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MINT Indicators Spreadsheet
Companion document:
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October 8, 2019
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SubjectIndicatorResult Type (qualitative vs quantitative)Spatial ResolutionTemporal ResolutionDescriptionRegional ExtentTemporal ExtentCommentVisualizationsData
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AgriculturePotential crop productionQuantitative0.5°x0.5°growing seasonPotential for crop production from biogeophysical modeling taking into account weather, soil type, cropping sequence, management practice, weed pressureSouth Sudan (all)yearly or seasonal (one point per harvest)(In progress)https://data.mint.isi.edu/files/simulation-runs/cycles/summary/
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Seasonal Crop Production IndexQuantitative0.5°x0.5°yearlyIndicator 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/
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Crop productionQuantitativeBasin/areayearlyCrop 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 Basinyearly or growing season(In progress)
https://data.mint.isi.edu/files/mint-runs/economic-model/summary/
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ClimateStandardized Precipitation IndexQuantitative0.05°x0.05° - 0.25°x0.25°monthlySPI 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 Ethiopia2000-2017
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
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South Sudan and Ethiopia2000-2017https://files.mint.isi.edu/s/NWCrxMPzMFiYmblMonthly 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


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South Sudan and Ethiopia2000-2017https://files.mint.isi.edu/s/3M1ewF9kBjIiGRjMonthly 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
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Standardized Precipitation Evapotranspiration IndexQuantitative0.25°x0.25°monthlyThe SPEI considers not only precipitation but the effect of temperature on frought conditions. South Sudan and Ethiopia2000-2017https://files.mint.isi.edu/s/evv68gpDoPJJjo3Monthly 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
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Potential EvapotranspirationQuantitative0.25°x0.25°monthlyPE 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 Ethiopia2000-2017https://files.mint.isi.edu/s/v1oql8lHrzpgjryMonthly PET calculated from GLDAS precipitation and temperature data: https://files.mint.isi.edu/s/mAG858uCKpAKS08
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HydrologyRiver Discharge (volume flow rate)Quantitative
0.0166°x0.0166°
hourly, dailySpatio-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 GambellaSimulated stormhttps://data.mint.isi.edu/files/indicators/Baro_Gam_River_Discharge_Movie.movhttps://data.mint.isi.edu/files/indicators/Baro_Gam_1min_Input.ziphttps://data.mint.isi.edu/files/indicators/Baro_Gam_1min_Output.zip
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River Flood Depth (overbank flow)Quantitative
0.0166°x0.0166°
hourly, dailySpatio-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 GambellaSimulated stormhttps://data.mint.isi.edu/files/indicators/Baro_Gam_Flood_Depth_Movie.movhttps://data.mint.isi.edu/files/indicators/Baro_Gam_1min_Input.ziphttps://data.mint.isi.edu/files/indicators/Baro_Gam_1min_Output.zip
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Streamflow Duration Index Quantitative0.5°x0.5°dailyCharacterizes 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.pngRaw 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.tgzOutput: http://data.mint.isi.edu/files/wings-data/pihm-output-pongo-2b47adc5c3f009ef2da98d6fa09eced9.tgzThe raw SDI index data: https://data.mint.isi.edu/files/indicators/Pongo_Streamflow_duration_index_data.xlsLOOK HERE
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Geospatial Flood Exceedance IndexQuantitative0.5°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 2017https://data.mint.isi.edu/files/indicators/Pongo_Geospatial_Flood_Exceedance_index.gifRaw 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.tgzOutput: http://data.mint.isi.edu/files/wings-data/pihm-output-pongo-2b47adc5c3f009ef2da98d6fa09eced9.tgz
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