Changes in Patterns of Streamflow From Unregulated Watersheds in Idaho,Western Wyoming,and Northern Nevada1

Clark, Gregory M., 2010. Changes in Patterns of Streamflow From Unregulated Watersheds in Idaho, Western Wyoming, and Northern Nevada. Journal of the American Water Resources Association (JAWRA) 46(3):486-497. DOI: 10.1111/j.1752-1688.2009.00416.x Abstract: Recent studies have identified a pattern of earlier spring runoff across much of North America. Earlier spring runoff potentially poses numerous problems, including increased risk of flooding and reduced summer water supply for irrigation, power generation, and migratory fish passage. To identify changing runoff patterns in Idaho streams, streamflow records were analyzed for 26 U.S. Geological Survey gaging stations in Idaho, western Wyoming, and northern Nevada, each with a minimum of 41 years of record. The 26 stations are located on 23 unregulated and relatively pristine streams that drain areas ranging from 28 to >35,000 km². Four runoff parameters were trend tested at each station for both the period of historical record and from 1967 through 2007. Parameters tested were annual mean streamflow, annual minimum daily streamflow, and the dates of the 25th and 50th percentiles of the annual total streamflow. Results of a nonparametric Mann-Kendall trend test revealed a trend toward lower annual mean and annual minimum streamflows at a majority of the stations, as well as a trend toward earlier snowmelt runoff. Significant downward trends over the period of historical record were most prevalent for the annual minimum streamflow (12 stations) and the 50th percentile of streamflow (11 stations). At most stations, trends were more pronounced during the period from 1967 through 2007. A regional Kendall test for water years 1967 through 2007 revealed significant regional trends in the percent change in the annual mean and annual minimum streamflows (0.67% less per year and 0.62% less per year, respectively), the 25th percentile of streamflow (12.3 days earlier), and the 50th percentile of streamflow (11.5 days earlier).     

ANALYSIS AND MODELING OF LONG-TERM STREAM TEMPERATURES ON THE STEAMBOAT CREEK BASIN,OREGON: IMPLICATIONS FOR LAND USE AND FISH HABITAT1

ABSTRACT: Steamboat Creek basin is an important source of timber and provides crucial spawning and rearing habitat for anadromous steelhead trout (Oncorhynchus mykiss). Because stream temperatures are near the upper limit of tolerance for the survival of juvenile steelhead, the possible long-term effect of clear-cut logging on stream temperatures was assessed. Twenty-year (1969–1989) records of summer stream temperature and flow from four tributaries and two reaches of Steamboat Creek and Boulder Creek (a nearby unlogged watershed) were analyzed. Logging records for the Steamboat Creek basin and air temperature records also were used in the analysis. A time-series model of the components of stream temperature (seasonal cycle of solar radiation, air temperature, streamflow, an autoregressive term of order 1, and a linear trend variable) was fitted to the water-temperature data. The linear trend variable was significant in all the fitted models except Bend Creek (a tributary fed by cool ground-water discharge) and Boulder Creek. Because no trends in either climate (i.e., air temperature) or streamflow were found in the data, the trend variable was associated with the pre-1969 loss and subsequent regrowth of riparian vegetation and shading canopies.     

TEMPORAL CHANGES IN THE YADKIN RIVER FLOW VERSUS SUSPENDED SEDIMENT CONCENTRATION RELATIONSHIP1

ABSTRACT: Dynamic linear models (DLM) and seasonal trend decomposition (STL) using local regression, or LOESS, were used to analyze the 50‐year time series of suspended sediment concentrations for the Yadkin River, measured at the U.S. Geological Survey station at Yadkin College, North Carolina. A DLM with constant trend, seasonality, and a log₁₀ streamflow regressor provided the best model to predict monthly mean log₁₀ suspended sediment concentrations, based on the forecast log likelihood. Using DLM, there was evidence (odds approximately 69:1) that the log₁₀ streamflow versus log₁₀ suspended sediment concentration relationship has changed, with an approximate 20 percent increase in the log₁₀ streamflow coefficient over the period 1981 to 1996. However, sediment concentrations in the Yadkin River have decreased during the decade of the 1990s, which has been accompanied by a concomitant increase in streamflow variability. Although STL has been shown to be a versatile trend analysis technique, DLM is shown to be more suitable for discovery and inference of structural changes (trends) in the model coefficient describing the relationship between flow and sediment concentration.     

