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The ongoing 22‐year drought in the Upper Colorado River Basin (UCRB) has been extremely severe, even in the context of the longest available tree‐ring reconstruction of annual flow at Lees Ferry, Arizona, dating back to 762 CE. While many southwestern drought assessments have been limited to the past 1,200 years, longer paleorecords of moisture variability do exist for the UCRB. Here, gridded drought‐atlas data in the UCRB domain along with naturalized streamflow data from the instrumental period (1906–2021) are used in a K‐nearest neighbor nonparametric algorithm to develop a streamflow reconstruction for the Lees Ferry gage starting in 1 CE. The reconstruction reveals a second‐century drought unmatched in severity by the current drought or by well‐documented medieval period droughts in the UCRB. Although data are sparse, analysis of individual long tree‐ring records and other paleoclimatic data also support the occurrence of an exceptional second‐century drought. Plain Language Summary: The Colorado River drought we currently are experiencing is severe in the context of the 116‐year gage record (1906–2021), but how severe is it in a long‐term context? Existing tree‐ring based reconstructions of Colorado River streamflow have suggested that the 22‐year period 2000–2021 could be the worst drought in the southwestern United States in 1,200 years. The purpose of this study is to extend the Colorado River reconstruction back 2,000 years and to evaluate the current drought in a long‐term context. We find that an even more extreme drought occurred and persisted over much of the second century. Data are sparse this far back in time, but evidence from both tree‐ring data and paleoclimate data from lakes, bogs, and caves supports the existence and severity of this drought in the context of the last two millennia. Additional work is needed to learn more about this drought and its causes, but we now know that drought more persistent than even the well‐documented medieval period droughts occurred in the past, expanding our understanding of the range of natural climate variability. Key Points: A new tree‐ring based ensemble streamflow reconstruction spanning the last two millennia was developed for the Colorado RiverThe new reconstruction reveals a second‐century drought unmatched in severity by any past droughts in the Upper Colorado River BasinThe ongoing 22‐year period of low Colorado River streamflow is a rare event, but is not the most severe drought in the past 2,000 years [ABSTRACT FROM AUTHOR]

Drought has affected much of the western United States since about the year 2000, including the Upper Colorado River Basin (UCRB). Using a time series of UCRB streamflow derived from a tree‐ring based reconstruction of UCRB streamflow for the years 1 CE through 1905 CE, together with naturalized UCRB streamflow values for 1906 CE through 2021 CE, we identify 51 drought events, including the 2000–2021 drought. Although the recent 2000–2021 drought has been relatively severe, it is not the most severe of the UCRB drought events we identified. Results also indicate that natural variability combined with projected climate warming can result in UCRB drought events that are more severe than any drought since 1 CE. Plain Language Summary: A long and severe drought has affected much of the western United States since about the year 2000 CE, including the Upper Colorado River Basin (UCRB). Comparing this drought to past UCRB droughts (during 1 CE through 2021 CE), we find that the 2000–2021 drought is not the most severe UCRB drought. The results also suggest that natural variability combined with projected climate warming could result in UCRB drought events that are more severe than any drought since 1 CE. Key Points: We identify 51 drought events for the years 1 CE through 2021 CE for the Upper Colorado River Basin (UCRB)Twelve of the 51 UCRB droughts we identify were more severe than the 2000–2021 droughtNatural variability and/or a 1°C temperature increase can trigger UCRB droughts that are more severe than the 2000–2021 drought [ABSTRACT FROM AUTHOR]

The Colorado River is an important source of water in the western United States, supplying the needs of more than 38 million people in the United States and Mexico. Groundwater discharge to streams has been shown to be a critical component of streamflow in the Upper Colorado River Basin ( UCRB), particularly during low-flow periods. Understanding impacts on groundwater in the basin from projected climate change will assist water managers in the region in planning for potential changes in the river and groundwater system. A previous study on changes in basin-wide groundwater recharge in the UCRB under projected climate change found substantial increases in temperature, moderate increases in precipitation, and mostly periods of stable or slight increases in simulated groundwater recharge through 2099. This study quantifies projected spatial and seasonal changes in groundwater recharge within the UCRB from recent historical (1950 to 2015) through future (2016 to 2099) time periods, using a distributed-parameter groundwater recharge model with downscaled climate data from 97 Coupled Model Intercomparison Project Phase 5 ( CMIP5) climate projections. Simulation results indicate that projected increases in basin-wide recharge of up to 15% are not distributed uniformly within the basin or throughout the year. Northernmost subregions within the UCRB are projected an increase in groundwater recharge, while recharge in other mainly southern subregions will decline. Seasonal changes in recharge also are projected within the UCRB, with decreases of 50% or more in summer months and increases of 50% or more in winter months for all subregions, and increases of 10% or more in spring months for many subregions. [ABSTRACT FROM AUTHOR]

