Factor: Environmental Hazards - Global Natural Hazards Data

[timlinux]

Data Source: National Centers for Environmental Information - downloadable from ArcGIS online

Reclassify input data to a standardized scale of 0 to 5 using a linear scaling process, such that 5 represents areas where there are no environmental hazards and 0 represents the areas with the highest level of hazard.

[ClaraIV]

Adjustments are needed as it is currently evaluated differently. Please use guidance above, included by Tim, for the computation.

[carolinamayh]

@osundwajeff The name of this factor has changed. In the original version of the tool, it was called Climatic something. Please ensure it is now correctly labeled as "Environmental Hazards."

[carolinamayh]

@osundwajeff This tab should enable users to input multiple rater datasets if they have additional information on environmental hazards in the area, such as heat hazards or landslides.

[bennyistanto]

Global Hazard Datasets for Environmental Risk Assessment

The implementation of the Environmental & Climatic Factors (ENV) component in GEEST requires reliable, up-to-date hazard data. Fortunately, numerous global initiatives have produced comprehensive, publicly available datasets covering a wide range of environmental hazards.
These resources offer consistent methodologies, global coverage and publicly available, making them ideal for comparative analyses across different regions. The following list highlights key global hazard datasets that can serve as primary or supplementary indicators for the ENV component, enabling GEEST users to conduct detailed environmental risk assessments tailored to specific project needs and geographical contexts.

1. Forest Fire, Actives Fires Density 2003-2022

   Summary: Active Fires density based on MODIS Collection 6 Active Fire Product MCD14ML. The density of fires is reported as the count of fires per km2 occurring into pixels of 0.1 decimal degree (the pixel dimension varies from 20 km2 at the Poles to 122 km2 at the equator). Only location points described as "presumed vegetation fire" in the attributes are included in the frequency calculation. The MODIS active fire product detects fires in 1-km pixels that are burning at the time of overpass under relatively cloud-free conditions using a contextual algorithm. Please see the MODIS Active Fire Product User's Guide for detailed information about the MODIS Active Fire product suite. This layer was produced by UNEP/GRID-Geneva.

   Licenses:

   • UNEP/GRID-Geneva: The data can be redistributed freely upon mention of the metadata.
   • NASA: NASA promotes the full and open sharing of all data with the research and applications communities, private industry, academia, and the general public. Read NASA's Data and Information Policy. If you provide NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) / Fire Information for Resource Management System (FIRMS) data to a third party, we request you follow these guidelines and replicate or provide a link to the disclaimer

   Reference: Giglio, L., Schroeder, W., & Justice, C. O. (2016). The collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178, 31–41. https://doi.org/10.1016/j.rse.2016.02.054

   Layer properties: GeoTIFF with 0.1deg spatial resolution

   Symbology: Classified using natural breaks into 5 class (Very Low to Very High) + 1 class for No Data or 0.

   Number of fires per km2	GEEST class	Color	HEX
   0 or No Data	5
   0 to 1	4		#fef0d9
   1 to 2	3		#fdcc8a
   2 to 5	2		#fc8d59
   5 to 8	1		#e34a33
   greater than 8	0		#b30000

   Download: https://datacore.unepgrid.ch/geoserver/wesr_risk/wcs?service=WCS&Version=2.0.1&request=GetCoverage&coverageId=fires_density_total&outputCRS=EPSG:4326&format=GEOTIFF&compression=DEFLATE

2. Earthquake, Global Seismic Hazard Map

   Summary: The Global Earthquake Model (GEM) Global Seismic Hazard Map (version 2023.1) depicts the geographic distribution of the Peak Ground Acceleration (PGA) with a 10% probability of being exceeded in 50 years, computed for reference rock conditions (shear wave velocity, Vs30, of 760-800 m/s). The map was created by collating maps computed using national and regional probabilistic seismic hazard models developed by various institutions and projects, in collaboration with GEM Foundation scientists. This version represents an update from the previous release from 2018 and features improvements in most regions of the world, as well as a higher spatial definition (approx. 2.5X) compared to the previous version.

   Licenses: CC BY-SA-NC 4.0

   Reference: K. Johnson, M. Villani, K. Bayliss, C. Brooks, S. Chandrasekhar, T. Chartier, Y. Chen, J. Garcia-Pelaez, R. Gee, R. Styron, A. Rood, M. Simionato, M. Pagani (2023). Global Earthquake Model (GEM) Seismic Hazard Map (version 2023.1 - June 2023), https://doi.org/10.5281/zenodo.8409647

   Layer properties: GeoTIFF in ZIP, with 0.05deg spatial resolution

   Symbology: The following is the color palette used by the GEM Foundation in the poster and PNG versions of the Global Seismic Hazard Map. Users are encouraged to use the same palette when presenting depictions of the map covering the globe in their applications.

