FbF Ethiopia 2022 Materials
FbF General Material
Introduction
Pillars
Impact-based drought risk analysis: historical information on the forecasted drought impacts represents a necessary knowledge base for drought management. The historical analysis of the occurrence and severity of drought hazards and their recorded impacts on the agricultural sector and people is crucial for stakeholders to better understand the risk and make informed decisions when selecting drought risk thresholds and validating forecast information.
Forecast generation: Forecast generation is a principal component of FbF. IRI-WFP are developing a suite of cutting-edge methodologies and tools for constructing seasonal climate forecast models, performing model validation, and producing forecasts.
Linking forecast to Anticipatory Action planning: In order to enable the translation of forecast outputs into an Impact-based trigger system executable within a national AA framework (SOP), IRI along with national stakeholders are co-designing a user-friendly tool for forecast trigger selection and monitoring.
How do we use forecasts for action?
The key is having tools for local experts to reconcile forecast triggers for humanitarian action with government datasets and/or crowdsourced farmer information.
It is difficult to use forecasts to act in preparation before a disaster. Its tricky to figure out how to use a forecast to take preventative action before a disaster strikes. Not only is it hard to coordinate the massive efforts between governments and international agencies, but forecasts can have errors–which one should be used, and for what actions should it be used?
Coordination is challenging. Major players all need to be on the same page when making big plans based on uncertain information. If international players do not coordinate to follow the lead of the national governments’ ministries, actions can be counterproductive. Coordinating and building trust in the solutions across entities is critical.
Planning is complex. Each player has a large set of elaborate actions, timelines (and budgets) to quickly there to protect large numbers people on the ground. These must be coordinated for a range of potential scenarios. Then, these workplans need to linked to the forecast itself, all while taking into account the uncertainty in the forecast.
Different actions require different forecasts. It is not one size fits all–different actions require different forecasts, and different timelines. For example, in a drought, planning a water storage program may require more months of lead time than a food program, and each will have different consequences if a disaster is not forecast, or if the forecasted disaster doesnt come.
Quantitative and intuitive understanding required for problem solving. People need to be provided adequate intuition to come up with innovative solutions and the quantitative information to inform design decisions.
New tools and workflows are starting to make this possible. We have been working with WFP, do build capacity within countries to operationalize Early Warning Systems with Forecasts for anticipatory humanitarian aid. There have been a lot of exciting breakthroughs recently.
The past can teach us about how to use forecasts in the future. Although we know the future will be different than the past, we must build upon our experiences and knowledge. Therefore a key strategy is to look at the past, to teach us how to use forecasts for the future.
Reconcile historical information. First, farmer or sector experts crowdsourced experiences are reconciled in workshops where players interact through design/data analysis tools, working with the humanitarian historical records of disasters and actions taken.
Reconcile plans to address historical events. Then, the stakeholders build and integrate what plans they would have wanted to be in place.
FbF Maptool
Summary
The Ethiopia FBF Maptool for the October-November-December (OND) season, with forecasts in July, August and September, can be found at http://iridl.ldeo.columbia.edu/fbfmaproom2/ethiopia-ond
The current prototype hosts different Forecast-based financing tools, including forecasts, triggers, and vulnerability data for Ethiopia. The control bar allows users to choose the forecast issue month (July, August, etc) for the OND season in their region and year of interest as well as selecting the thresholds to calculate the probabilities of non-exceeding the selected threshold/trigger.
There is also a version of this Maptool for the March-April-May (MAM) part of the season, with forecasts in December, January and February. It is available here: http://iridl.ldeo.columbia.edu/fbfmaproom2/ethiopia
The layout
The Maptool is sectioned into two areas, the map component, and the Gantt chart component. The map component includes the climate information such as forecast and rainfall rank, as well as data on which drought years were the most severe (“bad years”), which can come from farmers and / or expert review.
Control Bars
The control bar is comprised of 6 elements:
Mode - describing the spatial resolution in which the user would like to make the analysis; these range from the national level down to pixel level. This mode will affect the forecast calculation as it will be tailored to the selected spatial resolution.
Issue Month - This function determines the month in which the forecast is issued and acts as a lead time. The user can change the month depending on how early or close to the season they would like to utilize the forecast.
Season - This is the timing for which the forecast is modeled.
Year - The year selection strictly influences the map as the map will display the forecast of the year in question. Note that the table is not affected by the year change.
Severity - As early actions are designed based on the output of forecasts and their target frequency of reaching a threshold, labeling a trigger with a severity level enables the user to designate low, moderate, and severe scenarios in their planning. The severity function is used in the Gantt it section, but it is essentially used to label what type of events the user is planning (closely linked to function of Frequency of Triggered Forecasts).
Frequency of Triggered Forecasts - is a slider function in which the user can set the percentage frequency event at which the forecast is triggered. Upon choosing this target value, all the years in which the forecast probability reached that threshold will be highlighted (in the table below).
The Map
The map is a fairly straightforward feature that is affected by the Mode and Year settings in the control bar. The user can also move the pin to the desired place of interest in which the forecast displayed on the table will be for the place on the pin depending on the mode or spatial resolution chosen.
