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Publication Abstracts
The abstracts of all journal articles generated from research supported by Reclamation’s Upper and Lower Colorado Regions are presented below. They are in chronological order with links to their respective journals.
Medieval drought in the Upper Colorado River Basin
David Meko, Connie Woodhouse, Christopher Baisan, Troy Knight, Jeffrey Lukas, Malcom Hughes, and Matthew Salzer; Geophysical Research Letters 10(34); 2007
Abstract: New tree‐ring records of ring‐width from remnant preserved wood are analyzed to extend the record of reconstructed annual flows of the Colorado River at Lee Ferry into the Medieval Climate Anomaly, when epic droughts are hypothesized from other paleoclimatic evidence to have affected various parts of western North America. The most extreme low‐frequency feature of the new reconstruction, covering A.D. 762‐2005, is a hydrologic drought in the mid‐1100s. The drought is characterized by a decrease of more than 15% in mean annual flow averaged over 25 years, and by the absence of high annual flows over a longer period of about six decades. The drought is consistent in timing with dry conditions inferred from tree‐ring data in the Great Basin and Colorado Plateau, but regional differences in intensity emphasize the importance of basin‐specific paleoclimatic data in quantifying likely effects of drought on water supply.
Regional Analysis of Trend and Step Changes Observed in Hydroclimatic Variables around the Colorado River Basin
W. Paul Miller, Thomas Piechota; Journal of Hydrometeorology 9(5); 2008
Abstract: Recent research has suggested that changes in temperature and precipitation events due to climate change have had a significant impact on the availability and timing of streamflow. In this study, monthly temperature and precipitation data collected over 29 climate divisions covering the entire Colorado River basin and monthly natural flow data from 29 U.S. Geological Survey (USGS) gauge locations along the Colorado River are investigated for trend or step changes using parametric and nonparametric statistical tests. Temperature increases are persistent (at least 10 climate divisions over 6 months in trend analysis) throughout the year over the Colorado River basin, whereas precipitation only notably increased over 17 climate divisions (during trend analysis) during February and remained relatively unchanged otherwise. These results correspond with changes in naturalized streamflow throughout the year. Streamflow increases are recorded between November and February but exhibit a decreasing trend over the traditional peak runoff season (April through July). Under trend analysis, 18 flow stations exhibited increasing trends in January and 19 flow stations exhibited decreasing trends in June. It is likely that increasing temperature trends have affected the character of precipitation in the Colorado River basin, causing a change in the timing of runoff events.
A nonparametric approach for paleohydrologic reconstruction of annual streamflow ensembles
Subhrendu Gangopadhyay, Benjamin Harding, Balaji Rajagopalan, Jeffrey Lukas, Terrance Fulp; Water Resources Research 45(6); 2009
Abstract: As multicentury records of natural hydrologic variability, tree ring reconstructions of streamflow have proven valuable in water resources planning and management. All previous reconstructions have used parametric methods, most often regression, to develop a model relating a set of tree ring data to a target hydrology. In this paper, we present the first development and application of a K nearest neighbor (KNN) nonparametric method to reconstruct naturalized annual streamflow ensembles from tree ring chronology data in the Upper Colorado River Basin region. The method is developed using tree ring chronologies from the period 1400–2005 and naturalized streamflow from the period 1906–2005 at the important Lees Ferry, Arizona, gauge on the Colorado River to develop annual streamflow ensembles for this gauge for the 1400–1905 period. The proposed KNN algorithm was developed and tested using cross validation for the overlap period, i.e., the contemporary observed period for which both the tree ring and streamflow data are available (1906–2005). The cross-validated streamflow reconstructions for the selected contemporary period compare very well with the observed flows and also with published parametric streamflow reconstructions for this gauge. The proposed nonparametric method provides an ensemble of streamflows for each year in the paleohydrologic reconstruction period (1400–1905) and, consequently, a more realistic asymmetric confidence interval than one obtained through most parametric approaches. Also, the K nearest neighbors are obtained only from the tree ring chronology data, and thus, the method can be used to reconstruct structured and even nonnumerical data for use in water resources modeling.
Water supply risk on the Colorado River: Can management mitigate?
Balaji Rajagopalan, Kenneth Nowak, James Prairie, Martin Hoerling, Benjamin Harding, Joseph Barsugli, Andrea Ray, Bradley Udall; Water Resources Research 45(8); 2009
Abstract: Population growth and a changing climate will tax the future reliability of the Colorado River water supply. Using a heuristic model, we assess the annual risk to the Colorado River water supply for 2008–2057. Projected demand growth superimposed upon historical climate variability results in only a small probability of annual reservoir depletion through 2057. In contrast, a scenario of 20% reduction in the annual Colorado River flow due to climate change by 2057 results in a near tenfold increase in the probability of annual reservoir depletion by 2057. However, our analysis suggests that flexibility in current management practices could mitigate some of the increased risk due to climate change–induced reductions in flows.
