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USGS2013

Observed impacts of duration and seasonality of atmospheric-river landfalls on soil moisture and runoff in coastal northern California

This study is motivated by diverse needs for better forecasts of extreme precipitation and floods. It is enabled by unique hourly observations collected over six years near California’s Russian River and by recent advances in the science of atmospheric rivers (ARs). This study fills key gaps limiting the prediction of ARs and, especially, their impacts by quantifying the duration of AR conditions and the role of duration in modulating hydrometeorological impacts. Precursor soil moisture conditions and their relationship to streamflow are also shown. On the basis of 91 well-observed events during 2004-10, the study shows that the passage of ARs over a coastal site lasted 20 h on average and that 12% of the AR events exceeded 30 h. Differences in storm-total water vapor transport directed up the mountain slope contribute 74% of the variance in storm-total rainfall across the events and 61% of the variance in storm-total runoff volume. ARs with double the composite mean duration produced nearly 6 times greater peak streamflow and more than 7 times the storm-total runoff volume. When precursor soil moisture was less than 20%, even heavy rainfall did not lead to significant streamflow. Predicting which AR events are likely to produce extreme impacts on precipitation and runoff requires accurate prediction of AR duration at landfall and observations of precursor soil moisture conditions.

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USGS2005

Extraordinary flood response of a small urban watershed to short-duration convective rainfall

The 9.1 km2 Moores Run watershed in Baltimore, Maryland, experiences floods with unit discharge peaks exceeding 1 m3 s-1 km-2 12 times yr-1, on average. Few, if any, drainage basins in the continental United States have a higher frequency. A thunderstorm system on 13 June 2003 produced the record flood peak (13.2 m3 s-1 km-2) during the 6-yr stream gauging record of Moores Run. In this paper, the hydrometeorology, hydrology, and hydraulics of extreme floods in Moores Run are examined through analyses of the 13 June 2003 storm and flood, as well as other major storm and flood events during the 2000-03 time period. The 13 June 2003 flood, like most floods in Moores Run, was produced by an organized system of thunderstorms. Analyses of the 13 June 2003 storm, which are based on volume scan reflectivity observations from the Sterling, Virginia, WSR-88D radar, are used to characterize the spatial and temporal variability of flash flood producing rainfall. Hydrology of flood response in Moores Run is characterized by highly efficient concentration of runoff through the storm drain network and relatively low runoff ratios. A detailed survey of high-water marks for the 13 June 2003 flood is used, in combination with analyses based on a 2D, depth-averaged open channel flow model (TELEMAC 2D) to examine hydraulics of the 13 June 2003 flood. Hydraulic analyses are used to examine peak discharge estimates for the 13 June flood peak, propagation of flood waves in the Moores Run channel, and 2D flow features associated with channel and floodplain geometry. ?? 2005 American Meteorological Society.

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USGS2013

The landfall and inland penetration of a flood-producing atmospheric river in Arizona. Part I: observed synoptic-scale, orographic, and hydrometeorological characteristics

Atmospheric rivers (ARs) are a dominant mechanism for generating intense wintertime precipitation along the U.S. West Coast. While studies over the past 10 years have explored the impact of ARs in, and west of, California’s Sierra Nevada and the Pacific Northwest’s Cascade Mountains, their influence on the weather across the intermountain west remains an open question. This study utilizes gridded atmospheric datasets, satellite imagery, rawinsonde soundings, a 449-MHz wind profiler and global positioning system (GPS) receiver, and operational hydrometeorological observing networks to explore the dynamics and inland impacts of a landfalling, flood-producing AR across Arizona in January 2010. Plan-view, cross-section, and back-trajectory analyses quantify the synoptic and mesoscale forcing that led to widespread precipitation across the state. The analyses show that a strong AR formed in the lower midlatitudes over the northeastern Pacific Ocean via frontogenetic processes and sea surface latent-heat fluxes but without tapping into the adjacent tropical water vapor reservoir to the south. The wind profiler, GPS, and rawinsonde observations document strong orographic forcing in a moist neutral environment within the AR that led to extreme, orographically enhanced precipitation. The AR was oriented nearly orthogonal to the Mogollon Rim, a major escarpment crossing much of central Arizona, and was positioned between the high mountain ranges of northern Mexico. High melting levels during the heaviest precipitation contributed to region-wide flooding, while the high-altitude snowpack increased substantially. The characteristics of the AR that impacted Arizona in January 2010, and the resulting heavy orographic precipitation, are comparable to those of landfalling ARs and their impacts along the west coasts of midlatitude continents.

