability and fire size decreased in the red phase (0-5 years post-outbreak), increased in the gray phase (6-15 years post-outbreak), and depended on mortality level in the old phase (one to several decades post-outbreak). In the gray and old phases, surface fire size and probability did not respond to low levels of beetle-caused mortality (<=20%), increased during medium levels of mortality (>20% and <=50%), and remained elevated but did not change with mortality (during the gray phase) or decreased (during the old phase) when mortality was high (>50%). Wildfire responses also depended on fire regime. In fuel-limited locations, fire typically increased with increasing fuel loads, whereas in fuel-abundant (flammability-limited) systems, fire sometimes decreased due to decreases in fuel aridity. This modeling framework can improve our understanding of the mechanisms driving wildfire responses and aid managers in predicting when and where fire hazards will increase.

The entire western US is in the midst of a megadrought. Combined with high temperatures, increasingly severe droughts are causing widespread forest mortality. In the Sierra Nevada, CA in particular, the Mediterranean climate exposes montane forests to water stress due to the summer drought. Normally, the slow melting of the winter snowpack helps to alleviate summer water stress, especially in riparian ecosystems that benefit from subsurface lateral inputs along a hillslope. However, the loss of the snowpack due to snow drought could potentially eliminate these buffering effects. This research aims to address the role of subsurface lateral redistribution in mediating vegetation responses to drought along a hillslope. We apply a spatially-distributed ecohydrologic model (RHESSys) to an experimental hillslope in a snow-dominated watershed in the Sierra Nevada, CA. We incorporate observed sap flow data from the experimental hillslope to estimate the relative differences in onset of water stress for upslope and riparian sites, which is used to constrain RHESSys drainage parameter uncertainty. Then, we run hypothetical multi-year drought experiments to investigate how climate variability translates to water stress on a hillslope. Our results challenge the common assumption that riparian forests are buffered against drought stress by subsurface lateral inputs. For all drought types, both upslope and riparian sites experience severe losses of net primary productivity (NPP), and on average upslope sites are more adversely affected (upslope loss of NPP = 50% vs. riparian = 35%). But even in a wet year, as temperatures rise and the snowpack disappears (i.e., warm snow drought), vegetation approaches a threshold response that destabilizes the riparian buffering effect. Our results show that for 12% of all scenarios, riparian NPP decreases more than upslope NPP, as a consequence of earlier snowmelt. Interactions between climate variability and ecophysiological uncertainty produce scenarios that exhibit the riparian threshold response. By recognizing the conditions that determine riparian sensitivity to drought, management actions can be proactive in preserving this important hydrological refugia.

Earth system models synthesize the science of interactions among multiple biophysical and, increasingly, human processes across a wide range of scales. Ecohydrologic models are a subset of earth system models that focus particularly on the complex interactions between ecosystem processes and the storage and flux of water. Ecohydrologic models often focus at scales where direct observations occur: plots, hillslopes, streams, and watersheds, as well as where land and resource management decisions are implemented. These models complement field-based and data-driven science by combining theory and data to create virtual laboratories. Ecohydrologic models are tools that managers can use to ask “what if” questions and domain scientists can use to explore the implications of new theory or measurements. Recent decades have seen substantial advances in ecohydrologic models, building on both new domain science and advances in software engineering and data availability. The increasing sophistication of ecohydrologic models however, presents a barrier to their widespread use and credibility. Because they are “black boxes,” what the models actually do is rarely clear—even to those who design and use them—and this opacity leads to mistrust and complicates the interpretation of model results. For models to effectively advance our understanding of how plants and water interact, we must improve how we visualize not only model outputs, but also the underlying theories that are encoded within the models. In this paper, we outline a framework for increasing the usefulness of ecohydrologic models through better visualization. We outline four complementary approaches, ranging from simple best practices that leverage existing technologies, to ideas that would engage novel software engineering and cutting edge human-computer interface design. Our goal is to open the ecohydrologic model black box in ways that will engage multiple audiences, from novices to model developers, and support learning, new discovery, and environmental problem solving.