Skip to main content

Advertisement

SpringerLink
Log in

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

  • Research Article
  • Published:
Boundary-Layer Meteorology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Angevine WM, Bazile E, Legain D, Pino D (2014) Land surface spinup for episodic modeling. Atmos Chem Phys 14(15):8165–8172

    Article  Google Scholar 

  • Best M, Grimmond CSB (2014) Importance of initial state and atmospheric conditions for urban land surface models’ performance. Urban Clim 10:387–406. doi:10.1016/j.uclim.2013.10.006

    Article  Google Scholar 

  • Bonacci O, Pipan T, Culver DC (2009) A framework for karst ecohydrology. Environ Geol 56:891–900

    Article  Google Scholar 

  • Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320(5882):1444–1449

    Article  Google Scholar 

  • Case JL, Crosson WL, Kumar SV, Lapenta WM, Peters-Lidard CD (2008) Impacts of high-resolution land surface initialization on regional sensible weather forecasts from the WRF model. J Hydrometeorol 9(6):1249–1266

    Article  Google Scholar 

  • Chen F, Dudhia J (2001) Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129:569–585

    Article  Google Scholar 

  • Chen F, Manning KW, LeMone MA, Trier SB, Alfieri JG, Roberts R, Tewari M, Niyogi D, Horst TW, Oncley SP, Basara JB (2007) Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. J Appl Meteorol Climatol 46(6):694–713

    Article  Google Scholar 

  • Chou MD, Suarez MJ, Ho CH, Yan MMH, Lee KT (1998) Parameterizations for cloud overlapping and shortwave single-scattering properties for use in general circulation and cloud ensemble models. J Clim 11:202–214

    Article  Google Scholar 

  • Clark CA, Arritt RW (1995) Numerical simulations of the effect of soil moisture and vegetation cover on the development of deep convection. J Appl Meteorol 34:2029–2045

    Article  Google Scholar 

  • Collow TW, Robock A, Wu W (2014) Influences of soil moisture and vegetation on convective precipitation forecasts over the United States Great Plains. J Geophys Res Atmos 119:9338–9358. doi:10.1029/2014JD021454

    Article  Google Scholar 

  • Cosgrove BA, Lohmann D, Mitchell KE, Houser PR, Wood EF, Schaake JC, Robock A, Marshall C, Sheffield J, Duan Q, Luo L (2003) Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J Geophys Res Atmos 108(D22):GCP3-1

    Google Scholar 

  • Crowther J (1987) Ecological observations in tropical karst terrain, West Malaysia. III. Dynamics of the vegetation-soil-bedrock system. J Biogeogr 14:157–164

    Article  Google Scholar 

  • Dai Y, Dai Y, Zeng X, Dickinson RE, Baker I, Bonan GB, Bosilovich MG, Denning AS, Dirmeyer PA, Houser PR, Niu G, Oleson KW (2003) The common land model. Bull Am Meteorol Soc 84:1013–1023

    Article  Google Scholar 

  • Davies WE, Simpson JH, Ohlmacher GC, Kirk WE, Newton EG (1984) Digital engineering aspects of Karst Map. U.S. Geological Survey, National Atlas of the United States of America, GIS version by Tobin BD and Weary DJ. http://pubs.usgs.gov/of/2004/1352/

  • Durkee JD, Campbell L, Berry K, Jordan D, Goodrich G, Mahmood R, Foster S (2012) A synoptic perspective of the record 1–2 May 2010 mid-South heavy precipitation event. Bull Am Meteorol Soc 93(5):611–620

    Article  Google Scholar 

  • Fan X (2009) Impacts of soil heating condition on precipitation simulations in the Weather Research and Forecasting model. Mon Weather Rev 137:2263–2285

    Article  Google Scholar 

  • Ford D, Williams PD (2007) Karst hydrogeology and geomorphology. Wiley, New York, 576 pp

  • Fry J, Xian G, Jin S, Dewitz J, Homer C, Yang L, Barnes C, Herold N, Wickham J (2011) Completion of the 2006 national land cover database for the conterminous United States. PE & RS 77(9):858–864. http://www.mrlc.gov/nlcd2006.php

  • Gaines M (2012) Application of the weather research and forecasting (WRF) model to simulate a squall line: implications of choosing parameterization scheme combinations and model initialization data sets. Masters theses and specialist projects, paper 1181. http://digitalcommons.wku.edu/theses/1181

  • Gao J, Xue Y, Wu S (2013) Potential impacts on regional climate due to land degradation in the Guizhou Karst Plateau of China. Environ Res Lett 8(4):044037

    Article  Google Scholar 

  • Gonzalez GR, Verhoef A, Vidale PL, Braud I (2012) Incorporation of water vapor transfer in the JULES land surface model: implications for key soil variables and land surface fluxes. Water Resour Res 48:W05538. doi:10.1029/2011WR011811

    Google Scholar 

  • Groves C (2007) Hydrological methods. In: Goldscheider N, Drew D (eds) Methods in karst hydrology. Taylor & Francis, London, pp 45–64