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USGS2010

Assessment of extreme quantitative precipitation forecasts and development of regional extreme event thresholds using data from HMT-2006 and COOP observers

Extreme precipitation events, and the quantitative precipitation forecasts (QPFs) associated with them, are examined. The study uses data from the Hydrometeorology Testbed (HMT), which conducted its first field study in California during the 2005/06 cool season. National Weather Service River Forecast Center (NWS RFC) gridded QPFs for 24-h periods at 24-h (day 1), 48-h (day 2), and 72-h (day 3) forecast lead times plus 24-h quantitative precipitation estimates (QPEs) fromsites in California (CA) and Oregon-Washington (OR-WA) are used. During the 172-day period studied, some sites received more than 254 cm (100 in.) of precipitation. The winter season produced many extreme precipitation events, including 90 instances when a site received more than 7.6 cm (3.0 in.) of precipitation in 24 h (i.e., an "event") and 17 events that exceeded 12.7 cm (24 h)-1 [5.0 in. (24 h)-1]. For the 90 extreme events f.7.6 cm (24 h)-1 [3.0 in. (24 h)-1]g, almost 90% of all the 270 QPFs (days 1-3) were biased low, increasingly so with greater lead time. Of the 17 observed events exceeding 12.7 cm (24 h)-1 [5.0 in. (24 h)-1], only 1 of those events was predicted to be that extreme. Almost all of the extreme events correlated with the presence of atmospheric river conditions. Total seasonal QPF biases for all events fi.e., $0.025 cm (24 h)-1 [0.01 in. (24 h)-1]g were sensitive to local geography and were generally biased low in the California-Nevada River Forecast Center (CNRFC) region and high in the Northwest River Forecast Center(NWRFC) domain. The low bias in CA QPFs improved with shorter forecast lead time and worsened for extreme events. Differences were also noted between the CNRFC and NWRFC in terms of QPF and the frequency of extreme events. A key finding from this study is that there were more precipitation events .7.6 cm (24 h)-1 [3.0 in. (24 h)21] in CA than in OR-WA. Examination of 422 Cooperative Observer Program (COOP) sites in the NWRFC domain and 400 in the CNRFC domain found that the thresholds for the top 1% and top 0.1%of precipitation events were 7.6 cm (24 h)21 [3.0 in. (24 h)-1] and 14.2 cm (24 h)-1 [5.6 in. (24 h)-1] or greater for the CNRFC and only 5.1 cm (24 h)-1 [2.0 in. (24 h)-1] and 9.4 cm (24 h)-1 [3.7 in. (24 h)-1] for the NWRFC, respectively. Similar analyses for all NWS RFCs showed that the threshold for the top 1% of events varies from;3.8 cm (24 h)-1 [1.5 in. (24 h)-1] in the Colorado Basin River Forecast Center (CBRFC) to~5.1 cm (24 h)-1 [3.0 in. (24 h)-1] in the northern tier of RFCs and;7.6 cm (24 h)-1 [3.0 in. (24 h)-1] in both the southern tier and the CNRFC. It is recommended that NWS QPF performance in the future be assessed for extreme events using these thresholds. ?? 2010 American Meteorological Society.

