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1. Collaborative wildlife–snow science: Integrating wildlife and snow expertise to improve research and management

   https://doi.org/10.1002/ecs2.4094  

   Reinking, Adele_K ; Højlund_Pedersen, Stine ; Elder, Kelly ; Boelman, Natalie_T ; Glass, Thomas_W ; Oates, Brendan_A ; Bergen, Scott ; Roberts, Shane ; Prugh, Laura_R ; Brinkman, Todd_J ; et al ( June 2022 , Ecosphere)

   For wildlife inhabiting snowy environments, snow properties such as onset date, depth, strength, and distribution can influence many aspects of ecology, including movement, community dynamics, energy expenditure, and forage accessibility. As a result, snow plays a considerable role in individual fitness and ultimately population dynamics, and its evaluation is, therefore, important for comprehensive understanding of ecosystem processes in regions experiencing snow. Such understanding, and particularly study of how wildlife–snow relationships may be changing, grows more urgent as winter processes become less predictable and often more extreme under global climate change. However, studying and monitoring wildlife–snow relationships continue to be challenging because characterizing snow, an inherently complex and constantly changing environmental feature, and identifying, accessing, and applying relevant snow information at appropriate spatial and temporal scales, often require a detailed understanding of physical snow science and technologies that typically lie outside the expertise of wildlife researchers and managers. We argue that thoroughly assessing the role of snow in wildlife ecology requires substantive collaboration between researchers with expertise in each of these two fields, leveraging the discipline‐specific knowledge brought by both wildlife and snow professionals. To facilitate this collaboration and encourage more effective exploration of wildlife–snow questions, we provide a five‐step protocol: (1) identify relevant snow property information; (2) specify spatial, temporal, and informational requirements; (3) build the necessary datasets; (4) implement quality control procedures; and (5) incorporate snow information into wildlife analyses. Additionally, we explore the types of snow information that can be used within this collaborative framework. We illustrate, in the context of two examples, field observations, remote‐sensing datasets, and four example modeling tools that simulate spatiotemporal snow property distributions and, in some cases, evolutions. For each type of snow data, we highlight the collaborative opportunities for wildlife and snow professionals when designing snow data collection efforts, processing snow remote sensing products, producing tailored snow datasets, and applying the resulting snow information in wildlife analyses. We seek to provide a clear path for wildlife professionals to address wildlife–snow questions and improve ecological inference by integrating the best available snow science through collaboration with snow professionals.

    

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2. Strength and Memory of Precipitation's Control Over Streamflow Across the Conterminous United States

   https://doi.org/10.1029/2021WR030186  

   Moges, Edom ; Ruddell, Benjamin L. ; Zhang, Liang ; Driscoll, Jessica M. ; Larsen, Laurel G. ( March 2022 , Water Resources Research)

   How precipitation (P) is translated into streamflow (Q) and over what timescales (i.e., “memory”) is difficult to predict without calibration of site‐specific models or using geochemical approaches, posing barriers to prediction in ungauged basins or advancement of general theories. Here, we used a data‐driven approach to identify regional patterns and exogenous controls on P–Q interactions. We applied an information flow analysis, which quantifies uncertainty reduction, to a daily time series of P and Q from 671 watersheds across the conterminous United States. We first demonstrated that information transfer from P to Q primarily reflects the quickflow component of water‐budgets, based on a watershed model. Readily quantifiable information flows show a functional relationship with model parameters, suggesting utility for model calibration. Second, applied to real watersheds, P–Q information flows exhibit seasonally varying behavior within regions in a manner consistent with dominant runoff generation mechanisms. However, the timing and the magnitude of information flows also reflect considerable subregional heterogeneity, likely attributable to differences in watershed size, baseflow contributions, and variation in aerial coverage of preferential flow paths. A regression analysis showed that a combination of climate and watershed characteristics are predictive of P–Q information flows. Though information flows cannot, in most cases, uniquely determine dominant runoff mechanisms, they provide a means to quantify the heterogeneous outcomes of those mechanisms within regions, thereby serving as a benchmarking tool for models developed at the regional scale. Last, information flows characterize regionally specific ways in which catchment connectivity changes from the wet to dry season.

    

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3. Sources of uncertainty in estimating stream solute export from headwater catchments at three sites

   https://doi.org/10.1002/hyp.10265  

   Yanai, Ruth D. ; Tokuchi, Naoko ; Campbell, John L. ; Green, Mark B. ; Matsuzaki, Eiji ; Laseter, Stephanie N. ; Brown, Cindi L. ; Bailey, Amey S. ; Lyons, Pilar ; Levine, Carrie R. ; et al ( August 2014 , Hydrological Processes)