   PGA Range	Color	HEX
   0.00-0.01		#ffffff
   0.01-0.02		#d7e3ee
   0.02-0.03		#b5caff
   0.03-0.05		#8fb3ff
   0.05-0.08		#7f97ff
   0.08-0.13		#abcf63
   0.13-0.20		#e8f59e
   0.20-0.35		#fffa14
   0.35-0.55		#ffd121
   0.55-0.90		#ffa30a
   0.90-1.50		#ff4c00

   To translate the GEM Foundation's 11-class color palette into a 6-class system for GEEST, we need to consolidate the PGA ranges while maintaining a meaningful representation of earthquake hazard levels. Here's a suggested 5-class breakdown:

   PGA Range	GEEST class	Color	HEX
   0.01-0.02	5		#d7e3ee
   0.02-0.05	4		#8fb3ff
   0.05-0.13	3		#abcf63
   0.13-0.35	2		#fffa14
   0.35-0.90	1		#ffa30a
   0.90-1.90	0		#ff4c00

   This classification maintains the general progression of the hazard levels while simplifying the scale for easier interpretation in the GEEST framework. The chosen colors provide a clear visual distinction between the hazard levels.

   Download: https://cloud.openquake.org/s/6SnFk2f92JEr76H/download/GEM-GSHM_PGA-475y-rock_v2023.zip

3. Tsunami, Frequency of Tsunami as of 2022

   Summary: This dataset includes an estimate of tsunami frequency. It is based on two sources: (i) A comprehensive list of reports and scientific papers compiled and utilized in producing tsunami hazard maps as well as finding return periods of future events. (ii) Applying numerical tsunami models and zooming on selected areas. Unit is expected affected percentage of each pixel over a minimum return period of 500 years. This product was designed by International Centre for Geohazards /NGI for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing International Centre for Geohazards /NGI.

   Licenses: CC BY-NC 4.0

   Reference: https://datacore-gn.unepgrid.ch/geonetwork//srv/eng/catalog.search#/metadata/be12c66e-045a-4429-824b-ae3f9869a81c

   Layer properties: GeoTIFF, with 0.01deg spatial resolution

   Symbology: Classified using logaritmic scale into 5 class + 1 class for No Data or 0.

   Frequency	GEEST class	Color	HEX
   No Data or 0	5
   0 – 0.000001	4		#ffffcc
   0.000001 – 0.00001	3		#a1dab4
   0.00001 - 0.0001	2		#41b6c4
   0.0001 – 0.001	1		#2c7fb8
   0.001 – 0.002	0		#253494

   Download: https://datacore.unepgrid.ch/geoserver/ECO-DRR/wcs?service=WCS&Version=2.0.1&request=GetCoverage&coverageId=ts_freqaf_P3&outputCRS=EPSG:4326&format=GEOTIFF&compression=DEFLATE

4. Flood, Flood Hazard

   Summary: The GAR Atlas global flood hazard assessment uses a probabilistic approach for modelling riverine flood major river basins around the globe. This has been possible after compiling a global database of stream-flow data, merging different sources and gathering more than 8000 stations over the globe in order to calculate the range of possible discharges from very low to the maximum possible scales at different locations along the rivers. The calculated discharges were introduced in the river sections to model water levels downstream. This procedure allowed for the determination of stochastic event-sets of riverine floods from which hazard maps for several return periods (25, 50, 100, 200, 500, 1000 years) were obtained. The hazard maps are developed at 1kmx1km resolution and have been validated against satellite flood footprints from different sources (DFO archive, UNOSAT flood portal) performing well especially for big events For smaller events (lower return periods), the GAR Atlas flood hazard maps tend to overestimate with respect to similar maps produced locally (hazard maps where available for some countries and were used as benchmark). The main issue being that, due to the resolution, the GAR Atlas flood hazard maps do not take into account flood defenses that are normally present to preserve the value exposed to floods. More information about the flood hazard assessment can be found in the background paper (Rudari et al., 2015).

   License: Use limitation: All datasets are available for free for non commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers.

   Reference: https://www.preventionweb.net/english/hyogo/gar/2015/en/home/index.html

   Layer properties: GeoTIFF, with 1km spatial resolution

   Symbology: Classified into 5 class (Very Low to Very High) + 1 class for No Data or 0.