The Table
The table is dependent on most of the control bar settings and it shows all the necessary climate information displaying the years from 1982 to current along with some calculations as seen from the top row of the table:
Worthy-action - drought was forecasted, and a ‘bad year’ occurred (as compared to the “Baseline observations” dataset, described below)
Act-in-vain - drought was forecasted, but a ‘bad year’ did not occur
Fail-to-act - no drought was forecasted, but a ‘bad year’ occurred
Worthy-Inaction: no drought was forecasted, and no ‘bad year’ occurred
Rate - Overall performance (the number of correct action years over the total number of triggered years)
Threshold - This is the threshold (in terms of forecast probability or measured weather conditions) at which action would trigger, based on the chosen frequency and dataset.
The columns on the other hand emphasize the climate information and validating ground data/vulnerability data.
Year: refers to the forecast year
Forecast: displays all the historical flexible forecasts for the selected issue month and location
Baseline dataset: This is the main dataset on drought impact, against which the forecast performance metrics are calculated. By default, it is a list of reported bad years from last year’s expert workshop. The choice of baseline dataset can be changed (item 5)
Observational datasets: These are other observational datasets that may be useful for evaluating the forecast. By default, this column shows the actual rainfall for that year / season (what the forecast is trying to predict). You can add (item 6) other data such as the El Nino-La Nina state, vegetation, the previous seasons’ rainfall and vegetation, etc.
Baseline data selector: This menu allows you to toggle which data set is being used as the “baseline”, i.e. the benchmark for evaluating forecast performance.
Predictor selector: The menu allows you to select the datasets you would like to consider as potential triggers for action. This includes the forecast by default, but can also include observational data. You can select as many as you’d like.
Set trigger: Press this button to set a trigger based on the chosen dataset and frequency of action. When you press this button, you will be directed to the Activity Planning Tool, where you can define the specific activities associated with this trigger rule.
Activity Planning Tool
The Gantt it! button directs the user to the activity planning tool which is a Gantt chart that enables users to add and save activities as seen fit. The tool can be used to add or change activities in the planning chart and the trigger that has been set in the sliders is displayed at the top of the Gantt chart. Along with other more descriptive functionalities:
Project Description - Description from all the settings set up by the control bar are defaulted and saved here. 1.1 There is a feature there that can enable the user to make the project publish so that other users can view it. If not it will be private.
Save Project - Once the project is complete the user can save the project within their profile and give it a name and description
Export to PDF - The user can export the early action plan as PDF and distribute it as seen fit
Delete Project - If the project is no longer essential the button allows for deletion
Notice - This is just an extra description in case users collapse the previous notice
Complete Task - This depicts the green color coding label for tasks that have been completed within the early action Gantt chart
Forecast Independent SOP - Some actions are forecast independent and this shows that forecast independent SOPs will be shown in grey
Tasks - This is the Gantt chart itself which we will have all tasks displayed in it and will also allow users to add and modify the details of the specific tasks from duration to priority, stage, and even its dependency on the forecast. Additionally, the tasks can even be marked as completed.
My Projects - This shows the current page and allows users to create a new project
Dashboard - Shows all previous and public projects
Admin - Displays all other admins or users
Profile - This is the unique profile of each user, this is a restricted tool hence it requires credentials to be able to use the action planning tool hence this allows the users to review account/profile
Adding Tasks
Users can add tasks for the project by clicking on the + sign and the pop-up will allow them to describe the task entirely. Once the task is saved the user can also change these tasks as seen fit by clicking on the task.
Decisions So Far
Following discussion with the Technical Working Group, it was agreed that the triggers for OND season, July 2022 issue would be associated with the same frequencies of action decided upon for MAM 2022 - i.e., 20% frequency for high severity actions and 30% frequency for moderate severity actions.
The following table summaries the associated trigger thresholds for the July issue forecast, the forecast’s skill (as measured against observed historical rainfall) and sharpness ratio, and the forecasted probabilities for July 2022. This table is for the average forecast over all of Somali region.
On the basis of this forecast, the following anticipatory actions for July would be activated:
For details of the forecast by woreda, please navigate to “Monitor the Season Triggers” then “Monitor This Season” in the Dashboard:
https://fist-shiny.iri.columbia.edu/fbf_eth_dashboard/ (username: FBF, password: resilience)
NB: Every woreda in Somali region has a likely enough forecast probability of drought to trigger High severity action in July 2022.
For technical details on the forecast outlook and the associated climate conditions for July 2022, please consult the following presentation:
https://wiki.iri.columbia.edu/index.php?n=FbF.Forecast
The following sections of this document discuss next steps from here, and how the Anticipatory Action Plan could be improved for coming months.
Historical Drought Impact - Expert Review
The historical analysis of the occurrence and severity of drought hazards and their recorded impacts on the focused on sector and people is crucial for stakeholders to better understand the risk and make informed decisions when selecting drought risk thresholds and validating forecast information.
A preliminary list of bad years has already been generated by the TWG. One next step will be to review and refine this data, to better reflect impacts on the ground.
