Mark Stephen Raleigh

Oregon State University | OSU · College of Earth, Ocean and Atmospheric Sciences

The science of mountainous hydrology spans the atmosphere through the bedrock and inherently crosses physical and disciplinary boundaries: land-atmosphere interactions in complex terrain enhance clouds and precipitation, while watersheds retain and release water over a large range of spatial and temporal scales. Limited observations in complex terr...

Summer streamflow predictions are critical for managing water resources; however, warming‐induced shifts from snow to rain regimes impact low‐flow predictive models. Additionally, reductions in snowpack drive earlier peak flows and lower summer flows across the western United States increasing reliance on groundwater for maintaining summer streamfl...

Measurements of biophysical tree properties and hydrologic fluxes are necessary for improving models and monitoring the impact of disturbances. Prior research has demonstrated that measurements of tree sway frequency can be used to quantify important ecohydrologic processes, such as drought stress and snow interception, that otherwise require expen...

Snow cover mapping algorithms utilizing multispectral satellite data at various spatial resolutions are available, each treating subpixel variation differently. Past evaluations of snow mapping accuracy typically relied on satellite data collected at a higher spatial resolution than the data in question. However, these optical data cannot character...

We present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is...

Snow cover mapping algorithms utilizing multispectral satellite data at various spatial resolutions are available, each treating subpixel variation differently. Past evaluations of snow mapping accuracy typically relied on satellite data collected at a higher spatial resolution than the data in question. However, these optical data cannot character...

Trees are bioindicators of global climate change and regional urbanization, but available monitoring tools are ineffective for fine-scale observation of many species. Using six accelerometers mounted on two urban ash trees (Fraxinus americana), we looked at high-frequency tree vibrations, or change in periodicity of tree sway as a proxy for mass ch...

Snowpack accumulation in forested watersheds depends on the amount of snow intercepted in the canopy and its partitioning into sublimation, unloading, and melt. A lack of canopy snow measurements limits our ability to evaluate models that simulate canopy processes and predict snowpack. We tested whether monitoring changes in wind‐induced tree sway...

Intermittent snow depth observations can be leveraged with data assimilation (DA) to improve model estimates of snow water equivalent (SWE) at the point scale. A key consideration for scaling assimilation to the basin scale is its performance at forested locations—where canopy‐snow interactions affect snow accumulation and melt, yet are difficult t...

Snow duration in post‐fire forests is influenced by neighborhoods of trees, snags, and deadwood. We used annually resolved, spatially explicit tree and tree mortality data collected in an old‐growth, mixed‐conifer forest in the Sierra Nevada, California that burned at low to moderate severity to calculate ten tree neighborhood metrics within circle...

Understanding how the presence of a forest canopy influences the underlying snowpack is critical to making accurate model predictions of bulk snow density and snow water equivalent (SWE). To investigate the relative importance of forest processes on snow density and SWE, we applied the SUMMA (Structure for Unifying Multiple Modeling Alternatives) m...

The effect of forest canopy processes on snow water equivalent (SWE) beneath trees has been broadly studied. In contrast, little effort has been made to understand how forest canopy processes affect snow density. Yet, density is a fundamental property of snow, and often a critical element in SWE retrievals from remotely sensed data. To investigate...

Forested areas exhibit high spatial variability in the distribution of snow water equivalent (SWE). Previous work has focused on forested areas with respect to snow accumulation in adjacent clearings. There is generally less snow in forested areas with greater variability relative to open areas due to the influence of tree canopies. However, the le...

Maps of snow cover serve as early indicators for hydrologic forecasts and as inputs to hydrologic models that inform water management strategies. Advances in snow cover mapping have led to increasing accuracy, but unsatisfactory treatment of vegetation's interference when mapping snow has led to maps that have limited utility for water forecasting....

Snow depth observations can be leveraged with data assimilation (DA) to improve estimation of snow density and snow water equivalent (SWE). A key consideration for mission and campaign design is how snow depth retrieval characteristics (including observation timing/frequency and sampling error) influence SWE accuracy and uncertainty in a DA framewo...

Snow water equivalent (SWE), the amount of water stored in snowpack, is calculated as a product of snow depth and snow density. Accurately predicting SWE is central for managing drinking water, irrigation, hydropower, and reservoir storage. Remote sensing techniques provide high-resolution data sets of snow depth, while snow models are relied on to...

This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions' modelling at 10 sites. The long-term datasets (one maritime, one arctic, three boreal, and five mid-latitude alpine) are the reference sites chosen for evaluating models participating in the Earth System Model-Snow...

Snow depth observations and modeled snow density can be combined to calculate snow water equivalent (SWE). In this approach, SWE uncertainty is dominated by snow density uncertainty, which depends on meteorological data quality and process representation (e.g., compaction) in models. We test whether assimilating snow depth observations with the par...

This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions modelling at ten sites. The long-term datasets (one maritime, one arctic, three boreal and five mid-latitude alpine) are the reference sites chosen for evaluating models participating in the Earth System Model-Snow M...