   Flood Depth	GEEST class	Color	HEX
   No Data or 0	5
   Less than 180 cm	4		#eff3ff
   180 – 360 cm	3		#bdd7e7
   360 – 540 cm	2		#6baed6
   540 – 720 cm	1		#3182bd
   720 – 900 cm	0		#08519c

   Download:
   25 Year Return Period: https://datacore.unepgrid.ch/geoserver/wesr_risk/wcs?service=WCS&Version=2.0.1&request=GetCoverage&coverageId=fl_hazard_25_yrp&outputCRS=EPSG:4326&format=GEOTIFF&compression=DEFLATE
   50 Year Return Period: https://datacore.unepgrid.ch/geoserver/wesr_risk/wcs?service=WCS&Version=2.0.1&request=GetCoverage&coverageId=fl_hazard_50_yrp&outputCRS=EPSG:4326&format=GEOTIFF&compression=DEFLATE
   100 Year Return Period: https://datacore.unepgrid.ch/geoserver/wesr_risk/wcs?service=WCS&Version=2.0.1&request=GetCoverage&coverageId=fl_hazard_100_yrp&outputCRS=EPSG:4326&format=GEOTIFF&compression=DEFLATE (Recommended for GEEST)
   200 Year Return Period: https://datacore.unepgrid.ch/geoserver/wesr_risk/wcs?service=WCS&Version=2.0.1&request=GetCoverage&coverageId=fl_hazard_200_yrp&outputCRS=EPSG:4326&format=GEOTIFF&compression=DEFLATE
   500 Year Return Period: https://datacore.unepgrid.ch/geoserver/wesr_risk/wcs?service=WCS&Version=2.0.1&request=GetCoverage&coverageId=fl_hazard_500_yrp&outputCRS=EPSG:4326&format=GEOTIFF&compression=DEFLATE
   1000 Year Return Period: https://datacore.unepgrid.ch/geoserver/wesr_risk/wcs?service=WCS&Version=2.0.1&request=GetCoverage&coverageId=fl_hazard_1000_yrp&outputCRS=EPSG:4326&format=GEOTIFF&compression=DEFLATE

   Notes:
   For the GEEST project, it is recommended to use the 100-year return period flood hazard data from the GAR Atlas global flood hazard assessment. This recommendation is based on several factors:

   • Standard practice: The 100-year flood is widely used in flood risk management and urban planning as a benchmark for assessing flood hazards.
   • Regulatory alignment: Many countries and international organizations use the 100-year flood as a standard for flood risk assessment and mitigation planning.
   • Balance of risk representation: This return period provides a good balance between capturing significant flood events and avoiding overestimation of risk.
   • Infrastructure design considerations: Many infrastructure projects, including those in the renewable energy sector, often consider the 100-year flood in their design and risk assessment processes.
   • Data reliability: The 100-year return period typically offers a good compromise between data availability and statistical reliability.
   • Consistency with other hazard assessments: Using the 100-year return period allows for better integration with other hazard assessments that often use similar timeframes.

   It should be noted that while the 100-year flood data is recommended as the primary indicator, GEEST users may benefit from also considering the 50-year and 200-year return period data to provide a range of scenarios. This approach would offer a more comprehensive view of flood risks, especially in areas where climate change may be altering historical flood patterns.
   The selection of this return period should be documented, and users should be aware of the limitations mentioned in the GAR Atlas methodology, particularly the potential overestimation for smaller events due to the exclusion of local flood defenses in the model.
   For areas with critical infrastructure or regions highly susceptible to flooding, it may be prudent to consider the 500-year return period data for a more conservative assessment.

5. Landslide, Landslide Susceptibility

   Summary: NASA/GSFC developed the landslide susceptibility map to show where the terrain is most susceptible to landslides. The susceptibility model considers whether roads have been built, trees have been cut down or burned, a major tectonic fault is nearby, the local bedrock is weak, and/or the hillsides are steep.

   License: Public Domain

   Reference:

   • https://earthobservatory.nasa.gov/images/89937/a-global-view-of-landslide-susceptibility
   • Emberson, R., D. Kirschbaum, and T. Stanley. 2020. "New global characterisation of landslide exposure." Natural Hazards and Earth System Sciences, 20 (12): 3413-3424. https://doi.org/10.5194/nhess-20-3413-2020
   • Stanley, T., & Kirschbaum, D. B. (2017). A heuristic approach to global landslide susceptibility mapping. Natural Hazards, 87(1), 145-164. https://doi.org/10.1007/s11069-017-2757-y
   • Kirschbaum, D., Stanley, T., & Yatheendradas, S. (2016). Modeling landslide susceptibility over large regions with fuzzy overlay. Landslides, 13(3), 485-496. https://doi.org/10.1007/s10346-015-0577-2

   Layer properties: GeoTIFF, with 0.0083333deg ~ 900m spatial resolution

   Symbology: Classified into 5 class (Very Low to Very High) + 1 class for No Data or 0.

