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REVIEW 3 major objections 5 minor 37 references

Hurricane Impact Index for Assessing Direct and Indirect Hazards in Central America

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims a Hurricane Impact Index that separates direct track damage from indirect orographic wind-and-rain effects, and that validation against 2016–2022 disaster records confirms it works across Central America.

desk verdict A genuinely new direct/indirect hurricane impact decomposition, but the reliability claim outruns the evidence: the Pacific-coast finding rests on an underspecified angular proxy, and the validation is qualitative. read the letter →

arxiv 2506.19858 v1 pith:6DZQLAO4 submitted 2025-06-10 physics.ao-ph stat.AP

Manrique Camacho , Amanda Cedeño , Luis A. Barboza , Shu Wei Chou-Chen , Mario J. Gómez , Hugo G. Hidalgo This is my paper · ORCID
classification physics.ao-phstat.AP
keywords HurricaneImpactIndextropicalcyclonedirectandindirecteffectsvorticityprecipitationwindspeedorographicliftingCentralAmerica
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that one number, the Hurricane Impact Index (HII), can capture both the direct damage along a hurricane's path and the indirect damage it causes far from the path, and that the index is dependable enough for Central American hazard assessment. The paper argues that separating the two effects matters because places that rarely see landfall, such as the Pacific coast of southern Central America, still suffer serious consequences from nearby cyclones. Validated on six hurricanes between 2016 and 2022 using reported disaster records from Costa Rica, the index is claimed to reproduce observed impact patterns while also revealing a clear hotspot: the Pacific coast, driven mainly by indirect effects. Care matters because the index is designed to be cheap to compute from storm tracks and reanalysis fields, making it applicable to simulated or forecast hurricane tracks.

What carries the argument

The load-bearing object is the pair of effect formulas (equations (2) and (3)) and their normalized sum (equation (4)). The direct effect is a vorticity-thresholded indicator of being inside the storm's dynamical influence within 500 km, weighted by grid-cell length divided by the six-hour time step times translational speed, multiplied by the normalized product of wind speed and precipitation. The indirect effect is a wind-direction indicator: one if the local wind vector points within $\pm 45^\circ$ of the principal axis of the nearest mountain range above 500 m (an axis oriented near $90^\circ$ relative to the Pacific coast), weighted by normalized precipitation and the same storm-motion factor, and zero in cells already counted as direct. The two components are normalized separately to [0,1], summed, and normalized again, which makes direct and indirect contributions comparable and lets time series and spatial aggregations be read on a common scale.

What would settle it

Recompute the index on the same six storms with the $\pm 45^\circ$ window changed to $\pm 30^\circ$ and $\pm 60^\circ$ or removed: if the Pacific-coast hotspot shifts dramatically or agreement with the reported damage maps worsens, the threshold itself, not a physical process, is producing the headline result.

Watch

Extended reading notes

Core claim

The central claim is that the Hurricane Impact Index, defined as the sequentially min-max-normalized sum of a direct effect and an indirect effect, quantifies the total impact of a tropical cyclone on each $1^\circ\times 1^\circ$ grid cell. The direct effect is the storm's wind-and-precipitation influence within a vorticity-defined region along its track; the indirect effect is the precipitation-weighted influence in cells whose wind vectors lie within $\pm 45^\circ$ of the principal axis of the nearest mountain range above 500 m, a geometric proxy for orographic lifting. Aggregated over the six storms of 2016–2022, the index shows the Pacific coast of Central America as the region with highest combined impact, and this is carried by indirect effects: for Hurricane Nate the indirect impact reaches roughly five times the direct impact. Validation against reported disaster records in Costa Rica is taken as evidence that the index is reliable and applicable across diverse topographic and climatic conditions.

Load-bearing premise

The load-bearing premise is that a wind vector pointing within $\pm 45^\circ$ of the nearest mountain range (above 500 m, with its axis near $90^\circ$ to the Pacific coast) reliably indicates orographic lifting and pressure-gradient forcing; the paper asserts this proxy but does not test it.