Comment on “When will Lake Mead go dry?” by T. P. Barnett and D. W. Pierce
Joseph Barsugli, Kenneth Nowak, Balaji Rajagopalan, James Prairie, Benjamin Harding; Water Resources Research 45(9); 2009
Improving Seasonal Predictions of Climate Variability and Water Availability at the Catchment Scale
Matthew Switanek, Peter Troch, Christopher Castro; Journal of Hydrometeorology 10(6); 2009
Abstract: In a water-stressed region, such as the southwestern United States, it is essential to improve current seasonal hydroclimatic predictions. Typically, seasonal hydroclimatic predictions have been conditioned by standard climate indices, for example, Niño-3 and Pacific decadal oscillation (PDO). In this work, the statistically unique relationships between sea surface temperatures (SSTs) and particular basins’ hydroclimates are explored. The regions where global SSTs are most correlated with the Little Colorado River and Gunnison River basins’ hydroclimates are located throughout the year and at varying time lags. The SSTs, from these regions of highest correlation, are subsequently used as hydroclimatic predictors for the two basins. This methodology, named basin-specific climate prediction (BSCP), is further used to perform hindcasts. The hydroclimatic hindcasts obtained using BSCP are shown to be closer to the historical record, for both basins, than using the standard climate indices as predictors.
A Bimillennial-Length Tree-Ring Reconstruction of Precipitation for the Tavaputs Plateau, Northeastern Utah
Troy Knight, David Meko, Christopher Baisan; Quaternary Research 73(1); 2010
Abstract: Despite the extensive network of moisture-sensitive tree-ring chronologies in western North America, relatively few are long enough to document climatic variability before and during the Medieval Climate Anomaly (MCA) ca. AD 800-1300. We developed a 2300-yr tree-ring chronology extending to 323 BC utilizing live and remnant Douglas-fir (Pseudotsuga menziesii) from the Tavaputs Plateau in northeastern Utah. A resulting regression model accounts for 70% of the variance of precipitation for the AD 1918–2005 calibration period. Extreme wet and dry periods without modern analogues were identified in the reconstruction. The MCA is marked by several prolonged droughts, especially prominent in the mid AD 1100s and late 1200s, and a lack of wet or dry single-year extremes. The frequency of extended droughts is not markedly different, however, than before or after the MCA. A drought in the early AD 500s surpasses in magnitude any other drought during the last 1800 yr. A set of four long high-resolution records suggests this drought decreased in severity toward the south in the western United States. The spatial pattern is consistent with the western dipole of moisture anomaly driven by El Niño and is also similar to the spatial footprint of the AD 1930s “Dust Bowl” drought.
Twentieth Century Temperature Trends in Colorado’s San Juan Mountains
Imtiaz Rangwala, James Miller; Arctic, Antarctic, and Alpine Research 42(1); 2010
Abstract: We examine trends in surface air temperature for the San Juan Mountain region in southwestern Colorado from 1895 to 2005. Observations from both National Weather Service (NWS) and Snow Telemetry (SNOTEL) sites are analyzed. Results show a net warming of 1 °C between 1895 and 2005. Most of this warming occurred between 1990 and 2005, when the region experienced rapid and secular increases in temperature. Between 1950 and 1985, there was a cooling trend in the region during which there were significant decreases in the maximum temperature (Tmax) and almost no trend in the minimum temperature (Tmin). This cooling trend appears to be, in part, associated with increases in atmospheric aerosols. Between 1990 and 2005, the large increases in temperature anomalies are strongly correlated at the NWS and SNOTEL sites. Annual increases in Tmax and Tmin are similar between 1990 and 2005; however, they generally show greater increases during summer and winter, respectively. Spatially, there are similar increases in Tmax and Tmin except in the central mountain region, where the increases in Tmin are larger and started earlier.
