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At least 271 records · Page 15Linked to original sources

USGS2019

Using the precipitation-runoff modeling system to predict seasonal water availability in the upper Klamath River basin, Oregon and California

Accurate forecasts of the streamflow expected during late spring and summer in the Upper Klamath River Basin in southern-central Oregon and northern California are used by water management agencies to balance water allocations for agriculture, aquatic habitat, and hydropower-production needs. Streamflow forecasts are also used by irrigation farmers for planning. The forecasts are typically made twice a month starting as early in the water year as December. Multiple regression equations relating real-time snowpack and precipitation conditions to seasonal streamflow volumes have been used for many years in forecasting. However, with warming temperature trends and lower snowpack, such forecasts based on historical data could become less reliable in the future. If the timing and relation of snowpack and precipitation are outside of the range of the historical data used to create the equations, the forecasts become extrapolations. Statistical forecast equations are also limited in their ability to forecast streamflow in groundwater-dominated basins having inter-annual lag. As an additional method for seasonal streamflow forecasting, a physical-process-based hydrologic model employing the Precipitation-Runoff Modeling System (PRMS) was developed in cooperation with the U.S. Bureau of Reclamation for the Upper Klamath Basin in this study. The model was calibrated for the portion of the basin draining into Upper Klamath Lake. PRMS is a deterministic, distributed-parameter, physical-process-based modeling system developed by the U.S. Geological Survey. It simulates daily streamflow, snow, solar radiation, evapotranspiration, surface-water, and groundwater processes within the basin. A model calibration and validation period for water years 2000–15 and water years 1984–99, respectively, was used. The model was calibrated and validated using measured streamflow, snowpack, evapotranspiration, and solar radiation data sets. Interpolated daily precipitation and air temperature data from 32 meteorological stations within and surrounding the Upper Klamath Basin were used as model input. Performance statistics, used to evaluate how well simulated daily streamflow matched with measured streamflow included percent bias, percent relative error, and root-mean-square error. The statistics were computed annually, monthly, for October–March, and for April–September. With the exception of the October–March period, percent bias statistics were all within plus or minus 5-percent for both the calibration and validation periods. Limitations to using the model are error in the precipitation and air temperature input time series data, which include measurement error and error in the spatial interpolation method. Other errors include measured daily streamflow data, which were adjusted for consumptive use losses to make them more closely resemble natural streamflow for calibration. The model developed for the Upper Klamath Basin can be used to forecast streamflow from the Sprague and Williamson River Basins and inflow to Upper Klamath Lake. Reliable forecasts at these locations are needed for managing water for irrigation, ecosystem health, and power production. Using the models in a forecast application requires assembling model input data sets of anticipated daily precipitation and minimum and maximum air temperature for the period after the date the forecast is made and the end of the forecasted period. These climate data sets can be based on historical or synthetic records, at the discretion of the forecaster. With the Ensemble Streamflow Prediction method, a suite of streamflow scenarios is simulated using multiple years of climate data as model input. The forecasted streamflow is determined from knowing the exceedance probabilities of the simulated streamflows. In this study, the model and the Ensemble Streamflow Prediction method were used to forecast the volume of inflow to Upper Klamath Lake for a 6-month period from April 1, 2015, to September 30, 2015, using a range of climate data sets based on El Niño Southern Oscillation (ENSO) criteria. Because 2015 was a warm phase ENSO period, climate data for 10 warm phase ENSO years from 1980 to 2010 were used as input to the model. The simulated April–September 2015 UKL inflow volume based on measured 2015 climate data was 482,000 acre-feet, which was very close to the 50th percent exceedance probability computed from 10 simulated scenarios that used warm phase ENSO climate input data from 1980–2010.

California, Oregon↗

USGS2013

Simultaneous estimation of local-scale and flow path-scale dual-domain mass transfer parameters using geoelectrical monitoring

