Snowpack Telemetry Research Articles

Assessing the Performance of Multiple Regional Climate Model Simulations for Seasonal Mountain Snow in the Upper Colorado River Basin

Abstract This study assesses the performance of the regional climate model (RCM) simulations from the North American Regional Climate Change Assessment Program (NARCCAP) for the Upper Colorado River basin (UCRB), U.S. Rocky Mountains. The UCRB is a major contributor to the Colorado River’s runoff. Its significant snow-dominated hydrological regime makes it highly sensitive to climatic changes, and future water shortage in this region is likely. The RCMs are evaluated with a clear RCM output user’s perspective and a main focus on snow. Snow water equivalent (SWE) and snow duration, as well as air temperature and precipitation from five RCMs, are compared with snowpack telemetry (SNOTEL) observations, with National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) Reanalysis II (R2), which provides the boundary conditions for the RCM simulations, and with North American Regional Reanalysis (NARR). Overall, most RCMs were able to significantly improve on the results from the NCEP–NCAR reanalysis. However, in comparison with spatially aggregated point observations and NARR, the RCMs are generally too dry, too warm, simulate too little SWE, and have a too-short snow cover duration with a too-late start and a too-early end of a significant snow cover. The intermodel biases found are partly associated with inadequately resolved topography (at the spatial resolution of the RCMs), imperfect observational data, different forcing techniques (spectral nudging versus no nudging), and the different land surface schemes (LSS). Attributing the found biases to specific features of the RCMs remains difficult or even impossible without detailed knowledge of the physical and technical specification of the models.

Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model1

Harshburger, Brian J., Von P. Walden, Karen S. Humes, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2012. Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model. Journal of the American Water Resources Association (JAWRA) 48(4): 643‐655. DOI: 10.1111/j.1752‐1688.2012.00642.xAbstract: As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1‐15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt‐dominated basins in Idaho. Model inputs are derived from meteorological forecasts, snow cover imagery, and surface observations from Snowpack Telemetry stations. The model performed well at lead times up to 7 days, but has significant predictability out to 15 days. The timing of peak flow and the streamflow volume are captured well by the model, but the peak‐flow value is typically low. The model performance was assessed by computing the coefficient of determination (R2), percentage of volume difference (Dv%), and a skill score that quantifies the usefulness of the forecasts relative to climatology. The average R2 value for the mean ensemble is >0.8 for all three basins for lead times up to seven days. The Dv% is fairly unbiased (within ±10%) out to seven days in two of the basins, but the model underpredicts Dv% in the third. The average skill scores for all basins are >0.6 for lead times up to seven days, indicating that the ensemble model outperforms climatology. These results validate the usefulness of the ensemble forecasting approach for basins of this type, suggesting that the ensemble version of SRM might be applied successfully to other basins in the Intermountain West.

Multiscale assimilation of Advanced Microwave Scanning Radiometer–EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado

Eight years (2002–2010) of Advanced Microwave Scanning Radiometer–EOS (AMSR‐E) snow water equivalent (SWE) retrievals and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) observations are assimilated separately or jointly into the Noah land surface model over a domain in Northern Colorado. A multiscale ensemble Kalman filter (EnKF) is used, supplemented with a rule‐based update. The satellite data are either left unscaled or are scaled for anomaly assimilation. The results are validated against in situ observations at 14 high‐elevation Snowpack Telemetry (SNOTEL) sites with typically deep snow and at 4 lower‐elevation Cooperative Observer Program (COOP) sites. Assimilation of coarse‐scale AMSR‐E SWE and fine‐scale MODIS SCF observations both result in realistic spatial SWE patterns. At COOP sites with shallow snowpacks, AMSR‐E SWE and MODIS SCF data assimilation are beneficial separately, and joint SWE and SCF assimilation yields significantly improved root‐mean‐square error and correlation values for scaled and unscaled data assimilation. In areas of deep snow where the SNOTEL sites are located, however, AMSR‐E retrievals are typically biased low and assimilation without prior scaling leads to degraded SWE estimates. Anomaly SWE assimilation could not improve the interannual SWE variations in the assimilation results because the AMSR‐E retrievals lack realistic interannual variability in deep snowpacks. SCF assimilation has only a marginal impact at the SNOTEL locations because these sites experience extended periods of near‐complete snow cover. Across all sites, SCF assimilation improves the timing of the onset of the snow season but without a net improvement of SWE amounts.

