Need for judicious selection of runoff inputs in a global flood model
Numerous flood hazard assessment and risk management studies depend on hydrodynamic flood models, which require detailed inputs. However, these models face challenges when assessing flood hazards and risks at national scales due to the unavailability of input data and high computational demands. Rec...
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IOP Publishing
2025-01-01
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| Series: | Environmental Research Letters |
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| Online Access: | https://doi.org/10.1088/1748-9326/adaa89 |
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| author | Jayesh Parmar Mohit Prakash Mohanty Subhankar Karmakar |
| author_facet | Jayesh Parmar Mohit Prakash Mohanty Subhankar Karmakar |
| author_sort | Jayesh Parmar |
| collection | DOAJ |
| description | Numerous flood hazard assessment and risk management studies depend on hydrodynamic flood models, which require detailed inputs. However, these models face challenges when assessing flood hazards and risks at national scales due to the unavailability of input data and high computational demands. Recent advancements in global flood models (GFMs) have emerged as promising solutions. These widely adopted GFMs, capable of producing flood characteristics, require runoff input typically derived from land surface models (LSMs) or global hydrological models (GHMs), which are prone to inherit cascading uncertainties. Moreover, the utilization of a single runoff input into a GFM can produce biased and misinterpreted flood hazards due to underestimation or overestimation of GFM outputs. To highlight these implications, the present study examines GFM simulations forced with eight state-of-the-art model runoff datasets, including LSMs, GHMs, and reanalysis observations, uncovering unsafe inter-model flood depth variation (IMDV). Focusing on the flood-prone Mahanadi River Basin (MRB) of India, the study observes that IMDV surpasses the self-help range of humans (0.2 m) for 65% of the MRB region, and exceeds human and vehicle safety thresholds (2 m) for 15% of the region, based on four past flood events from the Dartmouth Flood Observatory. These regions exhibiting high IMDV overlap with densely populated areas, potentially affecting 1.66–3.65 million people. Thus, the injudicious use of runoff in GFM for flood disaster planning can lead to inaccurate flood hazard and risk assessments, significantly affecting populous regions. An alternative approach is recommended, advocating for the use of multiple simulations incorporating diverse runoff datasets. This approach would generate conservative and optimistic flood scenarios, leveraging each model’s strengths. Such comprehensive hazard scenarios would enhance flood management and decision-making for policymakers by addressing the uncertainty and providing possible impacts through risk assessments. |
| format | Article |
| id | doaj-art-59563212aed4441aba58138b6f81d245 |
| institution | Kabale University |
| issn | 1748-9326 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj-art-59563212aed4441aba58138b6f81d2452025-01-24T12:15:50ZengIOP PublishingEnvironmental Research Letters1748-93262025-01-0120202403210.1088/1748-9326/adaa89Need for judicious selection of runoff inputs in a global flood modelJayesh Parmar0https://orcid.org/0000-0001-6992-3549Mohit Prakash Mohanty1https://orcid.org/0000-0001-7783-2073Subhankar Karmakar2https://orcid.org/0000-0002-1132-1403Environmental Science and Engineering Department, Indian Institute of Technology Bombay , Mumbai 400076, IndiaDepartment of Water Resources Development and Management, Indian Institute of Technology Roorkee , Roorkee 247667, IndiaEnvironmental Science and Engineering Department, Indian Institute of Technology Bombay , Mumbai 400076, India; Centre for Climate Studies, Indian Institute of Technology Bombay , Mumbai 400076, IndiaNumerous flood hazard assessment and risk management studies depend on hydrodynamic flood models, which require detailed inputs. However, these models face challenges when assessing flood hazards and risks at national scales due to the unavailability of input data and high computational demands. Recent advancements in global flood models (GFMs) have emerged as promising solutions. These widely adopted GFMs, capable of producing flood characteristics, require runoff input typically derived from land surface models (LSMs) or global hydrological models (GHMs), which are prone to inherit cascading uncertainties. Moreover, the utilization of a single runoff input into a GFM can produce biased and misinterpreted flood hazards due to underestimation or overestimation of GFM outputs. To highlight these implications, the present study examines GFM simulations forced with eight state-of-the-art model runoff datasets, including LSMs, GHMs, and reanalysis observations, uncovering unsafe inter-model flood depth variation (IMDV). Focusing on the flood-prone Mahanadi River Basin (MRB) of India, the study observes that IMDV surpasses the self-help range of humans (0.2 m) for 65% of the MRB region, and exceeds human and vehicle safety thresholds (2 m) for 15% of the region, based on four past flood events from the Dartmouth Flood Observatory. These regions exhibiting high IMDV overlap with densely populated areas, potentially affecting 1.66–3.65 million people. Thus, the injudicious use of runoff in GFM for flood disaster planning can lead to inaccurate flood hazard and risk assessments, significantly affecting populous regions. An alternative approach is recommended, advocating for the use of multiple simulations incorporating diverse runoff datasets. This approach would generate conservative and optimistic flood scenarios, leveraging each model’s strengths. Such comprehensive hazard scenarios would enhance flood management and decision-making for policymakers by addressing the uncertainty and providing possible impacts through risk assessments.https://doi.org/10.1088/1748-9326/adaa89cascading uncertaintydisaster riskfloods hazardglobal flood modelsland surface modelsinter-model flood depth variation |
| spellingShingle | Jayesh Parmar Mohit Prakash Mohanty Subhankar Karmakar Need for judicious selection of runoff inputs in a global flood model Environmental Research Letters cascading uncertainty disaster risk floods hazard global flood models land surface models inter-model flood depth variation |
| title | Need for judicious selection of runoff inputs in a global flood model |
| title_full | Need for judicious selection of runoff inputs in a global flood model |
| title_fullStr | Need for judicious selection of runoff inputs in a global flood model |
| title_full_unstemmed | Need for judicious selection of runoff inputs in a global flood model |
| title_short | Need for judicious selection of runoff inputs in a global flood model |
| title_sort | need for judicious selection of runoff inputs in a global flood model |
| topic | cascading uncertainty disaster risk floods hazard global flood models land surface models inter-model flood depth variation |
| url | https://doi.org/10.1088/1748-9326/adaa89 |
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