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- Author or Editor: Norman L. Miller x
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Abstract
The impacts of snow on daily weather variability, as well as the mechanisms of snowmelt over the Sierra Nevada, California–Nevada, mountainous region, were studied using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) forced by 6-h reanalysis data from the National Centers for Environmental Prediction. The analysis of two-way nested 36–12-km MM5 simulations during the 1998 snowmelt season (April–June) shows that the snow water equivalent (SWE) is underestimated when there are conditions with higher temperature and greater precipitation than observations. An observed daily SWE dataset derived from the snow telemetry network was assimilated into the Noah land surface model within MM5. This SWE assimilation reduces the warm bias. The reduction of the warm bias results from suppressed upward sensible heat flux caused by the decreased skin temperature. This skin temperature reduction is the result of the longer assimilated snow duration than in the model run without SWE assimilation. Meanwhile, the cooled surface leads to a more stable atmosphere, resulting in a decrease in the exaggerated precipitation. Additionally, the detailed analysis of the snowmelt indicates that the absence of vegetation fraction in the most sophisticated land surface model (Noah) in the MM5 package results in an overestimation of solar radiation reaching the snow surface, giving rise to heavier snowmelt. An underestimated surface albedo also weakly contributes to the stronger snowmelt. The roles of the vegetation fraction and albedo in snowmelt are further verified by an additional offline simulation from a more realistic land surface model with advanced snow and vegetation schemes forced by the MM5 output. An improvement in SWE description is clearly seen in this offline simulation over the Sierra Nevada region.
Abstract
The impacts of snow on daily weather variability, as well as the mechanisms of snowmelt over the Sierra Nevada, California–Nevada, mountainous region, were studied using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) forced by 6-h reanalysis data from the National Centers for Environmental Prediction. The analysis of two-way nested 36–12-km MM5 simulations during the 1998 snowmelt season (April–June) shows that the snow water equivalent (SWE) is underestimated when there are conditions with higher temperature and greater precipitation than observations. An observed daily SWE dataset derived from the snow telemetry network was assimilated into the Noah land surface model within MM5. This SWE assimilation reduces the warm bias. The reduction of the warm bias results from suppressed upward sensible heat flux caused by the decreased skin temperature. This skin temperature reduction is the result of the longer assimilated snow duration than in the model run without SWE assimilation. Meanwhile, the cooled surface leads to a more stable atmosphere, resulting in a decrease in the exaggerated precipitation. Additionally, the detailed analysis of the snowmelt indicates that the absence of vegetation fraction in the most sophisticated land surface model (Noah) in the MM5 package results in an overestimation of solar radiation reaching the snow surface, giving rise to heavier snowmelt. An underestimated surface albedo also weakly contributes to the stronger snowmelt. The roles of the vegetation fraction and albedo in snowmelt are further verified by an additional offline simulation from a more realistic land surface model with advanced snow and vegetation schemes forced by the MM5 output. An improvement in SWE description is clearly seen in this offline simulation over the Sierra Nevada region.
Abstract
Traditional land surface models (LSMs) used for numerical weather simulation, climate projection, and as inputs to water management decision support systems, do not treat the LSM lower boundary in a fully process-based fashion. LSMs have evolved from a leaky-bucket approximation to more sophisticated land surface water and energy budget models that typically have a specified bottom layer flux to depict the lowest model layer exchange with deeper aquifers. The LSM lower boundary is often assumed zero flux or the soil moisture content is set to a constant value; an approach that while mass conservative, ignores processes that can alter surface fluxes, runoff, and water quantity and quality. Conversely, groundwater models (GWMs) for saturated and unsaturated water flow, while addressing important features such as subsurface heterogeneity and three-dimensional flow, often have overly simplified upper boundary conditions that ignore soil heating, runoff, snow, and root-zone uptake. In the present study, a state-of-the-art LSM (Common Land Model) and a variably saturated GWM (ParFlow) have been coupled as a single-column model.
A set of simulations based on synthetic data and data from the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS), version 2(d), 18-yr dataset from Valdai, Russia, demonstrate the temporal dynamics of this coupled modeling system. The soil moisture and water table depth simulated by the coupled model agree well with the Valdai observations. Differences in prediction between the coupled and uncoupled models demonstrate the effect of a dynamic water table on simulated watershed flow. Comparison of the coupled model predictions with observations indicates certain cold processes such as frozen soil and freeze/thaw processes have an important impact on predicted water table depth. Comparisons of soil moisture, latent heat, sensible heat, temperature, runoff, and predicted groundwater depth between the uncoupled and coupled models demonstrate the need for improved groundwater representation in land surface schemes.
