188 Research Report Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Giriraj Amarnath, Surajit Ghosh and Niranga Alahacoon Research Reports The publications in this series cover a wide range of subjects—from computer modeling to experience with water user associations—and vary in content from directly applicable research to more basic studies, on which applied work ultimately depends. Some research reports are narrowly focused, analytical and detailed empirical studies; others are wide-ranging and synthetic overviews of generic problems. Although most of the reports are published by IWMI staff and their collaborators, we welcome contributions from others. Each report is reviewed internally by IWMI staff, and by external reviewers. The reports are published and distributed both in hard copy and electronically (www.iwmi.org) and where possible all data and analyses will be available as separate downloadable files. Reports may be copied freely and cited with due acknowledgment. About IWMI The International Water Management Institute (IWMI) is an international, research-for-development organization that works with governments, civil society and the private sector to solve water problems in developing countries and scale up solutions. Through partnership, IWMI combines research on the sustainable use of water and land resources, knowledge services and products with capacity strengthening, dialogue and policy analysis to support implementation of water management solutions for agriculture, ecosystems, climate change and inclusive economic growth. Headquartered in Colombo, Sri Lanka, IWMI is a CGIAR Research Center with offices in 15 countries and a global network of scientists operating in more than 55 countries. www.iwmi.org IWMI Research Report 188 Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Giriraj Amarnath, Surajit Ghosh and Niranga Alahacoon International Water Management Institute (IWMI) P. O. Box 2075, Colombo, Sri Lanka The authors: Giriraj Amarnath, Principal Researcher – Disaster Risk Management and Climate Resilience, and Research Group Leader - Water Risk to Development and Resilience, International Water Management Institute (IWMI), Colombo, Sri Lanka. Surajit Ghosh, Regional Researcher – Water Risk and Data Sciences Specialist, IWMI, Colombo, Sri Lanka. Niranga Alahacoon, Researcher – Remote Sensing and Disaster Risk, all at the IWMI, Colombo, Sri Lanka. Amarnath, G.; Ghosh, S.; Alahacoon, N. 2023. Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool. Colombo, Sri Lanka: International Water Management Institute (IWMI). 53p. (IWMI Research Report 188). doi: https://doi.org/10.5337/2023.223 / drought indices / early warning systems / decision support systems / monitoring / earth observation satellites / remote sensing / extreme weather events / weather forecasting / precipitation / rainfall / temperature / snow cover / weather patterns / indicators / climate change mitigation / risk management / climate services / decision making / disaster preparedness / spatial data / datasets / maps / seasonal variation / institutions / finance / state intervention / surface water / irrigated farming / crop yield / food insecurity / Afghanistan / ISSN 1026-0862 ISBN 978-92-9090-958-3 Copyright © 2023, by IWMI. All rights reserved. IWMI encourages the use of its material provided that the organization is acknowledged and kept informed in all such instances. The boundaries and names shown and the designations used on maps do not imply official endorsement or acceptance by IWMI, CGIAR, our partner institutions, or donors. Please send inquiries and comments to IWMI-Publications@cgiar.org A free copy of this publication can be downloaded at www.iwmi.org/publications/iwmi-research-reports/ Acknowledgements The authors are grateful for the constructive feedback provided by Efrem Ferrari (Consultant, International Finance Corporation [IFC], Washington, DC, USA), Arati Belle (Senior Disaster Management Specialist, World Bank, Washington, DC, USA), Alice Soares (Senior Disaster Expert, World Bank, Washington, DC, USA), Azim Doosti (Disaster Risk Management Consultant, World Bank, Kabul, Afghanistan), and Alok Sikka (Country Representative – India, IWMI) which helped to improve the content of this report. Thanks also go to the various officials from the Afghanistan Meteorological Department (AMD), Afghanistan National Disaster Management Authority (ANDMA), National Water Affairs Regulation Authority (NWARA), and the National Statistics and Information Authority (NSIA) for their technical support and guidance in developing the Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool. Project The Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool was created as part of the Afghanistan Drought Early Warning System project with funding from the World Bank under Grant no. 7195415. The AF-DEWS Tool is an online platform that was created with the aim of building consensus, increasing coordination and supporting decision-making in efforts to mitigate the impacts of drought in Afghanistan. Donors World Bank, Washington, DC, USA INITIA TIVE O N This work is partially supported by the CGIAR Initiative on NEXUS NE XUS G ains Gains, which is grateful for the support of CGIAR Trust Fund contributors (https://www.cgiar.org/funders). Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - iii Contents Acronyms and Abbreviations vi Summary vii Introduction 1 Background 1 Objectives 2 Drought Monitoring and Early Warning using Satellite Remote Sensing 2 Drought in Afghanistan 2 Agroecological Region and Climatic Condition 2 Drought Type and Definition 4 The Importance of Early Warning 5 Remote Sensing and Drought Monitoring 6 Selection of Drought Indicators to Define the Types of Drought 6 Monitoring Different Types of Drought (Meteorological, Agricultural, Hydrological and Socioeconomic) 6 Key Indicators and Thresholds 11 Determination of Drought 11 Comparison of National- and Provincial-level Thresholds 14 Architecture of the AF-DEWS Tool 17 Evaluation of Two Decades of Drought Events in Afghanistan 19 Meteorological Drought Assessment 19 Hydrological Drought Assessment 23 Agricultural Drought Monitoring 26 Comparison of Multiple Indicators 27 Drought Impact Analysis using Crop Production 31 Drought Impact Assessment 33 Validation of the AF-DEWS Tool with Other Sources 36 The Way Forward 37 Convergence of the AF-DEWS Tool with Other Initiatives 37 Capacity Building and Knowledge Transfer 39 References 40 Annex 1. Cumulative Distribution Function 43 Annex 2. Provincial-level Threshold Values for Different Drought Classes 44 Annex 3. Glossary 45 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - v Acronyms and Abbreviations AF-DEWS Afghanistan Drought Early Warning Decision Support API Application Programming Interface ASCAT Advanced Scatterometer AVHRR Advanced Very High Resolution Radiometer CHIRPS Climate Hazards Group InfraRed Precipitation with Station data CYA Crop Yield Anomaly DEWC Drought Early Warning Committee ECI Evaporative Condition Index FAO Food and Agriculture Organization of the United Nations GCP Google Cloud Platform GEE Google Earth Engine GoIRA Government of the Islamic Republic of Afghanistan IDSI Integrated Drought Severity Index MAIL Ministry of Agriculture, Irrigation and Livestock MODIS Moderate Resolution Imaging Spectroradiometer PCC Percent crop cover PCI Precipitation Condition Index PSC Percent snow cover SPI Standardized Precipitation Index TCI Temperature Condition Index VCI Vegetation Condition Index VHI Vegetation Health Index IWMI - vi Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Summary The 2018 drought, the worst in a decade, prompted The AF-DEWS Tool is based on Google Earth Engine, a the Government of the Islamic Republic of Afghanistan web-based, global-scale geospatial analysis platform. It (GoIRA) to find a way to respond better to drought and hosts a vast array of raw satellite images and generated other drivers of food insecurity. Several consultations were datasets that can be accessed via a simple user interface. held with the government, humanitarian and development Users can view and perform their analyses on the communities, and civil society to discuss how to address datasets. food insecurity and famine risk in Afghanistan. As an outcome of these rigorous consultations, GoIRA initiated Each of the drought indices incorporated in the the Early Warning, Early Finance and Early Action system has a set of defined thresholds that, if (ENETAWF) project, funded by the World Bank, to support exceeded, will automatically trigger warnings to medium- to long-term drought resilience initiatives. The the user. For example, if the level of precipitation objectives of the project are given below: drops below a threshold for the time of a year, an initial drought warning is given. If the level breaches • Strengthen weather and climate services to develop a further thresholds, warnings for moderate or severe drought early warning decision support system. drought will be triggered. Data from drought events recorded over 20 years of historical satellite data • Establish a mechanism to distribute assistance to were used to determine the appropriate threshold chronically food-insecure people. levels for each index. • Develop financial instruments capable of scaling up The system makes it easy to quickly draw comparisons resources during extreme weather events, such as between, for example, conditions in different years. drought, that result in increased food insecurity. The AF-DEWS Tool provides regular outlook forecasts that include visual evidence of the state of different The International Water Management Institute (IWMI) parameters. These clearly highlight conditions of developed the Afghanistan Drought Early Warning concern, such as minimal snow cover, low reservoir Decision Support (AF-DEWS) Tool, a cloud-based online levels and poor vegetation growth in areas that are platform, to provide decision-makers with maps and data usually irrigated. to enable further analysis. The system combines satellite- based Earth observation data with weather forecasting Using Outputs from the AF-DEWS Tool to Inform model outputs to provide the following services: Action • Twenty-eight days of precipitation and temperature When there is a requirement to forecast future climate forecasts are updated weekly to guide future trends. shocks, Afghanistan’s authorities can confidently use the AF-DEWS Tool to identify the occurrence and trajectory of • Near real-time drought indicators, based on drought through a series of steps. multisource remote sensing data, covering meteorological, agricultural and hydrological droughts During December, January and February, the rainfall and with related thresholds to determine drought severity. snowfall indices reveal potential surface water deficits that may affect water availability in the following cropping • Calculation of a single, comprehensive composite season. If signs indicate that drought is emerging, the next drought index to monitor agricultural drought and step is to confirm this by analyzing, between February support decision-making. and March, indicators of impact, such as crop cover and vegetation health. Developing the AF-DEWS Tool If it becomes clear that drought is evolving to the extent In the last decade, Earth observation satellites and global that vegetation is affected, examining the Integrated environmental models have generated a large volume Drought Severity Index (IDSI) for April and May will of geospatial data that is freely available to science and help authorities identify the precise impacts of drought society. To support the storage and processing of these on agriculture and thus better plan the response datasets, novel technologies have been developed. measures. These mostly rely on cloud computing technology and In summary, the AF-DEWS Tool provides easily accessible, distributed databases, with web services used to access sub-seasonal forecasts and near real-time drought and process the data. This means that anyone with a monitoring information at both national and district desktop computer and internet connection can access levels. For effective use of the AF-DEWS Tool, it is crucial and process datasets that would previously have required to ensure multi-institutional arrangements for early action expensive computing equipment with a large processing to mitigate drought risks and address long-term impacts capability. on development. Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - vii Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Giriraj Amarnath, Surajit Ghosh and Niranga Alahacoon Introduction Background ● Community-level preparedness plans that tell people what they should do in the event of a In 2018, Afghanistan suffered the worst drought in drought. a decade. Reduced snowfall and water availability, coupled with high temperatures, caused widespread GoIRA commissioned the development of the crop failures. With 78% of the population reliant on Afghanistan Drought Early Warning Decision Support agriculture for their livelihoods (FAO 2018), this led to (AF-DEWS) Tool1 to fulfill the first two of the above more than 13 million people becoming severely food building blocks. This report provides an overview of insecure. The migration of large numbers of people how the AF-DEWS Tool was developed and how it can from rural to urban areas increased the nation’s already be used to systematically monitor, detect and forecast high poverty rate (Helgason 2020). drought conditions in Afghanistan. The tool is aimed at providing decision-makers with the information Afghanistan’s lack of an objective, forecast-based required to activate mitigation actions and response response mechanism meant it had been unable to measures. identify the onset of drought early on. As a result, the humanitarian response for those affected came The AF-DEWS Tool was established in 2020 as an online six months after the drought started. Such reactive platform. It combines near real-time satellite data with responses necessarily focus on saving lives, rather weather forecasts to provide three main services: (1) than on making communities more resilient to future weekly updates of daily precipitation and temperature drought. They also come at a higher cost to affected forecasts for four weeks to provide guidance on economies (Eckstein et al. 2021). future trends; (2) near real-time drought indicators covering meteorological, agricultural and hydrological The 2018 drought prompted the Government of the droughts, with related thresholds to determine drought Islamic Republic of Afghanistan (GoIRA) to find a way severity using multisource remote sensing data; and to respond better to drought and other drivers of (3) calculation of a single, comprehensive composite food insecurity. A nine-month consultation was held index, the Integrated Drought Severity Index (IDSI), with stakeholders in government, humanitarian and to support drought decision-making (Amarnath et al. development communities, and civil society on how to 2021). This cloud-based online platform implemented address water scarcity, food security and famine risk using Google Earth Engine (GEE) and Google Cloud in Afghanistan. This led to GoIRA initiating the Early Platform (GCP) provides decision-makers with quick Warning, Early Finance and Early Action (ENETAWF) and easy access to drought-related information. This project in February 2021. includes an easy-to-understand map, which can be downscaled from national to district level, and the An effective end-to-end drought early warning decision tools needed to easily download information for support system has the following four building blocks: conducting drought analyses. Data covering the period from 2001 to 2020 are available in the AF-DEWS Tool, ● Robust capabilities in weather and climate enabling the user to gain insights on past events and monitoring and forecasting to ensure weather patterns. environmental signals are detected early enough for people to mitigate the hazard. Assessing risks and vulnerabilities, and improving drought preparedness can minimize threats and avoid ● Well-run institutions with standard operating expensive post-event relief efforts. An early warning procedures and clear roles and responsibilities system is also required to detect signs of a slow onset to activate appropriate mitigation measures once of drought with a sufficient lead time for local decision- environmental signals are detected. makers to mitigate drought threats, for example, by arranging the provision of emergency food supplies, ● Alert systems to inform potentially affected initiating water conservation programs or introducing households. improved dryland farming initiatives. 1 http://af-dews.demo.iwmi.org:3000/ (accessed on November 2, 2023). Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 1 Objectives remote sensing datasets covering the entire country and to regional forecast modelling. In doing so, it is The AF-DEWS Tool represents an initial stage in the helping to address limitations imposed by the country’s establishment of an end-to-end drought early warning scarce network of surface weather observatories. system in Afghanistan. The objective is to use weather forecasts and near real-time meteorological and Another important aspect of an early warning system environmental data – based on satellite datasets and is the institutional mechanism responsible for drought field observations – to provide decision-makers with monitoring, declaring emergency conditions, and accessible and objective information on the current activating effective response actions in affected status and future prediction of drought conditions areas. In Afghanistan, while the legal framework and in the country. Providing timely, comprehensive mandate for disaster declaration exist, the processes and geographically explicit information facilitates to support them are unclear. For example, there are the targeting of alerts and warning messages many overlapping and fragmented responsibilities to affected communities, and the triggering of among entities mandated to take action before, during appropriate response measures from the relevant and after a drought event. There is, therefore, a critical agencies. A critical step in establishing a sound need to put in place predetermined processes and early warning platform is to strengthen the capacity standard operating procedures to ensure that response of hydrometeorological (hydromet) agencies in measures are timely and triggered based on evidence. Afghanistan towards providing modern and high- The development of the AF-DEWS Tool will help to quality services to their user groups. The AF-DEWS Tool overcome the limited coordination among key relevant provides these agencies with access to ready-to-use institutions. Drought Monitoring and Early Warning using Satellite Remote Sensing Drought in Afghanistan Drought is a recurring phenomenon in Afghanistan, with at Agricultural lands represent 58% of the country’s least one area affected almost every year since 1997, and geographical area, with most areas designated as two or three widespread droughts having occurred every permanent pastures (48%), leaving only 11.8% as arable 10 years for the past half century (FAO 2019). As shown in land (CIA 2019). Total arable land is 6.5 million hectares Figure 1, 22 out of the 34 provinces are chronically prone to (Mha), of which 3.1 Mha is irrigated, and 3.4 Mha is under droughts, with the northern plains and southern plateau rainfed conditions (FAO 2016). The types of agriculture having particularly high drought frequency (Qutbudin et by province are summarized in Table 2. Wheat, rice, al. 2019). Table 1 provides a summary of major drought barley and maize are the main cereal crops grown in events in Afghanistan. The worst event, in 2018, affected the country, with wheat accounting for 80.2% of total more than 13.5 million people. In recent years, drought cereal production. Thus, wheat is the most important has occurred more frequently. crop for the food security of the country (Chabot and Dorosh 2007). Agricultural production contributes to Agroecological Region and Climatic food security in Afghanistan and is largely dependent on irrigated farming, mostly utilizing surface water fed by Condition snowmelt (Pervez et al. 2014). The landscape of Afghanistan is characterized by high Irrigated areas are generally found throughout Afghanistan, mountains with snow-covered peaks, fertile valleys and especially along floodplains of rivers. However, their desert plains. The fertile lowland valleys and desert plains greatest concentrations are in the lowlands of the northern, are located in the northern, western, southwestern and western and southwestern parts of the country. Because southeastern areas, while the highlands are located in the of the high contribution of irrigated crops (> 80%) to total central, eastern and northeastern parts of the country. agricultural production, knowing the spatial distribution The total area of the country is 652,230 km2, with a and year-to-year variability in irrigated areas is imperative population of 34.9 million. to monitoring food security for the country. IWMI - 2 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Figure 1. Drought hazard map derived using the composite Integrated Drought Severity Index (2001–2019). Table 1. Summary of major drought events in Afghanistan. Year Province Affected Total population damages (USD '000) 1969 Pakteka province 48,000 200 1971 Central, Northwest, Northeast, West regions 2000 Kandahar, Hilmand, Nimroz, Zabul, Uruzgan provinces 2,580,000 50 (southwest), Hirat, Farah, Badghes provinces (west), Pakteka, Khost, Ghazni provinces (south), Baghlan, Kunduz, Takhar, Badakhshan provinces (northeast) 2006 Badakhshan, Badghes, Baghlan, Balkh, Bamyan, 1,900,000 Daykundi, Faryab, Ghor, Jawzjan, Kunduz, Samangan, Sar-e-Pul, Takhar, Uruzgan provinces 2008 Kunduz, Balkh, Faryab, Badghes provinces 280,000 2011 Balkh, Samangan, Takhar, Sar-e-Pul, Hirat, Badghes, 1,750,000 142,000 Faryab, Jawzjan, Baghlan, Kunduz, Badakshan, Bamyan, Daykundi, Ghor provinces 2018 Badghes, Daykundi, Hirat, Ghor, Daykundi, Badakhshan, 13,500,000 Farah, Helmand, Kandahar, Zabul, Nangarhar Data source: EM-DAT The International Disaster Database (https://www.emdat.be/ - accessed on November 30, 2020). Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 3 Table 2. Land use statistics on major agricultural systems in Afghanistan. Province Irrigated Rainfed Rangeland Irrigated and Area (km2) area (%) area (%) area (%) rainfed area (%) Badakhshan 1.15 6.58 56.66 7.73 43,391 Badghes 2.75 16.53 69.88 19.28 18,773 Baghlan 5.88 9.71 61.12 15.6 17,059 Balkh 15.33 15.58 21.92 30.92 17,486 Bamyan 3.39 0.87 79.59 4.26 18,023 Daykundi 3.86 0.6 67.19 4.46 13,650 Farah 5.31 0 11.39 5.32 39,373 Faryab 6.03 19.01 47 25.05 22,674 Ghazni 11.19 2.32 63.43 13.51 21,532 Ghor 1.7 2.64 84.18 4.35 38,972 Hilmand 5.24 0.66 8.61 5.91 59,988 Hirat 4.4 8.5 20.34 12.9 65,490 Jawzjan 16.74 8.41 12.72 25.15 8,745 Kabul 14.52 1.28 49.54 15.8 4,684 Kandahar 5.54 2.48 8.35 8.02 55,584 Kapisa 12.2 0.8 66.7 13 1,878 Khost 12.45 0.55 49.51 13 4,105 Kunarha 6.08 0.36 25.89 6.44 4,217 Kunduz 19.35 11.25 7.83 30.6 7,856 Laghman 5.65 0.02 37.44 5.66 3,898 Logar 10.11 3.65 45.25 13.76 4,368 Maydanwardag 5.88 1.95 75.89 7.83 10,791 Nangarhar 14.52 0.2 27.07 14.73 7,371 Nemroz 1.8 0 3.87 1.8 40,853 Noristan 0.79 0.06 61.14 0.85 9,578 Pakteka 7.37 0.63 49.42 8 18,857 Paktya 12.3 1.22 57.39 13.52 5,462 Panjsher 2.49 0.21 84.71 2.7 3,740 Parwan 6.39 2 66.99 8.39 5,577 Samangan 1.97 21.08 60.26 23.05 13,445 Sar-e-Pul 3.55 20.9 68.56 24.45 14,986 Takhar 6.94 34.15 38.96 41.09 12,414 Uruzgan 3.12 0.87 66.93 3.99 13,076 Zabul 7.73 0.56 44.57 8.3 15,833 Sources: Ministry of Agriculture, Irrigation and Livestock/Food and Agriculture Organization of the United Nations [FAO]). The annual average precipitation in Afghanistan varies Crop calendars, such as the one presented in Figure 2, between 50 mm in the southwest to over 1,000 mm provide information on the sowing, growing and harvesting in the east (Aich et al. 2017). The lowland plains in the stages of crops. When assessing drought impacts, they are south of Afghanistan experience extreme seasonal helpful, as they identify key growth periods when drought variations in temperature, from an average winter impacts are likely to be greatest. This is important when (December to February) low of 10 °C to more than 33 using satellite remote sensing data and weather forecast °C in the summer (June to August). Also, the annual information. Broad crop calendars for major crops were potential evapotranspiration is about six times higher provided by the Ministry of Agriculture, Irrigation and than the annual average precipitation, implying that the Livestock (MAIL); because of climatic variability and other direct recharge of precipitation to groundwater is likely factors, there can be a shift in the timing of sowing and to be extremely low (Banks and Soldal 2002; Reeling et harvesting of wheat over the years. al. 2012). As a result, 55–70% of total cultivated land is irrigated for successful production (Qureshi 2002; Tiwari et al. 2020), and 85% of that irrigation comes from Drought Type and Definition surface water, mostly in the form of snowmelt (Pervez et Drought can be defined as a long shortage of surface al. 2014). water and groundwater resources resulting from below IWMI - 4 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Maize Rice Spring wheat Winter grains (wheat and barley) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Sowing Growing Harvesting Figure 2. Crop calendar for Afghanistan. Source: https://ipad.fas.usda.gov/countrysummary/default.aspx?id=AF (accessed on October 26, 2022). average precipitation. Drought can last for months and The Importance of Early Warning years, and can even be detected after 15 days. Drought challenges the regional ecosystem and agriculture and In several countries, the availability of reliable data to brings the local and national economies under serious monitor and predict drought situations is not always risk. sufficient (Pozzi et al. 2013). With the advent of Earth observation (EO) data and atmospheric models, remote The following types of drought are recognized: sensing techniques, combined and verified with surface observations, made the monitoring and provision of early ● Meteorological drought: Deviation from the average warning information to various stakeholders possible. precipitation (rainfall/snowfall). It is usually calculated taking into account the degree of dryness and the An effective end-to-end drought early warning decision duration of the dry period (resulting from below- support system has four building blocks. First, average precipitation). strengthening capacities and capabilities in weather and climate monitoring and forecasting is vital to ● Agricultural drought: Deviation in vegetation health ensure that environmental signals can be detected and crop production. It is calculated by measuring sufficiently early for people to take action to mitigate the amount of moisture in the soil, and the state of the hazard. Second, institutions must be prepared to vegetation and yield. activate and enact appropriate mitigation measures once environmental signals are detected. These include ● Hydrological drought: Deviation from the average level issuing an early warning, releasing assistance to of surface water and groundwater. It is calculated affected households and providing targeted advisory as the decrease in water level below an established services. Therefore, an enabling institutional setting, statistical average level in rivers, lakes, reservoirs and encompassing standard operating procedures and clear aquifers. roles and responsibilities, is essential. Third, potentially affected households must be informed. This means ● Socioeconomic drought: Induced by a combination of that dissemination mechanisms, capable of alerting meteorological, agricultural and hydrological drought. all relevant people of the forthcoming risk, are critical. Socioeconomic drought is calculated using changes in Fourth, People must be aware of what to do in the event economic levels (assets, income flows, poverty levels) of an anticipated disaster or shock, such as drought. and social factors (out-migration, adverse coping Therefore, preparedness at the community level is key to strategies). reducing disaster impacts. Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 5 Remote Sensing and Drought on the relationship between vegetation indices and Monitoring Land Surface Temperature (LST), additional indices were developed to assess agricultural drought. These Recent technological advances in EO satellite data have include the Temperature Vegetation Dryness Index (TVDI) helped to address the complexities of decision making (Sandholt et al. 2002) and Vegetation Supply Water Index around environmental issues. EO-based satellite datasets (VSWI) (Rebel et al. 2012). Meanwhile, indices such as provide the opportunity for near real-time drought the Vegetation Health Index (VHI) (Kogan 1997) have monitoring across a time period of two to three decades been developed by combining VCI and TCI using a linear using satellite missions, such as National Oceanic and weighted method. Other helpful composite indices are Atmospheric Administration (NOAA) Advanced Very High the Composite Drought Index (CDI), which uses indicators Resolution Radiometer (AVHRR), Moderate Resolution such as precipitation, evapotranspiration and land surface Imaging Spectroradiometer (MODIS) Terra and Aqua, temperature, and the Surface Water Supply Index (SWSI), Landsat, and European Space Agency (ESA) Copernicus. which combines information on precipitation, streamflow, These large data sources can help in mapping and snow cover and storage. characterizing the onset, progression, extent and severity of drought over both space and time (Kogan 1997). Several Multiple indices derived from various indicator indicators are relevant for monitoring and assessing combinations have been used to quantify and drought using satellite data. These include rainfall, understand drought in different parts of the world. vegetation condition, soil moisture, evapotranspiration However, because drought is caused by many and many more. The presence of drought is made drivers, such as precipitation, soil moisture and apparent through the reduction of different indicator evapotranspiration, it is important to consider indicators values at specific times during the crop season and may for each of these relevant processes where datasets continue for more than one season. are available (Goodwell et al. 2018; Alahacoon and Edirisinghe 2022). The majority of existing drought The use of remote sensing data to monitor and monitoring programs rely on just one or two indicators, evaluate drought conditions over space and time is a limited approach that can undermine the accuracy well established. Most of the indices used are based of drought prediction. Considering the complexity of on long-term atmospheric and vegetation information drought, the best approach is to use multiple indicators (Martínez-Fernández et al. 2015; Cao et al. 2019), and and composite indices across the season to ensure include the Vegetation Condition Index (VCI), Temperature end users can accurately characterize the extent and Condition Index (TCI), Precipitation Condition Index severity of drought (Amarnath et al. 2019). This will (PCI) (Hao et al. 2015) and Soil Moisture Condition help to underpin the early warning process and drought Index (SMCI) (Kogan 1995; Bhuiyan et al. 2006). Based preparedness and mitigation measures. Selection of Drought Indicators to Define the Types of Drought The following section provides details of the satellite large swathes of land. Different parameters are useful in indices that are currently included in the AF-DEWS assessing the onset and magnitude of the different types Tool. The aim is to explain the use of, and parameters of drought: monitored by, each index. ● Meteorological drought – rainfall anomaly, duration of Monitoring Different Types of Drought dry spells. (Meteorological, Agricultural, ● Agricultural drought – progression of sowing, Hydrological and Socioeconomic) vegetation health anomalies, vegetation density, vegetation growth, soil moisture content. The parameters utilized by the AF-DEWS Tool to monitor droughts rely on near real-time satellite remote ● Hydrological drought – levels of lakes and reservoirs, sensing techniques. Satellite-based remote sensing of snow cover, streamflow and groundwater level. environmental parameters has been widely adopted globally due to its extensive spatial coverage, and regular ● Socioeconomic drought – water storage resilience, return periods allow for the frequent monitoring of inflow-demand reliability. IWMI - 6 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Of the various drought-related indices, each has hydrological and agricultural droughts, indices such as its advantages and limitations when it comes to Streamflow Drought Index (SDI), Land Surface Water Index evaluating drought. The Standardized Precipitation (LSWI), Snow Condition Index (SCI), Vegetation Health Index (SPI) (McKee et al. 1993) is simple for monitoring Index (VHI) and IDSI (Amarnath et al. 2021) are widely meteorological drought, relying solely on precipitation used. data. The Moisture Adequacy Index (MAI) measures the ratio of actual and potential evapotranspiration The parameters available in the AF-DEWS Tool for (Thornthwaite and Mather 1955). The Consecutive Dry monitoring the different types of drought are summarized Days (CDD) index (Nastos and Zerefos 2009) uses in Table 3, together with basic information on the source threshold values of specific rainfall units to characterize of data, temporal resolution, data period, and spatial and the type of drought and its severity. Similarly, for temporal resolution. Table 3. Summary of the indices available to monitor the different types of drought in the AF-DEWS Tool. Category Index Datasets Data period Spatial and Temporal Source temporal resolution resolution Meteorological Precipitation CHIRPS 1981–2022 5 km Daily GEEa drought Precipitation CHIRPS 1981–2022 5 km Daily -Do- anomaly Dry spell CHIRPS 1981–2022 5 km Daily -Do- Standardized CHIRPS 1981–2022 5 km Daily -Do- Precipitation Index (SPI) Precipitation MODIS 2001–2022 500 m Daily GEEb Condition Index (PCI) Agricultural Normalized MODIS 2001–2022 500 m Daily GEEc drought Difference Vegetation Index (NDVI) NDVI monthly MODIS 2001–2022 500 m 16 Day GEEd anomaly Vegetation MODIS 2001–2022 500 m Daily GEEe Condition Index (VCI) Temperature MODIS 2001–2022 500 m 8 Day GEEf Condition Index (TCI) Vegetation MODIS 2001–2022 500 m Daily GEEg Health GEEh Index (VHI) Moisture MODIS 2001–2022 500 m 8 Day GEEi Adequacy Index (MAI) Soil Moisture FLDAS, SMAP 2001–2022 10 km 10 day GEEj, k Condition Index (SMCI) Soil Water ASCAT 2001–2022 10 km 10 day Google Anomaly Cloud Drought Index Storage (SWADI) Integrated MODIS, FLDAS 2001–2022 250 m 8 day GEE and Drought and CHIRPS IWMI Severity Index (IDSI) Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 7 Table 3. Summary of the indices available to monitor the different types of drought in the AF-DEWS Tool. (Continued) Category Index Datasets Data period Spatial and Temporal Source temporal resolution resolution Hydrological Snow cover MODIS 2001–2022 500 m Daily GEEl drought Snow cover MODIS 2001–2022 500 m Daily GEEm anomaly Normalized MODIS 2001–2022 500 m Daily GEEn Difference Water Index (NDWI) Streamflow Observed 2001–2019 Station wise Daily - Drought data Index (SDI) Surface Water Observed + 2001–2019 Station wise Daily GEE Supply Index satellite data (SWSI) Drought Gross MODIS 2001–2022 500 m 8 day GEEo impact Primary Productivity (GPP) Drought hazard MODIS 2001–2019 500 m - GEE Asset Drought WorldPop 2018 100 m - GEE Asset exposure Sources: a https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY b https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1 c https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD09GA_006_NDVI d https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1 e https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1 f https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2 g https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1 h https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2 I https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD16A2 j https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP_soil_moisture k https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001 l https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD10A1 m https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD10A1 n https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD09GA_006_NDWI o https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A2H Notes: CHIRPS - Climate Hazards Group InfraRed Precipitation with Station data; FLDAS - Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System; SMAP – Soil Moisture Active Passive; IWMI – International Water Management Institute; ASCAT – Advanced Scatterometer; NDWI – Normalized Difference Water Index. Seasonal Weather Forecasts for Drought Early (CFSv2) model (Chattopadhyay et al. 2018). The forecasts, Warning automatically incorporated into the AF-DEWS Tool, include precipitation and minimum/maximum temperature. IMD In addition to indices to monitor the different types of provides rainfall and temperature datasets with a lead time drought in near real-time, the AF-DEWS Tool includes a of 31 days and a spatial resolution of 0.5 degrees. weather forecast component. Extended range forecasts2 are made available by the India Meteorological Department A brief description of the datasets used for the weather (IMD) through the Ensemble Prediction System (EPS) model. forecast component of the AF-DEWS Tool is described in This is based on the Climate Forecast System Version 2 Table 4. 2 Defined by the World Meteorological Organization (WMO) as: short range (from 12 hours to 72 hours); medium range (from 72 hours to 240 hours); extended range (from 10 days to 30 days); long range (from 30 days to 2 years), including seasonal outlook (loosely defined as a three-month period in the northern hemisphere, but varying in the tropical areas) - https://www.ecmwf.int/en/forecasts/documentation-and-support/extended-range-forecasts (accessed on November 9, 2022). IWMI - 8 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Table 4. Summary of the extended range forecast parameters obtained from the Extended Range Prediction and Analysis System (ERPAS) which are made available in the AF-DEWS Tool. Category Index Datasets Data period Spatial Temporal Source resolution resolution Weather Precipitation ERPAS 2020 – To date 50 km Daily IMD forecasts Maximum ERPAS 2020 – To date 100 km Daily IMD temperature Minimum ERPAS 2020 – To date 100 km Daily IMD temperature Meteorological Drought (viii) Temperature Condition Index (TCI): used to determine the stress on vegetation caused by Meteorological drought is defined as a deficiency of temperature and excessive wetness. Conditions are precipitation over a certain period (Stagge et al. 2015). As estimated relative to the maximum and minimum such, it is measured as the anomaly (or deviation) relative temperatures and modified to reflect different to what would be expected over the period (norm). vegetation responses to temperature. Meteorological drought is usually initiated by a long dry spell – defined by the World Meteorological Organization Agricultural Drought (WMO) as a period of abnormally dry weather lasting for at least five days with daily precipitation less than 1 mm Soil moisture content and plant growth are commonly used (Huang et al. 2015; Baig et al. 2020). to determine agricultural drought, along with secondary parameters such as precipitation and/or evapotranspiration Within the AF-DEWS Tool, several indices are included (Feng et al. 2019; Modanesi et al. 2020). The following for the Afghanistan Meteorological Department (AMD) to indices for monitoring plant growth and vegetation track meteorological drought: conditions are included in the AF-DEWS Tool: (i) Average daily temperature (T). (i) Normalized Difference Vegetation Index (NDVI) and NDVI anomaly: NDVI quantifies vegetation density (ii) Average daily precipitation (P). and health. The NDVI anomaly captures the state of vegetated areas relative to average conditions for a (iii) Precipitation anomaly: percentage of normal specific time range (16 days or more). precipitation over a certain period. (ii) Enhanced Vegetation Index (EVI) and EVI anomaly: (iv) Temperature anomaly: percentage of normal similar to NDVI, EVI can be used to quantify temperature over a certain period. vegetation greenness (density and health). However, EVI corrects for some atmospheric conditions and (v) Dry spell: duration of abnormally dry weather canopy background noise, and is more sensitive lasting for at least five days with daily precipitation in areas with dense vegetation. The EVI anomaly less than 1 mm (as per Nastos and Zerefos 2009). captures the state of vegetated areas relative to average conditions for a specific range of time (16 (vi) Standardized Precipitation Index (SPI): widely used days or more). to characterize meteorological drought on a range of timescales. It quantifies observed precipitation as a (iii) Vegetation Condition Index (VCI): this facilitates standardized departure from a selected probability monitoring vegetation vigor versus climatic distribution function of precipitation. variations. The range of VCI is 0–1, reflecting changes in vegetation conditions from the most (vii) Precipitation Condition Index (PCI): used to unfavorable to the conditions for optimal growth. normalize precipitation data over a certain period. Under meteorological drought conditions, the (iv) Vegetation Health Index (VHI): assesses the state of value of PCI is close to 0 (zero), while under wet vegetation. It is often used to monitor and identify conditions, the value of PCI is close to 1. the impacts of agricultural drought. Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 9 (v) Moisture Adequacy Index (MAI): provides (zero) to 1, reflecting changes in the fraction of snow information on the moisture status of the soil cover from extremely low to very high. relative to plant water needs. It is calculated as the ratio of actual evapotranspiration to potential (iii) Normalized Difference Water Index (NDWI): estimates evapotranspiration. the leaf water content at canopy level, so is sensitive to drought conditions affecting plant vegetative (vi) Soil Moisture Condition Index (SMCI): quantifies processes. the moisture within the uppermost soil layer. The index values range from 0 to 1, with 0 indicating (iv) Land Surface Water Index (LSWI) and Modified extreme dry conditions and 1 indicating extreme wet Land Surface Water Index (MLSWI): Very similar to conditions. the NDWI methodology, but uses two shortwave- infrared channels to monitor the water content within (vii) Soil Water Anomaly Drought Index (SWADI): the vegetation canopy. Changes observed in the assesses the moisture of the soil against the long- vegetation canopy help to identify periods of drought term average. stress. (viii) Evaporative Condition Index (ECI): based on the Socioeconomic drought current evaporation condition, with reference to historical condition. Actual evapotranspiration (ET) Socioeconomic drought evaluates the impacts of drought figures were used to calculate the ECI. (meteorological, agricultural and hydrological) on the supply and demand of economic goods such as fruits, (ix) Integrated Drought Severity Index (IDSI): this vegetables, grains and meat (WMO 2021). Socioeconomic composite index uses multiple indicators – drought occurs when the demand for a particular precipitation (input to the system), soil moisture commodity exceeds supply as a result of a weather- (storage within the system), actual ET (loss from related deficit in the water resource (Zisopoulou and the system) and VCI (vegetative response of the Panagoulia 2021). The AF-DEWS Tool incorporates some system). IDSI overcomes the drawback of using indices for assessing socioeconomic drought, including a single indicator/index to define drought and is the following: reliable for assessing the impacts of agricultural drought. (i) Gross Primary Productivity (GPP): this index measures changes in plant productivity, which is directly related (x) Percent crop cover (PCC): this drought indicator to water availability. This index is derived from MODIS uses NDVI with a specific threshold to map the satellite data, which are available at the global level. current vegetation extent. It provides an eight-day mean GPP at 1 km spatial resolution for the whole of Afghanistan. The index Hydrological Drought was used in the development of the AF-DEWS Tool to validate the IDSI and identify drought years between Over a prolonged period, meteorological drought 2001 and 2019. affects surface and subsurface water supply, reducing streamflow, snow cover, groundwater, and reservoir and (ii) Drought hazard: IDSI was used to identify drought lake levels (Van Loon 2015). This leads to a hydrological years and quantify the severity between 2001 and drought that can persist long after the meteorological 2019, with a view to developing a drought hazard drought has ended. Several indices are aimed at map for Afghanistan. The map was prepared by comprehensively characterizing drought impacts on the overlaying each of the eight-day IDSI maps showing hydrological cycle. Each of these indices requires different areas severely affected by drought. Seasonal to annual variables as input data for their formulas. The hydrological drought hazard maps are available in the AF-DEWS drought indices included in the AF-DEWS Tool are given Tool. below: (iii) Drought exposure: based on the Oak Ridge National (i) Snow cover (and anomaly): represents the area Laboratory LandScan dataset. This is an estimate covered by snow during a specific period and its for global population distribution data, covering difference from the long-term norm. Afghanistan with a 1 km spatial resolution. This index is fundamental to readily estimating the number of (ii) Snow Condition Index (SCI): used to normalize snow people affected by a drought event across a particular cover over a certain period. The SCI varies from 0 at-risk district or region. IWMI - 10 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Key Indicators and Thresholds This section provides an overview of the correlation between represents a very severe drought, then the thresholds different meteorological and agricultural indices and for a very severe drought are z2 and z3, and the return agricultural yield, as provided by GoIRA through the National period for a moderate drought is one in (1/(p3-p2)). For Statistics and Information Authority (NSIA) yearbook. example, if p2 and p3 are 0.15 and 0.35, respectively, Relating data from satellite-based indices to agricultural then the return period for a moderate drought is one productivity is important as it can be used to forecast where in 5 years. people may become food insecure – for directing relief efforts as well as informing import requirements. Statistical metrics ● The probability of a drought indicator falling above z3 were used to evaluate how different indicators correlate is (1-p3). If this represents an extremely wet year, then with crop yield. This correlation has been used to (1) the threshold for an extremely wet year is when the identify which index performs better, and (2) define precise drought indicator values fall above z3, and the return thresholds to identify drought conditions (and their different period is one in (1/(1-p3)) years. For example, if p3 is levels of severity). Importantly, the choice of indicators/ 0.95, then the return period of an extremely wet year indices is based on the specific characteristics of droughts is one in 20 years. most closely associated with the impacts of concern to the stakeholders. The drought thresholds for 3-month SPI, PCC, VHI and IDSI based on the estimated CDF are given in Annex 1. The Pearson correlation coefficient (R) between various drought indices (3-month SPI - precipitation over a specific 3-month period, PCC, VHI and IDSI) and yield was tested. Determination of Drought This exercise allowed the definition of a selection of indices Having explained how drought indices were selected and to be used preferably to identify and monitor different types drought thresholds were determined, this section explains of drought (meteorological, agricultural and hydrological). the procedure used to quantify the presence and severity These indices, defined as key indicators, have been selected of drought for a specific month within a given year. The because of their statistical performance in identifying past user will regularly monitor the weather forecasts, and drought events, as revealed in the analyses conducted by follow how the crop season progresses as the wet season IWMI and provided in Table 1. begins in October. As previously explained, the complexity of drought cannot be captured using just a single To define drought thresholds, cumulative distribution indicator or index from a specific month, but requires a functions were used. The cumulative distribution function more comprehensive understanding of various drought (CDF) describes the probability that a continuous random parameters, and supporting field inputs from the relevant variable, with a given probability distribution, will be agencies (Jiao et al. 2021). Table 5 provides a simple found at a value less than or equal to a given value. Thus, matrix for assessment of drought. the median value from the CDF will be 50% and the probability will not exceed a threshold value. Steps in the Determination of Drought The probabilities and percentiles are interpreted as The following steps are suggested for determining follows: drought: ● The probability of a drought indicator value falling Step 1: The ‘Mandatory Indices’, namely SPI or snow cover, below z1 is p1. If the occurrence of this event represents first evaluate whether the drought trigger has been set an extreme drought, then the threshold value for an off. For example, if 3-month SPI for a specific district or extreme drought is z1, and the return period for an province has values below -1.73 for December, this will extreme drought is 1 in (1/p1). For example, if p1 is indicate that an extreme drought is occurring. Similarly, 0.05, then the return period for an extreme drought is other values will indicate different outcomes. In addition one in 20 years. to SPI and snow cover, indices for rainfall deficit and dry spells can also be used to help determine the presence ● The probability of an indicator value falling between of meteorological drought. The monitoring of SPI or z1 and z2 is (p2-p1). If the occurrence of this event snow cover should continue during the entire wet season represents a very severe drought, then the thresholds (December to April) to continuously capture the evolving for a very severe drought are z1 and z2, and the meteorological conditions and, eventually, drought status. return period for a very severe drought is one in (1/ (p2-p1)). For example, if p1 and p2 are 0.05 and 0.15, Step 2: If the first drought trigger is set off during step respectively, then the return period for a very severe 1, the ‘Impact Indices’ (PCC, VHI and IDSI) should be drought is one in 10 years. examined to assess the severity of the drought, potential impacts and required actions. The relevant agencies ● The probability of an indicator value falling between should consider any three of the five types of impact z2 and z3 is (p3-p2). If the occurrence of this event indicators. Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 11 Table 5. Steps for determining drought. Monitoring Key variable Indicator/indices Thresholds Use of the period indicator December- Rainfall (Met) Standardized ≤ 1.73 ED Early Warning April Precipitation -1.73 to -1.20 SD (December) Index (SPI) -1.20 to -0.68 MD > -0.68 No drought January- Snow cover Percent snow < 5% ED Early Warning February (Hydro) cover (PSC) 5% - 10% SD (January) 10% - 15% MD > 15% No drought January-April Agri cover Percent crop < 5% ED Early Warning (Agri) cover (PCC) 5 - 10% SD (February) 10 - 15% MD > 15% No drought February-April Vegetation Vegetation < 20% ED Validate and condition Health Index 20 - 30% SD assess (Agri.) (VHI) 30 - 40% MD agricultural > 40% No drought drought (March) April-May Composite Integrated < 20% ED Initiate Early index Drought 20 - 25% SD Action and Severity Index 25 - 30% MD Early Finance (IDSI) > 30% No drought mechanisms (April) Note: ED - Extreme drought; SD - Severe drought; MD - Moderate drought. Rule-based Drought Declaration ● The third step is to look at the districts that are critically affected by drought using the IDSI composite The step-by-step procedure for monitoring indicators index for April and May to define the overall drought and determining whether to issue a drought impacts. declaration either at the national or provincial level is given below. ● Drawing on data showing historical variation in drought conditions and the deviation of indices ● The first step is to look at the Mandatory Indices from the norm (recognized in district/province-level – SPI and snow cover – for December, January triggers), a drought bulletin can now be prepared and February to determine if meteorological or giving early warning and to initiate preparedness hydrological droughts are occurring. The SPI or snow measures. cover monitoring should continue during the entire wet season (December to April) to continuously ● If at least two of the impact indices are in the capture the evolving meteorological conditions and, ‘extreme’ category, this indicates the presence of eventually, drought status. an ‘extreme drought’; if two of the three chosen impact indices are in the ‘moderate’ or ‘severe’ ● If a drought is identified, the second step is to look classes, this signifies a moderate or severe at two or three impact indices/indicators – such as drought, respectively. crop cover and vegetation health. Given the seasonal growth patterns, indices of agricultural drought