Commit 6b4756d8 authored by Mahé Perrette's avatar Mahé Perrette
Browse files

templates taken from isipedia-text-generator.py

parent 212ecd51
---
title: Template for preparing your ISIpedia report submission using RMarkdown
# Use letters for affiliations, numbers to show equal authorship (if applicable) and to indicate the corresponding author
author:
- name: Alice Anonymous
affiliation: a,1,2
- name: Bob Security
affiliation: a,b
address:
- code: a
address: Some Institute of Technology, Department, Street, City, State, Zip
- code: b
address: Another University Department, Street, City, State, Zip
corresponding_author:
- code: 2
text: "To whom correspondence should be addressed. E-mail: bob@email.com"
### Key messages: |
* Please provide the take home message of your study
* in bullet points
<place of Figure>
### How have we come to this figure?
The assessment provided here is based on our paper “title of the paper” published in “XYZ journal” in 2019. We have used simulations of ISIMIP...
### What we have found
Results
### Where are the most relevant gaps in our knowledge?
Models are simplified representations of reality, hence model simulations come with limitations and uncertainties
that have to be kept in mind. Regarding our ... simulations we mainly see the following issues:
### References
---------------
This ISIpedia portal template is provided to help you write your work in the
advised format. Instructions for use are provided below and in the "For authors" document.
Guide to using this template {.unnumbered}
========================================
Author Affiliations {#author-affiliations .unnumbered}
-------------------
Include department, institution, and complete address, with the
ZIP/postal code, for each author. Use lower case letters to match
authors with institutions, as shown in the example. Authors with an
ORCID ID may supply this information at submission.
Submitting Manuscripts {#submitting-manuscripts .unnumbered}
----------------------
All authors must submit their articles by email to the ISIpedia editorial team
(Barbara Templ, Mahé Perrette) via ISIpedia.editorial.team@pik-potsdam.de.
Format {#format .unnumbered}
------
Many authors find it useful to organize their manuscripts with the
following order of sections; Title, Author Affiliation, Key messages,
Results, Discussion, Materials and methods, and References.
Other orders and headings are permitted.
Data Archival {#data-archival .unnumbered}
-------------
ISIpedia must be able to archive the data essential to this study.
Language-Editing Services {#language-editing-services .unnumbered}
-------------------------
Prior to submission, authors who believe their manuscripts would benefit
from professional editing are encouraged to use a language-editing
service or ask the Editorial team for help.
### Areas affected by - people exposed to drought
Authors: Stefan Lange<sup>1</sup>, Ted Veldkamp<sup>2</sup>, Matthias Mengel<sup>1</sup>, Hannes Müller Schmied<sup>3</sup>, Katja Frieler<sup>1</sup>
Affilitations:
1. Potsdam Institute for Climate Impact Research, Germany
2. Vrije Universiteit Amsterdam, The Netherlands
3. Goethe University Frankfurt, Germany
Published: [to be added]
Doi: [to be added]
### Key messages
{% if country.name | lower != 'world' %}
* {{country.name}} ranks (ranking-value: land-abs-temp_{{country.code}} value: position temperature:2) with regards to absolute changes in land area affected by droughts (expressed as % of {{country.nameS}} land area) at 2°C of global warming in comparison to a situation without climate change. For the absolute changes in population exposed to droughts (expressed as % of {{country.nameS}} population), {{country.name}} ranks (ranking-value: pop-abs-temp_{{country.code}} value: position temperature:2).
{% endif %}
* At today’s levels of 1°C of global warming the simulated land area affected is already {{(land_abs_temp.get(1)/100*country.area) | round(-2)|int }} km<sup>2</sup> {%if land_abs_temp.get(1) > 0 %}larger{%else%}smaller{%endif%} ({{land_abs_temp.get(1) | round }}% of the national land area) than in a world without climate change where the annual area affected by droughts is {{(land_temp.get(0)/100*country.area) | round(-2)|int }} km<sup>2</sup> ({{land_temp.get(0) | round(1)}}% of {{country.nameS}} land area). The number of people exposed is {{(pop_abs_temp.get(1)/100*country.pop_total) | round(1) }} million ({{pop_abs_temp.get(1) | round(1)}}% of the national population) {%if pop_abs_temp.get(1) > 0 %}larger{%else%}smaller{%endif%} than without climate change where the annual number of people exposed to droughts was {{(pop_temp.get(0)/100*country.pop_total) | round(1) }} million ({{pop_temp.get(0) | round(1) }}% of {{country.nameS}} population).
* At 2°C of global warming the land area affected by droughts would {%if land_abs_temp.get(2) > 0 %}increase{%else%}decrease{%endif%} by {{(land_abs_temp.get(2)/100*country.area) | round(-2)|int }} km<sup>2</sup> ({{land_abs_temp.get(2) | round(1) }}% of the national land area) compared to a world without climate change, to {{land_temp.get(2) | round(1) }}% of the country’s land area. Assuming present-day population patterns, {{country.nameS}} population exposed to droughts would {%if pop_abs_temp.get(2) > 0 %}increase{%else%}decrease{%endif%} by {{pop_abs_temp.get(2)|round(1)}} million, to {{pop_temp.get(2)|round(1)}}% of the national population.
* Following the higher-emissions scenario (RCP6.0) which can entail over 3°C of global warming by the end of the century (2081-2100) ([Frieler et al. 2017](https://dx.doi.org/10.5194/gmd-10-4321-2017)) the land area affected by droughts would {%if land_abs_temp.get(3) > 0 %}increase{%else%}decrease{%endif%} by {{(land_abs_temp.get(3)/100*country.area) | round(-2)|int }} km<sup>2</sup> ({{land_abs_temp.get(3) | round(1) }}% of the national land area) and reach {{land_temp.get(3) | round(1) }}% of the country’s land area. Assuming present-day population patterns the population exposed would reach {{pop_temp.get(3)|round(1)}}% of {{country.nameS}} population, and {%if pop_abs_temp.get(3) > 0 %}increase{%else%}decrease{%endif%} by {{pop_abs_temp.get(3)|round(1)}} million compared to a situation without climate change.
### How have we got the results?
The assessment provided below is based on our paper “Change in exposure to climate impact events under global warming” submitted to the journal “Earth’s Future”. We have used simulations of {{pop_temp.impact_model_list|length}} different (link: glossary/global-hydrological-models text: global hydrological models) to estimate changes in drought conditions assuming different levels of global mean temperature change. To this end the global hydrological models were forced by climate projections from {{pop_temp.climate_model_list|length}} different global (link: glossary/climate-models text: climate models) following the ISIMIP2b scenario set-up. In the key messages we report the results for the median of the (link: glossary/model-ensemble text: model ensemble) of global hydrological models that ran simulations in (link: glossary/isimip2b text: ISIMIP2b). The median represents the middle of the ensemble, meaning that 50% of the ensemble members provide higher numbers and 50% provide lower numbers.
Among the multiple concepts of “drought”, our study refers to a situation where monthly soil moisture (agricultural drought concept) is extremely low for at least 7 consecutive months. “Extremely low” means dry conditions occurring less than 5 times in 200 years under reference conditions without climate change. We were interested in changes in the land area affected by droughts and the number of people exposed to droughts that we have to expect at different (link: glossary/levels-of-global-warming text: levels of global warming) (1°C, 2°C, and 3°C) or during different future time periods.
The ISIMIP2b simulations start in 1860 (before human greenhouse gas emissions started to change the climate) and end in 2100. They comprise a long run where weather only varies according to pre-industrial conditions without climate change. In addition, modellers have run two different future scenarios: one “business as usual” scenario (RCP6.0) reaching high levels of warming and one “low emissions scenario” (RCP2.6) with reduced levels of climate change.
As the “land area affected by drought” varies quite strongly from year to year we decided to average numbers over multiple years: for each year we calculate the deviation of the land area affected by droughts from the average reference level without climate change. These deviations are then averaged over a present-day period (2001-2020), a mid-century period (2041-2060) and an end-of-century period (2081-2100). To calculate the changes at different levels of global mean temperature change we do not average over years belonging to one of these periods but over all years with global mean warming levels close to 1°C, 2°C, and 3°C.
When calculating the “number of people exposed to droughts” we proceed in a similar way. We assume that only the rural population is exposed while the urban population does not necessarily “feel” any drought. For 1860 to 2005 we account for changes in population patterns ([Goldewijk et al., 2017](https://dx.doi.org/10.5194/essd-9-927-2017)). Afterwards the population data is considered constant -- not as a realistic assumption but an informative “thought experiment”: What would future climate change mean for present day societies? For all time periods, the pure effect of climate change on the “number of people exposed to droughts” is estimated by comparing the number of people exposed under climate change and a specific population pattern to the number of people exposed assuming the same population patterns but no climate change.
In the following we describe what we have found and how strongly {{country.name}} is affected compared to other countries.
### What we have found
### Land area affected by droughts
(line-plot: land-abs-temp_{{country.code}},land-abs-time_{{country.code}} first-temperature: 2 second-scenario: rcp26 second-time: 2041-2060)
Our definition of “drought” is quite strict, such that, without climate change, only {{land_temp.get(0)|round(1)}}% of {{country.nameS}} land area would be affected by droughts each year, on average.
However, at today’s level of 1°C global warming {{country.nameS}} annual land area affected by droughts is, on average, already {%if land_abs_temp.get(1) > 0 %}larger{%else%}smaller{%endif%} and amount to {{(land_abs_temp.get(1)/100*country.area)|round(-2)|int}} km<sup>2</sup> ({{land_abs_temp.get(1) | round(1) }}% of the national land area). The level of change ranges from {{land_abs_temp.getall(2) | min | round(1)}}% to {{land_abs_temp.getall(2) | max | round(1)}}% for the individual combinations of global hydrological models and global climate models. At 2°C of global warming, {{country.nameS}} annual land area affected by droughts is projected to {%if land_abs_temp.get(2) > 0 %}increase{%else%}decrease{%endif%} by {{(land_abs_temp.get(2)/100*country.area)|round(-2)|int}} km<sup>2</sup> (i.e. {{land_abs_temp.get(2) | round(1) }}% of the national land area) on average in comparison to a world without climate change. Under these conditions, {{land_temp.get(2) | round(1) }}% of the national land area would be affected by droughts each year, on average. Across the individual combinations of global hydrological models and global climate models this expected level of change ranges from {{(land_abs_temp.getall(2)|min /100*country.area) | round(-2)|int}} to {{(land_abs_temp.getall(2) |max /100*country.area) | round(-2)|int}} km<sup>2</sup>.
