Seasonal Snow Water Equivalent Research Articles

Seasonal Forecasting of Precipitation, Temperature, and Snow Mass over the Western United States by Combining Ensemble Postprocessing with Empirical Ocean–Atmosphere Teleconnections

Abstract Accurate and reliable seasonal forecasts are important for water and energy supply management. Recognizing the important role of snow water equivalent (SWE) for water management, here we include the seasonal forecast of SWE in addition to precipitation (P) and 2-m temperature (T2m) over hydrologically defined regions of the western United States. A two-stage process is applied to seasonal predictions from two models (NCEP CFSv2 and ECMWF SEAS5) through 1) postprocessing to remove biases in the mean, variance, and ensemble spread and 2) further reducing the residual errors by linear regression using climate indices. The adjusted forecasts from the two models are combined to form a superensemble using weights based on their prior skill. The adjusted forecasts are consistently improved over raw model forecasts probabilistically for all variables and deterministically for SWE forecasts. Overall skill of the superensemble usually improves upon the skill of forecasts from individual models; however, the percentage of seasons and regions with increased skill was approximately the same as those with decreased skill relative to the top performing postprocessed individual model. Seasonal SWE has the highest prediction skill, followed by T2m, with P showing lower prediction skill. Persistence contributes strongly to the skill of SWE and moderately to the skill of T2m. Furthermore, a distinct seasonality in the skill is seen in SWE, with a higher skill from late spring through early summer. Significance Statement Here we test the postprocessing of seasonal forecasts from two state-of-the-art seasonal prediction models for traditionally forecasted elements of precipitation and temperature as well as snowpack, which is important for water management. A two-stage procedure is utilized, including ocean and atmospheric teleconnection indices that have been shown to impact seasonal weather across the western United States. First, we adjust model output based on the average error in historic runs and then relate the remaining error to these teleconnection indices. A final step combines each adjusted model based on its historic performance. Forecasts are shown to improve upon the original models when assessed probabilistically. The snowpack forecasts perform better than temperature and precipitation forecasts with the best performance from late winter through early summer. Persistence is found to contribute strongly to the skill of snowpack and moderately to the skill of temperature.

IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)

Abstract. We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (∼ 301 × 103 km2) – a transitional continental-to-Mediterranean region where snow plays an important but still poorly constrained societal and ecological role. IT-SNOW provides ∼ 500 m daily maps of snow water equivalent (SWE), snow depth, bulk snow density, and liquid water content for the initial period 1 September 2010–31 August 2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel-2, MODIS (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 350 on-the-ground snow depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in situ snow data from three focus regions (Aosta Valley, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30–60 cm and 90–300 mm for in situ, measured snow depth and snow water equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak water volume in snow and annual streamflow that are in line with expectations for this mixed rain–snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March ± 10 d. Nearly 52 % of the mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23 %), and central Apennines (5 %). IT-SNOW is freely available at https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endeavor in a warming and drier climate.

Estimation of Seasonal Snow Mass Balance all over Japan Using a High-Resolution Atmosphere-Snow Model Chain

Despite the importance of seasonal snow in Japan, quantitative information on seasonal snow mass in the whole area is lacking. To understand the current mass balance of seasonal snow cover, we develop a method to quantify the mass balance of seasonal snow all over Japan using the Japan Meteorological Agency's operational regional atmospheric model, Local Forecast Model (LFM), as well as the snow physics model, Snow Metamorphism and Albedo Process (SMAP). Our model simulations using the LFM-SMAP model chain show that the seasonal snow water equivalent (SWE) evolution is mostly controlled by snowfall and runoff. It is estimated that the seasonal peak area-integrated SWE in Japan reaches 42.2 Gt on average during the 2017-2022 winters, and 60.1 Gt (+43% with respect to the average) and 22.0 Gt (−48%) during relatively heavy (2017-2018) and relatively light (2019-2020) snow years, respectively. The ratios between seasonal peak area-integrated SWE and winter-accumulated (November to February) snowfall amounts for the heavy, average, and light snow years are 83%, 74%, and 59%, respectively.

Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset

Abstract. Seasonal snowpack is an essential component in the hydrological cycle and plays a significant role in supplying water resources to downstream users. Yet the snow water equivalent (SWE) in seasonal snowpacks, and its space–time variation, remains highly uncertain, especially over mountainous areas with complex terrain and sparse observations, such as in High Mountain Asia (HMA). In this work, we assessed the spatiotemporal distribution of seasonal SWE, obtained from a new 18-year HMA Snow Reanalysis (HMASR) dataset, as part of the recent NASA High Mountain Asia Team (HiMAT) effort. A Bayesian snow reanalysis scheme previously developed to assimilate satellite-derived fractional snow-covered area (fSCA) products from Landsat and MODIS platforms has been applied to develop the HMASR dataset (at a spatial resolution of 16 arcsec (∼500 m) and daily temporal resolution) over the joint Landsat–MODIS period covering water years (WYs) 2000–2017. Based on the results, the HMA-wide total SWE volume is found to be around 163 km3 on average and ranges from 114 km3 (WY2001) to 227 km3 (WY2005) when assessed over 18 WYs. The most abundant snowpacks are found in the northwestern basins (e.g., Indus, Syr Darya and Amu Darya) that are mainly affected by the westerlies, accounting for around 66 % of total seasonal SWE volume. Seasonal snowpack in HMA is depicted by snow accumulating through October to March and April, typically peaking around April and depleting in July–October, with variations across basins and WYs. When examining the elevational distribution over the HMA domain, seasonal SWE volume peaks at mid-elevations (around 3500 m), with over 50 % of the volume stored above 3500 m. Above-average amounts of precipitation causes significant overall increase in SWE volumes across all elevations, while an increase in air temperature (∼1.5 K) from cooler to normal conditions leads to an redistribution in snow storage from lower elevations to mid-elevations. This work brings new insight into understanding the climatology and variability of seasonal snowpack over HMA, with the regional snow reanalysis constrained by remote-sensing data, providing a new reference dataset for future studies of seasonal snow and how it contributes to the water cycle and climate over the HMA region.

Solving Challenges of Assimilating Microwave Remote Sensing Signatures With a Physical Model to Estimate Snow Water Equivalent.

Global monitoring of seasonal snow water equivalent (SWE) has advanced significantly over the past decades. However, challenges remain when estimating SWE from passive and active microwave signatures, because a priori characterization of snow properties is required for SWE retrievals. Numerical experiments have shown that utilizing physical snow models to acquire snowpack characterization can potentially improve microwave‐based SWE retrievals. This study aims to identify the challenges of assimilating active and passive microwave signatures with physical snow models, and to examine solutions to those challenges. Guided by observations from a point‐based study, we designed a sensitivity experiment to quantify the effects of changes in the physically modeled SWE—and of corresponding changes to other snowpack properties—to the microwave‐based SWE retrievals. The results indicate that assimilating microwave signatures with physical snow models face some critical challenges associated with the physical relationship between SWE and snow microstructure. We demonstrate these challenges can be overcome if the microwave algorithms account for these relationships.

Factors Governing Winter Snow Accumulation and Ablation Susceptibility Across the Sierra Nevada, U.S.A.

AbstractSeasonal snow water equivalent (SWE) accumulation in California’s Sierra Nevada is primarily governed by a few orographically enhanced snowstorms. However, as air temperatures gradually rise, resulting in a shift from snow to rain, the governing processes determining SWE accumulation versus ablation become ambiguous. Using a network of 28 snow pillow measurements to represent an elevational and latitudinal gradient across the Sierra Nevada, we identify distributions of critical temperatures and corresponding storm and snowpack properties that describe how SWE accumulation varies across the range at an hourly timescale for water years 2010 through 2019. We also describe antecedent and prevailing conditions governing whether SWE accumulates or ablates during warm storms. Results show that atmospheric moisture regulates a temperature dependence of SWE accumulation. Conditions balancing precipitable water and snow formation requirements produce the most seasonal SWE, which was observed in the (low-elevation) northern and (middle-elevation) central Sierra Nevada. The high southern Sierra Nevada conservatively accumulates SWE with colder, drier air, resulting in less midwinter ablation. These differences explain a tendency for deep, low-density snowpacks to accumulate rather than ablate SWE during warm storms (having median temperatures exceeding 1.0°C), reflecting counteracting liquid storage and internal energy deficits. The storm events themselves in these cases are brief with modest moisture supplies or are otherwise followed immediately by ablation.