    Google Scholar 

  • Groves C, Meiman J (2005) Weathering, geomorphic work, and karst landscape evolution in the Cave City groundwater basin, Mammoth Cave, Kentucky. Geomorphology 67:115–126

    Article  Google Scholar 

  • Groves C, Bolster C, Meiman J (2005) Spatial and temporal variations in epikarst storage and flow in south central Kentucky’s Pennyroyal Plateau sinkhole plain. US Geol Surv Sci Investig Rep 2005–5160:64–73

    Google Scholar 

  • Hartmann A, Barbera JA, Lange J, Andreo B, Weiler M (2013) Progress in the hydrologic simulation of time variant recharge areas of karst systems—exemplified at a karst spring in Southern Spain. Adv Water Resour 54:149–160

    Article  Google Scholar 

  • Heilman JL, McInnes KJ, Kjelgaard JF, Owens MK, Schwinning S (2009) Energy balance and water use in a subtropical karst woodland on the Edwards Plateau, Texas. J Hydrol 373:426–435

    Article  Google Scholar 

  • Hess JW, White WB (1989) Chemical hydrology. In: White WB, White EL (eds) Karst hydrology: concepts from the Mammoth Cave area. Van Nostrand Reinhold, New York, pp 145–174

    Chapter  Google Scholar 

  • Jackson RB, Moore LA, Hoffmann W, Pockman WT, Linder CR (1999) Ecosystem rooting depth determined with caves and DNA. Proc Natl Acad Sci 96(20):11387–92

    Article  Google Scholar 

  • Ji Y, Zhou G, Wang S, Wang L (2015) Increase in flood and drought disasters during 1500–2000 in Southwest China. Nat Hazards 77(3):1853–1861

    Article  Google Scholar 

  • Jiang Z, Yuan D (1999) \(\text{ CO }_{2}\) source-sink in karst processes in karst areas of China. Episodes 22(1):33–35

    Google Scholar 

  • Kain J, Fritsch M (1993) Convective parameterization for mesoscale models: the Kain–Fritsch scheme. In: The representation of cumulus convection in numerical models. Meteorological monographs. American Meteor Society, vol 24, no 46, pp 165–170

  • Lawston PM, Santanello JA Jr, Zaitchik BF, Rodell M (2015) Impact of irrigation methods on land surface model spinup and initialization of WRF forecasts. J Hydrometeorol 16(3):1135–1154

    Article  Google Scholar 

  • Leeper R, Mahmood R, Quintanar AI (2011) Influence of karst landscape on planetary boundary layer atmosphere: a Weather Research and Forecasting (WRF) model-based comparison. J Hydrometeorol 12:1512–1529

    Article  Google Scholar 

  • Li E, Jiang Z, Cao J, Jiang G, Deng Y (2003) The comparison of properties of karst soil and karst erosion ratio under different successional stages of karst vegetation in Nongla, Guangxi. Acta Ecol Sin 24(6):1131–1139

    Google Scholar 

  • Lin Y (2011) GCIP/EOP surface: precipitation NCEP/EMC 4KM gridded data (GRIB) stage IV data. Version 1.0. UCAR/NCAR—Earth Observing Laboratory. http://data.eol.ucar.edu/dataset/21.093. Accessed 04 March 2017

  • Lin YL, Farley RD, Orville HD (1983) Bulk parameterization of the snow field in a cloud model. J Clim Appl Meteorol 22:1065–1092

    Article  Google Scholar 

  • McPherson RA (2007) A review of vegetation-atmosphere interactions and their influences on mesoscale phenomena. Prog Phys Geogr 31:261–285

    Article  Google Scholar 

  • Meng H, Wang L (2010) Advance in karst hydrological model. Prog Geogr 11:006

    Google Scholar 

  • Mesinger F et al (2006) North American regional reanalysis. Bull Am Meteorol Soc 87:343–360. doi:10.1175/BAMS-87-3-343

    Article  Google Scholar 

  • Mesinger F, Janjic ZI (1974) Noise due to time-dependent boundary conditions in limited area models. GARP Programme Numer Exp Rep 4:31–32

    Google Scholar 

  • Milanovic PT (1981) Karst hydrology. Water Resources Publications, Highlands Ranch, 434 pp

  • Mitchell K (2005) The community Noah land surface model (LSM)—user’s guide public release version 2.7.1. ftp://ftp.emc.ncep.noaa.gov/mmb/gcp/ldas/noahlsm/ver_2.7.1. Last visited 12 Sept 2014

  • Mlawer EJ, Taubman SJ, Brwon PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere; RRTM, a validated correlated-k model for the longwave. J Geophys Res 102(D14):16 663–16 682

    Article  Google Scholar 

  • Moser BK, Stevens GR, Watts CL (1989) The two-sample \(t\)-test versus Satterwaite’s approximate \(F\) test. Commun Stat Theory Methodol 18:3963–75