A multisite seasonal ensemble streamflow forecasting technique
Cameron Bracken, Balaji Rajagopalan, James Prairie; Water Resources Research 46(3); 2010
Abstract: We present a technique for providing seasonal ensemble streamflow forecasts at several locations simultaneously on a river network. The framework is an integration of two recent approaches: the nonparametric multimodel ensemble forecast technique and the nonparametric space‐time disaggregation technique. The four main components of the proposed framework are as follows: (1) an index gauge streamflow is constructed as the sum of flows at all the desired spatial locations; (2) potential predictors of the spring season (April–July) streamflow at this index gauge are identified from the large‐scale ocean‐atmosphere‐land system, including snow water equivalent; (3) the multimodel ensemble forecast approach is used to generate the ensemble flow forecast at the index gauge; and (4) the ensembles are disaggregated using a nonparametric space‐time disaggregation technique resulting in forecast ensembles at the desired locations and for all the months within the season. We demonstrate the utility of this technique in skillful forecast of spring seasonal streamflows at four locations in the Upper Colorado River Basin at different lead times. Where applicable, we compare the forecasts to the Colorado Basin River Forecast Center’s Ensemble Streamflow Prediction (ESP) and the National Resource Conservation Service “coordinated” forecast, which is a combination of the ESP, Statistical Water Supply, a principal component regression technique, and modeler knowledge. We find that overall, the proposed method is equally skillful to existing operational models while tending to better predict wet years. The forecasts from this approach can be a valuable input for efficient planning and management of water resources in the basin.
A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow
Kenneth Nowak, James Prairie, Balaji Rajagopalan, Umpanu Lall; Water Resources Research 46(8); 2010
Abstract: Streamflow disaggregation techniques are used to distribute a single aggregate flow value to multiple sites in both space and time while preserving distributional statistics (i.e., mean, variance, skewness, and maximum and minimum values) from observed data. A number of techniques exist for accomplishing this task through a variety of parametric and nonparametric approaches. However, most of these methods do not perform well for disaggregation to daily time scales. This is generally due to a mismatch between the parametric distributions appropriate for daily flows versus monthly or annual flows, the high dimension of the disaggregation problem, compounded uncertainty in parameter estimation for multistage approaches, and the inability to maintain flow continuity across disaggregation time period boundaries. We present a method that directly simulates daily data at multiple locations from a single annual flow value via K-nearest neighbor (K-NN) resampling of daily flow proportion vectors. The procedure is simple and data driven and captures observed statistics quite well. Furthermore, the generated daily data are continuous and display lag correlation structure consistent with that of the observed data. The utility and effectiveness of this approach is demonstrated for selected sites in the San Juan River Basin, located in southwestern Colorado, and later compared with the disaggregation technique of Prairie et al. (2007) for several locations in the Colorado River Basin.
Homogeneity of Gridded Precipitation Datasets for the Colorado River Basin
Galina Guentchev, Joseph Barsugli, Jon Eischeid; Journal of Applied Meteorology and Climatology 49(12); 2010
Abstract: Inhomogeneity in gridded meteorological data may arise from the inclusion of inhomogeneous station data or from aspects of the gridding procedure itself. However, the homogeneity of gridded datasets is rarely questioned, even though an analysis of trends or variability that uses inhomogeneous data could be misleading or even erroneous. Three gridded precipitation datasets that have been used in studies of the Upper Colorado River basin were tested for homogeneity in this study: that of Maurer et al., that of Beyene and Lettenmaier, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) dataset of Daly et al. Four absolute homogeneity tests were applied to annual precipitation amounts on a grid cell and on a hydrologic subregion spatial scale for the periods 1950–99 and 1916–2006. The analysis detects breakpoints in 1977 and 1978 at many locations in all three datasets that may be due to an anomalously rapid shift in the Pacific decadal oscillation. One dataset showed breakpoints in the 1940s that might be due to the widespread change in the number of available observing stations used as input for that dataset. The results also indicated that the time series from the three datasets are sufficiently homogeneous for variability analysis during the 1950–99 period when aggregated on a subregional scale.
Development of streamflow projections under changing climate conditions over Colorado River basin headwaters
W. Paul Miller, Thomas Piechota, Subhrendu Gangopadhyay, Tommy Pruitt; Hydrology and Earth System Sciences 15(7); 2011
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.
Trends in Western U.S. Snowpack and Related Upper Colorado River Basin Streamflow
W. Paul Miller, Thomas Piechota; Journal of the American Water Resources Association 47(6); 2011
Abstract: Water resource managers in the Western United States (U.S.) are currently faced with the challenge of adapting to unprecedented drought and uncertain impacts of climate change. Recent research has indicated increasing regional temperature and changes to precipitation and streamflow characteristics throughout the Western U.S. As such, there is increased uncertainty in hydroclimatological forecasts, which impact reservoir operations and water availability throughout the Western U.S., particularly in the Colorado River Basin. Previous research by the authors hypothesized a change in the character of precipitation (i.e., the frequency and amount of rainfall and snowfall events) throughout the Colorado River Basin. In the current study, 398 snowpack telemetry stations were investigated for trends in cumulative precipitation, snow water equivalent, and precipitation events. Observations of snow water equivalent characteristics were compared to observations in streamflow characteristics. Results indicate that the timing of the last day of the snow season corresponds well to the volume of runoff observed over the traditional peak flow season (April through July); conversely, the timing of the first day of the snow season does not correspond well to the volume of runoff observed over the peak flow season. This is significant to water resource managers and river forecasters, as snowpack characteristics may be indicative of a productive or unproductive runoff season.
