Gunnison River Basin Research Articles

Species Composition and Hybridization among Native and Nonnative Catostomid Fishes in Two Streams of the Gunnison River Basin, Colorado

We report results from a 3-year field investigation to limit hybridization between native and nonnative suckers in 2 small tributaries of the Gunnison River basin, Delta County, Colorado. Upstream movement to spawning habitat was selectively impeded by picket weir in intermittent Cottonwood Creek. There, native suckers were identified based on morphological characters in the field and were transferred upstream of the weir, whereas nonnative fishes were excluded from upstream movement. Another stream, Potter Creek, was not impeded by weir. Genetic identification of adult fishes in both streams was consistent with morphological identification in the field. Larval genetic identification was consistent with probable spawning species in Potter Creek. However, in Cottonwood Creek, larval fish proportions differed significantly from likely spawners. Proportions of nonnative suckers were high in Cottonwood Creek because exclusion structures were compromised by flooding during the spawning season. Yet, instream control of spawning by nonnative fishes has potential for reducing the incidence of hybridization, but practical challenges remain for implementation in the field.

An Intermittent Stream Supports Extensive Spawning of Large‐River Native Fishes

AbstractIntermittent or ephemeral streams make up a large percentage of all stream habitats and may have significant roles in spawning, foraging, refugia, and early life history habitat for many fishes. From 2015 to 2017 we examined the use of Cottonwood Creek, an intermittent tributary in the Gunnison River basin, Colorado, by spawning Flannelmouth Sucker Catostomus latipinnis, Bluehead Sucker C. discobolus, and Roundtail Chub Gila robusta. We used combinations of a stream‐spanning picket weir and PIT‐tag antennae near the mouth of the creek to determine fish movement in and out of the stream, which was more effective for the suckers than for Roundtail Chub. Large numbers of suckers used the stream each year despite very different flow regimes. The timing of initial fish entry varied by 6 weeks across 3 years of study from March 17 to May 6. Immigration was only loosely and inconsistently correlated to abiotic factors (various stream temperature metrics and discharge) among years and was most heavily dependent on the presence of sufficient water in the stream to permit fish passage. Average residency times of suckers ranged from 26 to 37 d depending on year and species, and spawning fish lost 10–18% of their body weight depending on species and sex. Both sucker species displayed a high level of annual site fidelity—61–71% of fish returned the year after being tagged. Larval drift was sporadic and many larvae failed to evacuate the stream before flow ceased each year. While further research is needed to assess the overall recruitment contribution from Cottonwood Creek to the regional population, this study revealed use of an intermittent tributary by thousands of native Colorado River fishes, highlighting the importance of nonperennial waters for the completion of the life histories of some large‐river fish species.

Assessing the Presence of Supercooled Liquid Water and Stability Using Radiometer Data Collected during the 2015-2016 Winter Cloud Seeding Program in the Upper Gunnison River Basin, Colorado

During the 2015-2016 Winter Cloud Seeding Program, a Radiometrics microwave radiometer was installed in the Upper Gunnison River Basin cloud seeding target area. The main purpose of using this device was to provide the ability to measure temperature and liquid water in frequent vertical profiles of the atmosphere above the radiometer. With observations of these two variables, the presence of supercooled liquid water can be identified. This information may be used in real-time to determine the “seedablity” of naturally occurring winter storms that impact the target area. Data may also be used in the post-analysis of these winter storms. For example, the radiometer data used in conjunction with the Rawindsonde Observation Program (RAOB), can be used to generate a vertical sounding of temperature dew point (no winds are observed from these radiometers). Such plots can be used to assess lower level atmospheric stability during storm periods that contain “supercooled” liquid water, which is the target of glaciogenic winter cloud seeding programs. The ensuing analyses indicated that stability is present in the lower valley levels upwind of the target area during most winter storms but that these inversions have a negligible effect on seeding operations primarily due to the very shallow nature of these inversions. Radiometer data were also compared to a nearby ground-based icing meter to determine if there was any correlation between the two types of observations.