    Article  Google Scholar 

  • Oke TR (1987) Boundary layer climates, 2nd edn. Routledge, London, pp 110–157

    Google Scholar 

  • Palmer AN (2007) Cave geology. Cave Books, Dayton, 454 pp

  • Petty GW (2008) A first course in atmospheric thermodynamics. Sundog Publishing, Madison, 337 pp

  • Pielke RA (2001) Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev Geophys 39(2):151–177

    Article  Google Scholar 

  • Quintanar AI, Mahmood R (2012) Ensemble forecast spread induced by soil moisture changes over mid-south and neighbouring mid-western region of the USA. Tellus A Dyn Meteorol Oceanogr 64:17156. doi:10.3402/tellusa.v64i0.17156

    Article  Google Scholar 

  • Quintanar AI, Mahmood R, Loughrin J, Lovanh NC (2008) A coupled MM5-Noah land surface model-based assessment of sensitivity of planetary boundary later variables to anomalous soil moisture conditions. Phys Geogr 29:54–78. doi:10.2747/0272-3646.29.1.54

    Article  Google Scholar 

  • Quintanar AI, Mahmood R, Motley MV, Yan J, Loughrin J, Lovanh N (2009) Simulation of boundary layer trajectory dispersion sensitivity to soil moisture conditions: MM5 and Noah-based investigation. Atmos Environ 43:3774–3785

    Article  Google Scholar 

  • Rodell M, Houser PR, Berg AA, Famigliett JS (2005) Evaluation of 10 methods for initializing a land surface model. J Hydrometeorol 6(2):146–155

    Article  Google Scholar 

  • Ruxton GD (2006) The unequal variance \(t\)-test is an underused alternative to Student’s \(t\)-test and the Mann–Whitney \(U\) test. Behav Ecol 17(4):688–690. doi:10.1093/beheco/ark016

    Article  Google Scholar 

  • Santanello JA Jr, Kumar SV, Peters-Lidard CD, Harrison K, Zhou S (2013) Impact of land model calibration on coupled land-atmosphere prediction. J Hydrometeorol 14(5):1373–1400

    Article  Google Scholar 

  • Schwinning S (2008) The water relations of two evergreen tree species in a karst savanna. Oecologia 158(3):373–83

    Article  Google Scholar 

  • Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang XY, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR tech note, NCAR/TN-475+STR

  • Suarez A, Mahmood R, Quintanar AI, Beltran-Przekurat A, Pielke RA Sr (2014) A comparison of the MM5 and the Regional Atmospheric Modeling System simulations for land–atmosphere interactions under varying soil moisture. Tellus A Dyn Meteorol Oceanogr 66:21486. doi:10.3402/tellusa.v66.21486

    Article  Google Scholar 

  • Weary DJ, Doctor DH (2014) Karst in the United States: a digital map compilation and database. US geological survey open-file report, pp 2014–1156

  • White WB (1988) Geomorphology and hydrology of karst terrains. Oxford University Press, New York, 480 pp

  • White WB, Culver DC, Herman JS, Kane TC, Mylroie JE (1995) Karst lands. Am Sci 83:450–459

    Google Scholar 

  • Wilks DS (2010) Statistical methods in the atmospheric sciences, 3rd edn. Academic Press, New York, 676 pp

  • Winchester J, Mahmood R, Rodgers W, Hossain F, Rappin E, Durkee J, Chronis T (2017) A model-based assessment of potential impacts of man-made reservoirs on precipitation. Earth Interact 21:1–31

  • Xue Y, Sellers PJ, Kinter JL, Shukla J (1991) A simplified biosphere model for global climate studies. J Clim 4:345–64

    Article  Google Scholar 

  • Zhang Q, Xu CY, Zhang Z, Chen X, Han Z (2010) Precipitation extremes in a karst region: a case study in the Guizhou province, southwest China. Theor Appl Climatol 101:53–65. doi:10.1007/s00704-009-0203-0

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Geography and Geology, Western Kentucky University, Bowling Green, KY, 42101, USA

    Christopher M. Johnson, Xingang Fan, Rezaul Mahmood, Chris Groves, Jason S. Polk & Jun Yan

  2. Kentucky Climate Center, Western Kentucky University, Bowling Green, KY, 42101, USA

    Christopher M. Johnson & Rezaul Mahmood

  3. Crawford Hydrology Laboratory, Western Kentucky University, Bowling Green, KY, 42101, USA

    Chris Groves

  4. Center for Human GeoEnvironmental Studies, Western Kentucky University, Bowling Green, KY, 42101, USA

    Jason S. Polk

Authors
  1. Christopher M. Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xingang Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rezaul Mahmood
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chris Groves
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jason S. Polk
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jun Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xingang Fan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Johnson, C.M., Fan, X., Mahmood, R. et al. Evaluating Weather Research and Forecasting Model Sensitivity to Land and Soil Conditions Representative of Karst Landscapes. Boundary-Layer Meteorol 166, 503–530 (2018). https://doi.org/10.1007/s10546-017-0312-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10546-017-0312-8

Keywords

Search

Navigation