Seasonal Estimates and Uncertainties of Snow Accumulation from CloudSat Precipitation Retrievals

CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.

Cool season precipitation projections for California and the Western United States in NA-CORDEX models

Understanding future precipitation changes is critical for water supply and flood risk applications in the western United States. The North American COordinated Regional Downscaling EXperiment (NA-CORDEX) matrix of global and regional climate models at multiple resolutions (~ 50-km and 25-km grid spacings) is used to evaluate mean monthly precipitation, extreme daily precipitation, and snow water equivalent (SWE) over the western United States, with a sub-regional focus on California. Results indicate significant model spread in mean monthly precipitation in several key water-sensitive areas in both historical and future projections, but suggest model agreement on increasing daily extreme precipitation magnitudes, decreasing seasonal snowpack, and a shortening of the wet season in California in particular. While the beginning and end of the California cool season are projected to dry according to most models, the core of the cool season (December, January, February) shows an overall wetter projected change pattern. Daily cool-season precipitation extremes generally increase for most models, particularly in California in the mid-winter months. Finally, a marked projected decrease in future seasonal SWE is found across all models, accompanied by earlier dates of maximum seasonal SWE, and thus a shortening of the period of snow cover as well. Results are discussed in the context of how the diverse model membership and variable resolutions offered by the NA-CORDEX ensemble can be best leveraged by stakeholders faced with future water planning challenges.

Snowpack affects soil microclimate throughout the year

Variations in snow depth have complex effects on soil microclimate. Snow insulates soil and thus regulates, along with air temperature, the nature, and extent of soil freezing. There is great uncertainty about the main drivers of soil freezing, which have important effects on ecosystem carbon and nitrogen cycling processes and might change as climate warms and snowfall decreases as part of climate change. Here, we utilitze sites from a variety of elevations and aspects within the northern hardwood forest at the Hubbard Brook Experimental Forest (New Hampshire, USA) to investigate relationships between seasonal snowpack, soil freezing, and soil microclimate across this gradient using 8 years of bi-weekly snowpack and soil frost-depth measurements, and continuous soil climate monitoring. We utilize a time-integrated snowpack descriptor and find that snowpacks with lower seasonal snow water equivalents result in more soil temperature variation and deeper soil frost but have no effect on variation in soil moisture. Seasonal snow water equivalent of the snowpack influences the date of rapid soil warming in the spring, which in turn influences both summer soil moisture and an index of annual cumulative soil heat. These results show that snowpack dynamics, which are highly sensitive to changes in climate, have wide-ranging effects on soil microclimate year-round and thus could have important implications for ecosystem carbon and nitrogen cycling processes.

Snowpack Contributions and Temperature Characterization of Landfalling Atmospheric Rivers in the Western Cordillera of the United States

AbstractAtmospheric rivers (ARs) are regions of high water vapor transport in the lower atmosphere. When these air masses encounter mountain ranges, they can produce significantly enhanced orographic precipitation. AR events may substantially influence seasonal mountain snow, an important ecologic and economic resource for Washington, Oregon, and California. To better understand how ARs affect the montane snowpack of these U.S. West Coast states, we used 33 years of Snow Telemetry and Topography Weather data to examine AR and non‐AR storm day temperatures and impacts on snow water equivalent (SWE). We found mean daily minimum temperatures of AR storm days to be 1 to 4 °C warmer and mean daily increases in SWE to be 23% to 57% higher than non‐AR storm days. AR storm days have contributed an average of 23% of effective snowfall to seasonal SWE in the California Sierra Nevada and 34% of effective snowfall to seasonal SWE in the Washington and Oregon Cascades.

The Utility of Infrequent Snow Depth Images for Deriving Continuous Space‐Time Estimates of Seasonal Snow Water Equivalent

AbstractSnow water equivalent (SWE), particularly in mountains regions, has been an elusive hydrologic measurement. We examine the utility of a data assimilation approach to generate space‐time continuous estimates of SWE from more readily available snow depth (SD) measurements. A multitemporal lidar data set provides a unique opportunity to assimilate single SD images and verify posterior estimates against SD images at nonassimilation times. Application over three water years shows significant improvement in the posterior estimates with an average correlation between estimated and measured SD fields of 0.88 compared to 0.52 for prior estimates that do not benefit from the assimilated SD data. We also show that posterior estimates are consistent with independent in situ SWE and streamflow measurements. This work demonstrates that using high‐resolution/high‐accuracy, but infrequent, SD measurements combined with a data assimilation framework could make significant inroads toward the goal of spatially distributed SWE and snowmelt estimates at the global scale.