Spatially distributed historical meteorological forcings (temperature and precipitation) are commonly incorporated into modeling efforts for long-term natural resources planning. For water management decisions, it is critical to understand the uncertainty associated with the different choices made in hydrologic impact assessments (choice of hydrologic model, choice of forcing dataset, calibration strategy, etc.). This paper evaluates differences among four commonly used historical meteorological datasets and their impacts on streamflow simulations produced using the Variable Infiltration Capacity (VIC) model. The four meteorological datasets examined here have substantial differences, particularly in minimum and maximum temperatures in high-elevation regions such as the Rocky Mountains. The temperature differences among meteorological forcing datasets are generally larger than the differences between calibration and validation periods. Of the four meteorological forcing datasets considered, there are substantial differences in calibrated model parameters and simulations of the water balance. However, no single dataset is superior to the others with respect to VIC simulations of streamflow. Also, optimal calibration parameter values vary across case study watersheds and select meteorological datasets, suggesting that there is enough flexibility in the calibration parameters to compensate for the effects of using select meteorological datasets. Evaluation of runoff sensitivity to changes in climate indicates that the choice of meteorological dataset may be as important in characterizing changes in runoff as climate change, supporting consideration of multiple sources of uncertainty in long-term planning studies. [ABSTRACT FROM AUTHOR]

Climate change scenarios generated by general circulation models have too coarse a spatial resolution to be useful in planning disaster risk reduction and climate change adaptation strategies at regional to river basin scales. This study presents a new non-parametric statistical K-nearest neighbor algorithm for downscaling climate change scenarios for the Rohini River Basin in Nepal. The study is an introduction to the methodology and discusses its strengths and limitations within the context of hindcasting basin precipitation for the period of 1976-2006. The actual downscaled climate change projections are not presented here. In general, we find that this method is quite robust and well suited to the data-poor situations common in developing countries. The method is able to replicate historical rainfall values in most months, except for January, September, and October. As with any downscaling technique, whether numerical or statistical, data limitations significantly constrain model ability. The method was able to confirm that the dataset available for the Rohini Basin does not capture long-term climatology. Yet, we do find that the hindcasts generated with this methodology do have enough skill to warrant pursuit of downscaling climate change scenarios for this particularly poor and vulnerable region of the world. [ABSTRACT FROM AUTHOR]

Summary: Multifaceted demands on western water supply, such as irrigation and mandated biological flows, coupled with climate variability are increasing the importance of supply forecasting to water managers. In this study, we develop and examine the accuracy of a seasonal ensemble streamflow forecasting model for the Yakima River Basin. The model incorporates large-scale climate information, related to the Pacific North American (PNA) pattern, with the objective of increasing the skill of the forecasts for water managers and stakeholders in the basin. Our study has found that spring runoff in the Yakima Basin is strongly correlated (correlation significant at the 5% significance level) with two of the large-scale circulation patterns associated with the PNA pattern from the preceding fall and winter seasons. Incorporating such climate information into our forecasts allowed a longer lead-time (a season in advance) planning period for water managers and stakeholders from the current practice of an April 1st forecast. The ensemble nature of our streamflow forecasts provides continuous probability distributions that will help the decision maker to objectively quantify the associated risk with selected streamflow values. [Copyright &y& Elsevier]