Editorial extensions

If this is right

  • Disaster managers can use HII maps to see that Pacific-coast communities, despite no direct landfall, are among the most hurricane-exposed in Central America.
  • Summing the index over storms identifies repeated-impact hotspots and supports prioritizing mitigation in those areas.
  • Because the index is computed from storm tracks and reanalysis fields, it can be applied to climate-model or forecast tracks to compare future and historical hurricane impacts at low computational cost.
  • The temporal decomposition shows when each event's impact peaks and reveals that weak storms such as Nate and Julia can produce combined impacts comparable to or greater than Category 4 hurricanes.
  • The same index structure can be transferred to other basins under regional names such as Cyclone Impact Index or Typhoon Impact Index.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension would be to treat the $\pm 45^\circ$ and 500 m thresholds as tunable parameters and map how the Pacific-coast hotspot shifts as they vary; the paper does not report this sensitivity analysis.
  • The indirect mechanism could be checked independently by comparing precipitation anomalies in flagged cells against cells outside the angular window at similar storm distances for the same six events.
  • Because validation rests on Costa Rica's continuous disaster record, independent loss data from other Central American countries would be needed to confirm the index's reliability elsewhere.
  • The same formula could be ported to other tropical basins with the mountain-axis orientation recomputed from local topography, making the indirect component region-specific rather than Central America-specific.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The manuscript proposes a Hurricane Impact Index (HII) for Central America that separates direct effects (vorticity-based proximity to the storm track, weighted by normalized wind and precipitation) from indirect effects (a binary indicator based on wind direction relative to mountain-range orientation, weighted by normalized precipitation). The index is demonstrated on six hurricanes (Otto 2016, Nate 2017, Eta and Iota 2020, Bonnie and Julia 2022), aggregated spatially into HII_R and temporally into HIITS1/2. The authors claim the index is validated by comparison with DesInventar disaster records, primarily for Costa Rica, and highlight that the Pacific coast experiences the largest indirect impacts.

Significance. If the HII were shown to be robust, it would provide a spatially explicit, decomposable measure of hurricane impact that explicitly separates direct from indirect (orographic/remote) effects—a gap in existing multi-hazard hurricane indices. The use of ERA5 and HURDAT2 data, with a clear mathematical structure, is a useful template for regional hazard assessment. However, the significance is currently limited by the lack of quantitative validation and by the under-specified and untested indirect-impact mechanism that drives the main spatial finding.

major comments (3)
  1. [§2.2, Eq. (3)] The indicator I_{i,h,t} that defines indirect effects is not operationally specified. The text states that wind vectors must lie within ±45° of 'the principal axis of the nearest mountain range—itself oriented near 90° relative to the Pacific coastline,' but no algorithm is given for identifying the nearest mountain range, computing its principal axis, or assigning the 90° reference. The thresholds (500 m elevation, ±45° window, 90° orientation) are asserted without sensitivity analysis or physical justification. Because this binary term is the mechanism that produces the paper's central result—high Pacific-coast impact from indirect effects (Figs. 3 and 4, §3 and §4)—the claim that the HII reliably quantifies indirect hazards is not supported until this component is defined precisely and tested.
  2. [§3.1 and Abstract/Introduction] The validation does not support the claim that 'validation using events from 2016 to 2022 confirms its applicability and reliability.' The comparison against DesInventar is qualitative and limited to Costa Rica (the only country with a continuous record), the same six events are used for both demonstration and validation, and the authors themselves note that DesInventar is biased toward densely populated areas and may underreport rural damage. No quantitative skill metric (e.g., spatial correlation, hit rate, false-alarm ratio) is provided, and there is no comparison against a null model or a simpler baseline. The validation also cannot isolate the indirect component, since HII_R sums direct and indirect effects.
  3. [§2.2, Eqs. (4)–(5) and §3, Fig. 2] The min-max normalization in Eq. (4) is applied per hurricane over grid cells, so HII values are only relative within a single event; numerical HII scores for different hurricanes are not directly comparable. The statement in §3 that 'Nate generated the most severe combined impact' is based on Eq. (8), which uses raw sums, but the main HII maps in Fig. 2 and the aggregated HII_R in Eq. (6) inherit this normalization. The manuscript should clarify that cross-event comparisons are only meaningful through the temporal indices (Eqs. 7–9), and should not attribute comparative severity to the HII scale shown in Figs. 2 and 3.
minor comments (5)
  1. [Data availability / Code availability] The Code availability statement says the code is available 'at this repository' but does not provide a URL; this makes it impossible to reproduce the computation of the indirect indicator I_{i,h,t}, which is critical given the ambiguity in its definition.
  2. [§2.2, Eq. (1)] The normalization in Eq. (1) uses the 10th and 90th percentiles of the cell's historical record for the event's month, but the reference period (1993–2023) is stated later in the same paragraph; please state the reference period explicitly in the equation description to avoid ambiguity.
  3. [§3.1 and Fig. 5] The qualitative comparison with DesInventar would be much more informative if the authors provided a quantitative agreement metric (e.g., the fraction of DesInventar cells falling within the high-HII_R category) or at least a side-by-side categorical map with the same color scale and spatial resolution; the current narrative is subjective.
  4. [Throughout] There are several typographical errors and inconsistencies: 'alings' for 'aligns', 'substancial' for 'substantial', and the paper refers to 'HIITS3' in the text while Eq. (9) defines 'HIITS2' (the text in §3 and Table 1 use HIITS2). These should be corrected.
  5. [Fig. 1b] The caption of Fig. 1b mentions 'the proposed angular mean restrictions' but the figure does not show the angular restrictions or the principal axis orientation; please include the ±45° sector or a schematic of the mountain-axis definition to make the method understandable.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the Hurricane Impact Index is constructed from independent atmospheric, trajectory, and orographic data and validated against an external disaster inventory, with no parameter fitted to the validation outcomes.