Following the higher-emissions scenario (RCP6.0) the land area affected by droughts is expected to {%if land_abs_time.get('2081-2100', 'rcp60') > 0 %}increase{%else%}decrease{%endif%} by {{(land_abs_time.get('2081-2100', 'rcp60')/100*country.area) | round(-2)|int}} km<sup>2</sup> ({{land_abs_time.get('2081-2100', 'rcp60')|round(1)}}% of the national land area) towards the end of the century (2081-2100). Following the low emission scenario (RCP2.6) the change would only reach {{(land_abs_time.get('2081-2100', 'rcp26')/100*country.area) | round(-2)|int}} km<sup>2</sup> ({{land_abs_time.get('2081-2100', 'rcp60')|round(1)}}% of the national land area). By the middle of the century, changes reach {{(land_abs_time.get('2041-2060', 'rcp26')/100*country.area) | round(-2)|int}} km<sup>2</sup> under RCP2.6 and {{(land_abs_time.get('2041-2060', 'rcp60')/100*country.area) | round(-2)|int}} km<sup>2</sup> under RCP6.0.
{{country.name}} is the (ranking-value: land-abs-temp_{{country.code}} value: position temperature:2) strongest affected by droughts at 2°C of global warming. For the absolute change in land area affected by droughts towards the end of the century under a high-emissions scenario (RCP6.0), {{country.nameS}} ranking is (ranking-value: land-abs-time_{{country.code}} value: position time:2081-2100 scenario:rcp60).
### Population exposed to droughts
(line-plot: pop-abs-temp_{{country.code}},pop-abs-time_{{country.code}} first-temperature: 2 second-scenario: rcp26 second-time: 2041-2060)
Our definition of “drought” is quite strict, such that, without climate change, only {{pop_abs_temp.get(0) | round(1)}}% of {{country.nameS}} population would be exposed to droughts each year, on average.
However, at today’s level of 1°C of global warming {{country.nameS}} annual population exposed to droughts is, on average, already {{(pop_abs_temp.get(1)/100*country.pop_total) | round(1) }} million (i.e. {{pop_abs_temp.get(1) | round(1)}}% of the total population) {%if pop_abs_temp.get(1) > 0 %}higher{%else%}lower{%endif%} than without climate change and amount to {{pop_temp.get(1) | round(1) }}% of the total population. The level of change ranges from {{land_abs_temp.getall(2) | min | round(1)}} % to {{land_abs_temp.getall(2) | max | round(1)}}% for the individual combinations of global hydrological models and global climate models. At 2°C of global warming, {{country.nameS}} annual population exposed to droughts is projected to {%if pop_abs_temp.get(2) > 0 %}increase{%else%}decrease{%endif%} by {{(pop_abs_temp.get(2)/100*country.pop_total)|int}} million (i.e. {{pop_abs_temp.get(2) | round(1)}}% of the population) on average in comparison to a world without climate change. Under these conditions, {{pop_temp.get(2) | round(1)}}% of the total population would be affected by droughts, on average. Across the individual combinations of global hydrological models and global climate models this expected level of change ranges from {{(pop_abs_temp.getall(2)|min /100*country.pop_total) | round(1)}} up to {{(pop_abs_temp.getall(2)|max /100*country.pop_total) | round(1)}} million people.
Following the higher-emissions scenario (RCP6.0) the population exposed to droughts is expected to {%if pop_abs_time.get('2081-2100', 'rcp60') > 0 %}increase{%else%}decrease{%endif%} by {{(pop_abs_time.get('2081-2100', 'rcp60')/100*country.pop_total)|round(1)}} million ({{pop_abs_time.get('2081-2100', 'rcp60')|round(1)}}% of the total population) towards the end of the century (2081-2100). Following the low emission scenario (RCP2.6) the change would expose {{(pop_abs_time.get('2081-2100', 'rcp26')/100*country.pop_total)|round(1)}} million people ({{pop_abs_time.get('2081-2100', 'rcp26')|round(1)}}% of the total population). By mid of the century changes expose {{(pop_abs_time.get('2041-2060', 'rcp26')/100*country.pop_total)|round(1)}} million people under RCP2.6 and {{(pop_abs_time.get('2041-2060', 'rcp60')/100*country.pop_total)|round(1)}} million people under RCP6.0.
{{country.name}} is the (ranking-value: pop-abs-temp_{{country.code}} value: position temperature:2) strongest affected by droughts at 2°C of global warming. For the absolute change in population exposed to droughts towards the end of the century under a higher-emissions scenario (RCP6.0), {{country.nameS}} ranking is (ranking-value: pop-abs-time_{{country.code}} value: position time:2081-2100 scenario:rcp60).
### How is soil moisture calculated?
Soil moisture is defined as the water stored in the soil in liquid or frozen form. It is calculated as the difference between the amount of water coming in as rain or snow, and the amount of water going out, either through evapotranspiration from the surface and vegetation, runoff, or through percolation of water towards the deeper groundwater layers. Global hydrological models (GHMs) include different representations of the (link: glossary/soil-water-column text: soil water column) with different numbers of soil water layer(s) (between 1 and 15 layers), and with different total depth of soil layers (between 1 and 42.1 meter).
Here, we use (link: glossary/rootzone-soil-moisture text: rootzone soil moisture) estimates, the portion of soil moisture that is within the rooting depth of plants, when directly provided by the GHMs. For GHMs that do not directly provide root zone soil moisture conditions, we approximated this variable by integrating soil moisture across multiple soil water layers in order to reach a depth of ~1 meter. Daily rootzone soil moisture values are finally aggregated into monthly average rootzone soil moisture conditions per grid cell.
### Where are the most relevant gaps in our knowledge?
Models are simplified representations of reality, hence model simulations come with limitations and uncertainties that have to be kept in mind ([Döll et al. 2016](https://dx.doi.org/10.1007/s10712-015-9343-1)). Regarding our drought simulations we mainly see the following issues.
* Representation of direct human influences
The presented drought projections reflect the isolated effect of climate change, while their local manifestations are expected to also be influenced by direct human drivers like changes in land use, land cover, and irrigation. Even in the historical period our simulations only account for water abstractions (with varying sectors among the models like irrigation, industry, domestic, livestock, desalinization) from groundwater and surface waters, reservoir management while other processes such as inter-basin water transfers and a more realistic reservoir management are also expected to be important. Including these drivers into our simulation would greatly improve the simulations ([Veldkamp et al. 2018](https://doi.org/10.1088/1748-9326/aab96f)). That it is not done is mainly an issue of missing knowledge about them.
* Representation of the CO2 fertilization effect
Another challenge lies in the representation of how vegetation cover affects evapotranspiration, a relationship that is subject to change under rising temperature and CO2 levels. For example, the effect of CO2 fertilization – the phenomenon through which photosynthesis, hence plant growth, should be enhanced in a CO2-richer atmosphere – is not represented in most global hydrological models as the vegetation components in those models are often simplified. Plants need less water to assimilate the same amount of carbon in a CO2-richer atmosphere. This effect alone would lead to a decrease of evapotranspiration. Yet the more efficient carbon assimilation lets plants grow better, which leads to bigger or more plants. This effect alone would lead to an increase of evapotranspiration. The overall CO2 fertilization effect on evapotranspiration, soil moisture conditions and drought risk is uncertain (Gerten et al., 2014; [Prudhomme et al., 2014](https://dx.doi.org/10.1073/pnas.1222473110); [Döll et al., 2016](https://dx.doi.org/10.1007/s10712-015-9343-1); [Kuzyakov et al., 2019](https://dx.doi.org/10.1016/j.soilbio.2018.10.005)).
* Representation of the soil compartment
The representation of the soil compartment vary with the total depth of soil layers (between 1 and 42.1 m), the number of soil layers (ranging from 1 up to 15) and the conceptualization of soil water processes. In many global hydrological models, we use here root zone soil moisture (related to agricultural needs) as an input for our drought assessment. However, in other models, we have to approximate this variable by integrating soil moisture across multiple water layers. Differences in the representation of the soil column and soil water processes can lead to significant differences in estimates of soil water availability, soil water saturation and drought conditions but this effect could not be investigated for the models used in this assessment.
* Representation of evapotranspiration in general
Much of the differences between the individual hydrological model simulations considered here is assumed to be due to different calculation of potential and actual evapotranspiration. Whereas some models directly calculates actual evapotranspiration (AET, the amount of water that is transferred through plants, bare soil, open water bodies or from water stored at canopies) directly using water transfer schemes or turbulent fluxes, the majority of the models use the potential evapotranspiration (PET) concept. By using various approaches with different demand on meteorological input variables, the potential water demand of the atmosphere (PET as upper limit) is calculated. The availability of water in the storages (e.g. soil water storage) can reduce the amount of water that is actually evapotranspired to the atmosphere (AET). Unfortunately, currently, we are still not able to decide which of the representations is most appropriate but first assessments of those representations have been done ([Wartenburger et al. 2018](https://doi.org/10.1088/1748-9326/aac4bb)). As a result, estimates of actual evapotranspiration can differ significantly across models despite having used uniform inputs from the global climate models, which may affect estimates of drought conditions much more than for example flooding which mainly depends on the amount of precipitation.
### Disclaimer
Note, that although this report is based on an article published in a peer-reviewed scientific journal, the original publication focused on a global analysis. Therefore, the results presented here for 200+ countries have not been reviewed individually. ISIpedia provides data on country level for convenience, but cannot be held responsible for any issues with the data. Please contact the ISIpedia editorial team (isipedia.editorial.team@pik-potsdam.de) for more information or questions about this report.
