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• Published: 03 August 2022

The challenge of unprecedented floods and droughts in risk management

• Heidi Kreibich   ORCID: orcid.org/0000-0001-6274-3625 1 ,
• Anne F. Van Loon   ORCID: orcid.org/0000-0003-2308-0392 2 ,
• Kai Schröter   ORCID: orcid.org/0000-0002-3173-7019 1 , 3 ,
• Philip J. Ward   ORCID: orcid.org/0000-0001-7702-7859 2 ,
• Maurizio Mazzoleni   ORCID: orcid.org/0000-0002-0913-9370 2 ,
• Nivedita Sairam   ORCID: orcid.org/0000-0003-4611-9894 1 ,
• Guta Wakbulcho Abeshu   ORCID: orcid.org/0000-0001-8775-3678 4 ,
• Svetlana Agafonova   ORCID: orcid.org/0000-0002-6392-1662 5 ,
• Amir AghaKouchak   ORCID: orcid.org/0000-0003-4689-8357 6 ,
• Hafzullah Aksoy   ORCID: orcid.org/0000-0001-5807-5660 7 ,
• Camila Alvarez-Garreton   ORCID: orcid.org/0000-0002-5381-4863 8 , 9 ,
• Blanca Aznar   ORCID: orcid.org/0000-0001-6952-0790 10 ,
• Laila Balkhi   ORCID: orcid.org/0000-0001-8224-3556 11 ,
• Marlies H. Barendrecht   ORCID: orcid.org/0000-0002-3825-0123 2 ,
• Sylvain Biancamaria   ORCID: orcid.org/0000-0002-6162-0436 12 ,
• Liduin Bos-Burgering   ORCID: orcid.org/0000-0002-8372-4519 13 ,
• Chris Bradley   ORCID: orcid.org/0000-0003-4042-867X 14 ,
• Yus Budiyono   ORCID: orcid.org/0000-0002-6288-6527 15 ,
• Wouter Buytaert   ORCID: orcid.org/0000-0001-6994-4454 16 ,
• Lucinda Capewell 14 ,
• Hayley Carlson 11 ,
• Yonca Cavus   ORCID: orcid.org/0000-0002-0528-284X 17 , 18 , 19 ,
• Anaïs Couasnon   ORCID: orcid.org/0000-0001-9372-841X 2 ,
• Gemma Coxon   ORCID: orcid.org/0000-0002-8837-460X 20 , 21 ,
• Ioannis Daliakopoulos   ORCID: orcid.org/0000-0001-9333-4963 22 ,
• Marleen C. de Ruiter   ORCID: orcid.org/0000-0001-5991-8842 2 ,
• Claire Delus   ORCID: orcid.org/0000-0002-6690-5326 23 ,
• Mathilde Erfurt   ORCID: orcid.org/0000-0003-1389-451X 19 ,
• Giuseppe Esposito   ORCID: orcid.org/0000-0001-5638-657X 24 ,
• Didier François 23 ,
• Frédéric Frappart   ORCID: orcid.org/0000-0002-4661-8274 25 ,
• Jim Freer 20 , 21 , 26 ,
• Natalia Frolova   ORCID: orcid.org/0000-0003-3576-285X 5 ,
• Animesh K. Gain   ORCID: orcid.org/0000-0003-3814-693X 27 , 28 ,
• Manolis Grillakis   ORCID: orcid.org/0000-0002-4228-1803 29 ,
• Jordi Oriol Grima 10 ,
• Diego A. Guzmán 30 ,
• Laurie S. Huning   ORCID: orcid.org/0000-0002-0296-4255 6 , 31 ,
• Monica Ionita   ORCID: orcid.org/0000-0001-8240-4380 32 , 33 , 34 ,
• Maxim Kharlamov   ORCID: orcid.org/0000-0002-4439-5193 5 , 35 ,
• Dao Nguyen Khoi   ORCID: orcid.org/0000-0002-1618-1948 36 ,
• Natalie Kieboom   ORCID: orcid.org/0000-0001-8497-0204 37 ,
• Maria Kireeva   ORCID: orcid.org/0000-0002-8285-9761 5 ,
• Aristeidis Koutroulis   ORCID: orcid.org/0000-0002-2999-7575 38 ,
• Waldo Lavado-Casimiro   ORCID: orcid.org/0000-0002-0051-0743 39 ,
• Hong-Yi Li   ORCID: orcid.org/0000-0002-9807-3851 4 ,
• María Carmen LLasat   ORCID: orcid.org/0000-0001-8720-4193 40 , 41 ,
• David Macdonald   ORCID: orcid.org/0000-0003-3475-636X 42 ,
• Johanna Mård   ORCID: orcid.org/0000-0002-8789-7628 43 , 44 ,
• Hannah Mathew-Richards 37 ,
• Andrew McKenzie   ORCID: orcid.org/0000-0001-8723-4325 42 ,
• Alfonso Mejia   ORCID: orcid.org/0000-0003-3891-1822 45 ,
• Eduardo Mario Mendiondo   ORCID: orcid.org/0000-0003-2319-2773 46 ,
• Marjolein Mens 47 ,
• Shifteh Mobini   ORCID: orcid.org/0000-0002-3365-7346 48 , 49 ,
• Guilherme Samprogna Mohor   ORCID: orcid.org/0000-0003-2348-6181 50 ,
• Viorica Nagavciuc   ORCID: orcid.org/0000-0003-1111-9616 32 , 34 ,
• Thanh Ngo-Duc   ORCID: orcid.org/0000-0003-1444-7498 51 ,
• Thi Thao Nguyen Huynh   ORCID: orcid.org/0000-0001-9071-1225 52 ,
• Pham Thi Thao Nhi   ORCID: orcid.org/0000-0003-4118-8479 36 ,
• Olga Petrucci   ORCID: orcid.org/0000-0001-6918-1135 24 ,
• Hong Quan Nguyen 52 , 53 ,
• Pere Quintana-Seguí   ORCID: orcid.org/0000-0002-7107-9671 54 ,
• Saman Razavi   ORCID: orcid.org/0000-0003-1870-5810 11 , 55 , 56 ,
• Elena Ridolfi   ORCID: orcid.org/0000-0002-4714-2511 57 ,
• Jannik Riegel 58 ,
• Md Shibly Sadik   ORCID: orcid.org/0000-0001-9205-4791 59 ,
• Elisa Savelli   ORCID: orcid.org/0000-0002-8948-0316 43 , 44 ,
• Alexey Sazonov 5 , 35 ,
• Sanjib Sharma   ORCID: orcid.org/0000-0003-2735-1241 60 ,
• Johanna Sörensen   ORCID: orcid.org/0000-0002-2312-4917 49 ,
• Felipe Augusto Arguello Souza   ORCID: orcid.org/0000-0002-2753-9896 46 ,
• Kerstin Stahl   ORCID: orcid.org/0000-0002-2159-9441 19 ,
• Max Steinhausen   ORCID: orcid.org/0000-0002-8692-8824 1 ,
• Michael Stoelzle   ORCID: orcid.org/0000-0003-0021-4351 19 ,
• Wiwiana Szalińska   ORCID: orcid.org/0000-0001-6828-6963 61 ,
• Qiuhong Tang 62 ,
• Fuqiang Tian   ORCID: orcid.org/0000-0001-9414-7019 63 ,
• Tamara Tokarczyk   ORCID: orcid.org/0000-0001-5862-6338 61 ,
• Carolina Tovar   ORCID: orcid.org/0000-0002-8256-9174 64 ,
• Thi Van Thu Tran   ORCID: orcid.org/0000-0003-1187-3520 52 ,
• Marjolein H. J. Van Huijgevoort   ORCID: orcid.org/0000-0002-9781-6852 65 ,
• Michelle T. H. van Vliet   ORCID: orcid.org/0000-0002-2597-8422 66 ,
• Sergiy Vorogushyn   ORCID: orcid.org/0000-0003-4639-7982 1 ,
• Thorsten Wagener   ORCID: orcid.org/0000-0003-3881-5849 21 , 50 , 67 ,
• Yueling Wang 62 ,
• Doris E. Wendt   ORCID: orcid.org/0000-0003-2315-7871 67 ,
• Elliot Wickham 68 ,
• Long Yang   ORCID: orcid.org/0000-0002-1872-0175 69 ,
• Mauricio Zambrano-Bigiarini   ORCID: orcid.org/0000-0002-9536-643X 8 , 9 ,
• Günter Blöschl   ORCID: orcid.org/0000-0003-2227-8225 70 &
• Giuliano Di Baldassarre   ORCID: orcid.org/0000-0002-8180-4996 43 , 44 , 71  

Nature volume  608 ,  pages 80–86 ( 2022 ) Cite this article

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• Natural hazards

Risk management has reduced vulnerability to floods and droughts globally 1 , 2 , yet their impacts are still increasing 3 . An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data 4 , 5 . On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change 3 .

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Observed decreasing trends in the vulnerability to floods and droughts, owing to effective risk management, are encouraging 1 . Globally, human and economic vulnerability dropped by approximately 6.5- and 5-fold, respectively, between the periods 1980–1989 and 2007–2016 (ref.  2 ). However, the impacts of floods and droughts are still severe and increasing in many parts of the world 6 . Climate change will probably lead to a further increase in their impacts owing to projected increases in the frequency and severity of floods and droughts 3 . The economic damage of floods is projected to double globally 7 and that of droughts to triple in Europe 8 , for a mean temperature increase of 2 °C.

The purpose of risk management is to reduce the impact of events through modification of the hazard, exposure and/or vulnerability: according to United Nations (UN) terminology 9 , disaster risk management is the application of disaster risk reduction policies and strategies to prevent new disaster risk, reduce existing disaster risk and manage residual risk, contributing to the strengthening of resilience against, and reduction of, disaster losses. Hazard is a process, phenomenon or human activity that may cause loss of life, injury or other health impacts, property damage, social and economic disruption or environmental degradation; exposure is the situation of people, infrastructure, housing, production capacities and other tangible human assets located in hazard-prone areas; and vulnerability is the conditions determined by physical, social, economic and environmental factors or processes 10 , 11 , 12 , 13 that increase the susceptibility of an individual, a community, assets or systems to the impacts of hazards. To be effective, risk management needs to be based on a sound understanding of these controlling risk drivers 14 , 15 . Past studies have identified increasing exposure as a primary driver of increasing impacts 3 , 4 , and vulnerability reduction has been identified as key for reduction of impacts 16 , 17 . However, ascertaining the combined effect of the drivers and the overall effectiveness of risk management has been hampered by a lack of empirical data 4 , 5 .

Here we analyse a new dataset of 45 pairs of flood or drought events that occurred in the same area on average 16 years apart (hereinafter referred to as paired events). The data comprise 26 flood and 19 drought paired events across different socioeconomic and hydroclimatic contexts from all continents (Fig. 1a ). We analyse floods and droughts together, because of the similarity of some of the management methods (for example, warning systems, water reservoir infrastructure), the potential for trade-offs in risk reduction between floods and droughts and therefore value for the management communities to learn from each other 18 . The impact, quantified by direct (fatalities, monetary damage), indirect (for example, disruption of traffic or tourism) and intangible impacts (for example, impact on human health or cultural heritage), is considered to be controlled by three drivers: hazard, exposure and vulnerability 3 . These drivers are quantified using a large range of different indices—for example, the standardized precipitation index, the number of houses in the affected area and risk awareness, respectively (Supplementary Table 1 ). These three drivers are considered to be exacerbated by management shortcomings. Hazard may be exacerbated by problems with water management infrastructure such as levees or reservoirs 19 . Exposure and vulnerability may be worsened by suboptimal implementation of non-structural measures such as risk-aware regional planning 20 or early warning 21 , respectively. We analyse management shortcomings and their effect on the three drivers explicitly, as this is the point at which improvements can start—for example, by the introduction of better strategies and policies. Data availability understandably varies among the paired events, and this can introduce inconsistency and subjectivity. The analyses are therefore based on indicators of change, to account for differences between paired events in respect of measured variables, data quality and uncertainty. These indicators of change represent the differences between the first event (baseline) and the second, categorized as large decreases/increases (−2/+2), small decreases/increases (−1/+1) and no change (0) (Supplementary Table 2 ). To minimize the subjectivity and uncertainty of indicator assignment, a quality assurance protocol is implemented and indicators of change with sub-indicators are used.

a , Location of flood and drought paired events ( n = 45). Numbers are paired-event IDs. b , Indicators of change, sorted by impact change. Impact is considered to be controlled by hazard, exposure and vulnerability, which are exacerbated by risk management shortcomings. Maps of the paired events coloured according to drivers and management shortcomings are shown in Extended Data Fig. 1 .

Source data

The majority of paired events show decreases in management shortcomings (71% of paired events; Fig. 1b ), which reflects that societies tend to learn from extreme events 22 . Most cases also show a decrease in vulnerability (80% of paired events) as societies typically reduce their vulnerability after the first event of a pair 21 . The five paired events with a large decrease in impact (dark blue, top left in Fig. 1b ) are associated with decreases or no change of all three drivers.

Drivers of changes in impact

Changes in flood impacts are significantly and positively correlated with changes in hazard ( r  = 0.64, P  ≤ 0.01), exposure ( r  = 0.55, P  ≤ 0.01) and vulnerability ( r  = 0.60, P  ≤ 0.01) (Fig. 2a ), which is in line with risk theory 3 . Although a previous analysis of eight case studies 21 identified vulnerability as a key to reduction of flood impacts, this new, more comprehensive, dataset suggests that changes in hazard, exposure and vulnerability are equally important, given that they correlate equally strongly with changes in flood impact. Changes in drought impacts are significantly correlated with changes in hazard and exposure, but not with changes in vulnerability (Fig. 2c ). This suggests that changes in vulnerability have been less successful in reducing drought impact than flood impact, which is also consistent with those event pairs for which only vulnerability changed (Extended Data Table 1 ). However, quantification of the contribution of individual drivers is difficult with this empirical approach because there are only a limited number of cases in which only one driver changed. There are three cases in which only vulnerability changed between events, two cases in which only hazard changed and no case in which only exposure changed (Extended Data Table 1 ). Additionally, paired events without a change in hazard (0) are analysed in more detail to better understand the role of exposure and vulnerability (Extended Data Fig. 2 ). In all these paired events, a reduction in impact was associated with a reduction in vulnerability, highlighting the importance of vulnerability. In five of these eight cases with a decrease in impact there was also a decrease in exposure, whereas in one case (floods in Jakarta, Indonesia in 2002 and 2007 (ID 18)) there was a large increase in exposure. In the paired event of droughts in California, United States (1987–1992 and 2011–2016, ID 36) an increase in exposure and a reduction in vulnerability increased impact, which points to the more important role of exposure in comparison with vulnerability in this drought case (Extended Data Fig. 2 ).

a , c , Correlation matrix of indicators of change for flood ( a ) and drought ( c ) paired events. Colours of squares indicate Spearman’s rank correlation coefficients and their size, the P  value. b , d ,Histograms of indicators of change of flood ( b ) and drought ( d ) stratified by decrease ( n  = 15 and n  = 5 paired events for flood and drought, respectively) and increase ( n  = 5 and n  = 8 paired events, respectively) in impact. The asterisk denotes the success stories of Box 1 ; double asterisks denote pairs for which the second event was much more hazardous than the first (that is, 'unprecedented'). Mgmt shortc, management shortcomings.

Generally the changes in drivers are not significantly correlated with each other, with the exception of hazard and exposure in the case of floods ( r  = 0.55, P  ≤ 0.01) (Fig. 2a ). This finding may be explained by the influence of hazard on the size of the inundation area, and thus on the numbers of people and assets affected, which represent exposure.

The sensitivity analysis suggests that the correlation pattern is robust, as visualized by the colours in Extended Data Fig. 3 . The pattern of P  values is also robust for flood cases, although these become less significant for drought because of the smaller sample size (Extended Data Fig. 3 ).

