Abstract
The subject of the research paper is the use of remote sensing in monitoring and analyzing the impact of climate change on the occurrence of extreme precipitation, and the cause-and-effect occurrence of floods in the area of the Sana River basin in Bosnia and Herzegovina. The goal is to process the “product” of remote sensing to identify the time intervals of occurrence of extreme precipitation, to assess their impact on water levels, and to map potential floods in space. Spatial identification of zones that are at risk of flooding is an integral part of the aforementioned goal. Precipitation monitoring was performed by processing Climate Hazards Group InfraRed Precipitation with Station Data through the Google Earth Engine platform. The observed 30-year period (1992–2022) was compared with the average precipitation for 2017, 2018 and 2019. The impact of extreme precipitation on the water level of the Sana River was analyzed. Flooding periods have been identified: February and December 2017, March 2018 and May 2019. Mapping of flooded areas was carried out by pre-processing and post-processing of Sentinel-1 radar satellite images. The total flooded area is: 710.38 ha (February 2017), 496.79 ha (December 2017), 417.86 ha (March 2018) and 422.42 ha (May 2019). Based on the identified flooded areas, a flood risk map was created on the main course of the Sana River. The research contributes to a better understanding of the changes that occur in the area under the influence of climate change, and the data presented are important for numerous practical issues in the field of water resource management and flood protection.
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References
Agency of Statistics of Bosnia and Herzegovina. (2019). Census of Population, Households and Dwellings in Bosnia and Herzegovina, 2013.
Amadio, M., Mysiak, J., Carrera, L., & Koks, E. (2016). Improving flood damage assessment models in Italy. Natural Hazards, 82, 2075–2088. https://doi.org/10.1007/s11069-016-2286-0
Bai, L., Shi, C., Li, L., Yang, Y., & Wu, J. (2018). Accuracy of CHIRPS satellite-rainfall products over mainland China. Remote Sensing, 10(3), 362. https://doi.org/10.3390/rs10030362
Beck, H., Vergopolan, N., Pan, M., Levizzani, V., Van Dijk, A., Weedon, G., Brocca, L., Pappenberger, F., Huffman, G., & Wood, E. F. (2017). Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrology and Earth System Sciences, 21(12), 6201–6217. https://doi.org/10.5194/hess-21-6201-2017
Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., & Wagner, W. (2019). SM2RAIN–ASCAT (2007–2018): Global daily satellite rainfall data from ASCAT soil moisture observations. Earth System Science Data, 11(4), 1583–1601. https://doi.org/10.5194/essd-11-1583-2019
Bucchignani, E., Zollo, A. L., Cattaneo, L., Montesarchio, M. & Mercogliano, P. (2016). Extreme weather events over China: Assessment of COSMO-CLM simulations and future scenarios. Internatioanl Journal of Climatology, 37, 1578–1594. https://doi.org/10.1002/joc.4798
Ciric, D., Nieto, R., Ramos, A., Drumond, A., & Gimeno, L. (2018). Contribution of Moisture from Mediterranean Sea to Extreme Precipitation Events over Danube River Basin. Water, 10(9), 1182. https://doi.org/10.3390/w10091182
Correia Filho, W. L. F., Oliveira Júnior, J. F., Santiago, D. B., Terassi, P. M. B., Teodoro, P. E., Gois, G., Blanco, C. J. C., Souza, P. H. A., Costa, M., Santos, P. J. (2019). Rainfall variability in the Brazilian northeast biomes and their interactions with meteorological systems and ENSO via CHELSA product. Big Earth Data, 3, 315–337. https://doi.org/10.1080/20964471.2019.1692298
DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J., & Lang, M. (2020). Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sensing of Environment, 240, 11664. https://doi.org/10.1016/j.rse.2020.111664
Dinku, T., Funk, C., Peterson, P., Maidment, R., Tadesse, T., Gadain, H., & Ceccato, P. (2018). Validation of the CHIRPS satellite rainfall estimates over Eastern Africa. Quarterly Journal of the Royal Meteorological Society, 144(S1), 292–312. https://doi.org/10.1002/qj.3244
Dong, S., Sun, Y., Li, C., Zhang, X., Min, S. K., & Kim, Y. H. (2021). Attribution of extreme precipitation with updated observations and CMIP6 simulations. Journal of Climate, 34, 871–881. https://doi.org/10.1175/JCLI-D-19-1017.1
Funk, C., Peterson, P., Landsfeld, M., Pedereros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2, 150066. https://doi.org/10.1038/sdata.2015.66