### References
Döll, P., Douville, H., Güntner, A., Müller Schmied, H., Wada, Y. (2016) Modelling Freshwater Resources at the Global Scale: Challenges and Prospects. Surveys in Geophysics, 37(2), 195–221. https://dx.doi.org/10.1007/s10712-015-9343-1
Frieler, K. and Lange, S. and Piontek, F. and Reyer, C. P. O. and Schewe, J. and Warszawski, L. and Zhao, F. and Chini, L. and Denvil, S. and Emanuel, K. and Geiger, T. and Halladay, K. and Hurtt, G. and Mengel, M. and Murakami, D. and Ostberg, S. and Popp, A. and Riva, R. and Stevanovic, M. and Suzuki, T. and Volkholz, J. and Burke, E. and Ciais, P. and Ebi, K. and Eddy, T. D. and Elliott, J. and Galbraith, E. and Gosling, S. N. and Hattermann, F. and Hickler, T. and Hinkel, J. and Hof, C. and Huber, V. and J\"agermeyr, J. and Krysanova, V. and Marc\'e, R. and M\"uller Schmied, H. and Mouratiadou, I. and Pierson, D. and Tittensor, D. P. and Vautard, R. and van Vliet, M. and Biber, M. F. and Betts, R. A. and Bodirsky, B. L. and Deryng, D. and Frolking, S. and Jones, C. D. and Lotze, H. K. and Lotze-Campen, H. and Sahajpal, R. and Thonicke, K. and Tian, H. and Yamagata, Y. (2017). Assessing the impacts of 1.5 °C global warming -- simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geoscientific Model Development,10 (12), 4321--4345. https://dx.doi.org/10.5194/gmd-10-4321-2017
Gerten, D. and Betts, R. and Döll, P. (2014). Cross-chapter box on the active role of vegetation in altering water flows under climate change. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate Change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the 5th assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 157–161
Goldewijk, K., Beusen, A., Doelman, J., Stehfest, E. (2017) Anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data, 9, 927–953. https://dx.doi.org/10.5194/essd-9-927-2017
Kuzyakov, Y., Horwath, W. R., Dorodnikov, M. and Blagodatskaya, E. (2019). Review and synthesis of the effects of elevated atmospheric CO2 on soil processes: No changes in pools, but increased fluxes and accelerated cycles, Soil Biology and Biochemistry, 128, 66-78, https://dx.doi.org/10.1016/j.soilbio.2018.10.005
Lange et al. Change in exposure to climate impact events under global warming. Submitted to Earth’s Future.
Prudhomme, C.; Giuntoli, I.; Robinson, E. L.; Clark, D. B.; Arnell, N. W.; Dankers, R.; Fekete, B. M.; Franssen, W.; Gerten, D.; Gosling, S. N.; Hagemann, S.; Hannah, D. M.; Kim, H.; Masaki, Y.; Satoh, Y.; Stacke, T.; Wada, Y. & Wisser, D. (2014). Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment, Proceedings of the National Academy of Sciences, National Academy of Sciences, 111, 3262-3267, https://dx.doi.org/10.1073/pnas.1222473110
Veldkamp, T. I.E., Zhao, F., Ward, P.J., de Moel, H., Aerts, J.C.J.H., Müller Schmied, H., Portmann, F.T., Masaki, Y., Pokhrel, Y., Liu, X., Satoh, Y., Gerten, D. Gosling, S.N., Zaherpour, J., Wada, Y. (2018) Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multimodel validation study. Environ. Res. Lett. 13 055008, https://doi.org/10.1088/1748-9326/aab96f
Wartenburger R., and Sonia I Seneviratne and Martin Hirschi and Jinfeng Chang and Philippe Ciais and Delphine Deryng and Joshua Elliott and Christian Folberth and Simon N Gosling and Lukas Gudmundsson and Alexandra-Jane Henrot and Thomas Hickler and Akihiko Ito and Nikolay Khabarov and Hyungjun Kim and Guoyong Leng and Junguo Liu and Xingcai Liu and Yoshimitsu Masaki and Catherine Morfopoulos and Christoph Müller and Hannes Müller Schmied and Kazuya Nishina and Rene Orth and Yadu Pokhrel and Thomas A M Pugh and Yusuke Satoh and Sibyll Schaphoff and Erwin Schmid and Justin Sheffield and Tobias Stacke and Joerg Steinkamp and Qiuhong Tang and Wim Thiery and Yoshihide Wada and Xuhui Wang and Graham P Weedon and Hong Yang and Tian Zhou. (2018) Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets. Environmental Research Letters,13 (7), 075001. https://dx.doi.org/10.1088/1748-9326/aac4bb
# Hurricanes
## Global overview
This is a minimalistic template for an hypothetical hurricane indicator.
Hurricanes will have a number of effect throughout the world.
In average, worldwide, they are projected to increase by {{world.time_var.get("2081-2100", "rcp85") | round(1)}} by the end of the century.
In that scenario, the three most affected countries will be:
- 1st: (ranking-area: time_var order: 1 value: name time: 2081-2100 scenario: rcp85)
- 2nd: (ranking-area: time_var order: 2 value: name time: 2081-2100 scenario: rcp85)
- 3rd: (ranking-area: time_var order: 3 value: name time: 2081-2100 scenario: rcp85)
## Detailed results
`{%if country.name != world %}`
{{country.name}} is the (ranking-value: time_var value: position time: 2081-2100 scenario: rcp85) most-affected country out of the (ranking-value: time_var value: total time: 2081-2100 scenario: rcp85) countries considered.
`{%endif%}`
Results:
<table>
<tr>
<th>Variable</th>
<th>2041-2060</th>
<th>2081-2100</th>
<th>2 degrees</th>
<th>4 degrees</th>
</tr>
<tr>
<th>time_var.name</th>
<th>time_var.get("2041-2060", "rcp85")</th>
<th>time_var.get("2081-2100", "rcp85")</th>
<th>temp_var.get(2)</th>
<th>temp_var.get(4)</th>
</tr>
</table>
(line-plot: time_var,temp_var first-time: 2041-2060 2 first-scenario: rcp60 second-temperature: 2)
## Authors
The data presented here was originally published by bla bla bla, literature reference and adapted to the isipedia platform by bla bla bla.
Note that although the methods and main results were presented in a peer-reviewed journal, the original publication focused on a global analysis, so that not all 200+ countries have been reviewed individually.
ISIpedia provides data on country level for convenience, but cannot be held responsible for any issues with the data. Please contact the authors (author@contact) for more information.
### Key messages
* Without human-made greenhouse gas emissions, {{land_temp.get(0)}}% of {{country.nameS}} land area and {{pop_temp.get(0)}}% of {{country.nameS}} population would be affected by river flood each year, on average.
* At today’s levels of 1 degrees Celsius of global warming the land area affected is {{land_rel_temp.get(1)/100 + 1}} times as much: {{land_rel_temp.get(1)}}% of the total land area, while the number of people affected is {{pop_rel_temp.get(1)/100 + 1}} times as much: {{pop_temp.get(1)}}% of the total population.
* At 2 degrees Celsius of global warming, {{country.nameS}} land area affected by river floods is projected to increase by a factor of {{land_rel_temp.get(2)}} compared to a world without human-made greenhouse gas emissions, to {{land_temp.get(0)}}%. Likewise, {{country.nameS}} population exposed to river floods is projected to increase by a factor {{pop_rel_temp.get(2)}}, to {{pop_temp.get(0)}}%.
* Following the higher-emissions scenario (RCP6.0) which can entail over 3 degrees Celsius of global warming by the end of the century (2081-2100), this factor is projected to reach {{land_rel_time.get('farfuture','rcp60')/100 + 1}} for the land area affected to river flood (to {{land_time.get('farfuture','rcp60')}}%) and {{pop_rel_time.get('farfuture','rcp60')/100 + 1}} for the population exposed to river floods (to {{pop_time.get('farfuture','rcp60')}}%).
* {{country.name}} ranks (ranking-value: land-rel-temp_PAK value: position temperature:2) with regards to its relative change in land area affected by river floods at 2 degrees Celsius of global warming in comparison to a situation without anthropogenic climate change. For the relative change in population exposed to river floods, {{country.nameS}} ranking is (ranking-value: pop-rel-temp_PAK value: position temperature:2).
This (link: glossary/climate-impact-assessment text: climate impact) assessment presents (link: glossary/projections text: projections) of the land area affected by river floods and the number of people exposed to river flood in {{country.name}}, based on {{land_time.impact_model_list | length}} different (link: glossary/global-hydrological-models text: global hydrological models) (GHMs) and {{land_time.climate_model_list | length}} different global (link: glossary/climate-models text: climate models)(GCMs) participating in [ISIMIP](https://www.isimip.org/).
Here, (link: glossary/river-floods text: river flood) refer to a situation where the daily (link: glossary/river-flow text: river flow) exceeds the so-called 100-year (link: glossary/return-level text: return level). For each (link: glossary/grid-cell text: grid cell), which covers an area of up to 50x50 square-kilometers affected to river flood, we calculate the percentage of area flooded by redistributing the (link: glossary/flood-volume text: flood volume) per cell in accordance variations in (link: glossary/surface-elevation text: surface elevation). To calculate the land area affected by river floods for {{country.name}}, the percentage of area flooded are added up across all grid cells belonging to {{country.name}}. To calculate the number of people exposed to river flood, we first multiply the percentage of area flooded of each grid cell by the number of people living in that grid cell to estimate the number of people exposed to river flood, and then add up those numbers across all grid cells belonging to {{country.name}}.
We analyze the land area affected by river floods and the number of people exposed to river flood at different (link: glossary/levels-of-global-warming text: levels of global warming)(0, 1, 2, and 3 degrees Celsius) and during different time periods. We cover the time before (link: glossary/anthropogenic-climate-change text: anthropogenic climate change) started (conventionally set at 1850), present-day (2001-2020), mid-century (2041-2060) and end-of-century (2081-2100). We compare the later periods against the climate conditions before anthropogenic climate change started (1850). A ranking of countries for each indicator is provided to allow comparison between countries on the basis of their (link: glossary/relative-change text: relative change) in the land area affected or the number of people exposed under different levels of global warming and for various time horizons, compared to conditions before anthropogenic climate change started. Here, a ranking of 1 implies that the country experiences a change in the (link: glossary/indicator text: indicator) that is more than all other ranked countries, whereas a lower ranking entails comparatively less effects due to climate change.
It is important to note that our (link: glossary/future-projections text: future projections) assume that the number of people living in an area, as well as the area’s (link: glossary/land-use text: land use) and (link: glossary/land-cover text: land cover), remain constant to that of the year 2005. This is not necessarily meant to be realistic, but to isolate the influence of climate change from the influence of other changes.
In the key messages we report the results for the (link: glossary/median text: median) of the (link: glossary/model-ensemble text: model ensemble) of global hydrological models providing data to ISIMIP. The median represents the middle of the ensemble, meaning that 50% of the ensemble members provide higher numbers and 50% provide lower numbers. After the key message, which only references the median, you can find the model-specific results in the figures, a discussion of the methodology and the restrictions of the analysis at the end of the article.