We split the paired events into groups of decreasing and increasing impact to evaluate their drivers separately (Fig. 2b,d ). Overall, the pattern is similar for floods and droughts. Most flood and drought pairs with decreasing impact show either a decrease in hazard (ten pairs, 50%) or no change (eight pairs, 40%). Exceptions are two flood pairs that are success stories of decreased impact despite an increase in hazard, as detailed in Box 1 . The change in exposure of the pairs with decreased impacts (Fig. 2b,d ) ranges from a large decrease to a large increase, whereas vulnerability always decreased. All cases with a large decrease in vulnerability (−2) are associated with a decrease in impacts. Overall, the pattern suggests that a decrease in impacts is mainly caused by a combination of lower hazard and vulnerability, despite an increase in exposure in 25% of cases.

The role of hazard and vulnerability in impact reduction can be exemplified by the pair of riverine floods in Jakarta, Indonesia (ID 4 in Fig. 1 ). The 2007 event had a flood return period of 50 years, whereas it was 30 years for the 2013 event 23 (that is, the hazard of the second event was smaller). Vulnerability had also decreased as a result of improved preparedness resulting from a flood risk mapping initiative and capacity building programmes implemented after the first flood, to improve citizens' emergency response, as well as by an improvement in official emergency management by establishment of the National Disaster Management Agency in 2008. Additionally, exposure was substantially reduced. Whilst the first flood caused 79 fatalities and direct damage of €1.3 billion, the second event caused 38 fatalities and €0.76 billion of direct damage.

Another example is a pair of Central European droughts (ID 9). During the 2003 event, the minimum 3-month Standardized Precipitation Evapotranspiration Index was −1.62 whereas in 2015 it was −1.18—that is, the hazard of the second event was smaller 24 . The vulnerability was also lower in the second event, because the first event had raised public awareness and triggered an improvement in institutional planning. For instance, the European Commission technical guidance on drought management plans 25 was implemented. Many reservoirs were kept filled until the beginning of summer 2015, which alleviated water shortages for various sectors and, in some cities (for example, Bratislava and Bucharest), water was supplied from tanks 26 . Additionally, water use and abstraction restrictions were implemented for non-priority uses including irrigation 26 . The impact was reduced from €17.1 billion to €2.2 billion, despite an increase in exposure because of the larger drought extent affecting almost all of Europe in 2013.

Most flood and drought pairs with an increase in impact also show a larger hazard (11 cases, 85%; Fig. 2b,d ). For six of these paired events (46%), the second event was much more hazardous than the first (hazard indicator-of-change +2), whereas this was never the case for the pairs with decreasing impact. Of those pairs with an increase in impact, 12 (92%) show an increase in exposure and nine (69%) show a small decrease in vulnerability (vulnerability indicator-of-change −1). Overall, the pattern suggests that the increase in impact is mainly caused by a combination of higher hazard and exposure, which is not compensated by a small decrease in vulnerability.

The role of hazard and exposure in increasing impact is illustrated by a pair of pluvial floods in Corigliano-Rossano City, Calabria, Italy (ID 40). This 2015 event was much more hazardous (+2) than that in 2000, with precipitation return periods of more than 100 and 10–20 years, respectively 27 . Also, the 2000 event occurred during the off-season for tourism in September whereas the exposure was much larger in 2015, because the event occurred in August when many tourists were present. Interruption of the peak holiday season caused severe indirect economic damage. Another example is a pair of droughts (ID 33) affecting North Carolina, United States. Between 2007 and 2009, about 65% of the state was affected by what was classified as an exceptional drought, with a composite drought indicator of the US Drought Monitor of 27 months 28 , whereas between 2000 and 2003 only about 30% of the state was affected by an exceptional drought of 24 months 28 . The crop losses in 2007–2009 were about €535 million, whereas they were €497 million in 2000–2003, even though vulnerability had been reduced due to drought early warning and management by the North Carolina Drought Management Council, established in 2003.

Box 1
 Success stories of decreased impact despite increased hazard

The dataset includes two cases in which a lower impact was achieved despite a larger hazard of the second event, making these interesting success stories (Fig. 3 ). Both cases are flood paired events, but of different types (that is, pluvial and riverine floods (Table 1 )). These cases have in common that institutional changes and improved flood risk management governance were introduced and high investments in integrated management were undertaken, which led to an effective implementation of structural and non-structural measures, such as improved early warning and emergency response to complement structural measures such as levees (Table 1 ).

Effects of changes in management on drivers

The correlations shown in Fig. 2a,c also shed light on how management affects hazard, exposure and vulnerability and thus, indirectly, impact. For flood paired events, changes in management shortcomings are significantly positively correlated with changes in vulnerability ( r  = 0.56, P  ≤ 0.01), and both are significantly positively correlated with changes in impact (Fig. 2a ). For drought, however, these correlations are not significant (Fig. 2c ). Thus, achieving decreases in vulnerability, and consequently in impact, by improving risk management (that is, reducing management shortcomings) seems to be more difficult for droughts than for floods. This difficulty may be related to spillover effects—that is, drought measures designed to reduce impacts in one sector can increase impacts in another. For example, irrigation to alleviate drought in agriculture may increase drought impacts on drinking water supply and ecology 29 .

The paired floods in the Piura region, Peru (ID 13) illustrate how effective management can reduce vulnerability, and consequently impact. At the Piura river, maximum flows of 3,367 and 2,755 m 3  s −1 were recorded during the 1998 and 2017 events, respectively (that is, hazard showed a small decrease (−1)). Around 2000, the national hydrometeorological service started to issue medium-range weather forecasts that allowed preparations months before the 2017 event. In 2011, the National Institute of Civil Defence and the National Centre for the Estimation, Prevention, and Reduction of Disaster Risk were founded which, together with newly established short-range river flow forecasts, allowed more efficient emergency management of the more recent event. Additionally, non-governmental organizations such as Practical Action had implemented disaster risk-reduction activities, including evacuation exercises and awareness campaigns 30 . All of these improvements in management decreased vulnerability. The impact of the second event was smaller, with 366 fatalities in 1998 compared with 159 in 2017, despite an increase in exposure due to urbanization and population increase.

When the hazard of the second event was larger than that of the first (+1, +2), in 11 out of 18 cases (61%) the impact of the second event was also larger, irrespective of small decreases in vulnerability in eight of these cases (light blue dots/triangles in Fig. 3 ). There are only two paired events in our dataset for which a decrease in impact was achieved despite the second event being more hazardous (highlighted by the green circle in Fig. 3 ). These cases are considered success stories and are further discussed in Box 1 . For the two paired events (ID 21 and 30) for which the only driver that changed was hazard (+1), the impacts did not change (0) (Extended Data Table 1 ). Water retention capacity of 189,881,000 m³ and good irrigation infrastructure with sprinkling machines were apparently sufficient to counteract the slight increase in hazard for the drought paired event in Poland in 2006 and 2015 (ID 21). The improved flood alleviation scheme implemented between the paired flood events (2016 and 2018), protected properties in Birmingham, United Kingdom (ID 30). There are, however, seven cases for which the second event was much more hazardous (+2) than the first (highlighted by the purple ellipse in Fig. 3 )—that is, events of a magnitude that locals had probably not previously experienced. We term these events, subjectively, as unprecedented; almost all had an increased impact despite improvements in management.

Categories are: lower hazard and lower impact, ten cases; higher hazard and higher impact, 11 cases; lower hazard and higher impact, one case; higher hazard and lower impact, two cases. Circles and triangles indicate drought and flood paired events, respectively; their colours indicate change in vulnerability. Green circle highlights success stories ( n  = 2) of reduced impact (−1) despite a small increase in hazard (+1). Purple ellipse indicates paired events ( n  = 7) with large increase in hazard (+2)—that is, events that were subjectively unprecedented and probably not previously experienced by local residents.

One unprecedented pluvial flood is the 2014 event in the city of Malmö, Sweden (ID 45). This event was much more hazardous than that experienced a few years before, with precipitation return periods on average of 135 and 24 years, respectively, for 6 h duration 31 . The largest 6 h precipitation measured at one of nine stations during the 2014 event corresponded to a return period of 300 years. The combined sewage system present in the more densely populated areas of the city was overwhelmed, leading to extensive basement flooding in 2014 (ref.  31 ). The direct monetary damage was about €66 million as opposed to €6 million in the first event. An unprecedented drought occurred in the Cape Town metropolitan area of South Africa, in 2015–2018 (ID 44). The drought was much longer (4 years) than that experienced previously in 2003–2004 (2 years). Although the Berg River Dam had been added to the city’s water supply system in 2009, and local authorities had developed various strategies for managing water demands (for example, water restrictions, tariff increases, communication campaign), the second event caused a much higher direct impact of about €180 million 32 because the water reserves were reduced to virtually zero.

Even though it is known that vulnerability reduction plays a key role in reducing risk, our paired-event cases reveal that when the hazard of the second event was higher than the first, a reduction in vulnerability alone was often not sufficient to reduce the impact of the second event to less than that of the first. Our analysis of drivers of impact change reveals the importance of reducing hazard, exposure and vulnerability to achieve an effective impact reduction (Fig. 2 ). Although previous studies have attributed a high priority to vulnerability reduction 17 , 21 , the importance of considering all three drivers identified here may reflect the sometimes limited efficiency of management decisions, resulting in unintended consequences. For example, levee construction aiming at reducing hazards may increase exposure through encouraging settlements in floodplains 33 , 34 . Similarly, construction of reservoirs to abate droughts may enhance exposure through encouraging agricultural development and thus increase water demand 35 , 36 .

Events that are much more hazardous than preceding events (termed unprecedented here) seem to be difficult to manage; in almost all the cases considered they led to increased impact (Fig. 3 ). This finding may be related to two factors. First, large infrastructure such as levees and water reservoirs play an important role in risk management. These structures usually have an upper design limit up to which they are effective but, once a threshold is exceeded, they become ineffective. For example, the unprecedented pluvial flood in 2014 in Malmö, Sweden (ID 45) exceeded the capacity of the sewer system 31 and the unprecedented drought in Cape Town (ID 44) exceeded the storage water capacity 37 . This means that infrastructure is effective in preventing damage during events of a previously experienced magnitude, but often fails for unprecedented events. Non-structural measures, such as risk-aware land-use planning, precautionary measures and early warning, can help mitigate the consequences of water infrastructure failure in such situations 21 , but a residual risk will always remain. Second, risk management is usually implemented after large floods and droughts, whereas proactive strategies are rare. Part of the reason for this behaviour is a cognitive bias associated with the rarity and uniqueness of extremes, and the nature of human risk perception, which makes people attach a large subjective probability to those events they have personally experienced 38 .

On the other hand, two case studies were identified in which impact was reduced despite an increase in hazard (Box 1 ). An analysis of these case studies identifies three success factors: (1) effective governance of risk and emergency management, including transnational collaboration such as in the Danube case; (2) high investments in structural and non-structural measures; and (3) improved early warning and real-time control systems such as in the Barcelona case. We believe there is potential for more universal application of these success factors to counteract the current trend of increasing impacts associated with climate change 3 . These factors may also be effective in the management of unprecedented events, provided they are implemented proactively.

The concept of paired events aims at comparing two events of the same hazard type that occurred in the same area 21 to learn from the differences and similarities. This concept is analogous to paired catchment studies, which compare two neighbouring catchments with different vegetation in terms of their water yield 39 . Our study follows the theoretical risk framework that considers impact as a result of three risk components or drivers 3 : hazard, exposure and vulnerability (Extended Data Fig. 4 ). Hazard reflects the intensity of an event, such as a flooded area or drought deficit—for example, measured by the standardized precipitation index. Exposure reflects the number of people and assets in the area affected by the event. Consequently, the change in exposure between events is influenced by changes in the population density and the assets in the affected area (socioeconomic developments), as well as by changes in the size of the affected area (change of hazard). Vulnerability is a complex concept, with an extensive literature from different disciplines on how to define, measure and quantify it 13 , 40 , 41 , 42 . For instance, Weichselgartner 43 lists more than 20 definitions of vulnerability, and frameworks differ quite substantially—for example, in terms of integration of exposure into vulnerability 11 or separating them 3 . Reviews and attempts to converge on the various vulnerability concepts stress that vulnerability is dynamic and that assessments should be conducted for defined human–environment systems at particular places 12 , 44 , 45 . Every vulnerability analysis requires an approach adapted to its specific objectives and scales 46 . The paired event approach allows detailed context and place-based vulnerability assessments that are presented in the paired event reports, as well as comparisons across paired events based on the indicators-of-change. The selection of sub-indicators for the characterization of vulnerability is undertaken with a particular focus on temporal changes at the same place. All three drivers—hazard, exposure and vulnerability—can be reduced by risk-management measures. Hazard can be reduced by structural measures such as levees or reservoirs 19 , exposure by risk-aware regional planning 20 and vulnerability by non-structural measures, such as early warning 21 .

Our comparative analysis is based on a novel dataset of 45 paired events from around the world, of which 26 event pairs are floods and 19 are droughts. The events occurred between 1947 and 2019, and the average period between the two events of a pair is 16 years. The number of paired events is sufficiently large to cover a broad range of hydroclimatic and socioeconomic settings around the world and allows differentiated, context-specific assessments on the basis of detailed in situ observations. Flood events include riverine, pluvial, groundwater and coastal floods 47 , 48 , 49 , 50 . Drought events include meteorological, soil moisture and hydrological (streamflow, groundwater) droughts 51 . The rationale for analysing floods and droughts together is based on their position at the two extremes of the same hydrological cycle, the similarity of some management strategies (for example, warning systems, water reservoir infrastructure), potential trade-offs in the operation of the same infrastructure 52 and more general interactions between these two risks (for example, water supply to illegal settlements that may spur development and therefore flood risk). There may therefore be value in management communities learning from each other 18 .

The dataset comprises: (1) detailed review-style reports about the events and key processes between the events, such as changes in risk management (open access data; Data Availability statement); (2) a key data table that contains the data (qualitative and quantitative) characterizing the indicators for the paired events, extracted from individual reports (open access data); and (3) an overview table providing indicators-of-change between the first and second events (Supplementary Table 3 ). To minimize the elements of subjectivity and uncertainty in the analysis, we (1) used indicators-of-change as opposed to indicators of absolute values, (2) calculated indicators from a set of sub-indicators (Supplementary Table 1 ) and (3) implemented a quality assurance protocol. Commonly, more than one variable was assessed per sub-indicator (for example, flood discharges at more than one stream gauge, or extreme rainfall at several meteorological stations). A combination or selection of the variables was used based on hydrological reasoning on the most relevant piece of information. Special attention was paid to this step during the quality assurance process, drawing on the in-depth expertise on events of one or more of our co-authors. The assignment of values for the indicators-of-change, including quality assurance, was inspired by the Delphi Method 53 that is built on structured discussion and consensus building among experts. The process was driven by a core group (H.K., A.F.V.L., K. Schröter, P.J.W. and G.D.B.) and was undertaken in the following steps: (1) on the basis of the detailed report, a core group member suggested values for all indicators-of-change for a paired event; (2) a second member of the core group reviewed these suggestions; in case of doubt, both core group members rechecked the paired event report and provided a joint suggestion; (3) all suggestions for the indicators-of-change for all paired events were discussed in the core group to improve consistency across paired events; (4) the suggested values of the indicators-of-change were reviewed by the authors of the paired-event report; and finally (5), the complete table of indicators-of-change (Supplementary Table 3 ) was reviewed by all authors to ensure consistency between paired events. Compound events were given special consideration, and the best possible attempt was made to isolate the direct effects of floods and droughts from those of concurrent phenomena on hazard, exposure and impact, based on expert knowledge of the events of one or more of the co-authors. For instance, in the course of this iterative process it became clear that fatalities during drought events were not caused by a lack of water, but by the concurrent heatwave. It was thus decided to omit the sub-indicator ‘fatalities’ in drought impact characterization. The potential biases introduced by compound events were further reduced by the use of the relative indicators-of-change between similar event types with similar importance of concurrent phenomena.

