Interactive comment on “ Large scale snow water status monitoring : comparison of different snow water products in the upper Colorado basins ”

2. The performance indicators of the different methods are (probably; although not explicitly stated) calculated for very different time intervals: monthly for MI and daily for the UEB model. This makes a huge difference! For MI the number of samples must be in the order of 10, for the UEB model in the order of 300. To me, it’s not ok to compare these performance indicators in the same table without clearly stating this.


Introduction
Every year large parts of the globe are seasonally covered by snow; for example, every year as much as half of the land surface in the Northern Hemisphere can be Figures snow-covered (Robinson and Kukla, 1985).Most of the water supply for those snowcovered areas comes from snowmelt runoff (Daly et al., 2000;Schmugge et al., 2002;Tekeli et al., 2005); over 60 % of the precipitation in the western United States falls as snow (Serreze et al., 1999).In the Upper Colorado Basin, 63 % of precipitation falls as snow (Fassnacht, 2006), and 70-80 % of total annual runoff comes from snowmelt (Daly et al., 2000;Schmugge et al., 2002).In the past few decades, some basins in the United States have seen historic floods that were induced and triggered from spring rain-on-snow events during years of above average winter snowfall, such as the floods of the Red River of 2009 and 2010.Monitoring the status of snowpack during winter and spring is important to the water resources and disaster management entities.
Several methods have been used to monitor snowpack status: snow course surveys, remote sensing, and snow accumulation/ablation modeling.Worldwide, few areas have reliable ground-observed snowpack status data collected regularly on a large scale, except for the western United States, which is monitored by the SNOwpack TELemetry system (SNOTEL).The representativeness of the snowpack characteristics estimated even from a data-extensive system such as SNOTEL is questioned by some investigators (Daly et al., 2000;Molotch and Bales, 2006).
Because of the limitations of the observational data, several snowpack status monitoring systems that rely on snowmelt models (Pan et al., 2003;Watson et al., 2006) have been described in the literature: snowmelt models combined with remotely sensed data (Cline et al., 1998), remotely sensed data combined with observed snow data (Carroll, 1995;Dressler et al., 2006), and models based solely on remote sensing methods (Bales et al., 2008;Schmugge et al., 2002;Tekeli et al., 2005).A system that utilizes assimilation of data (remotely sensed and insitu measured) and snow accumulation/ablation modeling is the NOAA National Operational Hydrologic Remote Sensing Center (NOHRC; NOHRC, 2004) Snow Data Assimilation System (SNODAS).
Efforts to monitor snowpack status for large areas from remotely sensed data have mainly focused on snow covered area (SCA) mapping (Bales et al., 2008;Kelly et al., 2003;Robinson et al., 1993;Tekeli et al., 2005); however, the snow water equivalent Introduction

Conclusions References
Tables Figures

Back Close
Full Despite their coarse spatial resolution and known shortcomings (Kelly et al., 2003), passive microwave sensors like the Scanning Multichannel Microwave Radiometer (SMMR) and the Special Sensor Microwave Imager (SSM/I) have gained some acceptance as tools to map SWE (Chen et al., 2001;Sun et al., 1996).
The objective of this study was to explore the possibility of monitoring the status of the snowpack at regional scales in real time with model and data that are available in even the most data-scarce regions of the globe.The specific aim of our study is to investigate how SWE that is modeled and one that was estimated from passive microwave sensors data compared with SWE values measured by SNOTEL and estimated by SNODAS.In the subsequent sections we will introduce a spatially distributed snow accumulation and ablation model that is forced with remotely sensed data and near-real-time meteorological data from forecast models.We will compare model-simulated SWE with the best available regional SWE datasets.In the comparison, we will include an SWE product estimated from SSM/I and SMMR to substantiate how useful they are in lieu of snowmelt-predicted SWE products.
The snowmelt model we used is a spatially distributed version of the Utah Energy Balance (UEB) model (Tarboton and Luce, 1996).The UEB model has been applied with good results to several basins from different parts the world (Koivusalo and Heikinheimo, 1999;Schulz and de Jong, 2004;Watson et al., 2006).In the subsequent sections, we will describe the model and data, and we will evaluate simulated SWE values over a region of the western United States that covers parts of the Upper Colorado Basin.