It is important that these years be ranked - so that they can be fairly compared against historical data - and that they identify impacts in the sector of interest. We may want to consider multiple sectors (such as pastoralists vs agro-pastoralists) and / or multiple sub-regions of Somali, especially if we think that the timing and severity of impactful climate events varies by sector or location.
Reviewing Early Actions (EA) and SOPs
Proposed Methodology
The draft Anticipatory Action Plan for Somali region has been entered into IRI’s tool, but should be reviewed and refined by the Technical Working Group.
Specifically, we want to capture more complex trigger rules (such as a higher frequency of action if pre-season rangeland conditions were sufficiently bad) as well as actions that might vary between parts of Somali Region.
In order for an Early Action plan to be properly designed and be best informed by forecast information, you should:
Identify the activities you have the capacity to implement in preparation for a probable drought year. These activities can be agreed upon by the stakeholders and partners engaged in the FbF programme. Actions can be identified by determining the target sector(s), the capacity of implementing agencies to act early, and the budget required to implement them.
Identify the timeline needed to carry out each activity in preparation for the rainy season. The identification of each early action should require the determination of the timing needed for its timely and complete execution. This equally requires pre-defining a budget for its execution, and the geographical extent of its coverage.
Identify the frequency at which each activity should be triggered. Defining the frequency at which an action should be carried out can rely on multiple factors which include the severity of the drought event that should prompt the early triggering of an activity, and the distribution of the budget allocated for the activity over the duration of a programme.
Template Sheet
Please use the following sheet as a template to input your contingency planning material. The following points are the details of each column:
- activity - Activity Name should be inserted here
- description - Provide a more detailed description for activity as seen fit
- start_date - Date when activity is proposed to start
- duration - Number of days this activity is active
- progress - Percentage of activity that is currently completed
- stage - The category of this activity of when it takes place. Please input: Preparation, Monitor&Evaluate or Activation
- priority - This should reflect on how important this task is. Please input: High, Normal or Low as seen fit
- budget - The monetary amount allocated to activity
- agency - Please input the agency that is carrying out the activity here
- sop - Please detail TRUE or FALSE if this activity is a standard operational procedure where it occurs every year
| activity | description | start_date | duration | progress | stage | priority | budget | agency | sop |
|---|---|---|---|---|---|---|---|---|---|
| Import Activity 1 | Description | 9/1/21 | 30 | 0 | Preparation | High | $100 | IRI | FALSE |
| Import Activity 2 | Description | 10/1/21 | 30 | 0 | Monitor&Evaluate | Normal | $100 | IRI | FALSE |
| Import Activity 3 | Description | 11/1/21 | 30 | 0 | Activation | Low | $100 | IRI | TRUE |
Setting Triggers
Summary
A climate scientist cannot set triggers for an FbF project without the help of experts and stakeholders. In an FbF project, triggers are set based on the expected frequency of occurrence of a hazard (drought) and the extent of its impact on the ground. The trigger setting process follows an impact-based approach:
To validate the performance of a drought forecast, we start by inspecting the spatial scales of historical impacts to identify the target frequency (trigger), determining the appropriate lead time(s), and assigning a severity level to the EA planned.
The probability output of the forecast determines if an EA (Early Action) is triggered or not.
Now that a decision maptool has been developed to help with the selection of all the variables needed to support the SOP development.
Now that we have historical drought years and we have managed to identify SOPs based on the severity level of these historical drought years, use the Maptool described above to set the triggers for the different severity level droughts. This is done by asking this following question for each severity level: “How often do these years happen?” Identifying the frequency in which these events happen in history will have allowed us to identify the frequency in which you would like the forecast to trigger.
Some activities might be of greater concern if the preceding season has been dry. This can be captured by setting triggers based on observational data (as opposed to the forecast), such as the previous month’s vegetation cover. Observational triggers might also be used as a validation check on the forecast as the season progresses.
Rule of Thumb for taking Action
To decide if decision maker should take action based on the forecast, at the desired frequency, decision makers should look at if the year in question is highlighted with the severity color or not on the forecast column. Once the year in question is highlighted take a look at the probability of non-exceedence value which is the number displayed on the forecast column.
If the probability of non-exceedence is 5% or more than the set frequency trigger on the sliders the decision maker should take action.
If the year is highlighted and is seen to have a probability of non-exceedence between 0-5% higher to the frequency trigger, decision makers should start to look at other historical years with similar probability of non-exceedence value (i.e., 0-5% higher than the trigger value) and see how many of them correspond to the reported bad years. If 50% or more of this historical years in question correspond to the reported bad years it makes sense for decision makers to take action.
If the probability of non-exceedence is less than the set frequency trigger on the sliders the decision maker should not take any action.
Severity
Below we have a few examples that showcase a few settings where we have two settings at which the frequency each activity would be triggered. An activity that should be triggered during a probably options of 35% and 20% frequenciesis presented below. The other settings are all for the Somali Region for a February lead time for the March - May season. From this analysis if we look at the forecast column, the frequency of 20%, by continuing to use these analysis tools experts can change the settings and find what best fits the phenomena on the ground based on historical data and use the rule of thumb above for the year being focused on.