Anomalous solute transport, modeled as rate-limited mass transfer, has an observable geoelectrical signature that can be exploited to infer the controlling parameters. Previous experiments indicate the combination of time-lapse geoelectrical and fluid conductivity measurements collected during ionic tracer experiments provides valuable insight into the exchange of solute between mobile and immobile porosity. Here, we use geoelectrical measurements to monitor tracer experiments at a former uranium mill tailings site in Naturita, Colorado. We use nonlinear regression to calibrate dual-domain mass transfer solute-transport models to field data. This method differs from previous approaches by calibrating the model simultaneously to observed fluid conductivity and geoelectrical tracer signals using two parameter scales: effective parameters for the flow path upgradient of the monitoring point and the parameters local to the monitoring point. We use regression statistics to rigorously evaluate the information content and sensitivity of fluid conductivity and geophysical data, demonstrating multiple scales of mass transfer parameters can simultaneously be estimated. Our results show, for the first time, field-scale spatial variability of mass transfer parameters (i.e., exchange-rate coefficient, porosity) between local and upgradient effective parameters; hence our approach provides insight into spatial variability and scaling behavior. Additional synthetic modeling is used to evaluate the scope of applicability of our approach, indicating greater range than earlier work using temporal moments and a Lagrangian-based Damköhler number. The introduced Eulerian-based Damköhler is useful for estimating tracer injection duration needed to evaluate mass transfer exchange rates that range over several orders of magnitude.

Water Resources Research↗

USGS2011

Geochemical and isotopic study of soils and waters from an Italian contaminated site: Agro Aversano (Campania)

Lead isotope applications have been widely used in recent years in environmental studies conducted on different kinds of sampled media. In the present paper, Pb isotope ratios have been used to determine the sources of metal pollution in soils and waters in the Agro Aversano area. During three different sampling phases, a total of 113 surface soils (5-20. cm), 20 samples from 2 soil profiles (0-1. m), 11 stream waters and 4 groundwaters were collected. Major element concentrations in sampled media have been analyzed by the ICP-MS technique. Surface soils (20 samples), all soil profiles and all waters have been also analyzed for Pb isotope compositions by thermal ionization (TIMS). The geochemical data were assessed using statistic methods and cartographically elaborated in order to have a clear picture of the level of disturbance of the area. Pb isotopic data were studied to discriminate between anthropogenic and geologic sources. Our results show that As (5.6-25.6. mg/kg), Cu (9-677. mg/kg), Pb (22-193. mg/kg), Tl (0.53-3.62. mg/kg), V (26-142. mg/kg) and Zn (34-215. mg//kg) contents in analyzed soils, exceed the intervention limits fixed by the Italian Environmental Law for residential areas in some of the sampled sites, while intervention limit for industrial areas is exceeded only for Cu concentrations. Lead isotopic data, show that there is a high similarity between the ratios measured in the leached soil samples and those deriving from anthropic activities. This similarity with anthropogenic Pb is also evident in the ratios measured in both groundwater and stream water samples. ?? 2010 Elsevier B.V.

Journal of Geochemical Exploration↗

USGS2015

Assessing the robustness of quantitative fatty acid signature analysis to assumption violations

Knowledge of animal diets can provide important insights into life history and ecology, relationships among species in a community and potential response to ecosystem change or perturbation. Quantitative fatty acid signature analysis (QFASA) is a method of estimating diets from data on the composition, or signature, of fatty acids stored in adipose tissue. Given data on signatures of potential prey, a predator diet is estimated by minimizing the distance between its signature and a mixture of prey signatures. Calibration coefficients, constants derived from feeding trials, are used to account for differential metabolism of individual fatty acids. QFASA has been widely applied since its introduction and several variants of the original estimator have appeared in the literature. However, work to compare the statistical properties of QFASA estimators has been limited. One important characteristic of an estimator is its robustness to violations of model assumptions. The primary assumptions of QFASA are that prey signature data contain representatives of all prey types consumed and the calibration coefficients are known without error. We investigated the robustness of two QFASA estimators to a range of violations of these assumptions using computer simulation and recorded the resulting bias in diet estimates. We found that the Aitchison distance measure was most robust to errors in the calibration coefficients. Conversely, the Kullback–Leibler distance measure was most robust to the consumption of prey without representation in the prey signature data. In most QFASA applications, investigators will generally have some knowledge of the prey available to predators and be able to assess the completeness of prey signature data and sample additional prey as necessary. Conversely, because calibration coefficients are derived from feeding trials with captive animals and their values may be sensitive to consumer physiology and nutritional status, their applicability to free-ranging animals is difficult to establish. We therefore recommend that investigators first make any improvements to the prey signature data that seem warranted and then base estimation on the Aitchison distance measure, as it appears to minimize risk from violations of the assumption that is most difficult to verify.