### Results
The figures below shows the relative change in {{country.nameS}} land area affected by and population exposed to river floods in comparison to the time period before anthropogenic climate change. Results are shown both for the (link: glossary/historical-period text: historical time period) and for the (link: glossary/future-projections text: future projections) and can be visualized with regards to their change over time as well as with regards to their change in terms of global warming. Under the ‘change in terms of time’ setting, the change in land area affected by and population exposed to river floods over time is shown for 20-year (link: glossary/time-slices text: time-slices) from 1861-1880 until the end-of-the-century (2081-2100). Under the ‘change in terms of global warming’, the change in land area affected by and population exposed to river floods is shown for 20-year (link: glossary/time-slices text time-slices) representing increasing levels of global warming from 0 degrees Celsius up to 3 degrees Celsius compared to the time period before anthropogenic climate change started.
In both cases, results are shown for all possible combinations of (link: glossary/global-hydrological-models text: global hydrological models) and (link: glossary/global-climate-models text: global climate models) (normal lines) as well as for the median of the model ensemble (thick line). The influence of (link: glossary/inter-annual-variability text: inter-annual variability) on the results is visualized by means of the shaded area in black. This inter-annual variability is only shown for the median model combination. For the future projections and under the ‘change in terms of time’ settings only, an additional distinction is made between the results of two future (link: glossary/emissions-trajectories text: emissions trajectories): a low-emissions trajectory limiting global mean temperature in the 21st century to below 2 degrees Celsius compared to before anthropogenic climate change started, called RCP2.6 (blue lines), and a higher-emissions scenario leading global mean temperature to above 3 degrees Celsius by 2100, compared to pre-climate change conditions, called RCP6.0 (red lines). The shaded areas in blue and red indicate the variety (or spread) in results between all combinations of global hydrological models and global climate models for these two emissions trajectories. A filtering menu top-right of the graph allows the user to select individual emissions trajectories, individual global climate models, and individual global hydrological models for visualization.
By hovering over or clicking on a particular value in the figure additional details, such as the specific global climate model or hydrological used, behind the presented value become available. The visualization below the graph shows, for the selected model run under this time-period or temperature-change level, how {{country.name}} ranks in comparison to other countries on its relative change in land area affected by or population exposed to river floods.
#### Land area affected by river floods
(line-plot: land-area-affected-by-drought-relative-changes_ISIMIP-projections_versus-temperature-change_{{country.code}},land-area-affected-by-drought-relative-changes_ISIMIP-projections_versus-timeslices_{{country.code}} first-temperature: 2 second-scenario: rcp26 second-time: 2141-2160)
Without human-made greenhouse gas emissions, {{land_temp.get(0)}}% of {{country.nameS}} land area would be affected by river flood each year, on average.
Under the ‘change in terms of global warming’ setting the figure shows that at today’s level of 1 degrees Celsius of global warming {{country.nameS}} land area affected by river floods is, on average, already {{land_rel_temp.get(1)/100 + 1}} times (or {{land_rel_temp.get(1)}}%) {% if land_rel_temp.get(1) > 100 %}higher{%else%}lower{%endif%} and amount to {{land_temp.get(1)}}% of the total land area. This level of change ranges from {{land_abs_temp.getall(2) | min}}% to {{land_abs_temp.getall(2) | max}}% for the individual combinations of global hydrological models and global climate models. At 2 degrees Celsius of global warming, {{country.nameS}} land area affected by river floods is projected to change by a factor of {{land_rel_temp.get(2)/100 + 1}} (or {{land_abs_temp.get(2)}}%) in comparison to a world without human-made greenhouse gas emissions. Under these conditions, {{land_temp.get(2)}}% of the total land area would be affected by river flood on a yearly basis, on average. Across the individual combinations of global hydrological models and global climate models this expected level of change ranges from {{land_abs_temp.getall(2) | min}}% up to {{land_abs_temp.getall(2) | max}}%.
When presenting the ‘change in terms of time’, we find that when following the higher-emissions scenario (RCP6.0) towards the end-of-the-century (equivalent to over 3 degrees Celsius change) would result in a change in the land area affected by river floods of, on average, a factor {{land_rel_time.get('farfuture','rcp60')/100 + 1}} ({{land_abs_time.get('farfuture','rcp60')}}%), towards: {{land_time.get('farfuture','rcp60')}}% of the total land area. Following the (link: glossary/climate-mitigation-emissions-scenario text: climate-mitigation emissions scenario)(RCP2.6) towards the end-of-the-century (entailing an average 2.5 degrees Celsius change) would result in a foreseen change in the land area affected by river floods of, on average, a factor {{land_rel_time.get('farfuture','rcp26')/100 + 1}} ({{land_abs_time.get('farfuture','rcp26')}}%), {{land_time.get('farfuture','rcp26')}}% of the total land area being affected.
Globally, the land area affected by river floods each year, on average, is {{world.land_temp.get(0)}}% in a situation without human-made greenhouse gas emissions. At 2 degrees Celsius global warming or by the end-of-the-century under a higher-emissions scenario (RCP6.0) these values are foreseen to increase globally by a factor {{world.land_rel_temp.get(2)/100+1}} and {{world.land_rel_time.get('farfuture','rcp60')/100 + 1}}%, respectively.
As such, {{country.name}} ranks (ranking-value: land-rel-temp_PAK value: position temperature:2) with regards to its relative change in land area affected by river floods at 2 degrees Celsius of global warming in comparison to a situation without anthropogenic climate change. For the relative change in land area affected by river floods towards the-end-of-the-century under a higher-emissions scenario (RCP6.0), {{country.nameS}} ranking is (ranking-value: land-rel-time_PAK value: position time:2081-2100 scenario: rcp60).
#### Population exposed to river floods
(line-plot: population-exposed-to-drought-relative-changes_ISIMIP-projections_versus-temperature-change_{{country.code}},population-exposed-to-drought-relative-changes_ISIMIP-projections_versus-timeslices_{{country.code}} first-temperature: 2 second-scenario: rcp26 second-time: 2141-2160)
Without human-made greenhouse gas emissions, {{pop_temp.get(0)}}% of {{country.nameS}} population would be exposed to river flood each year, on average.
Under the ‘change in terms of global warming’ setting the figure shows that at today’s level of 1 degrees Celsius of global warming {{country.nameS}} population exposed to river floods is, on average, already {{pop_rel_temp.get(1)/100 + 1}} times (or {{pop_rel_temp.get(1)}}%) {% if pop_rel_temp.get(1) > 100 %}higher{%else%}lower{%endif%}: {{pop_temp.get(1)}}% of the total land area. This level of change ranges from {{pop_abs_temp.getall(1) | min}}% to {{pop_abs_temp.getall(1) | max}}% for the individual combinations of global hydrological models and global climate models. At 2 degrees Celsius of global warming, {{country.nameS}} population exposed to river floods is projected to change by a factor of {{pop_rel_temp.get(2)/100+1}} (or {{pop_abs_temp.get(2)}}%) in comparison to a world without human-made greenhouse gas emissions. Under these conditions, {{pop_temp.get(2)}}% of the total population would be exposed to river flood on a yearly basis, on average. Across the individual combinations of global hydrological models and global climate models this expected level of change ranges from {{pop_abs_temp.getall(2) | min}}% up to {{pop_abs_temp.getall(2) | max}}%.
When presenting the ‘change in terms of time’, we find that when following the higher-emissions scenario (RCP6.0) towards the end-of-the-century (equivalent to over 3 degrees Celsius change) would result in a foreseen change in the population exposed to river floods of, on average, a factor {{pop_rel_time.get('farfuture','rcp60')/100 + 1}} ({{pop_abs_time.get('farfuture','rcp60')}}%),{{pop_time.get('farfuture','rcp60')}}% of the total population. Following the climate-mitigation emissions scenario (RCP2.6) towards the end-of-the-century (entailing an average 2.5 degrees Celsius change) would result in a foreseen change in the population exposed to river floods of, on average, a factor {{pop_rel_time.get('farfuture','rcp26')/100 + 1}} ({{pop_abs_time.get('farfuture','rcp26')}}%), {{pop_time.get('farfuture','rcp26')}}% of the total population being exposed.
Globally, the population exposed to river floods each year, on average, is {{world.pop_temp.get(0)}}% in a situation without human-made greenhouse gas emissions. At 2 degrees of global warming or by the end-of-the-century under a higher-emissions scenario (RCP6.0) these values are foreseen to increase globally by {{world.pop_rel_temp.get(2)/100+1}}% and {{world.pop_rel_time.get('farfuture','rcp60')/100 + 1}}% respectively.
As such, {{country.name}} ranks (ranking-value: pop-rel-temp_PAK value: position temperature:2) with regards to its relative change in population exposed to river floods at 2 degrees Celsius of global warming in comparison to a situation without anthropogenic climate change. For the relative change in population exposed to river floods towards the-end-of-the-century under a business-as-usual emissions scenario, {{country.nameS}} ranking is (ranking-value: pop-rel-time_PAK value: position time:2081-2100 scenario: rcp60).
### Methodology
#### What do we analyze?
We analyze the land area affected by river floods and the number of people exposed to river flood at different levels of global warming (0, 1, 2, and 3 degrees Celsius) and during different time periods. We cover the time before anthropogenic climate change started (1850), present-day (2001-2020), mid-century (2041-2060) and end-of-century (2081-2100). We compare the later periods against the climate conditions before anthropogenic climate change (1850).
#### How do we calculate where and when a flood occurs?
The calculation has several steps: 1) calculate (link: glossary/runoff text: runoff); 2) calculate daily river flow and its (link: glossary/annual-maximum text: annual maximum); 3) compare this annual maximum with the 100-year (link: glossary/return-level text: return level) which is calculated separately when the 100-year return level is exceeded, 4) calculate (link: glossary/flood-depth text: flood depth); and 5) calculate flooded land area fraction. All these steps are detailed below, after a short explanation of the spatial structure on which our models operate.
#### What is the spatial structure of the models?
The models we use cover the whole globe’s land area. The land area is divided into a grid. Each grid cell has a size of 0.5 degrees × 0.5 degrees (latitude by longitude). At the equator, this equals roughly 55 × 55 km; towards the North Pole or South Pole, where the land area covered per grid cell becomes smaller.
#### How is runoff calculated?
The calculation is done for each grid cell and each day. Information about temperature, precipitation, solar radiation, and other weather indicators is taken from the global climate models (GCMs) and used as input for the ISIMIP global hydrological models (GHMs). Additional spatial data, such as soil, land cover and water bodies are also inputted. Runoff is calculated as the difference between the amount of water coming in as rain or snow, and the amount of water going out, either through evapotranspiration from the surface and vegetation, or by seeping vertically into the ground. Runoff percolates horizontally through the ground (subsurface runoff) or flows on the surface (surface runoff) into the surface water bodies or river. The model calculates runoff as daily value for each grid cell.