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USGS2000

Predicting and downscaling ENSO impacts on intraseasonal precipitation statistics in California: The 1997/98 event

Three long-range forecasting methods have been evaluated for prediction and downscaling of seasonal and intraseasonal precipitation statistics in California. Full-statistical, hybrid-dynamical–statistical and full-dynamical approaches have been used to forecast El Niño–Southern Oscillation (ENSO)–related total precipitation, daily precipitation frequency, and average intensity anomalies during the January–March season. For El Niño winters, the hybrid approach emerges as the best performer, while La Niña forecasting skill is poor. The full-statistical forecasting method features reasonable forecasting skill for both La Niña and El Niño winters. The performance of the full-dynamical approach could not be evaluated as rigorously as that of the other two forecasting schemes. Although the full-dynamical forecasting approach is expected to outperform simpler forecasting schemes in the long run, evidence is presented to conclude that, at present, the full-dynamical forecasting approach is the least viable of the three, at least in California. The authors suggest that operational forecasting of any intraseasonal temperature, precipitation, or streamflow statistic derivable from the available records is possible now for ENSO-extreme years.

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USGS2000

Global characteristics of stream flow seasonality and variability

Monthly stream flow series from 1345 sites around the world are used to characterize geographic differences in the seasonality and year-to-year variability of stream flow. Stream flow seasonality varies regionally, depending on the timing of maximum precipitation, evapotranspiration, and contributions from snow and ice. Lags between peaks of precipitation and stream flow vary smoothly from long delays in high-latitude and mountainous regions to short delays in the warmest sectors. Stream flow is most variable from year to year in dry regions of the southwest United States and Mexico, the Sahel, and southern continents, and it varies more (relatively) than precipitation in the same regions. Tropical rivers have the steadiest flows. El Niño variations are correlated with stream flow in many parts of the Americas, Europe, and Australia. Many stream flow series from North America, Europe, and the Tropics reflect North Pacific climate, whereas series from the eastern United States, Europe, and tropical South America and Africa reflect North Atlantic climate variations.

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USGS2000

A hybrid orographic plus statistical model for downscaling daily precipitation in northern California

A hybrid (physical–statistical) scheme is developed to resolve the finescale distribution of daily precipitation over complex terrain. The scheme generates precipitation by combining information from the upper-air conditions and from sparsely distributed station measurements; thus, it proceeds in two steps. First, an initial estimate of the precipitation is made using a simplified orographic precipitation model. It is a steady-state, multilayer, and two-dimensional model following the concepts of Rhea. The model is driven by the 2.5° × 2.5° gridded National Oceanic and Atmospheric Administration–National Centers for Environmental Prediction upper-air profiles, and its parameters are tuned using the observed precipitation structure of the region. Precipitation is generated assuming a forced lifting of the air parcels as they cross the mountain barrier following a straight trajectory. Second, the precipitation is adjusted using errors between derived precipitation and observations from nearby sites. The study area covers the northern half of California, including coastal mountains, central valley, and the Sierra Nevada. The model is run for a 5-km rendition of terrain for days of January–March over the period of 1988–95. A jackknife analysis demonstrates the validity of the approach. The spatial and temporal distributions of the simulated precipitation field agree well with the observed precipitation. Further, a mapping of model performance indices (correlation coefficients, model bias, root-mean-square error, and threat scores) from an array of stations from the region indicates that the model performs satisfactorily in resolving daily precipitation at 5-km resolution.

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USGS2002

Primary modes and predictability of year-to-year snowpack variations in the Western United States from teleconnections with Pacific Ocean climate

Snowpack, as measured on 1 April, is the primary source of warm-season streamflow for most of the western United States and thus represents an important source of water supply. An understanding of climate factors that influence the variability of this water supply and thus its predictability is important for water resource management. In this study, principal component analysis is used to identify the primary modes of 1 April snowpack variability in the western United States. Two components account for 61% of the total snowpack variability in the western United States. Relations between these modes of variability and indices of Pacific Ocean climate [e.g., the Pacific decadal oscillation (PDO) and Nin??o-3 sea surface temperatures (SSTs)] are examined. The first mode of snowpack variability is closely associated with the PDO, whereas the second mode varies in concert with both the PDO and Nin??o-3 SSTs. Because these atmospheric-oceanic conditions change slowly from season to season, the observed teleconnections between the Pacific Ocean climate and 1 April snowpack may be useful to forecast 1 April snowpack using data that describe the Pacific Ocean climate in the previous summer and autumn seasons, especially for the northwestern United States.

