Articles | Volume 29, issue 4
https://doi.org/10.5194/hess-29-947-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-947-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
INSPIRE game: integration of vulnerability into impact-based forecasting of urban floods
Akshay Singhal
Indian Institute of Science Education and Research Bhopal, Madhya Pradesh, India
Univ. Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Louise Crochemore
Univ. Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Isabelle Ruin
Univ. Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Sanjeev K. Jha
CORRESPONDING AUTHOR
Indian Institute of Science Education and Research Bhopal, Madhya Pradesh, India
Related authors
Nibedita Samal, Meenakshi KV, Akshay Singhal, Sanjeev Kumar Jha, and Fabio Oriani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-155, https://doi.org/10.5194/hess-2024-155, 2024
Revised manuscript not accepted
Short summary
Short summary
A statistical approach is developed for the first time to generate hourly rainfall from daily records at multiple avalanche sites of mountainous terrain. The approach reproduces complex temporal patterns of past rainfall information in the future by using auxiliary information from nearby sites. The high temporal resolution data produced is reliable and also produces extreme rainfall patterns well. The hourly precipitation data can be used for better prediction of avalanches and landslides.
Nibedita Samal, Meenakshi KV, Akshay Singhal, Sanjeev Kumar Jha, and Fabio Oriani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-155, https://doi.org/10.5194/hess-2024-155, 2024
Revised manuscript not accepted
Short summary
Short summary
A statistical approach is developed for the first time to generate hourly rainfall from daily records at multiple avalanche sites of mountainous terrain. The approach reproduces complex temporal patterns of past rainfall information in the future by using auxiliary information from nearby sites. The high temporal resolution data produced is reliable and also produces extreme rainfall patterns well. The hourly precipitation data can be used for better prediction of avalanches and landslides.
Samuel Morin, Hugues François, Marion Réveillet, Eric Sauquet, Louise Crochemore, Flora Branger, Étienne Leblois, and Marie Dumont
Hydrol. Earth Syst. Sci., 27, 4257–4277, https://doi.org/10.5194/hess-27-4257-2023, https://doi.org/10.5194/hess-27-4257-2023, 2023
Short summary
Short summary
Ski resorts are a key socio-economic asset of several mountain areas. Grooming and snowmaking are routinely used to manage the snow cover on ski pistes, but despite vivid debate, little is known about their impact on water resources downstream. This study quantifies, for the pilot ski resort La Plagne in the French Alps, the impact of grooming and snowmaking on downstream river flow. Hydrological impacts are mostly apparent at the seasonal scale and rather neutral on the annual scale.
Alban de Lavenne, Vazken Andréassian, Louise Crochemore, Göran Lindström, and Berit Arheimer
Hydrol. Earth Syst. Sci., 26, 2715–2732, https://doi.org/10.5194/hess-26-2715-2022, https://doi.org/10.5194/hess-26-2715-2022, 2022
Short summary
Short summary
A watershed remembers the past to some extent, and this memory influences its behavior. This memory is defined by the ability to store past rainfall for several years. By releasing this water into the river or the atmosphere, it tends to forget. We describe how this memory fades over time in France and Sweden. A few watersheds show a multi-year memory. It increases with the influence of groundwater or dry conditions. After 3 or 4 years, they behave independently of the past.
Eva Boisson, Bruno Wilhelm, Emmanuel Garnier, Alain Mélo, Sandrine Anquetin, and Isabelle Ruin
Nat. Hazards Earth Syst. Sci., 22, 831–847, https://doi.org/10.5194/nhess-22-831-2022, https://doi.org/10.5194/nhess-22-831-2022, 2022
Short summary
Short summary
We present the database of Historical Impacts of Floods in the Arve Valley (HIFAVa). It reports flood occurrences and impacts (1850–2015) in a French Alpine catchment. Our results show an increasing occurrence of impacts from 1920 onwards, which is more likely related to indirect source effects and/or increasing exposure rather than hydrological changes. The analysis reveals that small mountain streams caused more impacts (67 %) than the main river.