Snow accumulation and ablation model
The Utah Energy Balance (UEB) model (Tarboton and Luce, 1996) was used for this work.The UEB model has been applied successfully to diverse basins with good results (Koivusalo and Heikinheimo, 1999;Schulz and de Jong, 2004;Watson et al., 2006).The UEB model solves the snow energy balance at the surface by means of three state variables, snow water equivalence, snow water content, and the age of the snow surface, using a lumped representation of the snowpack as a single layer.Full heterogeneity component that is due to the variability of the precipitation and solar radiation fields.
For model parameters, we kept the values of the UEB model parameters from Tarboton and Luce (1996) unchanged, except for the snow density, which was changed from 450 kg m −3 to 320 kg m −3 -value that is more appropriated for the study area (Josberger et al., 1996;Molotch and Bales, 2005).To estimate model parameters, Tarboton and Luce (1996) have a calibration dataset from the Central Sierra Snow Laboratory collected in the winter of [1985][1986].Even though the model has snow redistribution capability, there is no straightforward way to determine appropriate drift factor for every modeling grid.Besides, the sizes of our modeling grids (0.05 × 0.05 • and 0.1 × 0.1 • ) do not warrant modeling snow redistribution processes that usually take place at smaller scales.Therefore, snow redistribution was not taken into account in the simulation results that are presented here.

Data
The DisUEB model was run with inputs of air temperature, precipitation, wind speed, humidity, and radiation (longwave and shortwave) with temporal resolution of 6-h time steps and spatial resolutions of 0.05 × 0.05 and 0.1 × 0.1 degrees (about 5 km and 10 km).Six hours is the maximum time step that is sufficient to resolve the solar diurnal cycle (Tarboton and Luce, 1996).Precipitation is the most important meteorological model input variable.The precipitation data used was from sources: for the 0.05-degree resolution runs the source was the National Weather Service (NWS) regional River Forecast Centers (RFCs) Multi-sensor Precipitation Estimator (MPE) dataset, where the precipitation input for the 0.1-degree resolution runs was the TRMM precipitation estimates.In the subsequent paragraphs, we describe model input meteorological data and the data that was used to test simulated SWE ability to monitor the snowpack water content through the season and Table 1 summarizes the data.Introduction

Conclusions References
Tables Figures

Back Close
Full

Meteorological Data from Weather Forecast Model
The air temperature (Ta), relative humidity (RH), direct and diffuse solar radiation (Rs), and wind speed (U) were from the NOAA's Global Forecast System (GFS) model.To match the MPE resolution; the Ta, RH, and Rs were downscaled from their original spatial resolution of 0.375-degree resolution grid to a 0.05-degree resolution grid.The downscaling algorithms rely on the topographic data to downscale the coarse weather forecasting model's output fields to the higher resolution.To downscale the three variables terrain geomorphometric characteristics (aspect, slope, and sky-view factor) calculated from a digital elevation model (DEM) were utilized.To redistribute the solar radiation, we used the algorithms of Dozier and Frew (1990) and Dubayah and Van Katwijk (1992).Ta was downscaled with a moist adiabatic lapse rate model (Stone and Carlson, 1979), and RH was downscaled with the re-estimated Ta.

MPE
The MPE data is made by combining data from rain gages, radars, and satellite sensors.The original format of MPE data is in the Hydrologic Rainfall Analysis Project (HRAP) grid format and has an approximate spatial resolution of 4 km.Since the radar coverage of the mountainous areas of the western United States is poor (Wood et al., 2003), especially during the winter, the MPE product west of the Continental Divide is mainly made from gage reports and long-term climatologic precipitation data (PRISM).
The MPE has been operational since 2002, but only data from 2005 was available for download from NOAA's Web site at http://water.weather.gov/precip/download.php.

TRMM
The TRMM precipitation data we have used is the 3B42RT product at a 0.25-degree spatial resolution (Tian et al., 2007).The 3B42RT product is made by the combination of precipitation estimates from the TRMM microwave and infrared (IR) sensors.The Introduction

Conclusions References
Tables Figures

Back Close
Full microwave sensor provides the main estimates, and the IR sensors provide coverage for areas with gaps in the microwave precipitation estimates.Although the TRMM 3B42 estimates are considered better than the 3B42RT product, the 3B42 is not available in real time as the 3B42RT product is.The 3B42RT products are usually posted to the TRMM Web site about 6 h after the event.