Abstract
Traditional land surface models (LSMs) used for numerical weather simulation, climate projection, and as inputs to water management decision support systems, do not treat the LSM lower boundary in a fully process-based fashion. LSMs have evolved from a leaky-bucket approximation to more sophisticated land surface water and energy budget models that typically have a specified bottom layer flux to depict the lowest model layer exchange with deeper aquifers. The LSM lower boundary is often assumed zero flux or the soil moisture content is set to a constant value; an approach that while mass conservative, ignores processes that can alter surface fluxes, runoff, and water quantity and quality. Conversely, groundwater models (GWMs) for saturated and unsaturated water flow, while addressing important features such as subsurface heterogeneity and three-dimensional flow, often have overly simplified upper boundary conditions that ignore soil heating, runoff, snow, and root-zone uptake. In the present study, a state-of-the-art LSM (Common Land Model) and a variably saturated GWM (ParFlow) have been coupled as a single-column model.
A set of simulations based on synthetic data and data from the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS), version 2(d), 18-yr dataset from Valdai, Russia, demonstrate the temporal dynamics of this coupled modeling system. The soil moisture and water table depth simulated by the coupled model agree well with the Valdai observations. Differences in prediction between the coupled and uncoupled models demonstrate the effect of a dynamic water table on simulated watershed flow. Comparison of the coupled model predictions with observations indicates certain cold processes such as frozen soil and freeze/thaw processes have an important impact on predicted water table depth. Comparisons of soil moisture, latent heat, sensible heat, temperature, runoff, and predicted groundwater depth between the uncoupled and coupled models demonstrate the need for improved groundwater representation in land surface schemes.
Precipitation and river flow during a January 1995 flood event over the Russian River watershed in the northern Coastal Range of California were simulated using the University of California Lawrence Livermore National Laboratory's Coupled Atmosphere–River Flow Simulation (CARS) System. The CARS System unidirectionally links a primitive equation atmospheric mesoscale model to a physically based, fully distributed hydrologic model by employing an automated land analysis system. Using twice-daily National Meteorological Center eta model initial data to provide the large-scale forcing to the mesoscale model, the CARS System has closely simulated the observed river flow during the flooding stage, where the simulated river flow was within 10% of the observed river flow at the Hopland gauge station on the Russian River.
Precipitation and river flow during a January 1995 flood event over the Russian River watershed in the northern Coastal Range of California were simulated using the University of California Lawrence Livermore National Laboratory's Coupled Atmosphere–River Flow Simulation (CARS) System. The CARS System unidirectionally links a primitive equation atmospheric mesoscale model to a physically based, fully distributed hydrologic model by employing an automated land analysis system. Using twice-daily National Meteorological Center eta model initial data to provide the large-scale forcing to the mesoscale model, the CARS System has closely simulated the observed river flow during the flooding stage, where the simulated river flow was within 10% of the observed river flow at the Hopland gauge station on the Russian River.
Abstract
A Monte Carlo framework is adopted for propagating uncertainty in dynamically downscaled seasonal forecasts of area-averaged daily precipitation to associated streamflow response calculations. Daily precipitation is modeled as a mixture of two stochastic processes: a binary occurrence process and a continuous intensity process, both exhibiting serial correlation. The parameters of these processes (e.g., the proportion of wet days and the average wet-day precipitation intensity in a month) are derived from the forecast record. Parameter uncertainty is characterized via an empirical Bayesian model, whereby such parameters are modeled as random with a specific joint probability distribution. The hyperparameters specifying this probability distribution are derived from historical precipitation records at the study basin. Simulated parameter values are then generated using the Bayesian model, leading to alternative synthetic daily precipitation records simulated via the stochastic precipitation model. The set of such synthetic precipitation records is finally input to a physically based deterministic hydrologic model for propagating uncertainty in forecasted precipitation to hydrologic impact assessment studies.