#### How is river flow calculated?
Having calculated runoff in each grid cell, we use the global river model (link: glossary/camaflood text: CaMaFlood) to calculate the water flow once it is in the river. The river water flow is also known as discharge, and measured in cubic meters per second (m³/s). For each grid cell along the river, the amount of water flowing through the river is calculated by summing up the runoff coming from all grid cells located upstream. For each grid cell, we then calculate the annual maximum of the daily river flow values.
#### How do we know whether a flood occurs?
In our analysis, we assume that a flood occurs whenever the annual maximum daily river flow exceeds the so-called 100-year return level. This is the water flow that was exceeded only once every 100 years, on average, before anthropogenic climate change started. We only consider river flows larger than the 100-year return level, because we assume that for smaller flows, either no flooding occurs (e.g. protection measures such as levees prevent them) or the floods have minor impacts (e.g. people do not settle in these areas or the flood depth is very shallow). That is, we assume that societies have already adapted to more frequent events.
#### How do we know how often floods occurred before anthropogenic climate change started?
We use a separate model simulation in which the climate behaves as if there had been no human-made greenhouse gas emissions. This simulation runs for several centuries without any outside change, just letting the weather evolve naturally. We then calculate the maximum river flow level that occurs, on average, every 100 years in this simulation.
#### How is flood depth calculated?
Once there is a flood in a given grid cell and year, we obtain the flood depth from a calculation based on the unique relationship of flood depth with return level. This relationship between return level and flood depth is based on (link: glossary/calibrated-simulations text: calibrated simulations) from the global hydrological model (link: glossary/matsiro text: MATSIRO) in combination with CaMaFlood.
#### How is the flooded land area fraction calculated?
For each flood-affected grid cell, the flood-water volume is calculated from the flood depth and the size of the cell. This volume of water is then distributed onto a high-resolution elevation map (~100m x 100m at the equator). This map is part of CaMaFlood. The elevation map acts as a basin, with each point in the map assigned a height above sea-level.
The portion of each 0.5 degree x 0.5 degree grid cell that is submerged is calculated. For example, if it is a mountainous area, all the flood water will concentrate in the narrow valleys, while the mountain peaks do not get flooded. Thus, only a part of the 0.5 degree x 0.5 degree grid cell will be submerged even during a large flood. On the contrary, if the area is very flat, then the flood water will spread out and potentially submerge the entire grid cell.
#### How are the indicators land area affected by river floods and number of people exposed to river flood calculated?
To calculate the land area affected by flooding for {{country.name}}, the flooded land area fractions are added up across all 0.5 degree x 0.5 degree grid cells belonging to {{country.name}}. To calculate the number of people exposed to flooding, we first multiply the flooded land area fraction of each grid cell by the number of people living in that grid cell to estimate the number of people exposed to river flood, and then add up those numbers across all grid cells belonging to {{country.name}}.
#### What else should I know about the methodology?
We assume that the number of people living in an area, as well as the area’s land use and land cover (what fractions of the area are used for settlement, cropland, or pasture, or covered by forest), remain constant at the levels of year 2005 throughout the whole simulation. This is not meant to be realistic, but to isolate the influence of climate change from the influence of other changes.
In the key messages we report the results for the median (the model in the middle) of the group of global hydrological models that ran simulations in (link: glossary/isimip2b text: ISIMIP2b). All model-specific results are presented in the figures and a discussion of the limitations of the analysis is included at the end of the report.
A ranking of countries for each indicator is also provided to compare countries on the basis of their relative change in land area affected or number of people exposed under different levels of global warming and for various time horizons, compared to conditions without climate change. A ranking of 1 implies that the country experiences a change in the indicator higher than all other ranked countries, for example the strongest impacts from climate change; whereas a lower ranking entails comparatively less impacts.
### Discussion
The model simulations used for this report build on science that has been established through many peer-reviewed studies: e.g. by [Yamazaki et al. (2011)](https://doi.org/10.1029/2010WR009726),[Müller Schmied et al. (2016)](https://doi.org/10.5194/hess-20-2877-2016). Models are simplified representations of reality, hence model simulations come with limitations and uncertainties that have to be kept in mind. One major limitation of the global water models is the availability of global data sets that are needed to fully represent the global hydrological cycle. For example, many of the models do not consider glaciers which could influence river flood as well. Furthermore, the representation of the components of the hydrological cycle, such as soil hydrology, differ strongly between models (e.g. in terms of detail, but also the calculation approach) with large effects on runoff generation. Another major source of uncertainty are the climatic input data as they stem from climate models that have a coarser spatial resolution and their own (link: glossary/modelling-uncertainties text: modelling uncertainties). In particular, the adequate simulation of precipitation extremes is difficult for global climate models.
The performance of the global water models themselves can be tested by using (link: glossary/observed-historical-weather-information text: observed historical weather information) as inputs, and comparing the simulated river flow to river flow observed at different stations along rivers. One general challenge associated with the simulation of hydrological processes lies in the representation of how vegetation cover affects evapotranspiration and other surface properties that play a role in flood onset. Moreover, this relationship may change within a given river basin, or with rising temperature and CO2 levels. For example, the effect of CO2 fertilization – the phenomenon through which photosynthesis, hence plant growth, should be enhanced in a CO2-richer atmosphere – is not represented in most water models although it would affect the plants’ water use, with consequences for runoff and river flow. Nevertheless, although these vegetation-related processes are critical for the projection of drought conditions and low-flow events, they are of less importance for projections of river flood. A close relationship indeed exists between precipitation and peak discharge during flood events, which is only moderately affected by evapotranspiration.
The presented flood projections reflect the isolated effect of climate change, while their local manifestations are expected to also be influenced by direct human drivers like changes in irrigation, construction of new dams and reservoirs, levees and other flood protection infrastructure. The representation of these drivers is limited to (link: glossary/present-day-conditions text: present-day conditions), while their future changes are not explicitly modelled in the presented simulations. This serves the purpose of isolating the effect of anthropogenic climate change from other factors, but it means that actual flood exposure in the future could be more or less acute, depending on non-climatic human drivers, such as urbanization or adaptation.
Our methodology assumes that every event where river flow exceeds the local 100-year return level is a flood. This is a simplification; flood protection measures may have a higher or lower protection standard in reality. Most parts of the world are protected only against lower-intensity events that occur more frequently than once in 100 years. This means that more floods would actually occur than in our projections. In a few parts of the world (mainly the USA and a few European countries), the opposite is true, meaning that protection measures could prevent all river flow events that are simulated to occur up to as rarely as once in 200 years.
Finally, another caveat to keep in mind is that only a single river flood model (CaMaFlood) was used for the calculation of river flow and flooded area; and a single global hydrological model, MATSIRO, was used to derive the grid cell-specific relationship between return level and flood depth. It is, therefore, difficult to assess the uncertainty that arises from assumptions built into these models.
Regarding climate models and water models, it is known that generally both types of models contribute substantially to the overall spread in projected climate change impacts on river flow and other water-related variables. Because this report presents the results of a combination of climate models and water models that ran simulations based on the same experiment protocol, and whose outputs are thus directly comparable, it captures at least some of this spread in the projections.
This (link: glossary/climate-impact-assessment text: climate impact) assessment presents (link: glossary/projections text: projections) of the land area affected by river floods and the number of people exposed to river flood globally, based on {{land_time.impact_model_list }} different (link: glossary/global-hydrological-models text: global hydrological models) (GHMs) and {{land_time.climate_model_list | length}} different global (link: glossary/climate-models text: climate models)(GCMs) participating in [ISIMIP](https://www.isimip.org/).
Here, (link: glossary/river-floods text: river flood) refer to a situation where the daily (link: glossary/river-flow text: river flow) exceeds the so-called 100-year (link: glossary/return-level text: return level). For each (link: glossary/grid-cell text: grid cell), which covers an area of up to 50x50 square-kilometers affected to river flood, we calculate the percentage of area flooded by redistributing the (link: glossary/flood-volume text: flood volume) per cell in accordance variations in (link: glossary/surface-elevation text: surface elevation). To calculate the land area affected by river floods at the global scale, the percentage of area flooded are added up across all grid cells worldwide. To calculate the number of people exposed to river flood globally, we first multiply the percentage of area flooded of each grid cell by the number of people living in that grid cell to estimate the number of people exposed to river flood, and then add up those numbers across all grid cells worldwide.
We analyze the land area affected by river floods and the number of people exposed to river flood at different (link: glossary/levels-of-global-warming text: levels of global warming)(0, 1, 2, and 3 degrees Celsius) and during different time periods. We cover the time before (link: glossary/anthropogenic-climate-change text: anthropogenic climate change) started (conventionally set at 1850), present-day (2001-2020), mid-century (2041-2060) and end-of-century (2081-2100). We compare the later periods against the climate conditions before anthropogenic climate change started (1850). A ranking of countries for each indicator is provided to allow comparison between countries on the basis of their (link: glossary/relative-change text: relative change) in the land area affected or the number of people exposed under different levels of global warming and for various time horizons, compared to conditions before anthropogenic climate change started. Here, a ranking of 1 implies that the country experiences a change in the (link: glossary/indicator text: indicator) that is more than all other ranked countries, whereas a lower ranking entails comparatively less effects due to climate change.
It is important to note that our (link: glossary/future-projections text: future projections) assume that the number of people living in an area, as well as the area’s (link: glossary/land-use text: land use) and (link: glossary/land-cover text: land cover), remain constant to that of the year 2005. This is not necessarily meant to be realistic, but to isolate the influence of climate change from the influence of other changes.
In the key messages we report the results for the (link: glossary/median text: median) of the (link: glossary/model-ensemble text: model ensemble) of global hydrological models providing data to ISIMIP. The median represents the middle of the ensemble, meaning that 50% of the ensemble members provide higher numbers and 50% provide lower numbers. After the key message, which only references the median, you can find the model-specific results in the figures, a discussion of the methodology and the restrictions of the analysis at the end of the article.
### Key messages
* Without human-made greenhouse gas emissions, {{land_temp.get(0)}}% of the global land area and {{pop_temp.get(0)}}% of the world’s population would be affected by river flood each year, on average.