A comparison of SNOTEL and AMSR-E snow water equivalent data sets in western US watersheds

It is a consensus among earth scientists that climate change will result in an increased frequency of extreme events (e.g. floods, droughts). Streamflow forecasts and flood/drought analyses, given this high variability in the climatic driver (snowpack), are vital in the western USA. However, the ability to produce accurate forecasts and analyses is dependent upon the quality of these predictors. Run-off and stream volume analysis in the region is currently based upon in situ telemetry snow data products. Recent satellite deployments offer an alternative data source of regional snowpack. The proposed research investigates and compares remotely sensed snow water equivalent (SWE) data sets in western US watersheds in which snowpack is the primary driver of streamflow. Watersheds investigated include the North Platte, Upper Green and Upper Colorado. SWE data sets incorporated are in situ snowpack telemetry (SNOTEL) sites and the advanced microwave scanning radiometer-earth observing system (AMSR-E) aboard NASA's Aqua satellite. The time period analysed is 2003-2008, coincident with the deployment of the NASA Aqua satellite. Bivariate techniques between data sets are performed to provide valuable information on the time series of the snow products. Multivariate techniques including principal component analysis (PCA) and singular value decomposition (SVD) are also applied to determine similarities and differences between the data sets and investigate regional snowpack behaviours. Given the challenges (including costs, operation and maintenance) of deploying SNOTEL stations, the objective of the research is to determine whether remotely sensed SWE data provide a comparable option to in situ data sets. Correlation analysis resulted in only 11 of the 84 SNOTEL sites investigated being significant at 90% or greater with a corresponding AMSR-E cell. Agreement between SWE products was found to increase in lower elevation areas and later in the snowpack season. Two distinct snow regions were found to behave similarly between both data sets using a rotated PCA approach. Additionally, SVD linked both data products with streamflow in the region and found similar behaviour among data sets. However, when comparing SNOTEL data with the corresponding satellite cell, there was a consistent bias in the absolute magnitude (SWE) of the data sets. The streamflow forecasting results conclude regions that have few (or zero) land-based weather stations can incorporate the AMSR-E SWE product into a streamflow forecast model and obtain accurate values.

Modification of SNTHERM Albedo Algorithm and Response from Black Carbon in Snow

We carried out numerical experiments of snow accumulation and depletion as well as snow albedo for a SNOw pack TELemetry (SNOTEL) site, using a one-dimensinal energy and mass balance snow model (SNTHERM) and a two-stream, multi-layer Snow, Ice, and Aerosol Radiation model (SNICAR). We developed SNTHERM_SNICAR model by replacing the snow albedo algorithm in SNTHERM with SNICAR algorithm, to improve understanding of snow accumulation and snowpack ablation processes and response from black carbon (BC) in snow. Model was tested against observed snow water equivalent (SWE) during the water year 2000 and 2002. Results show that both models do a very good job at predicting SWE, although underestimates and earlier melt-out happen. Snow albedo got from SNTHERM_SNICAR model is lower than that got from SNTHERM. Decrease in albedo, duo to BC mixed in snow, leads to faster snowmelt process and shorter duration of snow cover.