Historical Groundwater Trends in Northern New England and Relations with Streamflow and Climatic Variables

Water‐level trends spanning 20, 30, 40, and 50 years were tested using month‐end groundwater levels in 26, 12, 10, and 3 wells in northern New England (Maine, New Hampshire, and Vermont), respectively. Groundwater levels for 77 wells were used in interannual correlations with meteorological and hydrologic variables related to groundwater. Trends in the contemporary groundwater record (20 and 30 years) indicate increases (rises) or no substantial change in groundwater levels in all months for most wells throughout northern New England. The highest percentage of increasing 20‐year trends was in February through March, May through August, and October through November. Forty‐year trend results were mixed, whereas 50‐year trends indicated increasing groundwater levels. Whereas most monthly groundwater levels correlate strongly with the previous month's level, monthly levels also correlate strongly with monthly streamflows in the same month; correlations of levels with monthly precipitation are less frequent and weaker than those with streamflow. Groundwater levels in May through August correlate strongly with annual (water year) streamflow. Correlations of groundwater levels with streamflow data and the relative richness of 50‐ to 100‐year historical streamflow data suggest useful proxies for quantifying historical groundwater levels in light of the relatively short and fragmented groundwater data records presently available.     

A COMPARISON OF STREAMFLOW GENERATION MODELS FOR RESERVOIR CAPACITY-YIELD ANALYSIS1

ABSTRACT: An analysis of four streamflow generation schemes for the use in the estimation of the required conservation storage for a single reservoir is presented. The comparison of the generating schemes should aid in the selection of an appropriate model type for the reservoir sizing problem. The streamflow generation models are compared using two criteria. The first comparison is between the statistics of the generated streamflow sequences and the corresponding statistics from the historical record. The second evaluation compares the median reservoir size determined by each model with the required storage based on the historical flow sequence. The results of a comparative analysis for monthly streamflow data for the Rzav River in Yugoslavia are presented and discussed. The results indicate that both evaluation criteria are required to discriminate between the various options.     

Long‐Term Streamflow Response to Climatic Variability in the Loess Plateau,China1

Abstract: The Loess Plateau region in northwestern China has experienced severe water resource shortages due to the combined impacts of climate and land use changes and water resource exploitation during the past decades. This study was designed to examine the impacts of climatic variability on streamflow characteristics of a 12‐km² watershed near Tianshui City, Gansu Province in northwestern China. Statistic analytical methods including Kendall’s trend test and stepwise regression were used to detect trends in relationship between observed streamflow and climatic variables. Sensitivity analysis based on an evapotranspiration model was used to detect quantitative hydrologic sensitivity to climatic variability. We found that precipitation (P), potential evapotranspiration (PET) and streamflow (Q) were not statistically significantly different (p > 0.05) over the study period between 1982 and 2003. Stepwise regression and sensitivity analysis all indicated that P was more influential than PET in affecting annual streamflow, but the similar relationship existed at the monthly scale. The sensitivity of streamflow response to variations of P and PET increased slightly with the increase in watershed dryness (PET/P) as well as the increase in runoff ratio (Q/P). This study concluded that future changes in climate, precipitation in particular, will significantly impact water resources in the Loess Plateau region an area that is already experiencing a decreasing trend in water yield.     

TREE RING RECONSTRUCTIONS OF STREAMFLOW FOR THREE CANADIAN PRAIRIE RIVERS1

ABSTRACT: Information regarding long term hydrological variability is critical for the effective management of surface water resources. In the Canadian Prairie region, growing dependence on major river systems for irrigation and other consumptive uses has resulted in an increasing vulnerability to hydrological drought and growing interprovincial tension. This study presents the first dendrochronological records of streamflow for Canadian Prairie rivers. We present 1,113‐year, 522‐year, and 325‐year reconstructions of total water year (October to September) streamflow for the North Saskatchewan, South Saskatchewan, and Saskatchewan Rivers, respectively. The reconstructions indicate relatively high flows during the 20th Century and provide evidence of past prolonged droughts. Low flows during the 1840s correspond with aridity that extended over much of the western United States. Similarly, an exceptional period of prolonged low flow conditions, approximately 900 A.D. to 1300 A.D., is coincident with evidence of sustained drought across central and western North America. The 16th Century megadrought of the western United States and Mexico, however, does not appear to have had a major impact on the Canadian rivers. The dendrohydrological records illustrate the risks involved if future water policy and infrastructure development in the Canadian Prairies are based solely on records of streamflow variability over the historical record.     