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USGS2002

Global modeling of land water and energy balances. Part I: The land dynamics (LaD) model

A simple model of large-scale land (continental) water and energy balances is presented. The model is an extension of an earlier scheme with a record of successful application in climate modeling. The most important changes from the original model include 1) introduction of non-water-stressed stomatal control of transpiration, in order to correct a tendency toward excessive evaporation: 2) conversion from globally constant parameters (with the exception of vegetation-dependent snow-free surface albedo) to more complete vegetation and soil dependence of all parameters, in order to provide more realistic representation of geographic variations in water and energy balances and to enable model-based investigations of land-cover change; 3) introduction of soil sensible heat storage and transport, in order to move toward realistic diurnal-cycle modeling; 4) a groundwater (saturated-zone) storage reservoir, in order to provide more realistic temporal variability of runoff; and 5) a rudimentary runoff-routing scheme for delivery of runoff to the ocean, in order to provide realistic freshwater forcing of the ocean general circulation model component of a global climate model. The new model is tested with forcing from the International Satellite Land Surface Climatology Project Initiative I global dataset and a recently produced observation-based water-balance dataset for major river basins of the world. Model performance is evaluated by comparing computed and observed runoff ratios from many major river basins of the world. Special attention is given to distinguishing between two components of the apparent runoff ratio error: the part due to intrinsic model error and the part due to errors in the assumed precipitation forcing. The pattern of discrepancies between modeled and observed runoff ratios is consistent with results from a companion study of precipitation estimation errors. The new model is tuned by adjustment of a globally constant scale factor for non-water-stressed stomatal resistance. After tuning, significant overestimation of runoff is found in environments where an overall arid climate includes a brief but intense wet season. It is shown that this error may be explained by the neglect of upward soil water diffusion from below the root zone during the dry season. With the exception of such basins, and in the absence of precipitation errors. It is estimated that annual runoff ratios simulated by the model would have a root-mean-square error of about 0.05. The new model matches observations better than its predecessor, which has a negative runoff bias and greater scatter.

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USGS2002

Global modeling of land water and energy balances. Part II: Land-characteristic contributions to spatial variability

Land water and energy balances vary around the globe because of variations in amount and temporal distribution of water and energy supplies and because of variations in land characteristics. The former control (water and energy supplies) explains much more variance in water and energy balances than the latter (land characteristics). A largely untested hypothesis underlying most global models of land water and energy balance is the assumption that parameter values based on estimated geographic distributions of soil and vegetation characteristics improve the performance of the models relative to the use of globally constant land parameters. This hypothesis is tested here through an evaluation of the improvement in performance of one land model associated with the introduction of geographic information on land characteristics. The capability of the model to reproduce annual runoff ratios of large river basins, with and without information on the global distribution of albedo, rooting depth, and stomatal resistance, is assessed. To allow a fair comparison, the model is calibrated in both cases by adjusting globally constant scale factors for snow-free albedo, non-water-stressed bulk stomatal resistance, and critical root density (which is used to determine effective root-zone depth). The test is made in stand-alone mode, that is, using prescribed radiative and atmospheric forcing. Model performance is evaluated by comparing modeled runoff ratios with observed runoff ratios for a set of basins where precipitation biases have been shown to be minimal. The withholding of information on global variations in these parameters leads to a significant degradation of the capability of the model to simulate the annual runoff ratio. An additional set of optimization experiments, in which the parameters are examined individually, reveals that the stomatal resistance is, by far, the parameter among these three whose spatial variations add the most predictive power to the model in stand-alone mode. Further single-parameter experiments with surface roughness length, available water capacity, thermal conductivity, and thermal diffusivity show very little sensitivity to estimated global variations in these parameters. Finally, it is found that even the constant-parameter model performance exceeds that of the Budyko and generalized Turc-Pike water-balance equations, suggesting that the model benefits also from information on the geographic variability of the temporal structure of forcing.