SWE from the microwave imagers
The SWE datasets estimated by microwave imagers that we used are the Global Monthly EASE-Grid SWE Climatology (Armstrong et al., 2007).The EASE-Grid SWE

SNOTEL
SNOTEL is an automated network of stations that record snow and meteorological vari-  Full

SNODAS
SNODAS is an NOAA National Operational Hydrologic Remote Sensing Center (NOHRC) SWE dataset (NOHRC, 2004).SNODAS is made by the assimilation of modeled SWE, remotely sensed SWE, and station-recorded SWE data.The SNODAS dataset covers the continental United States at 1-km spatial resolution and 24-h temporal resolution.Although we will consider hereafter the SWE as observed, we are not aware of any extensive validation done on the SNODAS SWE datasets.Because SNODAS assimilates all available observed snow data, it is difficult to validate the accuracy of the SNODAS product.Nevertheless, SNODAS has been used in several research studies and is the only publicly available large-scale SWE product.SNODAS datasets were downloaded from the NSIDC Web site (http://nsidc.org/data).Before comparing SNODAS with other datasets, the SNODAS data were re-gridded to 0.05-, 0.1-, and 0.25-degree resolution from the native 1-km resolution.Table 3 summarizes the spatial and temporal resolutions of the meteorological and snow data that were used in this study.

Performance indicators
For performance indicators we used the percent of bias, coefficient of determination, total root mean square error (RMSE), and parameters that are based on the RMSE outlined by Willmott (1982).Willmott (1982) decomposed the RMSE into the systematic error (RMSEs), which can be reduced with small improvements in model parameters and input data, and unsystematic RMSE (RMSEu), which cannot be reduced without extensive changes in the model structure and input data.The RMSE, RMSEs, and Introduction

Conclusions References
Tables Figures

Back Close
Full RMSEu parameters are defined (Willmott, 1982) as (1) (2) where n is the number of observations, O i is the observed value, P i is the predicted value, and Pi = a • O i + b.To describe how much the model underestimates or overestimates the variable of interest, the percent bias was calculated according to 4 Results and discussion

Snowmelt model meteorological inputs
We tested the precipitation reported by MPE and TRMM by comparing to precipitation value recorded at the 39 SNOTEL stations shown in Fig. 1.By comparing gridded data of varying spatial scales and point data there should not be an expectation of perfect agreement even if both data are correct.We compared precipitation totals accumulated in the snow accumulation/ablation periods of the three years of the simulation period -Introduction

Conclusions References
Tables Figures

Back Close
Full Both MPE and TRMM were negatively biased against SNOTEL precipitation as illustrated in Fig. 2; on average the percent bias of the MPE per season for the 39 locations was −26 % with mean and standard deviation of −84 ± 110 mm, where for the TRMM the bias was −51 % (−164 ± 124 mm).The correlation between the MPE and TRMM was even lower than the one the two datasets had with SNOTEL data (r = 0.53).Higher proportions of the precipitation differences with SNOTEL sets were systematic errors for both the TRMM (86 % of RMSE) and the MPE datasets (77 % of RMSE).The higher systematic errors of the TRMM and MPE data means that the data could be improved with simpler correction schemes.
The large discrepancy of the MPE compared with SNOTEL is difficult to explain even when the perils of comparing gridded precipitation with values from a single gage are taken into account.The discrepancies could be due to the difference between the methods used to calculate the MPE values east and west of the Continental Divide.The large magnitude of the discrepancy between some of the SNOTEL station-recorded precipitation and the MPE suggests that there is a need to improve the MPE estimation.Our results on the bias direction, being inclined for underestimation, are in line with what Habib et al. (2009) observed when they weighted precipitation values from MPE against raingage-recorded precipitation.
The GFS daily mean Ta extracted from grid-cells was compared to SNOTELrecorded Ta the 39 stations, which were described in the preceding sections.The GFS's Ta was created by averaging four 6-hourly Ta.The comparison period was same as the precipitation evaluation period -winter and spring, when the Ta influences the snow process.The 39 sites elevation ranges from 2268 m to 3487 m. Figure 3 shows the plots of the average daily GFS-and SNOTEL-recorded Ta for the 39 sites for the three seasons.GFS's Ta matches the seasonally of the SNOTEL recorded Ta (Fig. 3).The Ta of both GFS and SNOTEL were significantly correlated (R 2 = 0.61, d.f.= 171), but the GFS's Ta were negatively biased versus the Ta recorded at the SNOTEL sites (Fig. 4).The bias, between GFS and SNOTEL Ta, was not correlated with elevation Introduction