Trends in Western U.S. Snowpack and Related Upper Colorado River Basin Streamflow1

Miller, W. Paul and Thomas C. Piechota, 2011. Trends in Western U.S. Snowpack and Related Upper Colorado River Basin Streamflow. Journal of the American Water Resources Association (JAWRA) 47(6):1197–1210. DOI: 10.1111/j.1752-1688.2011.00565.x Abstract: Water resource managers in the Western United States (U.S.) are currently faced with the challenge of adapting to unprecedented drought and uncertain impacts of climate change. Recent research has indicated increasing regional temperature and changes to precipitation and streamflow characteristics throughout the Western U.S. As such, there is increased uncertainty in hydroclimatological forecasts, which impact reservoir operations and water availability throughout the Western U.S., particularly in the Colorado River Basin. Previous research by the authors hypothesized a change in the character of precipitation (i.e., the frequency and amount of rainfall and snowfall events) throughout the Colorado River Basin. In the current study, 398 snowpack telemetry stations were investigated for trends in cumulative precipitation, snow water equivalent, and precipitation events. Observations of snow water equivalent characteristics were compared to observations in streamflow characteristics. Results indicate that the timing of the last day of the snow season corresponds well to the volume of runoff observed over the traditional peak flow season (April through July); conversely, the timing of the first day of the snow season does not correspond well to the volume of runoff observed over the peak flow season. This is significant to water resource managers and river forecasters, as snowpack characteristics may be indicative of a productive or unproductive runoff season.

High-Resolution Coupled Climate Runoff Simulations of Seasonal Snowfall over Colorado: A Process Study of Current and Warmer Climate

Abstract Climate change is expected to accelerate the hydrologic cycle, increase the fraction of precipitation that is rain, and enhance snowpack melting. The enhanced hydrological cycle is also expected to increase snowfall amounts due to increased moisture availability. These processes are examined in this paper in the Colorado Headwaters region through the use of a coupled high-resolution climate–runoff model. Four high-resolution simulations of annual snowfall over Colorado are conducted. The simulations are verified using Snowpack Telemetry (SNOTEL) data. Results are then presented regarding the grid spacing needed for appropriate simulation of snowfall. Finally, climate sensitivity is explored using a pseudo–global warming approach. The results show that the proper spatial and temporal depiction of snowfall adequate for water resource and climate change purposes can be achieved with the appropriate choice of model grid spacing and parameterizations. The pseudo–global warming simulations indicate enhanced snowfall on the order of 10%–25% over the Colorado Headwaters region, with the enhancement being less in the core headwaters region due to the topographic reduction of precipitation upstream of the region (rain-shadow effect). The main climate change impacts are in the enhanced melting at the lower-elevation bound of the snowpack and the increased snowfall at higher elevations. The changes in peak snow mass are generally near zero due to these two compensating effects, and simulated wintertime total runoff is above current levels. The 1 April snow water equivalent (SWE) is reduced by 25% in the warmer climate, and the date of maximum SWE occurs 2–17 days prior to current climate results, consistent with previous studies.

Evaluation of a cloud-gap-filled MODIS daily snow cover product over the Pacific Northwest USA

Summary The cloud-gap-filled (CGF) method developed by Hall et al. (2010) is a novel method for efficiently mitigating cloud obscuration using the most recently available cloud-free observations from prior days at each pixel. In this paper, we extend this method not only using prior observations but also using subsequent observations. Four CGF using 3 or 5 days prior or subsequent observations are applied to the standard MODerate resolution Imaging Spectroradiometer (MODIS) snow cover product and are evaluated against 3-years, in situ observations at 244 SNOwpack TELemetry (SNOTEL) stations. Results indicate that daily CGF snow cover maps using prior or subsequent observations are obviously different due to daily cloud shifting. However, the monthly and annual cloud reductions are similar. Although the overall accuracies under all-sky conditions of CGF using prior or subsequent observations are very similar, CGF snow cover maps using subsequent images have less underestimation errors, except during the snow melting period, and higher snow accuracies. When the observation interval is expanded from 3 to 5 days, this method is able to fill more cloud obscurations and increase the overall accuracies by ∼30% and ∼37%, respectively; however, they introduce slightly more uncertainty. Monthly and regional analyses indicate that this method is more efficient during the snow-covered period or in high-elevation zones.