The stochastic simulation approach is applied for generating an ensemble (set) of synthetic area-averaged daily precipitation records at the Hopland basin in the northern California Coast Range for the winter months (December through February: DJF) of 1997/98. The parameters of the stochastic precipitation model are derived from a seasonal precipitation forecast based on the Regional Climate System Model (RCSM), available at a 36-km2 grid spacing. The large-scale forcing input to RCSM for dynamical downscaling was a seasonal prediction of the University of California, Los Angeles, Atmospheric General Circulation Model. A semidistributed deterministic hydrologic model (“TOPMODEL”) is then used for calculating the streamflow response for each member of the area-averaged precipitation ensemble set. Uncertainty in the parameters of the stochastic precipitation model is finally propagated to associated streamflow response, by considering parameter values derived from historical (DJF 1958–92) area-averaged precipitation records at Hopland.
Abstract
A Monte Carlo framework is adopted for propagating uncertainty in dynamically downscaled seasonal forecasts of area-averaged daily precipitation to associated streamflow response calculations. Daily precipitation is modeled as a mixture of two stochastic processes: a binary occurrence process and a continuous intensity process, both exhibiting serial correlation. The parameters of these processes (e.g., the proportion of wet days and the average wet-day precipitation intensity in a month) are derived from the forecast record. Parameter uncertainty is characterized via an empirical Bayesian model, whereby such parameters are modeled as random with a specific joint probability distribution. The hyperparameters specifying this probability distribution are derived from historical precipitation records at the study basin. Simulated parameter values are then generated using the Bayesian model, leading to alternative synthetic daily precipitation records simulated via the stochastic precipitation model. The set of such synthetic precipitation records is finally input to a physically based deterministic hydrologic model for propagating uncertainty in forecasted precipitation to hydrologic impact assessment studies.
The stochastic simulation approach is applied for generating an ensemble (set) of synthetic area-averaged daily precipitation records at the Hopland basin in the northern California Coast Range for the winter months (December through February: DJF) of 1997/98. The parameters of the stochastic precipitation model are derived from a seasonal precipitation forecast based on the Regional Climate System Model (RCSM), available at a 36-km2 grid spacing. The large-scale forcing input to RCSM for dynamical downscaling was a seasonal prediction of the University of California, Los Angeles, Atmospheric General Circulation Model. A semidistributed deterministic hydrologic model (“TOPMODEL”) is then used for calculating the streamflow response for each member of the area-averaged precipitation ensemble set. Uncertainty in the parameters of the stochastic precipitation model is finally propagated to associated streamflow response, by considering parameter values derived from historical (DJF 1958–92) area-averaged precipitation records at Hopland.
Abstract
A geostatistical framework for integrating lower-atmosphere state variables and terrain characteristics into the spatial interpolation of rainfall is presented. Lower-atmosphere state variables considered are specific humidity and wind, derived from an assimilated data product from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP–NCAR reanalysis). These variables, along with terrain elevation and its gradient from a 1-km-resolution digital elevation model, are used for constructing additional rainfall predictors, such as the amount of moisture subject to orographic lifting; these latter predictors quantify the interaction of lower-atmosphere characteristics with local terrain. A “first-guess” field of precipitation estimates is constructed via a multiple regression model using collocated rain gauge observations and rainfall predictors. The final map of rainfall estimates is derived by adding to this initial field a field of spatially interpolated residuals, which accounts for local deviations from the regression-based first-guess field. Several forms of spatial interpolation (kriging), which differ in the degree of complexity of the first-guess field, are considered for mapping the seasonal average of daily precipitation for the period from 1 November 1981 to 31 January 1982 over a region in northern California at 1-km resolution. The different interpolation schemes are compared in terms of cross-validation statistics and the spatial characteristics of cross-validation errors. The results indicate that integration of low-atmosphere and terrain information in a geostatistical framework could lead to more accurate representations of the spatial distribution of rainfall than those found in traditional analyses based only on rain gauge data. The magnitude of this latter improvement, however, would depend on the density of the rain gauge stations, on the spatial variability of the precipitation field, and on the degree of correlation between rainfall and its predictors.
