SHPYG V.M., SHCHEHLOV O.A. INFLUENCE OF LAND USE CATEGORIES ON WRF FORECAST AND ITS ACCURACY
DOI: https://doi.org/10.17721/2306-5680.2022.3.5
Hydrology, Hydrochemistry and Hydroecology. 2022. № 3 (65)
Publication language: English
Authors:
Shpyg V.M., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, Kyiv
Shchehlov O.A., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, Kyiv
In this research analysis of possibilities and methods of correction or reclassification of land use and land cover data, obtained modeling effects of using more accurate land use and land cover data and estimations of improvements of surface meteorological parameters simulations by mesoscale numerical atmospheric prediction (NWP) model. For the Kyiv region area, it is shown that the default WRF land use and land cover datasets are not ideal and contain various types of inaccuracies. Thus, compared to USGS, MODIS IGBP is characterized by a better spatial resolution, more correctly describing the urban built-up territories, forest areas and the Dnipro riverbed, but MODIS IGBP does not display small settlements. Analysis of other researches had shown that most of the reviewed studies are based on the case study approach, namely on an analysis of a small number of specific simulations. The disadvantage of this approach is that the sample size from which conclusions can be drawn is insignificant, so it is useful to compare similar studies for the consistency of the obtained results. The most consistent conclusions regarding the simulation improvement by the land use and land cover modification in the WRF model are related to the parameters of air temperature and humidity. Thus, the usage of more accurate and up-to-date land use and land cover databases in WRF simulations made it possible to reduce bias of forecasts and improved the accuracy of minimum and maximum daily temperature forecasts. The most significant improvement in the performed forecasts concerns the night temperature. The second parameter that were most often able to predict better is relative humidity. Improvement of simulated wind parameters using another land use and land cover dataset in the WRF model may be possible in urban areas and near the coast, especially as improving description of breeze circulation by the model. Assessments regarding the possibility of improving WRF precipitation simulation by land use and land cover dataset modification are not definite, although some works have noted the influence of the heat island of urbanized areas on redistribution of precipitation. On the one hand, in most of the analyzed scientific works, land use and land cover changes did not allow to significantly improve precipitation forecasts using the WRF model. On the other hand, it can be assumed that the physical and geographical conditions of a simulation domain as well as available moisture in the atmosphere is playing a more significant role in precipitation modeling than land use and land cover.
Keywords: Weather Research and Forecasting (WRF) Model, Land Use, Land Cover, Numerical weather prediction, Atmosphere Modelling, Accuracy.
References:
1. Achenafi Teklay, Yihun T. Dile, Dereje H. Asfaw, Haimanote K. Bayabil, Kibruyesfa Sisay, Impacts of land surface model and land use data on WRF model simulations of rainfall and temperature over Lake Tana Basin, Ethiopia // Heliyon. 2019. Volume 5. Issue 9. https://doi.org/10.1016/j.heliyon.2019.e02469
2. Achugbu I.C., Olufayo A.A., Balogun I.A. et al. Modeling the spatiotemporal response of dew point temperature, air temperature and rainfall to land use land cover change over West Africa // Model. Earth Syst. Environ. 2022. Vol. 8. P. 173-198. https://doi.org/10.1007/s40808-021-01094-8
3. Alshari Eman A., Gawali Bharti W. Development of classification system for LULC using remote sensing and GIS // Global Transitions Proceedings. 2021. Vol. 2, Issue 1. P. 8-17. https://doi.org/10.1016/j.gltp.2021.01.002
4. Arnold D. et al. High Resolution Modelling in Complex Terrain. Report on the HiRCoT 2012 Workshop, Vienna, 21-23 February 2012.
5. Arsanjani J.J. Dynamic Land Use / Cover Change Modelling : Geosimulation and Agent-Based Modelling. Vienna: University of Vienna; 2011.