ESTIMATION OF WATER TEMPERATURE EXCEEDANCE PROBABILITIES USING THERMO-HYDRODYNAMIC MODELING1

ABSTRACT: A continuous simulation approach is proposed for estimating water temperature exceedance probabilities using thermo-hydrodynamic modeling. The approach uses (1) a deterministic unsteady flow and heat transport model, (2) continuous hydrological and meteorological data for a long historical period, and (3) synthetic records of tributary water temperatures and other model inputs. Representative historical records of streamflow, air temperatures, and other hydrometeorological variables are obtained from nearby gages. Stochastic modeling methods are used to construct synthetic records for other model inputs, including inflow water temperatures. An application of this deterministic-stochastic approach is presented for a complex waterway in northeastern Illinois with heat discharges from several power plants and wastewater treatment plants. Statistical results from the continuous simulations are compared to results obtained from traditional event simulations. The application illustrates the information that engineers and biologists can obtain for (1) evaluating compliance with water temperature standards, and (2) assessing the effect of water temperatures on aquatic habitat.     

Assessment of Tree Rings as a Hydrologic Record in a Humid Subtropical Environment1

Crockett, Kris, Jonathan B. Martin, Henri D. Grissino-Mayer, Evan R. Larson, and Thomas Mirti, 2010. Assessment of Tree Rings as a Hydrologic Record in a Humid Subtropical Environment. Journal of the American Water Resources Association (JAWRA) 1-13. DOI: 10.1111/j.1752-1688.2010.00464.x Abstract: Information about long-term variability of streamflow is important to allocate water resources, but few historical records extend more than 75 years into the past, requiring proxy records to evaluate flow prior to that time. Flow proxies have been found in tree-ring widths in temperate regions, but have rarely been used in humid subtropical environments because the relationship between tree growth and climate was believed to be weakened by limited seasonality and stress on tree growth from drought conditions. Tree-ring residual chronologies from two forests sampled from northern Florida correlate well with historical annual discharge (r² values as high as 0.47) from 3 of 15 river-gauging stations that were used to compare with the tree-ring chronologies. The best correlations occur where streamflow has little contribution from spring discharge or continuous flow from lakes and wetlands. Streams lack correlations with the tree-ring residual chronologies (r² values as low as 0.0002) where springs and continuous discharge from lakes mute variations in their flow. Tree-ring chronologies appear to be useful for reconstruction of prehistorical variations of some streamflow in humid subtropical regions, but interpretations of the reconstructions must consider the local hydrologic conditions.     

Predicting streamflow response to fire-induced landcover change: implications of parameter uncertainty in the MIKE SHE model

Fire is a primary agent of landcover transformation in California semi-arid shrubland watersheds, however few studies have examined the impacts of fire and post-fire succession on streamflow dynamics in these basins. While it may seem intuitive that larger fires will have a greater impact on streamflow response than smaller fires in these watersheds, the nature of these relationships has not been determined. The effects of fire size on seasonal and annual streamflow responses were investigated for a medium-sized basin in central California using a modified version of the MIKE SHE model which had been previously calibrated and tested for this watershed using the Generalized Likelihood Uncertainty Estimation methodology. Model simulations were made for two contrasting periods, wet and dry, in order to assess whether fire size effects varied with weather regime. Results indicated that seasonal and annual streamflow response increased nearly linearly with fire size in a given year under both regimes. Annual flow response was generally higher in wetter years for both weather regimes, however a clear trend was confounded by the effect of stand age. These results expand our understanding of the effects of fire size on hydrologic response in chaparral watersheds, but it is important to note that the majority of model predictions were largely indistinguishable from the predictive uncertainty associated with the calibrated model - a key finding that highlights the importance of analyzing hydrologic predictions for altered landcover conditions in the context of model uncertainty. Future work is needed to examine how alternative decisions (e.g., different likelihood measures) may influence GLUE-based MIKE SHE streamflow predictions following different size fires, and how the effect of fire size on streamflow varies with other factors such as fire location.     