The indicator-of-change of impact is composed of the following sub-indicators: number of fatalities (for floods only), direct economic impact, indirect impact and intangible impact (Supplementary Table 1 ). Flood hazard is composed of the sub-indicators precipitation/weather severity, severity of flood, antecedent conditions (for pluvial and riverine floods only), as well as the following for coastal floods only: tidal level and storm surge. Drought hazard is composed of the duration and severity of drought. Exposure is composed of the two sub-indicators people/area/assets exposed and exposure hotspots. Vulnerability is composed of the four sub-indicators lack of awareness and precaution, lack of preparedness, imperfect official emergency/crisis management and imperfect coping capacity. Indicators-of-change, including sub-indicators, were designed such that consistently positive correlations with impact changes are expected (Supplementary Table 1 ). For instance, a decrease in 'lack of awareness' leads to a decrease in vulnerability and is thus expected to be positively correlated with a decrease in impacts. Management shortcomings are characterized by problems with water management infrastructure and non-structural risk management shortcomings, which means that non-structural measures were not optimally implemented. These sub-indicators were aggregated into indicators-of-change for impact, hazard, exposure, vulnerability and management shortcomings, to enable a consistent comparison between flood and drought paired events. This set of indicators is intended to be as complementary as possible, but overlaps are hard to avoid because of interactions between physical and socioeconomic processes that control flood and drought risk. Although the management shortcoming indicator is primarily related to the planned functioning of risk management measures, and hazard, exposure and vulnerability primarily reflect the concrete effects of measures during specific events, there is some overlap between the management shortcoming indicator and all three drivers. Supplementary Table 1 provides definitions and examples of description or measurement of sub-indicators for flood and drought paired events.

The changes are indicated by −2/2 for large decrease or increase, −1/1 for small decrease or increase and 0 for no change. In the case of quantitative comparisons (for example, precipitation intensities and monetary damage), a change of less than around 50% is usually treated as a small change and above approximately 50% as a large change, but always considering the specific measure and paired events. Supplementary Table 2 provides representative examples from flood and drought paired events showing how differences in quantitative variables and qualitative information between the two events of a pair correspond to the values of the sub-indicators, ranging from large decrease (−2) to large increase (+2). We assume that an event is unprecedented in a subjective way—that is, it has probably not been experienced before—if the second event of a pair is much more hazardous than the first (hazard indicator-of-change +2).

Spearman’s rank correlation coefficients are calculated for impact, drivers and management shortcomings, separated for flood and drought paired events. Despite the measures taken to minimize the subjectivity and uncertainty of indicator assignment, there will always be an element of subjectivity. To address this, we carried out a Monte Carlo analysis (1,000 iterations) to test the sensitivity of the results when randomly selecting 80% of flood and drought paired events. For each subsample correlation, coefficients and P  values were calculated to obtain a total of 1,000 correlation and 1,000  P  value matrices. The 25th and 75th quantiles of the correlation coefficients and P  values were calculated separately (Extended Data Fig. 3 ).

Data availability

The dataset containing the individual paired event reports, the key data table and Supplementary Tables 1 – 3 are openly available via GFZ Data Services ( https://doi.org/10.5880/GFZ.4.4.2022.002 ).  Source data are provided with this paper.

Jongman, B. et al. Declining vulnerability to river floods and the global benefits of adaptation. Proc. Natl Acad. Sci. USA 112 , E2271–E2280 (2015).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Formetta, G. & Feyen, L. Empirical evidence of declining global vulnerability to climate-related hazards. Glob. Environ. Change 57 , 101920 (2019).

Article   PubMed   PubMed Central   Google Scholar  

IPCC. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C.B. et al.) (Cambridge Univ. Press, 2012).

Bouwer, L. M. Have disaster losses increased due to anthropogenic climate change? Bull. Am. Meteorol. Soc. 92 , 39–46 (2011).

Article   ADS   Google Scholar  

Ward, P. J. et al. Natural hazard risk assessments at the global scale. Nat. Hazards Earth Syst. Sci. 20 , 1069–1096 (2020).

Economic Losses, Poverty & Disasters (1998–2017) (UNISDR (United Nations Office for Disaster Risk Reduction) and CRED (Centre for Research on the Epidemiology of Disasters), 2018); https://www.cred.be/unisdr-and-cred-report-economic-losses-poverty-disasters-1998-2017

Dottori, F. et al. Increased human and economic losses from river flooding with anthropogenic warming. Nat. Clim. Change 8 , 781–786 (2018).

Cammalleri, C. et al. Global Warming and Drought Impacts in the EU (Publications Office of the European Union, 2020); https://doi.org/10.2760/597045

Terminology on Disaster Risk Reduction (UNDRR (United Nations Office for Disaster Risk Reduction), 2017); www.undrr.org/terminology

Cutter, S. L., Boruff, B. J. & Shirley, W. L. Social vulnerability to environmental hazards. Soc. Sci. Q. 84 , 242–261 (2003).

Article   Google Scholar  

Turner, B. L. et al. A framework for vulnerability analysis in sustainability science. Proc. Natl Acad. Sci. USA 100 , 8074–8079 (2003).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Eakin, H. & Luers, A. L. Assessing the vulnerability of social-environmental systems. Annu. Rev. Environ. Resour. 31 , 365–394 (2006).

Eriksen, S. et al. Adaptation interventions and their effect on vulnerability in developing countries: help, hindrance or irrelevance? World Dev. Rev. 141 , 105383 (2020).

Kreibich, H. et al. Costing natural hazards. Nat. Clim. Change 4 , 303–306 (2014).

De Ruiter, M. C. et al. Why we can no longer ignore consecutive disasters. Earths Future 8 , e2019EF001425 (2020).

Di Baldassarre, G. A. et al. Perspectives on socio-hydrology: capturing feedbacks between physical and social processes. Water Resour. Res. 51 , 4770–4781 (2015).

Mechler, R. & Bouwer, L. M. Understanding trends and projections of disaster losses and climate change: is vulnerability the missing link? Clim. Change 133 , 23–35 (2015).

Ward, P. J. et al. The need to integrate flood and drought disaster risk reduction strategies. Water Secur. 11 , 100070 (2020).

Raikes, J. et al. Pre-disaster planning and preparedness for floods and droughts: a systematic review. Int. J. Disaster Risk Reduct. 38 , 101207 (2019).

Johnson, K. A. et al. A benefit–cost analysis of floodplain land acquisition for US flood damage reduction. Nat. Sustain. 3 , 56–62 (2019).

Kreibich, H. et al. Adaptation to flood risk: results of international paired flood event studies. Earths Future 5 , 953–965 (2017).

Birkland, T. A. Focusing events, mobilization, and agenda setting. J. Public Policy 18 , 53–74 (1998).

Budiyono, Y. et al. River flood risk in Jakarta under scenarios of future change. Nat. Hazards Earth Syst. Sci. 16 , 757–774 (2016).

Ionita, M. et al. The European 2015 drought from a climatological perspective. Hydrol. Earth Syst. Sci. 21 , 1397–1419 (2017).

Drought Management Plan Report (European Commission, 2007); https://ec.europa.eu/environment/water/quantity/pdf/dmp_report.pdf

Van Lanen, H. A. J. et al. Hydrology needed to manage droughts: the 2015 European case. Hydrol. Process. 30 , 3097–3104 (2016).

Petrucci, O. et al. Civil protection and damaging hydrogeological events: comparative analysis of the 2000 and 2015 events in Calabria (southern Italy). Adv. Geosci. 44 , 101–113 (2017).

NDMC (National Drought Mitigation Center) U.S. Drought Monitor https://droughtmonitor.unl.edu (2020).

Garrick, D. E. et al. Managing the cascading risks of droughts: institutional adaptation in transboundary river basins. Earths Future 6 , 809–827 (2018).

French, A. & Mechler, R. Managing El Niño Risks Under Uncertainty in Peru (International Institute for Applied Systems Analysis, 2017); http://pure.iiasa.ac.at/id/eprint/14849/1/French_Mechler_2017_El%20Ni%C3%B1o_Risk_Peru_Report.pdf

Sörensen, J. & Mobini, S. Pluvial, urban flood mechanisms and characteristics—assessment based on insurance claims. J. Hydrol. 555 , 51–67 (2017).

Muller, M. Cape Town’s drought: don’t blame climate change. Nature 559 , 174–176 (2018).

Article   ADS   CAS   PubMed   Google Scholar  

White, G. F. Human Adjustment to Floods (Univ. of Chicago Press, 1945).

Wenger, C. Better use and management of levees: reducing flood risk in a changing climate. Environ. Rev. 23 , 240–255 (2015).

Kallis, G. Coevolution in water resource development: the vicious cycle of water supply and demand in Athens, Greece. Ecol. Econ. 69 , 796–809 (2010).

Di Baldassarre, G. et al. Water shortages worsened by reservoir effects. Nat. Sustain. 1 , 617–622 (2018).

Savelli, E. et al. Don’t blame the rain: social power and the 2015–2017 drought in Cape Town. J. Hydrol. 594 , 125953 (2021).

Merz, B. et al. Charting unknown waters—on the role of surprise in flood risk assessment and management. Water Resour.Res. 51 , 6399–6416 (2015).

Brown, A. E. et al. A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation. J. Hydrol. 310 , 28–61 (2005).

Cutter, S. L. & Finch, C. Temporal and spatial changes in social vulnerability to natural hazards. Proc. Natl Acad. Sci. USA 105 , 2301–2306 (2008).

Hinkel, J. ‘‘Indicators of vulnerability and adaptive capacity’’: towards a clarification of the science–policy interface. Glob. Environ. Change 21 , 198–208 (2011).

Tate, E. Social vulnerability indices: a comparative assessment using uncertainty and sensitivity analysis. Nat. Hazards 63 , 325–347 (2012).

Weichselgartner, J. Disaster mitigation: the concept of vulnerability revisited. Disaster Prev. Manage. 10 , 85–94 (2001).

Adger, W. N. Vulnerability. Glob. Environ. Change 16 , 268–281 (2006).

Birkmann, J. Framing vulnerability, risk and societal responses: the MOVE framework. Nat. Hazards 67 , 193–211 (2013).

Thywissen, K. Components of Risk—a Comparative Glossary (UNU-EHS, 2006); http://collections.unu.edu/view/UNU:1869

Tarasova, L. et al. Causative classification of river flood events. Wiley Interdiscip. Rev. Water 6 , e1353 (2019).

Google Scholar  

Rosenzweig, B. R. et al. Pluvial flood risk and opportunities for resilience. Wiley Interdiscip. Rev. Water 5 , e1302 (2018).

Ascott, M. J. et al. Improved understanding of spatio‐temporal controls on regional scale groundwater flooding using hydrograph analysis and impulse response functions. Hydrol. Proc. 31 , 4586–4599 (2017).

Danard, M., Munro, A. & Murty, T. Storm surge hazard in Canada. Nat. Hazards 28 , 407–431 (2003).

Tallaksen, L. & Lanen, H. A. J. V. Hydrological Drought. Processes and Estimation Methods for Streamflow and Groundwater (Elsevier, 2004).

Van den Honert, R. C. & McAneney, J. The 2011 Brisbane floods: causes, impacts and implications. Water 3 , 1149–1173 (2011).

Okoli, C. & Pawlowski, S. D. The Delphi method as a research tool: an example, design considerations and applications. Inform. Manage. 42 , 15–29 (2004).

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Acknowledgements

The presented work was developed by the Panta Rhei Working Groups 'Changes in flood risk' and 'Drought in the Anthropocene' within the framework of the Panta Rhei Research Initiative of the International Association of Hydrological Sciences. We thank the Barcelona Cicle de l’Aigua S.A., Barcelona City Council, Environment Agency (United Kingdom), Länsförsäkringar Skåne, Steering Centre for Urban Flood Control Programme in HCMC (Vietnam), VA SYD and the West Berkshire Council (United Kingdom) for data. The work was partly undertaken under the framework of the following projects: Alexander von Humboldt Foundation Professorship endowed by the German Federal Ministry of Education and Research (BMBF); British Geological Survey’s Groundwater Resources Topic (core science funding); C3-RiskMed (no. PID2020-113638RB-C22), financed by the Ministry of Science and Innovation of Spain; Centre for Climate and Resilience Research (no. ANID/FONDAP/15110009); CNES, through the TOSCA GRANT SWHYM; DECIDER (BMBF, no. 01LZ1703G); Deltares research programme on water resources; Dutch Research Council VIDI grant (no. 016.161.324); FLOOD (no. BMBF 01LP1903E), as part of the ClimXtreme Research Network. Funding was provided by the Dutch Ministry of Economic Affairs and Climate; Global Water Futures programme of University of Saskatchewan; GlobalHydroPressure (Water JPI); HUMID project (no. CGL2017-85687-R, AEI/FEDER, UE); HydroSocialExtremes (ERC Consolidator Grant no. 771678); MYRIAD-EU (European Union’s Horizon 2020 research and innovation programme under grant agreement no. 101003276); PerfectSTORM (no. ERC-2020-StG 948601); Project EFA210/16 PIRAGUA, co-founded by ERDF through the POCTEFA 2014–2020 programme of the European Union; Research project nos. ANID/FSEQ210001 and ANID/NSFC190018, funded by the National Research and Development Agency of Chile; SECurITY (Marie Skłodowska-Curie grant agreement no. 787419); SPATE (FWF project I 4776-N, DFG research group FOR 2416); the UK Natural Environment Research Council-funded project Land Management in Lowland Catchments for Integrated Flood Risk Reduction (LANDWISE, grant no. NE/R004668/1); UK NERC grant no. NE/S013210/1 (RAHU) (W.B.); Vietnam National Foundation for Science and Technology Development under grant no. 105.06-2019.20.; and Vietnam National University–HCMC under grant no. C2018-48-01. D.M. and A. McKenzie publish with the permission of the Director, British Geological Survey. The views expressed in this paper are those of the authors and not the organizations for which they work.

Open access funding provided by Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum - GFZ.