full rationale

The derivation chain is self-contained. Equations (2)-(4) define DE, IE, and HII directly from HURDAT2 trajectories, ERA5 wind/precipitation/vorticity, and ASTER elevation data; no term in these equations is defined in terms of the DesInventar validation outcomes or of the final HII maps. The direct-effect region is set by vorticity and 500 km distance, and the indirect-effect indicator I in Eq. (3) is an angular wind/topography proxy; these are modeling assumptions, not fitted parameters. The paper's conclusion that indirect impacts are largest along the Pacific coast is a consequence of applying this proxy to the six chosen storms, not an independent discovery, but it is not circular because the proxy is fixed before the index is computed. The Section 3.1 comparison against DesInventar uses the same 2016-2022 events that are used for illustration, and the paper itself notes the DesInventar record is news-derived and only Costa Rica has a continuous record; this is an in-sample, small-sample validation weakness, and the reliability claim is therefore overstated, but no quantity in HII is calibrated to DesInventar, so the validation remains an external check rather than a self-fulfilling construction. Self-citations to Hidalgo et al. (2020, 2023) motivate the orographic-lifting mechanism but do not supply the equations or the validation. The untested angular constraint is a correctness/assumption risk, not a circularity.

Assumptions & free parameters 7 free parameters · 7 assumptions · 0 invented entities

The index introduces no new physical entity, but its reliability depends on a set of untested domain assumptions and hand-chosen thresholds. These are listed above.

free parameters (7)
  • Direct impact radius = 500 km
    Cells within this distance of the storm center are eligible for direct impact; selected by hand in Section 2.2.
  • Vorticity percentile threshold = 90th percentile
    Cells above the hurricane-specific 90th percentile of vorticity are considered directly affected; the percentile level is not derived.
  • Angular influence window = ±45 degrees
    Indirect impact is assigned when wind direction is within ±45 degrees of the nearest mountain range axis; chosen ad hoc in Section 2.2.
  • Mountain elevation threshold = 500 meters
    Only terrain above 500 meters is considered for orographic influence.
  • Spatial grid resolution = 1 degree by 1 degree
    Aggregation level chosen to balance spatial bias and variance.
  • Reference climatology period = 1993 to 2023
    Historical percentiles in equation (1) are computed over this period.
  • Percentile normalizer = P10 and P90
    The 10th and 90th percentiles define the normalization window in equation (1); chosen to reduce outlier effects.
assumptions (7)
  • domain assumption ERA5 reanalysis accurately represents wind speed, precipitation, and vorticity at 0.25 degree resolution for the hurricanes studied.
    Inputs to equations (2) and (3) are taken directly from ERA5 (Section 2.1.2).
  • domain assumption The average of vorticity at 850 hPa and 200 hPa captures the hurricane's structural influence.
    Used to define the direct-impact region in Section 2.2.
  • ad hoc to paper Wind vectors within ±45 degrees of the nearest mountain range axis, with mountains above 500 meters, identify cells under maximal indirect orographic influence.
    Defines the indirect indicator I in equation (3); the constraint is asserted, not validated.
  • domain assumption DesInventar DataCards is a valid proxy for overall hurricane damage.
    Used as the observed-impact benchmark in Section 3.1, with acknowledged bias from news-based reporting.
  • domain assumption Month-specific historical percentiles over 1993 to 2023 provide a stable baseline for normalization.
    Equation (1) depends on this reference period.
  • ad hoc to paper Min-max normalization across grid cells preserves meaningful relative impact comparisons.
    Equations (4) through (6) normalize each hurricane's index to [0,1], making values relative rather than absolute.
  • domain assumption HURDAT2 six-hourly positions and translational speeds are accurate for the storms studied.
    Trajectory and speed enter equations (2) and (3) directly (Section 2.1.1).