Methods in Ecology and Evolution↗

USGS2016

Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites

Evapotranspiration (ET) is an important component of the water cycle – ET from the land surface returns approximately 60% of the global precipitation back to the atmosphere. ET also plays an important role in energy transport among the biosphere, atmosphere, and hydrosphere. Current regional to global and daily to annual ET estimation relies mainly on surface energy balance (SEB) ET models or statistical and empirical methods driven by remote sensing data and various climatological databases. These models have uncertainties due to inevitable input errors, poorly defined parameters, and inadequate model structures. The eddy covariance measurements on water, energy, and carbon fluxes at the AmeriFlux tower sites provide an opportunity to assess the ET modeling uncertainties. In this study, we focused on uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model for ET estimation at multiple AmeriFlux tower sites with diverse land cover characteristics and climatic conditions. The 8-day composite 1-km MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) was used as input land surface temperature for the SSEBop algorithms. The other input data were taken from the AmeriFlux database. Results of statistical analysis indicated that the SSEBop model performed well in estimating ET with an R2 of 0.86 between estimated ET and eddy covariance measurements at 42 AmeriFlux tower sites during 2001–2007. It was encouraging to see that the best performance was observed for croplands, where R2 was 0.92 with a root mean square error of 13 mm/month. The uncertainties or random errors from input variables and parameters of the SSEBop model led to monthly ET estimates with relative errors less than 20% across multiple flux tower sites distributed across different biomes. This uncertainty of the SSEBop model lies within the error range of other SEB models, suggesting systematic error or bias of the SSEBop model is within the normal range. This finding implies that the simplified parameterization of the SSEBop model did not significantly affect the accuracy of the ET estimate while increasing the ease of model setup for operational applications. The sensitivity analysis indicated that the SSEBop model is most sensitive to input variables, land surface temperature (LST) and reference ET ( ET o ); and parameters, differential temperature ( dT ), and maximum ET scalar ( K max ), particularly during the non-growing season and in dry areas. In summary, the uncertainty assessment verifies that the SSEBop model is a reliable and robust method for large-area ET estimation. The SSEBop model estimates can be further improved by reducing errors in two input variables ( ET o and LST) and two key parameters ( K max and dT ).

Journal of Hydrology↗

USGS2016

Detection limits of quantitative and digital PCR assays and their influence in presence-absence surveys of environmental DNA

A set of universal guidelines is needed to determine the limit of detection (LOD) in PCR-based analyses of low concentration DNA. In particular, environmental DNA (eDNA) studies require sensitive and reliable methods to detect rare and cryptic species through shed genetic material in environmental samples. Current strategies for assessing detection limits of eDNA are either too stringent or subjective, possibly resulting in biased estimates of species’ presence. Here, a conservative LOD analysis grounded in analytical chemistry is proposed to correct for overestimated DNA concentrations predominantly caused by the concentration plateau, a nonlinear relationship between expected and measured DNA concentrations. We have used statistical criteria to establish formal mathematical models for both quantitative and droplet digital PCR. To assess the method, a new Grass Carp ( Ctenopharyngodon idella ) TaqMan assay was developed and tested on both PCR platforms using eDNA in water samples. The LOD adjustment reduced Grass Carp occupancy and detection estimates while increasing uncertainty – indicating that caution needs to be applied to eDNA data without LOD correction. Compared to quantitative PCR, digital PCR had higher occurrence estimates due to increased sensitivity and dilution of inhibitors at low concentrations. Without accurate LOD correction, species occurrence and detection probabilities based on eDNA estimates are prone to a source of bias that cannot be reduced by an increase in sample size or PCR replicates. Other applications also could benefit from a standardized LOD such as GMO food analysis, and forensic and clinical diagnostics.

Molecular Ecology Resources↗

USGS2023

Obtaining and applying public data for training students in technical statistical writing: Case studies with data from U.S. Geological Survey and general ecological literature

Effective undergraduate statistical education requires training using real-world data. Textbook datasets seldom match the complexities and messiness of real-world data and finding these datasets can be challenging for educators. Consulting and industrial datasets often have nondisclosure agreements. Academic datasets often require subject area expertise beyond those of a general education or lack connections to real-world applications. Many governments, including the United States, now require the release of data from projects they directly complete or fund though grants and contracts. We show how statistical educators may find datasets and incorporate them into courses. Specifically, we use two examples from the U.S. Geological Survey (USGS) and one example from the ecology literature. We demonstrate the use of these datasets in an upper-level analysis of variance (ANOVA) class. In addition to describing how we found the datasets, we describe how to include them into course work and the course’s student assessments. We have used these datasets over multiple semesters and included student feedback from these courses. Although our examples focus on an ANOVA class, the general methods for finding data shared here could be used for statistical classes ranging from high school to graduate education. Supplementary materials for this article are available online.

