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  • 1
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    The Use of Similarity Concepts to Represent Subgrid Variability in Land Surface Models: Case Study in a Snowmelt-Dominated Watershed (2014)
    American Meteorological Society
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    Publication Date: 2014-09-25
    Description: This paper develops a multivariate mosaic subgrid approach to represent subgrid variability in land surface models (LSMs). The k-means clustering is used to take an arbitrary number of input descriptors and objectively determine areas of similarity within a catchment or mesoscale model grid box. Two different classifications of hydrologic similarity are compared: an a priori classification, where clusters are based solely on known physiographic information, and an a posteriori classification, where clusters are defined based on high-resolution LSM simulations. Simulations from these clustering approaches are compared to high-resolution gridded simulations, as well as to three common mosaic approaches used in LSMs: the “lumped” approach (no subgrid variability), disaggregation by elevation bands, and disaggregation by vegetation types in two subcatchments. All watershed disaggregation methods are incorporated in the Noah Multi-Physics (Noah-MP) LSM and applied to snowmelt-dominated subcatchments within the Reynolds Creek watershed in Idaho. Results demonstrate that the a priori clustering method is able to capture the aggregate impact of finescale spatial variability with O(10) simulation points, which is practical for implementation into an LSM scheme for coupled predictions on continental–global scales. The multivariate a priori approach better represents snow cover and depth variability than the univariate mosaic approaches, critical in snowmelt-dominated areas. Catchment-averaged energy fluxes are generally within 10%–15% for the high-resolution and a priori simulations, while displaying more subgrid variability than the univariate mosaic methods. Examination of observed and simulated streamflow time series shows that the a priori method generally reproduces hydrograph characteristics better than the simple disaggregation approaches.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 2
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    How Does the Choice of Distributed Meteorological Data Affect Hydrologic Model Calibration and Streamflow Simulations? (2014)
    American Meteorological Society
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    Publication Date: 2014-07-30
    Description: Spatially distributed historical meteorological forcings (temperature and precipitation) are commonly incorporated into modeling efforts for long-term natural resources planning. For water management decisions, it is critical to understand the uncertainty associated with the different choices made in hydrologic impact assessments (choice of hydrologic model, choice of forcing dataset, calibration strategy, etc.). This paper evaluates differences among four commonly used historical meteorological datasets and their impacts on streamflow simulations produced using the Variable Infiltration Capacity (VIC) model. The four meteorological datasets examined here have substantial differences, particularly in minimum and maximum temperatures in high-elevation regions such as the Rocky Mountains. The temperature differences among meteorological forcing datasets are generally larger than the differences between calibration and validation periods. Of the four meteorological forcing datasets considered, there are substantial differences in calibrated model parameters and simulations of the water balance. However, no single dataset is superior to the others with respect to VIC simulations of streamflow. Also, optimal calibration parameter values vary across case study watersheds and select meteorological datasets, suggesting that there is enough flexibility in the calibration parameters to compensate for the effects of using select meteorological datasets. Evaluation of runoff sensitivity to changes in climate indicates that the choice of meteorological dataset may be as important in characterizing changes in runoff as climate change, supporting consideration of multiple sources of uncertainty in long-term planning studies.
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    Topics: Geography , Geosciences , Physics
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  • 3
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    Hydrologic Implications of Different Large-Scale Meteorological Model Forcing Datasets in Mountainous Regions (2014)
    American Meteorological Society
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    Publication Date: 2014-02-01
    Description: Process-based hydrologic models require extensive meteorological forcing data, including data on precipitation, temperature, shortwave and longwave radiation, humidity, surface pressure, and wind speed. Observations of precipitation and temperature are more common than other variables; consequently, radiation, humidity, pressure, and wind speed often must be either estimated using empirical relationships with precipitation and temperature or obtained from numerical weather prediction models. This study examines two climate forcing datasets using different methods to estimate radiative energy fluxes and humidity and investigates the effects of the choice of forcing data on hydrologic simulations over the mountainous upper Colorado River basin (293 472 km2). Comparisons of model simulations forced by two climate datasets illustrate that the methods used to estimate shortwave radiation impact hydrologic states and fluxes, particularly at high elevation (e.g., ~20% difference in runoff above 3000-m elevation), substantially altering the timing of snowmelt and runoff (~20 days difference) and the partitioning of precipitation between evapotranspiration and runoff. The different forcing datasets also exhibit differences in hydrologic sensitivity to interannual temperature at high elevation. The results suggest that the choice of forcing dataset is an important consideration when conducting climate impact assessments and the subsequent applications of these assessments for water resources planning and management.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 4
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    Challenges of Operational River Forecasting (2014)
    American Meteorological Society
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    Publication Date: 2014-07-30
    Description: Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors.
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    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 5
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    Climate Change Impacts on the Water Balance of the Colorado Headwaters: High-Resolution Regional Climate Model Simulations (2014)
    American Meteorological Society
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    Publication Date: 2014-06-01
    Description: A 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 positive increase of precipitation. Runoff decreased by an average of 6%, reflecting the increased aridity. Precipitation increased in the future winter by 12%, but decreased in the summer as a result of increased low-level inhibition to convection. The fraction of precipitation that fell as snow decreased from 0.83 in the current climate to 0.74 in the future. Future snowpack did not change significantly until January. From January to March the snowpack increased above ~3000 m MSL and decreased below that level. Snowpack decreased at all elevations in the future from April to July. The peak snowpack and runoff over the headwaters occurred 2–3 weeks earlier in the future simulation, in agreement with previous studies.
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    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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