Using aWhere Maps, Charts and Weather Data in Reports

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aWhere Training Tutorial


Congratulations! You have made it through the entire tutorial series and are now on the final tutorial which will show you how to combine the charts, maps, figures, and data you have extracted over the course of the training to make meaningful reports, case studies and more to build resilience to climate change. For this tutorial, we will leverage Microsoft Powerpoint to create a presentation that summarizes the trends in Zambia for the time represented in our outputs. If you do not have access to PowerPoint, you can also use Google Slides, or other data visualization tools.

Steps for building an effective case study or report

Before we start building our report, we will review a few essential steps to consider as you build your presentation.

  1. Initial Steps – Determine Purpose, Audience, Format
  2. Context – Country, crop, region, time period. Do you need to leverage additional contextual information such as crop calendars, crop production maps or a map of the population (from the aWhere country Template file)?
  3. Data Discovery & Analysis – Which aWhere tools should you use?
  4. Interpretation & Narrative – What does the data tell you? What are the implications?
  5. Reporting & Recommendations – How to turn the narrative into ACTION


Here are a few examples of how we have leveraged both R and QGIS to make quick reports using maps and charts.

Example 1: Using maps and charts together

The example below shows 3 maps, all from the same time period, that show 2 different variables: Accumulated precipitation, precipitation difference from the long-term normal (LTN), and the P/PET difference from the long-term normal. These three maps tell a fairly complete story of the rainfall patterns for this time period and coupled with the weekly climate charts from R, we can begin to add examples from specific locations. Main takeaways from the maps that could be added to a narrative include:

  1. The northeastern region of the country is drier than normal and crops in that area could be at risk
  2. The P/PET is lower than 0.1 (yellow-red areas) in many parts of the country which could indicate key crops such as maize and millet might begin to fail
  3. The location under the first map shows much lower than normal rainfall compared to normal as indicated by the red circle. The blue bars indicate the weekly rainfall totals and the orange line indicates the long-term normal for this location. There were many weeks where the rain did not fall in the same pattern as the historical trend.

Example 2: Creating a Case Study Report

The example below shows aWhere’s approach to a country case study meant for a public audience. This case study includes maps made in QGIS, an FAO crop calendar for context on impact on crops during the relevant months, and weekly climate charts made with training script #3 in R. These reports are highly useful for communicating current conditions to broad audiences. The most critical section is the implications on the second page – what do these trends mean for your audience? 

Making a Case Study: Zambia

We will be making a slide on the conditions in Zambia from January 17, 2020 – February 18, 2020, which is the peak of the rainy season. We will be leveraging the charts we created in earlier tutorials from script #4. The slide below was created using PowerPoint. Here are some key takeaways one might see from this analysis:

  1. The time period of the map is mid-January to mid-February 2020 which correlates to the highlighted time period on the FAO Crop Calendar. According to the calendar, January and February are key months for growing maize, millet, and sorghum – all staple crops. Any disruptions during this period could have a significant impact on the yields and potentially food security.
  2. Prolonged periods of drier than normal conditions can also lead to agricultural and/or hydrological drought, putting water availability at risk.
  3. The overall weekly precipitation totals are fairly aligned with the long-term normal but there are a few weeks in December in this location (latitude, longitude) when much rainfall was lower than normal.
  4. The “4-chart” on the lower left shows that the rolling average of P/PET was lowest in January and February and also that temperatures were higher than normal during this time.

This is a small sample of the types of outputs you can begin to integrate into your reports and bulletins. The weekly climate chart (from script #3) is a great place to start if you are just starting out with weather data!

Review of available tools

Throughout this series of tutorials we have shown you many ways you can access aWhere’s data. This image below illustrates the spectrum of tools available to you and the types of outputs you can expect from the different tools. The first 3 refer to tools in the adaptER Platform while the last 2 are available by running the R scripts. There is something for everyone in this toolkit!

What’s next? 

Thank you for making it through what we hope was an interesting series of tutorials. We are always open to questions and please let us know if you have any suggestions on how we can improve this series. We are always adding additional tutorials so check back regularly for new material that will help you start building resilience to climate change today!

Do you need additional access and more than 400 API Calls per month? Please visit our website to signup for a subscription – starting at just $50 a month!

If you have any questions, please contact

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