* At today’s levels of 1 degrees Celsius of global warming the land area affected is {{land_rel_temp.get(1)/100 + 1}} times as much: {{land_rel_temp.get(1)}}% of the total land area, while the number of people affected is {{pop_rel_temp.get(1)/100 + 1}} times as much: {{pop_temp.get(1)}}% of the total population.
* At 2 degrees Celsius of global warming, the global land area affected by river floods is projected to increase by a factor of {{land_rel_temp.get(2)}} compared to a world without human-made greenhouse gas emissions, to {{land_temp.get(0)}}%. Likewise, the world’s population exposed to river floods is projected to increase by a factor {{pop_rel_temp.get(2)}}, to {{pop_temp.get(0)}}%.
* Following the higher-emissions scenario (RCP6.0) which can entail over 3 degrees Celsius of global warming by the end of the century (2081-2100), this factor is projected to reach {{land_rel_time.get('farfuture','rcp60')/100 + 1}} for the global land area affected to river flood (to {{land_time.get('farfuture','rcp60')}}%) and {{pop_rel_time.get('farfuture','rcp60')/100 + 1}} for the world’s population exposed to river floods (to {{pop_time.get('farfuture','rcp60')}}%).
* The countries that rank highest with regards to their relative change in land area affected by river floods at 2 degrees Celsius of global warming in comparison to a situation without anthropogenic climate change, implying most severe impacts, are: (ranking-area: land_rel_temp order: 1 value: name temperature: 2 type: country), (ranking-area: land_rel_temp order: 2 value: name temperature: 2 type: country), (ranking-area: land_rel_temp order: 3 value: name temperature: 2 type: country), (ranking-area: land_rel_temp order: 4 value: name temperature: 2 type: country), (ranking-area: land_rel_temp order: 5 value: name temperature: 2 type: country).
* The countries that rank highest with regards to their relative change in population exposed to river floods at 2 degrees Celsius of global warming in comparison to a situation without anthropogenic climate change, implying most severe impacts, are: (ranking-area: pop_rel_temp order: 1 value: name temperature: 2 type: country), (ranking-area: pop_rel_temp order: 2 value: name temperature: 2 type: country), (ranking-area: pop_rel_temp order: 3 value: name temperature: 2 type: country), (ranking-area: pop_rel_temp order: 4 value: name temperature: 2 type: country), (ranking-area: pop_rel_temp order: 5 value: name temperature: 2 type: country).
### Results
The figures below shows the relative change in the global land area affected by and world’s population exposed to river floods in comparison to the time period before anthropogenic climate change. Results are shown both for the (link: glossary/historical-period text: historical time period) and for the (link: glossary/future-projections text: future projections) and can be visualized with regards to their change over time as well as with regards to their change in terms of global warming. Under the ‘change in terms of time’ setting, the change in global land area affected by and the world’s population exposed to river floods over time is shown for 20-year (link: glossary/time-slices text: time-slices) from 1861-1880 until the end-of-the-century (2081-2100). Under the ‘change in terms of global warming’, the change in global land area affected by and the world’s population exposed to river floods is shown for 20-year (link: glossary/time-slices text time-slices) representing increasing levels of global warming from 0 degrees Celsius up to 3 degrees Celsius compared to the time period before anthropogenic climate change started.
In both cases, results are shown for all possible combinations of (link: glossary/global-hydrological-models text: global hydrological models) and (link: glossary/global-climate-models text: global climate models) (normal lines) as well as for the median of the model ensemble (thick line). The influence of (link: glossary/inter-annual-variability text: inter-annual variability) on the results is visualized by means of the shaded area in black. This inter-annual variability is only shown for the median model combination. For the future projections and under the ‘change in terms of time’ settings only, an additional distinction is made between the results of two future (link: glossary/emissions-trajectories text: emissions trajectories): a low-emissions trajectory limiting global mean temperature in the 21st century to below 2 degrees Celsius compared to before anthropogenic climate change started, called RCP2.6 (blue lines), and a higher-emissions scenario leading global mean temperature to above 3 degrees Celsius by 2100, compared to pre-climate change conditions, called RCP6.0 (red lines). The shaded areas in blue and red indicate the variety (or spread) in results between all combinations of global hydrological models and global climate models for these two emissions trajectories. A filtering menu top-right of the graph allows the user to select individual emissions trajectories, individual global climate models, and individual global hydrological models for visualization.
By hovering over or clicking on a particular value in the figure additional details, such as the specific global climate model or hydrological used, behind the presented value become available. The visualization below the graph shows, for the selected model run under this time-period or temperature-change level, how countries ranks in comparison to other countries on their relative change in land area affected by or population exposed to river floods.
#### Land area affected by river floods
(line-plot: land-rel-temp_world,land-rel-time_world first-temperature: 2 second-scenario: rcp26 second-time: 2141-2160)
Without human-made greenhouse gas emissions, {{land_temp.get(0)}}% of the global land area would be affected by river flood each year, on average.
Under the ‘change in terms of global warming’ setting the figure shows that at today’s level of 1 degrees Celsius of global warming the global land area affected by river floods is, on average, already {{land_rel_temp.get(1)/100 + 1}} times (or {{land_rel_temp.get(1)}}%) {% if land_rel_temp.get(1) > 100 %}higher{%else%}lower{%endif%} and amount to {{land_temp.get(1)}}% of the total land area. This level of change ranges from {{land_abs_temp.getall(2) | min}}% to {{land_abs_temp.getall(2) | max}}% for the individual combinations of global hydrological models and global climate models. At 2 degrees Celsius of global warming, the global land area affected by river floods is projected to change by a factor of {{land_rel_temp.get(2)/100+1}} (or {{land_abs_temp.get(2)}}%) in comparison to a world without human-made greenhouse gas emissions. Under these conditions, {{land_temp.get(2)}}% of the total land area would be affected by river flood on a yearly basis, on average. Across the individual combinations of global hydrological models and global climate models this expected level of change ranges from {{land_abs_temp.getall(2) | min}}% up to {{land_abs_temp.getall(2) | max}}%.
When presenting the ‘change in terms of time’, we find that when following the higher-emissions scenario (RCP6.0) towards the end-of-the-century (equivalent to over 3 degrees Celsius change) would result in a change in the global land area affected by river floods of, on average, a factor {{land_rel_time.get('farfuture','rcp60')/100 + 1}} ({{land_abs_time.get('farfuture','rcp60')}}%), towards: {{land_time.get('farfuture','rcp60')}}% of the total land area. Following the (link: glossary/climate-mitigation-emissions-scenario text: climate-mitigation emissions scenario)(RCP2.6) towards the end-of-the-century (entailing an average 2.5 degrees Celsius change) would result in a foreseen change in the global land area affected by river floods of, on average, a factor {{land_rel_time.get('farfuture','rcp26')/100 + 1}} ({{land_abs_time.get('farfuture','rcp26')}}%), {{land_time.get('farfuture','rcp26')}}% of the total land area being affected.
Worldwide, countries that rank highest with regards to their relative change in land area affected by river floods at 2 degrees Celsius of global warming in comparison to a situation without anthropogenic climate change, implying most severe impacts, are: (ranking-area: land_rel_temp order: 1 value: name temperature: 2 type: country), (ranking-area: land_rel_temp order: 2 value: name temperature: 2 type: country), (ranking-area: land_rel_temp order: 3 value: name temperature: 2 type: country), (ranking-area: land_rel_temp order: 4 value: name temperature: 2 type: country), (ranking-area: land_rel_temp order: 5 value: name temperature: 2 type: country).
The countries that rank highest with regards to their relative change in land area affected by river floods towards the-end-of-the-century under a higher-emissions scenario (RCP6.0), in comparison to a situation without anthropogenic climate change, implying most severe impacts, are: (ranking-area: land-rel-time order: 1 value: name time: 2081-2100 scenario: rcp60 type: country), (ranking-area: land-rel-time order: 2 value: name time: 2081-2100 scenario: rcp60 type: country), (ranking-area: land-rel-time order: 3 value: name time: 2081-2100 scenario: rcp60 type: country), (ranking-area: land-rel-time order: 4 value: name time: 2081-2100 scenario: rcp60 type: country), (ranking-area: land-rel-time order: 5 value: name time: 2081-2100 scenario: rcp60 type: country).
#### Population exposed to river floods
(line-plot: pop-rel-temp_world,pop-rel-time_world first-temperature: 2 second-scenario: rcp26 second-time: 2141-2160)
Without human-made greenhouse gas emissions, {{pop_temp.get(0)}}% of the world’s population would be exposed to river flood each year, on average.
Under the ‘change in terms of global warming’ setting the figure shows that at today’s level of 1 degrees Celsius of global warming the world’s population exposed to river floods is, on average, already {{pop_rel_temp.get(1)/100 + 1}} times (or {{pop_rel_temp.get(1)}}%) {% if pop_rel_temp.get(1) > 100 %}higher{%else%}lower{%endif%}: {{pop_temp.get(1)}}% of the total land area. This level of change ranges from {{pop_abs_temp.getall(1) | min}}% to {{pop_abs_temp.getall(1) | max}}% for the individual combinations of global hydrological models and global climate models. At 2 degrees Celsius of global warming, the world’s population exposed to river floods is projected to change by a factor of {{pop_rel_temp.get(2)/100+1}} (or {{pop_abs_temp.get(2)}}%) in comparison to a world without human-made greenhouse gas emissions. Under these conditions, {{pop_temp.get(2)}}% of the total global population would be exposed to river flood on a yearly basis, on average. Across the individual combinations of global hydrological models and global climate models this expected level of change ranges from {{pop_abs_temp.getall(2) | min}}% up to {{pop_abs_temp.getall(2) | max}}%.
When presenting the ‘change in terms of time’, we find that when following the higher-emissions scenario (RCP6.0) towards the end-of-the-century (equivalent to over 3 degrees Celsius change) would result in a foreseen change in the world’s population exposed to river floods of, on average, a factor {{pop_rel_time.get('farfuture','rcp60')/100 + 1}} ({{pop_abs_time.get('farfuture','rcp60')}}%),{{pop_time.get('farfuture','rcp60')}}% of the total population. Following the climate-mitigation emissions scenario (RCP2.6) towards the end-of-the-century (entailing an average 2.5 degrees Celsius change) would result in a foreseen change in the world’s population exposed to river floods of, on average, a factor {{pop_rel_time.get('farfuture','rcp26')/100 + 1}} ({{pop_abs_time.get('farfuture','rcp26')}}%), {{pop_time.get('farfuture','rcp26')}}% of the total global population being exposed.