6. Cheng F.Y., Hsu Y.C., Lin P.L., Lin T.H. Investigation of the effects of different land use and land cover patterns on mesoscale meteorological simulations in the Taiwan area // Journal of Applied Meteorology and Climatology. 2013. 52(3), P. 570-587.
7. Doroshenko A. Numerical atmospheric models and their application in different areas of economics / Anatoliy Doroshenko, Vitalii Shpyg, Igor Budak, Kateryna Huda [In: Kvasniy L. And Tatomyr I. (eds) Ukraine in the context of global and national modern servisation processes and digital economy]: monograph. Praha: Oktan Print, 2020. – eBook ISBN 978-80-907863-4-9; Print ISBN 978-80-907863-3-2. – P. 155-171. DOI: 10.46489/UITCOG0909
8. Fang-Yi Cheng, Daewon W. Byun, Application of high resolution land use and land cover data for atmospheric modeling in the Houston–Galveston metropolitan area, Part I: Meteorological simulation results // Atmospheric Environment. 2008. Vol. 42, Issue 33. P. 7795-7811. https://doi.org/10.1016/j.atmosenv.2008.04.055
9. Friedl Mark A., Sulla-Menashe Damien, Tan Bin, Schneider Annemarie, Ramankutty Navin, Sibley Adam, Huang Xiaoman. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets // Remote Sensing of Environment. 2010. Vol. 114, Issue 1. P. 168-182. https://doi.org/10.1016/j.rse.2009.08.016
10. Global Land Cover Characteristics Data Base Version 2.0. URL: https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/GlobalLandCoverCharacteristicsDataBaseReadmeVersion2.pdf (date of access: 13 July 2022).
11. Golzio A., Ferrarese S., Cassardo C. et al. Land-Use Improvements in the Weather Research and Forecasting Model over Complex Mountainous Terrain and Comparison of Different Grid Sizes // Boundary-Layer Meteorology. 2021. Vol. 180. P. 319-351. https://doi.org/10.1007/s10546-021-00617-1
12. Grossman-Clarke S., Zehnder J.A., Stefanov W.L., Liu Y., Zoldak M.A. Urban Modifications in a Mesoscale Meteorological Model and the Effects on Near-Surface Variables in an Arid Metropolitan Region // Journal of Applied Meteorology. 2005. Vol. 44(9). P. 1281-1297. https://doi.org/10.1175/JAM2286.1
13. Jiménez-Esteve B., Udina M., Soler M., Pepin N., Mirò J. Land use and topography influence in a complex terrain area: a high resolution mesoscale modelling study over the Eastern Pyrenees using the WRF model // Atmos. Res. 2018. Vol. 202. P. 49-62. https://doi.org/10.1016/j.atmosres.2017.11.012
14. Jiménez-Esteve B. Land use influence in WRF model. A high resolution mesoscale modeling over Oriental Pyrenees. Master’s thesis, Facultat de Física, Universitat de Barcelona, 2015.