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GFZ German Research Centre for Geosciences, Section Hydrology, Potsdam, Germany

Heidi Kreibich, Kai Schröter, Nivedita Sairam, Max Steinhausen & Sergiy Vorogushyn

Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

Anne F. Van Loon, Philip J. Ward, Maurizio Mazzoleni, Marlies H. Barendrecht, Anaïs Couasnon & Marleen C. de Ruiter

Leichtweiss Institute for Hydraulic Engineering and Water Resources, Division of Hydrology and River basin management, Technische Universität Braunschweig, Braunschweig, Germany

Kai Schröter

Department of Civil and Environmental Engineering, University of Houston, Houston, TX, USA

Guta Wakbulcho Abeshu & Hong-Yi Li

Lomonosov Moscow State University, Moscow, Russia

Svetlana Agafonova, Natalia Frolova, Maxim Kharlamov, Maria Kireeva & Alexey Sazonov

University of California, Irvine, CA, USA

Amir AghaKouchak & Laurie S. Huning

Department of Civil Engineering, Istanbul Technical University, Istanbul, Turkey

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Center for Climate and Resilience Research, Santiago, Chile

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Department of Civil Engineering, Universidad de La Frontera, Temuco, Chile

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Global Institute for Water Security, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

Laila Balkhi, Hayley Carlson & Saman Razavi

LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France

Sylvain Biancamaria

Department of Groundwater Management, Deltares, Delft, the Netherlands

Liduin Bos-Burgering

School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK

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Agency for the Assessment and Application of Technology, Jakarta, Indonesia

Yus Budiyono

Department of Civil and Environmental Engineering, Imperial College London, London, UK

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Department of Civil Engineering, Beykent University, Istanbul, Turkey

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Graduate School, Istanbul Technical University, Istanbul, Turkey

Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany

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Geographical Sciences, University of Bristol, Bristol, UK

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Cabot Institute, University of Bristol, Bristol, UK

Gemma Coxon, Jim Freer & Thorsten Wagener

Department of Agriculture, Hellenic Mediterranean University, Iraklio, Greece

Ioannis Daliakopoulos

Université de Lorraine, LOTERR, Metz, France

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CNR-IRPI, Research Institute for Geo-Hydrological Protection, Cosenza, Italy

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University of Saskatchewan, Centre for Hydrology, Canmore, Alberta, Canada

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Animesh K. Gain

Department of Economics, Ca’ Foscari University of Venice, Venice, Italy

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Pontificia Bolivariana University, Faculty of Civil Engineering, Bucaramanga, Colombia

Diego A. Guzmán

California State University, Long Beach, CA, USA

Laurie S. Huning

Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Palaeoclimate Dynamics Group, Bremerhaven, Germany

Monica Ionita & Viorica Nagavciuc

Emil Racovita Institute of Speleology, Romanian Academy, Cluj-Napoca, Romania

Monica Ionita

Forest Biometrics Laboratory, Faculty of Forestry, Ștefan cel Mare University, Suceava, Romania

Water Problem Institute Russian Academy of Science, Moscow, Russia

Maxim Kharlamov & Alexey Sazonov

Faculty of Environment, University of Science, Ho Chi Minh City, Vietnam

Dao Nguyen Khoi & Pham Thi Thao Nhi

Environment Agency, Bristol, UK

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School of Chemical and Environmental Engineering, Technical University of Crete, Chania, Greece

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Servicio Nacional de Meteorología e Hidrología del Perú, Lima, Peru

Waldo Lavado-Casimiro

Department of Applied Physics, University of Barcelona, Barcelona, Spain

María Carmen LLasat

Water Research Institute, University of Barcelona, Barcelona, Spain

British Geological Survey, Wallingford, UK

David Macdonald & Andrew McKenzie

Centre of Natural Hazards and Disaster Science, Uppsala, Sweden

Johanna Mård, Elisa Savelli & Giuliano Di Baldassarre

Department of Earth Sciences, Uppsala University, Uppsala, Sweden

Civil and Environmental Engineering, The Pennsylvania State University, State College, PA, USA

Alfonso Mejia

Escola de Engenharia de Sao Carlos, University of São Paulo, São Paulo, Brasil

Eduardo Mario Mendiondo & Felipe Augusto Arguello Souza

Department of Water Resources & Delta Management, Deltares, Delft, the Netherlands

Marjolein Mens

Trelleborg municipality, Trelleborg, Sweden

Shifteh Mobini

Department of Water Resources Engineering, Lund University, Lund, Sweden

Shifteh Mobini & Johanna Sörensen

University of Potsdam, Institute of Environmental Science and Geography, Potsdam, Germany

Guilherme Samprogna Mohor & Thorsten Wagener

University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology, Hanoi, Vietnam

Thanh Ngo-Duc

Institute for Environment and Resources, Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam

Thi Thao Nguyen Huynh, Hong Quan Nguyen & Thi Van Thu Tran

Institute for Circular Economy Development, Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam

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Observatori de l’Ebre, Ramon Llull University – CSIC, Roquetes, Spain

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School of Environment and Sustainability, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

Saman Razavi

Department of Civil, Geological and Environmental Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

Dipartimento di Ingegneria Civile, Edile e Ambientale, Sapienza Università di Roma, Rome, Italy

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University of Applied Sciences, Magdeburg, Germany

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Earth and Environmental Systems Institute, The Pennsylvania State University, State College, PA, USA

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Wiwiana Szalińska & Tamara Tokarczyk

Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Qiuhong Tang & Yueling Wang

Department of Hydraulic Engineering, Tsinghua University, Beijing, China

Fuqiang Tian

Royal Botanical Gardens Kew, London, UK

Carolina Tovar

KWR Water Research Institute, Nieuwegein, the Netherlands

Marjolein H. J. Van Huijgevoort

Department of Physical Geography, Utrecht University, Utrecht, the Netherlands

Michelle T. H. van Vliet

Civil Engineering, University of Bristol, Bristol, UK

Thorsten Wagener & Doris E. Wendt

School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA

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School of Geography and Ocean Science, Nanjing University, Nanjing, China

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Contributions

H.K. initiated the study and led the work. H.K., A.F.V.L., K. Schröter, P.J.W. and G.D.B. coordinated data collection, designed the study and undertook analyses. All co-authors contributed data and provided conclusions and a synthesis of their case study (the authors of each paired event report were responsible for their case study). M. Mazzoleni additionally designed the figures, and he and N.S. contributed to the analyses. H.K., G.D.B., P.J.W., A.F.V.L., K. Schröter and G.D.B. wrote the manuscript with valuable contributions from all co-authors.

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Correspondence to Heidi Kreibich .

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Extended data figures and tables

Extended data fig. 1 location of flood and drought paired events coloured according to their indicators-of-change..

a , Change in hazard; b , change in exposure; c , change in vulnerability and d , change in management shortcomings.

Extended Data Fig. 2 Parallel plot of paired events with the same hazard of both events.

The hazard change is zero for all shown paired events. The lines show how the different combinations of indicators-of-change result in varying changes in impacts. Small offsets within the grey bars of the indicator-of-change values enable the visualization of all lines.

Extended Data Fig. 3 Results of the sensitivity analyses.

a–d Correlation matrix of indicators-of-change for 25th and 75th quantiles of correlation coefficients and p-values, respectively ( a , c ) and 75th and 25th quantiles of correlation coefficients and p-values, respectively ( b , d ) separate for flood and drought paired events. Quantiles of correlation coefficients and p-values were calculated separately; colours of squares indicate Spearman’s rank correlation coefficients; sizes of squares indicates p-values. Fig. 2a, c is added to the right to ease comparison.

Extended Data Fig. 4 Theoretical framework used in this study (adapted from IPCC 3 ).

This theoretical risk framework considers impact as a result of three risk components or drivers: hazard, exposure and vulnerability, which in turn are modified by management.

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Supplementary Tables 1–3.

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Kreibich, H., Van Loon, A.F., Schröter, K. et al. The challenge of unprecedented floods and droughts in risk management. Nature 608 , 80–86 (2022). https://doi.org/10.1038/s41586-022-04917-5

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Case Studies: Red Cross Red Crescent Disaster Risk Reduction in Action – What Works at Local Level, June 2018

Attachments.

Community/local action for resilience:

• Building the disaster resilience of asylum seekers

The Australian Red Cross in Queensland adapted a generic preparedness tool to support highrisk marginalised communities of asylum seekers to build their own resilience to disaster. Specific and relevant messaging was developed within a community education programme co-designed with members of the asylum seekers community, who became educators and facilitators to deliver the programme. The programme reached 900 people in a successful pilot, measured through positive shifts in knowledge of key actions to take in preparedness of disaster. The underlying achievement is the acceptance and trust of the communities, reflecting the respect for cultural and language diversity, and recognizing the capacity of asylum seekers communities to contribute and participate in their host country.

• Integrated Coastal Community Resilience and Disaster Risk Reduction in Demak, Central Java

Exacerbated erosion affected the ecology and increased vulnerability of coastal communities in Demak. The Indonesian Red Cross mobilized communities through Community-Based Action Teams to restore the ecosystem through mangrove plantation and implement livelihood generation to improve community resilience. Under an integrated approach, the community is connected with village authorities and scientists from the Bogor Agricultural Institute to implement sustainable local action. The programme has shown concrete results in reducing the risks of tidal disasters, while eco-tourism and crab cultivation farming have increased the income of the communities, along with their heightened awareness and preparedness for disaster.

• Winter shelters for rural herder communities

Rural herders in Mongolia must keep their livestock alive through extreme temperatures and exposure of harsh winters that follow after drought. In efforts to reduce livestock loss, the Red Cross supported herder communities to design and construct winters shelters for livestock in a participatory approach garnering the collective capacity of community, local government and the Red Cross. A strong community focus ensures that the herders drive the activities towards preserving their livelihoods and the traditional nomadic way of life under threat by climatic challenges.

• Youth-led actions for more resilient schools and communities: Mapping of School Safety approaches and Youth in School Safety training for youth facilitators

Over the last two years the Red Cross Red Crescent Southeast Asia Youth Network has improved Youth programming and networks on youth-led initiatives and solutions for DRR. A pilot Youth in School Safety Programme rolled out in six countries, training 150 youth volunteers who in turn conducted countless school safety actions. A comprehensive mapping of school safety actions in all 11 countries of South Asia is underway to showcase activities of RCRC Youth volunteers on the ground.

Private Sector Interventions:

• Australian Business Roundtable for Disaster Resilience and Safer Communities

Leaders of leading commercial organizations jointly commit resources to work constructively with government to make Australian communities safer and more resilient to natural disasters, by shifting national investment from recovery and response to preparedness and mitigation. The Australian Red Cross joins this Roundtable - contributing on emergency management and humanitarian aspects - to collectively deliver on community education, risk information, adaptation research, mitigation infrastructure and strategic alliances.

Disaster Risk Governance:

• A seat at the table: inclusive decision-making to strengthen local resilience

Disaster related laws and policies need to better include and protect those most at risk of disasters. This case study outlines the steps taken by the IFRC Disaster Law Programme - from global research undertaken jointly by IFRC and UNDP, to the provision of technical advice in supporting Asia Pacific National Societies, as the community-based actor and auxiliary to government, to ensure inclusive community empowerment and protection, gender and inclusion in national disaster laws and policies.

Gender and Inclusiveness:

• Participatory Campaign Planning for Inclusive DRR Knowledge and Messaging in Nepal

An innovative approach that embraces the essence of inclusiveness, the Participatory Campaign Planning methodology is applied to develop hazard messages and the means of communicating them that are tailored to different target groups, with the aim of making them more effective in creating behaviour change. This case study focuses on urban communities in Nepal and various elements to be considered within different target groups and their geographic environments.

• Community participatory action research on sexual and genderbased violence prevention and response during disasters

This collaborative research by the IFRC and the ASEAN Committee for Disaster Management was undertaken in recognizing that there are few SGBV studies that focus on low-income developing countries and fewer that go beyond the gendered effects on women and girls, overlooking men and boys and sexual minority groups. Key findings illustrate that the risks to SGBV are exacerbated during natural disaster situations in Indonesia, Lao PDR and the Philippines, and that “disaster responders” and actors addressing needs of SGBV survivors are not working together adequately to reduce these risks.

Early Warning and Early Action:

• Forecast-based Financing: Effective early actions to reduce flood impacts

When four pilot communities in the district of Bogura were affected by severe flood events in July and August of 2017, the Early Action Protocol of the Forecast-based Financing (FbF) approach was activated, and unconditional cash grant was chosen as the early action for floods to give people the flexibility to prepare individually for the impending flood and take the measures they see fit. This case study outlines the steps taken by Bangladesh Red Crescent Society and German Red Cross to implement FbF in Bangladesh. It analyses not only the effectiveness of the activation in Bogura, but the longer term impacts of this early action development.

• CPP Early Warning: Saving Thousands in Cyclone Mora

Through the Bangladesh Cyclone Preparedness Programme (CPP) interventions, a programme jointly run by the Government of Bangladesh and the Bangladesh Red Crescent Society (BDRCS), the communities of the coastal areas in Bangladesh have become more aware of the need to go to safe shelters during emergencies, have understood the significance of early warning and learned to pay heed to advice from CPP and youth volunteers. On 28 May 2017 - the eve of Cyclone Mora, more than 55,260 CPP volunteers and BDRCS youth volunteers were deployed to pass early warning message door to door in the coastal region, and announcing the danger of the approaching cyclone in the local language. Cyclone early warning messages were disseminated across a population area covering 11 million people, and almost half a million people were reached in this process and taken to safe places in less than 24 hours. The CPP has substantially reduce death tolls due to cyclones in Bangladesh.

• Flood Early Warning and Early Action System (FEWEAS)

The Flood Early Warning Early Action System (FEWEAS) was developed through a collaboration between the Indonesian Red Cross (PMI) and Institute Teknologi Bandung (ITB) to provide effective solutions for reducing disaster risk through a shared platform for community and government to address issues upstream and downstream in formulating appropriate strategy, planning and ground action for floods. FEWEAS is an internet-based application to predict and monitor rainfall and flooding. PMI Provincial and District staff and volunteers are using the FEWEAS to monitor floods along the Bengawan Solo River in East Java, and along the Citarum River in West Java. While the application provides flood alerts and updates to the community through smartphones, the communities and Community Based Action Teams can update their response, upload photos, videos and relevant information to further inform response actions.

• Forecast-based Financing for the vulnerable herders in Mongolia

The Mongolian Red Cross Society (MRCS) assisted 2,000 herder households in most-at-risk areas (40 soums in 12 provinces) with unrestricted cash grants in December 2017 and with animal care kits in January 2018, before the peak of the winter season. The MRCS used the Dzud Risk Map released by the Government in November 2017 to decide which soums to target for early action with the aim to reach the herders well before the loss of their livestock to reduce the impact of Dzud on the livelihoods of the herders. The Dzud Risk Map highlighted the risk of livestock death throughout the whole of Mongolia. A cost-benefit analysis is being conducted to further inform FbF in Mongolia.

• More than response: Building partnerships to engage communities in preparedness and early warning systems in the Pacific

A community early warning system (CEWS) model was developed in partnership by the Red Cross, government agencies and regional organizations in the Pacific to better link CEWS with national and sub-national systems. Taking these pilots to scale requires i) national mechanisms such as SOPs and action plans that systematically link warnings and climate information provided by National Meteorological Services to early preparedness actions at multiple scales, and; ii) available funding (at multiple scales) to support early actions. Recently a Roadmap for Forecast-based Financing for Drought Preparedness has been developed in the Solomon Islands. Through continued partnership approach, the Roadmap and outcomes from the regional ‘FINPAC’ CEWS project will be used to support the Government of the Solomon Islands and Solomon Islands Red Cross to implement a programme for communities, provincial and national authorities to apply forecast information for early action at scale. The drought thresholds developed in collaboration will form the basis of an FbF trigger system in the Solomon Islands.

Displacement and DRR:

• Preparing and reducing risks of disasters to displaced communities

Cox’s Bazar became the world’s most densely populated refugee settlement following the massive influx of people from Myanmar that started in August 2017. Being a coastal district prone to disaster, existing infrastructure and services cannot cope to cover the host population and incoming refugees, and preparedness interventions became critical. This case study follows actions taken to extend the coverage of the Cyclone Preparedness Programme, successfully integrating displaced people in camp settlements as temporary CPP camp volunteers, to support in establishing early warning system and ensure relevant preparedness and response action.

Urban Community/local action for resilience:

• What is an Urban ‘Community’? – New ways for local DRR actions in cities . Lessons learned from the 2015 Nepal earthquake response show that vulnerable populations in urban context do not often engage with or rely on local disaster management committees in the event of a disaster. Instead they organize themselves around their own networks, both informal and formal, such as family, temples, markets, service-providers, employment. A meaningful DRR intervention in urban communities must first recognize what defines an urban community and how they are organized to guide specific engagement and participatory-led approaches. The target group and network-based approach by Nepal Red Cross are innovations in organizing effective community-owned urban disaster resilience.

Green Response/ Enhancing Preparedness for Effective Response:

• Greening the IFRC Supply Chains; mapping of our GHG emissions

Under the Green Response initiative to improve environmental outcomes of life-saving operations, the IFRC in reviewing practices and policies is mapping the present level of greenhouse gas (GHG) emissions generated by relief operations and to implement GHG reduction activities to lower the environmental impact of emergency operations. The mapping contributes to the global emission baseline for IFRC supply chain monitoring, to design the reduction roadmap and build internal capacity.

• Environmental Field Advisor deployment in an emergency response

To improve the environmental outcomes and reduce negative impacts of operations and programmes, the IFRC deployed an Environmental Field Advisor (EFA) to the Population Movement Operation in Cox’s Bazar, Bangladesh. The EFA conducted an environmental impact assessment and worked with project leads to identify and implement improvements. A significant achievement to date is the IFRC joining the UNHCR/IOM/WFP/FAO to provide LPG as cooking fuel to camp community households to combat massive deforestation cause by firewood collection.