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Cite this review

Pith. "Pith review of Hurricane Impact Index for Assessing Direct and Indirect Hazards in Central America." pith.science (2026). https://pith.science/paper/6DZQLAO4

@misc{pith2026250619858,
  author       = {Pith},
  title        = {Pith review of: Hurricane Impact Index for Assessing Direct and Indirect Hazards in Central America},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6DZQLAO4}},
  note         = {Machine review of arXiv:2506.19858}
}
read the original abstract

Hurricanes rank among the most destructive natural hazards. They are complex phenomena that can cause both direct damage along their path and indirect impacts due to heavy rainfall and strong winds, with effects varying according to regional topography. In this paper, we propose a Hurricane Impact Index to assess both direct and indirect hazards, and we demonstrate its applicability to the Central American region. The index is constructed so that we can decompose these effects across multiple dimensions of time and space, enabling a detailed analysis of the intensity and distribution of hurricane impacts.

Figures

Figures reproduced from arXiv: 2506.19858 by the authors.

Figure 1
Figure 1. Panel a: Area of study and grid definition, using the resolution of 1 [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Direct Effect (First Column), Indirect Effect (Second Column), and HII (Third Column) for the Hurricane [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. HIIR for the Hurricanes Tracks of Otto (2016), Nate (2017), Julia (2022), Bonnie (2022), Iota (2020), and Eta (2020). The triangle indicates the hurricane’s start, while the square marks its end. Event Direct Indirect Combined Category (Saffir-Simpson) Nate October 6, 2017 October 5, 2017 October 5, 2017 TD-TS Bonnie July 02, 2022 July 03, 2022 July 02, 2022 TS Iota November 18, 2020 November 17, 2020 November 17, 2… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: HIITS1,h,t calculated using Equation (8). 9 [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Comparison Between DesInventar DataCards Variable (left column) with the HII [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

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Works this paper leans on

37 extracted references · 30 canonical work pages

  1. [1]

    An \'a lisis del impacto de fen \'o menos meteorol \'o gicos en Costa Rica, Am \'e rica Central, originados en los mares circundantes

    E Alfaro and Paula M P \'e rez-Brice \ n o. An \'a lisis del impacto de fen \'o menos meteorol \'o gicos en Costa Rica, Am \'e rica Central, originados en los mares circundantes . Revista de Climatolog \' a , 14: 0 1--11, 2014

    work page 2014

  2. [2]

    A multivariate scaling system is essential to characterize the tropical cyclones' risk

    Atieh Alipour, Farnaz Yarveysi, Hamed Moftakhari, Jae Yeol Song, and Hamid Moradkhani. A multivariate scaling system is essential to characterize the tropical cyclones' risk. Earth's Future, 10 0 (5): 0 e2021EF002635, 2022. doi:10.1029/2021ef002635

    work page doi:10.1029/2021ef002635 2022

  3. [3]

    Application for Extracting and Exploring Analysis Ready Samples (AppEEARS)

    AppEEARS Team . Application for Extracting and Exploring Analysis Ready Samples (AppEEARS). Ver. 3.76. NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center) . https://appeears.earthdatacloud.nasa.gov/, 2024. Accessed 28 September 2024

    work page 2024

  4. [4]