Ranch Owner Perceptions and Planned Actions in Response to a Proposed Endangered Species Act Listing

The Gunnison sage-grouse (GUSG) is an iconic species recently proposed for protection under the Endangered Species Act (ESA). In Colorado’s Upper Gunnison River Basin, ranchers own the majority of water rights and productive river bottoms as well as approximately 30% of the most important GUSG habitat. This project used mixed-methods interviews with 41 ranch owners to document how ranchers perceive the proposed ESA listing and how they plan to respond to a listing decision. Results show that ranchers support on-the-ground GUSG conservation but are concerned about listing implications. Ranchers are most concerned about their ability to manage public and private lands productively and continue permitted grazing on public lands. If the species is listed, landowners plan to decrease participation in conservation strategies, including plans to adopt conservation easements, participation in conservation programs, and willingness to allow access to private lands for GUSG monitoring. Landowners also express plans for increased sales of land and water, which could have negative consequences for GUSG habitat. This research suggests that changes in the application of the ESA could lead to beneficial conservation outcomes. These changes include increased transparency, ability to exclude stable populations from listing under the ESA, and commitment to work with local bodies if the species is listed. This project demonstrates the importance of qualitative research for understanding the indirect and unintended effects of species protections in an increasingly interconnected world.

Spatio-temporal drought forecasting within Bayesian networks

Spatial variations of future droughts across the Gunnison River Basin in CO, USA, are evaluated in this study, using a recently developed probabilistic forecast model. The Standardized Runoff Index (SRI) is employed to analyze drought status across the spatial extent of the basin. The runoff generated at each spatial unit of the basin is estimated by a distributed-parameter and physically-based hydrologic model. Using the historical runoff at each spatial unit, a statistical forecast model is developed within Bayesian networks. The forecast model applies a family of multivariate distribution functions to forecast future drought conditions given the drought status in the past. Given the runoff in the past (January–June), the forecast model is applied in estimating the runoff across the basin in the forecast period (July–December). The main advantage of the forecast model is its probabilistic features in analyzing future droughts. It develops conditional probabilities of a given forecast variable, and returns the highest probable forecast along with an assessment of the uncertainty around that value. Bayesian networks can also estimate the probability of future droughts with different severities, given the drought status of the predictor period. Moreover, the model can be used to generate maps showing the runoff variation over the basin with the particular chance of occurrence in the future. Our results indicate that the statistical method applied in this study is a useful procedure in probabilistic forecast of future droughts given the spatio-temporal characteristics of droughts in the past. The techniques presented in this manuscript are suitable for probabilistic drought forecasting and have potential to improve drought characterization in different applications.

A Bayesian Framework for Probabilistic Seasonal Drought Forecasting

Abstract Seasonal drought forecasting is presented within a multivariate probabilistic framework. The standardized streamflow index (SSI) is used to characterize hydrologic droughts with different severities across the Gunnison River basin in the upper Colorado River basin. Since streamflow, and subsequently hydrologic droughts, are autocorrelated variables in time, this study presents a multivariate probabilistic approach using copula functions to perform drought forecasting within a Bayesian framework. The spring flow (April–June) is considered as the forecast variable and found to have the highest correlations with the previous winter (January–March) and fall (October–December). Incorporating copula functions into the Bayesian framework, two different forecast models are established to estimate the hydrologic drought of spring given either the previous winter (first-order conditional model) or previous winter and fall (second-order conditional model). Conditional probability density functions (PDFs) and cumulative distribution functions (CDFs) are generated to characterize the significant probabilistic features of spring droughts. According to forecasts, the spring drought is more sensitive to the winter status than the fall status, which approves the results of prior correlation analysis. The 90% predictive bound of the spring-flow forecast indicates the efficiency of the proposed model in estimating the spring droughts. The proposed model is compared with the conventional forecast model, the ensemble streamflow prediction (ESP), and it is found that their forecasts are generally in agreement with each other. However, the forecast uncertainty of the new method is more reliable than the ESP method. The new probabilistic forecast model can provide insights to water resources managers and stakeholders to facilitate the decision making and developing drought mitigation plans.