Wavelet Auto-Regressive Method (WARM) for multi-site streamflow simulation of data with non-stationary spectra
Kenneth Nowak, Balaji Rajagopalan, Edith Zagona; Journal of Hydrology 410(1-2); 2011
Abstract: Traditional stochastic simulation methods that are crafted to capture measures such as mean, variance and skew fail to reproduce significant spectral properties of the observed data. A growing body of literature indicates that many geo-physical data, especially streamflow, exhibit quasi-periodic and non-stationary variability driven by large scale climate features. Thus, methods which accurately model this behavior, in particular, the time evolution of variability, frequency of wet/dry epochs, etc. are crucial for risk assessment and management of water resources. In this paper, a Wavelet based Auto Regression Modeling (WARM) framework is proposed for data with significant non-stationary spectral features. This approach has four broad steps – (i) the wavelet transform of a time series is reconstructed as several periodic components based on dominant variability frequencies, (ii) scale averaged wavelet power (SAWP) is computed for each band to capture the time varying power and the components are scaled by this, (iii) Auto Regressive (AR) models fit to the scaled components and, (iv) simulations are performed from the AR models, rescaled and combined to obtain simulations of the original time series. Step (ii) is a new and unique departure from the WARM proposed by Kwon et al. (2007). We demonstrate this approach on annual streamflow at the Lee’s Ferry gauge on the Colorado River. Furthermore, this is coupled with a spatial disaggregation method to generate streamflow ensembles at multiple locations upstream. We also show that this combination captures the spectral properties at several locations in a parsimonious manner.
Decadal prediction of Colorado River streamflow anomalies using ocean-atmosphere teleconnections
Matthew Switanek, Peter Troch; Geophysical Research Letters 38(23); 2011
Abstract: The Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) time series are used to forecast a decade ahead streamflow anomalies in the upper Colorado River at Lee’s Ferry. In the instrumental record, we obtain unusually high decadal forecast skill that is statistically significant at the 95% confidence level, suggesting strong ocean-atmosphere-land teleconnection. In order to test whether such teleconnection existed in the past, we compare the retrospective forecast skill to the skills obtained using the available ocean-atmosphere teleconnection and streamflow reconstructions derived from tree rings. We find much lower skill in the reconstructed record. Using frequency analysis, we show that the streamflow and sea surface temperature oscillations in the instrumental records all have dominant low frequency periodicities (>35 years) that explain much of the total variance. However, such dominant periodicities do not appear in the power spectra of the reconstructed records of AMO, PDO and streamflow. Given that these dominant low periodicities are likely responsible for the high prediction skill in the instrumental record, it remains uncertain whether reliable decadal streamflow predictions in the upper Colorado River basin will be possible in the years ahead.
The implications of climate change scenario selection for future streamflow projection in the Upper Colorado River Basin
Benjamin Harding, Andrew Wood, James Prairie; Hydrology and Earth System Sciences 16(11); 2012
Abstract: The impact of projected 21st century climate conditions on streamflow in the Upper Colorado River Basin was estimated using a multi-model ensemble approach wherein the downscaled outputs of 112 future climate projections from 16 global climate models (GCMs) were used to drive a macroscale hydrology model. By the middle of the century, the impacts on streamflow range, over the entire ensemble, from a decrease of approximately 30% to an increase of approximately the same magnitude. Although prior studies and associated media coverage have focused heavily on the likelihood of a drier future for the Colorado River Basin, approximately 25 to 35% of the ensemble of runs, by 2099 and 2039, respectively, result in no change or increases in streamflow. The broad range of projected impacts is primarily the result of uncertainty in projections of future precipitation, and a relatively small part of the variability of precipitation across the projections can be attributed to the effect of emissions pathways. The simulated evolution of future temperature is strongly influenced by emissions, but temperature has a smaller influence than precipitation on flow. Period change statistics (i.e., the change in flow from one 30-yr period to another) vary as much within a model ensemble as between models and emissions pathways. Even by the end of the current century, the variability across the projections is much greater than changes in the ensemble mean. The relatively large ensemble analysis described herein provides perspective on earlier studies that have used fewer scenarios, and suggests that impact analyses relying on one or a few climate scenarios are unacceptably influenced by the choice of projections.