Effects of Antecedent Streamflow and Sample Timing on Trend Assessments of Fish,Invertebrate, and Diatom Communities

Detecting trends in biological attributes is central to many stream monitoring programs; however, understanding how natural variability in environmental factors affects trend results is not well understood. We evaluated the influence of antecedent streamflow and sample timing (covariates) on trend estimates for fish, invertebrate, and diatom taxa richness and biological condition from 2002 to 2012 at 51 sites distributed across the conterminous United States. A combination of linear regression and Kendall‐tau test for trends were used to evaluate covariate influence on trend estimates. Adjusting for covariates changed the magnitude of trend estimates in two‐thirds of cases on average by 21%, most often reducing the estimated magnitude of the trend. Additionally, covariates influenced the interpretation of over one‐third of trend estimates by either strengthening or weakening trends after adjustment. Our findings clearly indicate that antecedent streamflow and sample timing influences trend estimates and subsequent interpretation. Accounting for covariates during trend analysis will enhance stream monitoring programs by providing a better understanding and interpretation of estimated changes in biological endpoints at monitored sites. Failure to account for antecedent streamflow and sample timing may lead to mischaracterization of a trend and/or misunderstanding of potential causes.     

Modeling Variability and Trends in Pesticide Concentrations in Streams1

Abstract: A parametric regression model was developed for assessing the variability and long‐term trends in pesticide concentrations in streams. The dependent variable is the logarithm of pesticide concentration and the explanatory variables are a seasonal wave, which represents the seasonal variability of concentration in response to seasonal application rates; a streamflow anomaly, which is the deviation of concurrent daily streamflow from average conditions for the previous 30 days; and a trend, which represents long‐term (inter‐annual) changes in concentration. Application of the model to selected herbicides and insecticides in four diverse streams indicated the model is robust with respect to pesticide type, stream location, and the degree of censoring (proportion of nondetections). An automatic model fitting and selection procedure for the seasonal wave and trend components was found to perform well for the datasets analyzed. Artificial censoring scenarios were used in a Monte Carlo simulation analysis to show that the fitted trends were unbiased and the approximate p‐values were accurate for as few as 10 uncensored concentrations during a three‐year period, assuming a sampling frequency of 15 samples per year. Trend estimates for the full model were compared with a model without the streamflow anomaly and a model in which the seasonality was modeled using standard trigonometric functions, rather than seasonal application rates. Exclusion of the streamflow anomaly resulted in substantial increases in the mean‐squared error and decreases in power for detecting trends. Incorrectly modeling the seasonal structure of the concentration data resulted in substantial estimation bias and moderate increases in mean‐squared error and decreases in power.     

Observed Changes in Climate and Streamflow in the Upper Rio Grande Basin

Observed streamflow and climate data are used to test the hypothesis that climate change is already affecting Rio Grande streamflow volume derived from snowmelt runoff in ways consistent with model‐based projections of 21st‐Century streamflow. Annual and monthly changes in streamflow volume and surface climate variables on the Upper Rio Grande, near its headwaters in southern Colorado, are assessed for water years 1958–2015. Results indicate winter and spring season temperatures in the basin have increased significantly, April 1 snow water equivalent (SWE) has decreased by approximately 25%, and streamflow has declined slightly in the April–July snowmelt runoff season. Small increases in precipitation have reduced the impact of declining snowpack on trends in streamflow. Changes in the snowpack–runoff relationship are noticeable in hydrographs of mean monthly streamflow, but are most apparent in the changing ratios of precipitation (rain + snow, and SWE) to streamflow and in the declining fraction of runoff attributable to snowpack or winter precipitation. The observed changes provide observational confirmation for model projections of decreasing runoff attributable to snowpack, and demonstrate the decreasing utility of snowpack for predicting subsequent streamflow on a seasonal basis in the Upper Rio Grande Basin.     

Climatic Fluctuations and Forecasting of Streamflow in the Lower Colorado River Basin1