Spatial Variability of Snow Water Equivalent – The Case Study from the Research Site in Khibiny Mountains, Russia

Abstract The aim of the investigation was assessment of spatial variability of the characteristics of snowpack, including the snow water equivalent (SWE) as the main hydrological characteristic of a seasonal snow cover. The study was performed in Khibiny Mountains (Russia), where snow density and snow cover stratigraphy were documented with the help of the SnowMicropen measurements, allowing to determine the exact position of the snow layers’ boundaries with accuracy of 0.1 cm. The study site was located at the geomorphologically and topographically uniform area with uniform vegetation cover. The measurement was conducted at maximum seasonal SWE on 27 March 2016. Twenty vertical profiles were measured along the 10 m long transect. Vertical resolution depended on the thickness of individual layers and was not less than 10 cm. The spatial variation of the measured snowpack characteristics was substantial even within such a homogeneous landscape. Bulk snow density variability was similar to the variability in snow height. The total variation of the snowpack SWE values along the transect was about 20%, which is more than the variability in snow height or snow density, and should be taken into account in analysis of the results of normally performed in operational hydrology snow course SWE estimations by snow tubes.

Modeling seasonal snowpack evolution in the complex terrain and forested Colorado Headwaters region: A model intercomparison study

AbstractCorrectly modeling snow is critical for climate models and for hydrologic applications. Snowpack simulated by six land surface models (LSM: Noah, Variable Infiltration Capacity, snow‐atmosphere‐soil transfer, Land Ecosystem‐Atmosphere Feedback, Noah with Multiparameterization, and Community Land Model) were evaluated against 1 year snow water equivalent (SWE) data at 112 Snow Telemetry (SNOTEL) sites in the Colorado River Headwaters region and 4 year flux tower data at two AmeriFlux sites. All models captured the main characteristics of the seasonal SWE evolution fairly well at 112 SNOTEL sites. No single model performed the best to capture the combined features of the peak SWE, the timing of peak SWE, and the length of snow season. Evaluating only simulated SWE is deceiving and does not reveal critical deficiencies in models, because the models could produce similar SWE for starkly different reasons. Sensitivity experiments revealed that the models responded differently to variations of forest coverage. The treatment of snow albedo and its cascading effects on surface energy deficit, surface temperature, stability correction, and turbulent fluxes was a major intermodel discrepancy. Six LSMs substantially overestimated (underestimated) radiative flux (heat flux), a crucial deficiency in representing winter land‐atmosphere feedback in coupled weather and climate models. Results showed significant intermodel differences in snowmelt efficiency and sublimation efficiency, and models with high rate of snow accumulation and melt were able to reproduce the observed seasonal evolution of SWE. This study highlights that the parameterization of cascading effects of snow albedo and below‐canopy turbulence and radiation transfer is critical not only for SWE simulation but also for correctly capturing the winter land‐atmosphere interactions.

A Bayesian hierarchical model for forecasting intermountain snow dynamics

Because of a continual increase in the demand for water as well as an ongoing regional drought, there is an imminent need to monitor and forecast water resources in the Western United States. In particular, water resources in the Intermountain West rely heavily on snow water storage. Thus, the need to improve seasonal forecasts of snowpack and considering new techniques would allow water resources to be more effectively managed throughout the entire water‐year. Many available models used in forecasting snow water equivalent (SWE) measurements require delicate calibrations. In contrast to the physical SWE models most commonly used for forecasting, we offer a statistical model. We present a data‐based statistical model that characterizes seasonal SWE in terms of a nested time series, with the large scale focusing on the inter‐annual periodicity of dominant signals and the small scale accommodating seasonal noise and autocorrelation. This model provides a framework for independently estimating mainly the temporal dynamics of SWE for the various snow telemetry sites. We use snow telemetry data from 10 stations in Utah over 34 water‐years to implement and validate this model. Copyright © 2014 John Wiley & Sons, Ltd.

