In mountain terrain, well-configured high-resolution atmospheric models are able to simulate total annual rain and snowfall better than spatial estimates derived from in situ observational networks ...of precipitation gauges, and significantly better than radar or satellite-derived estimates. This conclusion is primarily based on comparisons with streamflow and snow in basins across the western United States and in Iceland, Europe, and Asia. Even though they outperform gridded datasets based on gauge networks, atmospheric models still disagree with each other on annual average precipitation and often disagree more on their representation of individual storms. Research to address these difficulties must make use of a wide range of observations (snow, streamflow, ecology, radar, satellite) and bring together scientists from different disciplines and a wide range of communities.
Tropical cyclones have enormous costs to society through both loss of life and damage to infrastructure. There is good reason to believe that such storms will change in the future as a result of ...changes in the global climate system and that such changes may have important socioeconomic implications. Here a high-resolution regional climate modeling experiment is presented using the Weather Research and Forecasting (WRF) Model to investigate possible changes in tropical cyclones. These simulations were performed for the period 2001–13 using the ERA-Interim product for the boundary conditions, thus enabling a direct comparison between modeled and observed cyclone characteristics. The WRF simulation reproduced 30 of the 32 named storms that entered the model domain during this period. The model simulates the tropical cyclone tracks, storm radii, and translation speeds well, but the maximum wind speeds simulated were less than observed and the minimum central pressures were too large. This experiment is then repeated after imposing a future climate signal by adding changes in temperature, humidity, pressure, and wind speeds derived from phase 5 of the Coupled Model Intercomparison Project (CMIP5). In the current climate, 22 tracks were well simulated with little changes in future track locations. These simulations produced tropical cyclones with faster maximum winds, slower storm translation speeds, lower central pressures, and higher precipitation rates. Importantly, while these signals were statistically significant averaged across all 22 storms studied, changes varied substantially between individual storms. This illustrates the importance of using a large ensemble of storms to understand mean changes.
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.
Key Points:
Modeling template formulated using a general set of conservation equations
Evaluation focuses on flux parameterizations and spatial variability/connectivity
Systematic approach helps improve model fidelity and uncertainty characterization
Estimating spatially distributed parameters remains one of the biggest challenges for large‐domain hydrologic modeling. Many large‐domain modeling efforts rely on spatially inconsistent parameter ...fields, e.g., patchwork patterns resulting from individual basin calibrations, parameter fields generated through default transfer functions that relate geophysical attributes to model parameters, or spatially constant, default parameter values. This paper provides an initial assessment of a multiscale parameter regionalization (MPR) method over large geographical domains to derive seamless parameters in a spatially consistent manner. MPR applies transfer functions at the native scale of the geophysical data, and then scales these model parameters to the desired model resolution. We developed a stand‐alone framework called MPR‐flex for multimodel use and applied MPR‐flex to the variable infiltration capacity model to produce hydrologic simulations over the contiguous United States (CONUS). We first independently calibrate 531 basins across CONUS to obtain a performance benchmark for each basin. To derive the CONUS parameter fields, we perform a joint MPR calibration using all but the poorest behaved basins to obtain a single set of transfer function parameters that are applied to the entire CONUS. Results show that CONUS‐wide calibration has similar performance compared to previous simulations using a patchwork quilt of partially calibrated parameter sets, but without the spatial discontinuities in parameters that characterize some previous CONUS‐domain model simulations. Several avenues to improve CONUS‐wide calibration remain, including selection of calibration basins, objective function formulation, as well as MPR‐flex improvements including transfer function formulations and scaling operator optimization.
Plain Language Summary
Hydrologic models compute surface and subsurface water transmission and storage using many equations with parameters that vary spatially. Many parameter values are commonly estimated through systematic adjustment of parameter values to match historical streamflow over a watershed where a stream gauge is available. This method is difficult to apply to large domains contiguously. Consequently, many current large scale hydrologic assessments rely on spatially inconsistent parameter fields such as a patchwork quilt resulting from individual basin calibration or spatially constant parameters resulting from the adoption of default estimates. Facing this challenge, we developed a parameter estimation methodology that incorporates effects of landscape properties on parameter values to enable continental‐domain applications of hydrologic models in a spatially consistent way. We applied this methodology to a model that has been used for many hydrologic studies such as climate impact studies to generate a spatially smooth parameter set over the contiguous United States. Simulations using this parameter set produce similar accuracy to the previously developed parameter set.
Key Points
The Multiscale Parameter Regionalization (MPR) framework facilitates estimates of consistent hydrologic model parameters over large domains
A model‐independent MPR tool was developed for use with multiple models
Improvements of model simulation skill require enhancement of MPR and calibration strategies
Can we measure snow depth with GPS receivers? Larson, Kristine M.; Gutmann, Ethan D.; Zavorotny, Valery U. ...
