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At least 199 records · Page 11Linked to original sources
USGS2019

Updates to the Madison Lake (Minnesota) CE–QUAL–W2 water-quality model for assessing algal community dynamics

A previously developed CE–QUAL–W2 model for Madison Lake, Minnesota, simulated the algal community dynamics, water quality, and fish habitat suitability of Madison Lake under recent (2014) meteorological conditions. Additionally, this previously developed model simulated the complex interplay between external nutrient loading, internal nutrient loading from sediment release of phosphorus, and the organic matter decomposition of the algal biomass. However, the partitioning of Cyanophyta within the modeling framework was simplified to one group and did not account for how different Cyanophyta populations are affected by light conditions, use of nitrogen, temperature growth ranges, and differences in settling rates. Properly capturing Cyanophyta dynamics is important given the potential risks posed by potential large algal blooms. For example, when Cyanophyta form large blooms, recreational activities can become restricted in certain areas because of thick algal scums or algal mats, in addition to the possible production of a class of toxins, known as cyanotoxins, capable of threatening human health, domestic animals, and wildlife. Therefore, we updated the model to partition the Cyanophyta into a group that fixed nitrogen and a second, more buoyant Cyanophyta group that did not independently fix nitrogen. The U.S. Geological Survey, in cooperation with the St. Croix Watershed Research Station (Science Museum of Minnesota) with support from the Environmental and Natural Resources Trust Fund of Minnesota (Legislative-Citizen Commission on Minnesota Resources), updated the Madison Lake CE–QUAL–W2 model to address the shortcomings of simulating Cyanophyta in the previously developed model and better characterize Cyanophyta into two groups. In addition to updating the Cyanophyta group differentiation, the part of the model that handles the simulation of algal community dynamics was updated while preserving model predictive capabilities for nutrients, water temperature, and dissolved oxygen. The calibration and validation of the model was done under recent meteorological conditions with large and persistent Cyanophyta blooms (2014 and 2016). Overall, the model simulations predicted the persistently large total phosphorus concentrations in the hypolimnion of Madison Lake and key differences in nutrient concentrations between 2014 and 2016. The Cyanophyta bloom persistence throughout the summer was also simulated by the model in 2014 and 2016, a critical goal of the model update. Finally, monthly total phosphorus budgets were calculated for the updated Madison Lake model for 2014 and 2016.

Minnesota↗
USGS2007

Facing tomorrow’s challenges—U.S. Geological Survey science in the decade 2007–2017

Executive Summary In order for the U.S. Geological Survey (USGS) to respond to evolving national and global priorities, it must periodically reflect on, and optimize, its strategic directions. This report is the first comprehensive science strategy since the early 1990s to examine critically major USGS science goals and priorities. The development of this science strategy comes at a time of global trends and rapidly evolving societal needs that pose important natural-science challenges. The emergence of a global economy affects the demand for all resources. The last decade has witnessed the emergence of a new model for managing Federal lands—ecosystem-based management. The U.S. Climate Change Science Program predicts that the next few decades will see rapid changes in the Nation’s and the Earth’s environment. Finally, the natural environment continues to pose risks to society in the form of volcanoes, earthquakes, wildland fires, floods, droughts, invasive species, variable and changing climate, and natural and anthropogenic toxins, as well as animal-borne diseases that affect humans. The use of, and competition for, natural resources on the global scale, and natural threats to those resources, has the potential to impact the Nation’s ability to sustain its economy, national security, quality of life, and natural environment. Responding to these national priorities and global trends requires a science strategy that not only builds on existing USGS strengths and partnerships but also demands the innovation made possible by integrating the full breadth and depth of USGS capabilities. The USGS chooses to go forward in the science directions proposed here because the societal issues addressed by these science directions represent major challenges for the Nation’s future and for the stewards of Federal lands, both onshore and offshore. The six science directions proposed in this science strategy are summarized in the following paragraphs. The ecosystems strategy is listed first because it has a dual nature. It is itself an essential direction for the USGS to pursue to meet a pressing national and global need, but ecosystem-based approaches are also an underpinning of the other five directions, which all require ecosystem perspectives and tools for their execution. The remaining strategic directions are listed in alphabetical order.

