Twenty-first century central Rocky Mountain river discharge scenarios under greenhouse forcing (original) (raw)
Related papers
2010
To predict future river flows, empirical trend projection (ETP) analyses and extends historic trends, while hydroclimatic modelling (HCM) incorporates regional downscaling from global circulation model (GCM) outputs. We applied both approaches to the extensively allocated Oldman River Basin that drains the North American Rocky Mountains and provides an international focus for water sharing. For ETP, we analysed monthly discharges from 1912 to 2008 with non-parametric regression, and extrapolated changes to 2055. For modelling, we refined the physical models MTCLIM and SNOPAC to provide water inputs into RIVRQ (river discharge), a model that assesses the streamflow regime as involving dynamic peaks superimposed on stable baseflow. After parameterization with 1960-1989 data, we assessed climate forecasts from six GCMs: CGCM1-A, HadCM3, NCAR-CCM3, ECHAM4 and 5 and GCM2. Modelling reasonably reconstructed monthly hydrographs (R 2 about 0Ð7), and averaging over three decades closely reconstructed the monthly pattern (R 2 D 0Ð94). When applied to the GCM forecasts, the model predicted that summer flows would decline considerably, while winter and early spring flows would increase, producing a slight decline in the annual discharge (3%, 2005-2055). The ETP predicted similarly decreased summer flows but slight change in winter flows and greater annual flow reduction (9%). The partial convergence of the seasonal flow projections increases confidence in a composite analysis and we thus predict further declines in summer (about 15%) and annual flows (about 5%). This composite projection indicates a more modest change than had been anticipated based on earlier GCM analyses or trend projections that considered only three or four decades. For other river basins, we recommend the utilization of ETP based on the longest available streamflow records, and HCM with multiple GCMs. The degree of correspondence from these two independent approaches would provide a basis for assessing the confidence in projections for future river flows and surface water supplies.
Predicting regime shifts in flow of the Gunnison River under changing climate conditions
Water Resources Research, 2013
Water resource management agencies have traditionally relied upon past observations of historical hydrologic records for long-term planning. This assumption of stationarity, that the past is representative of the future, may no longer be valid under changing climate conditions. The Gunnison River Basin contributes approximately 16% of the annual natural streamflow within the Upper Colorado River Basin, affecting water supply availability over the entire Colorado River Basin. Recent studies indicate that streamflow over the Gunnison River Basin, a subbasin within the Colorado River Basin, may decrease on the order of 15% through 2099. Further study has developed a methodology to statistically characterize the risk of regime shifts using observations of past streamflow through the use of a twoparameter gamma distribution. In this study, regime characteristics derived using a paleoreconstruction of streamflow over the Gunnison River Basin are compared regime characteristics developed using 112 projections of future hydrology to better understand how the frequency and duration of persistent dry and wet periods may change as the impacts of climate change are realized over the subbasin. Results indicate that under changing climate conditions, similar regime characteristics may be expected through 2039. However, between 2040 and 2099, more frequent and persistent dry regimes increase on the order of 50%. Conversely, wet regimes are expected to be shorter and less frequent than observed over the paleoclimatic record, decreasing in frequency by as much as 50%.
Reconstructing river discharge trends from climate variables and prediction of future trends
Journal of Hydrology, 2014
A number of studies suggest a significant decline of river discharge in the Canadian Plains that drain the eastern slopes of the Canadian Rocky Mountains, and elsewhere in Canada. Analyses of these trends suggested that apparent decline rates may represent long-term discharge variation as a result of anthropogenic induced change in seasonal flow and/or may also represent true long-term declines in annual flow. Potential for significant declines in river discharge raises concern over future water supply for this region. However, extracting accurate trends in river discharge is challenging for basins with relatively short periods of record as quasi-periodic decadal and multi-decadal oscillations are found to be important components of long-term natural variability. In order to reconstruct historic river flows, a correlation model between river flow and climate variables (that normally have longer periods of record) was developed. This empirical relationship was used as a proxy to reconstruct natural modes of river discharge, allowing a means to extend short term discharge records further back in time. The Athabasca River was used as an example to demonstrate the application of the proposed methods. The resulting long-term Athabasca River flow trends show variation is strongly related to the Pacific Decadal Oscillation. Previous studies suggesting decline flows on this river have been biased by examining short-term records of flow, that by chance corresponded with the down limb of a long term cycle.
