chahank
commented
2 years ago Dear Alexander, thank you for your questions. Here a few quick answer:
First, it is important to understand that CLIMADA is a framework for climate risk assessment and adaptation option appraisal. The basic building blocks are the exposure, hazard, impact function (vulnerability) and measure. CLIMADA is not a model for said building blocks. However, a set of models that can be used as starting points are provided, and all are documented here. Each model also comes in general with a scientific publication.
To your specific questions:
- See the documentation here and here, and publication related to CLIMADA here.
- As said above, the specific model used for the hazard is not specified in CLIMADA and can be defined by the user. For further information please see the documentation. But yes, for certain hazard models (e.g. flood model from ISIMIP) it is as you described.
- Please see the ISIMIP documentation for the details about the flood model.
- For details on the CLIMADA computations please read carefully the paper here. I do not really understand your question, so I will let it be at this for the moment.
- The frequency is a property of each single hazard model. In the case of this flood model, the annual frequency is defined as you said.
- Please see this article
- You can resample your data to any resolution if you desire. You can also use any other flood model as input. The data on the API as of now is given only at 150as.
I am sorry that most answers are only referrals to other sources. However, I feel it is needed that these sources are consulted before further discussions, as these describe in all detail the flood model of interest.
alexsynadino
Hello,
I have been experimenting with Climada for some time in order to generate expected losses to properties in Belgium due to Floods. The idea being to compare the values outputted by Climada with the results obtained by using the current methodology proposed by the European central bank in its climate stress testing framework for banks (My Code is indicated below).
As a result I have some questions that I have not quite been able to answer by using the Climada documentation or other research. Those questions are listed below:
where can we find the most complete documentation on each model? I noticed interesting results came from the Matsiro model but I don't know what features make this a more suitable model for my analysis or not?
do I understand the modelling process correctly when I say that atmospheric data (climate drivers) contain certain variables that are the input to a certain ISIMIP model which then outputs (given RCP scenarios and timeperiod) a hasard forecast?
I am working with Hazard data on floods in belgium queried with the client api (see code snipet below). From my understanding a hazard class combines ISIMIP models with climate drivers (atmospheric data) which allows me to simulate a certain number of meters of flooding for each year under analysis and each model. As a result, where could I access the actual simulated flood data? for example, how many meters occured at a given centroid when using the Matsiro model run with the hadgem2-es climate drivers for the year 2057?
I believe that the resulting yearly damage is a result of the multiplication of the sum of damages across the different models and the frequency, correct?
The frequency appears to be the result of the following calculation : 1/ (Nb years Nb isimip models Nb climate drivers), correct? --> in my case 1/20 years6 impact simulators 4 climate drivers = 1/480 = 0.0020833 --> my hazard query below outputs that exact value for frequency. The value is fixed which means that each year, each model and each climate driver gets equal weighting
What prompted that equal weighting approach with the frequency?
With the client api I am limited to 'res_arcsec': ['150'] (which if I am not wrong amounts to 20km²), does climada offer any way to go to a more granular level? Bear in mind i have no additional data at this stage than what is found in your database that I query via api.
I know this is a long list of questions. If it is more suitable for you I would be more than happy to discuss this over a call. I understand that you do not wish to act as a "consulting" entity in any way, but that is not what I am seeking. The design of my analysis is my own, yet I feel I am missing a little context on what is happening within the climada engine to truly be able to validate my approach.
Kind Regards, Alexandre
CODE: from climada.util.api_client import Client client = Client() import pandas as pd
INFO
dtf = pd.DataFrame(data_types) dtf.sort_values(['data_type_group', 'data_type']) tc_dataset_infos = client.list_dataset_infos(data_type='river_flood') client.get_property_values(tc_dataset_infos, known_property_values = {'country_name':'Belgium'})
Import Hazard
from climada.hazard import Hazard FL_Belgium = client.get_hazard('river_flood', properties={'res_arcsec': ['150'],'country_name': 'Belgium', 'climate_scenario': 'rcp85', 'year_range': ['2050_2070']}) FL_Belgium.plot_intensity(0)
FL_Belgium.centroids.plot()
print(FL_Belgium.size) print(FL_Belgium.event_name) print(FL_Belgium.frequency)
house values obtained for notary database
notaris = pd.read_excel("climada_flood_article.xlsx", usecols='A:H')
notaris_full=notaris notaris_full['latitude']=notaris_full['lat'] notaris_full['longitude']=notaris_full['long'] notaris_full['value']=notaris_full['price']
flood_exp= Exposures(notaris_full) flood_exp.set_geometry_points() flood_exp.gdf.head()
import impact function set for RiverFlood using JRC damage functions () for 6 regions
import numpy as np from climada.entity import ImpactFunc, ImpactFuncSet impf = ImpactFunc() impf.id = 1 impf.haz_type='RF' impf.intensity_unit = 'm' impf.name = "Flood Europe JRC Residential noPAA" impf.continent = 'Europe' impf.intensity = np.array([0.00, 0.5, 1., 1.5, 2., 3., 4., 5., 6., 12.])
impf.mdd = np.array([0.00, 0.25, 0.40, 0.50, 0.60, 0.75, 0.85, 0.95,1.00, 1.00])
impf.mdr_concave = np.array([0.000, 0.010, 0.020, 0.025, 0.030, 0.100, 0.150, 0.200,0.250,0.400,0.700, 1.000])
impf.mdr = np.array([0.000, 0.250, 0.400, 0.500, 0.600, 0.750, 0.850, 0.950, 1.000, 1.000]) impf.paa = np.ones(impf.intensity.size)
imp_fun_set = ImpactFuncSet() imp_fun_set.append(impf)
hazard_type= 'RF' haz_id=1 impf.tag ="RF"
Exposures: rename column and assign id - OTHER PTF
flood_exp.gdf.rename(columns={"FLBelgium": "impf" + hazard_type }, inplace=True) floodexp.gdf['impf' + hazard_type] = haz_id floodexp.gdf['impf'] = haz_id
SET impf
impf_FL_1 = imp_fun_set.get_func('RF', 1)
Impact on Belgian PTF
from climada.engine import Impact imp = Impact() imp.calc(flood_exp, imp_fun_set, FL_Belgium,save_mat=True) imp.plot_scatter_eai_exposure(ignore_zero=False, buffer=0.8)