Predicting regime shifts in flow of the Gunnison River under changing climate conditions

Key Points Projected Gunnison River Basin hydrology is fitted to a gamma distribution Changes to wet and dry regime characteristics under climate change are expected Changing regime characteristics may influence future reservoir operations

Using large‐scale climatic patterns for improving long lead time streamflow forecasts for Gunnison and San Juan River Basins

AbstractIn a water‐stressed region, such as the western United States, it is essential to have long lead times for streamflow forecasts used in reservoir operations and water resources management. Current water supply forecasts provide a 3‐month to 6‐month lead time, depending on the time of year. However, there is a growing demand from stakeholders to have forecasts that run lead times of 1 year or more. In this study, a data‐driven model, the support vector machine (SVM) based on the statistical learning theory, was used to predict annual streamflow volume with a 1‐year lead time. Annual average oceanic–atmospheric indices consisting of the Pacific decadal oscillation, North Atlantic oscillation (NAO), Atlantic multidecadal oscillation, El Niño southern oscillation (ENSO), and a new sea surface temperature (SST) data set for the ‘Hondo’ region for the period of 1906–2006 were used to generate annual streamflow volumes for multiple sites in the Gunnison River Basin and San Juan River Basin, both located in the Upper Colorado River Basin. Based on the performance measures, the model showed very good forecasts, and the forecasts were in good agreement with measured streamflow volumes. Inclusion of SST information from the Hondo region improved the model's forecasting ability; in addition, the combination of NAO and Hondo region SST data resulted in the best streamflow forecasts for a 1‐year lead time. The results of the SVM model were found to be better than the feed‐forward, back propagation artificial neural network and multiple linear regression. The results from this study have the potential of providing useful information for the planning and management of water resources within these basins. Copyright © 2012 John Wiley & Sons, Ltd.

Prototype Decision Support System for Operations on the Gunnison Basin with Improved Forecasts

Numerous studies have developed new methods for skillful long-lead seasonal streamflow forecasts, especially in the western United States, and most assume that the streamflow forecast skills translate into improved water resources decision making, but there has been little comprehensive demonstration of this. This translation is not straightforward because the decision system is nonlinear. This paper develops a prototype decision support system (DSS) to systematically evaluate the translation of streamflow forecast skills to water resource operational variables and decision variables in the Gunnison River basin (GRB). The DSS consists of two main modules: (1) a multimodel ensemble streamflow forecast module that uses large-scale climate information and issues forecast ensembles, and (2) a decision support module (developed in the generalized decision tool, RiverWare) that captures the seasonal operations and management of the GRB water resources system. Ensembles of streamflow forecast scenarios drive the decision support module, resulting in an ensemble of various variables pertaining to operations of the reservoirs. The DSS is run with streamflow forecast ensembles for the spring season issued on January 1 and April 1 and also the climatological and observed streamflows. The skills of the various variables, e.g., power generated and end of season reservoir storages, are computed. The results demonstrate that the skills from the streamflow forecasts are well transferred to the operational variables, enabling more effective decision making as early as January, when the snow information is partial at best. This integrated and comprehensive prototype DSS is believed to be one of the early attempts to demonstrate the utility of streamflow forecasts in a decision-making context. This DSS framework can be easily transferred to other basins.

Development of streamflow projections under changing climate conditions over Colorado River basin headwaters