This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow mo...

This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes against local and global observations in a wide variety of settings, including snow schemes that are included in Earth System Models. The project aims at identifying crucial processes and snow characteristics that need to be improved in...

Reliable maps of snow-covered areas at scales of meters to tens of meters, with daily temporal resolution, are essential to understanding snow heterogeneity, melt runoff, energy exchange, and ecological processes. Here we develop a parsimonious downscaling routine that can be applied to fractional snow covered area (fSCA) products from satellite pl...

Lidar-measured snow depth and model-estimated snow density can be combined to map snow water equivalent (SWE). This approach has the potential to transform research and operations in snow dominated regions, but sources of uncertainty need quantification. We compared relative uncertainty contributions from lidar depth measurement and density modelin...

Physically based models facilitate understanding of seasonal snow processes but require meteorological forcing data beyond air temperature and precipitation (e.g., wind, humidity, shortwave radiation, and long- wave radiation) that are typically unavailable at automatic weather stations (AWSs) and instead are often represented with empirical estima...

Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit...

Forests cover almost 40% of the seasonally snow-covered regions in North America. However, operational snow networks are located primarily in forest clearings, and optical remote sensing cannot see through tree canopies to detect forest snowpack. Due to the complex influence of the forest on snowpack duration, ground observations in forests are ess...

The forcing irradiances irradiances (downwelling shortwave and longwave irradiances) are the primary drivers of snowmelt; however, in complex terrain, few observations, the use of estimated irradiances, and the influence of topography and elevation all lead to uncertainties in these radiative fluxes. The impact of uncertainties in the forcing irrad...

Physically based models provide insights into key hydrologic processes, but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit...

A hydrologic modeling dataset is presented for water years 2006 through 2012 from the Senator Beck Basin (SBB) study area. SBB is a high altitude, 291 ha catchment in southwest Colorado exhibiting a continental, radiation-driven, alpine snow climate. Elevations range from 3362m at the SBB pour point to 4118m. Two study plots provide hourly forcing...

Snow surface temperature (T s) is important to the snowmelt energy balance and land-atmosphere interactions, but in situ measurements are rare, thus limiting evaluation of remote sensing data sets and distributed models. Here we test simple T s approximations with standard height (2–4 m) air temperature (T a), wet-bulb temperature (T w), and dew po...

Climate plays an important role in determining the geographic ranges of species. With rapid climate change expected in the coming decades, ecologists have predicted that species ranges will shift large distances in elevation and latitude. However, most range shift assessments are based on coarse-scale climate models that ignore fine-scale heterogen...

Climatological context of the study. (PDF)

Model fitting and selection procedure. (PDF)

Soil characteristics in the subalpine and alpine biomes. (PDF)

Near-surface air temperature observations often have periods of missing data, and many applications using these datasets require filling in all missing periods. Multiple methods are available to fill missing data, but the comparative accuracy of these approaches has not been assessed. In this comparative study, five techniques were used to fill in...

There are many areas of uncertainty when solving the inverse problems of snow water equivalent (SWE) reconstruction. These include (i) the ability to infer the Final Date of the Seasonal Snow (FDSS) cover, particularly from remote sensing; (ii) errors in model forcing data (such as air temperature or radiation fluxes); and (iii) weaknesses in the s...

Background/Question/Methods Climate plays an important role in determining species geographic ranges. With the rapid rates of climate change expected for the coming decades, ecologists have predicted that species ranges will shift large distances in elevation and latitude, and that widespread extinctions could occur as climate shifts faster than...

Snow models such as SNOW-17 may estimate past snow water equivalent (SWE) using either a forward configuration based on spatial extrapolation of measured precipitation, such as with the parameter-elevation regressions on independent slopes model (PRISM), or a reconstruction configuration based on snow disappearance timing and back-calculated snowme...

Missing data is often found in temperature time series observations, and applications using these datasets frequently require filling in data during periods when it is missing. This may be required in order to avoid bias in long-term averages or to provide complete forcing time series for modeling applications. Multiple methods are available to fil...

Snowfall comprises up to 67% of annual precipitation in the middle elevations of the Sierra Nevada and over 90% in the higher elevations. Consequently, seasonal snowpack is a critical water resource and has significant interactions with local ecology and important feedbacks with the atmosphere. Yet, winter precipitation distributions between or bey...

The amount of water available in a snowpack, snow water equivalent (SWE), is an important parameter for many communities, such as water resource managers, climate scientists, and the agricultural sector. However, spatial patterns of SWE between observation stations are often unknown and precipitation gauge errors present challenges in acquiring rel...

Accurate forecasts of snowpack ablation in the Sierra Nevada are important to flood forecasters and water managers, but adequate surface meteorological observations required for snow models are often not readily available. In the Sierra Nevada, most energy available for snowpack ablation is provided by net radiation, but other energy fluxes can als...