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USGS2002

Global modeling of land water and energy balances. Part III: Interannual variability

The Land Dynamics (LaD) model is tested by comparison with observations of interannual variations in discharge from 44 large river basins for which relatively accurate time series of monthly precipitation (a primary model input) have recently been computed. When results are pooled across all basins, the model explains 67% of the interannual variance of annual runoff ratio anomalies (i.e., anomalies of annual discharge volume, normalized by long-term mean precipitation volume). The new estimates of basin precipitation appear to offer an improvement over those from a state-of-the-art analysis of global precipitation (the Climate Prediction Center Merged Analysis of Precipitation, CMAP), judging from comparisons of parallel model runs and of analyses of precipitation-discharge correlations. When the new precipitation estimates are used, the performance of the LaD model is comparable to, but not significantly better than, that of a simple, semiempirical water-balance relation that uses only annual totals of surface net radiation and precipitation. This implies that the LaD simulations of interannual runoff variability do not benefit substantially from information on geographical variability of land parameters or seasonal structure of interannual variability of precipitation. The aforementioned analyses necessitated the development of a method for downscaling of long-term monthly precipitation data to the relatively short timescales necessary for running the model. The method merges the long-term data with a reference dataset of 1-yr duration, having high temporal resolution. The success of the method, for the model and data considered here, was demonstrated in a series of model-model comparisons and in the comparisons of modeled and observed interannual variations of basin discharge.

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USGS2002

Relations between soil moisture and satellite vegetation indices in the U.S. Corn Belt

Satellite-derived vegetation indices extracted over locations representative of midwestern U.S. cropland and forest for the period 1990–94 are analyzed to determine the sensitivity of the indices to neutron probe soil moisture measurements of the Illinois Climate Network (ICN). The deseasoned (i.e., departures from multiyear mean annual cycle) soil moisture measurements are shown to be weakly correlated with the deseasoned full resolution (1 km × 1 km) normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) data over both land cover types. The association, measured by the Pearson-moment-correlation coefficient, is stronger over forest than over cropland during the growing season (April–September). The correlations improve successively when the NDVI and FVC pixel data are aggregated to 3 km × 3 km, 5 km × 5 km, and 7 km × 7 km areas. The improved correlations are partly explained by the reduction in satellite navigation errors as spatial aggregation occurs, as well as the apparent scale dependence of the NDVI–soil moisture association. Similarly, stronger relations are obtained with soil moisture data that are lagged by up to 8 weeks with respect to the vegetation indices, implying that soil moisture may be a useful predictor of warm season satellite-derived vegetation conditions. This study suggests that a “long-term” memory of several weeks is present in the near-surface hydrological characteristics, especially soil water content, of the Midwest Corn Belt. The memory is integrated into the satellite vegetation indices and may be useful for predicting crop yield estimates and surface temperature anomalies.