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U.S. Provides $350,000 to Help Communities in Sylhet and Sunamganj Districts Recover from Flooding

Wfp mobilizes the biggest anticipatory action as jamuna river rises critically [en/bn].

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Information on disaster risk management: case study of five countries: Jamaica

Título de la revista, issn de la revista, título del volumen, símbolo onu.

Jamaica, as a result of its location in the north-western Caribbean basin, is prone to numerous specific natural hazards. These include hurricanes, of which recent hurricanes experienced within the last few years (and in fact since 1988 with hurricane Gilbert), have reminded us of Jamaica's great vulnerability to the effects of this hazard. Next, it is also envisaged that a large earthquake could do considerable damage to sectors of the population and to infrastructure and could result in displacement and homelessness among large sections of the population, particularly in the highly urbanized areas of the Kingston Metropolitan Area (KMA). These two hazards, though perhaps not the most frequent, have the potential to do the most widespread damage to the population and to infrastructure. Floods are the most frequently occurring natural hazard in Jamaica, and are often linked with severe weather systems, frontal systems and troughs, and less often with hurricanes and storms. Next to floods, landslides are the most frequently occurring hazard for Jamaica. Tsunami events appear to be very infrequent around the coastline of Jamaica. Events have been recorded however, in 1755 and more recently in 1907.  In all, over 300,000 references on the general topic of disaster risk were found to exist. It is therefore clear that there is abundant data dealing with this issue. Largely due to the fact that these documents have been produced through research initiatives, or as commissioned studies, the quality of the data appears to be quite rich. With respect to the accessibility of these data, in the Jamaican context the central storage locations are primarily at ODPEM and at CARDIN.  The awareness of the public to floods is evident, as they are described in much detail after each occurrence in the visual and written media. In recent years a trend has been evident in which victims of flooding have either requested relocation or have agreed to relocation. It is clear that these residents have developed a full perception of this hazard, its possible effect (vulnerability) and the degree of loss likely to be experienced in the future and have decided not to accept the level of risk involved in remaining at the vulnerable location. From the perspective of hurricanes, hurricane Gilbert in 1988 and Ivan in 2004 have done much to sensitize the populace about the risks and vulnerabilities associated with this hazard. The threat of earthquakes, which has held a less prominent position in the public consciousness, has been boosted through the holding of simulation exercises that have been portrayed in the media.

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How to improve public participation in disaster risk management: A case study of Buein Zahra, a small city in Iran

Affiliations.

• 1 Department of Urban Planning, Buein Zahra Technical University, Buein Zahra, Islamic Republic of Iran.
• 2 Department of General Economic Affairs, Kharazmi University, Tehran, Islamic Republic of Iran.
• 3 Department of Civil Engineering, Buein Zahra Technical University, Buein Zahra, Islamic Republic of Iran.
• PMID: 31534638
• PMCID: PMC6739556
• DOI: 10.4102/jamba.v11i1.741

Identifying and providing basic solutions using a collaborative approach in earthquake-stricken cities of Iran has not yet been addressed. This article focuses on an area of practice and views disaster risk management from the point of view of volunteer groups to illustrate how the main components of disaster risk management affect the strengthening of public participation. In this article, Buein Zahra, a small city in Iran, is considered as a high-risk earthquake zone. The basic components of risk management are identified, namely public awareness, knowledge, skills, enabling environment, organisational development and social participation. An assessment of these indicators was done, and multidimensional relationships were established between them to enable an increase in the capacity for public participation. Accordingly, the results indicate that a mere increase in public awareness and knowledge, as seen today, and an improvement in enabling environment, although affecting disaster risk reduction, cannot by themselves lead to real public participation. Organisational development and strengthening of crisis coping skills are two key components to improving participation during crises in the small cities of Iran. According to the results of this study, institutional capacity and unreal political commitment have caused inefficiency of public participation in earthquake preparedness.

Keywords: community participation; disaster risk management; disaster risk reduction; earthquake risk management; enabling environment; organisational development.

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The authors declare no competing interests with regard to the writing of this article.

Basic goals of the national…

Basic goals of the national earthquake risk reduction programme.

Participation and risk management cycle.

Empirical model of factors affecting…

Empirical model of factors affecting participation in disaster risk management.

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• Dowrick D., 2009, ‘Earthquake risk reduction’, in Earthquake Resistant Design and Risk Reduction, 2nd edn., pp. 1–14, John Wiley & Sons, Hoboken, New Jersey: 10.1002/9780470747018 - DOI

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Please note you do not have access to teaching notes, social capital in disaster risk management; a case study of social capital mobilization following the 1934 kathmandu valley earthquake in nepal.

Disaster Prevention and Management

ISSN : 0965-3562

Article publication date: 29 July 2014

The purpose of this paper is to examine how social capital operated in the lives of 15 respondents from Lalitpur following the massive 1934 Kathmandu Valley earthquake. Based on experiences of the survivors, it attempts to understand how individuals and families utilized their social capital in the aftermath of the earthquake, and rebuild their lives and communities.

Design/methodology/approach

This is a qualitative study based on non-structured interviews and discussions with disaster victims on their own locality. Following Padgett's (2008) grounded theory approach, flexible method of data collection is adopted through interactions with respondents and following up on important cues or patterns as additional data emerged.

Participants described a process through which they relied on bonding, bridging and linking social capital in different stages of earthquake response and recovery. Close ties or bonding social capital were important for immediate support, but bridging and linking social capital offered pathways to longer term survival and wider neighbourhood and community revitalization. This paper also discusses how social capital inclusion in pre-disaster communities might be helpful to strengthen their response capacity.

Research limitations/implications

As the study participants were less than ten years old when the earthquake happened, they might have omitted or overlooked some important details about the event. The findings are based not only on participant's own memories, but they also shared stories told by their parents which were the indirect experiences.

Practical implications

This study indicates the potential value and need for including bonding, bridging and linking social capital and traditional social networks in disaster planning. A key outcome related to disaster policy would be what institutional condition or combinations of different dimensions of social capital may serve the public for better disaster response and recovery.

Originality/value

This study has paid attention to how social capital might be useful in disaster risk reduction both in post-disaster phase and in pre-disaster condition which may be rare in disaster studies. It also provides an insight into how community-based disaster management can take into account pre-existing social systems and traditional social networks to build local capacities.

• Social capital
• Disaster risk management
• Earthquakes
• Community organizations
• The 1934 Kathmandu Valley earthquake

Acknowledgements

The author wishes to acknowledge local respondents at Lalitpur, Nepal who graciously gave their time and participated in interviews. This research was carried out while the author was a Doctoral Student under Japanese Government fellowship at the Disaster Prevention Research Institute, Kyoto University, Japan.

Bhakta Bhandari, R. (2014), "Social capital in disaster risk management; a case study of social capital mobilization following the 1934 Kathmandu Valley earthquake in Nepal", Disaster Prevention and Management , Vol. 23 No. 4, pp. 314-328. https://doi.org/10.1108/DPM-06-2013-0105

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Regional probabilistic flood displacement risk assessment: the Horn of Africa case study

• Trasforini, Eva
• Campo, Lorenzo
• Ghizzoni, Tatiana
• Libertino, Andrea
• Ottonelli, Daria
• Ponserre, Sylvain
• Rossi, Lauro
• Rudari, Roberto

The risk of displacement caused by natural hazards has been increasingly impactful and emerges as a topical issue point in the field of disaster risk management. Given the potential escalation of this phenomenon due to climate change, population growth and urbanization, enhancing displacement risk assessment through reliable models and data has become increasingly crucial. Different applications require approaches that can be adapted at different spatial scales, from local to global scale. In pursuit of this goal, we have devised a probabilistic procedure for estimating the potential displacement of individuals due to riverine floods. The methodology is based on a novel approach to vulnerability assessment which considers that people's vulnerability depends on several physical and social factors such as direct impacts on houses, livelihoods and critical facilities (such as schools and hospitals). These concepts are seamlessly woven into a comprehensive probabilistic risk assessment. A modelling chain that incorporates climatic, hydrological, and hydraulic and exposure/vulnerability models can be run different resolution to predict impacts at different special scales, from local to global scale.This approach already demonstrated its validity for in Fiji and Vanuatu, where the small size of the countries allows for the definition of a building scale exposure model. In the present study, our focus turns towards adjusting the methodology for large countries, where using a high-resolution exposure model becomes impractical.For our case study, we selected three countries in the Horn of Africa—Ethiopia, Somalia, and Sudan—acknowledging their particular vulnerability to the challenges posed by recurrent floods and the resulting internal displacement.To properly match the 90m resolution of riverine flood hazard maps and avoid distortions in the final risk computations, a specific procedure for downscaling global exposure dataset, such as the 1-km resolution Global Exposure Socio-Economic and Building Layer (GESEBL), was implemented using high-resolution population distribution products. The resulting exposure layers are a set of population distributions associated to different sectorial assets (residential, industrial and agricultural production, services), characterized in terms of physical vulnerability to floods.Impacts of current and future flood scenarios on those assets may render them unable to provide their function, thus causing people to forcedly move. In this procedure we took special care to avoid double counting, i.e. those cases where people lose both habitual place of residence and livelihoods.Displacement risk expressed in annual average displacement and probable maximum displacement was evaluated under current and future climate conditions with optimistic and pessimistic scenarios. The results indicate a potential 2 to 4 times increase in average annual displacement for optimistic scenarios compared to current conditions, with even higher risk for pessimistic scenarios.The application of this methodology in larger countries paves the way for its implementation on a global scale.

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Leveraging geo-computational innovations for sustainable disaster management to enhance flood resilience

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• Published: 16 July 2024
• Volume 2 , article number  33 , ( 2024 )

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• Harshita Jain 1  

The increasing frequency of flood disasters around the globe highlights the need for creative approaches to improve disaster preparedness. This thorough analysis and assessment explore the topic of enhancing flood disaster resilience by utilising cutting-edge geo-computational techniques. By combining a variety of techniques, such as remote sensing, geographic information systems (GIS), LiDAR, unmanned aerial vehicles (UAVs), and cutting-edge technologies like machine learning and geospatial big data analytics, the study provides a complex framework for flood monitoring, risk assessment, and mitigation. By using remote sensing technology, flood occurrences can be tracked in real time and inundations may be precisely mapped, which makes proactive response plans possible. GIS facilitates effective evacuation planning by streamlining spatial analysis and decision-making procedures and providing critical insights into risky locations. High-resolution elevation data is provided by LiDAR technology, which is essential for precise flood modelling and simulation. Unmanned Aerial Vehicles (UAVs) may be quickly deployed to assist with situational awareness and damage assessment during a disaster. Furthermore, predictive skills are enhanced by the combination of machine learning and geographic big data analytics, opening the door to the creation of adaptive reaction plans and early warning systems. This investigation highlights how geo-computational tools may significantly improve community resilience and lessen the negative effects of flood disasters. After a thorough review of the literature and case studies, this study clarifies how these approaches might improve disaster response and preparation to a great extent.

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1 Introduction

The frequency and severity of flood disasters have alarmingly increased over the past few decades, posing serious threats to ecosystems, economy, and communities worldwide. Floods are the most frequent and destructive natural catastrophes, affecting millions of people and resulting in significant economic losses annually, according to the United Nations Office for Disaster Risk Reduction [ 51 ]. A number of causes, such as deforestation, fast urbanisation, climate change, and inadequate land management techniques, are responsible for the rising frequency of floods [ 71 ]. Communities are more vulnerable to flood threats as a result of changing climate patterns and an increase in the frequency of extreme weather events. This emphasises the critical need for preventative actions to increase disaster resilience.

Improving the resilience of flood disasters is crucial, considering the significant effects these calamities have on the environment and society. Floods have long-term effects on livelihoods, food security, and public health in addition to immediate threats to infrastructure and human lives. Furthermore, the long-term consequences of floods, like biodiversity loss, water pollution, and soil erosion, can have a lasting impact on the impacted areas, making it more difficult for them to recover and rebuild [ 69 ]. A greater understanding of the significance of implementing innovative flood disaster management techniques that go beyond conventional reactive measures is emerging in light of these difficulties. The dataset shown in Fig.  1 shows the total number of flood incidents that were reported worldwide each year between 1990 and 2020. Every data point shows the total number of documented flood incidents for a specific year. Flood activity has fluctuated over the past three decades, with certain years showing considerable surges. The years 2005, 2015, and 2018 had a notable rise in the number of flood events, with peaks of 221, 227, and 232 flood occurrences, respectively. On the other hand, there have been times of comparatively low flood frequency, including 1990, 1998, and 2020. These are times of relative stability. Overall, the trendline captures both short-term fluctuations and long-term patterns in worldwide flood activity, showing the wider trajectory of flood occurrence across the studied time span. This dataset can guide future analysis and research focused at improving flood disaster resilience globally, as well as providing a basis for understanding the temporal dynamics of flood episodes.

Global trends in flood occurrence (1990–2020)

By combining developments in geospatial technology and computational approaches to analyse, simulate, and visualise flood dangers, geo-computational techniques have emerged as a viable approach to improving resilience to flood disasters. These methods include a wide range of approaches, such as machine learning, LiDAR, unmanned aerial vehicles (UAVs), remote sensing, geographic information systems (GIS), and geospatial big data analytics. Each of these approaches has specific advantages for assessing and mitigating flood risk [ 8 , 22 ]. Through the integration of geospatial data with computational tools, stakeholders can evaluate vulnerability, obtain important insights into the dynamics of floods, and create focused policies to lessen the effects of floods on ecosystems and populations.

The potential of geo-computational tools in flood catastrophe management is underscored by a growing body of literature. For instance, the integration of high-resolution imaging and elevation data through remote sensing offers valuable insights for mapping flood extents and identifying vulnerable locations [ 9 ]. LiDAR technology delivers precise elevation data for flood modelling and simulation, while GIS enables spatial analysis tools for evaluating flood risk and planning evacuation routes [ 47 ]. After floods, UAVs with sensors and cameras can be used for quick damage assessment and situational awareness, allowing for prompt reaction and recovery operations [ 10 ]. Furthermore, by providing stakeholders with predictive skills for early warning systems and adaptive measures, machine learning algorithms and geospatial big data analytics improve communities’ resilience to future flood occurrences [ 28 ].

The incorporation of geo-computational methods into flood disaster management signifies a fundamental change in our strategy for enhancing resilience. These strategies allow decision-makers to make well-informed decisions and efficiently deploy resources in response to flood dangers by bridging the data-decision gap. Furthermore, communities' ability to adapt to shifting climatic conditions is strengthened by the iterative nature of geo-computational modelling, which enables constant development and refining of flood risk assessments [ 55 ]. As a result, there is a growing understanding of the significance of integrating geo-computational techniques into more comprehensive frameworks for climate adaptation and disaster risk reduction, as stated in international agreements like the Paris Climate Agreement and the Sendai Framework for Disaster Risk Reduction [ 24 ]. The goal of this study is to present a thorough analysis and assessment of the function of geo-computational methods in flood disaster mitigation. The uses of remote sensing, GIS, LiDAR, UAVs, machine learning, and geospatial big data analytics in flood catastrophe management, drawing on case studies, actual data, and already published research is inverstigated. The fundamental ideas behind each method, show how each contributes to flood risk assessment and mitigation, and pinpoint important findings, difficulties, and areas that need more investigation and application are looked. With the findings of this study, the transformative capacity of geo-computational methods in fostering resilient ecosystems and communities against escalating flood risks is demonstrated.