Abstract
A geostatistical framework for integrating lower-atmosphere state variables and terrain characteristics into the spatial interpolation of rainfall is presented. Lower-atmosphere state variables considered are specific humidity and wind, derived from an assimilated data product from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP–NCAR reanalysis). These variables, along with terrain elevation and its gradient from a 1-km-resolution digital elevation model, are used for constructing additional rainfall predictors, such as the amount of moisture subject to orographic lifting; these latter predictors quantify the interaction of lower-atmosphere characteristics with local terrain. A “first-guess” field of precipitation estimates is constructed via a multiple regression model using collocated rain gauge observations and rainfall predictors. The final map of rainfall estimates is derived by adding to this initial field a field of spatially interpolated residuals, which accounts for local deviations from the regression-based first-guess field. Several forms of spatial interpolation (kriging), which differ in the degree of complexity of the first-guess field, are considered for mapping the seasonal average of daily precipitation for the period from 1 November 1981 to 31 January 1982 over a region in northern California at 1-km resolution. The different interpolation schemes are compared in terms of cross-validation statistics and the spatial characteristics of cross-validation errors. The results indicate that integration of low-atmosphere and terrain information in a geostatistical framework could lead to more accurate representations of the spatial distribution of rainfall than those found in traditional analyses based only on rain gauge data. The magnitude of this latter improvement, however, would depend on the density of the rain gauge stations, on the spatial variability of the precipitation field, and on the degree of correlation between rainfall and its predictors.
Abstract
Over the twenty-first century, the frequency of extreme-heat events for major cities in heavily air conditioned California is projected to increase rapidly. Extreme heat is defined here as the temperature threshold for the 90th-percentile excedence probability (T90) of the local warmest summer days under the current climate. Climate projections from three atmosphere–ocean general circulation models, with a range of low to midhigh temperature sensitivity forced by the Special Report on Emission Scenarios higher, middle, and lower emission scenarios, indicate that these increases in temperature extremes and variance are projected to exceed the rate of increase in mean temperature. Overall, projected increases in extreme heat under the higher A1fi emission scenario by 2070–99 tend to be 20%–30% higher than those projected under the lower B1 emission scenario. Increases range from approximately 2 times the present-day number of days for inland California cities (e.g., Sacramento and Fresno), up to 4 times for previously temperate coastal cities (e.g., Los Angeles and San Diego), implying that present-day “heat wave” conditions may dominate summer months—and patterns of electricity demand—in the future. When the projected extreme heat and observed relationships between high temperature and electricity demand for California are mapped onto current availability, maintaining technology and population constant for demand-side calculations, a potential for electricity deficits as high as 17% during T90 peak electricity demand periods is found. Similar increases in extreme-heat days are likely for other southwestern U.S. urban locations, as well as for large cities in developing nations with rapidly increasing electricity demands. In light of the electricity response to recent extreme-heat events, such as the July 2006 heat waves in California, Missouri, and New York, these results suggest that future increases in peak electricity demand will challenge current transmission and supply methods as well as future planned supply capacities when population and income growth are taken into account.
Abstract
Over the twenty-first century, the frequency of extreme-heat events for major cities in heavily air conditioned California is projected to increase rapidly. Extreme heat is defined here as the temperature threshold for the 90th-percentile excedence probability (T90) of the local warmest summer days under the current climate. Climate projections from three atmosphere–ocean general circulation models, with a range of low to midhigh temperature sensitivity forced by the Special Report on Emission Scenarios higher, middle, and lower emission scenarios, indicate that these increases in temperature extremes and variance are projected to exceed the rate of increase in mean temperature. Overall, projected increases in extreme heat under the higher A1fi emission scenario by 2070–99 tend to be 20%–30% higher than those projected under the lower B1 emission scenario. Increases range from approximately 2 times the present-day number of days for inland California cities (e.g., Sacramento and Fresno), up to 4 times for previously temperate coastal cities (e.g., Los Angeles and San Diego), implying that present-day “heat wave” conditions may dominate summer months—and patterns of electricity demand—in the future. When the projected extreme heat and observed relationships between high temperature and electricity demand for California are mapped onto current availability, maintaining technology and population constant for demand-side calculations, a potential for electricity deficits as high as 17% during T90 peak electricity demand periods is found. Similar increases in extreme-heat days are likely for other southwestern U.S. urban locations, as well as for large cities in developing nations with rapidly increasing electricity demands. In light of the electricity response to recent extreme-heat events, such as the July 2006 heat waves in California, Missouri, and New York, these results suggest that future increases in peak electricity demand will challenge current transmission and supply methods as well as future planned supply capacities when population and income growth are taken into account.