Conclusions References
Tables Figures

Back Close
Full (Fig. 5).The negative bias of the GFS's Ta is counterintuitive given that usually the SNOTEL sites are located at higher elevations than the surrounding terrain.The presence of a negative bias within all elevation bands suggests that the elevation correction applied to the original GFS data was not the cause of the biases but rather a systematic GFS's underestimation bias.Others have reported similar results of negative biases of weather forecast model air temperature in the western United States during the winter months (Pan et al., 2003).

Spatial intercomparisons of the SWE datasets
The SWE grids simulated with the UEB model and the SWE grids estimated from MI were compared against SWE from SNOTEL and SNODAS.While the SWE from the UEB simulation and SNODAS system had only a few grids with missing data (grids over water bodies), the SWE estimated from the MI datasets has a high number of pixels with missing data.For example, MI-estimated SWE had missing data in 40 % of the area for February 2007 (Fig. 6).The evaluation of the SWE was done at the grids corresponding with the sites of the 39 SNOTEL sites shown in Fig. 1.The SNODAS grids used in the comparisons were upscaled from their native 1-km (∼ 0.01-degree) resolution to grids with 0.05-, 0.10-, and 0.025-degree resolution.Statistical indexes (correlation coefficients, percent biases, RMSE, RMSEs, and RMSEu) were calculated at each of the 39 validation sites between the SNODAS SWE and MI-and UEB-produced SWE.Additionally, to give a contextual frame-of-reference, the SWE products were compared to the SWE recorded at the 39 sites by SNOTEL system.The average monthly SWE value recorded at the SNOTEL sites was 259 ± 96 mm (mean ± standard deviation) and 240±98 mm for the periods January 2006-April 2008 (UEB simulations period) and January 2006-April 2007 (the period where MI-estimated SWE were available), respectively.Of the 39 sites the SWE products simulated with the UEB were significantly correlated with the SNODAS SWE (p = 0.05) in 37 and 23 sites for the MPE and TRMM precipitation, respectively (Fig. 7a).The SWE estimated from MI was not significantly correlated (p = 0.05) with the SNODAS SWE at 28 sites Introduction

Conclusions References
Tables Figures

Back Close
Full (Fig. 7a).The correlation between the SWE products and the SNOTEL recorded SWE was significant at 37, 23, and 12 sites for the UEB-MPE, and UEB-TRMM, and MI-SWE products, respectively (Fig. 7b). Figure 8a-f shows linear and box plots of the three SWE products contrasted with concurrent SNOTEL and SNODAS SWE.The MI-estimated SWE consistently underestimates the SWE depicted by the SNODAS or the SNOTEL (Fig. 8c).The SWE simulated with the UEB model forced with TRMM data for precipitation also consistently underpredicted the SWE most of the time (Fig. 8b).The SWE modeled with UEB driven with the MPE data was in good agreement with the SNODAS and SNOTEL SWE values, except for one location that had an extremely large SWE value (Fig. 8d).
Given the large difference in precipitation and elevation between the sites, it is fair to expect that SWE would vary greatly between sites.Accumulated precipitation recorded by the SNOTEL network at 39 sites for the snow melt/accumulation months of 2006-2008 ranged from 109 mm to 826 mm.The SWE simulated with the TRMM precipitation (Fig. 8e) and MI-estimated SWE had a much narrower interquartile range than the SWE simulated with MPE (Fig. 8f).The process of upscaling by itself narrows the interquartile range as shown by the SNODAS data (Fig. 8d, f).Although small the variability seen in Figures 8d the TRMM does not lacks the ability to differentiate sites with high snowfall from sites with small snowpack given the statistically significant correlation it has with SNODAS and SNOTEL SWE in more than half of the sites.We think that the lower variability of the TRMM-simulated SWE were in part due to the model grid resolution being sub-optimal to modeling the snow accumulation/ablation processes in the study area (Artan et al., 2000;Bl öschl, 1999).
Tables 4 and 5 summarize the statistical indices of the SWE comparisons.Of the three SWE products, the SWE simulated with UEB when forced with NOAA's MPE precipitation was the best performer.The SWE TRMM-simulated SWE and MI-estimated are not adept as site specific snowpack monitoring tools.
Figure 9a-f shows the relationship between the elevation and the correlations the SNODAS estimated SWE has with the SWE predicted from the UEB or the SWE Introduction