2 Background and context

For millennia, floods have shaped cultures, economics, and landscapes, leaving an enduring impression on human history. Floods are a frequent and destructive natural phenomenon that have affected civilizations all across the world throughout recorded history. Archaeological findings and historical documentation shed light on the frequency and severity of flood episodes in various locations. Frequent floods affected the social, economic, and cultural development of ancient civilizations, including those around the Tigris and Euphrates rivers in Mesopotamia and the Nile River in Egypt. Early civilizations flourished because of the rich soil that was made available for agriculture when river valleys were flooded. Floods, on the other hand, posed serious threats to settlements as well, resulting in extensive damage and fatalities. The effects of flooding on human populations have worsened in more recent times due to the industrial revolution and increased urbanisation. The number of individuals vulnerable to floods has increased dramatically over the past century [ 24 ] with urbanisation and population growth aggravating vulnerability in flood-prone areas. Additionally, research in the past has demonstrated how climate change affects flood frequency and intensity, with rising global temperatures causing more frequent and severe extreme weather events [ 18 ].

Communities are more susceptible to flood dangers due to a number of causes, including institutional, environmental, and socioeconomic ones. Floods are frequently made worse by socioeconomic inequality, which disproportionately affects marginalised populations in the wake of a disaster. Studies from the past highlight how poverty, substandard housing, and limited resource availability contribute to an increased risk of flooding. By changing natural hydrological processes and decreasing the landscape's ability to absorb and attenuate floodwaters, environmental degradation increases the danger of flooding. Communities are more vulnerable to flooding as a result of deforestation, soil erosion, and wetland loss, which reduce natural flood defences [ 66 ]. Communities' susceptibility to flood disasters is also influenced by institutional factors, such as poor infrastructure, incompetent land-use planning, and weak governance systems. Many studies emphasise how governance and institutional capabilities shape flood resilience and vulnerability. In many areas, weak flood defences, antiquated or laxly enforced building rules, and a lack of early warning systems increase vulnerability and impede efficient disaster response and recovery operations. Flood catastrophe management still faces several obstacles in spite of technological breakthroughs and enhancements in preparedness and response. One of the main issues is the absence of thorough risk mapping and assessment, which makes it more difficult for authorities to identify high-risk locations and set priorities for mitigation activities [ 57 ].

Another issue is inadequate flood defences and infrastructure, which is especially problematic in low-lying coastal areas and heavily populated urban centres. Communities are more vulnerable to catastrophic floods because ageing infrastructure may not be able to handle the number and intensity of flood events that are occurring more frequently. Furthermore, the length and intensity of flood catastrophes can be increased by gaps in early warning systems and communication networks that obstruct prompt evacuation and emergency response operations. Vulnerability is made worse by limited access to trustworthy information and resources, which makes community resilience-building initiatives more difficult, especially in rural and isolated locations [ 15 ]. Furthermore, efforts to address underlying vulnerabilities and effectively manage flood disasters can be hampered by sociopolitical variables such as bureaucratic inefficiency, corruption, and inadequate finance [ 65 ]. Disparate approaches to flood risk reduction and mitigation can frequently be the consequence of conflicting interests and a lack of cooperation amongst government agencies and stakeholders, which further complicates catastrophe response and recovery efforts. Therefore, a comprehensive strategy including risk assessment, infrastructure development, early warning systems, and community engagement is needed to solve the current issues in flood catastrophe management. Policymakers and practitioners can devise more efficacious methods for bolstering resilience and alleviating the effects of floods on susceptible communities by comprehending the historical patterns and variables that contribute to community susceptibility.

Our methodology was designed to ensure transparency, rigor, and comprehensiveness in the review process. Drawing on established protocols in the field [ 43 ], a methodical process was used to locate and compile research on the improvement of flood disaster resistance by using geo-computational methods. The search approach comprised a careful selection of databases, such as Scopus and Web of Science, that are well-known for their extensive coverage of academic literature. The evaluation focused on the link between flood catastrophe resilience and geo-computational approaches, therefore it was carefully selected a set of keywords to catch pertinent articles. The established inclusion criteria ensured that the studies that were chosen closely matched the study goals. Acceptable papers were those that used geo-computational techniques in the context of flood management, addressed research topics methodologically, and showed some relation to the overall theme of flood catastrophe resilience. Predefined criteria were used to evaluate each paper throughout the screening process, which included a detailed review of titles, abstracts, and full-text articles. In order to promote openness and dependability in the selection process, any disagreements or ambiguities that arose during screening were settled by consensus among the study team members.

To extract the most important insights from the chosen publications, a methodical process of data extraction and synthesis was followed. To promote methodological transparency and reproducibility, the procedure followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In order to augment openness, the paper features a PRISMA Flowchart (Fig.  2 ) that outlines the successive procedures entailed in the processes of data extraction and literature selection. This flowchart walks readers through the steps of the systematic review process, from article identification to final inclusion, by providing a visual depiction of our methodology. This deliberate and transparent approach aimed to provide an extensive overview of the literature on the application of geo-computational techniques to enhance flood disaster resilience. In order to ensure the authenticity and reliability of review results and further this vital field's understanding, process adhered to stringent criteria and processes.

PRISMA Flowchart illustrating literature selection and data extraction process

3 Overview of geo-computational techniques

Within the field of geospatial sciences, the application of computational methods has completely changed how we see, process, and organise spatial data. Leading the charge in this transformation is a group of approaches known as geo-computational techniques. These methods enable the extraction of useful insights from large and intricate geospatial datasets by combining computer science, geography, and environmental science. Remote sensing, which uses satellite and aerial sensors to gather precise information on the Earth's surface, is essential to this strategy. Researchers can map land cover, track changes in the environment, and identify natural dangers with previously unheard-of accuracy by using data from remote sensing [ 29 ]. The omnipresent Geographic Information Systems (GIS), which constitute the foundation for spatial data administration and analysis, are a valuable addition to remote sensing. GIS platforms enable users to perform geographic analysis, integrate various datasets, and visually represent geospatial data in an understandable and informative manner. By making it possible to investigate spatial relationships, patterns, and processes inside geographic datasets, spatial analysis techniques provide even more value to the analytical toolbox. These techniques, which range from proximity analysis to spatial statistics, make it easier to identify geographical trends and hotspots that are important for making decisions across a variety of industries. The capabilities of geo-computational approaches have been further extended by emerging technologies like machine learning and deep learning, which allow for automated feature extraction, categorization, and prediction from geographical data. These methods have great potential for solving difficult spatial issues as they develop and grow, from public health and disaster management to urban planning and environmental preservation [ 24 ]. Because of their computational power and interdisciplinary nature, geo-computational techniques have the potential to revolutionise the geospatial industry and spur innovation (Fig.  3 ).

Applications of geo-computational techniques in flood disaster management

3.1 Principles and applications of remote sensing for flood mapping and monitoring

A key component of contemporary geospatial analysis, remote sensing provides fast and thorough information on areas that are flooded, which is essential for flood monitoring and mapping. Fundamentally, remote sensing is the process of taking pictures of the Earth's surface using sensors on board satellites or aircraft. This allows for the identification and description of flood dynamics, impacts, and extent. Researchers and practitioners can monitor floods, evaluate their severity, and assist decision-making processes that aim to reduce risks and increase resilience by utilising a variety of remote sensing platforms and approaches. A synopsis of significant research on the application of remote sensing methods to flood mapping and monitoring is included in the Table  1 . Information about the remote sensing platform used, the data resolution used, and the particular use of remote sensing for flood monitoring is included with each study (M [ 41 ]). A wide range of applications are covered by the studies, such as mapping the extent and dynamics of floods, identifying and characterising the effects of floods, estimating agricultural productivity in flooded areas, modelling flood dynamics for early warning systems and prediction, tracking long-term trends in flood patterns, and assessing the impact of climate change on flood risk. This thorough overview emphasises the various ways that remote sensing is used in flood management and emphasises how crucial it is to improving our knowledge of flood threats and assisting with well-informed decision-making. The identification of flood extent and inundation dynamics is one of the main uses of remote sensing in flood monitoring. In particular, synthetic aperture radar (SAR) imaging has proven to be an effective technique for mapping the extent of floods, particularly in regions that frequently experience cloud cover or at night. High-resolution, precise flood maps may be created thanks to SAR's capacity to see through clouds and collect data in all weather. Research have shown how useful SAR imagery is for emergency response and disaster management activities by demonstrating how well it maps flood extent during significant flooding occurrences [ 2 ]. Figure  4 shows rapid flood risk mapping schematic diagram, in which to prepare a flood risk mapping, various data such as L8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) model are called upon and are pre-processed on the Google Earth Engine (GEE) platform. Then, Landsat 8 satellite images are used to generate four indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Soil Texture (ST) ST, and Land Use (LU). Additionally, the SRTM DEM model is used to produce six indices Elevation (El), Slope (Sl), Slope Aspect (SA), River Distance (RD), Waterway and River Density (WRD) and Topographic Wetness Index (TWI). In addition, one-day precipitation data related to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) satellite are used. Therefore, in general, 11 risk indices are generated and are used to model flood risk. In the next step, the RF model is fed by the values of each risk index, which are obtained from historical floods, for the performance of the training stage. After evaluating the RF model by the testing data, a flood risk map is generated for the values of all of the pixels and then the importance of each index is determined.

Rapid flood risk mapping schematic diagram regions [ 21 ]

Apart from measuring the extent of floods, remote sensing aids in evaluating the intensity of floods and their effects on both the natural and constructed environments. Important information about how floods affect vegetation health, water quality, and land cover can be gleaned from multispectral and hyperspectral photography. Researchers may measure these alterations and evaluate the degree of flood damage to infrastructure, ecosystems, and agricultural fields using spectral analysis techniques. For instance, a study conducted in 2019 by Anderson et al. used multispectral imaging to evaluate how flooding affected agricultural output. This helped farmers and policymakers comprehend the financial effects of flood disasters and guide recovery efforts [ 1 ]. By combining pictures with hydrological models and Geographic Information Systems (GIS), remote sensing aids in the modelling and prediction of flood dynamics as well. Through the integration of remotely sensed data with rainfall records, topographical details, and hydrological parameters, scientists may replicate flood occurrences, predict their patterns, and pinpoint regions susceptible to flooding. The effectiveness of remote sensing-based flood modelling techniques in generating early warnings and guiding evacuation plans has been shown in some studies which lowers the possible loss of life and property during flood occurrences [ 39 ]. In addition, remote sensing makes it easier to track long-term trends and modifications in flood patterns, which advances our knowledge of the effects of climate change and hydrological variability. Through the examination of past satellite images and time-series data, scientists may discern patterns in the frequency, length, and intensity of floods. This allows them to evaluate the impact of climate change on flood risk and susceptibility. Therefore, remote sensing is a useful technique for mapping and monitoring floods, giving vital information for risk assessment, disaster response, and resilience-building initiatives. We can better comprehend flood threats and make educated decisions at local, regional, and global levels thanks to remote sensing's capacity to identify flood extent, evaluate severity, model dynamics, and track long-term trends. The use of remote sensing in flood management is anticipated to increase as data availability and remote sensing technology progress, providing new avenues for improving flood resilience and lowering disaster risks in a world growing more prone to disasters.

3.2 Spatial analysis and decision support for flood risk assessment using geographic information systems (GIS)

Geographic Information Systems (GIS) have become essential instruments for decision support and spatial analysis in the evaluation of flood risk. Researchers and practitioners may evaluate flood threats, vulnerabilities, and impacts with accuracy and efficiency because to GIS's integration of geographical data with analytical capabilities. Capturing, storing, manipulating, analysing, and visualising geographic data is the fundamental function of GIS, which offers a thorough framework for comprehending intricate spatial relationships and patterns. Several studies have shown how useful GIS is for assessing flood risk, highlighting its adaptability and suitability in a variety of geographic contexts and scales (Table  2 ). For example, a study simulated flood scenarios and assessed the possible effects on metropolitan areas using GIS-based hydrological models [ 14 ]. Policymakers were able to efficiently allocate resources and prioritise mitigation measures by identifying high-risk zones prone to floods through the use of spatial analytic tools including terrain modelling and proximity analysis. In order to create thorough flood risk maps, GIS also makes it easier to integrate other datasets, such as topographic maps, hydrological models, land use/cover data, and socioeconomic indicators. These maps aid in land use planning, emergency preparedness, and disaster response activities by offering important insights into flood-prone areas, exposure levels, and population risk. Other studies showed the value of spatial analysis in identifying coastal vulnerabilities and guiding adaptation strategies in the face of sea-level rise and climate change. These studies used GIS-based approaches to develop flood risk maps for coastal regions [ 21 ]. Along with mapping flood risk, GIS helps decision-making processes by giving stakeholders access to spatially explicit information and decision support tools. Built on GIS platforms, decision support systems (DSS) combine models, data, and analytical tools to help with risk assessment, scenario analysis, and ambiguous decision-making. With the use of these tools, stakeholders can investigate various approaches to flood control, assess their efficacy, and determine the best course of action based on risk reduction goals and cost–benefit analyses. The use of GIS-based DSS for flood risk management in river basins was shown in research by [ 37 ], demonstrating its potential to strengthen community resilience to floods and improve decision-making processes. Furthermore, land use changes, climatic variability, and urbanisation trends are examples of dynamic elements impacting flood risk that can be assessed with the help of GIS. Researchers can quantify the effects of these elements on flood susceptibility, exposure, and resilience through spatial analysis and modelling tools, offering important insights for long-term planning and policy development. GIS-based methods were used in studies to evaluate the impact of changes in land use on the risk of flooding in urban areas. The findings highlighted the necessity of proactive land management techniques to lessen the effects of flooding and improve urban resilience [ 56 , 63 , 70 ]. Therefore, geographic information systems (GIS) are essential for decision support and spatial analysis related to flood risk assessment. GIS's analytical powers allow geographical data, modelling tools, and decision support systems to be integrated for a thorough assessment of flood hazards, vulnerabilities, and impacts. Geographic Information System (GIS) enables stakeholders to prioritise resources, make well-informed decisions, and implement efficient flood management methods by offering spatially explicit information and decision support tools. It is anticipated that the use of GIS in flood risk assessment and management would expand as a result of climate change and urbanisation, underscoring the need for continued study and innovation in this area and contributing to the frequency and severity of floods.

3.3 High-resolution elevation data for flood modelling and simulation using LiDAR technology

Because LiDAR (Light Detection and Ranging) technology provides high-resolution elevation data necessary for accurately portraying topographical and hydrological aspects, it has revolutionised flood modelling and simulation. LiDAR makes it possible to create accurate Digital Elevation Models (DEMs) and hydrological models by capturing detailed three-dimensional information about the Earth’s surface. This allows researchers and practitioners to simulate flood events with previously unheard-of accuracy and dependability. Several studies have proven the effectiveness of LiDAR technology in flood modelling and simulation, highlighting its adaptability and suitability for a variety of flood situations and geographical locations (Table  3 ). Some conducted research that employed LiDAR-derived DEMs to enhance the precision of flood inundation mapping. This led to the development of more dependable flood risk assessments and emergency response plans. The study was able to simulate flood dynamics and evaluate the effects of several flood scenarios on infrastructure and populations by merging LiDAR data with hydrological models [ 47 ].

Furthermore, through in-depth topography analysis and hydrological modelling, LiDAR technology makes it possible to identify and characterise locations that are vulnerable to flooding. Understanding topographic elements like slope, aspect, and drainage patterns is essential for comprehending surface runoff and flood paths. LiDAR-derived elevation data can help with this. According to other studies, high-resolution elevation data is crucial for precisely defining flood hazard zones. These studies used LiDAR-based hydrological models to identify floodplains and evaluate flood risk in coastal locations. LiDAR technology provides rapid and precise information about flood dynamics, which aids in the development of early warning systems and flood mitigation techniques in addition to flood modelling and risk assessment [ 47 , 49 , 56 ]. When combined with sophisticated data processing methods, real-time LiDAR data collection makes it possible to monitor flood occurrences' extents, water levels, and flow velocities. According to research, it is feasible to use LiDAR-based monitoring systems to track the advance of floods and notify at-risk areas in a timely manner, perhaps minimising the loss of life and property. Furthermore, by making it easier to analyse historical elevation data and hydrological changes over time, LiDAR technology advances our knowledge of long-term flood trends and repercussions. Researchers can distinguish patterns in urbanisation, changes in land cover, and alterations in river courses that affect flood resilience and susceptibility by contrasting LiDAR-derived DEMs from various time periods [ 17 ]. LiDAR data was used in studiesto evaluate the effects of land use changes on flood risk in urban settings. These studies emphasise the significance of temporal dynamics in flood modelling and management strategies. LiDAR technology is critical to flood modelling and simulation because it provides high-resolution elevation data that are necessary for precise terrain and hydrological feature modelling. LiDAR helps scientists and practitioners to better analyse flood risk, create early warning systems, and lessen the effects of floods on infrastructure and populations by capturing detailed three-dimensional information about the Earth's surface. LiDAR technology is predicted to become increasingly important in flood modelling and simulation as it develops and becomes more widely available. This will present new opportunities to improve flood resilience and lower the risk of disaster in areas that are already prone to flooding [ 14 , 31 ].