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USGS2002

Use of regional climate model output for hydrologic simulations

Daily precipitation and maximum and minimum temperature time series from a regional climate model (RegCM2) configured using the continental United States as a domain and run on a 52-km (approximately) spatial resolution were used as input to a distributed hydrologic model for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango. Colorado; east fork of the Carson River near Gardnerville, Nevada: and Cle Elum River near Roslyn, Washington). For comparison purposes, spatially averaged daily datasets of precipitation and maximum and minimum temperature were developed from measured data for each basin. These datasets included precipitation and temperature data for all stations (hereafter, All-Sta) located within the area of the RegCM2 output used for each basin, but excluded station data used to calibrate the hydrologic model. Both the RegCM2 output and All-Sta data capture the gross aspects of the seasonal cycles of precipitation and temperature. However, in all four basins, the RegCM2- and All-Sta-based simulations of runoff show little skill on a daily basis [Nash-Sutcliffe (NS) values range from 0.05 to 0.37 for RegCM2 and -0.08 to 0.65 for All-Sta]. When the precipitation and temperature biases are corrected in the RegCM2 output and All-Sta data (Bias-RegCM2 and Bias-All, respectively) the accuracy of the daily runoff simulations improve dramatically for the snowmelt-dominated basins (NS values range from 0.41 to 0.66 for RegCM2 and 0.60 to 0.76 for All-Sta). In the rainfall-dominated basin, runoff simulations based on the Bias-RegCM2 output show no skill (NS value of 0.09) whereas Bias-All simulated runoff improves (NS value improved from - 0.08 to 0.72). These results indicate that measured data at the coarse resolution of the RegCM2 output can be made appropriate for basin-scale modeling through bias correction (essentially a magnitude correction). However, RegCM2 output, even when bias corrected, does not contain the day-to-day variability present in the All-Sta dataset that is necessary for basin-scale modeling. Future work is warranted to identify the causes for systematic biases in RegCM2 simulations, develop methods to remove the biases, and improve RegCM2 simulations of daily variability in local climate.

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USGS2002

Seasonal and spatial patterns in diurnal cycles in streamflow in the western United States

The diurnal cycle in streamflow constitutes a significant part of the variability in many rivers in the western United States and can be used to understand some of the dominant processes affecting the water balance of a given river basin. Rivers in which water is added diurnally, as in snowmelt, and rivers in which water is removed diurnally, as in evapotranspiration and infiltration, exhibit substantial differences in the timing, relative magnitude, and shape of their diurnal flow variations. Snowmelt-dominated rivers achieve their highest sustained flow and largest diurnal fluctuations during the spring melt season. These fluctuations are characterized by sharp rises and gradual declines in discharge each day. In large snowmelt-dominated basins, at the end of the melt season, the hour of maximum discharge shifts to later in the day as the snow line retreats to higher elevations. Many evapotranspiration/infiltration-dominated rivers in the western states achieve their highest sustained flows during the winter rainy season but exhibit their strongest diurnal cycles during summer months, when discharge is low, and the diurnal fluctuations compose a large percentage of the total flow. In contrast to snowmelt-dominated rivers, the maximum discharge in evapotranspiration/infiltration-dominated rivers occurs consistently in the morning throughout the summer. In these rivers, diurnal changes are characterized by a gradual rise and sharp decline each day.

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USGS2003

The impact of a prominent rain shadow on flooding in California's Santa Cruz Mountains: A CALJET case study and sensitivity to the ENSO cycle

