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Introduction and methods of analysis for peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin
Flood-frequency analysis, also called peak-flow frequency or flood-flow frequency analysis, is essential to water resources management applications including critical structure design and floodplain mapping. Federal guidelines for doing flood-frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C is that for drainage basins without major hydrologic alterations, statistical properties of the distribution of annual peak streamflows (peak flows) are stationary; that is, the mean, variance, and skew are constant. The stationarity assumption has been widely accepted within the flood-frequency community; however, a better understanding of long-term climatic persistence and concerns about potential climate change and land-use change has caused a reexamination of the stationarity assumption. Flood-frequency analyses that do not incorporate observed trends and abrupt changes may result in a poor representation of the true flood risk. Bulletin 17C does not offer guidance for incorporating nonstationarities when estimating floods, and it describes a need for studies that incorporate changing climate or basin characteristics. In response to this need and a history of concern regarding nonstationarity peak flows in the region, this study was done by the U.S. Geological Survey, in cooperation with the Departments of Transportation of Illinois, Iowa, Michigan, Minnesota, Missouri, South Dakota, and Wisconsin; the Montana Department of Natural Resources and Conservation; and the North Dakota Department of Water Resources, to assess potential nonstationarity in peak flows in the north-central United States. This chapter summarizes the methods used to detect hydroclimatic changes in peak-flow data in the study region. A wide range of analyses and statistical approaches are applied to document the primary mechanisms controlling floods and characterize temporal changes in hydroclimatic variables and peak flow. Four periods were selected for analysis of peak flow, daily streamflow, and climate data. The periods are (1) a 100-year period, 1921–2020; (2) a 75-year period, 1946–2020; (3) a 50-year period, 1971–2020; and (4) a 30-year period, 1991–2020. The climate data consist of monthly time series estimates of temperature, precipitation, potential evapotranspiration, actual evapotranspiration, snowfall, soil moisture storage, snow water equivalent, and runoff on a 3.1-mile by 3.1-mile grid for the conterminous United States. Statistical and graphical analyses were used to investigate potential changes in hydrology and climate. The starting point for these analyses was the initial data analysis of peak flow described in Bulletin 17C, which includes plotting the peak flow and checking for autocorrelation, monotonic trends, and changes points. Analyses were added to examine additional features in the data. To examine potential causal drivers of changes, the climate data were analyzed graphically and statistically. Results are provided in a U.S. Geological Survey data release. The study limitations are documented for users of the results.
Applications of a broad-spectrum tool for conservation and fisheries analysis: Aquatic gap analysis
Natural resources support all of our social and economic activities, as well as our biological existence. Humans have little control over most of the physical, biological, and sociological conditions dictating the status and capacity of natural resources in any particular area. However, the most rapid and threatening influences on natural resources typically are anthropogenic overuse and degradation. In addition, living natural resources (i.e., organisms) do not respect political boundaries, but are aware of their optimal habitat and environmental conditions. Most organisms have wider spatial ranges than the jurisdictional boundaries of environmental agencies that deal with them; even within those jurisdictions, information is patchy and disconnected. Planning and projecting effects of ecological management are difficult, because many organisms, habitat conditions, and interactions are involved. Conservation and responsible resource use involves wise management and manipulation of the aspects of the environment and biological communities that can be effectively changed. Tools and data sets that provide new insights and analysis capabilities can enhance the ability of resource managers to make wise decisions and plan effective, long-term management strategies. Aquatic gap analysis has been developed to provide those benefits. Gap analysis is more than just the assessment of the match or mis-match (i.e., gaps) between habitats of ecological value and areas with an appropriate level of environmental protection (e.g., refuges, parks, preserves), as the name suggests. Rather, a Gap Analysis project is a process which leads to an organized database of georeferenced information and previously available tools to examine conservation and other ecological issues; it provides a geographic analysis platform that serves as a foundation for aquatic ecological studies. This analytical tool box allows one to conduct assessments of all habitat elements within an area of interest. Aquatic gap analysis naturally focuses on aquatic habitats. The analytical tools are largely based on specification of the species-habitat relations for the system and organism group of interest (Morrison et al. 2003; McKenna et al. 2006; Steen et al. 2006; Sowa et al. 2007). The Great Lakes Regional Aquatic Gap Analysis (GLGap) project focuses primarily on lotic habitat of the U.S. Great Lakes drainage basin and associated states and has been developed to address fish and fisheries issues. These tools are unique because they allow us to address problems at a range of scales from the region to the stream segment and include the ability to predict species specific occurrence or abundance for most of the fish species in the study area. The results and types of questions that can be addressed provide better global understanding of the ecological context within which specific natural resources fit (e.g., neighboring environments and resources, and large and small scale processes). The geographic analysis platform consists of broad and flexible geospatial tools (and associated data) with many potential applications. The objectives of this article are to provide a brief overview of GLGap methods and analysis tools, and demonstrate conservation and planning applications of those data and tools. Although there are many potential applications, we will highlight just three: (1) support for the Eastern Brook Trout Joint Venture (EBTJV), (2) Aquatic Life classification in Wisconsin, and (3) an educational tool that makes use of Google Earth (use of trade or product names does not imply endorsement by the U.S. Government) and Internet accessibility.