3.4 Rapid deployment of unmanned aerial vehicles (UAVs) for post-disaster assessment and response

In post-disaster evaluation and response operations, unmanned aerial vehicles (UAVs) have shown to be indispensable instruments for prompt deployment. Unmanned Aerial Vehicles (UAVs) are crucial in supporting targeted response actions and decision-making processes by providing critical situational awareness through their rapid navigation of disaster-affected areas and high-resolution imagery gathering. They are significant resources in disaster management because of their adaptability and accessibility, which help responders prioritise interventions, assess damage, and identify risks quickly. Several research works have demonstrated the use of UAVs in post-disaster scenarios, demonstrating its efficacy in a range of disaster contexts and geographical locations. For instance, research showed how to employ UAVs for post-hurricane damage assessment, wherein the amount of infrastructure damage was evaluated, and restoration activities were prioritised using footage collected by the UAV [ 38 ]. In a similar vein, research demonstrated the use of UAVs in post-earthquake evaluations, where data from the drones helped quickly identify collapsed structures and evaluate access routes for rescue operations. Furthermore, UAVs are clearly superior to conventional evaluation techniques, especially when it comes to securely accessing dangerous or difficult-to-reach locations [ 33 ]. UAVs can provide real-time aerial footage and infrared imaging to support search and rescue efforts and assess the amount of damage to ecosystems and infrastructure in catastrophe situations like floods, landslides, or wildfires. The use of UAVs in flood-affected areas was emphasised by studies, where they were used to monitor floodwaters, identify submerged risks, and evaluate the impact on residents and crops. Furthermore, by lowering the time and resources needed for data gathering and processing, UAVs improve the effectiveness and cost-effectiveness of post-disaster assessments (Table  4 ). UAVs provide high-resolution imagery at a fraction of the expense of traditional approaches like manned aerial surveys or ground-based evaluations due to their rapid deployment capabilities. In post-disaster assessments, studies showed how time-saving UAVs might be. UAV-derived data facilitated quick decision-making and response coordination, which improved disaster response and recovery efforts [ 23 ].

UAVs are being used more and more for long-term recovery and resilience-building projects, in addition to their function in the immediate aftermath of a disaster. UAVs assist with urban planning, infrastructure restoration, and hazard mitigation activities by offering comprehensive aerial surveys and 3D mapping of disaster-affected areas [ 67 ]. The use of UAVs in post-disaster reconstruction projects was demonstrated by studies, where data from UAVs was used to design resilient infrastructure and land-use zoning to lessen vulnerability to future disasters. UAVs are essential to post-disaster assessment and response operations because they offer high-resolution images, quick deployment capabilities, and reasonably priced data collecting for decision-making procedures. UAVs provide in better situational awareness, search and rescue operations, and long-term rehabilitation efforts in disaster-affected areas because of their adaptability and accessibility. UAVs are predicted to become more and more important in disaster management as technology develops and becomes more widely available. This will present new chances to improve disaster resilience and lessen the effects of calamities on infrastructure and communities in the future (Mohd [ 44 ]).

3.5 Predictive capabilities for early warning systems and adaptable strategies with machine learning and geospatial big data analytics

Geospatial big data analytics and machine learning (ML) have become potent instruments for improving early warning systems and adaptive plans in disaster management. Through the use of sophisticated algorithms and enormous volumes of geospatial data, machine learning techniques make it possible to predict disaster events with previously unheard-of precision and offer insightful information for proactive decision-making (Table  5 ). The potential for enhancing disaster resilience and response plans in a variety of scenarios is enormous due to the synergy between machine learning and geographic analytics. Numerous scholarly works demonstrate the efficacy of machine learning and geospatial big data analytics in diverse facets of catastrophe management. For example, a study showed how to use machine learning (ML) algorithms to forecast landslip susceptibility based on past landslip occurrences and terrain parameters. The study attained great accuracy in detecting landslide-prone locations by analysing geospatial data, including topography, land cover, and precipitation patterns. This allowed early warning systems to reduce the risk of landslides [ 3 ].

Furthermore, by examining meteorological data, hydrological models, and historical flood records, ML approaches have demonstrated promise in the fields of flood forecasting and early warning systems. ML algorithms were used in studies to create predictive models for flood inundation mapping. This allowed authorities to create evacuation plans and send timely warnings in areas that were susceptible to flooding. These models improve preparedness and response strategies by using geographical data, such as river discharge, rainfall intensity, and terrain elevation, to increase the accuracy and dependability of flood forecasts. Apart from their predictive powers, machine learning algorithms are essential for disaster response since they optimise resource allocation and adaptive tactics [ 13 ]. The effective delivery of supplies and manpower to affected areas was made, which illustrated the application of ML-based optimisation algorithms for route planning and resource allocation in disaster logistics. These algorithms increase the efficiency of disaster response operations by optimising response efforts and reducing response times by examining geographical data, such as road networks, population density, and infrastructure damage [ 20 ].

Furthermore, during disaster events, machine learning and geospatial analytics enable real-time monitoring and situational awareness, allowing authorities to analyse the evolving situation and modify response methods as necessary. Research demonstrated how ML algorithms may be used to track the development of wildfires and evaluate the effects on impacted communities by analysing data from social media and satellite imagery. Through the integration of real-time sensor data, social media feeds, and geospatial data, these systems enable prompt actions to safeguard property and people, giving decision-makers invaluable information. Additionally, the integration of diverse data sources and the extraction of useful information from sizable geographic datasets are made possible by ML approaches [ 32 , 33 ]. In order to determine how vulnerable vital infrastructure is to natural disasters, research showed how machine learning (ML) algorithms may be used to analyse multi-source geospatial data, such as satellite imaging, aerial surveys, and ground-based sensor data. These analytics assist adaptive methods for enhancing resilience and lessening the impact of disasters on communities and infrastructure by detecting vulnerable assets and prioritising mitigation efforts. For early warning systems and disaster response, machine learning and geospatial big data analytics provide predictive capabilities and adaptive methods. Machine learning (ML) techniques provide precise catastrophic event prediction, optimal resource allocation, real-time monitoring, and integration of diverse data sources by utilising sophisticated algorithms and copious volumes of geographical data. Integration of ML and GIS analytics into disaster management systems has the potential to improve response to natural hazards and resilience as these fields continue to advance [ 48 , 62 ].

4 Methodologies and approaches

With the introduction of geo-computational technology, disaster management methodologies and approaches have changed dramatically, providing creative ways to lessen the effects of flood disasters. Case studies demonstrate how these methods are applied in real-world settings and demonstrate how well they work to improve reaction plans, risk assessment, and early warning systems (Table  6 ). For instance, research emphasises the use of remote sensing and Geographic Information Systems (GIS) for flood mapping and vulnerability assessment in metropolitan settings. Authorities were able to prioritise mitigation measures and enhance community resilience by using the study's significant insights into flood-prone areas, which were obtained through the integration of satellite imagery and spatial analysis tools. A comparative examination of methods shows the wide variety of strategies used in flood catastrophe management and their applicability in various situations. Some research compares the efficacy of machine learning algorithms, hydraulic models, and hydrological models when it comes to flood modelling [ 34 ]. In order to help decision-makers choose the best approach for their particular requirements, the comparison analysis evaluates elements including accuracy, computational efficiency, and data requirements. Additionally, comparison analyses point out the advantages and disadvantages of each strategy, directing further study and development in the field of flood disaster management. It is clear that there are obstacles and restrictions related to the use of geo-computing approaches in flood disaster management. These range from problems with data availability and quality to demands for technical competence and computational limitations [ 9 ]. Data interoperability is a major obstacle to the integration of heterogeneous datasets from various sources, including socioeconomic indicators, weather data, and satellite images. The scalability of geo-computing methodologies is also limited by computational limitations, especially when processing large-scale datasets and doing real-time analysis during crisis situations. Furthermore, widespread adoption is hampered by the technical know-how needed to develop and understand complicated modelling techniques, especially in environments with limited resources. It will take multidisciplinary cooperation and creativity in a variety of fields, such as computer science, disaster management, and geographic science, to meet these difficulties. Studies from the past highlights the value of capacity building and training initiatives to improve practitioners' and decision-makers' technical proficiency in applying geo-computational methods [ 27 ]. Moreover, developments in big data analytics and cloud computing present viable ways to get over computational limitations and increase the scalability of geo-computational models. Cloud-based systems facilitate real-time analysis of large-scale geospatial datasets by utilising distributed computing resources and parallel processing techniques. This allows for more robust and rapid decision-making in flood catastrophe management. With the use of geo-computational tools, flood disaster management methodologies and approaches have advanced significantly. Case studies show how these methods are applied in real-world settings and show how well they work to improve reaction plans, risk assessment, and early warning systems. A comparative examination of methodologies helps decision-makers choose the best methodology for their particular requirements by illuminating the advantages and disadvantages of various strategies. But problems like data interoperability, computational limitations, and the need for technical skills still exist, and their effective resolution calls for interdisciplinary cooperation and creativity. Geo-computational tools have the ability to transform flood disaster management and enhance community resilience against more frequent and severe flood disasters by surmounting these obstacles [ 33 ].

5 Consequences for disaster resiliency in flooding

The key insights and conclusions drawn from the analysis underscore the transformative possibilities offered by geo-computational methods in fostering flood-resistant communities. Through comprehensive scrutiny of case studies, comparative analyses, and challenges, this examination unveils several significant implications for enhancing flood disaster resilience. The use of geo-computational methodologies facilitates a more precise and prompt evaluation of flood risk, thus augmenting readiness and reaction endeavours. Research shows how machine learning algorithms, GIS, and remote sensing may be used to identify flood-prone locations and increase the accuracy of flood forecasts. Decision-makers can reduce the impact of floods on vulnerable areas by using predictive modelling and advanced spatial analysis to prioritise mitigation measures and distribute resources more effectively. Geo-computational techniques also make it easier to create reliable early warning systems, which means that preventive actions can be done before flood catastrophes happen [ 54 ]. The integration of UAVs for quick flood damage assessment allows for real-time aerial imagery to improve situational awareness and guide emergency response activities. Authorities can more efficiently coordinate rescue and relief efforts, detect vital infrastructure damage, and rapidly assess the extent and severity of flooding by utilising unmanned aerial vehicles (UAVs) and remote sensing capabilities. The analysis also emphasises how crucial interdisciplinary cooperation and knowledge exchange are to increasing flood disaster resistance [ 12 ]. The necessity of technical training programmes and capacity building activities to improve practitioners’ and decision-makers' abilities to use geo-computational approaches. Collaborating among researchers, policymakers, and practitioners can yield useful insights and facilitate the sharing of best practices to address shared concerns and enhance flood disaster management techniques. Furthermore, geo-computational methodologies have the capacity to significantly impact long-term resilience building and adaptation tactics in addition to urgent reaction efforts [ 50 ]. Comparative studies demonstrate the efficacy of various flood modelling techniques in a range of geographical situations, offering planners and policymakers insightful information to help them choose the best course of action for their areas. Through the integration of hydrological modelling, machine learning algorithms, and spatial analysis, communities may create comprehensive strategies for managing flood risks that take into consideration evolving environmental circumstances and climate projections. In conclusion, stakeholders may successfully lessen the effects of flood disasters and create more resilient communities by putting these suggestions into practice and utilising the transformative potential of geo-computational tools [ 40 ]. The combination of sophisticated geographical analysis and predictive modelling can open the door to a more resilient future in the face of growing dangers associated with climate change through cooperative efforts and creative solutions.

6 Discussion and future directions

The field of flood catastrophe resilience is changing quickly thanks to the ongoing development of geo-computational methods. Although our evaluation has offered a thorough analysis of the available literature, more investigation and synthesis of the results are necessary to completely clarify the consequences and provide possible future paths in this important area. The application of geo-computational technologies for flood catastrophe resistance is highlighted by our examination of new advances and trends in this field. We acknowledge that the significance of our results warrants more investigation beyond simple listing. For example, we may provide concrete instances of the effectiveness of geo-computational approaches in improving flood resilience by examining particular case studies where they have been applied successfully [ 4 ]. Furthermore, filling in the gaps in the literature and suggesting new lines of inquiry can enhance our conversation, point scholars in new areas, and encourage creativity in flood catastrophe management.

In flood risk management, it is critical to close the gap between theoretical developments and real-world implementation. We may help the smooth incorporation of geo-computational approaches into present practices by clarifying the practical implications of our findings for practitioners, policymakers, and community stakeholders. Giving stakeholders specific examples of how these results might influence decision-making procedures will enable them to take proactive steps to reduce the danger of flooding and improve their preparation for emergencies [ 45 ]. The intricacy of flood catastrophe resilience demands interdisciplinary and cross-sector collaboration. Stakeholders may work together to overcome issues like limited computing power and data interoperability by collaborating across disciplines. Additionally, promoting standardisation of risk assessment protocols and modelling techniques would encourage knowledge exchange and interoperability among stakeholders, enabling more effective and efficient flood disaster management. Encouraging practitioners and decision-makers to apply geo-computational approaches with greater technical competency requires investments in training programmes and capacity building activities. Prioritising professional development and education will enable people and organisations to use innovative ideas and state-of-the-art technology in flood resilience initiatives [ 52 ].

New developments and trends in geo-computational methods for flood catastrophe resistance portend a bright future for disaster management. Novel applications of geo-computational techniques are being investigated to improve flood resistance at different scales as technology advances. The use of machine learning (ML) and artificial intelligence (AI) algorithms into models for assessing flood risk is one such trend. Predictive models and early warning systems can now analyse large, complicated datasets and find minute patterns in the dynamics of floods thanks to these sophisticated methodologies. Furthermore, during flood disasters, there are never-before-seen possibilities for real-time monitoring and decision-making thanks to the development of cloud computing and big data analytics. Through the utilisation of scalable data processing systems and distributed computing resources, stakeholders can optimise response plans in near real-time and obtain vital insights into flood dynamics. Furthermore, new opportunities for data gathering and monitoring in flood-prone areas are presented by the development of cutting-edge sensor technologies and unmanned aerial vehicles (UAVs) [ 54 ]. Accurate flood modelling and risk assessment are made possible by the deep insights into terrain form and flood extent provided by high-resolution elevation data acquired by LiDAR technology and UAV-based images. Furthermore, the incorporation of citizen science projects and crowdsourced data enables local people to actively engage in flood monitoring and response operations, promoting resilience at the local level. In spite of tremendous progress, more study and cooperation are still desperately needed in the area of flood disaster resistance [ 64 ]. Researchers, legislators, practitioners, and community stakeholders must work together to address complicated issues like data interoperability, computational limitations, and technological skills. Developing comprehensive and context-specific solutions to lessen the effects of flood disasters requires interdisciplinary cooperation that span the gaps between academic institutions, governmental bodies, non-profit organisations, and local communities. Standardisation of modelling approaches, risk assessment procedures, and data protocols is also necessary in order to promote knowledge sharing and interoperability among various industries and geographical areas. Stakeholders can expedite data sharing and collaboration, leading to more efficient decision-making and resource allocation in flood catastrophe management, by instituting standard frameworks and best practices. Additionally, training programmes and capacity development activities are crucial for improving the technical proficiency and knowledge base of practitioners and decision-makers in the efficient application of geo-computational methodologies. By making investments in professional development and education, people and organisations will be better equipped to prepare for and implement flood resilience using cutting-edge technologies and creative solutions.

