Impact Forecasting to Support Emergency Management of Natural Hazards

Merz, Bruno; Kuhlicke, Christian; Kunz, Michael; Pittore, Massimiliano; Babeyko, Andrey; Bresch, David N.; Domeisen, Daniela I. V.; Feser, Frauke; Koszalka, Inga Monika; Kreibich, Heidi; Pantillon, Florian; Parolai, Stefano; Pinto, Joaquim G.; Punge, Heinz Jürgen; Rivalta, Eleonora; Schröter, Kai; Strehlow, K.; Weisse, Ralf; Wurpts, Andreas

doi:10.1029/2020rg000704

Cited by 141 publications

(104 citation statements)

References 394 publications

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“…These identified drivers of impact can serve as a basis for effective early warning systems that provide valuable information to decision makers (Merz et al, 2020). Acting in advance can be critical to avoid crop loss and associated socio-economic consequences.…”

Section: Discussionmentioning

confidence: 99%

Impacts of hot-dry compound extremes on US soybean yields

Hamed

Loon

Aerts

et al. 2021

Preprint

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Abstract. The US agriculture system supplies more than one-third of globally-traded soybean and with 90 % of US soybean produced under rainfed agriculture, soybean trade is particularly sensitive to weather and climate variability. Average growing season climate conditions can explain about one-third of US soybean yield variability. Additionally, crops can be sensitive to specific short-term weather extremes, occurring in isolation or compounding at key moments throughout crop development. Here, we identify the dominant within-season climate drivers that can explain soybean yield variability in the US, and explore synergistic effects between drivers that can lead to severe impacts. The study combines weather data from reanalysis, satellite-based evapotranspiration and root-zone soil moisture with sub-national crop yields using statistical methods that account for interaction effects. Our model can explain on average about half of the year-to-year yield variability (60 % on all years and 40 % on out-of-sample predictions). The largest negative influence on soybean yields is driven by high temperature and low soil moisture during the summer crop reproductive period. Moreover, due to synergistic effects, heat is considerably more damaging to soybean crops during dry conditions, and less so during wet conditions. Compound and interacting hot and dry August conditions (defined by the 95th and 5th percentiles of temperature and soil moisture, respectively) reduce yields by 1.25 standard deviation. This sensitivity is, respectively, 6 and 3 times larger than the sensitivity to hot or dry conditions alone. Other important drivers of negative yield responses are lower evapotranspiration early in the season and lower minimum temperature late in the season, both likely reflecting an increased risk of frost. The sensitivity to the identified drivers varies across the spatial domain with higher latitudes, and thus colder regions, being less sensitive to hot-dry August months. Historic trends in identified drivers indicates that US soybean has generally benefited from recent shifts in weather. Overall warming conditions have reduced the risk of frost in early and late-season and potentially allowed for earlier sowing dates. More importantly, summers have been getting cooler and wetter over eastern US. Still, despite these positive changes, we show that the frequency of compound hot-dry August month has remained unchanged over 1946–2016. Moreover, in the longer term, climate models project substantially warmer summers for the continental US which likely creates risks for soybean production.

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Section: Discussionmentioning

confidence: 99%

Impacts of hot-dry compound extremes on US soybean yields

Hamed

Loon

Aerts

et al. 2021

Preprint

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“…Hence, there is a need for automated and fast computational methods which exclude both the mobilization of hydraulician's expert knowledge and thorough calibration of models. Running a variety of scenarios with different boundary conditions and/or parameters to represent uncertainties, and/or integrating mapping approaches in real-time forecasting chains, may make the question of computational time even more critical (Savage et al, 2016;Dottori et al, 2017;Morsy et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Performance of automated methods for flash flood inundation mapping: a comparison of a digital terrain model (DTM) filling and two hydrodynamic methods