Noah land surface model modifications to improve snowpack prediction in the Colorado Rocky Mountains

Simulated snowpack by the Noah land surface model (LSM) shows an early depletion due to excessive sublimation and too early onset of snowmelt. To mitigate these deficiencies, five model modifications are tested to improve snowpack performance: (1) time‐varying snow albedo, (2) solar radiation adjustment for terrain slope and orientation, (3) reducing the surface exchange coefficient for stable boundary layers, (4) increase of fresh snow albedo, and (5) adjusting surface roughness length when snow is present. The Noah LSM is executed from 1 November 2007 to 1 August 2008 for the headwater region in the Colorado Rocky Mountains with complex terrain, and its results are evaluated against 1 km Snow Data Assimilation System (SNODAS) output and individual Natural Resources Conservation Service Snowpack Telemetry (SNOTEL) sites. The most effective way to improve magnitude and timing of seasonal maximum snow water equivalent (SWE) is the introduction of the time‐varying albedo formulation and the increase in fresh snow albedo. Minor improvement is obtained by reducing nighttime sublimation through adjusting the stable boundary layer surface exchange coefficient. Modifying the surface roughness length over snow surfaces and adding a terrain slope and orientation adjustment for radiation has little effect on average SWE conditions for the entire modeling domain, though it can have a significant effect in certain regions. The net effect of all changes is to improve the magnitude and timing of seasonal maximum SWE, but the snow period end is now somewhat too long. Adding the terrain slope and orientation effects does have an effect on local surface energy flux components depending on the cell slope and orientation.

Noah LSM Snow Model Diagnostics and Enhancements

Abstract A negative snow water equivalent (SWE) bias in the snow model of the Noah land surface scheme used in the NCEP suite of numerical weather and climate prediction models has been noted by several investigators. This bias motivated a series of offline tests of model extensions and improvements intended to reduce or eliminate the bias. These improvements consist of changes to the model’s albedo formulation that include a parameterization for snowpack aging, changes to how pack temperature is computed, and inclusion of a provision for refreeze of liquid water in the pack. Less extensive testing was done on the performance of model extensions with alternate areal depletion parameterizations. Model improvements were evaluated through comparisons of point simulations with National Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) SWE data for deep-mountain snowpacks at selected stations in the western United States, as well as simulations of snow areal extent over the conterminous United States (CONUS) domain, compared with observational data from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS). The combination of snow-albedo decay and liquid-water refreeze results in substantial improvements in the magnitude and timing of peak SWE, as well as increased snow-covered extent at large scales. Modifications to areal snow depletion thresholds yielded more realistic snow-covered albedos at large scales.

Changes in the Timing of Snowmelt and Streamflow in Colorado: A Response to Recent Warming

Abstract Trends in the timing of snowmelt and associated runoff in Colorado were evaluated for the 1978–2007 water years using the regional Kendall test (RKT) on daily snow-water equivalent (SWE) data from snowpack telemetry (SNOTEL) sites and daily streamflow data from headwater streams. The RKT is a robust, nonparametric test that provides an increased power of trend detection by grouping data from multiple sites within a given geographic region. The RKT analyses indicated strong, pervasive trends in snowmelt and streamflow timing, which have shifted toward earlier in the year by a median of 2–3 weeks over the 29-yr study period. In contrast, relatively few statistically significant trends were detected using simple linear regression. RKT analyses also indicated that November–May air temperatures increased by a median of 0.9°C decade−1, while 1 April SWE and maximum SWE declined by a median of 4.1 and 3.6 cm decade−1, respectively. Multiple linear regression models were created, using monthly air temperatures, snowfall, latitude, and elevation as explanatory variables to identify major controlling factors on snowmelt timing. The models accounted for 45% of the variance in snowmelt onset, and 78% of the variance in the snowmelt center of mass (when half the snowpack had melted). Variations in springtime air temperature and SWE explained most of the interannual variability in snowmelt timing. Regression coefficients for air temperature were negative, indicating that warm temperatures promote early melt. Regression coefficients for SWE, latitude, and elevation were positive, indicating that abundant snowfall tends to delay snowmelt, and snowmelt tends to occur later at northern latitudes and high elevations. Results from this study indicate that even the mountains of Colorado, with their high elevations and cold snowpacks, are experiencing substantial shifts in the timing of snowmelt and snowmelt runoff toward earlier in the year.