Visualizing gridded time series data with self organizing maps: An application to multi-year snow dynamics in the Northern Hemisphere

Gridded time-series data are increasingly available for climatology research. Microwave imagery of snow water equivalent (SWE) has been accumulated at daily basis for over two decades, but complex spatial-temporal patterns in SWE dataset pose great challenges for exploration and interpretation. This paper introduces the use of several perspectives from a tri-space conceptualization of a time series of SWE grids combined with dimensionality reduction via the self-organizing map (SOM) method. While SOM has been predominantly viewed as a clustering mechanism within climatology research, we expand the visual-analytic potential of SOM for climate research with a series of conceptual, computational, and visual transformations. Specifically, we apply a medium-resolution SOM to an SWE dataset covering the Northern Hemisphere over a 20-year period, with high temporal resolution. Through clustering and visualization a number of distinct SWE patterns are identified, including mountainous, coastal, and continental regions. A subset of cells from six areas are selected for transition analysis, including mountainous (Sierra Nevada, Western Himalaya, Eastern Himalaya) and continental (central Siberia, western Russia and Midwest United States) regions. By combining with trajectory analysis, this SOM documents notable transitions in seasonal SWE accumulation and melt patterns in mountain ranges, suggesting that SWE in some geographic locations alternates between different discrete annual patterns. In the Sierra Nevada, transitions to classes with high SWE are shown to be related to the Southern Oscillation Index, demonstrating that the annual patterns and transitions in SWE regime identified by the SOM correspond to synoptic climate conditions.

Evaluating global snow water equivalent products for testing land surface models

This paper compares three global snow water equivalent (SWE) products, SSM/I (NSIDC), AMSR-E (NSIDC) and Globsnow (v1.0, ESA) to each other, snow covered area (SCA), ground measures of snow depth and meteorological data in an attempt to determine which might be most suitable for testing and developing land surface models. Particular attention is paid to which gives the most accurate peak accumulation, seasonal SWE changes and first and last dates of snow cover.SSM/I and AMSR-E are pure earth observation (EO) derived products whilst Globsnow is a combination of EO and ground data. The results suggest that the pure EO products can saturate in deeper snow (SWE>80–150mm), can show spurious features during melt and can overestimate SWE due to strong thermal gradients and erroneous forest cover correction factors. Along with the comparison to ground data (only a single point) this suggests that Globsnow is the more accurate product for determining peak accumulation and seasonal SWE cycle.The snow start and end dates of the three SWE products were compared to an optically derived SCA (MOD10C1, taken as truth) and found to give large errors of snow start date (root mean square error of 20+ days, though SSM/I was correct on average). The snow end dates had lower errors (a bias of 1–6days) although the spread was still on the order of three weeks.During the investigation, occasional abrupt changes in Globsnow were observed (in the v1.0 and v1.2 daily and v1.2 weekly products). These only occurred in around 1% of cases examined and seem to be spurious. Care should be taken to corrector avoid these jumps if using Globsnow to validate land surface models or in an assimilation scheme.

Multiscale observations of snow accumulation and peak snowpack following widespread, insect‐induced lodgepole pine mortality

ABSTRACTSeasonal snowpack in forested lands is the primary source of fresh water in western North America, where mountain pine beetle (MPB) infestation has resulted in rapid and extensive tree die‐off. Forests significantly influence the amount and spatial distribution of peak seasonal snowpack, but the impacts of large‐scale tree mortality on the processes controlling peak snowpack are not well understood. We evaluate the effects of widespread tree mortality on winter snow accumulation and peak seasonal snowpack across multiple spatial scales and several levels of MPB impact in the Central Rocky Mountains. Observations for winters 2010 and 2011 include continuous snow depths in 20 plots, distributed snow surveys at peak accumulation and climate observations above and below canopy including precipitation, temperature, humidity, wind and shortwave radiation. Stable water isotopes were observed for fresh snowfall and for snowpack. Plot‐scale snowfall observations showed 20% lower interception (p < 0.05) in grey‐phase stands (needles lost) than in unimpacted stands. However, distributed snow surveys found no differences in peak seasonal snow water equivalent between unimpacted and grey‐phase stands. Water isotopes of snowpack from MPB‐killed stands indicated kinetic fractionation; enriched values demonstrated higher winter snowpack sublimation in MPB‐killed forest. Following MPB infestation, reduced canopy sublimation of intercepted snow appeared to be compensated by increased snowpack sublimation, consistent with observations of higher snowpack insolation. Consequently, the effects of widespread tree mortality on peak seasonal snowpack, which is crucial for downstream water resources, will be influenced by compensation for lower interception by higher snowpack sublimation. Copyright © 2012 John Wiley & Sons, Ltd.