such ● The drought bulletin should be circulated for a rapid as crop cover and vegetation health (i.e., VHI, NDVI, ‘ground-truthing’ survey at the provincial level to etc.) should be monitored from February/March validate the drought severity. onwards because vegetation is dormant in most of the country (winter vegetation pause) prior to that. Once the severity levels of drought are Impact indices will, therefore, be used to assess determined through these processes, a Drought whether an agricultural drought is emerging and, if so, Declaration Report can be produced by high-level to determine the severity. policymakers. IWMI - 12 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Threshold Classification for Key Drought the CDF analysis (Annex 1), thresholds for the drought Indices indices were set and classified into four classes. Three classes identify the different intensities of drought as A threshold classification for five drought indices was ‘extreme’, ‘severe’ and ‘moderate’ while the remaining developed by analyzing the frequency distribution of class signifies ‘no drought’. The individual drought classes drought over 20 years throughout the country. Based on are illustrated in Table 6. Table 6. Thresholds for key drought indices and their classification into four drought classes - ED - extreme drought, SD - severe drought, MD - moderate drought and ND - no drought. Indicator/ Drought Explanation and its possible impact Drought intensity indices category Standardized ED Extremely dry conditions due to the lack of rainfall 1 in 20 years Precipitation over several months Meteorological drought Index (SPI) SD Severe dry conditions due to the lack of rainfall 1 in 10 years over several months Meteorological drought MD Moderately dry conditions with below average 1 in 5 years rainfall during the rainy season Meteorological drought ND Good rainfall explains healthy vegetation condition No drought Percent Snow ED Extreme drought due to the lack of snow cover 1 in 20 years Cover (PSC) accumulation over several months, impacting Hydrological drought agriculture, energy and livelihoods SD Severe drought due to the lack of snow cover 1 in 10 years accumulation over several months with Hydrological drought widespread impacts across agricultural systems MD Moderate drought impact on the rainfed or 1 in 5 years rangeland areas due to a shortage in snow cover Hydrological drought ND Good snow cover accumulation explains available No drought soil moisture and healthy vegetation condition Percent Crop ED With the lack of rainfall and snow cover over 1 in 20 years Cover (PCC) several months, there is an exceptional impact Agricultural drought on the agricultural area, which will result in food insecurity among smallholder farmers and fodder shortages SD Major crop/pasture losses with widespread water 1 in 10 years shortages or restrictions Agricultural drought MD Certain crop or pasture areas will have a lack of 1 in 5 years water availability or stress due to an unseasonal Agricultural drought reduction in rainfall or snow cover ND Good crop cover explains an optimal condition No drought which results in healthy crop production and increased incomes among smallholder farmers Vegetation ED Abnormal dry conditions in the agricultural areas 1 in 20 years Health Index due to a lack of rainfall/high temperature Agricultural drought (VHI) including water shortages in reservoirs and streams, and poor access to the irrigated system SD Severe drought due to the lack of snow cover 1 in 10 years accumulation over several months with widespread Agricultural drought impacts across the region MD Moderate drought due to unseasonal rainfall or 1 in 5 years water shortages or reduced water availability Agricultural drought across the agricultural system ND Healthy vegetation conditions are favorable with the availability of water and/or soil moisture No drought Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 13 Table 6. Thresholds for key drought indices and their classification into four drought classes - ED - extreme drought, SD - severe drought, MD - moderate drought and ND - no drought. (continued) Indicator/ Drought Explanation and its possible impact Drought intensity indices category Integrated ED Exceptional and widespread crop/pasture losses, 1 in 20 years Drought Severity including shortages of water in reservoirs, streams Agricultural drought Index (IDSI) and agro-wells SD Major crop/pasture losses with widespread water 1 in 10 years shortages or restrictions Agricultural drought MD Crop or pasture losses due to unseasonal rainfall 1 in 5 years or water shortages or reduced water availability. Agricultural drought Some damage to crops and pastures with unseasonal and delayed rainfall at different stages of crop growth as well as water shortages ND Conditions are favorable with optimal vegetation No drought and the availability of water and/or soil moisture Comparison of National- and When analyzing thresholds at the provincial level, the Provincial-level Thresholds trigger values vary significantly. For example, VHI drought classes for ED are at various figures below 20%, the The following section details the results of an analysis figures for SD range from 20 to 30%, and the figures for carried out to evaluate the appropriateness of MD range from 30 to 40%. Such variability results from national versus provincial thresholds. The country's the complex climatic and agroecological conditions wide climatic and geographic variability makes it across Afghanistan. For PCC, the desert plain region of appropriate to adopt localized, provincial-level Nemroz shows minimum values of 0.03% (ED), 0.04% thresholds rather than applying one set of thresholds (SD) and 0.12% (MD), whereas the arable farming region to the entire country. However, applying provincial- of Badghes has values of 33.83% (ED), 35.26% (SD) and level thresholds increases the overall complexity of 36.32% (MD). It is estimated that PCC is 1,000 times the monitoring. The assessment, therefore, aimed larger in Badghes than in Nemroz province. The same to guide decision-makers on when: (1) a single set scenario was observed for PSC, with the absence of snow of thresholds for the various indicators at a national cover in Nemroz, and Badakhshan showing 55%, 61% and level would be appropriate, or (2) based on past 63% for ED, SD and MD, respectively. observations, statistics indicate that a provincial-level approach would be more appropriate. The details In summary, GoIRA should utilize key drought indices of provincial-level thresholds are available in Annex and country-level thresholds during the initial phases 2 (3-month SPI, PSC, PCC, VHI and IDSI). Table 7 of establishing drought monitoring in the country using provides details of thresholds for these five indicators the AF-DEWS Tool. It can be noted that all drought for selected provinces. Figure 3 provides a snapshot indices – the 3-month SPI, PSC, PCC, VHI and IDSI – were of the country and national thresholds for the drought normalized and the CDF method was applied to develop year 2018. The national-level threshold was calculated drought trigger values. In the long term, given the varied by taking the average of indicator values for all agroclimatic conditions across the country, it would provinces. This yielded the SPI index values of -1.73 for be appropriate to utilize provincial-level thresholds to extreme drought (ED), -1.20 for severe drought (SD) enhance the reliability of drought forecasting from early and -0.68 for moderate drought (MD). These trigger warning to drought declaration. This requires a greater values form the basis for defining meteorological level of institutional coordination, as well as validating droughts using a 3-month SPI at the country level. field observations from district to provincial level. IWMI - 14 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 15 Table 7. Provincial thresholds for five indicators for selected provinces (see Annex 2 for more details). SPI (December) PSC (January) PCC (February) VHI (March) IDSI (April) Province ED SD MD ED SD MD ED SD MD ED SD MD ED SD MD Badakhshan -1.75 -1.25 -0.94 55.44 61.04 63.22 1.91 3.30 4.48 0.35 0.37 0.38 0.35 0.35 0.36 Badghes -2.40 -1.04 -0.52 0.50 1.01 2.33 33.83 35.26 36.32 0.16 0.19 0.32 0.17 0.19 0.29 Balkh -1.91 -0.94 -0.57 0.87 1.04 1.37 11.40 12.57 19.12 0.19 0.20 0.34 0.17 0.18 0.29 Bamyan -1.89 -1.06 -0.52 30.71 35.90 45.62 0.05 0.14 0.20 0.38 0.42 0.44 0.32 0.36 0.39 Daykundi -1.45 -1.17 -1.04 7.08 10.14 17.22 0.32 0.97 1.95 0.43 0.43 0.45 0.37 0.40 0.41 Farah -1.81 -0.86 -0.28 0.01 0.06 0.12 2.47 2.57 2.76 0.19 0.22 0.25 0.17 0.21 0.26 Faryab -1.85 -1.15 -0.38 2.88 3.32 5.94 18.36 18.97 19.31 0.17 0.20 0.27 0.16 0.19 0.28 Hilmand -1.49 -0.86 -0.66 0.08 0.09 0.14 3.94 4.32 4.83 0.22 0.23 0.30 0.16 0.21 0.29 Hirat -2.29 -0.92 -0.24 0.43 1.00 1.46 6.95 8.02 9.08 0.24 0.25 0.28 0.21 0.26 0.28 Jawzjan -2.15 -0.93 -0.57 0.02 0.03 0.07 12.91 13.98 15.64 0.19 0.22 0.27 0.07 0.11 0.20 Kunduz -1.60 -1.01 -0.46 0.01 0.01 0.02 26.09 29.13 33.24 0.22 0.31 0.37 0.22 0.23 0.34 Nemroz -1.22 -0.92 -0.62 0.00 0.00 0.00 0.03 0.04 0.12 0.18 0.20 0.26 0.24 0.24 0.34 Sar-e-Pul -1.67 -1.04 -0.55 12.41 14.17 18.06 20.20 21.11 24.79 0.27 0.28 0.37 0.25 0.29 0.36 Takhar -1.89 -1.23 -0.64 8.15 10.08 11.74 12.14 16.73 25.25 0.19 0.28 0.32 0.24 0.25 0.41 IWMI - 16 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Figure 3. Three-month SPI, VHI and IDSI maps based on thresholds at the country level (top) and provincial level (bottom) for 2018. Architecture of the AF-DEWS Tool In recent decades, a variety of EO satellites and global satellite data suited to multiple land and water models have generated a large volume of geospatial data resources management applications – used by that are freely available in the public domain and can be researchers, nonprofit organizations and government used to develop science-based knowledge products and agencies. tools to assist decision-making. However, making full use of this asset with standard computing technology The AF-DEWS Tool was developed using cloud services calls for an innovative approach to data access, storage through GEE, which offers high security standards, and processing. This is now being achieved through easy access and straightforward maintenance. It cloud infrastructure and platforms such as Google Cloud incorporates data sources such as weather Platform (GCP), Amazon Web Services, and Microsoft information and near real-time satellite data; a Azure. Thus, the cloud platform and related systems offer pre-configured drought algorithm that includes enormous opportunities for scaling and sustainability of thresholds; and robust data analytics tools for rapid projects, even with limited resources. drought monitoring and early warning, supporting drought preparedness and response strategies. Figure GEE, which runs on GCP, is unique in that it offers 4 summarizes the AF-DEWS cloud-based drought early free access to a large repository of near real-time warning decision support tool. Figure 4. Conceptual diagram of the AF-DEWS Tool, which uses Google cloud services. The AF-DEWS Tool architecture consists of three the GEE server, where it processes user requests and components (Figure 5): forms that into appropriate GEE API requests. This server hosts all of the programming code, such as a. Client: The web browser on the client side that renders javascript (both on the server side and client side), the web application received from the server. Hyper Text Markup Language (HTML) and Cascading Style Sheets (CSS). b. Node.js server: The server that handles and processes requests from users. Since the application is based on c. GEE servers: These are external servers for the GEE Application Programming Interfaces (API), where application. Various data processing is chained most of the processing happens, the node.js server through the javascript API. To access these servers, also acts as an intermediary between the client and proper authentication is required. Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 17 note.js server GEE authentication GEE Client GEE JS API to request GEE layers servers geoJSON files for map Figure 5. The system architecture of the AF-DEWS Tool. The AF-DEWS Tool has powerful visualization capabilities Through easy-to-use interfaces, users can filter large that facilitate the rapid display of drought indices collections of images to quickly select areas of interest, through maps, charts and other statistical data (Figure choose drought indices, and compute statistics through 6). These alert users to the current weather situation space and time without the need to download derived and any need to instigate early warning procedures. products. Figure 6. Screenshot of the AF-DEWS Tool displaying the NDVI map and time series data. IWMI - 18 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Evaluation of Two Decades of Drought Events in Afghanistan The focus is on the 2018 drought in Afghanistan because it ● The assessment of the 2018 drought using the was one of the most severe during the last two decades. AF-DEWS Tool correlated with the IPC analysis of It spanned 22 out of the 34 provinces and directly affected September 2018, which highlighted that: 13.5 million two-thirds of the population. The impacts were felt across people were facing a ‘crisis’ or worse levels of food the country’s agriculture, livestock, irrigation, water, insecurity, of which 9.8 million people (43.6% of the health and economic sectors. At least 300,000 people rural population) were estimated to be in a ‘crisis’ were internally displaced due to drought, and 13.5 million while 3.6 million were facing ‘emergency’ levels people faced a ‘crisis’ or worse levels of food insecurity nationwide. This was six million more than in 2017 in September 2018 (according to the Integrated Food (FEWS NET). Security Phase Classification [IPC] Acute Food Insecurity Classification in September 2018).3 Meteorological Drought Assessment Using the AF-DEWS Tool to assess the 2018 drought in Rainfall Anomaly Afghanistan Rainfall anomalies are known to have deleterious ● Using LandScan gridded population data, the AF- impacts on agricultural yields (Modanesi et al. 2020). DEWS Tool successfully identified that the 2018 The Rainfall Anomaly Index calculates the deviation drought affected more than 13 million people. from the long-term average, whether positive or negative. It is a comparison of current rainfall variation ● The tool identified that 22 out of the 34 provinces were from the historical period. The maps in Figure 7 show affected by drought, with 14 provinces falling under rainfall anomalies in units of mm/month for January the severe to extreme drought category. 