Circular↗
USGS2012

Quantification of aquifer properties with surface nuclear magnetic resonance in the Platte River valley, central Nebraska, using a novel inversion method

Surface nuclear magnetic resonance, a noninvasive geophysical method, measures a signal directly related to the amount of water in the subsurface. This allows for low-cost quantitative estimates of hydraulic parameters. In practice, however, additional factors influence the signal, complicating interpretation. The U.S. Geological Survey, in cooperation with the Central Platte Natural Resources District, evaluated whether hydraulic parameters derived from surface nuclear magnetic resonance data could provide valuable input into groundwater models used for evaluating water-management practices. Two calibration sites in Dawson County, Nebraska, were chosen based on previous detailed hydrogeologic and geophysical investigations. At both sites, surface nuclear magnetic resonance data were collected, and derived parameters were compared with results from four constant-discharge aquifer tests previously conducted at those same sites. Additionally, borehole electromagnetic-induction flowmeter data were analyzed as a less-expensive surrogate for traditional aquifer tests. Building on recent work, a novel surface nuclear magnetic resonance modeling and inversion method was developed that incorporates electrical conductivity and effects due to magnetic-field inhomogeneities, both of which can have a substantial impact on the data. After comparing surface nuclear magnetic resonance inversions at the two calibration sites, the nuclear magnetic-resonance-derived parameters were compared with previously performed aquifer tests in the Central Platte Natural Resources District. This comparison served as a blind test for the developed method. The nuclear magnetic-resonance-derived aquifer parameters were in agreement with results of aquifer tests where the environmental noise allowed data collection and the aquifer test zones overlapped with the surface nuclear magnetic resonance testing. In some cases, the previously performed aquifer tests were not designed fully to characterize the aquifer, and the surface nuclear magnetic resonance was able to provide missing data. In favorable locations, surface nuclear magnetic resonance is able to provide valuable noninvasive information about aquifer parameters and should be a useful tool for groundwater managers in Nebraska.

Nebraska↗
USGS1982

Optimal stochastic control in natural resource management: Framework and examples

A framework is presented for the application of optimal control methods to natural resource problems. An expression of the optimal control problem appropriate for renewable natural resources is given and its application to Markovian systems is presented in some detail. Three general approaches are outlined for determining optimal control of infinite time horizon systems and three examples from the natural resource literature are used for illustration.

Ecological Modelling↗
USGS2020

Drivers and consequences of alternative landscape futures on wildlife distributions in New England, United States

In an era of rapid climate and land transformation, it is increasingly important to understand how future changes impact natural systems. Scenario studies can offer the structure and perspective needed to understand the impacts of change and help inform management and conservation decisions. We implemented a scenario-based approach to assess how two high impact drivers of landscape change influence the distributions of managed wildlife species (n = 10) in the New England region of the northeastern United States. We used expert derived species distribution models (SDMs) and scenarios developed by the New England Landscape Futures Project (NELFP) to estimate how species distributions change under various trajectories (n = 5) of landscape change. The NELFP scenarios were built around two primary drivers – Socio-Economic Connectedness (SEC) and Natural Resource Planning and Innovation (NRPI) – and provide plausible alternatives for how the New England region may change over fifty years (2010 to 2060). Our models generally resulted in species occurrence and richness declines by 2060. The majority of species (7 of 10) experienced declines in regional occurrence for all NELFP scenarios, and one species experienced a projected increase in mean regional occurrence for all scenarios. Our results indicate that the NRPI and SEC drivers strongly influenced projected distribution changes compared to baseline projections. NRPI had a greater impact on distribution change for five species (coyote, moose, striped skunk, white-tailed deer, and wild turkey), while SEC had a greater impact on four species (American black bear, bobcat, raccoon, and red fox); one species (gray fox) was equally influenced by both NRPI and SEC. These results emphasize the importance of integrating both natural resource planning and socio-economic factors when addressing issues of distribution change and offer insights that can inform proactive management and conservation planning.