A Physically Based Daily Hydrometeorological Model for Complex Mountain Terrain

Abstract This paper describes the continued development of the physically based hydrometeorological model Generate Earth Systems Science input (GENESYS) and its application in simulating snowpack in the St. Mary (STM) River watershed, Montana. GENESYS is designed to operate a high spatial and temporal resolution over complex mountainous terrain. The intent of this paper is to assess the performance of the model in simulating daily snowpack and the spatial extent of snow cover over the St. Mary River watershed. A new precipitation estimation method that uses snowpack telemetry (SNOTEL) and snow survey data is presented and compared with two other methods, including Parameter-elevation Regressions on Independent Slopes Model (PRISM) precipitation data. A method for determining daily temperature lapse rates from NCEP reanalysis data is also presented and the effect of temperature lapse rate on snowpack simulations is determined. Simulated daily snowpack values compare well with observed values at the Many Glacier SNOTEL site, with varying degrees of accuracy, dependent on the method used to estimate precipitation. The spatial snow cover extent compares well with Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products for three dates selected to represent snow accumulation and ablation periods.

Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States

Abstract Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density. The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.

Changing Properties of Snowpack in the Great Salt Lake Basin, Western United States, from a 26-Year SNOTEL Record

Daily observations from automated snowpack telemetry (SNOTEL) stations from within the drainage basin of the Great Salt Lake over the period from 1982 to 2007 are analyzed. The major finding is a shift toward an earlier date of peak snow water equivalent (SWE) by around fifteen days. Less robust findings are reductions in the amounts of peak SWE and 1 April SWE. This suggests increased chances of late-summer water shortages, especially when combined with rapid recent population growth. Less freshwater is likely to be available to flow into the Great Salt Lake, increasing its salinity and potentially affecting its ecology.

Unraveling uncertainties in hydrologic model calibration: Addressing the problem of compensatory parameters

In a previous paper, Vrugt et al. (2005) presented a combined parameter and state estimation method, entitled SODA, to improve the treatment of input, output, parameter and model structural error during model calibration. The argument for using SODA is that explicit treatment of all sources of uncertainty should result in parameter estimates that closer represent system properties, instead of parameter values that are compensating for input, output and model structural errors. In this study we provide further support for this claim by applying the SODA method to the calibration of a simple 2‐parameter snow model, using data from the Lake Eldora SNOwpack TELemetry (SNOTEL) site in Colorado, USA.

SNOTEL representativeness in the Rio Grande headwaters on the basis of physiographics and remotely sensed snow cover persistence

AbstractIn this study we identify the physiographic and snowpack conditions currently represented by snowpack telemetry (SNOTEL) stations in the Rio Grande headwaters. Based on 8 years of advanced very high‐resolution radiometer data (1995–2002) a snow cover persistence index was derived. Snow cover persistence values at the seven SNOTEL sites ranged from 3·9 to 4·75, with an average 14% greater than the mean persistence of the watershed. Using elevation, western barrier distance, and vegetation density, a 32‐node binary classification tree model explained 75% of the variability in average snow cover persistence. Terrain classes encompassing the Lily Pond, Middle Creek, and Slumgullion SNOTEL sites represented 4·1%, 6·4%, and 4·0% of the watershed area respectively. SNOTEL stations do not exist in the spatially extensive (e.g. 11% of the watershed) terrain classes located in the upper elevations above the timberline. The results and techniques presented here will be useful for spatially distributed hydrologic analyses, in that we have identified the physiographic conditions currently represented by SNOTEL stations (i.e. the snowpack regimes at which snow water equivalent estimation uncertainty can be determined). Further, we outlined a statistically unbiased approach for designing future observation networks tailored for spatially distributed applications. Copyright © 2006 John Wiley & Sons, Ltd.