Response of streamflow to weather variability under climate change in the Colorado Rockies
Journal of Hydrologic Engineering, 2007
We examine the response of streamflow to long-term rainfall variability under climate change by coupling downscaled global climate model precipitation to a distributed hydrologic model. We use daily output of the coupled global climate model ͑CGCM2͒ of the Canadian Centre for Climate Modelling and Analysis corresponding to the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios B2 scenario. The B2 scenario envisions slower population growth ͑10.4 billion by 2100͒ with a more rapidly evolving economy and more emphasis on environmental protection. We use the Hydrologic Modeling System of the Hydrologic Engineering Center for distributed hydrologic modeling. Because of the incongruence between the spatial scale of the CGCM2 output and that of the hydrologic model, a new space-time stochastic random cascade model was implemented in order to downscale the CGCM2 precipitation. The downscaling model accounts for the observed spatial intermittency of precipitation as well as for the self-similar scaling structure of its spatial distribution. For the South Platte basin, results show that the distribution of peak flow rate is more sensitive to the spatial variability of rainfall than total runoff volume. Results also show that the relative impact of long-term rainfall variation associated with climate change on total runoff and peak flow can be much greater than the magnitude of the rainfall variation itself, and that the magnitude of the impact depends strongly on the magnitude of the associated change in evapotranspiration.
Journal of Hydrology, 2009
The study presented here utilized long-term streamflow records (over 500 years) to investigate the influence of interannual/interdecadal climate variability on the Colorado River basin. 19 unimpaired water year streamflow stations were reconstructed utilizing partial least square regression using standard tree ring chronologies. The spatial and temporal variability of drought was evaluated for all the stations for the different centuries in the record. Finally, the relationship between individual impact of ENSO, PDO, and AMO and its combined effect on streamflow was determined using the non parametric Rank Sum test for different lag years (0, +1, +2, and +3) of streamflow. This research also determined the change in streamflow volume with respect to the long-term mean volume of the basin due to individual and coupled effect of oceanic climate influences. Results indicate that there is an increase in streamflow during El Niño and decreased streamflow during La Niña in the basin. Similarly, PDO warm/cold resulted in increased/decreased streamflow. There were few stations related to the AMO in the basin. Finally, the differences in the Upper and Lower basin were noted in the magnitude of changes in streamflow (in terms of percentage) under different individual and coupled influences of ENSO, PDO, and AMO.
Journal of Hydrology, 2013
The ability to predict spatial variation in streamflow at the watershed scale is essential to understanding the potential impacts of projected climate change on aquatic systems in this century. However, problems associated with single outlet-based model calibration and validation procedures can confound the prediction of spatial variation in streamflow under future climate change scenarios. The goal of this study is to calibrate and validate a distributed hydrologic model, the Soil and Water Assessment Tool (SWAT), using distributed streamflow data (1978-2009), and to assess the potential impacts of climate change on future streamflow (2051-2060 and 2086-2095) for the Rock River (RRW), Illinois River (IRW), Kaskaskia River (KRW), and Wabash River (WRW) watersheds in the Midwestern United States, primarily in Illinois. The potential impacts of climate change on future water resources are assessed using SWAT streamflow simulations driven by projections from nine global climate models (GCMs) under a maximum of three SRES scenarios (A1B, A2, and B1). Results from model validation indicate reasonable spatial and temporal predictions of streamflow, suggesting that a multi-site calibration strategy is necessary to accurately predict spatial variation in watershed hydrology. Compared with past streamflow records, predicted future streamflow based on climate change scenarios will tend to increase in the winter but decrease in the summer. According to 26 GCM projections, annual streamflows from 2051-2060 (2086-2095) are projected to decrease up to 45.2% (61.3%), 48.7% (49.8%), 48.7% (56.6%), and 41.1% (44.6%) in the RRW, IRW, KRW, and WRW, respectively. In addition, under the projected changes in climate, intra-and inter-annual streamflow variability generally does not increase over time. Results suggest that increased temperature could change the rate of evapotranspiration and the form of precipitation, subsequently influencing monthly streamflow patterns. Moreover, the spatially varying pattern of streamflow variability under future climate conditions suggests different buffering capabilities among regions. As such, regionally specific management strategies are necessary to mitigate the potential impacts of climate change and preserve aquatic ecosystems and water resources.
Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse ( 1 /168) and fine ( 1 /1208) spatial resolutions were evaluated against observed streamflows from 217 watersheds. In particular, the adequacy of VIC simulations in groundwater-versus runoff-dominated watersheds using a range of flow metrics relevant for water supply and aquatic habitat was examined. These flow metrics were 1) total annual streamflow; 2) total fall, winter, spring, and summer season streamflows; and 3) 5th, 25th, 50th, 75th, and 95th flow percentiles. The effect of climate on model performance was also evaluated by comparing the observed and simulated streamflow sensitivities to temperature and precipitation. Model performance was evaluated using four quantitative statistics: nonparametric rank correlation r, normalized Nash-Sutcliffe efficiency NNSE, root-mean-square error RMSE, and percent bias PBIAS. The VIC model captured the sensitivity of streamflow for temperature better than for precipitation and was in poor agreement with the corresponding temperature and precipitation sensitivities derived from observed streamflow. The model was able to capture the hydrologic behavior of the study watersheds with reasonable accuracy. Both total streamflow and flow percentiles, however, are subject to strong systematic model bias. For example, summer streamflows were underpredicted (PBIAS 5 213%) in groundwater-dominated watersheds and overpredicted (PBIAS 5 48%) in runoff-dominated watersheds. Similarly, the 5th flow percentile was underpredicted (PBIAS 5 251%) in groundwater-dominated watersheds and overpredicted (PBIAS 5 19%) in runoff-dominated watersheds. These results provide a foundation for improving model parameterization and calibration in ungauged basins.
Modeling the effects of climate change on water resources in the Gunnison River Basin, Colorado
Hydrologic models provide a framework in which to conceptualize and investigate the relationships between climate and water resources. A review of current studies that assess the impacts of climate change using hydrologic models indicates a number of problem areas common to the variety of models applied. These problem areas include parameter estimation, scale, model validation, climate scenario generation, and data. Research needs to address these problems include development of (1) a more physically based understanding of hydrologic processes and their interactions; (2) parameter measurement and estimation techniques for application over a range of spatial and temporal scales; (3) quantitative measures of uncertainty in model parameters and model results; (4) improved methodologies of climate scenario generation; (5) detailed data sets in a variety of climatic and physiographic regions; and (6) modular modeling tools to provide a framework to facilitate interdisciplinary research. Solutions to these problems would significantly improve the capability of models to assess the effects of climate change.
Basis for Extending Long-Term Streamflow Forecasts in the Colorado River Basin
The National Weather Service (NWS) maintains a collection of computer models used to perform various functions for managing the rivers of the United States. One function of the NWS's river forecast centers is to provide long-term resource forecasts for the main river basins in the United States. By using singular value decomposition (SVD) analysis, the research presented here identifies a new sea surface temperature (SST) index which demonstrates significant, long-lead covariance with streamflow in the Colorado River Basin. This index is compared with other existing climate indices by using the nonparametric rank sum test and by also using the index in a forecasting scenario.
Summer streamflows in the Pacific Northwest are largely derived from melting snow and groundwater discharge. As the climate warms, diminishing snowpack and earlier snowmelt will cause reductions in summer stream-flow. Most regional-scale assessments of climate change impacts on streamflow use downscaled temperature and precipitation projections from general circulation models (GCMs) coupled with large-scale hydrologic models. Here we develop and apply an analytical hydrogeologic framework for characterizing summer streamflow sensitivity to a change in the timing and magnitude of recharge in a spatially explicit fashion. In particular, we incorporate the role of deep groundwater, which large-scale hydrologic models generally fail to capture, into streamflow sensitivity assessments. We validate our analytical streamflow sensitivities against two empirical measures of sensitivity derived using historical observations of temperature, precipitation, and streamflow from 217 watersheds. In general, empirically and analytically derived streamflow sensitivity values correspond. Although the selected watersheds cover a range of hydrologic regimes (e.g., rain-dominated, mixture of rain and snow, and snow-dominated), sensitivity validation was primarily driven by the snow-dominated watersheds, which are subjected to a wider range of change in recharge timing and magnitude as a result of increased temperature. Overall, two patterns emerge from this analysis: first, areas with high streamflow sensitivity also have higher summer streamflows as compared to low-sensitivity areas. Second, the level of sensitivity and spatial extent of highly sensitive areas diminishes over time as the summer progresses. Results of this analysis point to a robust, practical, and scalable approach that can help assess risk at the landscape scale, complement the downscaling approach, be applied to any climate scenario of interest, and provide a framework to assist land and water managers in adapting to an uncertain and potentially challenging future.