Journal of Statistics and Data Science Education↗

USGS2017

Translating statistical species-habitat models to interactive decision support tools

Understanding species-habitat relationships is vital to successful conservation, but the tools used to communicate species-habitat relationships are often poorly suited to the information needs of conservation practitioners. Here we present a novel method for translating a statistical species-habitat model, a regression analysis relating ring-necked pheasant abundance to landcover, into an interactive online tool. The Pheasant Habitat Simulator combines the analytical power of the R programming environment with the user-friendly Shiny web interface to create an online platform in which wildlife professionals can explore the effects of variation in local landcover on relative pheasant habitat suitability within spatial scales relevant to individual wildlife managers. Our tool allows users to virtually manipulate the landcover composition of a simulated space to explore how changes in landcover may affect pheasant relative habitat suitability, and guides users through the economic tradeoffs of landscape changes. We offer suggestions for development of similar interactive applications and demonstrate their potential as innovative science delivery tools for diverse professional and public audiences.

PLoS ONE↗

USGS2012

Interlaboratory comparison of three microbial source tracking quantitative polymerase chain reaction (qPCR) assays from fecal-source and environmental samples

During summer and early fall 2010, 15 river samples and 6 fecal-source samples were collected in West Virginia. These samples were analyzed by three laboratories for three microbial source tracking (MST) markers: AllBac, a general fecal indicator; BacHum, a human-associated fecal indicator; and BoBac, a ruminant-associated fecal indicator. MST markers were analyzed by means of the quantitative polymerase chain reaction (qPCR) method. The aim was to assess interlaboratory precision when the three laboratories used the same MST marker and shared deoxyribonucleic acid (DNA) extracts of the samples, but different equipment, reagents, and analyst experience levels. The term assay refers to both the markers and the procedure differences listed above. Interlaboratory precision was best for all three MST assays when using the geometric mean absolute relative percent difference (ARPD) and Friedman's statistical test as a measure of interlaboratory precision. Adjustment factors (one for each MST assay) were calculated using results from fecal-source samples analyzed by all three laboratories and applied retrospectively to sample concentrations to account for differences in qPCR results among labs using different standards and procedures. Following the application of adjustment factors to qPCR results, ARPDs were lower; however, statistically significant differences between labs were still observed for the BacHum and BoBac assays. This was a small study and two of the MST assays had 52 percent of samples with concentrations at or below the limit of accurate quantification; hence, more testing could be done to determine if the adjustment factors would work better if the majority of sample concentrations were above the quantification limit.

West Virginia↗

USGS2008

Testing small-aperture array analysis on well-located earthquakes, and application to the location of deep tremor

We have here analyzed local and regional earthquakes using array techniques with the double aim of quantifying the errors associated with the estimation of propagation parameters of seismic signals and testing the suitability of a probabilistic location method for the analysis of nonimpulsive signals. We have applied the zero-lag cross-correlation method to earthquakes recorded by three dense arrays in Puget Sound and Vancouver Island to estimate the slowness and back azimuth of direct P waves and S waves. The results are compared with the slowness and back azimuth computed from the source location obtained by the analysis of data recorded by the Pacific Northwest seismic network (PNSN). This comparison has allowed a quantification of the errors associated with the estimation of slowness and back azimuth obtained through the analysis of array data. The statistical analysis gives ??BP = 10?? and ??BS = 8?? as standard deviations for the back azimuth and ??SP = 0.021 sec/km and ??SS = 0.033 sec /km for the slowness results of the P and S phases, respectively. These values are consistent with the theoretical relationship between slowness and back azimuth and their uncertainties. We have tested a probabilistic source location method on the local earthquakes based on the use of the slowness estimated for two or three arrays without taking into account travel-time information. Then we applied the probabilistic method to the deep, nonvolcanic tremor recorded by the arrays during July 2004. The results of the tremor location using the probabilistic method are in good agreement with those obtained by other techniques. The wide depth range, of between 10 and 70 km, and the source migration with time are evident in our results. The method is useful for locating the source of signals characterized by the absence of pickable seismic phases.

