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Boundary-Layer Meteorology

, Volume 166, Issue 3, pp 503–530 | Cite as

Evaluating Weather Research and Forecasting Model Sensitivity to Land and Soil Conditions Representative of Karst Landscapes

  • Christopher M. Johnson
  • Xingang FanEmail author
  • Rezaul Mahmood
  • Chris Groves
  • Jason S. Polk
  • Jun Yan
Research Article

Abstract

Due to their particular physiographic, geomorphic, soil cover, and complex surface-subsurface hydrologic conditions, karst regions produce distinct land–atmosphere interactions. It has been found that floods and droughts over karst regions can be more pronounced than those in non-karst regions following a given rainfall event. Five convective weather events are simulated using the Weather Research and Forecasting model to explore the potential impacts of land-surface conditions on weather simulations over karst regions. Since no existing weather or climate model has the ability to represent karst landscapes, simulation experiments in this exploratory study consist of a control (default land-cover/soil types) and three land-surface conditions, including barren ground, forest, and sandy soils over the karst areas, which mimic certain karst characteristics. Results from sensitivity experiments are compared with the control simulation, as well as with the National Centers for Environmental Prediction multi-sensor precipitation analysis Stage-IV data, and near-surface atmospheric observations. Mesoscale features of surface energy partition, surface water and energy exchange, the resulting surface-air temperature and humidity, and low-level instability and convective energy are analyzed to investigate the potential land-surface impact on weather over karst regions. We conclude that: (1) barren ground used over karst regions has a pronounced effect on the overall simulation of precipitation. Barren ground provides the overall lowest root-mean-square errors and bias scores in precipitation over the peak-rain periods. Contingency table-based equitable threat and frequency bias scores suggest that the barren and forest experiments are more successful in simulating light to moderate rainfall. Variables dependent on local surface conditions show stronger contrasts between karst and non-karst regions than variables dominated by large-scale synoptic systems; (2) significant sensitivity responses are found over the karst regions, including pronounced warming and cooling effects on the near-surface atmosphere from barren and forested land cover, respectively; (3) the barren ground in the karst regions provides conditions favourable for convective development under certain conditions. Therefore, it is suggested that karst and non-karst landscapes should be distinguished, and their physical processes should be considered for future model development.

Keywords

Barren land cover Karst landscapes Model precipitation events Model sensitivity Weather Research and Forecasting model 

Notes

Acknowledgements

The detailed information of datasets used is provided in Sect. 2. The digital karst maps incorporated into the WRF model were obtained from USGS at http://pubs.usgs.gov/of/2004/1352/. The 2006 National Land Cover Data were obtained from the USGS at http://www.mrlc.gov/nlcd2006.php. The North American Regional Reanalysis data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, were obtained from http://www.esrl.noaa.gov/psd/. The NCEP Stage-IV precipitation data for model evaluation were obtained from the NCAR/UCAR Earth Observing Laboratory at http://data.eol.ucar.edu/codiac/dss/id=21.093. This research was funded by the Kentucky Climate Center and supported by the Western Kentucky University Interdisciplinary Research and Creative Activity grant and the Faculty Undergraduate Student Engagement grant. Support was also provided in part from a USDA-ARS Grant #58-6445-6-068. The authors thank Dr. Joshua Durkee for valuable discussions and William Rodgers for technical assistance. We also thank Ryan Difani, Tyler Binkley, Allie Durham, and Andrew Schuler for their initial involvement in a related atmospheric modelling class project.

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Department of Geography and GeologyWestern Kentucky UniversityBowling GreenUSA
  2. 2.Kentucky Climate CenterWestern Kentucky UniversityBowling GreenUSA
  3. 3.Crawford Hydrology LaboratoryWestern Kentucky UniversityBowling GreenUSA
  4. 4.Center for Human GeoEnvironmental StudiesWestern Kentucky UniversityBowling GreenUSA

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