Dendrochronology and links to streamflow
David Meko, Connie Woodhouse, Kiyomi Morino; Journal of Hydrology 412-413; 2012
Abstract: Streamflow variability on timescales of decades to centuries becomes increasingly important as water managers grapple with shortages imposed by increasing demand and limited supply, and possibly exacerbated by climate change. Two applications of dendrochronology to the study of flow variability are illustrated for an existing 1244-yr reconstruction of annual flows of the Colorado River at Lees Ferry, Arizona, USA: (1) identification and climatological interpretation of rare flow events, and (2) assessment of vulnerability of water-supply systems to climatic variability. Analysis centers on a sustained drought of the mid-1100s characterized by persistent low flows on both the Colorado and Sacramento Rivers. Analysis of geopotential height anomalies during modern joint-droughts suggests more than one mode of circulation might accompany joint-drought in the two basins. Monte Carlo simulation is used to demonstrate that a drought as severe as that in the 1100s on the Colorado River might be expected about once in every 4–6 centuries by chance alone given the time-series properties of the modern gaged flows. Application of a river-management model suggests a mid-1100s-style drought, were it to occur today, would drop reservoir levels in Lake Mead to dead-pool within a few decades. Uncertainty presents challenges to accurately quantifying severe sustained droughts from streamflow reconstructions, especially early in the tree-ring record. Corroboration by multiple proxy records is essential. Future improvements are likely to require a combination of methodological advancements and expanded basic data.
Water Management Decisions Using Multiple Hydrologic Models within the San Juan River Basin under Changing Climate Conditions
W. Paul Miller, R. Alan Butler, Thomas Piechota, James Prairie, Katrina Grantz, Gina DeRosa; Journal of Water Resources Planning and Management 138(5); 2012
Abstract: A modified version of the U.S. Bureau of Reclamation (Reclamation) long-term planning model, Colorado River Simulation System (CRSS), is used to evaluate whether hydrologic model choice has an impact on critical decision variables within the San Juan River Basin when evaluating potential effects of climate change through 2099. The distributed variable infiltration capacity (VIC) model and the lumped National Weather Service (NWS) River Forecast System (RFS) were each used to project future streamflow; these projections of streamflow were then used to force Reclamation’s CRSS model over the San Juan River Basin. Both hydrologic models were compared to evaluate whether or not uncertainty in climatic input generated from general circulation models outweighed differences between the hydrologic models. Differences in methodologies employed by each hydrologic model had a significant effect on projected streamflow within the basin. Both models project decreased water availability under changing climate conditions within the San Juan River Basin, but disagree on the magnitude of the decrease. On average, total naturalized inflow within the San Juan River Basin into the Navajo Reservoir is approximately 15% higher using inflows derived using the VIC model than those inflows developed using the RFS model; average projected tributary inflow from the San Juan River Basin to the Colorado River is approximately 25% higher using inflows derived by using the VIC model than those inflows developed by using the RFS. Overall, there is a higher risk and magnitude of shortage within the San Juan River Basin using streamflow developed with the RFS model as compared with inflow scenarios developed by using the VIC model. Model choice was found to have a significant effect on the evaluation of climate change impacts over the San Juan River Basin.
Colorado River Basin Hydroclimatic Variability
Kenneth Nowak, Martin Hoerling, Balaji Rajagopalan, Edith Zagona; Journal of Climate 25(12); 2012
Abstract: An analysis of annual hydroclimatic variability in the Upper Colorado River basin (UCRB) for the period of 1906–2006 was performed to understand the dominant modes of multidecadal variability. First, waveletbased spectral analysis was employed for streamflow at Lees Ferry, Arizona (aggregate location for UCRB flow), which identified two significant modes: a ‘‘low frequency’’ (~64-yr period) mode and a strong ‘‘decadal’’ (~15-yr period) component active only in recent decades. Subsequent investigation of temperature and precipitation data for the UCRB indicated that the low-frequency variability is associated with temperature via modulation of runoff efficiency while the decadal is strongly tied to moisture delivery. Simple hydrology and climate model experiments are also provided to support the aforementioned findings.