Bulletin of the Seismological Society of America↗

USGS1984

Spectral analysis of aeromagnetic profiles for depth estimation principles, software, and practical application

Fourier spectral analysis in recent years has become a widely utilized tool for the processing and interpretation of potential field data. It is particularly well suited to analysis of aeromagnetic maps and profiles, where coverage commonly is of broad scope and statistical treatment is appropriate. The techniques developed by earlier workers for map data are readily adapted for depth estimates using aeromagnetic profiles. Three subroutines are presented: "FRQAN", which employs the complete Fourier transform to convert field intensity to the frequency domain and then computes the logarithmic energy spectrum; "CSIZE", which refines the spectrum to correct for the finite horizontal dimensions of magnetic sources, and "ENSMTH", which smooths the spectrum to clarify its decay characteristics. The average depths to sources of ensembles are obtained by manually fitting a straight line to each linear interval of the logarithmic energy-decay curve. If proper account is taken of the constraints of the method, it is capable of providing depth estimates to within an accuracy of about 10 percent under suitable circumstances. The estimates are unaffected by source magnetization and are relatively insensitive to assumptions as to source shape or distribution. The validity of the method is demonstrated by analyses of synthetic profiles and profiles recorded over Harrat Rahat, Saudi Arabia, and Diyur, Egypt, where source depths have been proved by drilling.

Open-File Report↗

USGS2001

Critical habitat for ovigerous Dungeness crabs

The Dungeness crab, Cancer magister , supports an important fishery in the northeastern Pacific Ocean, yet there is limited knowledge of ovigerous female brooding locations and brooding behavior. Our earlier research suggests that ovigerous crabs aggregate at the same brooding locations for many years. Within these locations, ovigerous females occur in high densities, with the majority of the aggregation buried within the sediment. These locations often have similar water depths and sediment types and appear to be critical for this life history stage. Our study was designed to examine the bathymetric distribution of Dungeness crabs in bays with and without sea otters at eight locations within the Glacier Bay area by conducting transects with a video-equipped manned submersible. Two of the bays investigated contained large aggregations of ovigerous females. At both sites the substrate was composed primarily of sand. However, only a small percentage of the 33 km of transects were classified as sand. These data suggest that sand substrate may be a limiting resource. Since crab brooding aggregations represent a large portion of the crab population within a small area, and because they are a critical component of Dungeness life history, areas with these characteristics need to be investigated, mapped, and protected from development or exploitation. The areas requiring protection from the impact of anthropogenic wastes, fishing, and logging activities could be quite small, thus limiting conflict with alternative users.

Conference Paper↗

USGS2017

Methods for estimating annual exceedance-probability streamflows for streams in Kansas based on data through water year 2015

A study was done by the U.S. Geological Survey in cooperation with the Kansas Department of Transportation and the Federal Emergency Management Agency to develop regression models to estimate peak streamflows of annual exceedance probabilities of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent at ungaged locations in Kansas. Peak streamflow frequency statistics from selected streamgages were related to contributing drainage area and average precipitation using generalized least-squares regression analysis. The peak streamflow statistics were derived from 151 streamgages with at least 25 years of streamflow data through 2015. The developed equations can be used to predict peak streamflow magnitude and frequency within two hydrologic regions that were defined based on the effects of irrigation. The equations developed in this report are applicable to streams in Kansas that are not substantially affected by regulation, surface-water diversions, or urbanization. The equations are intended for use for streams with contributing drainage areas ranging from 0.17 to 14,901 square miles in the nonirrigation effects region and, 1.02 to 3,555 square miles in the irrigation-affected region, corresponding to the range of drainage areas of the streamgages used in the development of the regional equations.

Colorado, Kansas, Missouri, Nebraska, Oklahoma↗

USGS1971

Estimating magnitude and frequency of floods in Wisconsin

This report provides methods for estimating flood characteristics at most sites where flood information may be needed for planning and design and summatizes the significant flood data and related information available on Wisconsin streams. Individual equations are presented for estimating flood discharges for selected recurrence intervals up to a 25-year flood for drainage areas 0.5 square miles and larger, a 50-year flood for drainage areas 20 square miles and larger, and a 100-year flood for drainage areas 50 square miles and larger. A ratio method is used for estimating a 50-year flood for drainage areas 0.5 to 20 square miles. The equations were defined from multiple-regression analysis of flood peak records and basin characteristics for 119 continuous-record gaging stations and 114 crest-stage partial-record stations in Wisconsin and adjoining States. Of the severai basin characteristics used in this study, only drainage area, main-channel slope, lake and marsh area, and areal factors were found to be statistically significant at the 99 percent effectiveness level for all flood frequencies. Solution of a hypothetical problem is given for using the flood-frequency equations. Graphs are presented for solution of flood discharges on regulated streams where the formulas are not applicable. Flood-frequency characteristics, 2-year flood to 100-year flood, and drainage basin characteristics for stations used in the multiple regression are presented in the appendices of this report.