Abstract:  Water‐resource managers need to forecast streamflow in the Lower Colorado River Basin to plan for water‐resource projects and to operate reservoirs for water supply. Statistical forecasts of streamflow based on historical records of streamflow can be useful, but statistical assumptions, such as stationarity of flows, need to be evaluated. This study evaluated the relation between climatic fluctuations and stationarity and developed regression equations to forecast streamflow by using climatic fluctuations as explanatory variables. Climatic fluctuations were represented by the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and Southern Oscillation Index (SOI). Historical streamflow within the 25‐ to 30‐year positive or negative phases of AMO or PDO was generally stationary. Monotonic trends in annual mean flows were tested at the 21 sites evaluated in this study; 76% of the sites had no significant trends within phases of AMO and 86% of the sites had no significant trends within phases of PDO. As climatic phases shifted in signs, however, many sites had nonstationary flows; 67% of the sites had significant changes in annual mean flow as AMO shifted in signs. The regression equations developed in this study to forecast streamflow incorporate these shifts in climate and streamflow, thus that source of nonstationarity is accounted for. The R² value of regression equations that forecast individual years of annual flow for the central part of the study area ranged from 0.28 to 0.49 and averaged 0.39. AMO was the most significant variable, and a combination of indices from both the Atlantic and Pacific Oceans explained much more variation in flows than only the Pacific Ocean indices. The average R² value for equations with PDO and SOI was 0.15.     

Modeling Discharge Rates Using a Coupled Modeled Approach for the Merced River in Yosemite National Park

This study describes the application of the NASA version of the Carnegie‐Ames‐Stanford Approach (CASA) ecosystem model coupled with a surface hydrologic routing scheme previously called the Hydrological Routing Algorithm (HYDRA) to model monthly discharge rates from 2000 to 2007 on the Merced River drainage in Yosemite National Park, California. To assess CASA‐HYDRA's capability to estimate actual water flows in extreme precipitation years, the focus of this study is the 2007 water year, which was very dry, and the 2005 water year, which was a moderately wet year in the historical record. Prior to comparisons to gauge records, CASA‐HYDRA snowmelt algorithms were modified with equations from the U.S. Department of Agriculture Snowmelt‐Runoff Model (SRM), which has been designed to predict daily streamflow in mountain basins where snowmelt is a major runoff factor. Results show that model predictions closely matched monthly flow rates at the Pohono Bridge gauge station (USGS#11266500), with R² = 0.67 and Nash‐Sutcliffe (E) = 0.65. By subdividing the upper Merced River basin into subbasins with high spatial resolution in the gridded modeling approach, we were able to determine which biophysical characteristics in the Sierra differed to the largest degree in extreme low‐flow and high‐flow years. Average elevation and snowpack accumulation were found to be the most important explanatory variables to understand subbasin contributions to monthly discharge rates.     

Using Paleo Reconstructions to Improve Streamflow Forecast Lead Time in the Western United States

In water stressed regions, water managers are exploring new horizons that would help in long‐range streamflow forecasts. Oceanic‐atmospheric oscillations have been shown to influence streamflow variability. In this study, long‐lead time streamflow forecasts are made using a multiclass kernel‐based data‐driven support vector machine (SVM) model. The extended streamflow records based on tree ring reconstructions were used to provide a longer time series data. Reconstructed data were used from 1658 to 1952 and the instrumental record was used from 1953 to 2007. Reconstructions for oceanic‐atmospheric oscillations included the El Niño‐Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, and North Atlantic Oscillation. Streamflow forecasts using all four oscillations were made with one‐year to five‐year lead times for 21 gages in the western United States. This is the first study that uses both instrumental and reconstructed data of oscillations in SVM model to improve streamflow forecast lead time. SVM model was able to provide “satisfactory” to “very good” forecasts with one‐ to five‐year lead time for the selected gages. The use of all the oscillation indices helped in achieving better predictability compared to using individual oscillations. The SVM modeling results are better when compared with multiple linear regression model forecasts. The findings are statistical in nature and are expected to be useful for long‐term water resources planning and management.     

Filyos River Streamflow Reconstruction from Tree‐Ring Chronologies with Nonparametric Approaches

The article presents nonparametric methods based on K nearest neighbors (KNNs), modified KNNs, and local polynomial techniques to reconstruct streamflow ensembles from tree‐ring data in Filyos River region (Turkey). Three methods were tested using cross‐validation for the overlap period, 1963‐1997 for which the tree‐ring and streamflow data are available. It was found that for the study where the length of the overlap period was limited, a nonparametric method based on a local polynomial technique provides simulations that have a slightly better solution than the other methods. After verification using standard statistical techniques, these methods were utilized to develop streamflow reconstructions from tree‐ring data for the paleo‐hydrologic period (1657‐1963). These reconstructions of seasonal low and high flows were discussed with the obtained flood duration curve. They were also compared with the historical archives and other tree‐ring reconstructions data available in the same river. Overall, the utility and limitations of these methods and the resulting streamflow simulations were discussed to assess the long‐term discharge behavior of Filyos River and to evaluate water supply reliability.     