7 Conclusion

Mitigating the effects of flood disasters and constructing future-ready communities require proactive efforts. Through increased risk assessment, early warning systems, and adaptive methods, the integration of geo-computational approaches presents hitherto unseen prospects to improve flood resistance. Stakeholders can create more sustainable and effective solutions to the intricate problems presented by flood disasters by utilising new technological trends and developments. However, cooperation and coordinated efforts from a variety of stakeholders are needed to make significant progress. Everyone may contribute to increasing flood catastrophe resilience from academics and politicians to practitioners and community members. We can create a future that is more robust to flood disasters and more capable of thriving in the midst of hardship by cooperating, exchanging information and resources, and embracing innovation.

Data availability

No datasets were generated or analysed during the current study.

Abdelkareem M, Mansour AMJNH. Risk assessment and management of vulnerable areas to flash flood hazards in arid regions using remote sensing and GIS-based knowledge-driven techniques. Natural Hazards. 2023;117:2269–95.

Article   Google Scholar  

Adjovu GE, Stephen H, James D, Ahmad SJRS. Overview of the application of remote sensing in effective monitoring of water quality parameters. Remote Sensing. 2023;15:1938.

Ado M, Amitab K, Maji AK, Jasińska E, Gono R, Leonowicz Z, Jasiński M. Landslide susceptibility mapping using machine learning: a literature survey. Remote Sensing. 2022;14(13):3029.

Auliagisni W, Wilkinson S, Elkharboutly M. Learning from floods—how a community develops future resilience. Water. 2022;14:3238.

Banjara M, Bhusal A, Ghimire AB, Kalra A. Impact of land use and land cover change on hydrological processes in urban watersheds: analysis and forecasting for flood risk management. Geosciences. 2024. https://doi.org/10.3390/geosciences14020040 .

Bernasconi A. Data quality-aware genomic data integration. Computer Methods Programs Biomed Update. 2021;1:100009.

Brêda JPLF, Cauduro Dias Paiva R, Siqueira VAC. Assessing climate change impact on flood discharge in South America and the influence of its main drivers. J Hydrol. 2023;619:129284.

Bulti DT, Girma B, Megento TL. Community flood resilience assessment frameworks: a review. SN Applied Sciences. 2019;1:1663.

Chan SW, Abid SK, Sulaiman N, Nazir U, Azam K. A systematic review of the flood vulnerability using geographic information system. Heliyon. 2022;8: e09075.

Damaševičius R, Bacanin N, Misra S. From sensors to safety: internet of emergency services (IoES) for emergency response and disaster management. J Sensor Actuator Networks. 2023;12:41.

Dasgupta A, Grimaldi S, Ramsankaran R, Pauwels VRN, Walker JP, Chini M, Hostache R, Matgen P. Flood mapping using synthetic aperture radar sensors from local to global scales. Global Flood Hazard. 2018;3:55.

de Bruijn KM, Jafino BA, Merz B, Doorn N, Priest SJ, Dahm RJ, Zevenbergen C, Aerts JCJH, Comes T. Flood risk management through a resilience lens. Commun Earth Environ. 2022;3:285.

De Maio C, Fenza G, Gallo M, Loia V, Volpe A. A perceived risk index leveraging social media data: assessing severity of fire on microblogging. Cognitive Comput. 2024;10(1):11.

Google Scholar  

Dey NN, Rakib AL, Kafy A, Raikwar AA. Geospatial modelling of changes in land use/land cover dynamics using multi-layer Perceptron Markov chain model in Rajshahi City Bangladesh. Environ Challenges. 2021;4:100148.

Dhiman R, Vishnuradhan R, Eldho TI, Inamdar A. Flood risk and adaptation in Indian coastal cities: recent scenarios. Appl Water Sci. 2018;9:5.

Duarte L, Teodoro AC, Gonçalves H. Evaluation of spatial thinking ability based on exposure to geographical information systems (GIS) concepts in the context of higher education. ISPRS Int J Geo-Inform. 2022;11(8):417.

Duminda P, Ousmane S, Jetal A, Hamid M, Mohamed R. Challenges and technical advances in flood early warning systems (FEWSs). In: Guangwei H, editor. Flood impact mitigation and resilience enhancement. Rijeka: IntechOpen; 2020.

Ebi KL, Vanos J, Baldwin JW, Bell JE, Hondula DM, Errett NA, Hayes K, Reid CE, Saha S, Spector J, Berry P. Extreme weather and climate change: population health and health system implications. Annu Rev Public Health. 2021;42:293–315.

Efraimidou E, Spiliotis M. A GIS-based flood risk assessment using the decision-making trial and evaluation laboratory approach at a regional scale. Environ Process. 2024;11:9.

Ezequiel CAF, Cua M, Libatique NC, Tangonan GL, Alampay R, Labuguen RT, Favila CM, Honrado JLE, Caños V, Devaney C, Loreto AB, Bacusmo J, Palma B. UAV aerial imaging applications for post-disaster assessment, environmental management and infrastructure development. Int Conf Unmanned Aircraft Syst (ICUAS). 2014;1:274–83.

Farhadi H, Najafzadeh M. Flood risk mapping by remote sensing data and random forest technique. Water. 2021;13:15.

Gao M, Wang Z, Yang H. Review of urban flood resilience: insights from scientometric and systematic analysis. Int J Environ Res Public Health. 2022. https://doi.org/10.3390/ijerph19148837 .

Giardina G, Macchiarulo V, Foroughnia F, Jones JN, Whitworth MRZ, Voelker B, Milillo P, Penney C, Adams K, Kijewski-Correa T. Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions. Bull Earthquake Eng. 2023;22(7):3415.

Gibson TD, Pelling M, Ghosh A, Matyas D, Siddiqi A, Solecki W, Johnson L, Kenney C, Johnston D, du Plessis R. Pathways for transformation: disaster risk management to enhance resilience to extreme events. J Extreme Events. 2016;03:1671002.

Grygoruk M, Kochanek K, Mirosław-Świątek D. Analysis of long-term changes in inundation characteristics of near-natural temperate riparian habitats in the lower Basin of the Biebrza Valley. Poland J Hydrol Regional Stud. 2021;36: 100844.

Hu L, Zhang C, Zhang M, Shi Y, Lu J, Fang Z. Enhancing FAIR data services in agricultural disaster: a review. Remote Sensing. 2023;15:8.

Hulayyil SB, Li S, Xu L. Machine-learning-based vulnerability detection and classification in internet of things device security. Electronics. 2023;12:3927.

Hussain M, Tayyab M, Ullah K, Ullah S, Rahman ZU, Zhang J, Al-Shaibah B. Development of a new integrated flood resilience model using machine learning with GIS-based multi-criteria decision analysis. Urban Climate. 2023;50: 101589.

Hussain Z, Khan IA, Hassan MJPGR. Machine learning approaches for dengue prediction: a review of algorithms and applications. Pak Geogr Rev. 2023;78:15–36.

Iqbal U, Riaz MZ, Zhao J, Barthelemy J, Perez P. Drones for flood monitoring, mapping and detection: a bibliometric review. Drones. 2023;7(1):32.

Jakovljević G, Govedarica M. Water body extraction and flood risk assessment using lidar and open data. In: Leal Filho W, Trbic G, Filipovic D, editors. Climate Change Adaptation in Eastern Europe: Managing Risks and Building Resilience to Climate Change. Cham: Springer International Publishing; 2019.

Jiang Y, Yuan Y. Emergency logistics in a large-scale disaster context: achievements and challenges. Int J Environ Res Publ Health. 2019;16:779.

Kanwal R, Rafaqat W, Iqbal M, Weiguo S. Data-driven approaches for wildfire mapping and prediction assessment using a convolutional neural network (CNN). Remote Sensing. 2023;15:5099.

Khan SM, Shafi I, Butt WH, Díez ID, Flores MA, Galvlán JC, Ashraf I. Model driven approach for efficient flood disaster management with meta model support. Land. 2023;12:1538.

Kumar V, Sharma KV, Caloiero T, Mehta DJ, Singh K. Comprehensive overview of flood modeling approaches: a review of recent advances. Hydrology. 2023;10:141.

Li L, Xu T, Chen Y. Improved urban flooding mapping from remote sensing images using generalized regression neural network-based super-resolution algorithm. Remote Sensing. 2016;8:625.

Li Z, Zhang Q, Wu F, Wang P. Decision support system for integrated and adaptive water governance. In: Deng X, Gibson J, editors. River Basin Management. Singapore: Springer; 2019.

Lin C, Li Y, Liu Y, Wang X, Geng S. building damage assessment from post-hurricane imageries using unsupervised domain adaptation with enhanced feature discrimination. IEEE Trans Geosci Remote Sens. 2022;60:1–10.

Liu Z, Jiayi X, Liu M, Yin Z, Liu X, Yin L, Zheng W. Remote sensing and geostatistics in urban water-resource monitoring: a review. Mar Freshw Res. 2023. https://doi.org/10.1071/MF22167 .

Lwin KK, Pal I, Shrestha S, Warnitchai P. Assessing social resilience of flood-vulnerable communities in Ayeyarwady Delta, Myanmar. Int J Dis Risk Reduct. 2020;51: 101745.

Amen M, Mustafa AR, Kareem A, Hameed DA, Mirza HM, Szydłowski AA, Saleem MM. Mapping of flood-prone areas utilizing GIS techniques and remote sensing: a case study of Duhok Kurdistan Region of Iraq. Remote Sensing. 2023;15:1102.

Mason DC, Bevington J, Dance SL, Revilla-Romero B, Smith R, Vetra-Carvalho S, Cloke HL. Improving urban flood mapping by merging synthetic aperture radar-derived flood footprints with flood hazard maps. Water. 2021;13:1577.

Moghadas M, Asadzadeh A, Vafeidis A, Fekete A, Kötter T. A multi-criteria approach for assessing urban flood resilience in Tehran. Iran Int J Disaster Risk Reduct. 2019;35: 101069.

Daud SMSM, Yusof MYPM, Heo CC, Khoo LS, Singh MKC, Mahmood MS, Nawawi H. Applications of drone in disaster management: a scoping review. Sci Justice. 2022;62(30):42.

Morrison A, Westbrook CJ, Noble BF. A review of the flood risk management governance and resilience literature. J Flood Risk Management. 2018;11(291):304.

Mosavi A, Ozturk P, Chau K-W. Flood prediction using machine learning models: literature review. Water. 2018;10:1536.

Muhadi NA, Abdullah AF, Bejo SK, Mahadi MR, Mijic A. The use of LiDAR-derived DEM in flood applications: a review. Remote Sensing. 2020;12:2308.

Muñoz P, Orellana-Alvear J, Bendix J, Feyen J, Célleri R. Flood early warning systems using machine learning techniques: the case of the tomebamba catchment at the southern andes of ecuador. Hydrology. 2021;8:183.

Nandam V, Patel PL. A framework to assess suitability of global digital elevation models for hydrodynamic modelling in data scarce regions. J Hydrol. 2024;630: 130654.

Obuobi-donkor G, Eboreime E, Shalaby R, Agyapong B, Adu MK, Owusu E, Mao W, Oluwasina F, Pazderka H, Agyapong VIO. Evaluating Community resilience and associated factors one year after the catastrophic fort mcmurray flood. Int J Environ Res Public Health. 2022;19:16153.

Prashar N, Lakra HS, Shaw R, Kaur H. Urban flood resilience: a comprehensive review of assessment methods, tools, and techniques to manage disaster. Progress in Disaster Science. 2023;20: 100299.

Rahman M, Ningsheng C, Mahmud GI, Islam MM, Pourghasemi HR, Ahmad H, Habumugisha JM, Washakh RMA, Alam M, Liu E, Han Z, Ni H, Shufeng T, Dewan A. Flooding and its relationship with land cover change, population growth, and road density. Geosci Front. 2021;12: 101224.

Rahman MS, Di L, Yu E, Lin L, Yu Z. Remote sensing based rapid assessment of flood crop damage using novel disaster vegetation damage index (DVDI). Int J Dis Risk Sci. 2021;12:90–110.

Reynoso Vanderhorst HD, Pathirage C, Proverbs D. Navigating Flood resilience: challenges, solutions, and lessons learnt from the dominican Republic. Water. 2024;16:382.

Rezvani SM, Falcão MJ, Komljenovic D, de Almeida NM. A Systematic literature review on urban resilience enabled with asset and disaster risk management approaches and gis-based decision support tools. Appl Sci. 2023;13(4):2223.

Article   CAS   Google Scholar  

Roldán-Valcarce A, Jato-Espino D, Manchado C, Bach PM, Kuller M. Vulnerability to urban flooding assessed based on spatial demographic, socio-economic and infrastructure inequalities. Int J Disaster Risk Reduct. 2023;95: 103894.

Rosmadi HS, Ahmed MF, Mokhtar MB, Lim CK. Reviewing challenges of flood risk management in Malaysia. Water. 2023;15:2390.

Schumann G, Giustarini L, Tarpanelli A, Jarihani B, Martinis S. Flood modeling and prediction using earth observation data. Surv Geophys. 2023;44:1553–78.

Sitas N, Reyers B, Cundill G, Prozesky HE, Nel JL, Esler KJ. Fostering collaboration for knowledge and action in disaster management in South Africa. Current Opin Environ Sustain. 2016;19:94–102.

Skakun S, Kussul N, Shelestov A, Kussul O. Flood hazard and flood risk assessment using a time series of satellite images: a case study in Namibia. Risk Anal. 2014;34:1521–37.

Skoulikaris C. Large-scale hydrological models and transboundary river basins. Water. 2024;16:878.

Tang Y, Sun Y, Han Z, Soomro S-E-H, Wu Q, Tan B, Hu C. flood forecasting based on machine learning pattern recognition and dynamic migration of parameters. J Hydrol Regional Stud. 2023;47: 101406.

Tanim AH, McRae CB, Tavakol-Davani H, Goharian E. Flood detection in urban areas using satellite imagery and machine learning. Water. 2022;14(7):1140.

Trevisani S, Omodeo PD. Mapping transitions and alterations in complex environments paradigms & approaches introduction to designing environments. Chem: Springer; 2023.

Wang L, Cui S, Li Y, Huang H, Manandhar B, Nitivattananon V, Fang X, Huang W. A review of the flood management: from flood control to flood resilience. Heliyon. 2022;8: e11763.

Ward PJ, de Ruiter MC, Mård J, Schröter K, van Loon A, Veldkamp T, von Uexkull N, Wanders N, Aghakouchak A, Arnbjerg-Nielsen K, Capewell L. The need to integrate flood and drought disaster risk reduction strategies. Water Security. 2020;11: 100070.

Wu L, Tong J, Wang Z, Li J, Li M, Li H, Feng Y. Post-flood disaster damaged houses classification based on dual-view image fusion and concentration-based attention module. Sustain Cities Soc. 2024;103: 105234.

Ye X-W, Ma S-Y, Liu Z-X, Ding Y, Li Z-X, Jin T. Post-earthquake damage recognition and condition assessment of bridges using UAV integrated with deep learning approach. Struct Control Health Monit. 2022;29: e3128.

Zhai L, Lee JE. Exploring and enhancing community disaster resilience: perspectives from different types of communities. Water. 2024;16:881.

Zhang W, Hu B, Liu Y, Zhang X, Li Z. Urban flood risk assessment through the integration of natural and human resilience based on machine learning models. Remote Sensing. 2023;15:3678.

Zhu S, Feng H, Arashpour M, Zhang F. Enhancing urban flood resilience: a coupling coordinated evaluation and geographical factor analysis under SES-PSR framework. Int J Disaster Risk Reduct. 2024;101: 104243.

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Jain, H. Leveraging geo-computational innovations for sustainable disaster management to enhance flood resilience. Discov Geosci 2 , 33 (2024). https://doi.org/10.1007/s44288-024-00042-0

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