Abstract:Land‐use and climate change are significantly affecting stream ecosystems, yet understanding of their long‐term impacts is hindered by the few studies that have simultaneously investigated their interaction and high variability among future projections. We modeled possible effects of a suite of 2030, 2060, and 2090 land‐use and climate scenarios on the condition of 70,772 small streams in the Chesapeake Bay watershed, United States. The Chesapeake Basin‐wide Index of Biotic Integrity, a benthic macroinvertebra… Show more
“…The quadrant and individual-projection approaches capture uncertainty stemming from GCMs and RCPs without differentiating between the two sources, which is useful for the scenario planning context focused on capturing the broadest range of plausible conditions. Treating this uncertainty together has precedent in the literature (Battaglin et al 2020;BOR 2014;Hay and McCabe 2019;Maloney et al 2020).…”
Scenario planning has emerged as a widely used planning process for resource management in situations of consequential, irreducible uncertainty. Because it explicitly incorporates uncertainty, scenario planning is regularly employed in climate change adaptation. An early and essential step in developing scenarios is identifying “climate futures”—descriptions of the physical attributes of plausible future climates that could occur at a specific place and time. Divergent climate futures that describe the broadest possible range of plausible conditions support information needs of decision makers, including understanding the spectrum of potential resource responses to climate change, developing strategies robust to that range, avoiding highly consequential surprises, and averting maladaptation. Here, we discuss three approaches for generating climate futures: a Representative Concentration Pathway (RCP)-ensemble, a quadrant-average, and an individual-projection approach. All are designed to capture relevant uncertainty, but they differ in utility for different applications, complexity, and effort required to implement. Using an application from Big Bend National Park as an example of numerous similar efforts to develop climate futures for National Park Service applications over the past decade, we compare these approaches, focusing on their ability to capture among-projection divergence during early-, mid-, and late-twenty-first century periods to align with near-, mid-, and long-term planning efforts. The quadrant-average approach and especially the individual-projection approach captured a broader range of plausible future conditions than the RCP-ensemble approach, particularly in the near term. Therefore, the individual-projection approach supports decision makers seeking to understand the broadest potential characterization of future conditions. We discuss tradeoffs associated with different climate future approaches and highlight suitable applications.
“…The quadrant and individual-projection approaches capture uncertainty stemming from GCMs and RCPs without differentiating between the two sources, which is useful for the scenario planning context focused on capturing the broadest range of plausible conditions. Treating this uncertainty together has precedent in the literature (Battaglin et al 2020;BOR 2014;Hay and McCabe 2019;Maloney et al 2020).…”
Scenario planning has emerged as a widely used planning process for resource management in situations of consequential, irreducible uncertainty. Because it explicitly incorporates uncertainty, scenario planning is regularly employed in climate change adaptation. An early and essential step in developing scenarios is identifying “climate futures”—descriptions of the physical attributes of plausible future climates that could occur at a specific place and time. Divergent climate futures that describe the broadest possible range of plausible conditions support information needs of decision makers, including understanding the spectrum of potential resource responses to climate change, developing strategies robust to that range, avoiding highly consequential surprises, and averting maladaptation. Here, we discuss three approaches for generating climate futures: a Representative Concentration Pathway (RCP)-ensemble, a quadrant-average, and an individual-projection approach. All are designed to capture relevant uncertainty, but they differ in utility for different applications, complexity, and effort required to implement. Using an application from Big Bend National Park as an example of numerous similar efforts to develop climate futures for National Park Service applications over the past decade, we compare these approaches, focusing on their ability to capture among-projection divergence during early-, mid-, and late-twenty-first century periods to align with near-, mid-, and long-term planning efforts. The quadrant-average approach and especially the individual-projection approach captured a broader range of plausible future conditions than the RCP-ensemble approach, particularly in the near term. Therefore, the individual-projection approach supports decision makers seeking to understand the broadest potential characterization of future conditions. We discuss tradeoffs associated with different climate future approaches and highlight suitable applications.