Abstract
The authors present a seasonal hindcast and prediction of precipitation in the western United States and stream flow in a northern California coastal basin for December 1997–February 1998 (DJF) using the Regional Climate System Model (RCSM). In the seasonal hindcast simulation, in which the twice-daily National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis was used for the initial conditions and time-dependent boundary forcing, RCSM has simulated realistically the temporal and spatial variations of precipitation in California and stream flow in a northern California coastal basin. For the headwater basin of the Russian River in the northern California Coast Ranges, the Topography-Based Hydrologic Model (TOPMODEL) forced by observed daily precipitation resulted in a correlation coefficient of 0.88 between observed and simulated DJF stream flow. In the coupled stream flow hindcast, the authors obtained a correlation coefficient of 0.7 between simulated and observed stream flow for the same period. The coupled hindcast has generally overestimated (underestimated) low (high) flow events in the basin. Errors in the simulated stream flow were due mostly to the errors in the simulated precipitation. A seasonal hydroclimate prediction experiment, in which RCSM was nested within the global forecast data from the University of California, Los Angeles, GCM, has predicted well the season-total precipitation in the western United States. Temporal variations of predicted precipitation were affected strongly by the predictability of the general circulation model. The predicted DJF-total snowfall agrees well with the snowfall simulated in the hindcast, especially in the central Cascades and the Sierra Nevada, where snowfall was the heaviest.
Abstract
The authors present a seasonal hindcast and prediction of precipitation in the western United States and stream flow in a northern California coastal basin for December 1997–February 1998 (DJF) using the Regional Climate System Model (RCSM). In the seasonal hindcast simulation, in which the twice-daily National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis was used for the initial conditions and time-dependent boundary forcing, RCSM has simulated realistically the temporal and spatial variations of precipitation in California and stream flow in a northern California coastal basin. For the headwater basin of the Russian River in the northern California Coast Ranges, the Topography-Based Hydrologic Model (TOPMODEL) forced by observed daily precipitation resulted in a correlation coefficient of 0.88 between observed and simulated DJF stream flow. In the coupled stream flow hindcast, the authors obtained a correlation coefficient of 0.7 between simulated and observed stream flow for the same period. The coupled hindcast has generally overestimated (underestimated) low (high) flow events in the basin. Errors in the simulated stream flow were due mostly to the errors in the simulated precipitation. A seasonal hydroclimate prediction experiment, in which RCSM was nested within the global forecast data from the University of California, Los Angeles, GCM, has predicted well the season-total precipitation in the western United States. Temporal variations of predicted precipitation were affected strongly by the predictability of the general circulation model. The predicted DJF-total snowfall agrees well with the snowfall simulated in the hindcast, especially in the central Cascades and the Sierra Nevada, where snowfall was the heaviest.
Abstract
Previous studies of changes in visibility over a period of years have indicated either a general trend toward better horizontal visibilities or no change. In this study, visibility, relative humidity, wind direction, and other related data from three National Weather Service Offices (Akron, Ohio; Lexington, Ky.; Memphis, Tenn.) are used to determine changes in daylight visibilities during the summer seasons of 1962–69. Analyses of the data indicate that the percent of restricted visibility was greater during the period 1966–69 than during the period 1962–65 both before and after adjustment for the effects of location, time of day, humidity, and wind.
Abstract
Previous studies of changes in visibility over a period of years have indicated either a general trend toward better horizontal visibilities or no change. In this study, visibility, relative humidity, wind direction, and other related data from three National Weather Service Offices (Akron, Ohio; Lexington, Ky.; Memphis, Tenn.) are used to determine changes in daylight visibilities during the summer seasons of 1962–69. Analyses of the data indicate that the percent of restricted visibility was greater during the period 1966–69 than during the period 1962–65 both before and after adjustment for the effects of location, time of day, humidity, and wind.
Abstract
Regional-scale projections of climate change signals due to increases in atmospheric CO2 are generated for the western United States using a regional climate model (RCM) nested within two global scenarios from a GCM. The downscaled control climate improved the local accuracy of the GCM results substantially. The downscaled control climate is reasonably close to the results of an 8-yr regional climate hindcast using the same RCM nested within the NCEP–NCAR reanalysis, despite wet biases in high-elevation regions along the Pacific coast.