To test the accuracy of statistically downscaled precipitation estimates from numerical weather prediction models, a set of experiments to study what space and time scales are appropriate to obtain downscaled precipitation forecasts with maximum skill have been carried out. Fourteen-day forecasts from the 1998 version of the National Centers for Environmental Prediction (NCEP) Medium-Range Forecast (MRF) model were used in this study. It has been previously found that downscaled temperature fields have significant skill even up to 5 days of forecast lead time, but there is practically no valuable skill in the downscaled precipitation forecasts. Low skill in precipitation forecasts revolves around two main issues. First, the (intermittent) precipitation variability on daily and subdaily time scales could be too noisy to derive meaningful relationships with atmospheric predictors. Second, the model parameterizations and the coarse spatial resolution of the current generation of global-scale forecast models might be unable to resolve the local-scale variability in precipitation. Both of these issues may be addressed by spatial and temporal averaging. In this paper the authors present a diagnostic study using a set of numerical experiments to understand how spatial and temporal aggregations affect the skill of downscaled precipitation forecasts in the upper Colorado River basin. The question addressed is, if the same set of predictor variables from numerical weather prediction models is used, what space (e.g., station versus regional average) and time (e.g., subdaily versus daily) scales optimize regression-based downscaling models so as to maximize forecast skill for precipitation? Results in general show that spatial and temporal averaging increased the skill of downscaled precipitation estimates. At subdaily (6 hourly) and daily time scales, the skill of downscaled estimates at spatial scales greater than 50 km was generally higher than the skill of downscaled estimates at individual stations. For the 6-hourly time scale both for stations and for mean areal precipitation estimates the maximum forecast skill was found to be approximately half that of the daily time scale. At forecast lead times of 5 days, when there is very little skill at daily and subdaily time scales, useful skill emerged when station data are aggregated to 3- and 5-day averages. [ABSTRACT FROM AUTHOR]

A number of statistical methods that are used to provide local-scale ensemble forecasts of precipitation and temperature do not contain realistic spatial covariability between neighboring stations or realistic temporal persistence for subsequent forecast lead times. To demonstrate this point, output from a global-scale numerical weather prediction model is used in a stepwise multiple linear regression approach to downscale precipitation and temperature to individual stations located in and around four study basins in the United States. Output from the forecast model is downscaled for lead times up to 14 days. Residuals in the regression equation are modeled stochastically to provide 100 ensemble forecasts. The precipitation and temperature ensembles from this approach have a poor representation of the spatial variability and temporal persistence. The spatial correlations for downscaled output are considerably lower than observed spatial correlations at short forecast lead times (e.g., less than 5 days) when there is high accuracy in the forecasts. At longer forecast lead times, the downscaled spatial correlations are close to zero. Similarly, the observed temporal persistence is only partly present at short forecast lead times. A method is presented for reordering the ensemble output in order to recover the space–time variability in precipitation and temperature fields. In this approach, the ensemble members for a given forecast day are ranked and matched with the rank of precipitation and temperature data from days randomly selected from similar dates in the historical record. The ensembles are then reordered to correspond to the original order of the selection of historical data. Using this approach, the observed intersite correlations, intervariable correlations, and the observed temporal persistence are almost entirely recovered. This reordering methodology also has applications for recovering the space–time variability in modeled streamflow. [ABSTRACT FROM AUTHOR]

Presents information on a study which developed and tested principal component analysis, a data reduction technique used to evaluate a ground water monitoring network. Design of a ground water monitoring network; Description of the principal component analysis; Study area and steps in analysis; Discussion; Summary and conclusions.

Presents information on a study which characterized the subsurface profiles using artificial neural network (ANN) and geographic information system (GIS). Details on distributed parameter ground-water models; Development of ANN-GIS; Conclusions.

Groundwater is a critical resource in the Grand Canyon region, supplying nearly all water needs for residents and millions of visitors. Additionally, groundwater discharging at hundreds of spring locations in and near Grand Canyon supports important ecosystems in this mostly arid environment. The security of groundwater supplies is of critical importance for both people and ecosystems in the region and the potential for changes to groundwater systems from projected climate change is a cause for concern. In this study, we analyze recent historical and projected precipitation and temperature data for the Grand Canyon region. Projected climate scenarios are then used in Soil Water Balance groundwater infiltration simulations to understand the state-of-the-science on projected changes to groundwater resources in the area. Historical climate data from 1896 through 2019 indicate multi-decadal cyclical patterns in both precipitation and temperature for most of the time period. Since the 1970s, however, a significant rising trend in temperature is observed in the area. All 10-year periods since 1993 are characterized by both below average precipitation and above average temperature. Downscaled and bias-corrected precipitation and temperature output from 97 CMIP5 global climate models for the water-year 2020–2099 time period indicate projected precipitation patterns similar to recent historical (water-year 1951–2015) data. Projected temperature for the Grand Canyon area, however, is expected to rise by as much as 3.4 °C by the end of the century, relative to the recent historical average. Integrating the effects of projected precipitation and temperature changes on groundwater infiltration, simulation results indicate that > 76% of future decades will experience average potential groundwater infiltration less than that of the recent historical period. [ABSTRACT FROM AUTHOR]

