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Creators/Authors contains: "Ren, Jianning"

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  1. Abstract

    Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.

     
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  2. Abstract

    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 in the soil 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 to streams. Modeled N export was sensitive to three major factors (a) the abundance of hotspots in a watershed: N export increased linearly and then reached an asymptote with increasing hotspot abundance; this occurred because carbon and N inputs eventually became limiting as hotspots displaced vegetation cover, (b) the soil moisture threshold required for subsurface flow from hotspots to reestablish: peak streamflow N export increased and then decreased with an increasing threshold due to tradeoffs between N accumulation and export that occur with increasingly disconnected hotspots, and (c) the rate at which water diffused out of hotspots as soils dried down: N export was generally higher when the rate was slow because more N could accumulate in hotspots over dry periods, and then be flushed more rapidly to streams at the onset of rain. 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. N export further increased in response to interannual precipitation variability, particularly when multiple dry years were followed by a wet year. This modeling framework can improve projections of N export in watersheds where hotspots play an increasingly important role in water quality.

     
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  3. Abstract. Mountain pine beetle (MPB) outbreaks in the western United States result inwidespread tree mortality, transforming forest structure within watersheds.While there is evidence that these changes can alter the timing and quantity of streamflow, there is substantial variation in both the magnitude and direction of hydrologic responses, and the climatic and environmental mechanisms driving this variation are not well understood. Herein, we coupled an eco-hydrologic model (RHESSys) with a beetle effects model and applied it to a semiarid watershed, Trail Creek, in the Bigwood River basin in central Idaho, USA, to examine how varying degrees of beetle-caused tree mortality influence water yield. Simulation results show that water yield during the first 15 years after beetle outbreak is controlled by interactions between interannual climate variability, the extent of vegetation mortality, and long-term aridity. During wet years, water yield after a beetle outbreak increased with greater tree mortality; this was driven by mortality-caused decreases in evapotranspiration. During dry years, water yield decreased at low-to-medium mortality but increased at high mortality. The mortality threshold for the direction of change was location specific. The change in water yield also varied spatially along aridity gradients during dry years. In wetter areas of the Trail Creek basin, post-outbreak water yield decreased at low mortality (driven by an increase in ground evaporation) and increased when vegetation mortality was greater than 40 % (driven by a decrease in canopy evaporation and transpiration). In contrast, in more water-limited areas, water yield typically decreased after beetle outbreaks, regardless of mortality level (although the driving mechanisms varied). Our findings highlight the complexity and variability of hydrologic responses and suggest that long-term (i.e., multi-decadal mean) aridity can be a useful indicator for the direction of water yield changes after a disturbance. 
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  4. Abstract

    Extreme wildfires are increasing in frequency globally, prompting new efforts to mitigate risk. The ecological appropriateness of risk mitigation strategies, however, depends on what factors are driving these increases. While regional syntheses attribute increases in fire activity to both climate change and fuel accumulation through fire exclusion, they have not disaggregated causal drivers at scales where land management is implemented. Recent advances in fire regime modeling can help us understand which drivers dominate at management-relevant scales. We conducted fire regime simulations using historical climate and fire exclusion scenarios across two watersheds in the Inland Northwestern U.S., which occur at different positions along an aridity continuum. In one watershed, climate change was the key driver increasing burn probability and the frequency of large fires; in the other, fire exclusion dominated in some locations. We also demonstrate that some areas become more fuel-limited as fire-season aridity increases due to climate change. Thus, even within watersheds, fuel management must be spatially and temporally explicit to optimize effectiveness. To guide management, we show that spatial estimates of soil aridity (or temporally averaged soil moisture) can provide a relatively simple, first-order indicator of where in a watershed fire regime is climate vs. fuel-limited and where fire regimes are most vulnerable to change.

     
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  5. Abstract

    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 ecohydrologic‐fire regime‐beetle effects model (RHESSys‐WMFire‐Beetle) in a semiarid watershed in the western US. We found that in the red phase (0–5 years post‐outbreak), surface fire extent, burn probability, and surface and crown fire severity all decreased. In the gray phase (6–15 years post‐outbreak), both surface fire extent and surface and crown fire severity increased with increasing mortality. However, fire probability reached a plateau during high mortality levels (>50% in terms of carbon removed). In the old phase (one to several decades post‐outbreak), fire extent and severity still increased in all mortality levels. However, fire probability increased during low to medium mortality (≤50%) but decreased during high mortality levels (>50%). Wildfire responses also depended on the fire regime. In fuel‐limited locations, fire probability increased with increasing fuel loads, whereas in fuel‐abundant (flammability‐limited) systems, fire probability decreased due to decreases in fuel aridity from reduced plant water demand. 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.

     
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  6. Abstract

    Climate change has lengthened wildfire seasons and transformed fire regimes throughout the world. Thus, capturing fuel and fire dynamics is critical for projecting Earth system processes in warmer and drier future. Recent advances in fire regime modeling have linked land surface models with fire behavior models. Such models often rely on fine surface fuels to drive fire behavior and effects, and while many models can simulate processes that control how these fuels change through time (i.e., fine fuel accumulation), fuel loading estimates remain highly uncertain, largely due to uncertainties in the algorithms controlling decomposition. Uncertainties are often amplified in climate change forecasts when initial conditions and feedbacks are not well represented. The goal of this review is to highlight fine fuel decomposition as a key uncertainty in model systems. We review the current understanding of mechanisms controlling decomposition, describe how they are incorporated into models, and evaluate the uncertainties associated with different approaches. We also use three state‐of‐the‐art land surface fire regime models to demonstrate the sensitivity of decomposition and subsequent wildfire projections to both parameter and model structure uncertainty and show that sensitivity can increase substantially under future climate warming. Given that many of the governing decomposition equations are based on individual case studies from a single location, and because key parameters are often hard coded, critical uncertainties are currently ignored. It is essential to be transparent about these uncertainties as the domain of land surface models is expanded to include the evaluation of future wildfire regimes.

     
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  7. Abstract

    Fire regimes are influenced by both exogenous drivers (e.g., increases in atmospheric CO2and 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 United States throughout the 21st century. We used a coupled ecohydrologic and fire regime model to examine how climate change and CO2scenarios influence fire regimes. In this semiarid watershed, we found an increase in burned area and burn probability in the mid‐21st century (2040s) as the CO2fertilization effect on vegetation productivity outstripped the effects of climate change‐induced fuel decreases, resulting in greater fuel loading. However, by the late‐21st century (2070s), climatic warming dominated over CO2fertilization, thus reducing fuel loading and burned area. Fire regimes were shown to shift from flammability‐ to fuel‐limited or become increasingly fuel‐limited in response to climate change. We identified a metric to identify when fire regimes shift from flammability‐ to fuel‐limited: 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 CO2effects on vegetation.

     
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