Author: Pavel Hájek (WIRELESSINFO) – (Challenge #6, Kampala INSPIRE Hackathon)
The idea of Challenge # 6 called Climate Change Trends for Africa was to provide a proof of concept scenario in which a user enters coordinates (i.e. choose a locality) and he/she will get information about several climatic variables and their changes in time.
The workflow of Challenge 6 was as follows:
- Define an area of interest – West of Victoria Lake on border of Uganda and Tanzania
- Define suitable climatic variables of interest – such as Dates for fall nitrogen application, Solar radiation 1990-2019, Growing degree units and Heat stress units for C3/C4 crops 2010-2019 and Number of days with growing temperatures for C3, C4 crops
- Design algorithms for particular chosen variable variable in the previous step
- Find necessary data, in order to be able to run the algorithms – Climate Data Store of Copernicus project – ERA5-Land hourly data from 1981 to the present
- Run the algorithm using Jupyter notebook computing platform of EUXDAT project (www.euxdat.eu)
- Present the results in a form of maps of the chosen area and graphs for trends for particular points in the area if interest
This team aims to demonstrate several options for meteorological data exploitation for the needs of African farmers. The farmers’ needs were collected and are described in the Vaughan et al. (2017) study:
During previous INSPIRE Hackathons, we worked on an Agroclimatic map of a selected region, on a tool alerting farmers about forecasted severe weather conditions, or on a Climatic Services for Africa.
The team has developed a concept service returning:
- Growth plan – a time interval when to start planting to maximize yield
- Nitrogen plan – a time interval when to insert nitrogen fertilisation to maximize its effect
- Insect pests alert – alert when a risk of insect pest attack is high
This challenge for Kampala INSPIRE Hackathon is to search for trends in climatic data because future climatic conditions can be inspected and potentially forecasted based on temporal trends in climatic data.
Therefore we plan to provide a proof of concept scenario in which a user enters coordinates (i.e. choose a locality), he/she will get information about several climatic variables (e. g. Last spring / First fall frost date, Annual/Seasonal Evapotranspiration and precipitation, Soil temperature, Solar radiation, etc.) and their evolvement in time. The initial visualization can be depicted by Figure 1 below, where an Annual average temperature and the trend of such a variable is shown (this figure is taken from Stargate project). More-over we would like to incorporate information about the uncertainty of such a variable in the graph as well, in order to capture the credibility of the used data.