   Land Susceptibility and Value	GEEST class	Color	HEX
   No Data or 0	5
   Slight (1)	4		#420a68
   Low Moderate1 (2)	3		#932667
   Moderate (3)	2		#dd513a
   High-Moderate2 (4)	1		#fca50a
   Severe (5)	0		#fcffa4

   Download: https://gpm.nasa.gov/sites/default/files/downloads/global-landslide-susceptibility-map-2-27-23.tif

6. Tropical Cyclone, Frequency of Tropical Cyclone

   Summary: This dataset includes an estimate of tropical cyclone frequency of Saffir-Simpson category 5. It is based on two sources: (i) IBTrACS v02r01 (1969 - 2008, http://www.ncdc.noaa.gov/oa/ibtracs/), year 2009 completed by online data from JMA, JTWC, UNISYS, Meteo France and data sent by Alan Sharp from the Australian Bureau of Meteorology. (ii) A GIS modeling based on an initial equation from Greg Holland, which was further modified to take into consideration the movement of the cyclones through time. Unit is expected average number of event per 100 years multiplied by 100. This product was designed by UNEP/GRID-Geneva for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: Raw data: IBTrACS, compilation and GIS processing UNEP/GRID-Geneva.

   License: Use limitation: All datasets are available for free for non commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers.

   Reference: https://develop.unepgrid.ch/en/datasetproxy/MX-E69IN-F14ZL-8UCLQ

   Layer properties: GeoTIFF, with ~2km spatial resolution

   Symbology: Classified into 5 class (Very Low to Very High) + 1 class for No Data or 0.

   Tropical Cyclone frequency per 100 years	GEEST class	Color	HEX
   No Data or 0	5
   Less than 25 event	4		#edf8e9
   25 – 50 events	3		#bae4b3
   50 – 75 events	2		#74c476
   75 – 100 events	1		#31a354
   More than 100 events	0		#006d2c

   Download: https://datacore.unepgrid.ch/geoserver/wesr_risk/wcs?service=WCS&Version=2.0.1&request=GetCoverage&coverageId=cy_frequency&outputCRS=EPSG:4326&format=GEOTIFF&compression=DEFLATE

7. Drought, Global Drought Hazard based on SPEI

   Summary: The Global Drought Hazard project is a collection of spatial raster datasets that provide access to statistical extreme value analyses of the Standardised Precipitation Evapotranspiration Index (6-month SPEI) to identify high risk areas on a global scale. The source data are monthly spatial raster datasets from the Climatology and Climate Services Laboratory's Global SPEI Database for the period from January 1902 to December 2018. Each raster dataset has a spatial resolution of about 0.5 degrees, the values of which we converted into time series for each grid cell. These time series are used to model the number of months above selected SPEI thresholds (e.g. SPEI -1.5 or lower) for a range of return periods (e.g. 100 years) via Poisson-Generalized Pareto Point Process models. Since extreme value analyses are highly influenced by non-stationarity and time dependence of the data, time-varying parameters to deal with non-stationarity, declustering to reduce tail dependence, and spatiotemporally weighted error term corrections were incorporated into the modelling process for each cell. The modelling results for each return period are projected bilinearly onto a spatial grid with a higher resolution of about 0.0083 degrees for publication.

   License: Creative Commons Attribution International

   Reference: Institute for International Law of Peace and Armed Conflict - https://data.humdata.org/dataset/global-drought-hazard

   Layer properties: GeoTIFF, with 0.5deg spatial resolution

   Symbology: Classified using natural break into 5 class (Very Low to Very High) + 1 class for No Data or 0.