Wisconsin↗

USGS2010

Estimation of distributional parameters for censored trace level water quality data: 2. Verification and applications

Estimates of distributional parameters (mean, standard deviation, median, interquartile range) are often desired for data sets containing censored observations. Eight methods for estimating these parameters have been evaluated by R. J. Gilliom and D. R. Helsel (this issue) using Monte Carlo simulations. To verify those findings, the same methods are now applied to actual water quality data. The best method (lowest root-mean-squared error (rmse)) over all parameters, sample sizes, and censoring levels is log probability regression (LR), the method found best in the Monte Carlo simulations. Best methods for estimating moment or percentile parameters separately are also identical to the simulations. Reliability of these estimates can be expressed as confidence intervals using rmse and bias values taken from the simulation results. Finally, a new simulation study shows that best methods for estimating uncensored sample statistics from censored data sets are identical to those for estimating population parameters. Thus this study and the companion study by Gilliom and Helsel form the basis for making the best possible estimates of either population parameters or sample statistics from censored water quality data, and for assessments of their reliability.

Water Resources Research↗

USGS1992

Robustness of survival estimates for radio-marked animals

Telemetry techniques are often used to study the survival of birds and mammals; particularly whcn mark-recapture approaches are unsuitable. Both parametric and nonparametric methods to estimate survival have becn developed or modified from other applications. An implicit assumption in these approaches is that the probability of re-locating an animal with a functioning transmitter is one. A Monte Carlo study was conducted to determine the bias and variance of the Kaplan-Meier estimator and an estimator based also on the assumption of constant hazard and to eva!uate the performance of the two-sample tests associated with each. Modifications of each estimator which allow a re-Iocation probability of less than one are described and evaluated. Generallv the unmodified estimators were biased but had lower variance. At low sample sizes all estimators performed poorly. Under the null hypothesis, the distribution of all test statistics reasonably approximated the null distribution when survival was low but not when it was high. The power of the two-sample tests were similar.

Biometric Bulletin↗

USGS2016

Bayesian analysis of Jolly-Seber type models

We propose the use of finite mixtures of continuous distributions in modelling the process by which new individuals, that arrive in groups, become part of a wildlife population. We demonstrate this approach using a data set of migrating semipalmated sandpipers ( Calidris pussila ) for which we extend existing stopover models to allow for individuals to have different behaviour in terms of their stopover duration at the site. We demonstrate the use of reversible jump MCMC methods to derive posterior distributions for the model parameters and the models, simultaneously. The algorithm moves between models with different numbers of arrival groups as well as between models with different numbers of behavioural groups. The approach is shown to provide new ecological insights about the stopover behaviour of semipalmated sandpipers but is generally applicable to any population in which animals arrive in groups and potentially exhibit heterogeneity in terms of one or more other processes.

Environmental and Ecological Statistics↗

USGS2026

Potential flood events in the lower Missouri River basin over multiple centuries identified using tree-ring based multi-model streamflow reconstructions

The Missouri River basin (MRB), the largest river basin in the United States, presents major water management challenges due to its complex topology and extensive infrastructure designed to manage high annual flows. Severe hydroclimatic events, particularly floods in 1993, 2011, and 2019, have highlighted vulnerabilities, leading the U.S. Army Corps of Engineers to enhance flood risk assessments in 2023. This study focuses on the full MRB, using tree-ring based streamflow reconstructions to extend the Basin's historic streamflow perspective. We developed annual streamflow reconstructions for the Hermann, Missouri streamgage from water years 1360–1900 using K-nearest neighbor and principal component regression methods. These reconstructions incorporate 183 tree-ring chronologies (1360–2020) and modeled natural streamflow (1901–‍2020). Reconstructions were evaluated for consistency using statistical measures and wavelet analysis. Both methods produced similar results, confirming their reliability in reconstructing multi-century streamflows. This novel methodology, guided by wavelet analysis, was developed to identify potential flood events in the lower Missouri River basin (LMRB), resulting in the identification of 172 potential flood events from water years 1360–2020, with 126 events common to the two versions of the MRB reconstructions. The reconstructed streamflow records showed an increase in the number of events until the late-18th- or mid-19th-century. These findings provide insights into evolving flood characteristics in the LMRB. The novel methodology developed in this study can be used to identify potential flood events from tree-ring based annual streamflow reconstructions and generally be applicable to other river basins.

Colorado, Iowa, Kansas, Missouri, Nebraska, Wyomin↗