Connecticut, Maine, Massachusetts, New Hampshire, ↗
USGS2022

Multi-objective modeling as a decision-support tool for free-roaming horse management

Decisions related to controversial problems in natural resource management receive the greatest support when they account for multiple objectives of stakeholders in a structured and transparent fashion. In the United States, management of free-roaming horses ( Equus caballus ; horses) is a controversial multiple-objective problem because disparate stakeholder groups have varying objectives and opinions about how to manage fast-growing horse populations in ways that sustain both natural ecosystems and healthy horses. Despite much decision-support research on management alternatives that prevent excessive population size or cost, horse management decisions still receive resistance from a variety of stakeholder groups, potentially because decisions fail to explicitly or transparently account for multiple objectives of diverse stakeholders. Here, we used a predictive model for horse populations to evaluate the degree to which alternative management strategies involving removals and fertility control treatment with the immunocontraceptive vaccine PZP-22 maximize 4 objectives in horse management: maximize ecosystem health, maximize horse health, minimize effects on horse behavior, and minimize management cost. We simulated scenarios varying in management action, frequency, magnitude, and starting population size over a 10-year interval and evaluated scenario performance with a weighted multiple-objective utility reward function. Management involving high-magnitude removals along with PZP-22 treatment generally outperformed other alternatives by achieving higher reward relative to alternatives in 2 scenario analyses. Simulation of 1,372 scenarios at 5 starting population sizes generally found that management with biannual removals and 2 doses of PZP-22 treatment for half of eligible females during years 1 and 5 generated the most rewarding outcomes. However, a removal scenario with more frequent PZP-22 application generated the greatest reward when starting population size was already within target population size range. Our paper demonstrates how values and objectives of diverse stakeholders can be used to support management decisions in ways that might lead to greater acceptance of decisions by a broad array of stakeholder groups.

Human-Wildlife Interactions↗
USGS2015

MMI: Multimodel inference or models with management implications?

We consider a variety of regression modeling strategies for analyzing observational data associated with typical wildlife studies, including all subsets and stepwise regression, a single full model, and Akaike's Information Criterion (AIC)-based multimodel inference. Although there are advantages and disadvantages to each approach, we suggest that there is no unique best way to analyze data. Further, we argue that, although multimodel inference can be useful in natural resource management, the importance of considering causality and accurately estimating effect sizes is greater than simply considering a variety of models. Determining causation is far more valuable than simply indicating how the response variable and explanatory variables covaried within a data set, especially when the data set did not arise from a controlled experiment. Understanding the causal mechanism will provide much better predictions beyond the range of data observed. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Journal of Wildlife Management↗
USGS2022

Economical defence of resources structures territorial space use in a cooperative carnivore

Ecologists have long sought to understand space use and mechanisms underlying patterns observed in nature. We developed an optimality landscape and mechanistic territory model to understand mechanisms driving space use and compared model predictions to empirical reality. We demonstrate our approach using grey wolves ( Canis lupus ). In the model, simulated animals selected territories to economically acquire resources by selecting patches with greatest value, accounting for benefits, costs and trade-offs of defending and using space on the optimality landscape. Our approach successfully predicted and explained first- and second-order space use of wolves, including the population's distribution, territories of individual packs, and influences of prey density, competitor density, human-caused mortality risk and seasonality. It accomplished this using simple behavioural rules and limited data to inform the optimality landscape. Results contribute evidence that economical territory selection is a mechanistic bridge between space use and animal distribution on the landscape. This approach and resulting gains in knowledge enable predicting effects of a wide range of environmental conditions, contributing to both basic ecological understanding of natural systems and conservation. We expect this approach will demonstrate applicability across diverse habitats and species, and that its foundation can help continue to advance understanding of spatial behaviour.

Montana↗
USGS2023

Nest-site selection model for endangered Everglade snail kites to inform ecosystem restoration

dictors of nesting for snail kites in south Florida. The results of our modeling indicate that hydrology, percent canopy cover, and proximity to recently burned areas were the most important factors associated with nest-site selection for snail kites. Water depths between 75 and 100 cm, water recession rates between 0 and 1.25 cm/day, percent canopy covers <20%, and areas <10 km from recently burned habitat were associated with the greatest likelihood of nest-site selection. KiteNest is applicable to natural resource management decisions in the Everglades and may be useful independently or in conjunction with other ecological models for restoration decision support.