Abstract. The current drought over the Colorado River Basin has raised concerns that the US Department of the Interior, Bureau of Reclamation (Reclamation) may impose water shortages over the lower portion of the basin for the first time in history. The guidelines that determine levels of shortage are affected by relatively short-term (3 to 7 month) forecasts determined by the Colorado Basin River Forecast Center (CBRFC) using the National Weather Service (NWS) River Forecasting System (RFS) hydrologic model. While these forecasts by the CBRFC are useful, water managers within the basin are interested in long-term projections of streamflow, particularly under changing climate conditions. In this study, a bias-corrected, statistically downscaled dataset of projected climate is used to force the NWS RFS utilized by the CBRFC to derive projections of streamflow over the Green, Gunnison, and San Juan River headwater basins located within the Colorado River Basin. This study evaluates the impact of changing climate to evapotranspiration rates and contributes to a better understanding of how hydrologic processes change under varying climate conditions. The impact to evapotranspiration rates is taken into consideration and incorporated into the development of streamflow projections over Colorado River headwater basins in this study. Additionally, the NWS RFS is modified to account for impacts to evapotranspiration due to changing temperature over the basin. Adjusting evapotranspiration demands resulted in a 6 % to 13 % average decrease in runoff over the Gunnison River Basin when compared to static evapotranspiration rates. Streamflow projections derived using projections of future climate and the NWS RFS provided by the CBRFC resulted in decreased runoff in 2 of the 3 basins considered. Over the Gunnison and San Juan River basins, a 10 % to 15 % average decrease in basin runoff is projected through the year 2099. However, over the Green River basin, a 5 % to 8 % increase in basin runoff is projected through 2099. Evidence of nonstationary behavior is apparent over the Gunnison and San Juan River basins.

A multimodel ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin

We propose a multimodel ensemble forecast framework for streamflow forecasts at multiple locations that incorporates large‐scale climate information. It has four broad steps: (1) Principal component analysis is performed on the spatial streamflows to identify the dominant modes of variability. (2) Potential predictors of the dominant streamflow modes are identified from several large‐scale climate features and snow water equivalent information. (3) Objective criterion is used to select a suite of candidate nonlinear regression models each with different predictors. (4) Ensemble forecasts of the dominant streamflow modes are generated from the candidate models and are combined objectively to produce a multimodel ensemble, which are then back transformed to produce spatially coherent streamflow forecasts at all the locations. The utility of the framework is demonstrated in the skillful forecast of spring seasonal streamflows at six locations in the Gunnison River Basin at several lead times. The generated ensemble streamflow forecast provides valuable and useful information for optimal management and planning of water resources in the basin.

A new method to produce categorical streamflow forecasts

Categorical forecasts of streamflow are important for effective water resources management. Typically, these are obtained by generating ensemble forecasts of streamflow and counting the proportion of ensembles in the desired category. Here we develop a simple and direct method to produce categorical streamflow forecasts at multiple sites. The method involves predicting the probability of the leading mode (or principal component) of the basin streamflows above a given threshold and subsequently translating the predicted probabilities to all the sites in the basin. The categorical probabilistic forecasts are obtained via logistic regression using a set of large‐scale climate predictors. Application to categorical forecasts of the spring (April–June) streamflows at six locations in the Gunnison River Basin exhibited significant long‐lead forecast skill.

A 431-Yr Reconstruction of Western Colorado Snowpack from Tree Rings

Abstract A tree-ring-based reconstruction for 1 April snow water equivalent (SWE) is generated for the Gunnison River basin region in western Colorado. The reconstruction explains 63% of the variance in the instrumental record and extends from 1569 to 1999. When the twentieth-century part of the record is compared to the full record, the variability and extremes in the twentieth century appear representative of the long-term record. However, years of extreme SWE (low and high) and persistent low SWE events are not evenly distributed throughout the record. The twentieth century is notable for several periods that lack extreme years, and along with the nineteenth century and the second half of the eighteenth century, contains many fewer persistent low SWE events than the first half of the reconstruction. Low SWE in the western United States is associated with several circulation patterns, including the Pacific–North American (PNA) pattern and those related to El Niño–Southern Oscillation (ENSO), but the Gunnison River basin is on the edge of the area with a strong relationship to the PNA and is generally in a transitional zone with respect to regional ENSO influences. Tree-ring chronologies from Oregon and New Mexico, regions impacted by ENSO, were used as rough proxies of northwestern and southwestern U.S. winter precipitation to explore possible associations between Gunnison SWE and winter climate in these two regions over the past four centuries.