Worldwide, countries that rank highest with regards to their relative change in population exposed to river floods at 2 degrees Celsius of global warming in comparison to a situation without anthropogenic climate change, implying most severe impacts, are: (ranking-area: pop_rel_temp order: 1 value: name temperature: 2 type: country), (ranking-area: pop_rel_temp order: 2 value: name temperature: 2 type: country), (ranking-area: pop_rel_temp order: 3 value: name temperature: 2 type: country), (ranking-area: pop_rel_temp order: 4 value: name temperature: 2 type: country), (ranking-area: pop_rel_temp order: 5 value: name temperature: 2 type: country).
The countries that rank highest with regards to their relative change in population exposed to river floods towards the-end-of-the-century under a higher-emissions scenario (RCP6.0), in comparison to a situation without anthropogenic climate change, implying most severe impacts, are: (ranking-area: pop_rel_time order: 1 value: name time: 2081-2100 scenario: rcp60 type: country), (ranking-area: pop_rel_time order: 2 value: name time: 2081-2100 scenario: rcp60 type: country), (ranking-area: pop_rel_time order: 3 value: name time: 2081-2100 scenario: rcp60 type: country), (ranking-area: pop_rel_time order: 4 value: name time: 2081-2100 scenario: rcp60 type: country), (ranking-area: pop_rel_time order: 5 value: name time: 2081-2100 scenario: rcp60 type: country).
### Methodology
#### What do we analyze?
We analyze the land area affected by river floods and the number of people exposed to river flood at different levels of global warming (0, 1, 2, and 3 degrees Celsius) and during different time periods. We cover the time before anthropogenic climate change started (1850), present-day (2001-2020), mid-century (2041-2060) and end-of-century (2081-2100). We compare the later periods against the climate conditions before anthropogenic climate change (1850).
#### How do we calculate where and when a flood occurs?
The calculation has several steps: 1) calculate (link: glossary/runoff text: runoff); 2) calculate daily river flow and its (link: glossary/annual-maximum text: annual maximum); 3) compare this annual maximum with the 100-year (link: glossary/return-level text: return level) which is calculated separately when the 100-year return level is exceeded, 4) calculate (link: glossary/flood-depth text: flood depth); and 5) calculate flooded land area fraction. All these steps are detailed below, after a short explanation of the spatial structure on which our models operate.
#### What is the spatial structure of the models?
The models we use cover the whole globe’s land area. The land area is divided into a grid. Each grid cell has a size of 0.5 degrees × 0.5 degrees (latitude by longitude). At the equator, this equals roughly 55 × 55 km; towards the North Pole or South Pole, where the land area covered per grid cell becomes smaller.
#### How is runoff calculated?
The calculation is done for each grid cell and each day. Information about temperature, precipitation, solar radiation, and other weather indicators is taken from the global climate models (GCMs) and used as input for the ISIMIP global hydrological models (GHMs). Additional spatial data, such as soil, land cover and water bodies are also inputted. Runoff is calculated as the difference between the amount of water coming in as rain or snow, and the amount of water going out, either through evapotranspiration from the surface and vegetation, or by seeping vertically into the ground. Runoff percolates horizontally through the ground (subsurface runoff) or flows on the surface (surface runoff) into the surface water bodies or river. The model calculates runoff as daily value for each grid cell.
#### How is river flow calculated?
Having calculated runoff in each grid cell, we use the global river model (link: glossary/camaflood text: CaMaFlood) to calculate the water flow once it is in the river. The river water flow is also known as discharge, and measured in cubic meters per second (m³/s). For each grid cell along the river, the amount of water flowing through the river is calculated by summing up the runoff coming from all grid cells located upstream. For each grid cell, we then calculate the annual maximum of the daily river flow values.
#### How do we know whether a flood occurs?
In our analysis, we assume that a flood occurs whenever the annual maximum daily river flow exceeds the so-called 100-year return level. This is the water flow that was exceeded only once every 100 years, on average, before anthropogenic climate change started. We only consider river flows larger than the 100-year return level, because we assume that for smaller flows, either no flooding occurs (e.g. protection measures such as levees prevent them) or the floods have minor impacts (e.g. people do not settle in these areas or the flood depth is very shallow). That is, we assume that societies have already adapted to more frequent events.
#### How do we know how often floods occurred before anthropogenic climate change started?
We use a separate model simulation in which the climate behaves as if there had been no human-made greenhouse gas emissions. This simulation runs for several centuries without any outside change, just letting the weather evolve naturally. We then calculate the maximum river flow level that occurs, on average, every 100 years in this simulation.
#### How is flood depth calculated?
Once there is a flood in a given grid cell and year, we obtain the flood depth from a calculation based on the unique relationship of flood depth with return level. This relationship between return level and flood depth is based on (link: glossary/calibrated-simulations text: calibrated simulations) from the global hydrological model (link: glossary/matsiro text: MATSIRO) in combination with CaMaFlood.
#### How is the flooded land area fraction calculated?
For each flood-affected grid cell, the flood-water volume is calculated from the flood depth and the size of the cell. This volume of water is then distributed onto a high-resolution elevation map (~100m x 100m at the equator). This map is part of CaMaFlood. The elevation map acts as a basin, with each point in the map assigned a height above sea-level.
The portion of each 0.5 degree x 0.5 degree grid cell that is submerged is calculated. For example, if it is a mountainous area, all the flood water will concentrate in the narrow valleys, while the mountain peaks do not get flooded. Thus, only a part of the 0.5 degree x 0.5 degree grid cell will be submerged even during a large flood. On the contrary, if the area is very flat, then the flood water will spread out and potentially submerge the entire grid cell.
#### How are the indicators land area affected by river floods and number of people exposed to river flood calculated?
To calculate the global land area affected by flooding, the flooded land area fractions are added up across all 0.5 degree x 0.5 degree grid cells worldwide, except for Antarctica and Greenland. To calculate the global number of people exposed to flooding, we first multiply the flooded land area fraction of each grid cell by the number of people living in that grid cell to estimate the number of people exposed to river flood, and then add up those numbers across all grid cells worldwide, except for Antarctica and Greenland.
#### What else should I know about the methodology?
We assume that the number of people living in an area, as well as the area’s land use and land cover (what fractions of the area are used for settlement, cropland, or pasture, or covered by forest), remain constant at the levels of year 2005 throughout the whole simulation. This is not meant to be realistic, but to isolate the influence of climate change from the influence of other changes.
In the key messages we report the results for the median (the model in the middle) of the group of global hydrological models that ran simulations in (link: glossary/isimip2b text: ISIMIP2b). All model-specific results are presented in the figures and a discussion of the limitations of the analysis is included at the end of the report.
A ranking of countries for each indicator is also provided to compare countries on the basis of their relative change in land area affected or number of people exposed under different levels of global warming and for various time horizons, compared to conditions without climate change. A ranking of 1 implies that the country experiences a change in the indicator higher than all other ranked countries, for example the strongest impacts from climate change; whereas a lower ranking entails comparatively less impacts.
### Discussion
The model simulations used for this report build on science that has been established through many peer-reviewed studies: e.g. by [Yamazaki et al. (2011)](https://doi.org/10.1029/2010WR009726),[Müller Schmied et al. (2016)](https://doi.org/10.5194/hess-20-2877-2016). Models are simplified representations of reality, hence model simulations come with limitations and uncertainties that have to be kept in mind. One major limitation of the global water models is the availability of global data sets that are needed to fully represent the global hydrological cycle. For example, many of the models do not consider glaciers which could influence river flood as well. Furthermore, the representation of the components of the hydrological cycle, such as soil hydrology, differ strongly between models (e.g. in terms of detail, but also the calculation approach) with large effects on runoff generation. Another major source of uncertainty are the climatic input data as they stem from climate models that have a coarser spatial resolution and their own (link: glossary/modelling-uncertainties text: modelling uncertainties). In particular, the adequate simulation of precipitation extremes is difficult for global climate models.
The performance of the global water models themselves can be tested by using (link: glossary/observed-historical-weather-information text: observed historical weather information) as inputs, and comparing the simulated river flow to river flow observed at different stations along rivers. One general challenge associated with the simulation of hydrological processes lies in the representation of how vegetation cover affects evapotranspiration and other surface properties that play a role in flood onset. Moreover, this relationship may change within a given river basin, or with rising temperature and CO2 levels. For example, the effect of CO2 fertilization – the phenomenon through which photosynthesis, hence plant growth, should be enhanced in a CO2-richer atmosphere – is not represented in most water models although it would affect the plants’ water use, with consequences for runoff and river flow. Nevertheless, although these vegetation-related processes are critical for the projection of river-flood conditions and low-flow events, they are of less importance for projections of river flood. A close relationship indeed exists between precipitation and peak discharge during flood events, which is only moderately affected by evapotranspiration.
The presented flood projections reflect the isolated effect of climate change, while their local manifestations are expected to also be influenced by direct human drivers like changes in irrigation, construction of new dams and reservoirs, levees and other flood protection infrastructure. The representation of these drivers is limited to (link: glossary/present-day-conditions text: present-day conditions), while their future changes are not explicitly modelled in the presented simulations. This serves the purpose of isolating the effect of anthropogenic climate change from other factors, but it means that actual flood exposure in the future could be more or less acute, depending on non-climatic human drivers, such as urbanization or adaptation.
Our methodology assumes that every event where river flow exceeds the local 100-year return level is a flood. This is a simplification; flood protection measures may have a higher or lower protection standard in reality. Most parts of the world are protected only against lower-intensity events that occur more frequently than once in 100 years. This means that more floods would actually occur than in our projections. In a few parts of the world (mainly the USA and a few European countries), the opposite is true, meaning that protection measures could prevent all river flow events that are simulated to occur up to as rarely as once in 200 years.
Finally, another caveat to keep in mind is that only a single river flood model (CaMaFlood) was used for the calculation of river flow and flooded area; and a single global hydrological model, MATSIRO, was used to derive the grid cell-specific relationship between return level and flood depth. It is, therefore, difficult to assess the uncertainty that arises from assumptions built into these models.
Regarding climate models and water models, it is known that generally both types of models contribute substantially to the overall spread in projected climate change impacts on river flow and other water-related variables. Because this report presents the results of a combination of climate models and water models that ran simulations based on the same experiment protocol, and whose outputs are thus directly comparable, it captures at least some of this spread in the projections.
