Remote Sensing and GIS Assessment of Flood Zones: A Case Study of the Bhima River Basin
Keywords:
GIS, flood hazard, flood zone, mapping, remote sensing, weighted overlay analysisAbstract
Assessing flood zones represent a paramount challenge in hydrology due to its critical nature. This task is crucial as it plays a pivotal role in mitigating economic and human losses. Real-time flood forecasting is a highly effective non-structural approach in flood management. However, issuing flood warnings to communities involves substantial uncertainty. Throughout history, this uncertainty has predominantly stemmed from the data and models utilized for flood forecasting. Floodplain management employs applied methods where river engineering plays a crucial role in implementing river training practices. Simplifying the complex hydraulic behavior of rivers is essential for this purpose. This study demonstrates the application of these technologies in a flood forecasting context and explores strategies for effectively communicating uncertainties to the public. Modern GIS technology has emerged as a potent instrument for delineating flood risk zones, crucial for planning and managing natural hazards. The Bhīma river basin, a significant hydrological region in India, served as the focal area for this research. Satellite imagery, along with base maps encompassing river basins. The definitive flood zone map is divided into four categories: high, medium, low, and very low. The main conclusion of this study reveals that the upper and central sections of the Bhīma river basin are exposed to considerable too high and medium flood risk. This research intends to assist authorities in formulating development strategies to recognize risks in the Bhīma river basin area.
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R. S. Ajin, R. R. Krishnamurthy, M Jayaprakash, and P Vinod, “Flood hazard assessment of Vamanapuram River Basin, Kerala, India: An approach using Remote Sensing & GIS techniques,” Advances in Applied Science Research, vol. 4, no. 3, pp. 263–274, 2013.
C. P. Leahy, S. Srikanthan, G. Amirthanathan, and Soori, “Objective assessment and communication of uncertainty in flood warnings,” Hydrology unit, Australia in 5th flood management conference Warrnambool, pp. 9–12, Oct. 2007.
H. Chen, Y. Ito, M. Sawamukai, and T. Tokunaga, “Flood hazard assessment in the Kujukuri Plain of Chiba Prefecture, Japan, based on GIS and multicriteria decision analysis,” Natural Hazards, vol. 78, no. 1, pp. 105–120, Aug. 2015, doi: 10.1007/s11069-015-1699-5.
D. N. Kumar and T. V. Reshmidevi, “Remote sensing applications in water resources,” Journal of the Indian Institute of Science, vol. 93, no. 2, pp. 163–188, 2014.
ERDAS Spatial Modeler Language Reference Manual, 2015.
F. Silvestro and N. Rebora, “Impact of precipitation forecast uncertainties and initial soil moisture conditions on a probabilistic on a flood forecasting chain,” Journal of Hydrology, vol. 2014, pp. 1052–1067, 2014.
Govt. of India, Central Water Commission, Flood Forecast Monitoring Directorate, “Flood forecasting and warning network performance appraisal report 2011,” 2012.
G. Joshi, S. Rana, and N. J. Shrimali, “Peak flood probability analysis for Tapi river,” Indian Journal of Applied Research, vol. 4, no. 1, Jan. 2014.
G. A. Schultz, “Use of remote sensing data in a GIS environment for water resources management,” Remote Sensing and Geographic Information Systems for Design and Operation of Water Resources Systems, vol. 242, Apr. 1997.
H. Pachri, Y. Mitani, H. Ikemi, I. Djamaiuddin, and A. Morita, “Development of water management modeling by using GIS in Chirchik river basin, Uzbekistan,” Procedia Earth and Planetary Science, vol. 6, pp. 169–176, 2013.
H. Ji, W. Songlin, W. Qinglin, and C. Xiaonan, “Douhe reservoir flood forecasting model based on data mining technology,” Procedia Environmental Sciences, vol. 12 Part A, pp. 93–98, 2012.