Cited articles
Ahlgrimm, M., Forbes, R. M., Morcrette, J.-J., and Neggers, R. A. J.: ARM's Impact on Numerical Weather Prediction at ECMWF, Meteor. Mon., 57, 28.1–28.13, https://doi.org/10.1175/amsmonographs-d-15-0032.1, 2016. a
Arnal, L., Ramos, M.-H., Coughlan de Perez, E., Cloke, H. L., Stephens, E., Wetterhall, F., van Andel, S. J., and Pappenberger, F.: Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game, Hydrol. Earth Syst. Sci., 20, 3109–3128, https://doi.org/10.5194/hess-20-3109-2016, 2016. a, b, c
Aubert, A. H., Bauer, R., and Lienert, J.: A review of water-related serious games to specify use in environmental Multi-Criteria Decision Analysis, Environ. Modell. Softw., 105, 64–78, https://doi.org/10.1016/j.envsoft.2018.03.023, 2018. a
Aubert, A. H., Medema, W., and Wals, A. E.: Towards a Framework for Designing and Assessing Game-Based Approaches for Sustainable Water Governance, Water, 11, 869, https://doi.org/10.3390/W11040869, 2019. a, b
Balaganesh, G., Malhotra, R., Sendhil, R., Sirohi, S., Maiti, S., Ponnusamy, K., and Sharma, A. K.: Development of composite vulnerability index and district level mapping of climate change induced drought in Tamil Nadu, India, Ecol. Indic., 113, 106197, https://doi.org/10.1016/j.ecolind.2020.106197, 2020. a
Ballard, S. P., Li, Z., Simonin, D., and Caron, J. F.: Performance of 4D-Var NWP-based nowcasting of precipitation at the Met Office for summer 2012, Q. J. Roy. Meteor. Soc., 142, 472–487, https://doi.org/10.1002/QJ.2665, 2016. a
Bohra, A. K., Basu, S., Rajagopal, E. N., Iyengar, G. R., Das Gupta, M., Ashrit, R., and Athiyaman, B.: Heavy rainfall episode over Mumbai on 26 July 2005: Assessment of NWP guidance, Curr. Sci. India, 90, 1188–1194, 2006. a
Coughlan de Perez, E., van den Hurk, B., van Aalst, M. K., Jongman, B., Klose, T., and Suarez, P.: Forecast-based financing: an approach for catalyzing humanitarian action based on extreme weather and climate forecasts, Nat. Hazards Earth Syst. Sci., 15, 895–904, https://doi.org/10.5194/nhess-15-895-2015, 2015. a
Craven, J., Angarita, H., Corzo Perez, G. A., and Vasquez, D.: Development and testing of a river basin management simulation game for integrated management of the Magdalena-Cauca river basin, Environ. Modell. Softw., 90, 78–88, https://doi.org/10.1016/j.envsoft.2017.01.002, 2017. a
Crochemore, L., Ramos, M. H., Pappppenberger, F., Van Andel, S. J., and Wood, A. W.: An Experiment on Risk-Based Decision-Making in Water Management Using Monthly Probabilistic Forecasts, B. Am. Meteorol. Soc., 97, 541–551, https://doi.org/10.1175/BAMS-D-14-00270.1, 2016. a, b
Crochemore, L., Cantone, C., Pechlivanidis, I. G., and Photiadou, C. S.: How Does Seasonal Forecast Performance Influence Decision-Making? Insights from a Serious Game, B. Am. Meteorol. Soc., 102, E1682–E1699, https://doi.org/10.1175/BAMS-D-20-0169.1, 2021. a
Flood, S., Cradock-Henry, N. A., Blackett, P., and Edwards, P.: Adaptive and interactive climate futures: systematic review of “serious games” for engagement and decision-making, Environ. Res. Lett., 13, 063005, https://doi.org/10.1088/1748-9326/AAC1C6, 2018. a
Forrest, S. A., Kubíková, M., and Macháč, J.: Serious gaming in flood risk management, WIRes Water, 9, e1589, https://doi.org/10.1002/WAT2.1589, 2022. a
Gallopín, G. C.: Linkages between vulnerability, resilience, and adaptive capacity, Global Environ. Chang., 16, 293–303, https://doi.org/10.1016/j.gloenvcha.2006.02.004, 2006. a
Geurts, J. L., Duke, R. D., and Vermeulen, P. A.: Policy Gaming for Strategy and Change, Long Range Plann., 40, 535–558, https://doi.org/10.1016/J.LRP.2007.07.004, 2007. a
Government of Maharashtra: Report of the Fact-Finding Committee (FFC) on Mumbai Flood, government report, Government of Maharashtra, vol. 1, 2006. a