2018 and 2019, based on precipitation estimates from the CHIRPS dataset. The period used for computing the ● The tool helps in mapping the drought frequency climatology was 2000–2019. Blue areas in Figure 7(b) and severity using the predetermined thresholds for indicate where precipitation is above the long-term individual drought indicators, such as SPI and IDSI. normal for the month, and the red areas in Figure 7(a) indicate where precipitation is below the normal. Total ● A comprehensive assessment was carried out rainfall during the period December–January (Table to identify the type of drought (meteorological, 8) indicates that there is a 35% deficit in 2017–2018, hydrological and agricultural) using various drought and a more than 50% excess during 2018–2019. The indices; this quantified the impacts on the population December 2017–January 2018 period was the third and agricultural systems. driest for the past 20 years. (a) (b) Figure 7. Rainfall anomaly map for (a) January 2018 (drought), and (b) January 2019 (normal weather conditions). 3 https://www.ipcinfo.org/ipc-country-analysis/details-map/en/c/1151733/?iso3=AFG Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 19 Table 8. Accumulated rainfall obtained from CHIRPS gridded rainfall products (December–January) for the period 2000–2019 for the whole of Afghanistan. Year Rainfall (December–January) (mm) 2000–2001 50.57 2001–2002 80.28 2002–2003 97.07 2003–2004 77.23 2004–2005 97.48 2005–2006 104.51 2006–2007 102.22 2007–2008 81.90 2008–2009 112.77 2009–2010 106.94 2010–2011 103.50 2011–2012 91.48 2012–2013 96.35 2013–2014 64.87 2014-2015 118.03 2015-2016 45.76 2016–2017 165.00 2017–2018 62.66 2018–2019 150.24 Standardized Precipitation Index (SPI) lowest 3-month SPI values in Table 9 reveal that most provinces experienced droughts (SPI <= -1.75) during the The 3-month SPI values were calculated from December drought period. Table 9 further highlights the difference to March to characterize meteorological drought and its in 3-month SPI values in both December and February in severity. The analysis was extended from December to drought and non-drought years. April to coincide with the rainfall season in Afghanistan. The key finding of this analysis was that changes in rainfall The maps shown in Figure 8 were generated using indicate drought in two ways. First, it indicates very low the drought thresholds calculated using the CDF to 3-month SPI values as a result of decreased rainfall due understand the spatial distribution of the drought to the delayed onset of the rainy season. For the 2018 based on the 3-month SPI values (i.e., December SPI drought, the lowest 3-month SPI values were observed in values include rainfall information between October and December 2017. This signifies a decrease in the rainfall December) at the country level. Results of the 3-month required for the early stages of crop cultivation. Second, SPI show that the drought in 2018 affected all provinces it indicates a decrease in 3-month SPI values during the of Afghanistan with varying degrees of severity, and months when maximum rainfall is expected (February more than 75% of the provinces were in the extreme and March). For example, low 3-month SPI values were drought category. However, no province was affected by observed for February and March in Afghanistan’s 2001 the drought in 2009, and northern, northeastern and and 2008 drought years. Since positive 3-month SPI values northwestern provinces were mostly affected by the were observed in both December 2018 and February 2019, drought in 2008. it was possible to accurately predict 2019 as a drought- free year (Table 9). These indicators made it possible to The classification scheme used at the provincial level accurately estimate drought or non-drought conditions. shows how drought can vary within a province. Figure 9 clearly shows the changes in drought severity within the Table 9 shows the changes in 3-month SPI values at the province at the district level. provincial level, with the two scenarios of December and January described above clearly able to define drought SPI was also calculated to understand the variation of the conditions. The provinces shown in Table 9 are the index at different timescales over the past 30 years. SPI with areas where most of Afghanistan’s rainfed agriculture different timescales provides meaningful information about is practiced and which are, therefore, highly prone to short- and long-term droughts in a very simple way. SPI changes in rainfall. During the period 2000-2019, the values at different timescales were plotted for two locations average 3-month SPI values for the drought years in the in Kunduz and Badghes provinces (Figure 10). It is clearly provinces shown in Table 9 ranged from -1.94 to -0.67. apparent from Figure 10 that a meteorological drought This indicates that most droughts range from moderate occurred in 2001, 2008 and 2018, and that there was a to extreme, according to the SPI classification. The prolonged drought condition before early 2000 as well. IWMI - 20 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Table 9. Historical drought and non-drought events detected by the December and February 3-month SPI values. Provinces 3-month SPI (December) 3-month SPI (February) 2000 2007 2017 2018 2001 2008 2018 2019 Badakhshan 0.33 -0.39 -1.86 0.88 -2.17 -1.26 -1.22 0.47 Badghes -0.58 -0.24 -2.19 0.43 -1.46 -1.11 0.16 2.00 Baghlan -0.51 -1.00 -1.83 1.68 -2.12 -1.07 -0.38 2.25 Balkh -0.84 -0.41 -1.72 1.67 -1.24 -1.21 -0.29 2.75 Bamyan -0.41 -0.24 -2.10 1.73 -1.71 -0.49 0.21 2.36 Daykundi 0.05 0.21 -2.18 1.30 -1.58 -0.27 1.62 1.73 Farah -0.08 -0.63 -2.22 -0.22 -1.69 -1.58 0.31 0.79 Faryab -1.08 0.13 -1.91 0.63 -1.83 -1.12 -0.31 2.26 Ghazni 0.05 0.11 -1.37 0.49 -1.83 -0.05 -0.01 1.04 Ghor -0.32 0.26 -1.93 0.99 -1.74 -0.20 1.12 1.77 Hilmand -0.37 0.51 -2.09 -0.47 -1.27 -0.01 0.28 1.54 Hirat 0.26 -0.14 -2.38 0.23 -1.18 -1.27 0.12 2.08 Jawzjan -0.80 -0.01 -2.05 0.98 -1.66 -1.05 -0.36 2.75 Kabul -0.72 -0.55 -1.71 1.08 -1.69 -0.42 -0.91 0.97 Kandahar 0.23 0.43 -1.82 -0.37 -0.95 -0.14 -0.13 1.35 Kapisa -0.73 -0.58 -1.58 1.34 -1.58 -0.39 -0.96 1.42 Khost -0.81 -0.03 -1.34 0.12 -1.72 -1.29 -0.49 -0.08 Kunarha -0.56 0.50 -1.18 -0.01 -2.11 -0.84 -1.49 0.10 Kunduz -0.39 -1.21 -1.77 1.52 -1.06 -1.83 -0.46 2.01 Laghman -0.68 -0.21 -1.45 0.37 -1.98 -0.83 -1.01 0.46 Logar -0.54 0.09 -1.67 0.78 -1.88 -0.36 -0.76 0.81 Maydanwardag -0.27 0.04 -1.86 1.33 -1.81 -0.03 -0.34 1.56 Nangarhar -1.33 0.08 -1.03 0.07 -2.07 -1.03 -0.95 -0.05 Nemroz -0.89 0.15 -2.12 -1.22 -1.63 -0.48 -0.40 0.30 Noristan -0.02 0.19 -1.33 0.24 -2.08 -0.80 -1.42 0.38 Pakteka -0.47 -0.08 -1.13 0.24 -1.86 -0.90 -0.47 0.56 Paktya -0.63 0.09 -1.72 0.29 -1.81 -1.00 -0.76 0.26 Panjsher -0.53 -0.76 -2.00 1.19 -2.26 -0.58 -0.94 1.56 Parwan -0.41 -0.57 -1.51 1.43 -1.79 -0.28 -0.82 1.69 Samangan -0.59 -0.59 -1.53 1.53 -1.47 -1.26 0.26 2.64 Sar-e-Pul -0.97 -0.36 -1.56 1.72 -1.67 -1.29 0.55 2.65 Takhar -0.24 -1.04 -1.82 1.27 -1.79 -1.88 -0.54 1.49 Uruzgan -0.18 0.27 -1.82 0.63 -1.43 -0.04 1.08 1.50 Zabul -0.09 0.43 -1.65 0.01 -1.62 -0.02 0.05 1.02 Figure 8. Spatial distribution of drought thresholds derived from the 3-month SPI values for (a) 2008, (b) 2009, and (c) 2018. Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 21 Figure 9. Spatial distribution of provincial drought thresholds derived from 3-month SPI values for (a) 2008, (b) 2009, and (c) 2018. Figure 10. SPI values of two locations in (a) Badghes and (b) Kunduz provinces, and (c) 2018. IWMI - 22 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool The number of districts and provinces that experienced event can be determined based on the number of districts drought during the period 2001-2019 were calculated at and provinces affected. On this basis, 2001, 2011 and 2018 the provincial and district levels using the country-level can be identified as years with extreme drought, 2004 drought threshold. As shown in Table 10, more provinces as a severe drought, and 2002 and 2008 as years with and districts were affected by drought in 2001, 2004, moderate drought. Drought years are indicated by the use 2008, 2011 and 2018, but less drought or no drought was of a color, with the different intensities used to indicate experienced in other years. The severity of each drought the severity of the drought. Table 10. Assessment of past drought events using 3-month SPI (December and February) for a country threshold in Afghanistan. Districts Provinces Year Extreme Severe Moderate No drought Extreme Severe Moderate No drought 2001 231 137 22 11 16 15 3 0 2002 54 75 110 162 2 12 6 14 2003 0 1 16 384 0 0 2 32 2004 92 140 129 40 5 17 12 0 2005 0 0 0 401 0 0 0 34 2006 0 4 18 379 0 0 0 34 2007 0 0 1 400 0 0 0 34 2008 22 105 87 187 2 7 10 15 2009 0 0 28 373 0 0 0 34 2010 0 0 52 349 0 0 4 30 2011 285 90 23 3 23 10 1 0 2012 0 0 2 399 0 0 0 34 2013 0 0 12 389 0 0 1 33 2014 2 21 113 265 0 1 9 24 2015 1 13 90 297 0 0 10 24 2016 0 1 0 400 0 0 0 34 2017 0 0 40 361 0 0 0 34 2018 209 124 46 22 18 13 3 0 2019 3 6 8 384 0 1 0 33 Notes: Extreme drought is shown in dark orange, severe drought in light orange, and moderate drought in yellow. Hydrological Drought Assessment Snow Cover Index (SCI) Hydrological droughts relate to a period with The Moderate Resolution Imaging Spectroradiometer inadequate surface and subsurface water resources (MODIS) products MODIS/Terra (MOD10A1) and Aqua for established water uses in a given water resources (MYD10A1), which provide cloud-free daily snow cover at management system (Srivastava and Chinnasamy 500 m grid cells, were extracted to represent the snow 2021). It is important to investigate how drought cover area (SCA). This serves as a reliable source of snow evolves from a meteorological to hydrological measurements for hydrological studies. The assessment drought, and to examine the factors that may drive was carried out for monthly time intervals over 19 years the drought propagation process, as understanding (2001–2019) to understand the variation in snow cover this is key to mitigation measures. The SCI, SDI during the critical months that were used in determining and SWSI were used to characterize hydrological a hydrological drought. Table 11 shows the lack of snow droughts. The transmission of meteorological cover for January and February for selected provinces droughts to hydrological droughts was also in drought years, i.e., 2001, 2008, 2011 and 2018, in investigated. reference to normal years i.e., 2006, 2007 and 2010, highlighting a strong correlation between snow cover and meteorological drought. Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 23 IWMI - 24 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Table 11. Variation in snow cover between 2001 and 2019 for selected provinces in Afghanistan. Year Badghes Balkh Faryab Hirat Jawzjan Kunduz Sar-e-Pul J F M J F M J F M J F M J F M J F M J F M 2001 2.8 6.8 0.7 1.4 5.7 0.7 5.9 12.1 1.5 6.6 5.1 1.8 0.0 0.1 0.0 0.0 0.0 0.0 19.3 27.7 11.3 2002 32.0 24.9 4.2 23.2 9.2 1.6 25.0 21.1 6.3 16.1 15.7 4.7 5.5 2.1 0.5 28.4 1.9 0.3 62.9 36.8 21.2 2003 12.4 14.9 12.4 3.1 4.5 12.4 10.7 13.2 15.2 7.2 17.0 7.7 0.0 0.0 1.4 0.0 0.0 5.6 25.4 27.4 40.3 2004 25.1 5.1 2.6 6.2 2.2 1.4 16.3 10.3 6.5 21.9 5.2 3.2 0.1 0.1 0.1 0.0 0.0 0.0 31.8 26.5 22.1 2005 32.8 57.0 2.2 17.3 26.8 1.2 24.8 25.0 5.4 35.7 39.4 5.0 2.9 3.1 0.3 2.6 6.9 0.1 57.9 76.2 19.9 2006 89.2 33.3 2.2 17.4 14.0 1.4 75.0 26.8 5.4 40.9 12.5 3.2 16.7 5.1 0.0 5.7 23.4 0.0 62.8 75.8 23.2 2007 94.7 15.1 14.6 32.1 5.1 3.4 61.1 15.8 15.9 51.6 16.2 10.4 16.1 0.5 0.6 15.8 6.1 0.0 89.2 29.3 29.9 2008 10.5 24.8 10.3 7.0 17.7 8.4 12.9 27.2 18.5 5.9 11.3 11.5 0.6 5.7 3.0 0.1 8.4 2.4 27.6 65.2 34.0 2009 7.0 25.7 13.3 1.1 11.7 3.4 6.8 22.1 12.0 9.7 23.2 17.6 0.0 4.7 0.1 0.0 4.1 0.1 21.9 43.6 27.3 2010 20.7 13.7 77.7 25.3 4.3 37.4 24.2 17.1 61.1 13.8 10.8 20.8 4.7 1.4 31.5 6.7 0.0 70.1 80.3 36.0 95.0 2011 0.0 20.9 19.9 0.5 10.7 10.2 0.0 17.6 17.2 0.0 15.9 23.1 0.0 1.5 1.0 0.0 1.2 0.4 3.8 32.1 39.0 2012 9.9 27.2 58.2 31.1 8.2 30.9 22.1 22.3 40.3 4.2 18.6 27.7 7.3 1.6 12.1 40.6 1.0 20.3 68.5 34.6 82.6 2013 23.1 42.4 20.0 8.7 14.1 4.1 22.4 29.7 15.9 18.6 16.4 18.3 1.4 6.1 0.4 2.8 16.2 64.9 35.6 67.3 27.7 2014 25.9 3.0 41.0 20.5 1.7 44.2 21.2 7.8 34.1 5.0 4.0 23.8 7.9 0.0 25.8 10.1 0.0 79.6 66.7 17.3 89.1 2015 1.1 18.8 12.5 0.9 10.2 7.5 3.3 22.4 15.0 1.2 10.6 10.4 0.1 1.8 0.0 0.2 0.5 0.1 14.4 39.1 30.9 2016 15.8 9.1 5.0 26.3 7.1 11.3 30.7 12.4 8.8 1.6 8.0 4.1 11.4 0.3 0.1 12.0 0.0 15.4 62.3 31.2 33.0 2017 1.7 33.7 7.9 3.0 36.3 4.1 5.9 28.2 11.0 1.1 23.7 6.6 0.4 5.6 0.0 0.7 67.2 29.2 16.3 83.2 30.0 2018 0.6 23.0 1.3 1.4 24.5 1.1 3.2 17.0 3.6 0.5 11.9 2.4 0.1 0.7 0.1 3.4 37.3 0.0 13.4 66.3 22.2 2019 30.5 23.3 13.3 17.4 21.4 3.7 22.7 23.6 13.6 13.5 22.7 15.7 2.4 5.1 0.1 4.5 27.8 0.0 44.0 54.7 29.7 Note: J – January, F – February, M – March. Streamflow Drought Index (SDI) al. 2021). To study a hydrological drought, the SDI was developed using cumulative monthly flow data spanning Streamflow data are widely used in hydrological analyses 30 years. Specifically, flow data of Pul-i-Bang and Chahar because the agricultural response to drought is a crucial Dara stations in the Panj Amo River Basin (Figure 11) were variable in determining drought severity (Aghelpour et used to calculate SDI. Figure 11. Geographical location of the Panj Amo River Basin and spatial distribution of hydrometric stations. Monthly SDI values for Pul-i-Bang and Chahar Dara is advantageous because it can flexibly utilize various stations are shown in Figure 12. This station has higher hydrometeorological components depending on the negative values of SDI for 2018, explaining the long characteristics of the basin in question. SWSI is based duration of drought severity. A similar observation is on probability distributions of monthly time series of noticed for Pul-i-Bang station in 2018, where the months individual component indices and is calculated using between March and May show an SDI value of −2.53. four hydrometeorological components: snow cover, During normal years, the SDI has higher positive values precipitation, streamflow and reservoir storage. It is between 2.5 and 3 in reference to the period 2016-2019. a particularly appropriate drought indicator to use in The lag time for peak drought severity was, on average, snow-dominated regions within the northern provinces of 0.59 months between SDI, SPI and SCI. In comparison with Afghanistan. SDI, the maximum delay was two months for SPI and SCI. Although the severity levels of a meteorological drought In this study, the years 2001, 2008 and 2018 were are relatively low, the impacts of a hydrological drought considered because severe drought occurred nationally. are extreme because factors such as surface water and In the 2018 drought event, the average rainfall amount groundwater depletion lead to an agricultural drought was as high as 62 mm from December 2017 to January and have large-scale implications for current crops and 2018, which is a deficit of 35% from the average rainfall cultivation in the next season. received between December and January over the last 20 years across Afghanistan. On the other hand, the country Surface Water Supply Index (SWSI) received excess rainfall of 57% during the same period (2018-2019). Annual water use for irrigation is estimated The SWSI (Shafer and Dezman 1982) was selected because to be around 20 billion cubic meters (BCM), drawn mostly it is a well-known hydrological drought index. SWSI from surface water. Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 25 Figure 12. SDI series for the (a) Pul-i-Bang, and (b) Chahar Dara stations in Panj Amo River Basin for the reference period 2015–2019. Figure 13 shows that, in 2018, the values of SWSI from sub-basins to mitigate droughts and guide early warning April were mostly negative in the Panj Amo River Basin, strategies. with the peak deficit occurring in October. A similar drought trend existed across the country, with the central provinces appearing near normal or experiencing Agricultural Drought Monitoring a slight drought. In 2018, the values of SWSI showed Three parameters, primarily VHI, IDSI and PCC, were used stronger drought intensities in some sub-basins for at different periods of the crop season to map agricultural each hydrometeorological component – precipitation, drought in Afghanistan. The April IDSI value was used to streamflow and snow cover. In the normal year, i.e., determine the impact of drought; the spatial distribution 2017, which is considered as one of the wetter years, the of 2001, 2008 and 2018 drought events derived using IDSI values of SWSI were highly positive (2 to 3 index values) is presented in Figure 14, and a comparison of PCC, VHI with increased precipitation (up to 143%) and snow cover and IDSI is given in Table 12. As an example, the province (23%). Therefore, it is reasonable to conclude that a of Jawzjan has low IDSI values (2.80 in 2001 and 8.81 hydrological drought occurred in the Panj Amo River Basin in 2018) for the drought years in reference to a normal in 2018. It can be argued that integrating observations year (36.83 in 2019). This is well correlated with PCC, from existing stations into the AF-DEWS Tool can help to with less than 5% in 2018 compared to over 21.4% in the monitor drought severity accurately, and can contribute normal year. Similar observations can be made in several to managing water resources in more spatially segmented provinces across the country. IWMI - 26 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool 2017 2018 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 4 3 2 1 0 -1 -2 -3 Figure 13. SWSI time series between normal (2017) and drought (2018) years for the Panj Amo River Basin. Figure 14. Spatial distribution of provincial drought derived from April IDSI value in (a) 2001, (b) 2008, and (c) 2018. Since the agricultural and meteorological drought the other drought indices and their impacts on rainfed and monitoring parameters represent a close relationship, a rangeland areas. In summary, all the key drought indices linear regression analysis was performed between SPI- accurately illustrate the likely situation of an agricultural VHI and SPI-PCC for Jawzjan, Kunduz, Faryab and Hirat drought, which can be monitored from the peak stage to provinces separately to understand the correlation among the end of the crop season. Combining the drought indices these drought indicators as discussed in the section with crop calendars across the crop growth stages will Meteorological Drought Assessment. help stakeholders to determine the onset of drought and quantify the likely severity of drought impacts. Comparison of Multiple Indicators Figure 15 shows how multiple indicators in the months This study analyzed two major drought events, i.e., from December reveal the 2018 drought and 2019 non- in 2008 and 2018. During these times, the country drought conditions across the country. Combining the experienced a severe to extreme precipitation deficit relevant drought information, i.e., the 3-month SPI (Table 8). The lack of snow cover and the resulting impact for December and February, explains the severity of on streamflow gave rise to a hydrological drought (Figure meteorological drought, which, in 2018, subsequently 12). We selected key drought indices – 3-month SPI, led to hydrological and agricultural droughts and related PCC, VHI and IDSI – to undertake a detailed evaluation yield loss. Using PCC for February, derived from NDVI, can of the major drought events and their impacts on crop help in identifying the crop sown area, e.g., wheat, which production. We used both satellite-derived (MODIS) Gross is either in the early or delayed stage due to the drought Primary Productivity (GPP) data and observed wheat situation. In March, VHI can help to determine the health production data provided by NSIA.4 condition of crops. It is clear that crop cover and VHI generally increased from January to March in the normal Spatio-temporal characterization of drought severity and year, indicating a healthy condition. This correlates well extent was developed using selected drought indices with the IDSI for April, the peak crop maturity stage as derived from a range of satellite products. Figure 15 shows per the crop calendar, which reflects good yields. For the the drought condition for 2018 for affected provinces in drought year, the situation is very different. The IDSI for western and northwestern Afghanistan, using the 3-month April indicates the drought condition and related yield SPI, PCC, VHI and IDSI indices with reference to the 2019 losses. Thus, the remote sensing-derived indices provide normal year. It is clear that the composite index IDSI comprehensive drought monitoring indicators to identify reflects an agricultural drought; this also relates well with the progression, extent, duration and severity of drought. 4 http://www.data.gov.af/about-us (accessed on April 6, 2020). Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 27 Table 12. PCC, VHI and IDSI distribution in drought (2001 and 2018) and non-drought (2009 and 2019) years. PCC (February) VHI (March) IDSI (April) Province 2001 2018 2009 2019 2001 2018 2009 2019 2001 2018 2009 2019 Badakhshan 5.15 5.75 5.08 5.90 0.41 0.48 0.40 0.39 30.59 54.60 44.93 69.51 Badghes 29.29 18.51 34.57 36.92 0.28 0.18 0.56 0.59 3.32 13.34 89.85 73.69 Baghlan 10.31 8.35 9.56 16.56 0.41 0.49 0.45 0.46 17.08 37.01 62.98 72.21 Balkh 12.62 12.19 17.08 20.37 0.26 0.32 0.44 0.45 10.78 27.94 62.85 53.89 Bamyan 0.24 0.56 0.16 0.26 0.48 0.57 0.35 0.36 25.96 51.49 25.75 51.99 Daykundi 1.09 2.13 1.04 2.29 0.48 0.58 0.44 0.39 26.90 53.45 43.30 46.55 Farah 1.06 5.38 4.84 6.25 0.22 0.39 0.36 0.44 8.43 17.78 42.03 36.89 Faryab 16.73 11.98 20.50 18.71 0.21 0.20 0.46 0.40 4.45 17.45 76.96 60.58 Ghazni 0.72 1.59 1.00 0.92 0.55 0.67 0.61 0.35 23.82 44.40 52.55 30.62 Ghor 0.43 2.03 0.30 0.70 0.58 0.66 0.62 0.41 22.56 50.41 63.61 47.44 Hilmand 2.45 6.29 5.81 6.90 0.22 0.54 0.51 0.52 10.04 18.42 54.56 37.15 Hirat 6.79 4.94 10.05 9.81 0.37 0.29 0.57 0.34 15.34 20.06 73.10 47.41 Jawzjan 14.45 4.11 14.99 13.41 0.24 0.20 0.47 0.36 2.80 8.81 63.37 36.83 Kabul 3.03 7.24 4.56 3.01 0.42 0.52 0.48 0.36 23.76 30.84 45.93 29.91 Kandahar 0.31 1.21 2.05 1.85 0.20 0.30 0.43 0.35 13.65 16.00 65.99 38.81 Kapisa 12.17 14.85 13.73 12.49 0.42 0.60 0.50 0.43 34.47 33.79 50.26 27.17 Khost 8.68 12.50 10.97 10.89 0.28 0.57 0.56 0.43 15.33 46.33 54.94 47.34 Kunarha 6.55 9.17 7.43 7.78 0.42 0.58 0.53 0.56 28.02 43.09 No data 52.84 Kunduz 35.02 23.75 32.32 35.66 0.47 0.39 0.53 0.58 22.45 23.41 73.12 48.69 Laghman 5.29 10.45 8.99 7.36 0.56 0.58 0.59 0.55 23.15 39.07 61.07 37.05 Logar 2.65 3.49 2.83 0.09 0.51 0.62 0.61 0.32 21.70 35.53 48.91 25.62 Maydanwardag 0.15 1.05 0.04 0.00 0.54 0.68 0.46 0.29 28.61 45.01 29.55 37.53 Nangarhar 8.54 13.20 9.72 10.31 0.36 0.51 0.51 0.50 13.40 34.75 52.16 45.13 Nemroz 0.04 0.18 0.08 0.17 0.24 0.44 0.29 0.36 9.51 12.48 18.18 14.46 Noristan 1.29 1.33 0.91 1.06 0.41 0.53 0.35 0.35 37.97 45.81 45.03 36.94 Pakteka 0.62 1.10 0.65 0.13 0.36 0.58 0.51 0.36 6.83 37.63 58.44 28.72 Paktya 3.72 4.49 2.67 1.95 0.53 0.66 0.58 0.38 27.98 48.08 36.85 21.12 Panjsher 0.50 1.50 0.08 0.00 0.46 0.55 0.32 0.34 39.27 52.47 4.08 54.07 Parwan 8.25 9.74 9.37 9.41 0.40 0.58 0.43 0.36 37.15 42.70 48.02 43.28 Samangan 10.35 8.67 10.34 11.92 0.44 0.48 0.48 0.45 17.70 37.16 56.17 57.84 Sar-e-Pul 24.77 12.22 17.94 27.94 0.40 0.34 0.44 0.53 20.61 37.08 74.93 68.77 Takhar 24.52 15.75 18.94 32.94 0.51 0.46 0.49 0.58 22.65 43.06 69.80 64.60 Uruzgan 1.94 2.54 4.90 4.86 0.37 0.51 0.50 0.50 17.52 22.49 79.36 34.49 Zabul 0.25 0.69 0.59 0.59 0.27 0.42 0.52 0.33 14.20 23.77 76.67 25.31 IWMI - 28 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool A1) 3-month SPI (December 2017) A2) 3-month SPI (December 2018) B1) NDVI (February 2018) B2) NDVI (February 2019) B3) NDVI (April 2018) B4) NDVI (April 2019) Figure 15. The use of multiple drought indices, and comparison of drought and normal years across Afghanistan. Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 29 C1) VHI (March 2018) C2) VHI (March 2019) D1) IDSI (April 2018) D2) IDSI (April 2019) E1) Composite index, i.e., IDSI (April 2018): Closure view E2) Composite index, i.e., IDSI (April 2019): Closure of of Badghes province Badghes province Figure 15. The use of multiple drought indices, and comparison of drought and normal years across Afghanistan. IWMI - 30 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Drought Impact Analysis using Crop clear that studying the rainfall conditions using the Production 3-month SPI for December will provide an indicator of drought behavior for the following months. Therefore, The above section compared the use of key drought the 3-month SPI can be used as the first drought indices for assessing drought progression from the start warning indicator at the beginning of the rainy season. to the end of the crop season. This section describes the It is also emphasized that crop cover in February can comparative analysis of drought indicators in reference be correlated with more than 55% accuracy using to ground-based Crop Yield Anomaly (CYA) and satellite- the December rainfall variation, as there is a good derived CYA using MODIS GPP in rainfed and irrigated correlation between 3-month SPI and PCC. areas. Pearson correlation analysis (Table 13) was carried out in Correlation Analysis of Drought Indices and reference to CYA and key drought indices – 3-month SPI CYA (December), PSC (January), PCC (February), VHI (March) and IDSI (April) – data to evaluate statistical significance. The SPI and VHI scatterplots in Figure 16 correspond to The analysis was undertaken for both drought and normal the 3-month SPI for December and the VHI for March. years, and it is evident that the CYA (Dutta et al. 2013) Similarly, the SPI and PCC values relate well with the is highly correlated with the drought indices (Figure 16; 3-month SPI for December and the PCC for February. Table 13). The correlation of IDSI (April) with CYA is 0.83, One point in the scatterplot corresponds to a one-year for example, while that of VHI (March) and CYA is 0.82 and value, so there are 19 records to represent the study that of 3-month SPI (December) and CYA is 0.78. Among period from 2001 to 2019. Since 3-month SPI and VHI all the drought indices, the combined IDSI of April has the have shown a good correlation for all provinces, it is highest significance with CYA. Table 13. Correlation (Pearson) matrix between crop yield anomaly (CYA) and key drought indices. 