Florida↗
USGS2015

Integrated Environmental Modelling: Human decisions, human challenges

Integrated Environmental Modelling (IEM) is an invaluable tool for understanding the complex, dynamic ecosystems that house our natural resources and control our environments. Human behaviour affects the ways in which the science of IEM is assembled and used for meaningful societal applications. In particular, human biases and heuristics reflect adaptation and experiential learning to issues with frequent, sharply distinguished, feedbacks. Unfortunately, human behaviour is not adapted to the more diffusely experienced problems that IEM typically seeks to address. Twelve biases are identified that affect IEM (and science in general). These biases are supported by personal observations and by the findings of behavioural scientists. A process for critical analysis is proposed that addresses some human challenges of IEM and solicits explicit description of (1) represented processes and information, (2) unrepresented processes and information, and (3) accounting for, and cognizance of, potential human biases. Several other suggestions are also made that generally complement maintaining attitudes of watchful humility, open-mindedness, honesty and transparent accountability. These suggestions include (1) creating a new area of study in the behavioural biogeosciences, (2) using structured processes for engaging the modelling and stakeholder communities in IEM, and (3) using &lsquo;red teams&rsquo; to increase resilience of IEM constructs and use.

Geological Society of London Special Publications↗
USGS2020

Sustaining Environmental Capital Initiative summary report

Federal agencies need credible scientific information to determine the production and value of ecosystem services in an efficient and timely manner. The U.S. Geological Survey addresses this scientific information need through the Sustaining Environmental Capital Initiative project. The project has relied on U.S. Geological Survey expertise related to water, fisheries, advanced modeling, and economics and other social sciences to conduct eight case studies across a range of environment types, including water-based environments, deserts, sagebrush ecosystems, floodplains, and forests. The Sustaining Environmental Capital Initiative also supported the development and expansion of four tools with the intent of adding content and usability for partners’ decision-making needs. The tools are the Natural Value Resource Center, Benefit Transfer Toolkit, Riverine Environmental Flow Decision Support System, and Artificial Intelligence for Ecosystem Services modeling platform.

Open-File Report↗
USGS2014

W(h)ither the Oracle? Cognitive biases and other human challenges of integrated environmental modeling

Integrated environmental modeling (IEM) can organize and increase our knowledge of the complex, dynamic ecosystems that house our natural resources and control the quality of our environments. Human behavior, however, must be taken into account. Human biases/heuristics reflect adaptation over our evolutionary past to frequently experienced situations that affected our survival and that provided sharply distinguished feedbacks at the level of the individual. Unfortunately, human behavior is not adapted to the more diffusely experienced, less frequently encountered, problems and issues that IEM typically seeks to address in the simulation of natural resources and environments. While seeking inspiration from the prophetic traditions of the Oracle of Delphi, several human biases are identified that may affect how the science base of IEM is assembled, and how IEM results are interpreted and used. These biases are supported by personal observations, and by the findings of behavioral scientists. A process for critical analysis is proposed that solicits explicit accounting and cognizance of potential human biases. A number of suggestions are made to address the human challenges of IEM, in addition to maintaining attitudes of watchful humility, open-mindedness, honesty, and transparent accountability. These include creating a new area of study in the behavioral biogeosciences, using structured processes for engaging the modeling and stakeholder community in IEM, and using “red teams” to increase resilience of IEM constructs and use.

Conference Paper↗
USGS2016

Participatory modeling and structured decision making

Structured decision making (SDM) provides a framework for making sound decisions even when faced with uncertainty, and is a transparent, defensible, and replicable method used to understand complex problems. A hallmark of SDM is the explicit incorporation of values and science, which often includes participation from multiple stakeholders, helping to garner trust and ultimately result in a decision that is more likely to be implemented. The core steps in the SDM process are used to structure thinking about natural resources management choices, and include: (1) properly defining the problem and the decision context, (2) determining the objectives that help describe the aspirations of the decision maker, (3) devising management actions or alternatives that can achieve those objectives, (4) evaluating the outcomes or consequences of each alternative on each of the objectives, (5) evaluating trade-offs, and (6) implementing the decision. Participatory modeling for SDM includes engaging stakeholders in some or all of the steps of the SDM process listed above. In addition, participatory modeling often is crucial for creating qualitative and quantitative models of how the system works, providing data for these models, and eliciting expert opinion when data are unavailable. In these ways, SDM provides a framework for decision making in natural resources management that includes participation from stakeholder groups throughout the process, including the modeling phase.