Mid-21st century projections in temperature extremes in the southern Colorado Rocky Mountains from regional climate models
Imtiaz Rangwala, Joseph Barsugli, Karen Cozzetto, Jason Neff, James Prairie; Climate Dynamics 39(7); 2012
Abstract: This study analyzes mid-21st century projections of daily surface air minimum (Tmin) and maximum (Tmax) temperatures, by season and elevation, over the southern range of the Colorado Rocky Mountains. The projections are from four regional climate models (RCMs) that are part of the North American Regional Climate Change Assessment Program (NARCCAP). All four RCMs project 2°C or higher increases in Tmin and Tmax for all seasons. However, there are much greater (>3°C) increases in Tmax during summer at higher elevations and in Tmin during winter at lower elevations. Tmax increases during summer are associated with drying conditions. The models simulate large reductions in latent heat fluxes and increases in sensible heat fluxes that are, in part, caused by decreases in precipitation and soil moisture. Tmin increases during winter are found to be associated with decreases in surface snow cover, and increases in soil moisture and atmospheric water vapor. The increased moistening of the soil and atmosphere facilitates a greater diurnal retention of the daytime solar energy in the land surface and amplifies the longwave heating of the land surface at night. We hypothesize that the presence of significant surface moisture fluxes can modify the effects of snow-albedo feedback and results in greater wintertime warming at night than during the day.
Climate change in mountains: a review of elevation-dependent warming and its possible causes
Imtiaz Rangwala, James Miller; Climatic Change 114(3); 2012
Abstract: Available observations suggest that some mountain regions are experiencing seasonal warming rates that are greater than the global land average. There is also evidence from observational and modeling studies for an elevation-dependent climate response within some mountain regions. Our understanding of climate change in mountains, however, remains challenging owing to inadequacies in observations and models. In fact, it is still uncertain whether mountainous regions generally are warming at a different rate than the rest of the global land surface, or whether elevation-based sensitivities in warming rates are prevalent within mountains. We review studies of four high mountain regions – the Swiss Alps, the Colorado Rocky Mountains, the Tibetan Plateau/Himalayas, and the Tropical Andes – to examine questions related to the sensitivity of climate change to surface elevation. We explore processes that could lead to enhanced warming within mountain regions and possible mechanisms that can produce altitudinal gradients in warming rates on different time scales. A conclusive understanding of these responses will continue to elude us in the absence of a more comprehensive network of climate monitoring in mountains.
Amplified water vapour feedback at high altitudes during winter
Imtiaz Rangwala; International Journal of Climatology 33(4); 2013
Abstract: During the last five decades, the Tibetan Plateau has experienced a warming trend of 0.4 °C/decade in winter, which is at least twice that of any other season. Some studies have suggested that this anomalous winter warming is caused, in part, by the observed increases in near-surface water vapour and its amplifying effect on the surface longwave downward radiation (LDR). This study uses observations of surface-specific humidity (q) and temperature as input to a one-dimensional radiative transfer model to assess the influence of lower atmospheric increases in water vapour on surface LDR, and the sensitivity of this process to different elevations and seasons on the Tibetan Plateau. The results from three idealized experiments are examined based on realistic atmospheric column profiles of temperature and moisture. They show that when an equal mass of water vapour is added into the atmospheric boundary layer during winter, a substantially greater increase (8×) in LDR is found at the high-elevation site relative to the low-elevation site. During summer, the LDR increases are much smaller as are the differences between the two sites. Experiments, where both q and temperature are increased, suggest that the influence of temperature changes on LDR is much greater than those caused by changes in q in all cases, except for the high-elevation-winter case when the opposite is true. These results provide further evidence for the possibility of a strong modulation of surface LDR caused by increases in atmospheric water vapour in high altitude regions (>3000 m) during the cold season.
A hidden Markov model combined with climate indices for multidecadal streamflow simulation
Cameron Bracken, Balaji Rajagopalan, Edith Zagona; Water Resources Research 50(10); 2014
Abstract: Hydroclimate time series often exhibit very low year-to-year autocorrelation while showing prolonged wet and dry epochs reminiscent of regime-shifting behavior. Traditional stochastic time series models cannot capture the regime-shifting features thereby misrepresenting the risk of prolonged wet and dry periods, consequently impacting management and planning efforts. Upper Colorado River Basin (UCRB) annual flow series highlights this clearly. To address this, a simulation framework is developed using a hidden Markov (HM) model in combination with large-scale climate indices that drive multidecadal variability. We demonstrate this on the UCRB flows and show that the simulations are able to capture the regime features by reproducing the multidecadal spectral features present in the data where a basic HM model without climate information cannot.