“…We had no a priori reason to select any bioregion as a baseline and thus used Blue Ridge (BR) as the default (alphabetically first) contrast for Bioregion in the model. Selected landscape predictors were identified in previous studies to be important drivers of stream condition (Hill et al 2017 ) and the Chessie BIBI (Maloney et al 2018 ; Maloney et al 2020 ). Exploratory model testing found that incorporating nonlinear effects did not improve the ability to accurately predict Chessie BIBI scores in a validation dataset (Table S3 ); therefore, we built a single logistic regression model that included only linear effects (glm function in R with a binomial logit link function).…”
Section: Methodsmentioning
confidence: 99%
“…We used this dataset to examine the likelihood of a degraded stream condition in a flow-altered site for both the observed (Eng et al 2019 ) and our modeled estimates of flow-alteration class (altered, unaltered) for each of the 12 HMs using contingency tables (flow-alteration class × stream condition) and Fisher’s Exact Tests for significant odds ratios as specified above. Limited sample size and lack of samples in each bioregion (Table S4 ), given its importance in previous studies (Maloney et al 2018 , 2020 ), precluded use of a logistic regression model. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Flow drives multiple in-stream physical, chemical, and thermal variables (Power et al 1995 ; Poff et al 1997 ; Bunn and Arthington 2002 ) that together comprise the habitat template. Understanding how altered flows affect these habitat variables and biological communities has been a focus of study for many years (Poff and Zimmerman 2010 ) and is crucial for improving conservation and restoration of these systems, especially given projected future changes to landscapes and climate and their potential effects on stream systems (Pyne and Poff 2017 ; Maloney et al 2020 ). However, identifying an effect of altered flows on aquatic communities is complicated by the myriad of factors simultaneously influencing these communities, often through nonlinear and interactive pathways (Matthaei et al 2010 ; Buchanan et al 2013 ; Schmidt et al 2019 ).…”
Regionally scaled assessments of hydrologic alteration for small streams and its effects on freshwater taxa are often inhibited by a low number of stream gages. To overcome this limitation, we paired modeled estimates of hydrologic alteration to a benthic macroinvertebrate index of biotic integrity data for 4522 stream reaches across the Chesapeake Bay watershed. Using separate random-forest models, we predicted flow status (inflated, diminished, or indeterminant) for 12 published hydrologic metrics (HMs) that characterize the main components of flow regimes. We used these models to predict each HM status for each stream reach in the watershed, and linked predictions to macroinvertebrate condition samples collected from streams with drainage areas less than 200 km2. Flow alteration was calculated as the number of HMs with inflated or diminished status and ranged from 0 (no HM inflated or diminished) to 12 (all 12 HMs inflated or diminished). When focused solely on the stream condition and flow-alteration relationship, degraded macroinvertebrate condition was, depending on the number of HMs used, 3.8–4.7 times more likely in a flow-altered site; this likelihood was over twofold higher in the urban-focused dataset (8.7–10.8), and was never significant in the agriculture-focused dataset. Logistic regression analysis using the entire dataset showed for every unit increase in flow-alteration intensity, the odds of a degraded condition increased 3.7%. Our results provide an indication of whether altered streamflow is a possible driver of degraded biological conditions, information that could help managers prioritize management actions and lead to more effective restoration efforts.
“…To predict the flow metrics for the end‐of‐century time period, the random forest model was applied with the same static predictors, but with the end‐of‐century precipitation data for the wet, dry and moderate periods. Some static variables like land cover change can impact stream habitat (Maloney et al, 2020); however, because our region includes only the unaltered watersheds predominantly in mountainous areas, many of which are protected, we do not anticipate substantial urbanization in future years. All processing for the streamflow metrics was completed in R (R Core Team, 2018).…”
Climate change will alter stream habitats through precipitation and air temperature changes and potentially threaten species that rely on contemporary streamflow and stream temperature regimes. Habitat projections are therefore critical to inform management decisions. Past and ongoing research has improved streamflow and temperature modelling in ungauged regions, but no studies merge these advancements with climate modelling for regional streamflow and stream temperatures predictions that describe stream habitat change. Here, we predict change in streamflow and stream temperature at the reach scale using projections from downscaled global climate models (GCMs) and the ‘business as usual’ carbon emission scenario. We focus on unaltered streams in six southern California watersheds using data from baseline (1982–2014) and projected end‐of‐century (2082–2100). Stream temperature is projected to increase regionally, with high‐elevation stream reaches increasing most rapidly. There is less consistency in the streamflow projections, but a spatial and temporal homogenization of stream flow characteristics was predicted, that is, flows become more similar across the region with less inter‐annual variation. Additionally, there is a regional trend towards larger high flow magnitudes and more storm events. Despite the increased frequency and magnitude of storm events, high‐elevation streams are predicted to become drier for a greater portion of the year. Conversely, low‐elevation streams are predicted to have longer hydroperiods. Mapping future streamflow and stream temperatures at the reach scale can direct conservation efforts to streams that remain suitable, restoration to areas that decrease in suitability for target species, and support water policies that consider future stream condition.
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