Book chapter↗
USGS2005

Upper Auglaize watershed AGNPS modeling project

The Upper Auglaize Watershed agricultural non-point source modeling project was an interagency effort to use a Geographic Information System (GIS)-based modeling approach for assessing and reducing pollution from agricultural runoff and other non-point sources. This project applied the U.S. Department of Agriculture (USDA), Agricultural Research Service’s AGricultural Non-Point Source (AGNPS) suite of models to the Upper Auglaize River Watershed, a major watershed within the Maumee River Basin. This modeling project was conducted by an interagency team consisting of a partnership between the: (1) USDA, Agricultural Research Service (ARS); (2) USDA, Natural Resources Conservation Service (NRCS); (3) U.S. Army Corps of Engineers (USACE); (4) U.S. Geological Survey (USGS); (5) Ohio State University; (6) University of Toledo (UT); (7) Heidelberg College; (8) Ohio Department of Natural Resources (ODNR), Division of Soil and Water Conservation; (9) Ohio Environmental Protection Agency (OEPA); and (10) Allen, Auglaize, Van Wert, and Putnam Soil and Water Conservation Districts. The partnership was the first step in a process to eventually apply the model in a portioned subset of watersheds for the Maumee Basin, and then to link them to form a comprehensive basin-wide model This work was performed under the authority of Section 516(e) of the Water Resources Development Act (WRDA) of 1996, as amended, for the purpose of assisting State and local watershed managers with their evaluation, prioritization and implementation of alternatives for soil conservation, sediment trapping and non-point source pollution prevention in the Upper Auglaize River watershed. The project team, working in a cooperative effort, used the models to determine sediment sources, contributing locations, and the effect of application of best management practices (BMPs) on rates of sediment delivery to the mouth of the watershed. The results will be used to guide conservation incentive and land treatment programs. The team relied heavily on Geographic Information System (GIS)-based applications to expedite the application of the model. The results of the analysis demonstrated that the application of BMPs would have a positive effect on reducing the loadings of sediment leaving the mouth of the Upper Auglaize Watershed. An application of 17 percent new no-till acres and eight percent new grassland acres, when randomly applied to the watershed, reduced loadings at the mouth to 82 percent of the simulated existing condition loadings. No-till, conversion of cropland to grassland, other uses including grass buffers, and reforestation of parts of the watershed, were all shown by the model to have a measurable effect on reducing sediment loads. Conversion of all of the cropland in the watershed to no-till would reduce the average unit load (tons of sediment per acre) leaving the mouth of the watershed to a level that is 42 percent of the simulated existing condition load. Ephemeral gullies were found to be the primary source of erosion (72 percent), sediment yield (73 percent), and sediment loading (73 percent). Controlling sediment load means controlling gully erosion and possibly trapping sediment yield before it reaches the stream system. Most BMPs (e.g., no-till, conversion of cropland, etc.) that reduce sheet and rill erosion and its sediment yield will also reduce gully erosion and its sediment yield. However, grassed waterways, which have no effect on sheet and rill erosion, are frequently an effective BMP to prevent ephemeral gullies. And, of course, riparian vegetation and sediment traps would reduce the delivery ratios of all types of landscape erosion. New techniques were developed by the team to quantify the ephemeral gully erosion within the model. When calibrated to available stream gage data the model suggests that more (73% in the existing condition simulation) of the sediment load originates from ephemeral gully erosion than from traditional sheet and rill erosion. The model quantified the value of tile drainage in reducing the sediment load from the watershed. Loadings under drained conditions were always less than loadings under undrained conditions for otherwise identical land uses. The average sediment load of all alternatives for drained loadings was 89.2 percent of the load for the corresponding undrained loadings. The model established that while many conservation incentive programs treat tile drainage as a production practice, significant erosion and sediment control benefits are provided by the practice in comparison to cultivation in an undrained state.

Ohio↗
USGS2023

Forecasting natural regeneration of sagebrush after wildfires using population models and spatial matching