Paleohydrologic reconstructions of water-year streamflow for 105 sites across the western United States (West) were used to compute the likelihood (risk) of regime (wet/dry state) shifts given the length of time in a specific regime and for a specified time in the future. The spatial variability of risks was examined and indicates that regime shift risks are variable across the West. The Pacific-Northwest region is associated with low risks of regime shifts, indicating persistence controlled by prevalent low frequency variability in flow (periods above 64 years). Other areas in the West indicate higher risks compared to the Pacific-Northwest due to flow variability in the mid-to-high frequencies (periods of 32 to 16 years). Understanding risks of regime shifts provides critical information for improved management of water supplies, particularly during periods of extended low flows. The method presented here has global applicability as a decision-making framework for risk-based planning and management. [ABSTRACT FROM AUTHOR]

Hydrologic model input datasets such as climate, land use, elevation, soil, and geology information are available in a range of scales for use in water resources investigations. Smaller spatial and temporal scale input data allow groundwater recharge models to simulate more physically realistic processes and presumably result in more accurate estimates of groundwater recharge. Projected climate data are, therefore, often downscaled to smaller spatial and temporal scales for use in these models. It is unknown, however, if increasingly smaller-scale climate data produce substantially different simulated recharge results, either in magnitude or trend. Also, even if simulated recharge results are different at a higher space and time resolution, simulation at coarser resolution might be adequate to provide recharge information at decision scales (e.g., meeting Colorado River compact requirements on a ten-year moving average basis). Historical climate datasets at three spatial (∼800 m, ∼4 km, and ∼12 km) and two temporal (daily and monthly) scales were used in a Soil Water Balance (SWB) model of the upper Colorado River basin (UCRB) to simulate groundwater recharge over the water-year 1982–2014 time period. The magnitude of annual and moving ten-year annual average recharge results for daily climate data were within 5% and 7% of ∼4 km results for ∼800 m and ∼12 km climate data, respectively, with deviations from 1982 to 2014 means within 1% and 3% (median), respectively. Comparison of simulated recharge results using the coarsest spatial and temporal climate data with results from the finest scale data indicated similar small differences over ten-year moving annual averages, over water years, and during high recharge months. While differences in simulated groundwater recharge magnitude, which may be important for groundwater-flow simulations, were substantial during some seasonal comparisons, trends in recharge were almost identical across scales, leading to similar conclusions about change from “normal”. Considering the uncertainty inherent in projected climate data, coarser spatial and longer temporal scale input data may be sufficient for water resources managers who need to understand changes in trends in groundwater recharge over water-year or longer time periods. [ABSTRACT FROM AUTHOR]

Highlights: [•] This study analyzes the impact of aquifer characterization on land energy fluxes. [•] We use an integrated physical hydrology model with a coupled land surface model. [•] We simulate the Upper Klamath basin using four subsurface parameterizations. [•] Subsurface heterogeneity does not fundamentally alter groundwater surface water connections. [•] Regional results are consistent but local differences are significant between scenarios. [ABSTRACT FROM AUTHOR]

Across the Upper Missouri River Basin, the recent drought of 2000 to 2010, known as the "turn-of-the-century drought," was likely more severe than any in the instrumental record including the Dust Bowl drought. However, until now, adequate proxy records needed to better understand this event with regard to long-term variability have been lacking. Here we examine 1,200 y of streamflow from a network of 17 new tree-ring-based reconstructions for gages across the upper Missouri basin and an independent reconstruction of warm-season regional temperature in order to place the recent drought in a long-term climate context. We find that temperature has increasingly influenced the severity of drought events by decreasing runoff efficiency in the basin since the late 20th century (1980s) onward. The occurrence of extreme heat, higher evapotranspiration, and associated low-flow conditions across the basin has increased substantially over the 20th and 21st centuries, and recent warming aligns with increasing drought severities that rival or exceed any estimated over the last 12 centuries. Future warming is anticipated to cause increasingly severe droughts by enhancing water deficits that could prove challenging for water management. [ABSTRACT FROM AUTHOR]

Process-based hydrologic models require extensive meteorological forcing data, including data on precipitation, temperature, shortwave and longwave radiation, humidity, surface pressure, and wind speed. Observations of precipitation and temperature are more common than other variables; consequently, radiation, humidity, pressure, and wind speed often must be either estimated using empirical relationships with precipitation and temperature or obtained from numerical weather prediction models. This study examines two climate forcing datasets using different methods to estimate radiative energy fluxes and humidity and investigates the effects of the choice of forcing data on hydrologic simulations over the mountainous upper Colorado River basin (293 472 km2). Comparisons of model simulations forced by two climate datasets illustrate that the methods used to estimate shortwave radiation impact hydrologic states and fluxes, particularly at high elevation (e.g., ~20% difference in runoff above 3000-m elevation), substantially altering the timing of snowmelt and runoff (~20 days difference) and the partitioning of precipitation between evapotranspiration and runoff. The different forcing datasets also exhibit differences in hydrologic sensitivity to interannual temperature at high elevation. The results suggest that the choice of forcing dataset is an important consideration when conducting climate impact assessments and the subsequent applications of these assessments for water resources planning and management. [ABSTRACT FROM AUTHOR]