Envisioning the future of wildlife in a changing climate: Collaborative learning for adaptation planning
Natural resource managers are tasked with assessing the impacts of climate change on conservation targets and developing adaptation strategies to meet agency goals. The complex, transboundary nature of climate change demands the collaboration of scientists, managers, and stakeholders in this effort. To share, integrate, and apply knowledge from these diverse perspectives, we must engage in social learning. In 2009, we initiated a process to engage university researchers and agency scientists and managers in collaborative learning to assess the impacts of climate change on terrestrial fauna in the state of Wisconsin, USA. We constructed conceptual Bayesian networks to depict the influence of climate change, key biotic and abiotic factors, and existing stressors on the distribution and abundance of 3 species: greater prairie-chicken ( Tympanuchus cupido ), wood frog ( Lithobates sylvaticus ), and Karner blue butterfly ( Plebejus melissa samuelis ). For each species, we completed a 2-stage expert review that elicited dialogue on information gaps, management opportunities, and research priorities. From our experience, collaborative network modeling proved to be a powerful tool to develop a common vision of the potential impacts of climate change on conservation targets.
Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020
Peak-flow (flood) frequency analysis is essential to water-resources management applications, including the design of critical infrastructure such as bridges and culverts, and floodplain mapping. Federal guidelines for performing peak-flow flood frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C, which documents the guidelines for determining annual peak streamflow frequency, is that, for basins without major hydrologic alterations (for example, regulation, diversion, and urbanization), statistical properties of the distribution of annual peak streamflows are stationary; that is, the mean, variance, and skew are constant through time. Nonstationarity is a statistical property of a peak-flow series such that the long-term (on the order of decades) distributional properties change one or more times either gradually or abruptly through time. Individual nonstationarities may be attributed to one source such as flow regulation, land-use change, or climate but are often the result of a combination of sources, making detection and attribution of nonstationarities challenging. In response to a growing concern regarding nonstationarity in peak streamflows in the region, the U.S. Geological Survey, in cooperation with the Departments of Transportation of Illinois, Iowa, Michigan, Minnesota, Missouri, South Dakota, and Wisconsin; the Montana Department of Natural Resources and Conservation; and the North Dakota Department of Water Resources, assessed the potential nonstationarity in peak streamflows in the north-central United States. This chapter characterizes the effects of natural hydroclimatic shifts and potential climate change on annual peak streamflows in the State of South Dakota. Annual peak and daily streamflow as well as model-simulated gridded climatic data were examined for temporal monotonic trends, change points, and other statistical properties indicative of changing climatic and environmental conditions. Changes in annual peak and daily flows were evaluated among 13, 35, and 81 qualifying U.S. Geological Survey streamgages for the 75-, 50-, and 30-year trend periods through water year 2020 (the period from October 1, 2019, to September 30, 2020) in South Dakota, respectively. No qualifying streamgages were in the 100-year trend period in the State. Statistical tests for autocorrelation (independent and identically distributed assumption), monotonic trends, and change points in the median and scale are analyzed to evaluate potential stationarity violations (nonstationarity) for performing at-site peak-flow flood-frequency analysis. The trends are reported using a likelihood approach as an alternative to simply reporting significant trends with an arbitrary p -value cutoff point. A distinct east-west spatial pattern of likely upward and downward monotonic trends and change points, respectively, was detected in 75- and 50-year trend periods, but an inconsistent spatial pattern was detected in the 30-year trend period. Additionally, change points in the median annual peak streamflows were detected in the late 1970s and early 1980s in the western part of the State, but in the east, the change point was more commonly detected in 1992–93. A similar east-west spatial pattern of likely upward and downward trends was detected in the annual peak-flow timing, the day of the year of the annal peak streamflow. In the western part of the State, the annual peak streamflows are arriving earlier, but in the east, the annual peak streamflows are arriving later. A peaks-over-threshold (POT) analysis where, on average, there are two events per year (POT2) and four events per year (POT4) was also used to evaluate changes in the frequency (count) of daily streamflows exceeding the threshold. Similar to detected changes in the annual peak streamflow, an east-west likely upward or downward change