The more extreme nature of U.S. warm season climate in the recent observational record and two “well-performing” dynamically downscaled CMIP3 models
Hsin‐I Chang, Christopher L. Castro, Carlos M. Carrillo, Francina Dominguez; Journal of Geophysical Research: Atmospheres 120(16); 2015
Abstract: Arid and semiarid regions located in subtropical zones are projected to experience the most adverse impacts of climate change. During the warm season, observations and Intergovernmental Panel on Climate Change global climate models generally support a “wet gets wetter, dry gets drier” hypothesis in these regions, which acts to amplify the climatological transitions in the context of the annual cycle. In this study, we consider changes in U.S. early warm season precipitation in the observational record and regional climate model simulations driven by two “well‐performing” dynamically downscaled Coupled Model Intercomparison Project phase 3 (CMIP3) models (Hadley Centre Coupled Model, version 3 and Max Planck Institute (MPI) European Centre/Hamburg Model 5) that have a robust climatological representation of the North American Monsoon System (NAMS). Both observations and model results show amplification in historical seasonal transitions of temperature and precipitation associated with NAMS development, with Weather Research and Forecasting (WRF)‐MPI better representing the observed signal. Assuming the influence of remote Pacific sea surface temperature (SST) forcing associated with the El Niño–Southern Oscillation and Pacific Decadal Variability (ENSO‐PDV) on U.S. regional climate remains the same in the 21st century, similar extreme trends are also projected by WRF‐MPI for the next 30 years. A methodology is also developed to objectively analyze how climate change may be synergistically interacting with ENSO‐PDV variability during the early warm season. Our analysis suggests that interannual variability of warm season temperature and precipitation associated with Pacific SST forcing is becoming more extreme, and the signal is stronger in the observed record.
Wavelet-based time series bootstrap model for multidecadal streamflow simulation using climate indicators
Solomon Erkyihun; Balaji Rajagopalan, Edith Zagona, Upmanu Lall, Kenneth Nowak; Water Resources Research 52(5); 2016
Abstract: A model to generate stochastic streamflow projections conditioned on quasi-oscillatory climate indices such as Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO) is presented. Recognizing that each climate index has underlying band-limited components that contribute most of the energy of the signals, we first pursue a wavelet decomposition of the signals to identify and reconstruct these features from annually resolved historical data and proxy based paleoreconstructions of each climate index covering the period from 1650 to 2012. A K-Nearest Neighbor block bootstrap approach is then developed to simulate the total signal of each of these climate index series while preserving its time-frequency structure and marginal distributions. Finally, given the simulated climate signal time series, a K-Nearest Neighbor bootstrap is used to simulate annual streamflow series conditional on the joint state space defined by the simulated climate index for each year. We demonstrate this method by applying it to simulation of streamflow at Lees Ferry gauge on the Colorado River using indices of two large scale climate forcings: Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO), which are known to modulate the Colorado River Basin (CRB) hydrology at multidecadal time scales. Skill in stochastic simulation of multidecadal projections of flow using this approach is demonstrated.
Wavelet and Hidden Markov-Based Stochastic Simulation Methods Comparison on Colorado River Streamflow
Solomon Erkyhun, Edith Zagona, Balaji Rajagopalan; Journal of Hydrologic Engineering 22(9); 2017
Abstract: Wavelet and hidden Markov-based modeling frameworks were developed to better capture the nonstationarity and non-Gaussian characteristics of streamflow that linear models cannot. Climate-based conditional streamflow simulation techniques recently have been shown to perform even better in capturing the spectral characteristics of streamflow coupled with block bootstrap simulation. This paper presents a comparison of three recently developed time series models in these frameworks: the climate wavelet autoregressive model (CWARM), the climate hidden Markov model (CHMM), and the climate wavelet-based k-nearest neighbor (K-NN) time series bootstrap (CWKNN) model. The purpose is to determine their applicability in water resources planning and management. These three methods incorporate two large-scale climate forcings, Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO)—recognized as the drivers of underlying nonstationarity—to condition the streamflow simulation. Comparisons are made of performance in both simulation and projection modes using the Lees Ferry (Arizona) flow in the Colorado River basin (CRB). The three methods are generally very good at capturing the distributional statistics and nonstationary features of the historical data in simulation mode. For short-term projections (1–8 years), important for midterm reservoir operations and planning, the CHMM appears to perform slightly better than the other two models. For longer-term projections (∼20 years), useful for decadal and multidecadal water resources planning, the CWKNN performs much better.