Gao, F., Zhang, Y., Chen, Q., Wang, P., Yang, H., Yao, Y., & Cai, W. (2018). Comparison of two long-term and high-resolution satellite precipitation datasets in Xinjiang, China. Atmospheric Research, 212, 150–157. https://doi.org/10.1016/j.atmosres.2018.05.016
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Gnjato, S. (2018). Analysis of the Water Discharge at the Sana River. Гласник/Herald, 22, 103–116. https://doi.org/10.7251/HER2218103G
IPCC (2021). Summary for Policymakers, Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
Ivanišević, M., Savić, S., Pavić, D., Gnjato, S., & Popov, T. (2022). Spatio-Temporal Patterns of Flooded Areas in the Lower Part of the Sana River Basin (Bosnia and Herzegovina). Bulletin of the Serbian Geographical Society, 102(2), 67–82. https://doi.org/10.2298/GSGD2202067I
Katsanos, D., Retalis, A., Tymvios, F., & Michaelides, S. (2016). Analysis of precipitation extremes based on satellite (CHIRPS) and in situ data set over Cyprus. Natural Hazards, 8353–63. https://doi.org/10.1007/s11069-016-2335-8
Kidd, C., Becker, A., Huffman, G. J., Muller, C. L., Joe, P., Skofronick-Jackson, G., & Kirschbaum, D. B. (2017). So, how much of the Earth’s surface is covered by rain gauges? Bulletin of the American Meteorological Society, 98(1), 69–78. https://doi.org/10.1175/BAMS-D-14-00283.1
Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259–263. https://doi.org/10.1127/0941-2948/2006/0130
Kumar, T. V. L., Barbosa, H. A., Thakur, M. K., & Paredes-Trejo, F. (2019). Validation of satellite (TMPA and IMERG) rainfall products with the IMD gridded data sets over monsoon core region of India. In: Rustamov R. B. (Ed.). Satellite Information Classification and Interpretation. https://doi.org/10.5772/intechopen.84999
Loew, A., Bell, W., Brocca, L., Bulgin, C. E., Burdanowitz, J., Calbet, X., Donner, R. V., Ghent, D., Gruber, A., Kaminski, T., Kinzel, J., Klepp, C., Lambert, J. C., Schaepman-Strub, G., & Schröder, M. (2017). Reviews of Geophysics, 55(3), 779–817. https://doi.org/10.1002/2017RG000562
Maggioni, V., Sapiano, M. R. P., & Adler, R. F. (2016). Estimating uncertainties in high-resolution satellite precipitation products: Systematic or random error? Journal of Hydrometeorology, 17(4), 1119–1129. https://doi.org/10.1175/JHM-D-15-0094.1
Meresa, H., Tischbein, B., & Mekonnen, T. (2022). Climate change impact on extreme precipitation and peak flood magnitude and frequency: observations from CMIP6 and hydrological models. Natural Hazards, 111, 2649–2679. https://doi.org/10.1007/s11069-021-05152-3
Michaelides, S., Levizzani, V., Anagnostou, E., Bauer, P., Kasparis, T., & Lane, J. E. (2009). Precipitation: Measurement, remote sensing, climatology and modeling. Atmospheric Research, 94(4), 512–533. https://doi.org/10.1016/j.atmosres.2009.08.017
Moel, H., Alphen, J., & Aerts, J. (2009). Flood maps in Europe–methods, availability and use. Natural Hazards and Earth System Science, 9, 289–301. https://doi.org/10.5194/nhess-9-289-2009
Nghia, B., Pal, I., Chollacoop, N., & Mukhopadhyay, A. (2022). Applying Google earth engine for flood mapping and monitoring in the downstream provinces of Mekong River. Progress in Disaster Science, 14, 100235. https://doi.org/10.1016/j.pdisas.2022.100235
Pandey, A., Kaushik, K., & Parida, B. (2022). Google Earth Engine for Large-Scale Flood Mapping Using SAR Data and Impact Assessment on Agriculture and Population of Ganga-Brahmaputra Basin. Sustainability, 14(7), 4210. https://doi.org/10.3390/su14074210
Paredes-Trejo, F., Alves Barbosa, H., Venkata Lakshmi Kumar, T., Kumar Thakur, M., & de Oliveira Buriti, C. (2021). Assessment of the CHIRPS-Based Satellite Precipitation Estimates. IntechOpen. https://doi.org/10.5772/intechopen.91472
Plank, S. (2014). Rapid Damage Assessment by Means of Multi-Temporal SAR—A Comprehensive Review and Outlook to Sentinel-1. Remote Sensing, 6(6), 4870–4906. https://doi.org/10.3390/rs6064870
Popov, T., Gnjato, S., Trbić, G., & Ivanišević, M. (2017). Trends in extreme daily precipitation indices in Bosnia and Herzegovina. Zbornik radova - Geografski fakultet Univerziteta u Beogradu, 65(1), 5–24. https://doi.org/10.5937/zrgfub1765005P
Prokić, M., Savić, S., & Pavić, D. (2019). Pluvial flooding in Urban Areas Across the European Continent. Geographica Pannonica, 23(4), 216–232. https://doi.org/10.5937/gp23-23508
Raghavan S. (2013). Radar Meteorology. Springer Dordrecht. https://doi.org/10.1007/978-94-017-0201-0
Republika Srpska Institute of Statistics. (2017). Census of population, households and dwellings in Republika Srpska, 2013.
