Ethan D. Gutmann

How Well Are We Measuring Snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed

This paper presents recent efforts to understand the relative accuracies of different instrumentation and gauges with various windshield configurations to measure snowfall. Results from the National Center for Atmospheric Research (NCAR) Marshall Field Site will be highlighted. This site hosts a test bed to assess various solid precipitation measurement techniques and is a joint collaboration between the National Oceanic and Atmospheric Administration (NOAA), NCAR, the National Weather Service (NWS), and Federal Aviation Administration (FAA). The collaboration involves testing new gauges and other solid precipitation measurement techniques in comparison with World Meteorological Organization (WMO) reference snowfall measurements. This assessment is critical for any ongoing studies and applications, such as climate monitoring and aircraft deicing, that rely on accurate and consistent precipitation measurements.

High resolution coupled climate-runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate

AbstractClimate 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 enha...

Cascading impacts of bark beetle‐caused tree mortality on coupled biogeophysical and biogeochemical processes

Recent, large-scale outbreaks of bark beetle infestations have affected millions of hectares of forest in western North America, covering an area similar in size to that impacted by fire. Bark beetles kill host trees in affected areas, thereby altering water supply, carbon storage, and nutrient cycling in forests; for example, the timing and amount of snow melt may be substantially modified following bark beetle infestation, which impacts water resources for many western US states. The quality of water from infested forests may also be diminished as a result of increased nutrient export. Understanding the impacts of bark beetle outbreaks on forest ecosystems is therefore important for resource management. Here, we develop a conceptual framework of the impacts on coupled biogeophysical and biogeochemical processes following a mountain pine beetle (Dendroctonus ponderosae) outbreak in lodgepole pine (Pinus contorta Douglas var latifolia) forests in the weeks to decades after an infestation, and highlight fu...

GPS Multipath and Its Relation to Near-Surface Soil Moisture Content

Measurements of soil moisture at various spatial and temporal scales are needed to study the water and carbon cycles. While satellite missions have been planned to measure soil moisture at global scales, these missions also need ground-based soil moisture data to validate their observations and retrieval algorithms. Here, we demonstrate that signals routinely recorded by Global Positioning System (GPS) receivers installed to measure crustal deformation for geophysical studies could be used to provide a global network of soil moisture sensors. The sensitivity to soil moisture is seen in reflected GPS signals, which are quantified by using the GPS signal to noise ratio data. We show that these data are sensitive to soil moisture variations for areas of 1000 m2 horizontally and 1-6 cm vertically. It is demonstrated that GPS signals penetrate deeper when the soil is dry than when it is wet. This change in penetration or ¿reflector¿ depth, along with the change in dielectric constant, causes the GPS signal strength to change its frequency and amplitude. Comparisons with conventional water content reflectometer sensors show good agreement (r2=0.9 to 0.76) with the variation in frequencies of the reflected GPS signals over a period of 7 months, with most of the disagreement occurring when soil moisture content is less than 0.1 cm3/cm3.

A unified approach for process-based hydrologic modeling: 1. Modeling concept

This work advances a unified approach to process-based hydrologic modeling to enable controlled and systematic evaluation of multiple model representations (hypotheses) of hydrologic processes and scaling behavior. Our approach, which we term the Structure for Unifying Multiple Modeling Alternatives (SUMMA), formulates a general set of conservation equations, providing the flexibility to experiment with different spatial representations, different flux parameterizations, different model parameter values, and different time stepping schemes. In this paper, we introduce the general approach used in SUMMA, detailing the spatial organization and model simplifications, and how different representations of multiple physical processes can be combined within a single modeling framework. We discuss how SUMMA can be used to systematically pursue the method of multiple working hypotheses in hydrology. In particular, we discuss how SUMMA can help tackle major hydrologic modeling challenges, including defining the appropriate complexity of a model, selecting among competing flux parameterizations, representing spatial variability across a hierarchy of scales, identifying potential improvements in computational efficiency and numerical accuracy as part of the numerical solver, and improving understanding of the various sources of model uncertainty.