The downscaled near-surface temperature signal ranges from 3 to 5 K in the western United States. The projected warming signals generally increase with increasing elevation, consistent with earlier studies for the Swiss Alps and the northwestern United States. In addition to the snow–albedo feedback, seasonal variations of the low-level flow and soil moisture appear to play important roles in the spatial pattern of warming signals. Projected changes in precipitation characteristics are mainly associated with increased moisture fluxes from the Pacific Ocean and the increase in elevation of freezing levels during the cold season. Projected cold season precipitation increases substantially in mountainous areas along the Pacific Ocean. Most of the projected precipitation increase over the Sierra Nevada and the Cascades is in rainfall, while snowfall generally decreases except above 2500 m. Projected changes in summer rainfall are small. The snow budget signals are characterized by decreased (increased) cold season snowfall (snowmelt) and reduced snowmelt during spring and summer. The projected cold season runoff from high-elevation regions increases substantially in response to increased cold season rainfall and snowmelt, while the spring runoff decreases due to an earlier depletion of snow, except above 2500 m.
Abstract
Regional-scale projections of climate change signals due to increases in atmospheric CO2 are generated for the western United States using a regional climate model (RCM) nested within two global scenarios from a GCM. The downscaled control climate improved the local accuracy of the GCM results substantially. The downscaled control climate is reasonably close to the results of an 8-yr regional climate hindcast using the same RCM nested within the NCEP–NCAR reanalysis, despite wet biases in high-elevation regions along the Pacific coast.
The downscaled near-surface temperature signal ranges from 3 to 5 K in the western United States. The projected warming signals generally increase with increasing elevation, consistent with earlier studies for the Swiss Alps and the northwestern United States. In addition to the snow–albedo feedback, seasonal variations of the low-level flow and soil moisture appear to play important roles in the spatial pattern of warming signals. Projected changes in precipitation characteristics are mainly associated with increased moisture fluxes from the Pacific Ocean and the increase in elevation of freezing levels during the cold season. Projected cold season precipitation increases substantially in mountainous areas along the Pacific Ocean. Most of the projected precipitation increase over the Sierra Nevada and the Cascades is in rainfall, while snowfall generally decreases except above 2500 m. Projected changes in summer rainfall are small. The snow budget signals are characterized by decreased (increased) cold season snowfall (snowmelt) and reduced snowmelt during spring and summer. The projected cold season runoff from high-elevation regions increases substantially in response to increased cold season rainfall and snowmelt, while the spring runoff decreases due to an earlier depletion of snow, except above 2500 m.
Abstract
A numerical study of precipitation and river flow from November 1994 to May 1995 at two California basins is presented. The Hopland watershed of the Russian River in the northern California Coastal Range and the headwater of the North Fork American River in the northern Sierra Nevada were selected to investigate the hydroclimate, snow budget, and streamflow at different elevations. Simulated precipitation and streamflow at the Hopland basin closely approximated observed values. An intercomparison between the semidistributed TOPMODEL and two versions of the lumped Sacramento model for the severe storm event of January 1995 indicates that both types of models predicted a similar response of river outflows from this basin, with the exception that TOPMODEL predicted a faster recession of river flow with less base flow after precipitation ended. Precipitation in this low-elevation watershed was predominantly in the form of rain, causing a fast streamflow response. The high-elevation Sierra Nevada watershed received most of its precipitation as snowfall. As a result, the frozen water held in surface storage delayed runoff and streamflow. Application of a simple elevation-dependent snowfall and rainfall partitioning scheme showed the significance of finescale terrain variation in the surface hydrology at high-elevation watersheds.
Abstract
A numerical study of precipitation and river flow from November 1994 to May 1995 at two California basins is presented. The Hopland watershed of the Russian River in the northern California Coastal Range and the headwater of the North Fork American River in the northern Sierra Nevada were selected to investigate the hydroclimate, snow budget, and streamflow at different elevations. Simulated precipitation and streamflow at the Hopland basin closely approximated observed values. An intercomparison between the semidistributed TOPMODEL and two versions of the lumped Sacramento model for the severe storm event of January 1995 indicates that both types of models predicted a similar response of river outflows from this basin, with the exception that TOPMODEL predicted a faster recession of river flow with less base flow after precipitation ended. Precipitation in this low-elevation watershed was predominantly in the form of rain, causing a fast streamflow response. The high-elevation Sierra Nevada watershed received most of its precipitation as snowfall. As a result, the frozen water held in surface storage delayed runoff and streamflow. Application of a simple elevation-dependent snowfall and rainfall partitioning scheme showed the significance of finescale terrain variation in the surface hydrology at high-elevation watersheds.