   Number of month above SPEI threshold	GEEST class	Color	HEX
   No Data or 0	5
   0 - 1	4		#ffffb2
   1 - 2	3		#fecc5c
   2 - 3	2		#fd8d3c
   3 - 4	1		#f03b20
   4 - 5	0		#bd0026

   Download: https://data.humdata.org/dataset/global-drought-hazard

   Notes: Global Drought SPEI available in certain threshold and return period with following naming convention: Global Drought SPEI {spei_value} Return Period {return_period} Years.tif

   Where {spei_value} available for:

   • 1.0+
   • 1.5+
   • 2.0+
   • 2.5+
   • 3.0+

   And {return_period} available for:

   • 25 Years
   • 50
   • 100
   • 250
   • 500
   • 1000

   For the GEEST project, it is recommended to use the Global Drought Hazard data based on SPEI with the following parameters:
   SPEI value: -1.5
   Return period: 100 years

   Download Link: https://data.humdata.org/dataset/30b85665-4c3d-4dc3-b543-3a567a3dea37/resource/6744572e-d5d1-4033-9d64-c87dc565586a/download/global-drought-spei-1.5-return-period-100-years.tif

   This recommendation is based on several academic and practical considerations:

   • SPEI Threshold: The -1.5 SPEI threshold is considered appropriate because:
     a) It represents a moderate to severe drought condition, as defined by McKee et al. 1993 and widely accepted in drought literature. b) It captures significant drought events without being overly extreme, allowing for a balanced representation of drought risk. c) This threshold is often used in drought management plans and agricultural assessments Vicente-Serrano et al., 2010.
   • Return Period: The 100-year return period is recommended because: a) It aligns with standard practices in natural hazard assessment and infrastructure planning Koks et al., 2019. b) This timeframe provides a good balance between capturing rare, high-impact events and maintaining statistical reliability. c) It is consistent with other hazard assessments often used in multi-hazard frameworks, facilitating integration with other risk factors in GEEST UNDRR, 2022.
   • Relevance to Renewable Energy Sector: This combination is particularly relevant for the renewable energy sector because: a) Long-term drought conditions can significantly impact hydropower generation and cooling water availability for various energy systems van Vliet et al., 2016. b) A 100-year event horizon aligns well with the expected lifespan of many renewable energy infrastructure projects.
   • Compatibility with Climate Change Projections: Using a 100-year return period allows for better integration with climate change projections, which often use similar time horizons IPCC, 2021.
   • Statistical Robustness: The chosen parameters provide a good balance between data availability and statistical reliability, considering the temporal range of the source data (1902-2018).
   • Policy Relevance: This level of drought severity and frequency is often considered in national and international policy frameworks for disaster risk reduction and climate adaptation Wilhite et al., 2014.

   It is important to note that while these parameters are recommended, the specific needs of the GEEST project and the characteristics of the regions being assessed should be considered. For areas with particular sensitivity to drought or where water resources are critical for renewable energy development, it may be prudent to also consider the -2.0 SPEI threshold or longer return periods as supplementary information.

8. Air Pollution, Satellite-derived PM2.5

   Summary: Global and regional PM2.5 concentrations are estimated using information from satellite-, simulation- and monitor-based sources. Aerosol optical depth from multiple satellites (MODIS, VIIRS, MISR, SeaWiFS, and VIIRS) and their respective retrievals (Dark Target, Deep Blue, MAIAC) is combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations to produce geophysical estimates that explain most of the variance in ground-based PM2.5 measurements. A subsequent statistical fusion incorporates additional information from PM2.5 measurements. We estimate annual and monthly ground-level fine particulate matter (PM2.5) for 2000-2019 by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, SeaWIFS, and VIIRS with the GEOS-Chem chemical transport model, and subsequently calibrating to global ground-based observations using a residual Convolutional Neural Network (CNN), as detailed in the below reference for V6.GL.01. V6.GL.02.02 follows the methodology of V6.GL.01 but updates the ground-based observations used to calibrate the geophysical PM2.5 estimates for the entire time series, extends temporal coverage through 1998 – 2022, and includes retrievals from the SNPP VIIRS instrument.

   License: CC BY 4.0

   Reference:

   • https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V6.GL.02
   • Shen, S. Li, C. van Donkelaar, A. Jacobs, N. Wang, C. Martin, R. V.: Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. (2024) ACS ES&T Air. https://pubs.acs.org/doi/full/10.1021/acsestair.3c00054

   Layer properties: NetCDF, Annual, with 0.01deg ~ 1.1km spatial resolution

   Symbology: Classified using natural break into 6 class.