This (link: glossary/climate-impact-assessment text: climate impact) assessment presents (link: glossary/projections text: projections) of the land area affected by test-indicators and the number of people exposed to test-indicators globally, based on 1 (link: glossary/global-hydrological-models text: global hydrological model) (GHMs) and 3 different global (link: glossary/climate-models text: climate models)(GCMs) participating in [ISIMIP](https://www.isimip.org/).
Here, (link: glossary/test-indicators text: test-indicators) refer to a situation where the monthly (link: glossary/soil-moisture text: soil moisture) conditions fall short of the 2.5th percentile (glossary/link: variable-monthly-threshold text: variable monthly threshold) for at least 7 consecutive months. To calculate the total land area affected by test-indicator at the global scale the land area exposed to test-indicator is summed up across all grid cells worldwide. Multiplying, at each grid cell, with the number of people living in the grid cell yields the number of people exposed to test-indicator globally.
We analyze the land area affected by test-indicators and the number of people exposed to test-indicators at different (link: glossary/levels-of-global-warming text: levels of global warming)(0, 1, 2, and 3 degrees Celsius) and during different time periods. We cover the time before (link: glossary/anthropogenic-climate-change text: anthropogenic climate change) started (conventionally set at 1850), present-day (2001-2020), mid-century (2041-2060) and end-of-century (2081-2100). We compare the later periods against the climate conditions before anthropogenic climate change started (1850). A ranking of countries for each indicator is provided to allow comparison between countries on the basis of their (link: glossary/relative-change text: relative change) in the land area affected or the number of people exposed under different levels of global warming and for various time horizons, compared to conditions before anthropogenic climate change started. Here, a ranking of 1 implies that the country experiences a change in the (link: glossary/indicator text: indicator) that is more than all other ranked countries, whereas a lower ranking entails comparatively less effects due to climate change.
It is important to note that our (link: glossary/future-projections text: future projections) assume that the number of people living in an area, as well as the area’s (link: glossary/land-use text: land use) and (link: glossary/land-cover text: land cover), remain constant to that of the year 2005. This is not necessarily meant to be realistic, but to isolate the influence of climate change from the influence of other changes.
In the key messages we report the results for the (link: glossary/median text: median) of the (link: glossary/model-ensemble text: model ensemble) of global hydrological models providing data to ISIMIP. The median represents the middle of the ensemble, meaning that 50% of the ensemble members provide higher numbers and 50% provide lower numbers. After the key message, which only references the median, you can find the model-specific results in the figures, a discussion of the methodology and the restrictions of the analysis at the end of the article.
### Key messages
* Without human-made greenhouse gas emissions, 2.377% of the global land area and 0.601% of the world’s population would be affected by test-indicators each year, on average.
* At today’s levels of 1 degrees Celsius of global warming the land area affected is 1.787 times as much: 78.66% of the total land area, while the number of people affected is 1.767 times as much: 0.851% of the total population.
* At 2 degrees Celsius of global warming, the global land area affected by test-indicators is projected to increase by a factor of 210.614 compared to a world without human-made greenhouse gas emissions, to 2.377%. Likewise, the world’s population exposed to test-indicators is projected to increase by a factor 226.391, to 0.601%.
* Following the higher-emissions scenario (RCP6.0) which can entail over 3 degrees Celsius of global warming by the end of the century (2081-2100), this factor is projected to reach 1.067 for the global land area affected to test-indicators (to 1.067%) and 1.025 for the world’s population exposed to test-indicators (to 2.479%).
* The countries that rank highest with regards to their relative change in land area affected by test-indicators at 2 degrees Celsius of global warming in comparison to a situation without anthropogenic climate change, implying most severe impacts, are: (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-temperature-change order: 1 value: name temperature: 2 type: country), (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-temperature-change order: 2 value: name temperature: 2 type: country), (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-temperature-change order: 3 value: name temperature: 2 type: country), (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-temperature-change order: 4 value: name temperature: 2 type: country), (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-temperature-change order: 5 value: name temperature: 2 type: country).
* The countries that rank highest with regards to their relative change in population exposed to test-indicators at 2 degrees Celsius of global warming in comparison to a situation without anthropogenic climate change, implying most severe impacts, are: (ranking-area: population-exposed-to-test-indicator-relative-changes_test-category_versus-temperature-change order: 1 value: name temperature: 2 type: country), (ranking-area: population-exposed-to-test-indicator-relative-changes_test-category_versus-temperature-change order: 2 value: name temperature: 2 type: country), (ranking-area: population-exposed-to-test-indicator-relative-changes_test-category_versus-temperature-change order: 3 value: name temperature: 2 type: country), (ranking-area: population-exposed-to-test-indicator-relative-changes_test-category_versus-temperature-change order: 4 value: name temperature: 2 type: country), (ranking-area: population-exposed-to-test-indicator-relative-changes_test-category_versus-temperature-change order: 5 value: name temperature: 2 type: country).
### Results
The figures below shows the relative change in the global land area affected by and world’s population exposed to test-indicators in comparison to the time period before anthropogenic climate change. Results are shown both for the (link: glossary/historical-period text: historical time period) and for the (link: glossary/future-projections text: future projections) and can be visualized with regards to their change over time as well as with regards to their change in terms of global warming. Under the ‘change in terms of time’ setting, the change in global land area affected by and the world’s population exposed to test-indicators over time is shown for 20-year (link: glossary/time-slices text: time-slices) from 1861-1880 until the end-of-the-century (2081-2100). Under the ‘change in terms of global warming’, the change in global land area affected by and the world’s population exposed to test-indicators is shown for 20-year (link: glossary/time-slices text time-slices) representing increasing levels of global warming from 0 degrees Celsius up to 3 degrees Celsius compared to the time period before anthropogenic climate change started.
In both cases, results are shown for all possible combinations of (link: glossary/global-hydrological-models text: global hydrological models) and (link: glossary/global-climate-models text: global climate models) (normal lines) as well as for the median of the model ensemble (thick line). The influence of (link: glossary/inter-annual-variability text: inter-annual variability) on the results is visualized by means of the shaded area in black. This inter-annual variability is only shown for the median model combination. For the future projections and under the ‘change in terms of time’ settings only, an additional distinction is made between the results of two future (link: glossary/emissions-trajectories text: emissions trajectories): a low-emissions trajectory limiting global mean temperature in the 21st century to below 2 degrees Celsius compared to before anthropogenic climate change started, called RCP2.6 (blue lines), and a higher-emissions scenario leading global mean temperature to above 3 degrees Celsius by 2100, compared to pre-climate change conditions, called RCP6.0 (red lines). The shaded areas in blue and red indicate the variety (or spread) in results between all combinations of global hydrological models and global climate models for these two emissions trajectories. A filtering menu top-right of the graph allows the user to select individual emissions trajectories, individual global climate models, and individual global hydrological models for visualization.
By hovering over or clicking on a particular value in the figure additional details, such as the specific global climate model or hydrological used, behind the presented value become available. The visualization below the graph shows, for the selected model run under this time-period or temperature-change level, how countries ranks in comparison to other countries on their relative change in land area affected by or population exposed to test-indicators.
#### Land area affected by test-indicators
(line-plot: land-area-affected-by-test-indicator-relative-changes_test-category_versus-temperature-change_world,land-area-affected-by-test-indicator-relative-changes_test-category_versus-timeslices_world first-temperature: 2 second-scenario: rcp26 second-time: 2041-2060)
Now without relative nor absolute transformation.
(line-plot: land-area-affected-by-test-indicator_test-category_versus-temperature-change_world,land-area-affected-by-test-indicator_test-category_versus-timeslices_world first-temperature: 2 second-scenario: rcp26 second-time: 2041-2060)
Here a test of ranking map.
(ranking-map: land-area-affected-by-test-indicator_test-category_versus-temperature-change_world temperature: 2 climate-model: IPSL-CM5A-
LR impact-model: GEPIC)
Without human-made greenhouse gas emissions, 2.377% of the global land area would be affected by test-indicators each year, on average.
Under the ‘change in terms of global warming’ setting the figure shows that at today’s level of 1 degrees Celsius of global warming the global land area affected by test-indicators is, on average, already 1.787 times (or 78.66%) lower and amount to 3.806% of the total land area. This level of change ranges from 2.912% to 5.046% for the individual combinations of global hydrological models and global climate models. At 2 degrees Celsius of global warming, the global land area affected by test-indicators is projected to change by a factor of 3.106 (or 3.979%) in comparison to a world without human-made greenhouse gas emissions. Under these conditions, 5.705% of the total land area would be affected by test-indicators on a yearly basis, on average. Across the individual combinations of global hydrological models and global climate models this expected level of change ranges from 2.912% up to 5.046%.
When presenting the ‘change in terms of time’, we find that when following the higher-emissions scenario (RCP6.0) towards the end-of-the-century (equivalent to over 3 degrees Celsius change) would result in a change in the global land area affected by test-indicators of, on average, a factor 7.014 (5.707%), towards: 6.705% of the total land area. Following the (link: glossary/climate-mitigation-emissions-scenario text: climate-mitigation emissions scenario)(RCP2.6) towards the end-of-the-century (entailing an average 2.5 degrees Celsius change) would result in a foreseen change in the global land area affected by test-indicators of, on average, a factor 5.091 (4.082%), 5.08% of the total land area being affected.
Worldwide, countries that rank highest with regards to their relative change in land area affected by test-indicators at 2 degrees Celsius of global warming in comparison to a situation without anthropogenic climate change, implying most severe impacts, are: (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-temperature-change order: 1 value: name temperature: 2 type: country), (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-temperature-change order: 2 value: name temperature: 2 type: country), (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-temperature-change order: 3 value: name temperature: 2 type: country), (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-temperature-change order: 4 value: name temperature: 2 type: country), (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-temperature-change order: 5 value: name temperature: 2 type: country).
The countries that rank highest with regards to their relative change in land area affected by test-indicators towards the-end-of-the-century under a higher-emissions scenario (RCP6.0), in comparison to a situation without anthropogenic climate change, implying most severe impacts, are: (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-timeslices order: 1 value: name time: 2081-2100 scenario: rcp60 type: country), (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-timeslices order: 2 value: name time: 2081-2100 scenario: rcp60 type: country), (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-timeslices order: 3 value: name time: 2081-2100 scenario: rcp60 type: country), (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-timeslices order: 4 value: name time: 2081-2100 scenario: rcp60 type: country), (ranking-area: land-area-affected-by-test-indicator-relative-changes_test-category_versus-timeslices order: 5 value: name time: 2081-2100 scenario: rcp60 type: country).
#### Population exposed to test-indicators