Hocini

Payrastre

Bourgin

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Flash floods observed in headwater catchments often cause catastrophic material and human damage worldwide. Considering the large number of small watercourses possibly affected, the use of automated methods for flood inundation mapping at a regional scale can be of great help for the identification of threatened areas and the prediction of potential impacts of these floods. An application of three mapping methods of increasing level of complexity is presented herein, including a digital terrain model (DTM) filling approach (height above nearest drainage/Manning–Strickler or HAND/MS) and two hydrodynamic methods (caRtino 1D and Floodos 2D). These methods are used to estimate the flooded areas of three major flash floods observed during the last 10 years in southeastern France, i.e., the 15 June 2010 flooding of the Argens river and its tributaries (585 km of river reaches), the 3 October 2015 flooding of small coastal rivers of the French Riviera (131 km of river reaches) and the 15 October 2018 flooding of the Aude river and its tributaries (561 km of river reaches). The common features of the three mapping approaches are their high level of automation, their application based on a high-resolution (5 m) DTM, and their reasonable computation times. Hydraulic simulations are run in steady-state regime, based on peak discharges estimated using a rainfall–runoff model preliminarily adjusted for each event. The simulation results are compared with the reported flood extent maps and the high water level marks. A clear grading of the tested methods is revealed, illustrating some limits of the HAND/MS approach and an overall better performance of hydraulic models which solve the shallow water equations. With these methods, a good retrieval of the inundated areas is illustrated by critical success index (CSI) median values close to 80 %, and the errors on water levels remain mostly below 80 cm for the 2D Floodos approach. The most important remaining errors are related to limits of the DTM, such as the lack of bathymetric information, uncertainties on embankment elevation, and possible bridge blockages not accounted for in the models.

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“…This reflects the current differences in the warning capabilities of river floods and convective storms or flash floods: while river floods, particularly at reaches downstream, can be forecasted several days in advance; forecasting convective storms that cause pluvial flooding is more 375 challenging due to the dynamic formation of convective cells. Hence, lead times are restricted to a few hours, if at all (Merz et al, 2020). This is illustrated by the average lead time that is particularly short for the flash floods in 2016 (Table 4).…”

Section: Penning-rowsell and Green 2000) 360mentioning

confidence: 99%

Compound flood events: different pathways–different impacts–different coping options?

Thieken¹,

Mohor²,

Kreibich³

et al. 2021

Preprint

Self Cite

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Abstract. Several severe flood events hit Germany in recent years, with events in 2013 and 2016 being the most destructive ones although dynamics and flood processes were very different. While the 2013-event was a slowly rising widespread fluvial flood accompanied by some severe dike breaches, the events in 2016 were fast onset pluvial floods, which resulted in some places in surface water flooding due to limited capacities of the drainage systems and in others, particularly in small steep catchments, in destructive flash floods with high sediment loads. Hence, different pathways, i.e. different routes that the water takes to reach (and potentially damage) receptors, in our case private households, can be identified in both events. They can thus be regarded as spatially compound flood events. This paper analyses how affected residents coped with these different flood types (fluvial and pluvial) and their impacts while accounting for the different pathways (river flood, dike breach, surface water flooding and flash flood) within the events. The analyses are based on two data sets with 1652 (for the 2013-flood) and 601 (for the 2016-flood) affected residents who were surveyed around nine months after each flood, revealing little socio-economic differences–except for income–between the two samples. The four pathways showed significant differences with regard to their hydraulic and financial impacts, recovery, warning processes as well as coping and adaptive behaviour. There are no or just small differences with regard to perceived self-efficacy and responsibility offering entry points for tailored risk communication and support.

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Impact Forecasting to Support Emergency Management of Natural Hazards

Cited by 141 publications

References 394 publications

Impacts of hot-dry compound extremes on US soybean yields

Impacts of hot-dry compound extremes on US soybean yields

Performance of automated methods for flash flood inundation mapping: a comparison of a digital terrain model (DTM) filling and two hydrodynamic methods

Compound flood events: different pathways–different impacts–different coping options?

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