Dominant modes of individual and joint variability in global sea surface temperatures (SST) and global Palmer drought severity index (PDSI) values for the twentieth century are identified through a multivariate frequency domain singular value decomposition. This analysis indicates that a secular trend and variability related to the El Niño–Southern Oscillation (ENSO) are the dominant modes of variance shared among the global datasets. For the SST data the secular trend corresponds to a positive trend in Indian Ocean and South Atlantic SSTs, and a negative trend in North Pacific and North Atlantic SSTs. The ENSO reconstruction shows a strong signal in the tropical Pacific, North Pacific, and Indian Ocean regions. For the PDSI data, the secular trend reconstruction shows high amplitudes over central Africa including the Sahel, whereas the regions with strong ENSO amplitudes in PDSI are the southwestern and northwestern United States, South Africa, northeastern Brazil, central Africa, the Indian subcontinent, and Australia. An additional significant frequency, multidecadal variability, is identified for the Northern Hemisphere. This multidecadal frequency appears to be related to the Atlantic multidecadal oscillation (AMO). The multidecadal frequency is statistically significant in the Northern Hemisphere SST data, but is statistically nonsignificant in the PDSI data. [ABSTRACT FROM AUTHOR]

This study introduces medium-range meteorological ensemble inputs of temperature and precipitation into the Ensemble Streamflow Prediction component of the National Weather Service River Forecast System (NWSRFS). The Climate Diagnostics Center (CDC) produced a reforecast archive of model forecast runs from a dynamically frozen version of the Medium-Range Forecast (MRF) model. This archive was used to derive statistical relationships between MRF variables and historical basin-average precipitation and temperatures. The latter are used to feed the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two sets of ESP reforecasts were produced: A control run based on historically observed temperature and precipitation and an experimental run based on MRF-derived temperature and precipitation. This study found the MRF reforecasts to be generally superior to the control reforecasts, although there were situations when the downscaled MRF output actually degraded the forecast. Forecast improvements were most pronounced during the rising limb of the hydrograph—at this time accurate temperature forecasts improve predictions of the rate of snowmelt. Further improvements in streamflow forecasts at short forecast lead times may be possible by incorporating output from high-resolution regional atmospheric models into the NWSRFS. [ABSTRACT FROM AUTHOR]

This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Niño–Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for using a climate index (e.g., Niño-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques. [ABSTRACT FROM AUTHOR]

Paleohydrologic records can provide unique, long-term perspectives on streamflow variability and hydroclimate for use in water resource planning. Such long-term records can also play a key role in placing both present day events and projected future conditions into a broader context than that offered by instrumental observations. However, relative to other major river basins across the western United States, a paucity of streamflow reconstructions has to date prevented the full application of such paleohydrologic information in the Upper Missouri River Basin. Here we utilize a set of naturalized streamflow records for the Upper Missouri and an expanded network of tree-ring records to reconstruct streamflow at thirty-one gaging locations across the major headwaters of the basin. The reconstructions explain an average of 68% of the variability in the observed streamflow records and extend available records of streamflow back to 886 CE on average. Basin-wide analyses suggest unprecedented hydroclimatic variability over the region during the Medieval period, similar to that observed in the Upper Colorado River Basin, and show considerable synchrony of persistent wet-dry phasing with the Colorado River over the last 1200 years. Streamflow estimates in individual sub-basins of the Upper Missouri demonstrate increased spatial variability in discharge during the Little Ice Age (∼1400–1850 CE) compared with the Medieval Climate Anomaly (∼800–1400 CE). The network of streamflow reconstructions presented here fills a major geographical void in paleohydrologic understanding and now allows for a long-term assessment of hydrological variability over the majority of the western U.S. • Upper Missouri River streamflow is reconstructed using tree-rings. • Persistent wet-dry cycles in the Upper Missouri track those in the Upper Colorado. • Medieval, Little Ice Age, and 20th century streamflow variability differed. [ABSTRACT FROM AUTHOR]