For us to better understand a typical season at one of the research sites, we had the pleasure to sit down and discuss with some of the local farmers. Sketching down the information on a piece of paper resulted in this graph.
The 2013-2014 season was in short a good year (for the home-garden farmers), water supplies were good, etc. We will further compile the information we got from the other farmers that kindly received us on the 19th of May, 2014.
In the figure below, I have taken the data from the Mokwakwaila office station. We are looking at the measured data from the most shallow sensor. In addition to this, I have created a model that takes the precipitation into account, as well as evaporation and run-off. The numbers are displayed in per cent.
The model looks something like
topModel(m) = topModel(m-1) + scaleRain*(rainfall(m) + rainfall(m-1)) - topModel(m-1)*topModel(m-1)*(evaporation + dillution)*(timeVector(m)-timeVector(m-1));The current, modelled value of soil moisture is equal to the previous one plus rain times a scaling factor. In addition to this, moisture will disappear through air and ground, this is a time-dependent parameter and it will also depend on the soil moisture itself. I have assumed a square model. The more moist it is, the more will evaporate.
Evaporation, etc., is also a function of air and soil temperature, not included here, that would further refine the model. Evaporation and dillution, is approximately 2% per hour.
Just since I prepared the files for some eye inspection, I thought we could add them here too.
Below you find the data underneath each other and in the same scale. The data is recorded at the Mokwakwaila office and at the Morwatshela high school. The plots are spanning from June 2013 until today. Notice that one of the sensors were installed some three months later than the other. This explains the long straight line from June to November 2013.
With the two graphs in front of us, we can at least see some correlation between the values with the naked eye.
The office station also has a rain gauge and we can see that there was quite heavy rain at the end of January. This also forms a peak in the soil moisture at the school. If we look at the accumulated rain over seven day periods over the year, Mokwakwaila received 75 mm during end of January. Interestingly, there was an earlier rain fall, flood, on Jan 6, where 61 mm was registered during 6 hours.
Just for the interested, a quick comparison of the different soil moisture values are given in the figure bellow. The curves can be identified from above (green: deep@school, yellow: shallow@office, red: deep@school, blue@office).
ANT/ANT+ are hybrids of the Bluetooth communication standard and can be used for short-range communication between electronic devices. Nowaday, ANT/ANT+ typically is used for training gadgets, healthcare and similar.
Today, when we visited Mokwakwaila to check on the sensors we found that there was a huge ant nest there! Crowded with them. Unfortunately, this has also made the sensor malfunction and data is lost.
We have to reinstall the sensor in September for the next season. We will probably have to move the sensor a bit to, and it will be a challenge to get the sensor up from the ground given all the ants that will be there...
The El Niño Southern Oscillation (ENSO) phenomenon describes the year-to-year variations in sea- surface temperatures across the equatorial Pacific Ocean. This affects atmospheric circulation and weather for many regions of the globe.
ENSO occurs in 3 phases as below:
ENSO typically affects weather in Southern Africa as below:
The “skill” or reliability of a seasonal forecast also varies according to which ENSO phase is in force during the current rainfall season. In general for Southern Africa, seasonal forecasts for conditions of both El Nino and La Nina can have relatively good skill, meaning that they are more likely to come true. Under Neutral conditions, however, the skill of the forecast is low, which means that one should not put much trust in such forecasts.
We have added forecasts for the Letaba and Luvuvhu regions with analyzed data from CSIR and CSAG. Please find them here.
We will update the forecast pages accordingly.