Geophysical research letters,
September 2009, Letnik:
36, Številka:
17
Journal Article
Recenzirano
Odprti dostop
Snow is an important component of the climate system and a critical storage component in the hydrologic cycle. However, in situ observations of snow distribution are sparse, and remotely sensed ...products are imprecise and only available at a coarse spatial scale. GPS geodesists have long recognized that snow can affect a GPS signal, but it has not been shown that a GPS receiver placed in a standard geodetic orientation can be used to measure snow depth. In this paper, it is shown that changes in snow depth can be clearly tracked in the corresponding multipath modulation of the GPS signal. Results for two spring 2009 snowstorms in Colorado show strong agreement between GPS snow depth estimates, field measurements, and nearby ultrasonic snow depth sensors. Because there are hundreds of geodetic GPS receivers operating in snowy regions of the U.S., it is possible that GPS receivers installed for plate deformation studies, surveying, and weather monitoring could be used to also estimate snow depth.
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 future research needs and management implications of this widespread disturbance event.
Statistical 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 the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models North American Regional Climate Change Assessment Program (NARCCAP), and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation froma 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.
Measurements of soil moisture, both its global distribution and temporal variations, are required to study the water and carbon cycles. A global network of in situ soil moisture stations is needed to ...supplement datasets from satellite sensors. We demonstrate that signals routinely recorded by Global Positioning System (GPS) receivers for precise positioning applications can also be related to surface soil moisture variations. Over a three month interval, GPS‐derived estimates from a 300 m2 area closely match soil moisture fluctuations in the top 5 cm of soil measured with conventional sensors, including the rate and amount of drying following six precipitation events. Thousands of GPS receivers that exist worldwide could be used to estimate soil moisture in near real‐time, with L‐band signals that complement future satellite missions.
Prior research confirmed the substantial bias from using precipitation‐based intensity‐duration‐frequency curves (PREC‐IDF) in design flood estimates and proposed next‐generation IDF curves (NG‐IDF) ...that represent both rainfall and snow processes in runoff generation. This study improves the NG‐IDF technology for a snow‐dominated test basin in the Sierra Nevada. A well‐validated physics‐based hydrologic model, the Distributed Hydrology Soil Vegetation Model (DHSVM), is used to continuously simulate snowmelt and streamflow that are used as benchmark data sets to systematically assess the NG‐IDF technology. We find that, for the studied small snow‐dominated basin, the use of standard rainfall hyetographs in the NG‐IDF technology leads to substantial underestimation of design floods. Thus, we propose probabilistic hyetographs that can represent unique patterns of events with different underlying mechanisms. For the test basin where flooding events are generated entirely by snowmelt, we develop a hyetograph that characterizes snowmelt temporal patterns, which greatly improves the performance of NG‐IDF technology in design flood estimates. In contrast to the standard rainfall hyetographs characterized by a symmetrically peaked, bell‐shaped curve, the snowmelt hyetograph displays a more rapid rise (i.e., greater intensity) and a distinct diurnal pattern influenced by solar energy input. The results also show that the uncertainty of hyetography plays an important role in design flood estimation and can have important implications for future flood projections.
Plain Language Summary
In recent years, flood hazards have gained increasing attention from national and international homeland security communities. Accurately assessing floods is crucial for many hydrologic applications, including infrastructure design, planning, and renewal, as well as the national flood insurance program. This research focuses on evaluating and enhancing the next‐generation flood design technology, which is an improvement over the traditional rainfall‐based method that does not account for snow processes in flood generation. Our study reveals a significant underestimation of floods when using standard rainfall temporal pattern in a small snow‐dominated basin. To address this issue, we propose probabilistic curves that consider the temporal patterns of snowmelt, resulting in a considerable reduction in flood estimation errors. In contrast to the standard rainfall temporal pattern characterized by a symmetrically peaked, bell‐shaped curve, the snowmelt temporal pattern displays a more rapid rise (i.e., greater intensity) and a distinct diurnal pattern influenced by solar energy input. The results demonstrate that the next‐generation flood design technology has the potential to complement the traditional method for hydrologic design in snow‐dominated regions, providing a consistent design approach in both rain‐dominated and snow‐dominated areas.
Key Points
Standard rainfall hyetographs substantially underestimate floods in a small snow‐dominated basin in the Sierra Nevada
Snowmelt hyetograph shows a more rapid rise (i.e., higher intensity) compared to the standard rainfall hyetographs used in hydrologic design
A general method to develop probabilistic hyetographs that represent the underling flood‐generation mechanism is described
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.
Key Points:
Flexible model implementation enables evaluation of key modeling decisions
Case studies illustrate capabilities to identify preferable modeling approaches
Accelerates improvements in model fidelity & uncertainty characterization
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