Evaluation Indicators

	A	B	C	D	E	F	G	H	I	J	K	L	M	N	O
1	MINT Indicators Spreadsheet			Companion document:
2	October 8, 2019
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4	Subject	Indicator	Result Type (qualitative vs quantitative)	Spatial Resolution	Temporal Resolution	Description	Regional Extent	Temporal Extent	Comment	Visualizations	Data
5	Agriculture	Potential crop production	Quantitative	0.5°x0.5°	growing season	Potential for crop production from biogeophysical modeling taking into account weather, soil type, cropping sequence, management practice, weed pressure	South Sudan (all)	yearly or seasonal (one point per harvest)		(In progress)	https://data.mint.isi.edu/files/simulation-runs/cycles/summary/
6		Seasonal Crop Production Index	Quantitative	0.5°x0.5°	yearly	Indicator uses the median for location x planting x fertility x weed pressure to estimate the yield divide by median yield in that situation. When index > 1 then situation is better than average. When index < 1 situation is worse than average. Easy for analyst to compare years. Completing simulations for second growing season will allow tracking seasonal and yearly local food shortages.	South Sudan (all)	yearly or yearly x seasonal (one point per harvest)	one growing season (harvest July/Aug 2017), effects since Jan 2017	(In progress)	https://data.mint.isi.edu/files/simulation-runs/cycles/summary/
7		Crop production	Quantitative	Basin/area	yearly	Crop production (maize, cassava, sorghum, groundnuts, sesame) taking into account the behavioral response of farming households to biophysical factors, economic conditions (e.g., market prices fluctuation, changses of production cost, fertilizer subsidies) and policy constraints (land tenure arrangement). By varying related parameters (price, land/fertilizer cost) one at a time between -50 and 50 (increment by 10) within MINT, simulated crop production results are generated to predict how farmers react to potential economic condition changes.	Pongo Basin	yearly or growing season		(In progress)	https://data.mint.isi.edu/files/mint-runs/economic-model/summary/
8	Climate	Standardized Precipitation Index	Quantitative	0.05°x0.05° - 0.25°x0.25°	monthly	SPI characterizes meteorological drought. On short timescale, it is related to soil moisture. On longer timescales, it can be related to groundwater and reservoir storage. The SPI can be compared across regions with markedly different climates. It quantifies observed precipitation as a standardized departure from a selected probability distribution function that models the raw precipitation data.	South Sudan and Ethiopia	2000-2017		https://files.mint.isi.edu/s/Jx3Pf0t8cFs32qd	Monthly SPI inferred from CHIRPS precipitation data, with a reference period between 1981 and 2010 for South Sudan and Ethiopia : https://files.mint.isi.edu/s/74wQUomwpvrOOog
9							South Sudan and Ethiopia	2000-2017		https://files.mint.isi.edu/s/NWCrxMPzMFiYmbl	Monthly SPI inferred from CHIRPS precipitation data, with a reference period between 2000 and 2017 for South Sudan and Ethiopia: https://files.mint.isi.edu/s/1K5nnHsWejgfbqR
10							South Sudan and Ethiopia	2000-2017		https://files.mint.isi.edu/s/3M1ewF9kBjIiGRj	Monthly SPI inferred from GLDAS precipitation data, with a reference period between 2000 and 2017 for South Sudan and Ethiopia: https://files.mint.isi.edu/s/UQ7waJLbVbV4cfn
11		Standardized Precipitation Evapotranspiration Index	Quantitative	0.25°x0.25°	monthly	The SPEI considers not only precipitation but the effect of temperature on frought conditions.	South Sudan and Ethiopia	2000-2017		https://files.mint.isi.edu/s/evv68gpDoPJJjo3	Monthly SPEI calculated from GLDAS precipitation and temperature (through PET in 3) data, with a reference period between 2000 and 2017 for South Sudan and Ethiopia: https://files.mint.isi.edu/s/umeT1kYTiZyYoJC
12		Potential Evapotranspiration	Quantitative	0.25°x0.25°	monthly	PE is the demand or maximum amount of water that would be evapotranspired if enough water were available (from precipitation and soil moisture).	South Sudan and Ethiopia	2000-2017		https://files.mint.isi.edu/s/v1oql8lHrzpgjry	Monthly PET calculated from GLDAS precipitation and temperature data: https://files.mint.isi.edu/s/mAG858uCKpAKS08
13	Hydrology	River Discharge (volume flow rate)	Quantitative	0.0166°x0.0166°	hourly, daily	Spatio-temporal, stack of grids that vary in time, showing river discharge in response to a single storm. Spatial grid cell size is 60 arcseconds (about 1.8 km). TopoFlow output.	Baro River basin as it drains to the town of Gambella	Simulated storm		https://data.mint.isi.edu/files/indicators/Baro_Gam_River_Discharge_Movie.mov	https://data.mint.isi.edu/files/indicators/Baro_Gam_1min_Input.zip	https://data.mint.isi.edu/files/indicators/Baro_Gam_1min_Output.zip
14		River Flood Depth (overbank flow)	Quantitative	0.0166°x0.0166°	hourly, daily	Spatio-temporal, stack of grids that vary in time, showing depth of water in the flood plain in response to a single storm. Spatial grid cell size is 60 arcseconds (about 1.8 km). TopoFlow output.	Baro River basin as it drains to the town of Gambella	Simulated storm		https://data.mint.isi.edu/files/indicators/Baro_Gam_Flood_Depth_Movie.mov	https://data.mint.isi.edu/files/indicators/Baro_Gam_1min_Input.zip	https://data.mint.isi.edu/files/indicators/Baro_Gam_1min_Output.zip
15		Streamflow Duration Index	Quantitative	0.5°x0.5°	daily	Characterizes discharge vs. percent of time that a particular discharge was equaled or exceeded. The area under the flow duration curve gives the average daily flow, and the median daily flow is the 50% value.	Lol-Kuru (Pongo)			https://data.mint.isi.edu/files/indicators/Pongo_Streamflow_duration_index.png	Raw output of the model, including animated gif files of the raw output: https://data.mint.isi.edu/files/simulation-runs/pihm/	Input: http://data.mint.isi.edu/files/wings-data/pihm-input-pongo-4b6fb3d30834208001e5771580e47eb4.tgz	Output: http://data.mint.isi.edu/files/wings-data/pihm-output-pongo-2b47adc5c3f009ef2da98d6fa09eced9.tgz	The raw SDI index data: https://data.mint.isi.edu/files/indicators/Pongo_Streamflow_duration_index_data.xls	LOOK HERE
16		Geospatial Flood Exceedance Index	Quantitative	0.5°x0.5°	daily (monthly averages)	Spatially characterizes depth of flow that was equaled or exceeded in a particular watershed during a given month of the year.	Lol-Kuru (Pongo)	Jan-Dec 2017		https://data.mint.isi.edu/files/indicators/Pongo_Geospatial_Flood_Exceedance_index.gif	Raw output of the model, including animated gif files of the raw output are here: https://data.mint.isi.edu/files/simulation-runs/pihm/	Input: http://data.mint.isi.edu/files/wings-data/pihm-input-pongo-4b6fb3d30834208001e5771580e47eb4.tgz	Output: http://data.mint.isi.edu/files/wings-data/pihm-output-pongo-2b47adc5c3f009ef2da98d6fa09eced9.tgz
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