DETECTING BASE FLOW IMPACTS IN COASTAL PLAIN STREAMS1

ABSTRACT: An established trend analysis methodology was applied to the problem of identifying and quantifying stream base flow impacts from water withdrawals and water loss through interbasin transfers. Impacts were simulated using base flow values selected from two U.S. Geological Survey (USGS) continuous record streamflow sites located within the Pinelands of southern New Jersey. Study site base flows were regressed against index site base flows with monotonic and step trend tests applied to the residuals from the regression model. The smallest, significantly detectable (α= 0.10) percentage reduction within a given simulation was used as an estimate of the sensitivity of a trend test. Evaluation of the trend analysis methodology led to the following practical considerations regarding trend test sensitivity. The proportion of study site base flow variability explained by index site base flows should be maximized, while at the same time minimizing positive, first-order autocorrelation in the regression residuals. Given the importance of detecting autocorrelation, missing values should be avoided or minimized. The quarterly (three-month) interval reduced the magnitude of autocorrelation relative to a shorter two-month sampling interval. Sensitivity appeared to improve when equalizing the number of values before and after a base flow impact(s) while seasonally biased sampling appeared to reduce sensitivity. Based primarily on past trend detection studies, nonparametric tests were deemed a better choice over their parametric counterparts, due to the lack of stringent data distributional requirements coupled with little or no loss of power even when applied to normally distributed data.     

Sensitivity of Stream flow and Water Table Depth to Potential Climatic Variability in a Coastal Forested Watershed1

Dai, Zhaohua, Carl C. Trettin, Changsheng Li, Devendra M. Amatya, Ge Sun, and Harbin Li, 2010. Sensitivity of Streamflow and Water Table Depth to Potential Climatic Variability in a Coastal Forested Watershed. Journal of the American Water Resources Association (JAWRA) 1–13. DOI: 10.1111/j.1752-1688.2010.00474.x Abstract: A physically based distributed hydrological model, MIKE SHE, was used to evaluate the effects of altered temperature and precipitation regimes on the streamflow and water table in a forested watershed on the southeastern Atlantic coastal plain. The model calibration and validation against both streamflow and water table depth showed that the MIKE SHE was applicable for predicting the streamflow and water table dynamics for this watershed with an acceptable model efficiency (E > 0.5 for daily streamflow and >0.75 for monthly streamflow). The simulation results from changing temperature and precipitation scenarios indicate that climate change influences both streamflow and water table in the forested watershed. Compared to current climate conditions, the annual average streamflow increased or decreased by 2.4% with one percentage increase or decrease in precipitation; a quadratic polynomial relationship between changes in water table depth (cm) and precipitation (%) was found. The annual average water table depth and annual average streamflow linearly decreased with an increase in temperature within the range of temperature change scenarios (0-6°C). The simulation results from the potential climate change scenarios indicate that future climate change will substantially impact the hydrological regime of upland and wetland forests on the coastal plain with corresponding implications to altered ecosystem functions that are dependent on water.     

Use of observed hydroclimatic trends to constrain projections of snowmelt season runoff in the Rio Grande headwaters

A method is developed for choosing 21st Century streamflow projections among widely varying results from a large ensemble of climate model-driven simulations. We quantify observed trends in climate–streamflow relationships in the Rio Grande headwaters, which has experienced warming temperature and declining snowpack since the mid-20th Century. Prominent trends in the snowmelt runoff season are used to assess corresponding statistics in downscaled global climate model projections. We define “Observationally Consistent (OC)” simulations as those that reproduce historical changes to linear statistics of diminished snowpack–streamflow coupling in the headwaters and an associated increase in the contribution of spring season (post-peak snowpack) precipitation to streamflow. Only a modest fraction of the ensemble of simulations meets these consistency metrics. The subset of OC simulations projects significant decreases in headwaters flow, whereas the simulations that poorly replicate historical trends exhibit a much wider range of projected changes. These results bolster confidence in model-based projections of declining runoff in the Rio Grande headwaters in the snowmelt runoff season and offer an example of a methodology for evaluating model-based projections in basins with similar hydroclimates that have experienced pronounced climate changes in the recent historical record.