SELENIUM BUDGETS FOR LAKE POWELL AND THE UPPER COLORADO RWER BASIN1

ABSTRACT: A selenium budget for Lake Powell, Utah‐Arizona was determined based on selenium loads at the principal stream input sites to and the output site from the lake. Based on data collected during 1985‐1994, 83 percent of the selenium entering Lake Powell is accounted for at the output site. The rest of the selenium may be incorporated by lake sediment or used by the biota. Considerably more selenium per unit area is produced from the Colorado River Basin above the Colorado River‐Green River confluence than from the Green River Basin and the San Juan River Basin combined. The Gunnison River Basin and the Grand Valley in Colorado produce an estimated 31 and 30 percent of the selenium that reaches Lake Powell, respectively. Irrigation‐related activities are thought to be responsible for mobilizing 71 percent of the selenium that reaches Lake Powell. Selenium concentrations in water at Imperial Dam on the Colorado River upstream of the United States‐Mexico international border are similar to those at the output site of Lake Powell. Therefore, most selenium observed in downstream areas of the Colorado River therefore probably is derived mostly from the Colorado River Basin above Lake Powell.

Salinity Trends in Surface Waters of the Upper Colorado River Basin, Colorado

AbstractDissolved‐solids data collected in the Upper Colorado River Basin upstream from Cameo, Colorado, and in the Gunnison River Basin were analyzed for trends in flow‐adjusted dissolved‐solids concentrations and loads for water years 1970 to 1993, 1980 to 1993, and 1986 to 1993. Trend results for flow‐adjusted periodic dissolved‐solids concentrations for the Colorado River Basin upstream from Cameo, CO, generally were downward or no trend was indicated. Trends in flow‐adjusted monthly and annual dissolved‐solids loads primarily were downward or absent. These trend results partly agree with the downward trends reported by a previous investigation for the Colorado River near Cameo site. In the Gunnison River Basin, trends in flow‐adjusted dissolved‐solids concentrations and loads were not detected for more than one‐half of the site/analysis‐period combinations. Of the trends that were present, most indicated decreases in concentrations and loads rather than increases. In both the Colorado River Basin upstream from Cameo, CO, and the Gunnison River Basin, trends in flow‐adjusted dissolved‐solids concentrations and flow‐adjusted monthly and annual dissolved‐solids loads may be affected by a variety of factors. These include channel evolution and hydrologic variation, water quality and flow rate of groundwater discharges and springs, sample size and period of record of dissolved‐solids data, and changes in land use in the basin.

Stochastic calibration of an orographic precipitation model

In this study a stochastic approach to calibration of an orographic precipitation model (Rhea, 1978) was applied in the Gunnison River Basin of south-western Colorado. The stochastic approach to model calibration was used to determine: (1) the model parameter uncertainty and sensitivity; (2) the grid-cell resolution to run the model (10 or 5 km grids); (3) the model grid rotation increment; and (4) the basin subdivision by elevation band for parameter definition. Results from the stochastic calibration are location and data dependent. Uncertainty, sensitivity and range in the final parameter sets were found to vary by grid-cell resolution and elevation. Ten km grids were found to be a more robust model configuration than 5 km grids. Grid rotation increment, tested using only 10 km grids, indicated increments of less than 10 degrees to be superior. Basin subdivision into two elevation bands was found to produce ‘optimal’ results for both 10 and 5 km grids. © 1998 John Wiley & Sons, Ltd.