    Ruby Leung, David R

    Karthik Balaguru, Wenwei Xu, Chuan-Chieh Chang, L. Ruby Leung, David R. Judi, Samson M. Hagos, Michael F. Wehner, James P. Kossin, and Mingfang Ting. Increased U.S. coastal hurricane risk under climate change . Science Advances, 9 0 (14): 0 eadf0259, 2023. doi:10.1126/sciadv.adf0259

    doi:10.1126/sciadv.adf0259 2023

  5. [5]

    Adequately reflecting the severity of tropical cyclones using the new Tropical Cyclone Severity Scale

    Nadia Bloemendaal, Hans de Moel, Jantsje M Mol, Priscilla R M Bosma, Amy N Polen, and Jennifer M Collins. Adequately reflecting the severity of tropical cyclones using the new Tropical Cyclone Severity Scale . Environmental Research Letters, 16 0 (1): 0 014048, 2021. doi:10.1088/1748-9326/abd131

    work page doi:10.1088/1748-9326/abd131 2021

  6. [6]

    Projected increases in hurricane damage in the United States: The role of climate change and coastal development

    Terry Dinan. Projected increases in hurricane damage in the United States: The role of climate change and coastal development . Ecological Economics, 138: 0 186--198, 2017. ISSN 0921-8009. doi:10.1016/j.ecolecon.2017.03.034

    work page doi:10.1016/j.ecolecon.2017.03.034 2017

  7. [7]

    ECMWF Reanalysis v5 (ERA5)

    ECMWF . ECMWF Reanalysis v5 (ERA5) . https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5, 2024. Accessed 07 August 2024

    work page 2024

  8. [8]

    Response of global tropical cyclone activity to increasing CO2: Results from downscaling CMIP6 models

    Kerry Emanuel. Response of global tropical cyclone activity to increasing CO2: Results from downscaling CMIP6 models . Journal of Climate, 34 0 (1): 0 57 -- 70, 2021. doi:10.1175/jcli-d-20-0367.1

    work page doi:10.1175/jcli-d-20-0367.1 2021

Show all 37 references
  1. [9]

    Fischer, Paul D

    Michael S. Fischer, Paul D. Reasor, Robert F. Rogers, and John F. Gamache. An analysis of tropical cyclone vortex and convective characteristics in relation to storm intensity using a novel airborne Doppler radar database . Monthly Weather Review, 150 0 (9): 0 2255 -- 2278, 20...

    2022 doi

  2. [10]

    Tropical cyclone climatology change greatly exacerbates US extreme rainfall--surge hazard

    Avantika Gori, Ning Lin, Dazhi Xi, and Kerry Emanuel. Tropical cyclone climatology change greatly exacerbates US extreme rainfall--surge hazard . Nature Climate Change, 12 0 (2): 0 171--178, 2022. ISSN 1758-6798. doi:10.1038/s41558-021-01272-7

    2022 doi

  3. [11]

    Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, Adrian Simmons, Cornel Soci, Saleh Abdalla, Xavier Abellan, Gianpaolo Balsamo, Peter Bechtold, Gionata Biavati, Jean B...

    1999

  4. [12]

    Hidalgo, Eric J

    Hugo G. Hidalgo, Eric J. Alfaro, Franklin Hernández-Castro, and Paula M. Pérez-Briceño. Identification of tropical cyclones’ critical positions associated with extreme precipitation events in Central America . Atmosphere, 11 0 (10), 2020. ISSN 2073-4433. doi:10.3390/atmos11101123

    2020 doi

  5. [13]

    Hidalgo, Eric J

    Hugo G. Hidalgo, Eric J. Alfaro, Kenneth T. Valverde, and Juan Bazo. Probability of induced extreme precipitation events in Central America due to tropical cyclone positions in the surrounding oceans . Natural Hazards, 116 0 (3): 0 2917--2933, Apr 2023. ISSN 1573-0840. doi:10....