15. Kamal S., Huang H., Myint S.W. The Influence of Urbanization on the Climate of the Las Vegas Metropolitan Area: A Numerical Study // Journal of Applied Meteorology and Climatology. 2015. 54(11). P. 2157-2177. https://doi.org/10.1175/JAMC-D-15-0003.1
16. Katsalova L.M., Shpyg V.M. The choice of optimal lag for Kriging interpolation of NWP model forecast // Meteorology, Hydrology and Water Management. 2016. Vol. 4, Issue 2. P. 23-28. DOI: https://doi.org/10.26491/mhwm/64292
17. Kun Wang, Yali Tong, Jiajia Gao, Chao Gao, Kai Wu, Tao Yue, Sida Qin, Chenlong Wang, Impacts of LULC, FDDA, Topo-wind and UCM schemes on WRF-CMAQ over the Beijing-Tianjin-Hebei region, China // Atmospheric Pollution Research. 2021. Vol. 12, Issue 2., P. 292-304. https://doi.org/10.1016/j.apr.2020.11.011 18. Kusaka H., Kimura F. Coupling a single-layer urban canopy model with a simple atmospheric model: Impact on urban heat island simulation for an idealized case // J. Meteor. Soc. Japan. 2004. Vol. 82. P. 67-80. https://doi.org/10.2151/jmsj.82.67
19. Kusaka H., Nawata K., Suzuki-Parker A., Takane Y., Furuhashi N. Mechanism of precipitation increase with urbanization in Tokyo as revealed by ensemble climate simulations // J. Appl. Meteor. Climatol. 2014. Vol. 53. P. 824-839. DOI: 10.1175/JAMC-D-13-065.1
20. Li H., Zhang H., Mamtimin A., Fan S., Ju C. A New Land-Use Dataset for the Weather Research and Forecasting (WRF) Model // Atmosphere. 2020. 11(4), 350. https://doi.org/10.3390/atmos11040350
21. Lin C.Y.,. Chen W.C., Chang P.L., Sheng Y.F. Impact of the urban heat island effect on precipitation over a complex geographic environment in northern Taiwan // J. Appl. Meteor. Climatol. 2011. Vol. 50. P. 339-353. DOI:10.1175/2010JAMC2504.1
22. Liping C., Yujun S., Saeed S. Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques – A case study of a hilly area, Jiangle, China // PLoS ONE. 2018. 13(7). e0200493. https://doi.org/10.1371/journal.pone.0200493
23. López-Espinoza E.D., Zavala-Hidalgo J., Mahmood R., Gómez-Ramos O. Assessing the Impact of Land Use and Land Cover Data Representation on Weather Forecast Quality: A Case Study in Central Mexico // Atmosphere. 2020. 11(11), 1242. https://doi.org/10.3390/atmos11111242
24. Manual on the Global Observing System (WMO No 544), Vol. I
25. McConnell W.J. Land Change: The Merger of Land Cover and Land use Dynamics A2 – Wright, James D. // International Encyclopedia of the Social & Behavioral Sciences (Second Edition). Oxford: Elsevier; 2015. p. 220-3.
26. Nguyen L.H., Joshi D.R., Clay D.E., Henebry G.M. Characterizing land cover/land use from multiple years of Landsat and MODIS time series: A novel approach using land surface phenology modeling and random forest classify // Remote Sens. Environ. 2020. 238, 111017.
27. Pedruzzi Rizzieri et al. Update of land use/land cover and soil texture for Brazil: Impact on WRF modeling results over São Paulo // Atmospheric Environment. 2022. Vol. 268, 118760. https://doi.org/10.1016/j.atmosenv.2021.118760
28. Pielke R.A., Sr., Pitman A., Niyogi D., Mahmood R., McAlpine C., Hossain F. et al. Land use/land cover changes and climate: modeling analysis and observational evidence // Wires Clim. Change. 2011. 2(6). P. 828-850. DOI: 10.1002/wcc.144
29. Pineda N., Jorba O., Jorge J., Baldasano J.M. Using NOAA AVHRR and SPOT VGT data to estimate surface parameters: application to a mesoscale meteorological model // International Journal of Remote Sensing. 2004. 25(1). P. 129-143.
