In this exercise you will combine all the skills you have gathered in the previous exercises to develop annual/seasonal gap-filled ETa maps for Miandoab Irrigation Scheme (MIS). The focus study period is from October 2018 to September 2019.
The Miandoab Irrigation Scheme (MIS) is of strategic importance for food security in western Iran. The scheme was built in 1985 with a loan received from the World Bank. Through the years the MIS has been a reliable source for food production and has improved the livelihood of farmers. However, these agricultural activities have resulted in a reduction of the flow into Urmia Lake and are accounted for one of the reasons behind the shrinkage of the lake. Lake Urmia is the second biggest hypersaline lake in the world and its sphere of influence for biodiversity and the ecosystem goes beyond Iran. It also generates numerous economic benefits for Iran. Hence to help restore the lake the government of Iran has decided to reduce water allocated to MIS by 40% by increasing the performance of the scheme without compromising food production. The recent statistics show that the productivity of wheat and sugar beet in the MIS has fallen by an average of 20% in the past 5 years in comparison with the ten year average of 2000-2009.
The crop year for MIS is from October to September next year, covering two major cropping seasons. The winter cropping season is from October to June next year and the Summer cropping season is from May to September the same year. Major winter crops are Wheat and Barley, while Summer crops are mainly vegetables and sugar beet. There are all year crops like Orchards and Alfa-Alfa. A typical crop calendar in the region is shown in Figure 1.
Figure 1: Typical crop calendar in the region
Terms of Reference
The participant is expected to conduct a thorough study in the MIS to analyze the crop water consumption in MIS by computing Actual EvapoTranspiration maps, to help the government in devising informed strategies for reducing water consumption in the scheme while the crop production is increasing.
The exercise is divided into Four parts:
Part 1: Running PySEBAL for all the Landsat images (Less than 50% cloud cover) for the crop year October 2018 to September 2019
- Compute ETa using PySEBAL from Landsat 8 data for the year 2018/19 and apply gapfilling procedure to develop monthly ETa maps at 30m spatial resolution.
- Install PySEBAL - [Tutorial]. Get the PySEBAL source code from this link
- Run PySEBAL for assigned dates to you between October 2018 to September 2019(Check with Task force at Sharif University)
- Share the PySEBAL outputs (ETa and NDVI maps) with all participants
- Run the gapfilling script to get gapfilled monthly ETa maps from October 2018 to September 2019
Part 2: Aggregation to seasonal/Annual maps and crop based statistics
- Aggregate from monthly ETa maps to seasonal maps and extract crop based/Landcover based statistics (crop type maps are provided to you.
- Aggregate the gapfilled monthly ETa maps to Winter season (October to May), Summer season (June to September) and Annual (October 2018 to September 2019) ETa maps
- Extract crop based statistics using Seasonal/Annual ETa maps and crop type maps provided to you [Tutorial 1]
- Reflection on the temporal variation of monthly ETa over crop type/land cover map (crop type map of each year is provided)
Part 3: Comparison with FAO WaPOR data
- Monthly comparison between ETa from PySEBAL and AETI from WaPOR over crop type and report on the correlations. Report using tables and scatter plots
- Use sampling tools in QGIS to create a random sampling of 500 points in the MIS and compare the reported value of these sample points in WaPOR annual ETI and Annual aggregate PySEBAL ET map, using tables and scatter plots.
- Compute mean statistics of monthly ETa from PySEBAL and WaPOR AETI (data provided to you as part of Exercise 1) over each croptype/landcover type
- Aggregate the monthly ETa maps from PySEBAL and WaPOR to annual maps (Check above tutorials)
- Follow Exercise 1 to get scaled AETI monthly, seasonal and annual WaPOR data in Geotif (Note: You will have to clip the WaPOR data into MIS)
- Random sampling to 500 points over MIS
- Extract Annual ETa values for the 500 points and export to a table
- Prepare scatter plots and report correlation indicators(Rsq). i) Monthly ETa over each croptype from PySEBAL vs WaPOR; ii) Annual PySEBAL vs WaPOR over 500 sampled points
What you need to submit:
Individual reports containing:
Description of data and methods
Seasonal and annual gap-filled maps of evapotranspiration for the MIS for study year and the statistics over MIS and per land cover type
Reflection on the temporal variation of ETa over crop type/land cover map (use the crop map that has the highest accuracy among the group members)
- Results of comparison between PySEBAL ETa and WaPOR AETI
Deadline: 15 August 2021
Follow up work: Repeat the above analysis for crop years 2017/18 and 2019/20 (Not part of this assignment.