   Uncertainty in the estimation of hydrologic export of solutes has never been fully evaluated at the scale of a small‐watershed ecosystem. We used data from the Gomadansan Experimental Forest, Japan, Hubbard Brook Experimental Forest, USA, and Coweeta Hydrologic Laboratory, USA, to evaluate many sources of uncertainty, including the precision and accuracy of measurements, selection of models, and spatial and temporal variation. Uncertainty in the analysis of stream chemistry samples was generally small but could be large in relative terms for solutes near detection limits, as is common for ammonium and phosphate in forested catchments. Instantaneous flow deviated from the theoretical curve relating height to discharge by up to 10% at Hubbard Brook, but the resulting corrections to the theoretical curve generally amounted to <0.5% of annual flows. Calibrations were limited to low flows; uncertainties at high flows were not evaluated because of the difficulties in performing calibrations during events. However, high flows likely contribute more uncertainty to annual flows because of the greater volume of water that is exported during these events. Uncertainty in catchment area was as much as 5%, based on a comparison of digital elevation maps with ground surveys. Three different interpolation methods are used at the three sites to combine periodic chemistry samples with streamflow to calculate fluxes. The three methods differed by <5% in annual export calculations for calcium, but up to 12% for nitrate exports, when applied to a stream at Hubbard Brook for 1997–2008; nitrate has higher weekly variation at this site. Natural variation was larger than most other sources of uncertainty. Specifically, coefficients of variation across streams or across years, within site, for runoff and weighted annual concentrations of calcium, magnesium, potassium, sodium, sulphate, chloride, and silicate ranged from 5 to 50% and were even higher for nitrate. Uncertainty analysis can be used to guide efforts to improve confidence in estimated stream fluxes and also to optimize design of monitoring programmes. © 2014 The Authors.Hydrological Processespublished John Wiley & Sons, Ltd.

    

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4. Quantifying uncertainty in annual runoff due to missing data

   https://doi.org/10.7717/peerj.9531  

   See, Craig R. ; Green, Mark B. ; Yanai, Ruth D. ; Bailey, Amey S. ; Campbell, John L. ; Hayward, Jeremy ( January 2020 , PeerJ)

   null (Ed.)

   Long-term streamflow datasets inevitably include gaps, which must be filled to allow estimates of runoff and ultimately catchment water budgets. Uncertainty introduced by filling gaps in discharge records is rarely, if ever, reported. We characterized the uncertainty due to streamflow gaps in a reference watershed at the Hubbard Brook Experimental Forest (HBEF) from 1996 to 2009 by simulating artificial gaps of varying duration and flow rate, with the objective of quantifying their contribution to uncertainty in annual streamflow. Gaps were filled using an ensemble of regressions relating discharge from nearby streams, and the predicted flow was compared to the actual flow. Differences between the predicted and actual runoff increased with both gap length and flow rate, averaging 2.8% of the runoff during the gap. At the HBEF, the sum of gaps averaged 22 days per year, with the lowest and highest annual uncertainties due to gaps ranging from 1.5 mm (95% confidence interval surrounding mean runoff) to 21.1 mm. As a percentage of annual runoff, uncertainty due to gap filling ranged from 0.2–2.1%, depending on the year. Uncertainty in annual runoff due to gaps was small at the HBEF, where infilling models are based on multiple similar catchments in close proximity to the catchment of interest. The method demonstrated here can be used to quantify uncertainty due to gaps in any long-term streamflow data set, regardless of the gap-filling model applied. 

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5. Identifying and quantifying sources of temporal and spatial uncertainty in assessing salmonid responses to watershed‐scale restoration

   https://doi.org/10.1002/rra.3956  

   Rogers, Mark ; Selker, John ; Peterson, James ; Arismendi, Ivan ( March 2022 , River Research and Applications)

   Despite significant investments in watershed‐scale restoration projects, evaluation of their impacts on salmonids is often limited by inadequate experimental design. This project aimed to strengthen study designs by identifying and quantifying sources of temporal and spatial uncertainty while assessing population‐level salmonid responses in Before‐After‐Control‐Impact (BACI) restoration experiments. To evaluate sources of temporal uncertainty, meta‐analysis of 32 annual BACI experiments from the Pacific Northwest, USA was conducted. Experimental error was determined to be a function of the total temporal variation of both restoration and control salmonid population metrics and the degree of covariation, or synchrony, between these metrics (r² = 1). However, synchrony was both weak ( = 0.18) and unrelated to experimental error (r = 0.01) while temporal variability was found to account for 91% of this error. Because synchrony did not reduce experimental error, we conclude that BACI designs will not normally exhibit greater power over uncontrolled Before‐After (BA) designs. To evaluate spatial uncertainty, hierarchical BACI designs were simulated. It was found that spatial variability of hypothetical steelhead (Oncorhynchus mykiss) growth values within watersheds can cause mis‐estimation of the restoration effect and reduce power. While hierarchical BACI designs can examine both reach and watershed‐scale restoration effects simultaneously, due to probable mis‐estimation of the restoration effect size, these scales should be examined separately. Paired‐reach designs such as Extensive Post‐Treatment (EPT) provide powerful replicated local‐scale restoration experiments, which can build understanding of restoration‐ecological mechanisms. Knowledge gained from reach‐scale experiments should then be implemented on watershed‐scales and monitored within a non‐hierarchical framework.

    

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