A comparison of statistical and dynamical downscaling of winter precipitation over complex terrain

AbstractStatistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between th...

A Physical Model for GPS Multipath Caused by Land Reflections: Toward Bare Soil Moisture Retrievals

Reflected Global Positioning System (GPS) signals can be used to infer information about soil moisture in the vicinity of the GPS antenna. Interference of direct and reflected signals causes the composite signal, observed using signal-to-noise ratio (SNR) data, to undulate with time while the GPS satellite ascends or descends at relatively low elevation angles. The soil moisture change affects both the phase of the SNR modulation pattern and its magnitude. In order to more thoroughly understand the mechanism of how the soil moisture change leads to a change in the SNR modulation, we built an electrodynamic model of GPS direct and reflected signal interference, i.e., multipath, that has a bare-soil model as the input and the total GPS received power as the output. This model treats soil as a continuously stratified medium with a specific composition of material ingredients having complex dielectric permittivity according to well-known mixing models. The critical part of this electrodynamic model is a numerical algorithm that allows us to calculate polarization-dependent reflection coefficients of such media with various profiles of dielectric permittivity dictated by the soil type and moisture. In this paper, we demonstrate how this model can reproduce and explain the main features of experimental multipath modulation patterns such as changes in phase and amplitude. We also discuss the interplay between true penetration depth and effective reflector depth. Based on these modeling comparisons, we formulate recommendations to improve the performance of bare soil moisture retrievals from the data obtained using GPS multipath modulation.

An intercomparison of statistical downscaling methods used for water resource assessments in the United States

Information relevant for most hydrologic applications cannot be obtained directly from the native-scale outputs of climate models. As a result the climate model output must be downscaled, often using statistical methods. The plethora of statistical downscaling methods requires end-users to make a selection. This work is intended to provide end-users with aid in making an informed selection. We assess four commonly used statistical downscaling methods: daily and monthly disaggregated-to-daily Bias Corrected Spatial Disaggregation (BCSDd, BCSDm), Asynchronous Regression (AR), and Bias Corrected Constructed Analog (BCCA) as applied to a continental-scale domain and a regional domain (BCCAr). These methods are applied to the NCEP/NCAR Reanalysis, as a surrogate for a climate model, to downscale precipitation to a 12 km gridded observation data set. Skill is evaluated by comparing precipitation at daily, monthly, and annual temporal resolutions at individual grid cells and at aggregated scales. BCSDd and the BCCA methods overestimate wet day fraction, and underestimate extreme events. The AR method reproduces extreme events and wet day fraction well at the grid-cell scale, but over (under) estimates extreme events (wet day fraction) at aggregated scales. BCSDm reproduces extreme events and wet day fractions well at all space and time scales, but is limited to rescaling current weather patterns. In addition, we analyze the choice of calibration data set by looking at both a 12 km and a 6 km observational data set; the 6 km observed data set has more wet days and smaller extreme events than the 12 km product, the opposite of expected scaling.

A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies

This work advances a unified approach to process-based hydrologic modeling, which we term the “Structure for Unifying Multiple Modeling Alternatives (SUMMA).” The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within-canopy and below-canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty.

Climate Change Impacts on the Water Balance of the Colorado Headwaters: High-Resolution Regional Climate Model Simulations

AbstractA high-resolution climate model (4-km horizontal grid spacing) is used to examine the following question: How will long-term changes in climate impact the partitioning of annual precipitation between evapotranspiration and runoff in the Colorado Headwaters?This question is examined using a climate sensitivity approach in which eight years of current climate is compared to a future climate created by modifying the current climate signal with perturbation from the NCAR Community Climate System Model, version 3 (CCSM3), model forced by the A1B scenario for greenhouse gases out to 2050. The current climate period is shown to agree well with Snowpack Telemetry (SNOTEL) surface observations of precipitation (P) and snowpack, as well as streamflow and AmeriFlux evapotranspiration (ET) observations. The results show that the annual evaporative fraction (ET/P) for the Colorado Headwaters is 0.81 for the current climate and 0.83 for the future climate, indicating increasing aridity in the future despite a p...