Effects of more extreme precipitation regimes on maximum seasonal snow water equivalent

Many climate change forecasts project more extreme precipitation regimes (MEPR) in the future, characterized by more intense but less frequent storms. Plausible impacts of MEPR have drawn considerable attention from hydrologists and ecologists, but the hydrological impacts of MEPR in snowfall‐dominated regions are less clear. Here we quantify the impacts of MEPR on the maximum seasonal snow water equivalent (SWEmax) based on simulated snowpack dynamics. We show that between any two scenarios with the same amount of precipitation, the one with more intense snowfalls generally has larger SWEmax. Under a warmer climate, SWEmax increases enough with MEPR to partially offset higher melt rates and rain/snow ratios, especially in warm and dry winters. This study demonstrates how MEPR might affect seasonal snow water resources and potentially alleviate the decreasing trend in SWEmax caused by climate warming under specific conditions.

Analyzing the uncertainty of suspended sediment load prediction using sequential data assimilation

► Estimated snow melt and streamflow using SNOW-17 and SAC-SMA models. ► Applied ensemble data assimilation to improve predictions and estimate uncertainty. ► Developed an approach to dynamically adjust model error variance scaling factors. ► Linked streamflow ensemble to non-linear regression model of suspended sediment. ► Estimated annual suspended sediment load for watershed in the Lake Tahoe Basin. A first step in understanding the impacts of sediment and controlling the sources of sediment is to quantify the mass loading. Since mass loading is the product of flow and concentration, the quantification of loads first requires the quantification of runoff volume. Using the National Weather Service’s SNOW-17 and the Sacramento Soil Moisture Accounting (SAC-SMA) models, this study employed particle filter based Bayesian data assimilation methods to predict seasonal snow water equivalent (SWE) and runoff within a small watershed in the Lake Tahoe Basin located in California, USA. A procedure was developed to scale the variance multipliers (a.k.a hyperparameters) for model parameters and predictions based on the accuracy of the mean predictions relative to the ensemble spread. In addition, an online bias correction algorithm based on the lagged average bias was implemented to detect and correct for systematic bias in model forecasts prior to updating with the particle filter. Both of these methods significantly improved the performance of the particle filter without requiring excessively wide prediction bounds. The flow ensemble was linked to a non-linear regression model that was used to predict suspended sediment concentrations (SSCs) based on runoff rate and time of year. Runoff volumes and SSC were then combined to produce an ensemble of suspended sediment load estimates. Annual suspended sediment loads for the 5 years of simulation were finally computed along with 95% prediction intervals that account for uncertainty in both the SSC regression model and flow rate estimates. Understanding the uncertainty associated with annual suspended sediment load predictions is critical for making sound watershed management decisions aimed at maintaining the exceptional clarity of Lake Tahoe. The computational methods developed and applied in this research could assist with similar studies where it is important to quantify the predictive uncertainty of pollutant load estimates.

Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements

The key variable describing global seasonal snow cover is snow water equivalent (SWE). However, reliable information on the hemispheric scale variability of SWE is lacking because traditional methods such as interpolation of ground-based measurements and stand-alone algorithms applied to space-borne observations are highly uncertain with respect to the spatial distribution of snow mass and its evolution. In this paper, an algorithm assimilating synoptic weather station data on snow depth with satellite passive microwave radiometer data is applied to produce a 30-year-long time-series of seasonal SWE for the northern hemisphere. This data set is validated using independent SWE reference data from Russia, the former Soviet Union, Finland and Canada. The validation of SWE time-series indicates overall strong retrieval performance with root mean square errors below 40 mm for cases when SWE < 150 mm. Retrieval uncertainty increases when SWE is above this threshold. The SWE estimates are also compared with results obtained by a typical stand-alone satellite passive microwave algorithm. This comparison demonstrates the benefits of the newly developed assimilation approach. Additionally, the trends and inter-annual variability of northern hemisphere snow mass during the era of satellite passive microwave measurements are shown.