2  Introduction

This is a Work in Progress (WIP).

• This book provides the different analyses considered during the development of the Afghanistan - Drought AA framework.

• The majority of analyses here are based on environment/climatic remotely sensed data. As the data sets and analyses resemble many other environmental analyses it’s important to clarify what the objectives are and are not:

  • The current analyses target the March-April-May season in Northern Afghanistan which coincides with the Spring wheat growing season.

  • The current analyses are aimed at understanding if any environmental indicators or combinations of environmental indicators can be useful in anticipating drought conditions over this growing season that would result in negative humanitarian consequences.

  • We seek to understand if we can establish thresholds on data sets at specified trigger moments (moments in time) where if breached pre-arranged finances can be released for pre-arranged activities.

  • The goal is not to build a comprehensive drought monitoring system the extends beyond the area of interest or season of interest.

2.1 Data Sets Explored

• Precipitation forecast:

  • The European Centre for Medium-Range Weather Forecasts—System 5 (ECMWF-SEAS5) Johnson et al. (2019)

• Precipitation observed:

  • The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) Funk et al. (2015)
  • ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate (C3S) (2017)

• Vegetation/crop development

  • FAO Agricultural Stress Index System (ASIS) Food and United Nations (FAO) (2024)

  • FA0 Vegetation Health Index (VHI) - Near Real Time (Dekadal) Food and United Nations (FAO) (2024)

  • NDVI - MODIS Terra & Aqua Didan (2015a) Didan (2015b)

• Snow

  • NDSI - MODIS Hall and Riggs (2015)

  • SWE ERA5 McNally et al. (2017)

  • Snow depth water equivalen - ERA5-Land: monthly averaged data from 1981 to present Muñoz Sabater (2019)

• Soil Moisture

  • Volumetric soil moisture layers 1- 4 Muñoz Sabater (2019)

• Runoff

  • Muñoz Sabater (2019)

• Temperature

  • (C3S) (2017)

This is a book created from markdown and executable code.

See Knuth (1984) for additional discussion of literate programming.

(C3S), Copernicus Climate Change Service. 2017. “ERA5: Fifth Generation of ECMWF Atmospheric Reanalyses of the Global Climate.” Copernicus Climate Change Service Climate Data Store (CDS) 15 (2): 2020.

Didan, Kamel. 2015a. “MOD13A2 MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V006.” (No Title).

———. 2015b. “MYD13Q1 MODIS/Aqua Vegetation Indices 16-Day L3 Global 250m SIN Grid V006.” NASA EOSDIS Land Processes DAAC, 10.

Food, and Agriculture Organization of the United Nations (FAO). 2024. “Agricultural Stress Index System (ASIS).” https://www.fao.org/giews/earthobservation/.

Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, et al. 2015. “The Climate Hazards Infrared Precipitation with Stations—a New Environmental Record for Monitoring Extremes.” Scientific Data 2 (1): 1–21.

Hall, Dorothy K, and George A Riggs. 2015. “MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid.” (No Title).

Johnson, Stephanie J, Timothy N Stockdale, Laura Ferranti, Magdalena A Balmaseda, Franco Molteni, Linus Magnusson, Steffen Tietsche, et al. 2019. “SEAS5: The New ECMWF Seasonal Forecast System.” Geoscientific Model Development 12 (3): 1087–117.

Knuth, Donald E. 1984. “Literate Programming.” Comput. J. 27 (2): 97–111. https://doi.org/10.1093/comjnl/27.2.97.

McNally, Amy, Kristi Arsenault, Sujay Kumar, Shraddhanand Shukla, Pete Peterson, Shugong Wang, Chris Funk, Christa D Peters-Lidard, and James P Verdin. 2017. “A Land Data Assimilation System for Sub-Saharan Africa Food and Water Security Applications.” Scientific Data 4 (1): 1–19.

Muñoz Sabater, Joaquı́n. 2019. “ERA5-Land Monthly Averaged Data from 1981 to Present.” Copernicus Climate Change Service (C3S) Climate Data Store (CDS) 10.