Rincón-Avalos, P., Khouakhi, A., Mendoza-Cano, O., López-De la Cruz, J., & Paredes-Bonilla, K. M. (2022). Evaluation of satellite precipitation products over Mexico using Google Earth Engine. Journal of Hydroinformatics, 24(4), 711–729. https://doi.org/10.2166/hydro.2022.122
Sabljić, L., & Bajić, D. (2021). Mapping of flooded areas using remote sensing on the example of the Sana river. Гласник/Herald, 25, 109–120. https://doi.org/10.7251/HER2125109S
Sarkadi, N., Pirkhoffer, E., Lóczy, D., Balatonyi, L. B., Geresdi, I., Fábián, S. Á., Varga, G., Balogh, R., Gradwohl Valkay, A., Halmai, Á., & Czigány, S. (2022). Generation of a flood susceptibility map of evenly weighted conditioning factors for Hungary. Geographica Pannonica, 26(3), 200-214. https://doi.org/10.5937/gp26-38969
Samuele, P., Filippo, S., & Enrico, B-M. (2021). Multi-temporal mapping of flood damage to crops using sentinel-1 imagery: a case study of the Sesia River (October 2020). Remote Sensing Letters, 12(5), 459–469. https://doi.org/10.1080/2150704X.2021.1890262
Tabari, H. (2020). Climate change impact on flood and extreme precipitation increases with water availability. Scientific Reports, 10, 13768. https://doi.org/10.1038/s41598-020-70816-2
Tran, K. H., Menenti, M., & Jia, L. (2022). Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold. Remote Sensing, 14(22), 5721. https://doi.org/10.3390/rs14225721
Vanama, V., Mandal, D., & Rao, Y. (2020). GEE4FLOOD: rapid mapping of flood areas using temporal Sentinel-1 SAR images with Google Earth Engine cloud platform. Journal of Applied Remote Sensing, 14(3), 034505. https://doi.org/10.1117/1.JRS.14.034505
Ward, P. J., Jongman, B., Weiland, F., Bouwman, A., van Beek, R., Bierkens, M., Ligtvoet, W., & Winsemius, H. (2013). Assessing flood risk at the global scale: Model setup, results, and sensitivity. Environmental Research Letters, 8, 044019. https://doi.org/10.1088/1748-9326/8/4/044019
Wu, W., Li, Y., Luo, X., Zhang, Y., Ji, X., & Li, X. (2019). Performance evaluation of the CHIRPS precipitation dataset and its utility in drought monitoring over Yunnan Province, China. Geomatics, Natural Hazards and Risk, 10(1), 2145–2162. https://doi.org/10.1080/19475705.2019.1683082
Zhang, Y., Wu, C., Yeh, P. J., Li, J., Hu, B., Feng, P., & Jun, C. (2022). Evaluation and comparison of precipitation estimates and hydrologic utility of CHIRPS, TRMM 3B42 V7 and PERSIANN-CDR products in various climate regimes. Atmospheric Research, 265, 105881. https://doi.org/10.1016/j.atmosres.2021.105881