Context Addressing ecosystem degradation in the Anthropocene will require ecological restoration across large spatial extents. Identifying areas where natural regeneration will occur without direct resource investment will improve scalability of restoration actions. Objectives An ecoregion in need of large scale restoration is the Great Basin of the Western US, where increasingly large and frequent wildfires threaten ecosystem integrity and its foundational shrub species. We develop a framework to forecast where post-wildfire regeneration of sagebrush cover ( Artemisia spp.) is likely to occur within the burnt areas across the region (> 900,000 km 2 ). Methods First, we parameterized population models using Landsat satellite-derived time series of sagebrush cover. Second, we evaluated the out-of-sample performance by predicting natural regeneration in wildfires not used for model training. This model assessment reproduces a management-oriented scenario: making restoration decisions shortly after wildfires with minimal local information. Third, we asked how accounting for increasingly fine-scale spatial heterogeneity could improve model forecasting accuracy. Results Regional-level models revealed that sagebrush post-fire recovery is slow, estimating > 80-year time horizon to reach an average cover at equilibrium of 16.6% (CI95% 9–25). Accounting for wildfire and within-wildfire spatial heterogeneity improved out-of-sample forecasts, resulting in a mean absolute error of 3.5 ± 4.3% cover, compared to the regional model with an error of 7.2 ± 5.1% cover. Conclusions We demonstrate that combining population models and non-parametric spatial matching provides a flexible framework for forecasting plant population recovery. Models for population recovery applied to Landsat-derived time series will assist restoration decision-making, including identifying priority targets for restoration.

Great Basin↗
USGS2018

Williston Basin groundwater availability, United States and Canada

The Williston Basin contains important oil and gas resources for the Nation. Freshwater supplies are limited in this semiarid area, and oil and gas development can require large volumes of freshwater. Groundwater is the primary source of water for many water users in the Williston Basin, so to better understand these resources, the U.S. Geological Survey (USGS) assessed the groundwater availability in this area. The final phase of this assessment included a computer model that simulates how groundwater flows in the aquifer systems and simulates how changes in water use and natural conditions may affect the water resources. These results provide a tool for land and water-resource managers to determine how water can be used for multiple purposes in the Williston Basin. For additional information about this assessment and more in-depth descriptions and results, see Long and others (2018).

Williston Basin↗
USGS2014

A precipitation-runoff model for simulating natural streamflow conditions in the Smith River watershed, Montana, water years 1996-2008

This report documents the construction of a precipitation-runoff model for simulating natural streamflow in the Smith River watershed, Montana. This Precipitation-Runoff Modeling System model, constructed in cooperation with the Meagher County Conservation District, can be used to examine the general hydrologic framework of the Smith River watershed, including quantification of precipitation, evapotranspiration, and streamflow; partitioning of streamflow between surface runoff and subsurface flow; and quantifying contributions to streamflow from several parts of the watershed. The model was constructed by using spatial datasets describing watershed topography, the streams, and the hydrologic characteristics of the basin soils and vegetation. Time-series data (daily total precipitation, and daily minimum and maximum temperature) were input to the model to simulate daily streamflow. The model was calibrated for water years 2002–2007 and evaluated for water years 1996–2001. Though water year 2008 was included in the study period to evaluate water-budget components, calibration and evaluation data were unavailable for that year. During the calibration and evaluation periods, simulated-natural flow values were compared to reconstructed-natural streamflow data. These reconstructed-natural streamflow data were calculated by adding Bureau of Reclamation’s depletions data to the observed streamflows. Reconstructed-natural streamflows represent estimates of streamflows for water years 1996–2007 assuming there was no agricultural water-resources development in the watershed. Additional calibration targets were basin mean monthly solar radiation and potential evapotranspiration. The model estimated the hydrologic processes in the Smith River watershed during the calibration and evaluation periods. Simulated-natural mean annual and mean monthly flows generally were the same or higher than the reconstructed-natural streamflow values during the calibration period, whereas they were lower during the evaluation period. The shape of the annual hydrographs for the simulated-natural daily streamflow values matched the shape of the hydrographs for the reconstructed-natural values for most of the calibration period, but daily streamflow values were underestimated during the evaluation period for water years 1996–1998. The model enabled a detailed evaluation of the components of the water budget within the Smith River watershed during the water year 1996–2008 study period. During this study period, simulated mean annual precipitation across the Smith River watershed was 16 inches, out of which 14 inches evaporated or transpired and 2 inches left the basin as streamflow. Per the precipitation-runoff model simulations, during most of the year, surface runoff rarely (less than 2 percent of the time during water years 2002–2008) makes up more than 10 percent of the total streamflow. Subsurface flow (the combination of interflow and groundwater flow) makes up most of the total streamflow (99 or more percent of total streamflow for 71 percent of the time during water years 2002–2008).

Montana↗