corresponding to an increase or decrease, respectively, in the frequency of daily streamflow greater than a POT2 and POT4 threshold was detected. A monthly water-balance model was used to evaluate hydroclimatic variation in annual and seasonal precipitation, snowfall, potential evapotranspiration, and soil moisture storage for all qualifying streamgages in the 75-, 50-, and 30-year trend periods. Detected trends in the annual hydroclimatic metrics for the 75- and 50-year trend periods indicate a spatially consistent statewide increase in precipitation, decrease in snowfall, increase in potential evapotranspiration, and increase in soil moisture storage. Furthermore, detected trends in seasonal precipitation in the 75- and 50-year trend periods highlight a pronounced change in precipitation in winter and later into the summer season, especially in the 50-year trend period in the eastern part of the State. Statewide increases in seasonal soil moisture storage were also detected, highlighting year-round increasing flood magnitudes, particularly in the eastern part of the State. Based on the results of these stationarity tests for the qualifying streamgages in South Dakota among the 75-, 50-, and 30-year trend periods, consistent temporal and spatial patterns of nonstationarity were detected among the 75- and 50-year trend periods. Furthermore, when nonstationarity is detected in daily streamflow, increased streamflow and volume (increasing frequency in POT), as well as potentially bridge scour, may have implications on culvert and highway design in the eastern part of South Dakota. Thus, when performing at-site peak-flow flood-frequency analyses in South Dakota, potential nonstationarities and alternative approaches are important considerations.
Potential impacts of land-management schedules on grassland bird nests and fledglings
Trade-offs exist between timing of grassland habitat management activities (e.g., weed and brush control) while also ensuring other conservation objectives (e.g., grassland bird reproductive success) are met. On land set aside for grassland conservation (e.g., lands enrolled in the U.S. Department of Agriculture Conservation Reserve Program and some state and federal wildlife management areas), federal and state agencies set time frames for land management based on avoiding disturbance of grassland bird nests. However, recent calls for considering the needs of dependent fledglings in conservation plans imply a need to evaluate whether current time frames are adequate. We used initiation and end dates for 3,257 nests of 8 obligate grassland bird species in Illinois and Wisconsin and a simulation approach for fledgling survival to estimate the potential proportion of active nests at risk when management (e.g., mowing, fire) occurred on the 1st and 15th day of May, June, July, and August during the breeding season. Management during June had the potential to impact 24–61% of nests, depending on the species. Management during July had the potential to impact 4–52% of nests, depending on the species. If disturbance occurred during August, proportionately few active nests were at risk for any species (0–9% of nests on 1 August, 0–<0.5% on 15 August). Potential proportions of fledglings at risk from management were greatest in July for 7 of the 8 species (7–18%). Our study suggests that on lands where a primary conservation objective is to increase or ensure survival of grassland birds, delaying management activities until August could be beneficial for protecting both grassland bird nests and fledglings.
Detecting trends in raptor counts: power and type I error rates of various statistical tests
We conducted simulations that estimated power and type I error rates of statistical tests for detecting trends in raptor population count data collected from a single monitoring site. Results of the simulations were used to help analyze count data of bald eagles (Haliaeetus leucocephalus) from 7 national forests in Michigan, Minnesota, and Wisconsin during 1980-1989. Seven statistical tests were evaluated, including simple linear regression on the log scale and linear regression with a permutation test. Using 1,000 replications each, we simulated n = 10 and n = 50 years of count data and trends ranging from -5 to 5% change/year. We evaluated the tests at 3 critical levels (alpha = 0.01, 0.05, and 0.10) for both upper- and lower-tailed tests. Exponential count data were simulated by adding sampling error with a coefficient of variation of 40% from either a log-normal or autocorrelated log-normal distribution. Not surprisingly, tests performed with 50 years of data were much more powerful than tests with 10 years of data. Positive autocorrelation inflated alpha-levels upward from their nominal levels, making the tests less conservative and more likely to reject the null hypothesis of no trend. Of the tests studied, Cox and Stuart's test and Pollard's test clearly had lower power than the others. Surprisingly, the linear regression t-test, Collins' linear regression permutation test, and the nonparametric Lehmann's and Mann's tests all had similar power in our simulations. Analyses of the count data suggested that bald eagles had increasing trends on at least 2 of the 7 national forests during 1980-1989.