   PM2.5	GEEST class	Color	HEX
   0 - 10	5		#4575b4
   10 - 20	4		#91bfdb
   20 - 30	3		#e0f3f8
   30 - 50	2		#fee090
   50 - 80	1		#fc8d59
   80 - 300	0		#d73027

   Download: https://wustl.app.box.com/s/iwvi2avusnz3fpabl6v5ouyobavbt70a/folder/274011040200

   The data available annually from 1998-2022. Lets use the latest data for GEEST. Link for 2022

Footnotes

1. Low-Moderate indicates a level of risk higher than Slight but not yet reaching Moderate. ↩

2. High-Moderate suggests a risk level that is more significant than Moderate but not as extreme as Severe. ↩

[carolinamayh]

@osundwajeff @bennyistanto @ClaraIV @javaftw
For this indicator, only the following five hazards should be considered:

1. Forest Fire: Active Fires Density (2003-2022)
2. Flood: Flood Hazard
3. Landslide: Landslide Susceptibility
4. Tropical Cyclone: Frequency of Tropical Cyclones
5. Drought: Global Drought Hazard based on the Standardized Precipitation Evapotranspiration Index (SPEI)

Users should be able to select between 1 to 3 of these hazards that are most relevant to their specific context. For each selected hazard, the tool will generate raster cells of 100m x 100m and assign a score ranging from 0 to 5, standardized according to the hazard's scale. A score of 5 represents no hazard, while a score of 0 indicates areas at the highest risk. The final score will be the average of the scores from the selected hazards.

[ClaraIV]

@osundwajeff we requested documentation of internal QA, where can we find it?

[mvmaltitz]

Inputs:

Output:
Aggregate - This is correct according to the different output files and takes few files into considerations.

The individual outputs are below:
Cyclone:

Landslide:

Flood:

Fire:

Drought:

@dragosgontariu some of the output files came out as solid colour due to there not being enough detail in the input files. This is correct according to the input files used.

[dragosgontariu]

Hi @mvmaltitz , @osundwajeff @bennyistanto @ClaraIV @carolinamayh

I have noticed some differences in the outputs, which we need to address. Below are the observations related to the input data for each hazard event:

Drought: The coverage is limited to the northern part of the country, not the entire country.
Fires: There is noticeable variability in the data.
Cyclones: Some variability is present in the southern part of the country.
Landslides: The data appears to be correct.
Floods: We did not find any coverage from the input you used, specifically the file fl_hazard_1000_yrp - there are only some coastal floods

Did you consider the classification provided by Benny for each input?

It would be helpful to have a quick call to better understand the process and ensure everything is functioning as expected. I'm available today from 17:00 RO time (EEST). Please let me know if this works for you.

My best,
Dragos

[bennyistanto]

The example of individual hazard picture from Michelle is correct, except for Drought (which should be same as Dragos's picture, in northern part classified as class 2, while other areas is class 5)

Responding to Dragos comment:
We decided to use publicly available global hazard or risk data, and usually global data are available in medium-to-low resolution. This is the limitation that we should accept, especially if working on very small island countries.

If in a certain areas there is no hazard information, we can assume if there is no hazard in those areas (GEEST class 5), this will applied to:

• Drought, in southern part of the country
• Flood, as the 100 yr Flood hazard from GAR is focused on riverine flood, and from the data, all the countries are categorized as class 5.

[ClaraIV]

@mvmaltitz @osundwajeff please update the descriptive text in the corresponding plugin tab to match the current input data/processing, thanks!

[osundwajeff]

Hi @dragosgontariu, I have seen the issue pointed out under landslides and I am not able to replicate it. I have tested it on both windows and linux.

Dataset: https://gpm.nasa.gov/sites/default/files/downloads/global-landslide-susceptibility-map-2-27-23.tif

On Linux:

On windows:

[mvmaltitz]

@timlinux

Progress Label show that Processing Complete!, but unfortunately no output files are generated. See the Processing log below.