A Large-Droplet Mode and Prognostic Number Concentration of Cloud Droplets in the Colorado State University Regional Atmospheric Modeling System (RAMS). Part II: Sensitivity to a Colorado Winter Snowfall Event

Abstract This paper is the second in a two-part series describing recent additions to the microphysics module of the Regional Atmospheric Modeling System (RAMS) at Colorado State University. These changes include the addition of a large-cloud-droplet mode (40–80 μm in diameter) into the liquid-droplet spectrum and the parameterization of cloud-droplet nucleation through activation of cloud condensation nuclei (CCN) and giant CCN (GCCN). The large-droplet mode was introduced to represent more precisely the natural dual mode of the cloud-droplet distribution. The parameterized droplet nucleation replaces the former estimation of cloud-droplet formation solely from supersaturation calculations. In Part I of this series, details of the improvements to the microphysics were presented, including the set of equations governing the development of cloud droplets in the Lagrangian parcel model that was employed to parameterize this complex process. Supercell simulations were examined with respect to the model sensitivity to the presence and concentration of large cloud droplets, CCN, and GCCN. Part II examines the sensitivity of the model microphysics to imposed aerosol variations in a wintertime snowfall event that occurred over Colorado on 28–29 February 2004. Model analyses and sensitivity are compared with the real-time forecast version 4.3 of RAMS as well as selected snowpack telemetry (SNOTEL) accumulated precipitation data and surface data from Storm Peak Laboratory in Steamboat Springs, Colorado.

A comparison of MODIS and NOHRSC snow‐cover products for simulating streamflow using the Snowmelt Runoff Model

AbstractRemote sensing is an important source of snow‐cover extent for input into the Snowmelt Runoff Model (SRM) and other snowmelt models. Since February 2000, daily global snow‐cover maps have been produced from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS). The usefulness of this snow‐cover product for streamflow prediction is assessed by comparing SRM simulated streamflow using the MODIS snow‐cover product with streamflow simulated using snow maps from the National Operational Hydrologic Remote Sensing Center (NOHRSC). Simulations were conducted for two tributary watersheds of the Upper Rio Grande basin during the 2001 snowmelt season using representative SRM parameter values. Snow depletion curves developed from MODIS and NOHRSC snow maps were generally comparable in both watersheds: satisfactory streamflow simulations were obtained using both snow‐cover products in larger watershed (volume difference: MODIS, 2·6%; NOHRSC, 14·0%) and less satisfactory streamflow simulations in smaller watershed (volume difference: MODIS, −33·1%; NOHRSC, −18·6%). The snow water equivalent (SWE) on 1 April in the third zone of each basin was computed using the modified depletion curve produced by the SRM and was compared with in situ SWE measured at Snowpack Telemetry sites located in the third zone of each basin. The SRM‐calculated SWEs using both snow products agree with the measured SWEs in both watersheds. Based on these results, the MODIS snow‐cover product appears to be of sufficient quality for streamflow prediction using the SRM in the snowmelt‐dominated basins. Copyright © 2005 John Wiley & Sons, Ltd.

Statistical evaluation of remotely sensed snow-cover products with constraints from streamflow and SNOTEL measurements