Guido, Z., McMahan, B., Hoy, D., Larsen, C., Delgado, B., Granillo, R. L., and Crimmins, M.: Public Engagement on Weather and Climate with a Monsoon Fantasy Forecasting Game, B. Am. Meteorol. Soc., 104, E249–E256, https://doi.org/10.1175/BAMS-D-22-0003.1, 2023. a
Gupta, K.: Urban flood resilience planning and management and lessons for the future: a case study of Mumbai, India, Urban Water J., 4, 183–194, https://doi.org/10.1080/15730620701464141, 2007. a
Hemingway, R. and Robbins, J.: Developing a hazard-impact model to support impact-based forecasts and warnings: The Vehicle OverTurning (VOT) Model, Meteorol. Appl., 27, e1819, https://doi.org/10.1002/MET.1819, 2020. a
Jenamani, R. K., Bhan, S. C., and Kalsi, S. R.: Observational/forecasting aspects of the meteorological event that caused a record highest rainfall in Mumbai, Current Science, 90, 1344–1362, 2006. a
Kaltenberger, R., Schaffhauser, A., and Staudinger, M.: “What the weather will do” – results of a survey on impact-oriented and impact-based warnings in European NMHSs, Adv. Sci. Res., 17, 29–38, https://doi.org/10.5194/asr-17-29-2020, 2020. a
Kim, H. B., Choi, S., Kim, B., and Pop-Eleches, C.: The role of education interventions in improving economic rationality, Science, 362, 83–86, https://doi.org/10.1126/science.aar6987, 2018. a
Kirkwood, C., Economou, T., Odbert, H., and Pugeault, N.: A framework for probabilistic weather forecast post-processing across models and lead times using machine learning, Philos. T. Roy. Soc. A, 379, 20200099, https://doi.org/10.1098/rsta.2020.0099, 2021. a
Kox, T., Lüder, C., and Gerhold, L.: Anticipation and Response: Emergency Services in Severe Weather Situations in Germany, Int. J. Disast. Risk Sc., 9, 116–128, https://doi.org/10.1007/S13753-018-0163-Z, 2018. a
Lala, J., Bazo, J., Anand, V., and Block, P.: Optimizing forecast-based actions for extreme rainfall events, Climate Risk Management, 34, 100374, https://doi.org/10.1016/J.CRM.2021.100374, 2021. a
Mayer, I. S.: The Gaming of Policy and the Politics of Gaming: A Review, Simulation and Gaming, 40, 825–862, https://doi.org/10.1177/1046878109346456, 2009. a
MCGM: Flood Preparedness Guidelines, Tech. rep., https://dm.mcgm.gov.in/flood-preparedness-guidelines (last access: 10 October 2024), 2022. a
Misra, S., Roberts, P., and Rhodes, M.: Information overload, stress, and emergency managerial thinking, Int. J. Disast. Risk Re., 51, 101762, https://doi.org/10.1016/j.ijdrr.2020.101762, 2020. a
Murthy, C. S., Laxman, B., and Sesha Sai, M. V.: Geospatial analysis of agricultural drought vulnerability using a composite index based on exposure, sensitivity and adaptive capacity, Int. J. Disast. Risk Re., 12, 163–171, https://doi.org/10.1016/j.ijdrr.2015.01.004, 2015. a, b
Næss, L. O., Norland, I. T., Lafferty, W. M., and Aall, C.: Data and processes linking vulnerability assessment to adaptation decision-making on climate change in Norway, Global Environ. Chang., 16, 221–233, https://doi.org/10.1016/j.gloenvcha.2006.01.007, 2006. a
Nanditha, J. S. and Mishra, V.: On the need of ensemble flood forecast in India, Water Security, 12, 100086, https://doi.org/10.1016/j.wasec.2021.100086, 2021. a
Omerkhil, N., Chand, T., Valente, D., Alatalo, J. M., and Pandey, R.: Climate change vulnerability and adaptation strategies for smallholder farmers in Yangi Qala District, Takhar, Afghanistan, Ecol. Indic., 110, 105863, https://doi.org/10.1016/j.ecolind.2019.105863, 2020. a
Papagiannaki, K., Lagouvardos, K., Kotroni, V., and Bezes, A.: Flash flood occurrence and relation to the rainfall hazard in a highly urbanized area, Nat. Hazards Earth Syst. Sci., 15, 1859–1871, https://doi.org/10.5194/nhess-15-1859-2015, 2015. a
Parker, L., Bourgoin, C., Martinez-Valle, A., and Läderach, P.: Vulnerability of the agricultural sector to climate change: The development of a pan-tropical Climate Risk Vulnerability Assessment to inform sub-national decision making, PLOS ONE, 14, e0213641, https://doi.org/10.1371/journal.pone.0213641, 2019. a