30. Pongratz J., Schwingshackl C., Bultan S. et al. Land Use Effects on Climate: Current State, Recent Progress, and Emerging Topics // Curr. Clim. Change Rep. 2021. Vol. 7. P. 99-120. https://doi.org/10.1007/s40641-021-00178-y
31. QGIS.org, 2022. QGIS Geographic Information System. QGIS Association. http://www.qgis.org
32. Rawat J.S., Kumar M. Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India // The Egyptian Journal of Remote Sensing and Space Science. 2015. 18(1). P. 77-84. https://doi.org/10.1016/j.ejrs.2015.02.002
33. Santos M.J., Smith A.B., Dekker S.C. et al. The role of land use and land cover change in climate change vulnerability assessments of biodiversity: a systematic review // Landscape Ecol. 2021. Vol. 36. P. 3367-3382. https://doi.org/10.1007/s10980-021-01276-w
34. Schicker I., Arnold Arias D., Seibert, P. Influences of updated land-use datasets on WRF simulations for two Austrian regions // Meteorol. Atmos. Phys. 2016. Vol. 128. P. 279-301. https://doi.org/10.1007/s00703-015-0416-y
35. Shpyg V. et al. The application of regional NWP models to operational weather forecasting in Ukraine // CAS Technical Conference on “Responding to the Environmental Stressors of the 21st Century”: 18–19 November 2013: Conf. Materials. 2013. URL: https://www.researchgate.net/publication/312488806_The_Application_of_Regional_NWP_Models_to_Operational_Weather_Forecasting_in_Ukraine (date of access: 13 July 2022).
36. Shpyg V., Budak I. WRF reflectivity simulation and verification of thunderstorm forecast by radar and surface observation // 16th International Radar Symposium : 24-26 June 2015. Symposium Materials. Dresden, 2015. P. 610-615. DOI: 10.1109/IRS.2015.7226388
37. Shpyg V., Budak I. Estimation of effectiveness of thunderstorms observation and their forecast by the instability indices // 7th International Verification Methods Workshop. 03-11 May 2017, Berlin, Germany. Annalen der Meteorologie, 2017. Vol. 51. P. 81.
38. Siewert J., Kroszczynski K. GIS Data as a Valuable Source of Information for Increasing Resolution of the WRF Model for Warsaw // Remote Sensing. 2020. 12(11), 1881. https://doi.org/10.3390/rs12111881
39. Skamarock W.C. et al. A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR. 2008. 113 pp. https://doi.org/10.5065/D68S4MVH.
40. Sulla-Menashe Damien, Gray Josh M., Parker Abercrombie S., Friedl Mark A., Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product // Remote Sensing of Environment. 2019. Vol. 222. P. 183-194. https://doi.org/10.1016/j.rse.2018.12.013
41. Temimi M. Assessing the Impact of Changes in Land Surface Conditions on WRF Predictions in Arid Regions // Journal of Hydrometeorology. 2020. 21(12). P. 2829-2853. https://doi.org/10.1175/JHM-D-20-0083.1
42. Tewari M., Chen F., Kusaka H. Implementation and evaluation of a single-layer urban canopy model in WRF/Noah // 7-th WRF Users’ Workshop, Boulder, CO, NCAR, 2006. URL: http://www2.mmm.ucar.edu/wrf/users/workshops/WS2006/abstracts/Session05/5_6_Tewari.pdf (date of access: 13 July 2022)
43. USGS EROS Archive – Land Cover Products – Global Land Cover Characterization (GLCC). DOI: 10.5066/F7GB230D
44. Xuejie G., Yong L., Wantao L., Zongci Z., Giorgi F. Simulation of effects of land use change on climate in China by a regional climate model // Adv. Atmos. Sci. 2003. Vol. 20. P. 583-592. https://doi.org/10.1007/BF02915501
45. Zhaohui Yang (Joey), Dominguez Francina, Gupta Hoshin Vijai, Zeng Xubin and Norman Laura M. Urban Effects on Regional Climate: A Case Study in the Phoenix and Tucson “Sun Corridor” // Earth Interactions. 2016. Vol. 20. P. 1-25. https://doi.org/10.1175/EI-D-15-0027.1
HOW TO CITE
Shpyg, V.M., Shchehlov, O.A (2022). Influence of land use categories on WRF forecast and its accuracy. Hidrolohiia, hidrokhimiia i hidroekolohiia [Hydrology, Hydrochemistry and Hydroecology], 3(65), 68-78 (in English, abstr. in Ukrainian). https://doi.org/10.17721/2306-5680.2022.3.5.