Using streamflow and Snowpack Telemetry (SNOTEL) measurements as constraints, the evaluation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily and 8-day snow-cover products is carried out using the Upper Rio Grande River Basin as a test site. A time series of the snow areal extent (SAE) of the Upper Rio Grande Basin is retrieved from the MODIS tile h09v05 covering the time period from February 2000 to June 2004 using an automatic Geographic Information System (GIS)-based algorithm developed for this study. Statistical analysis between the streamflow at Otowi (NM) station and the SAE retrieved from the two MODIS snow-cover products shows that there is a statistically significant correlation between the streamflow and SAE for both products. This relationship can be disturbed by heavy rainstorms in the later springtime, especially in May. Correlation analyses show that the MODIS 8-day product has a better correlation ( r=−0.404) with streamflow and has less percentage of spurious snowmelt events in wintertime than the MODIS daily product ( r=−0.300). Intercomparison of these two products, with the SNOTEL data sets as the ground truth, shows that (1) the MODIS 8-day product has higher classification accuracy for both snow and land; (2) the omission error of misclassifying snow as land is similar for both products, both are low; (3) the MODIS 8-day product has a slightly higher commission error of misclassifying land as snow than the MODIS daily product; and (4) the MODIS daily product has higher omission errors of misclassifying both snow and land as clouds. Clouds are the major cause for reduction of the overall accuracy of the MODIS daily product. Improvement in suppressing clouds in the 8-day product is obvious from this comparison study. The sacrifice is the temporal resolution that is reduced from 1 to 8 days. The significance of the results is that the 8-day product will be more useful in evaluating the streamflow response to the snow-cover extent changes, especially from the long-term point of view considering its lower temporal resolution than the daily product. For clear days, the MODIS daily algorithm works quite well or even better than the MODIS 8-day algorithm.

VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 27:151-170 (2004) - doi:10.3354/cr027151 VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA T. G. F. Kittel1,2,5,*, N. A. Rosenbloom1, J. A. Royle1,6, C. Daly3, W. P. Gibson3, H. H. Fisher1, P. Thornton1, D. N. Yates1, S. Aulenbach1, C. Kaufman1, R. McKeown2, D. Bachelet4, D. S. Schimel1, VEMAP2 Participants§ 1National Center for Atmospheric Research, PO Box 3000, Boulder, Colorado 80307, USA2Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado 80523, USA3Spatial Climate Analysis Service and 4Department of Bioengineering, Oregon State University, Corvallis, Oregon 97331, USAPresent addresses: 5INSTAAR, Campus Box 450, University of Colorado, Boulder, Colorado 80309-0450, USA6USDI Fis and Wildlife Service, Laurel, Maryland 20708, USA§R. Neilson, J. Lenihan, R. Drapek (USDA Forest Service, Pacific Northwest Research Station, Corvallis, OR, USA); D. S. Ojima, W. J. Parton (Natural Resource Ecology Laboratory, Colorado University, Fort Collins, CO, USA); J. M. Melillo, D. W. Kicklighter, H. Tian (Ecosystems Center, Marine Biological Laboratory, Woods Hole, MA, USA); A. D. McGuire (US Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit, Fairbanks, AK, USA); M. T. Sykes, B. Smith, S. Cowling, T. Hickler (Physical Geography & Ecosystems Analysis, Lund University, Sweden); I. C. Prentice (Max Planck Institute for Biogeochemistry, Jena, Germany); S. Running (University of Montana, Missoula, MT, USA); K. A. Hibbard (College of Forestry, Oregon State University, Corvallis, OR, USA); W. M. Post, A. W. King (Oak Ridge National Laboratory, Oak Ridge, TN, USA); T. Smith, B. Rizzo (Dept. of Environmental Sciences, University of Virginia, Charlottesville, VA, USA); F. I. Woodward (Dept. of Animal and Plant Sciences, University of Sheffield, UK) *Email: kittel@colorado.edu ABSTRACT: Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895-1993 on a 0.5° latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables are: precipitation, mininimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed techniques that rely on geostatistical and physical relationships to create the temporally and spatially complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to grid the infilled monthly station data. We implemented a stochastic weather generator (modified WGEN) to disaggregate the gridded monthly series to dailies. Radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100 yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. The VEMAP dataset exhibits statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion datasets are available online at data archive centers. KEY WORDS: Climate dataset · Climate variability · Climate change · Ecosystem dynamics · Vegetation · Ecological modeling · VEMAP · Geostatistics · Palmer Drought Severity Index · PDSI Full article in pdf format PreviousExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 27, No. 2. Online publication date: October 07, 2004 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2004 Inter-Research.