2024-09-12T22:07:47     INFO    Results: {'OUTPUT': 'D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/countryUTMLayerBuf.shp'}
2024-09-12T22:07:47     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -dstnodata -9999.0 -tr 100.0 100.0 -r near -te 2521404.4815840046 -2672812.0677260184 2957862.1149176797 -1597099.3760041117 -te_srs EPSG:8859 -of GTiff D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_firedensity_8859.tif D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvResample.tif
2024-09-12T22:07:47     INFO    GDAL command:
2024-09-12T22:07:47     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -dstnodata -9999.0 -tr 100.0 100.0 -r near -te 2521404.4815840046 -2672812.0677260184 2957862.1149176797 -1597099.3760041117 -te_srs EPSG:8859 -of GTiff D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_firedensity_8859.tif D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvResample.tif
2024-09-12T22:07:47     INFO    GDAL command output:
2024-09-12T22:07:49     INFO    Process completed successfully
2024-09-12T22:07:49     INFO    Results: {'OUTPUT': 'D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvResample.tif'}
2024-09-12T22:07:59     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -of GTiff -cutline D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_polbnda_adm0_country_8859.shp -cl fji_polbnda_adm0_country_8859 -crop_to_cutline -dstnodata -9999.0 D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvClipResample.tif Hazard_Fire_Density.tif
2024-09-12T22:07:59     INFO    GDAL command:
2024-09-12T22:07:59     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -of GTiff -cutline D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_polbnda_adm0_country_8859.shp -cl fji_polbnda_adm0_country_8859 -crop_to_cutline -dstnodata -9999.0 D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvClipResample.tif Hazard_Fire_Density.tif
2024-09-12T22:07:59     INFO    GDAL command output:
2024-09-12T22:08:00     CRITICAL    Process returned error code 1
2024-09-12T22:08:00     INFO    Results: {'OUTPUT': 'Hazard_Fire_Density.tif'}
2024-09-12T22:08:00     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -dstnodata -9999.0 -tr 100.0 100.0 -r near -te 2521404.4815840046 -2672812.0677260184 2957862.1149176797 -1597099.3760041117 -te_srs EPSG:8859 -of GTiff D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_land_susceptibility_nasa_8859.tif D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvResample.tif
2024-09-12T22:08:00     INFO    GDAL command:
2024-09-12T22:08:00     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -dstnodata -9999.0 -tr 100.0 100.0 -r near -te 2521404.4815840046 -2672812.0677260184 2957862.1149176797 -1597099.3760041117 -te_srs EPSG:8859 -of GTiff D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_land_susceptibility_nasa_8859.tif D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvResample.tif
2024-09-12T22:08:00     INFO    GDAL command output:
2024-09-12T22:08:01     INFO    Process completed successfully
2024-09-12T22:08:01     INFO    Results: {'OUTPUT': 'D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvResample.tif'}
2024-09-12T22:08:13     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -of GTiff -cutline D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_polbnda_adm0_country_8859.shp -cl fji_polbnda_adm0_country_8859 -crop_to_cutline -dstnodata -9999.0 D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvClipResample.tif Hazard_Landslide_Susceptibility.tif
2024-09-12T22:08:13     INFO    GDAL command:
2024-09-12T22:08:13     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -of GTiff -cutline D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_polbnda_adm0_country_8859.shp -cl fji_polbnda_adm0_country_8859 -crop_to_cutline -dstnodata -9999.0 D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvClipResample.tif Hazard_Landslide_Susceptibility.tif
2024-09-12T22:08:13     INFO    GDAL command output:
2024-09-12T22:08:14     CRITICAL    Process returned error code 1
2024-09-12T22:08:14     INFO    Results: {'OUTPUT': 'Hazard_Landslide_Susceptibility.tif'}
2024-09-12T22:08:14     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -dstnodata -9999.0 -tr 100.0 100.0 -r near -te 2521404.4815840046 -2672812.0677260184 2957862.1149176797 -1597099.3760041117 -te_srs EPSG:8859 -of GTiff D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_cyclone_100yr_8859.tif D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvResample.tif
2024-09-12T22:08:14     INFO    GDAL command:
2024-09-12T22:08:14     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -dstnodata -9999.0 -tr 100.0 100.0 -r near -te 2521404.4815840046 -2672812.0677260184 2957862.1149176797 -1597099.3760041117 -te_srs EPSG:8859 -of GTiff D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_cyclone_100yr_8859.tif D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvResample.tif
2024-09-12T22:08:14     INFO    GDAL command output:
2024-09-12T22:08:15     INFO    Process completed successfully
2024-09-12T22:08:15     INFO    Results: {'OUTPUT': 'D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvResample.tif'}
2024-09-12T22:08:34     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -of GTiff -cutline D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_polbnda_adm0_country_8859.shp -cl fji_polbnda_adm0_country_8859 -crop_to_cutline -dstnodata -9999.0 D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvClipResample.tif Hazard_Cyclones.tif