M. S. Tehrany, B. Pradhan, S. Mansor, and N. Ahmad, “Flood susceptibility assessment using GIS-based support vector machine model with different kernel types,” CATENA, vol. 125, pp. 91–101, Feb. 2015.
K. Solaimani, “Flood forecasting based on geographical information system,” African Journal of Agricultural Research, vol. 4, pp. 950–956, Nov. 2009.
K. H. V. D. Rao, V. V. Rao, V. K. Dadhwal, G. Behera, and J. R. Sharma, “A distributed model for real-time flood forecasting in the Godavari Basin using space inputs,” International Journal of Disaster Risk Science, vol. 2, no. 3, pp. 31–40, 2011.
L. du Plessis, “A review of effective flood forecasting, warning and response system for application in South Africa,” Water SA, vol. 28, no. 2, pp. 129–138, Apr. 2002.
M. Borga, E. N. Anagnostou, G. Blöschl, and J.-D. Creutin, “Flash flood forecasting, warning and risk management: the HYDRATE project,” Environmental Science & Policy, vol. 14, no. 7, pp. 834–844, Nov. 2011.
Coherent Digital (Firm), Options for Improving Flood Forecasting in Alberta. Canadian Electronic Library, 2014.
P. V. Timbadiya, P. L. Patel, and P. D. Porey, “One-Dimensional hydrodynamic modelling of flooding and stage hydrographs in the lower Tapi river in India,” Current Science, vol. 106, no. 5, pp. 708–716, 2014.
P. Supriya, M. Krishnaveni, and M. Subbulakshmi, “Regression analysis of annual maximum daily rainfall and stream flow for flood forecasting in Vellar river basin,” Aquatic Procedia, vol. 4, pp. 957–963, 2015.
P. Bidkar, M. Unde, and N. Telore, “Evaluation of morphometric analysis of Bhima river basin, Maharashtra using geospatial techniques,” in Advances In Geographical Research, 1st ed., Jyotikiran, 2020, pp. 45–55.
D. Patel, P. Srivastava, S. K. Singh, C. Prieto, M. Bray, and D. Han, “Flood hydrodynamic modelling of river discharge carrying capacity - A case study of the Tapi river 2006 flood in Surat city of India,” in 20th EGU General Assembly, Vienna, Austria, 2018, p. 1406.
L. B. L. Santos et al., “An RS-GIS-based comprehensive impact assessment of floods—A case study in Madeira river, Western Brazilian Amazon,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 9, pp. 1614–1617, Sep. 2017.
S. H. Elsafi, “Artificial Neural Networks (ANNs) for flood forecasting at Dongola Station in the River Nile, Sudan,” Alexandria Engineering Journal, vol. 53, no. 3, pp. 655–662, Sep. 2014.
S. R. Warghat, S. Das, A. Doad, S. Mali, and V. Moon, “Flood vulnerability analysis of the part of Karad region, Satara district, Maharashtra using remote sensing and geographic information system technique,” International Journal of Advancements in Research & Technology, vol. 1, no. 2, pp. 140–146, Jul. 2012.
S. Bera and A. Bhandari, “Assessment of flood hazard zone using remote sensing & GIS – A case study of Subarnarekha river basin,” International Journal of Science and Research (IJSR), vol. 5, no. 9, pp. 1607–1612, 2016.
S. K. Jain et al., “A Brief review of flood forecasting techniques and their applications,” International Journal of River Basin Management, vol. 16, no. 3, pp. 329–344, Jul. 2018.
T. Alharbi, “A weighted overlay analysis for assessing urban flood risks in arid lands: A case study of Riyadh, Saudi Arabia,” Water, vol. 16, no. 3, p. 397, Jan. 2024.
V. Demir and O. Kisi, “Flood hazard mapping by using geographic information system and hydraulic model: Mert river, Samsun, Turkey,” Advances in Meteorology, vol. 2016, pp. 1–9, 2016.
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