    2023 doi

  6. [14]

    Abdou Khouakhi, Gabriele Villarini, and Gabriel A. Vecchi. Contribution of tropical cyclones to rainfall at the global scale . Journal of Climate, 30 0 (1): 0 359 -- 372, 2017. doi:10.1175/JCLI-D-16-0298.1

    2017 doi

  7. [15]

    Camargo, Johnny C

    Thomas Knutson, Suzana J. Camargo, Johnny C. L. Chan, Kerry Emanuel, Chang-Hoi Ho, James Kossin, Mrutyunjay Mohapatra, Masaki Satoh, Masato Sugi, Kevin Walsh, and Liguang Wu. Tropical cyclones and climate change assessment: Part II: Projected response to anthropogenic warming ...

    2020 doi

  8. [16]

    Landsea and James L

    Christopher W. Landsea and James L. Franklin. Atlantic hurricane database uncertainty and presentation of a new database format . Monthly Weather Review, 141 0 (10): 0 3576 -- 3592, 2013. doi:10.1175/mwr-d-12-00254.1

    2013 doi

  9. [17]

    Little, Radley M

    Christopher M. Little, Radley M. Horton, Robert E. Kopp, Michael Oppenheimer, Gabriel A. Vecchi, and Gabriele Villarini. Joint projections of US East Coast sea level and storm surge . Nature Climate Change, 5 0 (12): 0 1114--1120, 2015. ISSN 1758-6798. doi:10.1038/nclimate2801

    2015 doi

  10. [18]

    Luis-Carlos Martinez, David Romero, and Eric J. Alfaro. Assessment of the spatial variation in the occurrence and intensity of major hurricanes in the western hemisphere . Climate, 11 0 (1), 2023. ISSN 2225-1154. doi:10.3390/cli11010015

    2023 doi

  11. [19]

    NHC data archive

    National Hurricane Center . NHC data archive. https://www.nhc.noaa.gov/data/, 2024. Accessed 21 April 2024

    2024

  12. [20]

    Historical hurricane tracks [data set]

    NOAA Office for Coastal Management . Historical hurricane tracks [data set]. https://coast.noaa.gov/hurricanes/, 2025. URL https://coast.noaa.gov/hurricanes/. National Ocean Service, National Oceanic and Atmospheric Administration. Data derived from IBTrACS and HURDAT2; update...

    2025

  13. [21]

    The economics of hurricanes in the U nited S tates

    William D Nordhaus. The economics of hurricanes in the U nited S tates. Working Paper 12813, National Bureau of Economic Research, December 2006

    2006

  14. [22]

    Why relatively fewer people died? The case of Bangladesh's Cyclone Sidr

    Bimal Kanti Paul. Why relatively fewer people died? The case of Bangladesh's Cyclone Sidr . Natural Hazards, 50 0 (2): 0 289--304, Aug 2009. ISSN 1573-0840. doi:10.1007/s11069-008-9340-5

    2009 doi

  15. [23]

    Peduzzi, B

    P. Peduzzi, B. Chatenoux, H. Dao, A. De Bono, C. Herold, J. Kossin, F. Mouton, and O. Nordbeck. Global trends in tropical cyclone risk. Nature Climate Change, 2 0 (4): 0 289--294, Apr 2012. ISSN 1758-6798. doi:10.1038/nclimate1410

    2012 doi

  16. [24]

    Distribuci \'o n espacial de impactos de eventos hidrometeorol \'o gicos en am \'e rica central

    Paula M P \'e rez-Brice \ n o, Eric J Alfaro, H Hidalgo, and Francisco Jim \'e nez. Distribuci \'o n espacial de impactos de eventos hidrometeorol \'o gicos en am \'e rica central. Revista de Climatolog \' a , 16: 0 63--75, 2016

    2016

  17. [25]

    MalaquÍas Peña and Michael W. Douglas. Characteristics of wet and dry spells over the Pacific side of Central America during the rainy season . Monthly Weather Review, 130 0 (12): 0 3054 -- 3073, 2002. doi:10.1175/1520-0493(2002)130<3054:cowads>2.0.co;2

    2002 doi

  18. [26]

    Multi-factor evaluation indicator method for the risk assessment of atmospheric and oceanic hazard group due to the attack of tropical cyclones

    Peng Qi and Mei Du. Multi-factor evaluation indicator method for the risk assessment of atmospheric and oceanic hazard group due to the attack of tropical cyclones. International Journal of Applied Earth Observation and Geoinformation, 68: 0 1--7, 2018. ISSN 1569-8432. doi:10....