Conclusions References
Tables Figures

Back Close
Full estimated from the MI imagery.The MI-estimated SWE products have a significant negative correlation (r = −0.45,d.f.= 37, p < 0.05) with the elevation value of the sites, but neither of the two SWE simulated with UEB exhibited any relationship with elevation (Fig. 9b, c).The MI provides better prediction of SWE at lower elevation terrains.All three SWE products had the lowest skills in the southwest of the study area.
Among the three SWE datasets we evaluated to reproduce SWE values seen in the SNODAS and SNOTEL datasets, the performance of the MI-estimated SWE was the worst in most of the correlation metrics.The MI SWE had the lowest correlation with the SNODAS and SNOTEL SWE.Both, MI estimated and UEB-TRMM simulated, SWE had a relatively large systematic errors.Also, the skills of the MI-estimated SWE to reproduce the values recorded by SNOTEL and SNODAS were negatively correlated with the elevation.Other researchers have also reached similar conclusions on the poor performance of MI-estimated SWE for the mountainous western United States; for example, Dong et al. (2005) found that in the western United States, the complex nature of the terrain and climate causes a significant error in the estimation of SWE from microwave imagery.The low random error component, once the source of the errors are fully know, should make possible the improvement of the UEB-TRMM and Mi-estimated SWE product.

Temporal intercomparisons of the SWE datasets
The average SWE value at the 39 SNOTEL sites was calculated at every time step (14 month of data for all products except the MI-estimated SWE only 9 months of data were available) for the simulated or observed SWE datasets.Figure 10a-c shows the time series plots of the evolution through the season of the average SWE in the study area from SNOTEL, SNODAS, estimated from MI, and simulated by the UEB.All of the SWE products displayed a similar evaluation of the SWE temporal pattern.
The SWE estimated from the MI showed (Fig. 10a) an earlier start of the melt season than either SNODAS or SNOTEL, but the snowpack simulated with the UEB model (Fig. 10b, c) started the melt season about 10 days later than SNODAS.Although the Introduction

Conclusions References
Tables Figures

Back Close
Full SWE estimated from the MI has a monthly time step that makes it difficult to quantify accurately the exact date of the start of the melt season, prediction of the start of the melt season of one month earlier by the MI will decrease the usefulness of the SWE-MI product for monitoring purposes.The SNODAS SWE start of the melt period for two seasons (2006/2007 and 2007/2008) was about one week earlier than SNOTEL's.
Figure 11a-d presents the linear relationships between the average monthly values of the SWE products.The SWE estimated from the MI was not significantly correlated with the SNODAS SWE (Fig. 11a).But the SWE simulated with the UEB models was in good agreement with the SNODAS-estimated SWE (Fig. 11b, c) with a clear linear relationship.The UEB-simulated SWE mostly captured the SNODAS SWE evolution through the season, and the only major diverges noticeable from the linear line is during the period of rapid melts.During such a period, the SNODAS SWE trends lower, where the SWE simulated with UEB was still in the snow accumulation period.The later start of the melt season seen in the plots of UEB-simulated SWE (Fig. 10b, c and Fig. 11b, c) was due to the negative bias seen in model input air temperature (Fig. 4) and elucidates the effects of the errors in the input meteorological data on the UEBsimulated SWE. Figure 11d shows the average area-wide SWE from the SNODAS and SNOTEL datasets.The striking feature of Fig. 11d is the great agreement between the two products in the three years of the comparison (14 months).
For skills to predict snow water content nevertheless had good qualitative skills to predict snow water given the high correlation it exhibited when compared with SNODAS data.From a practical point-of-view, we found the MI-estimated SWE to be unreliable sources for mapping SWE in the study area and to have a large underestimation bias compared with the SNOTEL SWE or the SWE estimated by the SNODAS system.
Our results on the negative biases of the MI-estimated SWE are different from what Mote et al. (2003) reported.Mote et al. (2003) found that SWE estimated from SSM/I overpredicted during the melting period for five sites in the northern Great Plains.