VERIFICATION OF THE RHEA‐OROGRAPHIC‐PRECIPITATION MODEL1

ABSTRACT: Observed April 1 snowpack accumulations within and near the Gunnison River basin in southwestern Colorado are compared with simulations from the Rhea‐orographic‐precipitation model to determine if the model simulates reliable magnitudes and temporal and spatial variability in winter precipitation for the basin. Twenty simulations of the Rhea model were performed using‘optimal’parameter sets determined for 10‐kilometer (km) grids (10‐km by 10‐km grid cells) through stochastic calibration. Comparisons of Rhea‐model simulations of winter precipitation with April 1 snowpack accumulations at 32 snowcourse stations were performed for the years 1972–1990. For most stations and most years the Rhea model reliably simulates the temporal and spatial variability in April 1 snowpack accumulations. However, in general, the Rhea‐model underestimates April 1 snowpack accumulations in the Gunnison River basin area, and the underestimation is greatest for locations that receive the largest amount of snow. A significant portion of the error in Rhea‐model simulations is due to the calibration of the Rhea model using gauge‐catch precipitation measurements which can be as much as 50 percent below actual snowfall accumulations. Additional error in the Rhea‐model simulations is a result of the comparison of gridded precipitation values to observed values measured at points.

Stochastic calibration of an orographic precipitation model

In this study a stochastic approach to calibration of an orographic precipitation model (Rhea, 1978) was applied in the Gunnison River Basin of south-western Colorado. The stochastic approach to model calibration was used to determine: (1) the model parameter uncertainty and sensitivity; (2) the grid-cell resolution to run the model (10 or 5 km grids); (3) the model grid rotation increment; and (4) the basin subdivision by elevation band for parameter definition. Results from the stochastic calibration are location and data dependent. Uncertainty, sensitivity and range in the final parameter sets were found to vary by grid-cell resolution and elevation. Ten km grids were found to be a more robust model configuration than 5 km grids. Grid rotation increment, tested using only 10 km grids, indicated increments of less than 10 degrees to be superior. Basin subdivision into two elevation bands was found to produce 'optimal' results for both 10 and 5 km grids.

Validation and sensitivity of a convective precipitation model for mountainous areas

A convective precipitation model for use in regions of complex terrain has been developed and applied to the Gunnison River Basin in southwestern Colorado. Spring snowfall in the Rocky Mountain region often has a significant convective component which orographic precipitation models are unable to simulate. Additionally, summertime precipitation is predominately convective in this area and is responsible for a large portion of summer streamflow variability. Streamflow typically increases by 50 to 100 percent of baseflow for moderate rainfall events for periods of up to one week. Larger precipitation episodes can produce peak discharges that exceed the spring snowmelt peaks. Convective precipitation also is important for plant growth, minimum streamflows and fire hazard conditions. In addition, an accurate assessment of the response of hydrologic systems to climate variability and change requires an accurate estimate of convective precipitation in mountainous areas. The convective model accurately reproduced the trend and amount of observed precipitation for the test period August 14-20, 1989. The convective model has applicability for downscaling large-scale model precipitation to smaller scales for use in water quality and quantity assessments.

Hydrological effects of hypothetical climate change in the East River basin, Colorado, USA

Abstract In 1988, the US Geological Survey began a study of the effects of potential climate change on the water resources of the Gunnison River basin. The Gunnison River, in southwestern Colorado, is an important tributary of the Colorado River, contributing approximately 40% of the flow of the Colorado River at the Colorado/Utah stateline. As part of the study, the sensitivity of annual and seasonal runoff in the East River basin, a sub-basin of the Gunnison River basin, to changes in temperature and precipitation was examined. To perform the sensitivity analyses, hypothetical climate changes were used to alter current time series of temperature and precipitation. The altered time series were then used as inputs to a hydrological model that translated these inputs into estimates of runoff. Mean annual and seasonal runoff resulting from a range of hypothetical climate changes were compared and evaluated. Results indicated that in general, changes in precipitation had a larger effect on changes in runoff than did changes in temperature. Changes in precipitation had significant effects on runoff during all seasons. Changes in temperature primarily affected the temporal distribution of runoff through the year. Changes in temperature affected the timing of snowmelt and the ratio of rain to snow, and therefore the effects of temperature were particularly significant during the spring and summer seasons. On an annual basis, increases in temperature led only to slight decreases in runoff. Results also indicated that the effects of an increase in mean annual temperature of 4°C on annual runoff could be offset by an increase in annual precipitation of between 4 and 5%, and that the magnitude of natural climatic variability was large and might mask the effects of long term climate changes.