Data from the California Land-Falling Jets Experiment (CALJET) are used to explore the causes of variations in flood severity in adjacent coastal watersheds within the Santa Cruz Mountains on 2-3 February 1998. While Pescadero Creek (rural) experienced its flood of record, the adjacent San Lorenzo Creek (heavily populated), attained only its fourth-highest flow. This difference resulted from conditions present while the warm sector of the storm, with its associated low-level jet, high moisture content, and weak static stability, was overhead. Rainfall in the warm sector was dominated by orographic forcing. While the wind speed strongly modulated rain rates on windward slopes, the wind direction positioned the edge of a rain shadow cast by the Santa Lucia Mountains partially over the San Lorenzo basin, thus protecting the city of Santa Cruz from a more severe flood. Roughly 26% ?? 9% of the streamflow at flood peak on Pescadero Creek resulted from the warm-sector rainfall. Without this rainfall, the peak flow on Pescadero Creek would likely not have attained record status. These results are complemented by a climatological analysis based on ???50-yr-duration streamflow records for these and two other nearby windward watersheds situated ???20 to 40 km farther to the east, and a comparison of this climatological analysis with composites of NCEP-NCAR reanalysis fields. The westernmost watersheds were found to have their greatest floods during El Nin??o winters, while the easternmost watersheds peaked during non-El Nin??o episodes. These results are consistent with the case study, that showed that the composite 925-mb, meridionally oriented wind direction during El Nin??os favors a rain shadow over the eastern watersheds. During non-El Nin??o periods, the composite, zonally oriented wind direction indicates that the sheltering effect of the rain shadow on the eastern watersheds is reduced, while weaker winds, less water vapor, and stronger stratification reduce the peak runoff in the western watersheds relative to El Nin??o periods. These case study and climatological results illustrate the importance of conditions in the moisture-rich warm sector of landfalling Pacific winter storms. Although many other variables can influence flooding, this study shows that variations of ??10?? in wind direction can modulate the location of orographically enhanced floods. While terrain can increase predictability (e.g., rainfall typically increases with altitude), the predictability is reduced when conditions are near a threshold separating different regimes (e.g., in or out of a rain shadow ).

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USGS2004

Use of medium-range numerical weather prediction model output to produce forecasts of streamflow

This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3°C . In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases he accuracy of precipitation forecasts over the northeastern United States, but overall, the accuracy of MOS-based precipitation forecasts is slightly lower than the raw NCEP forecasts. Four basins in the United States were chosen as case studies to evaluate the value of MRF output for predictions of streamflow. Streamflow forecasts using MRF output were generated for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado: East Fork of the Carson River near Gardnerville, Nevada: and Cle Elum River near Roslyn, Washington). Hydrologic model output forced with measured-station data were used as "truth" to focus attention on the hydrologic effects of errors in the MRF forecasts. Eight-day streamflow forecasts produced using the MOS-corrected MRF output as input (MOS) were compared with those produced using the climatic Ensemble Streamflow Prediction (ESP) technique. MOS-based streamflow forecasts showed increased skill in the snowmelt-dominated river basins, where daily variations in streamflow are strongly forced by temperature. In contrast, the skill of MOS forecasts in the rainfall-dominated basin (the Alapaha River) were equivalent to the skill of the ESP forecasts. Further improvements in streamflow forecasts require more accurate local-scale forecasts of precipitation and temperature, more accurate specification of basin initial conditions, and more accurate model simulations of streamflow.

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USGS2004

The Schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields

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.

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USGS2004

Spring onset in the Sierra Nevada: When is snowmelt independent of elevation?

Short-term climate and weather systems can have a strong influence on mountain snowmelt, sometimes overwhelming the effects of elevation and aspect. Although most years exhibit a spring onset that starts first at lowest and moves to highest elevations, in spring 2002, flow in a variety of streams within the Tuolumne and Merced River basins of the southern Sierra Nevada all rose synchronously on 29 March. Flow in streams draining small high-altitude glacial subcatchments rose at the same time as that draining much larger basins gauged at lower altitudes, and streams from north- and south-facing cirques rose and fell together. Historical analysis demonstrates that 2002 was one among only 8 yr with such synchronous flow onsets during the past 87 yr, recognized by having simultaneous onsets of snowmelt at over 70% of snow pillow sites, having discharge in over 70% of monitored streams increase simultaneously, and having temperatures increase over 12°C within a 5-day period. Synchronous springs tend to begin with a low pressure trough over California during late winter, followed by the onset of a strong ridge and unusually warm temperatures. Synchronous springs are characterized by warmer than average winters and cooler than average March temperatures in California. In the most elevation-dependent, nonsynchronous years, periods of little or no storm activity, with warmer than average March temperatures, precede the onset of spring snowmelt, allowing elevation and aspect to influence snowmelt as spring arrives gradually.

Sierra Nevada↗