Conclusions
We presented a distributed snow accumulation and ablation model; build on the UEB model, that uses data from weather forecast models as forcing input.Besides the weather forecast model (GFS) data, the snowmelt model was forced with two precipitation datasets: the NWS's MPE and the TRMM precipitation estimates.The model was run at a 0.050-and 0.100-degree resolution for the MPE and TRMM, respectively.We compared model-simulated SWE and MI-estimated SWE with co-located  Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (SWE) status is what interests water resources and disaster risk managers the most.

Figure 1
Figure 1 depicts the geographic extent of the study area and of the SNOTEL sites that were used in the model verification.The area (43 • 48 N, 116 • 6 W) encompasses a modeling domain of 1 504 800 km 2 .The area is rugged and straddles the Continental Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ables in the western United States and Alaska.SNOTEL is a Natural Resources Conservation Service (NRCS) network.Most SNOTEL sites are located at higher elevations.We downloaded from the NRCS's Web site (http://www.wcc.nrcs.usda.gov/snow)SWE, precipitation, and air temperature data recorded at 39 stations located in the areas shown in Fig.1for the period October 2005-September 2008 and summarized in Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1 January 2006-30 April 2006, 1 January 2007-30 April 2007, and 1 January 2008-28 April 2008 (d.f.= 115).
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | most of the time, the UEB model underestimated SWE compared to the SWE values recorded at the SNOTEL stations and SNODAS.Our findings, on the underestimation of the simulated SWE, are consistent with the findings of other research on the underestimation biases of simulated SWE in the mountainous western United States (Pan et al., 2003).The underestimation of the simulated SWE was consistent with the negative biases that MPE and TRMM precipitation datasets had when contrasted with SNOTEL precipitation recorded at a location inside the MPE or TRMM grid.Overall, the UEB-simulated SWE showed remarkable predictive skills compared to the SWE predicted by the SNODAS, and the agreement between the SNODAS-and SNOTELrecorded SWE was marginal.Although the TRMM simulated SWE had low quantitative Discussion Paper | Discussion Paper | Discussion Paper | SWE datasets recorded by the SNOTEL network or estimated by the SNODAS system.The SWE simulated by the UEB model was strongly correlated with the SWE estimated by SNODAS (R 2 = 0.54 and R 2 = 0.90 for model input precipitation as TRMM and MPE, respectively) and the SWE recorded by SNOTEL (R 2 = 0.40 and R 2 = 0.81) when the seasonal average SWE values were compared.The MI-estimated SWE was not significantly correlated with either of the SNOTEL or SNODAS SWE products (R 2 of 0.0 and 0.02, respectively).Both of the UEB-simulated and MI-estimated SWEs underestimated the SWE reported by the SNOTEL or SNODAS systems but were found to be useful in mapping the SWE.The MI-estimated SWE underestimated the SWE values seen in the SNO-TEL and SNODAS datasets and lacked a discernable variability between sites seen in the SNOTEL and SNODAS SWE data and were found to be unreliable sources for Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 1 .Fig. 4 .Fig. 5 .Fig. 6 .
Fig. 1.A shaded relief map of the study area and locations of the SNOTEL sites with an outline of the Colorado Basin and western US States.
Divide and has a mean elevation of 2 203 m (σ = 517 m).The SNOTEL sites used for validation are mainly in the Upper Colorado Basin.The average yearly precipitation that falls on the Upper Colorado Basin, estimated from 39 SNOTEL stations, was 700 mm (±184 mm) for the three water years of thestudy -2006, 2007, and 2008.
Table 1 lists input, output, and model state variables.By using spatially distributed meteorological fields, we assumed that we would able to account for the snow cover Introduction

Table 2 .
Locations of the SNOTEL station where simulated SWE and MI-estimated SWE were validated.