Poletti, M. L., Silvestro, F., Davolio, S., Pignone, F., and Rebora, N.: Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts, Hydrol. Earth Syst. Sci., 23, 3823–3841, https://doi.org/10.5194/hess-23-3823-2019, 2019. a
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., Prudden, R., Mandhane, A., Clark, A., Brock, A., Simonyan, K., Hadsell, R., Robinson, N., Clancy, E., Arribas, A., and Mohamed, S.: Skilful precipitation nowcasting using deep generative models of radar, Nature, 597, 672–677, https://doi.org/10.1038/s41586-021-03854-z, 2021. a
Robbins, J. C. and Titley, H. A.: Evaluating high-impact precipitation forecasts from the Met Office Global Hazard Map (GHM) using a global impact database, Meteorol. Appl., 25, 548–560, https://doi.org/10.1002/met.1720, 2018. a
Rumore, D., Schenk, T., and Susskind, L.: Role-play simulations for climate change adaptation education and engagement, Nat. Clim. Change, 6, 745–750, https://doi.org/10.1038/nclimate3084, 2016. a
Rusca, M., Heun, J., and Schwartz, K.: Water management simulation games and the construction of knowledge, Hydrol. Earth Syst. Sci., 16, 2749–2757, https://doi.org/10.5194/hess-16-2749-2012, 2012. a
Samal, N., Ashwin, R., Singhal, A., Jha, S. K., and Robertson, D. E.: Using a Bayesian joint probability approach to improve the skill of medium-range forecasts of the Indian summer monsoon rainfall, Journal of Hydrology: Regional Studies, 45, 101284, https://doi.org/10.1016/j.ejrh.2022.101284, 2023. a
Sermet, Y., Demir, I., and Muste, M.: A serious gaming framework for decision support on hydrological hazards, Sci. Total Environ., 728, 138895, https://doi.org/10.1016/j.scitotenv.2020.138895, 2020. a
Singhal, A. and Jha, S. K.: Can the approach of vulnerability assessment facilitate identification of suitable adaptation models for risk reduction?, Int. J. Disast. Risk Re., 63, 102469, https://doi.org/10.1016/j.ijdrr.2021.102469, 2021. a, b
Singhal, A., Raman, A., and Jha, S. K.: Potential Use of Extreme Rainfall Forecast and Socio-Economic Data for Impact-Based Forecasting at the District Level in Northern India, Front. Earth Sci., 10, 846113, https://doi.org/10.3389/feart.2022.846113, 2022. a
Singhal, A., Cheriyamparambil, A., Samal, N., and Jha, S. K.: Relating forecast and satellite precipitation to generate future skillful ensemble forecasts over the northwest Himalayas at major avalanche and glacier sites, J. Hydrol., 616, 128795, https://doi.org/10.1016/j.jhydrol.2022.128795, 2023. a
Terti, G., Ruin, I., Kalas, M., Láng, I., Cangròs i Alonso, A., Sabbatini, T., and Lorini, V.: ANYCaRE: a role-playing game to investigate crisis decision-making and communication challenges in weather-related hazards, Nat. Hazards Earth Syst. Sci., 19, 507–533, https://doi.org/10.5194/nhess-19-507-2019, 2019. a, b, c
van den Homberg, M., Monné, R., and Spruit, M.: Bridging the information gap of disaster responders by optimizing data selection using cost and quality, Comput. Geosci., 120, 60–72, https://doi.org/10.1016/j.cageo.2018.06.002, 2018. a
Weis, S. W. M., Agostini, V. N., Roth, L. M., Gilmer, B., Schill, S. R., Knowles, J. E., Blyther, R., Margles Weis, S. W., and Org, S. W.: Assessing vulnerability: an integrated approach for mapping adaptive capacity, sensitivity, and exposure, Climatic Change, 136, 615–629, https://doi.org/10.1007/s10584-016-1642-0, 2016. a
Short summary
A serious game experiment is presented which assesses the interplay between hazard, exposure, and vulnerability in a flash flood event. The results show that participants' use of information to make decisions was based on the severity of the situation. Participants used precipitation forecast and exposure to make correct decisions in the first round, while they used precipitation forecast and vulnerability information in the second round.
A serious game experiment is presented which assesses the interplay between hazard, exposure,...