    2018 doi

  19. [27]

    Hidalgo, and Eric J

    Adolfo Quesada-Román, Hugo G. Hidalgo, and Eric J. Alfaro. Assessing the impact of tropical cyclones on economic sectors in Costa Rica, Central America . Tropical Cyclone Research and Review, 13 0 (3): 0 196--207, 2024. ISSN 2225-6032. doi:10.1016/j.tcrr.2024.08.001

    2024 doi

  20. [28]

    Mehdi Rezapour and Tom E. Baldock. Classification of hurricane hazards: The importance of rainfall. Weather and Forecasting, 29 0 (6): 0 1319 -- 1331, 2014. doi:10.1175/waf-d-14-00014.1

    2014 doi

  21. [29]

    Hurricane risk assessment in a multi-hazard context for Dominica in the Caribbean

    Peter Sammonds, Akhtar Alam, Simon Day, Katerina Stavrianaki, and Ilan Kelman. Hurricane risk assessment in a multi-hazard context for Dominica in the Caribbean . Scientific Reports, 13 0 (1): 0 20565, 2023. ISSN 2045-2322. doi:10.1038/s41598-023-47527-5

    2023 doi

  22. [30]

    Niggol Seo and Laura A

    S. Niggol Seo and Laura A. Bakkensen. Is tropical cyclone surge, not intensity, what kills so many people in South Asia? Weather, Climate, and Society, 9 0 (2): 0 171 -- 181, 2017. doi:10.1175/wcas-d-16-0059.1

    2017 doi

  23. [31]

    Sivakumar

    Mannava V.K. Sivakumar. Impacts of natural disasters in agriculture, rangeland and forestry: An overview, pages 1--22. Springer Berlin Heidelberg, Berlin, Heidelberg, 2005. ISBN 978-3-540-28307-2. doi:10.1007/3-540-28307-2_1

    2005 doi

  24. [32]

    R. B. Smith, P. Schafer, D. Kirshbaum, and E. Regina. Orographic enhancement of precipitation inside hurricane Dean . Journal of Hydrometeorology, 10 0 (3): 0 820 -- 831, 2009. doi:10.1175/2008jhm1057.1

    2009 doi

  25. [33]

    Increasing wind threat of Atlantic tropical cyclones based on a comprehensive risk analysis using Multi-Hazard Hurricane Index and Social Vulnerability Index

    Jae Yeol Song and Eun-Sung Chung. Increasing wind threat of Atlantic tropical cyclones based on a comprehensive risk analysis using Multi-Hazard Hurricane Index and Social Vulnerability Index . Earth Systems and Environment, 8 0 (4): 0 951--962, 2024. ISSN 2509-9434. doi:10.10...

    2024 doi

  26. [34]

    Tripathy, Keighobad Jafarzadegan, Hamed Moftakhari, and Hamid Moradkhani

    Shrabani S. Tripathy, Keighobad Jafarzadegan, Hamed Moftakhari, and Hamid Moradkhani. Dynamic bivariate hazard forecasting of hurricanes for improved disaster preparedness. Communications Earth & Environment , 5 0 (1): 0 12, Jan 2024. ISSN 2662-4435. doi:10.1038/s43247-023-01198-2

    2024 doi

  27. [35]

    Desinventar as a disaster information management system

    UNDRR . Desinventar as a disaster information management system. https://www.desinventar.net/whatisdesinventar.html, 2024. Accessed 20 October 2024

    2024

  28. [36]

    Woodruff, Jennifer L

    Jonathan D. Woodruff, Jennifer L. Irish, and Suzana J. Camargo. Coastal flooding by tropical cyclones and sea-level rise. Nature, 504 0 (7478): 0 44--52, 2013. ISSN 1476-4687. doi:10.1038/nature12855

    2013 doi

  29. [37]

    Zachry, William J

    Brian C. Zachry, William J. Booth, Jamie R. Rhome, and Tarah M. Sharon. A national view of storm surge risk and inundation. Weather, Climate, and Society, 7 0 (2): 0 109 -- 117, 2015. doi:10.1175/wcas-d-14-00049.1

    2015 doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.