2024-09-12T22:08:34     INFO    GDAL command:
2024-09-12T22:08:34     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -of GTiff -cutline D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_polbnda_adm0_country_8859.shp -cl fji_polbnda_adm0_country_8859 -crop_to_cutline -dstnodata -9999.0 D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvClipResample.tif Hazard_Cyclones.tif
2024-09-12T22:08:34     INFO    GDAL command output:
2024-09-12T22:08:34     CRITICAL    Process returned error code 1
2024-09-12T22:08:34     INFO    Results: {'OUTPUT': 'Hazard_Cyclones.tif'}
2024-09-12T22:08:34     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -dstnodata -9999.0 -tr 100.0 100.0 -r near -te 2521404.4815840046 -2672812.0677260184 2957862.1149176797 -1597099.3760041117 -te_srs EPSG:8859 -of GTiff D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_drought_spei_1dot5_100yr_8859.tif D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvResample.tif
2024-09-12T22:08:34     INFO    GDAL command:
2024-09-12T22:08:34     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -dstnodata -9999.0 -tr 100.0 100.0 -r near -te 2521404.4815840046 -2672812.0677260184 2957862.1149176797 -1597099.3760041117 -te_srs EPSG:8859 -of GTiff D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_drought_spei_1dot5_100yr_8859.tif D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvResample.tif
2024-09-12T22:08:34     INFO    GDAL command output:
2024-09-12T22:08:35     INFO    Process completed successfully
2024-09-12T22:08:35     INFO    Results: {'OUTPUT': 'D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvResample.tif'}
2024-09-12T22:09:26     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -of GTiff -cutline D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_polbnda_adm0_country_8859.shp -cl fji_polbnda_adm0_country_8859 -crop_to_cutline -dstnodata -9999.0 D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvClipResample.tif Hazard_Drought.tif
2024-09-12T22:09:26     INFO    GDAL command:
2024-09-12T22:09:26     INFO    gdalwarp -overwrite -t_srs EPSG:8859 -of GTiff -cutline D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_polbnda_adm0_country_8859.shp -cl fji_polbnda_adm0_country_8859 -crop_to_cutline -dstnodata -9999.0 D:/temp/geest/geest_qgis_templates/output/fji_test_20240912/temp/tempEnvClipResample.tif Hazard_Drought.tif
2024-09-12T22:09:26     INFO    GDAL command output:
2024-09-12T22:09:26     CRITICAL    Process returned error code 1
2024-09-12T22:09:26     INFO    Results: {'OUTPUT': 'Hazard_Drought.tif'}
2024-09-12T22:09:27     INFO    Creating output file that is 4365P x 10757L. 
             Processing D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_firedensity_8859.tif [1/1] : 0...10...20...30...40...50...60...70...80...90...100 - done.
2024-09-12T22:09:27     CRITICAL    Warning 1: Ring Self-intersection at or near point 2617160.3599800956 -2438488.8009277876
2024-09-12T22:09:27     CRITICAL    ERROR 1: Cutline polygon is invalid.
2024-09-12T22:09:27     CRITICAL    Warning 1: for band 1, destination nodata value has been clamped to -128, the original value being out of range.
2024-09-12T22:09:27     INFO    Creating output file that is 4365P x 10757L. 
             Processing D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_land_susceptibility_nasa_8859.tif [1/1] : 0Using internal nodata values (e.g. 127) for image D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_land_susceptibility_nasa_8859.tif. 
             ...10...20...30...40...50...60...70...80...90...100 - done.
2024-09-12T22:09:27     CRITICAL    Warning 1: Ring Self-intersection at or near point 2617160.3599800956 -2438488.8009277876
2024-09-12T22:09:27     CRITICAL    ERROR 1: Cutline polygon is invalid.
2024-09-12T22:09:27     INFO    Creating output file that is 4365P x 10757L. 
             Processing D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_cyclone_100yr_8859.tif [1/1] : 0Using internal nodata values (e.g. 1.70141e+38) for image D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_cyclone_100yr_8859.tif. 
             ...10...20...30...40...50...60...70...80...90...100 - done.
2024-09-12T22:09:27     CRITICAL    Warning 1: Ring Self-intersection at or near point 2617160.3599800956 -2438488.8009277876
2024-09-12T22:09:27     CRITICAL    ERROR 1: Cutline polygon is invalid.
2024-09-12T22:09:27     INFO    Creating output file that is 4365P x 10757L. 
             Processing D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_drought_spei_1dot5_100yr_8859.tif [1/1] : 0Using internal nodata values (e.g. -3.4e+38) for image D:\temp\geest\geest_qgis_templates\layers\iso3\fji_input\fji_place_env_drought_spei_1dot5_100yr_8859.tif. 
             ...10...20...30...40...50...60...70...80...90...100 - done.
2024-09-12T22:09:27     CRITICAL    Warning 1: Ring Self-intersection at or near point 2617160.3599800956 -2438488.8009277876
2024-09-12T22:09:27     CRITICAL    ERROR 1: Cutline polygon is invalid.

Using self and manually generated Hazard layer and renamed following the Plugin requirement, the Aggregate Natural Disaster Risk also tested and it works perfectly with some warning in projection

No transform is available between WGS_1984_Equal_Earth_Asia_Pacific and EPSG:8859 - WGS 84 / Equal Earth Asia-Pacific.
No coordinate operations are available between these two reference systems

See #134 and #135 for full testing and input data