Multi-year climate variability in the Southwestern United States within a context of a dynamically downscaled twentieth century reanalysis
Carlos M. Carrillo, Christopher L. Castro, Hsin-I Chang, Thang M. Luong; Climate Dynamics 49(11-12); 2017
Abstract: This investigation evaluates whether there is coherency in warm and cool season precipitation at the low-frequency scale that may be responsible for multi-year droughts in the US Southwest. This low-frequency climate variability at the decadal scale and longer is studied within the context of a twentieth-century reanalysis (20CR) and its dynamically-downscaled version (DD-20CR). A spectral domain matrix methods technique (Multiple-Taper-Method Singular Value Decomposition) is applied to these datasets to identify statistically significant spatiotemporal precipitation patterns for the cool (November–April) and warm (July–August) seasons. The low-frequency variability in the 20CR is evaluated by exploring global to continental-scale spatiotemporal variability in moisture flux convergence (MFC) to the occurrence of multiyear droughts and pluvials in Central America, as this region has a demonstrated anti-phase relationship in low-frequency climate variability with northern Mexico and the southwestern US By using the MFC in lieu of precipitation, this study reveals that the 20CR is able to resolve well the low-frequency, multiyear climate variability. In the context of the DD-20CR, multiyear droughts and pluvials in the southwestern US (in the early twentieth century) are significantly related to this low-frequency climate variability. The precipitation anomalies at these low-frequency timescales are in phase between the cool and warm seasons, consistent with the concept of dual-season drought as has been suggested in tree ring studies.
Developing Subseasonal to Seasonal Climate Forecast Products for Hydrology and Water Management
Sarah A. Baker, Andrew W. Wood, Balaji Rajagopalan; Journal of the American Water Resources Association 55(4); 2019
Abstract: We describe a new effort to enhance climate forecast relevance and usability through the development of a system for evaluating and displaying real‐time subseasonal to seasonal (S2S) climate forecasts on a watershed scale. Water managers may not use climate forecasts to their full potential due to perceived low skill, mismatched spatial and temporal resolutions, or lack of knowledge or tools to ingest data. Most forecasts are disseminated as large‐domain maps or gridded datasets and may be systematically biased relative to watershed climatologies. Forecasts presented on a watershed scale allow water managers to view forecasts for their specific basins, thereby increasing the usability and relevance of climate forecasts. This paper describes the formulation of S2S climate forecast products based on the Climate Forecast System version 2 (CFSv2) and the North American Multi‐Model Ensemble (NMME). Forecast products include bi‐weekly CFSv2 forecasts, and monthly and seasonal NMME forecasts. Precipitation and temperature forecasts are aggregated spatially to a United States Geological Survey (USGS) hydrologic unit code 4 (HUC‐4) watershed scale. Forecast verification reveals appreciable skill in the first two bi‐weekly periods (Weeks 1–2 and 2–3) from CFSv2, and usable skill in NMME Month 1 forecast with varying skills at longer lead times dependent on the season. Application of a bias‐correction technique (quantile mapping) eliminates forecast bias in the CFSv2 reforecasts, without adding significantly to correlation skill.
Application of Postprocessing to Watershed-Scale Subseasonal Climate Forecasts over the Contiguous United States.
Sarah A. Baker, Andrew W. Wood, Balaji Rajagopalan; Journal of Hydrometeorology 21(5); 2020
Abstract: Subseasonal to seasonal (S2S) climate forecasting has become a central component of climate services aimed at improving water management. In some cases, operational S2S climate predictions are translated into inputs for follow-on analyses or models, whereas the S2S predictions on their own may provide for qualitative situational awareness. At the spatial scales of water management, however, S2S climate forecasts often suffer from systematic biases, and low skill and reliability. This study assesses the potential to improve S2S forecast skill and salience for watershed applications through the use of postprocessing to harness skills in large-scale fields from the global climate model forecast outputs. To this end, the components-based technique—partial least squares regression (PLSR)—is used to improve the skill of biweekly temperature and precipitation forecasts from the Climate Forecast System version 2 (CFSv2). The PLSR method forms predictor components based on a cross-validated analysis of hindcasts from CFSv2 climate and land surface fields, and the results are benchmarked against raw CFSv2 forecasts, remapped to intermediate-scale watershed areas. We find that postprocessing affords marginal to moderate gains in skill in many watersheds, raising climate forecast skill above a usability threshold over the four seasons analyzed. In other locations, however, postprocessing fails to improve skill, particularly for extreme events, and can lead to unreliably narrow forecast ranges. This work presents evidence that the statistical postprocessing of climate forecast system outputs has potential to improve forecast skill, but that more thorough study of alternative approaches and predictors may be needed to achieve comprehensively positive outcomes.
For additional information, please contact us via email at: CRB-R2O@usbr.gov
Last updated: 2020-06-11
