Spatial financeWhere capital meets hazard, exposure and transition
Hydrology and water science for reinsurance and catastrophe
Hydrology explains how rainfall becomes river and surface water flooding. In catastrophe reinsurance it supplies the flood hazard maps, event footprints and defence assumptions behind flood models and post-event claims analysis.
Hydrology studies the movement, distribution and quality of water across the land surface, subsurface and atmosphere. It underpins the assessment of floods, droughts and water supply, which affect food, energy, health and infrastructure together. In insurance and catastrophe, climate and hazards and disaster and crisis, hydrological estimates set design standards for dams, levees and drainage and inform water allocation between competing users. They link climate hazards to downstream effects on power generation, crops and public health. The canonical description is onthe STEM Commons.
River flood event sets
Spatially coherent rainfall and flow events are simulated across basins so that a single event can affect many locations at once.
Flood defence assumptions
Defence standards and failure probabilities are represented explicitly, since small changes in them change modelled flood loss sharply.
Pluvial flood mapping
Surface water flooding from intense rain is mapped at fine resolution where drainage data allow.
03Spatial finance
A drought index against its attachment point.
How a dry season is read against the point where drought cover starts to pay. Synthetic season and design terms.
A rainfall season across the Sahel, read against the points where drought cover would start and stop paying.Synthetic season and design terms; no cover is implied.
04داتا
What the method consumes.
Datasets and records used by hydrology and water science on this platform, with the role each plays and the label its values carry. Sources are listed inthe source register.
داتا
ڕۆڵ
ناونیشان
River gauge records
Flow statistics
چاودێریکراو
Flood extent observations
Calibration
چاودێریکراو
Digital elevation models
Terrain
چاودێریکراو
Defence databases
پاراستن
REPORTED
05Loss model
Loss exceedance, average annual loss and probable maximum loss.
The loss a portfolio in the footprint can expect per year and at the 1 in 100 and 1 in 250 levels that insurers and lenders price, from the same three blocks everycatastrophe modeluses. Teaching model with stated illustrative values, not a prediction. The map on this page shows The Gulf of Mexico coast; the model itself is not tied to a place.
کۆنترۆڵەکان
Typical annual flood depth (location)
0.40 m water depth
بڵاوبوونەوەی لێکەوتە توندەکان (پێوەر)
0.45 m water depth
توندییەک کە تێیدا نیوەی بەها لەدەست دەچێت
1.60 m water depth
بڵاوبوونەوەی لاوازی
0.55
بەشی بەها لە شوێنپێی مەترسیدەردا
40%
ستانداردی پاراستن (ماوەی گەڕانەوە)
50 ساڵ
خوێندنەوەکان
تێکڕای زیانی ساڵانە
0.64% of value
زیان لە 1 لە 100 ساڵدا
31.4% of value
زیان لە 1 لە 250 ساڵدا
34.3% of value
توندی لە 1 لە 100 ساڵدا
2.47 m water depth
ژمارە، ڕێگا و سەرچاوەکان
ماوەی گەڕانەوە (ساڵ)
Intensity (m water depth)
زیان (% ی بەها)
1
−0.11
0.00
2
0.55
0.00
4
0.91
0.00
7
1.23
0.00
13
1.53
0.00
24
1.81
0.00
44
2.10
0.00
82
2.38
30.62
154
2.66
32.93
287
2.95
34.66
536
3.23
35.96
1,000
3.51
36.93
ڕێگاکان
مەترسیدەری بەهای زۆرداری Gumbel Gumbel (1958)، Statistics of Extremes، Columbia University Press
چەمکی ئەگەری تێپەڕبوون و تێکڕای زیانی ساڵانە Grossi و Kunreuther (2005)، Catastrophe Modeling، Springer
کاریگەری بەربەست Di Baldassarre و ئەوانی دیکە (2013)، Socio-hydrology، HESS 17
پەنجەی نادڵنیایی (مۆنتی کارلۆی پارامیتەر) Spiegelhalter, Pearson و Short (2011)، Visualising uncertainty about the future، Science 333
هەستیاریی یەک-یەک (تۆرناد) Saltelli و ئەوانی دیکە (2008)، Global Sensitivity Analysis: The Primer، Wiley
داتا
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
بەرینی دروستکراو و نیشتەجێبوون، 2010 (HBASE، Landsat) CIESIN/Columbia, NASA SEDAC HBASE · register sedac.context-urban-layers
Loss exceedance, average annual loss and probable maximum loss. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: Gumbel extreme-value hazard (Gumbel (1958), Statistics of Extremes, Columbia University Press); Lognormal fragility (vulnerability) (FEMA (2003), HAZUS-MH Technical Manual); Exceedance-probability curve and average annual loss (Grossi and Kunreuther (2005), Catastrophe Modeling, Springer).
Models are calibrated on one period of observed flows and tested on an independent period or neighbouring catchment. Skill scores and comparison with surveyed flood extents are used to judge performance.
بپشکنە
How it is done
Event footprint realism
Simulated events are compared with observed multi-basin floods
Defence sensitivity
Results are shown with and without defence assumptions
Resolution adequacy
Map resolution is matched to the exposure data used
Assumptions of stationarity weaken as climate and land use change. Ungauged basins, karst aquifers and rapid urbanisation are poorly represented by parameters fitted elsewhere.
07شبیهسازی دووانە و زنجیرەیی
Trace a shock to the balance sheet.
Assembled from theNexus Standardsarea profileبیمە و دووبارەبیمە: the shocks it names, theسیستەمەکانand sectors that carry the loss, the controls that dampen it and the data that observe it. Pick an initiating shock and step through the cascade to see which exposures it reaches and where losses could concentrate. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise, not a forecast, a valuation, a rating or investment advice. To model a place of your own choosing, open the fullSystem Builder.
Where exposure in hydrology and water science for reinsurance and catastrophe travels.
The sectors that carry exposure in hydrology and water science for reinsurance and catastrophe and how a loss in one passes to the others, across portfolios and public finances.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Exposure and loss at return periods
How losses from hydrology and water science for reinsurance and catastrophe grow with severity, gross and net of reinsurance, against capital.
Return period
Annual probability
Gross loss
Net loss
Net, capital = 100
1 in 10
0.10
14
10
10
1 in 50
0.02
33
21
21
1 in 100
0.01
46
27
27
1 in 250
0.004
68
39
39
تێکڕای زیانی ساڵانە
3.1
2.2
2.2
Illustrative loss index for a example portfolio (capital = 100): the exceedance-probability form insurers and supervisors read. Not data on any institution, not a price or a rating.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
What are the limits of hydrology and water science in this field?
Assumptions of stationarity weaken as climate and land use change. Ungauged basins, karst aquifers and rapid urbanisation are poorly represented by parameters fitted elsewhere.
How are results checked?
Models are calibrated on one period of observed flows and tested on an independent period or neighbouring catchment. Skill scores and comparison with surveyed flood extents are used to judge performance.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Reportsis the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
Global Risks Allianceبیمەی ئەتکایی و کارەساتئامادەیی دارایی و بیمەڕێگا و تەکنیکی
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