3-month SPI PSC PCC VHI IDSI CYA 3-month SPI 1 0.31 0.52 0.74 0.84 0.78 PSC 0.31 1 0.2 0.14 0.29 0.32 PCC 0.52 0.2 1 0.67 0.91 0.6 VHI 0.74 0.14 0.67 1 0.91 0.82 IDSI 0.84 0.29 0.61 0.91 1 0.83 CYA 0.78 0.32 0.6 0.82 0.83 1 Ground-based Crop Yield Anomaly (CYA) in potential to help identify the impact of drought and assist Rainfed and Irrigated Areas in developing drought-response strategies. Crop yield anomaly was measured for the current year in Assessment of Satellite-derived CYA and its reference to historical yields. Table 14 shows year-wise Comparison with Observed Crop Production CYA for wheat production in rainfed and irrigated areas at Data the provincial level from 2006 to 2018 using data obtained from NSIA.5 It is evident from the interannual comparison Table 15 shows estimated crop yield (kgha-1) derived from that for the drought years 2008, 2011 and 2018, CYA satellite data covering the rainfed and irrigated areas for shows higher negative values. Similarly, for normal years, selected provinces of Afghanistan. It is evident from the such as 2007, 2009 and 2013, CYA has higher positive table that the yield estimates for the drought years 2008, values. It is evident from the analysis that CYA has the 2011 and 2018 are very low compared to the normal years. 5 http://www.data.gov.af/dataset/wheat-area-and-production-province (accessed on August 10, 2020). Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 31 Figure 16. Scatterplot and histogram comparison of crop yield anomaly (CYA) and drought indices. Table 14. Assessment of Crop Yield Anomaly (CYA) for selected provinces in rainfed (R) and irrigated (I) areas. The years highlighted indicate when severe drought affected those provinces. Year Balkh Badghes Faryab Hirat Jawzjan Kunduz Sar-e-Pul R I R I R I R I R I R I R I 2006 1.00 0.33 1.58 0.52 1.33 0.34 0.87 -0.25 0.68 1.08 -0.71 -0.59 1.47 0.19 2007 1.09 0.40 1.51 1.19 1.19 0.34 1.50 -0.25 0.97 1.28 -0.52 -0.54 1.34 0.31 2008 -1.01 -0.80 -1.09 0.05 -1.42 -0.44 -1.01 -1.26 -1.01 -0.97 -1.29 -1.35 -1.07 -0.42 2009 1.01 0.40 1.41 2.23 1.44 0.44 1.39 0.21 1.37 0.95 -0.04 -0.13 1.12 0.39 2010 0.07 0.79 0.12 0.10 1.02 2.22 -0.30 -0.72 0.61 0.88 -0.01 0.57 0.43 1.65 2011 -1.24 0.79 -1.09 0.62 -1.24 -0.84 -0.80 -0.36 -0.63 -0.77 -1.22 -0.84 -1.52 -0.62 2012 1.22 0.64 0.66 0.41 1.36 -0.21 -0.16 -0.55 2.16 1.52 0.12 0.31 0.32 0.72 2013 1.36 1.04 0.75 -0.04 -0.43 -0.68 1.86 -0.40 0.52 0.14 0.41 0.23 0.81 2.08 2014 1.05 0.97 -0.47 -1.03 -0.58 -0.59 -0.26 -0.31 0.40 -0.93 2.32 2.14 0.75 0.32 2015 0.02 1.09 -0.44 -1.39 -0.88 0.34 -0.05 0.38 -0.65 -0.07 0.90 1.14 -0.68 -0.24 2016 -0.49 -0.15 -0.26 0.36 -0.03 0.88 -0.07 1.96 -0.99 -1.26 0.67 -0.03 -0.71 -0.85 2017 -1.25 -1.93 -0.34 -0.59 0.12 1.24 -0.53 1.51 -1.15 -0.71 1.46 0.82 -1.11 -1.80 2018 -1.39 -1.35 -1.65 -1.58 -1.12 -1.93 -1.66 1.05 -1.20 -1.32 -1.35 -0.01 -1.37 -0.75 IWMI - 32 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Table 15. Yield estimates obtained using MODIS Terra and Aqua GPP (kgha-1) covering rainfed and irrigated areas for selected provinces of Afghanistan. The shades from red to green color show lower to higher production values. Yea r Balk h Badghes Farya b Hir at Jawzjan Kunduz Sar-e-Pul 2005 90.84 100.01 86.73 75.86 96.31 142.90 80.20 2006 82.79 73.66 74.83 60.59 83.76 135.21 80.20 2007 85.75 103.92 87.98 75.74 92.44 137.20 82.65 2008 50.28 57.56 51.93 50.50 46.56 87.05 52.00 2009 107.69 125.47 106.39 87.21 116.67 174.28 99.42 2010 107.67 108.25 99.73 80.24 103.19 157.46 98.36 2011 55.49 78.71 59.83 59.41 47.51 98.45 62.05 2012 82.91 94.13 75.18 61.05 76.25 132.85 79.66 2013 89.45 100.72 94.79 75.52 89.29 156.29 88.44 2014 75.90 70.68 69.20 57.34 68.39 141.04 69.96 2015 89.31 93.66 86.78 67.21 89.57 150.43 83.78 2016 76.67 93.26 86.95 71.74 75.14 132.62 78.61 2017 86.33 79.03 74.72 65.69 76.96 137.22 77.77 2018 66.24 56.63 49.97 60.45 37.62 139.72 62.77 2019 109.51 128.12 103.41 87.00 90.78 174.31 102.84 It is clear from Figure 17 that the satellite-derived of Afghanistan. Out of 401 districts, 80 districts fall under production estimates indicate drought for the years the ‘very high’ drought hazard category, 80 districts 2008 and 2018, in contrast to the normal year 2019. The are in the ‘high’ category, 86 districts are classified as rainfed-dominated provinces, such as Badghes, Jawzjan ‘moderate’, 113 districts are ‘low’, and the remaining 42 and Faryab, are severely affected by drought compared to districts are ‘very low’ or have ‘no’ likelihood of being the irrigated areas such as Kunduz, Balkh and Helmand, affected by drought. According to Figure 19(b), the which are dependent on water released from reservoirs. north, northwestern and central provinces are critical The comparative analysis (Figure 18) of production areas, where drought could have severely impacted the estimates using ground-derived and satellite-derived CYA population in terms of food availability and livelihoods. shows a high correlation, indicating that this satellite- derived yield estimation can be used to assess the impact Finally, the drought risk analysis combined drought hazard of drought on yield losses to support drought declaration and population exposure to determine the overall impact, and food security. using spatial aggregation to define drought-risk classes from ‘very high’ to ‘very low’. Out of 401 districts, there are Drought Impact Assessment 60 districts categorized as ‘very high’ risk, 107 districts are ‘high’ risk, 63 are ‘moderate’ risk, 111 are ‘low’ risk, and 60 The project evaluated the frequency and intensity of are ‘very low ’ risk (Figure 20). The map highlights where drought using the April IDSI value in relation to agricultural drought risks are highest, and indicates where historical data (2001–2019) for the three drought classes the greatest impacts are likely to be on population and food – extreme, severe and moderate – in agricultural areas security. The critical drought-prone provinces are mostly in to assess the impact on the population. Figure 19(a) the rainfed and rangeland areas such as Badghes, Faryab, shows a district-level drought hazard map for the whole Kunduz, Sar-e-Pul, Balkh, Jawzjan and Hirat. Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 33 Figure 17. MODIS-based GPP for the drought years 2008 and 2018, and the normal year 2019. The shades of orange indicate lower production in rainfed areas and the shades of green indicate higher production in both rainfed and irrigated areas. 3 2 CYA-OBS CYA-GPP 1 0 -1 -2 -3 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 (b) 3 2 CYA-OBS CYA-GPP 1 0 -1 -2 -3 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Figure 18. Comparison of crop yield anomaly (CYA) of (a) Jawzjan, and (b) Badghes with Observed (OBS) and GPP-based estimation. IWMI - 34 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool CYA CYA Figure 19. (a) Drought hazard map developed with the AF-DEWS Tool, using historical IDSI data from 2001 to 2019, and (b) population exposure to drought. Figure 20. Drought risk map created using drought hazard and population exposure for Afghanistan. Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 35 Validation of the AF-DEWS Tool with The agricultural production losses reported by the AF- Other Sources DEWS Tool for the drought years 2001 and 2018 were also reported by FAO. These were the most severe droughts There have been several years of drought in Afghanistan in ever recorded in Afghanistan (Figure 21) (FAO 2019). the last 20 years. According to our assessment, localized The drought risk maps produced using the AF-DEWS droughts have a periodicity of between three and five Tool compare well with other published sources, such years, with droughts covering large areas and recurring as reports of the drought in The International Disaster every 9 to 11 years. Importantly, the 2018 drought affected Database (EM-DAT) of the Centre for Research on the more than two-thirds of Afghanistan (22 out of the 34 Epidemiology of Disasters (CRED) and maps published provinces), with more than 10.5 million people (of the by FEWS NET and United Nations agencies (Figure 22). total 17 million in these 22 provinces) severely affected This demonstrates the capabilities of the AF-DEWS (UNDRR 2020). Tool in supporting drought early warning and informing preparedness and risk reduction measures. Figure 21. Wheat production statistics for Afghanistan between 2011 and 2018. Source: FAO 2019. IWMI - 36 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Source: https://immap.org/news/immap-presents-its-contributions-to- Source: https://ipad.fas.usda.gov/highlights/2008/08/Afghanistan%20 drr-in-afghanistan-during-national-conference/ (accessed on October Drought/(accessed on November 30, 2021). 27, 2020) Source: https://fews.net/central-asia/afghanistan/food-security-outlook/ Source: https://fews.net/sites/default/files/documents/reports/ october-2018 (accessed on October 25, 2022). afghanistan_OL_Q3Q4_final.pdf (accessed on November 30, 2019). Figure 22. Drought maps produced by other agencies. The Way Forward This section explains coordination of the AF-DEWS Tool (created by the United States Agency for International with other initiatives in Afghanistan, and strengthening Development [USAID]) for monitoring food insecurity drought risk management from early warning to early and iMMAP for humanitarian coordination, and national- action and early finance. to provincial-level institutions in Afghanistan. It can support efforts to improve drought management6 across Convergence of the AF-DEWS Tool with three pillars: monitoring and early warning systems; vulnerability and impact assessment; and mitigation, Other Initiatives preparedness and response. Table 16 highlights the strengths and opportunities presented by the AF-DEWS There is great potential for the convergence of the Tool towards building a strategic partnership with GoIRA, AF-DEWS Tool with other initiatives, such as FEWS NET United Nations and other partners. 6 https://www.droughtmanagement.info/about/ (accessed on November 30, 2021). Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 37 Table 16. Comparison of the AF-DEWS Tool with FEWS NET and iMMAP. Features AF-DEWS FEWS NET iMMAP Background The prototype was developed in Established in 1985 in East and Established in 2020 and 2020 and funded by the World West Africa, and funded by funded by USAID, the Bank and GoIRA. IWMI is the USAID; works in cooperation with organization provides prime contractor to implement three other US government information management the AF-DEWS Tool in the wider agencies – National Aeronautics services to humanitarian and framework of drought Early and Space Administration (NASA), development communities, Warning, Early Action and Early United States Geological Survey which will support informed Finance to promote (USGS) and United States decision-making processes. comprehensive drought risk Department of Agriculture (USDA). management. Chemonics is the prime contractor to provide critical data to monitor rising or waning food insecurity situations specific to Afghanistan. Pros and cons Pros: provides ready-to-use, Pros: combines many parameters Pros: integrates spatial and constantly updated satellite- from climate to food prices; non-spatial data for based indices for monitoring frequently updated; very emergency response by all meteorological, agricultural comprehensive. actors. and hydrological droughts. Cons: not specifically intended Cons: mostly static maps; Cons: skilled capacities needed for drought; information limited applicability to to ensure platform sustainability embedded within the system drought monitoring and early and usability. cannot be directly accessed by warning. GoIRA. Platform Robust, transparent and FEWS NET Data Center provides iMMAP Afghanistan Spatial operational. It is the first a range of products through Data Center is a dissemination drought early warning system multiple services/platforms for platform for disaster risk (DEWS) available in the cloud food security assessment, and reduction data and other environment to enable rapid is limited for drought declaration baseline information. It is not drought declaration. purposes. implemented in the cloud framework. Cost and Very low (~USD 200 per month) High cost with the involvement High cost with the maintenance through Microsoft Azure and of several commercial partners. involvement of several Google Cloud Platform with commercial partners. basic maintenance and limited Human Resource involvement. FAIR data High Moderate Moderate principles (Findable, Accessible, Interoperable, Reusable) Figure 23 highlights the strengths of the AF-DEWS allows emergency and relief agencies to provide Tool, from accurate and efficient monitoring and timely drought alerts for promoting early action, and early warning to using historical drought records for bring together multiple sectoral and service delivery long-term impact assessments, guiding institutions agencies towards mitigating the worst impacts of at the national, provincial and district levels. It also drought. IWMI - 38 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Figure 23. Use of the AF-DEWS Tool within broader drought risk management initiatives. Capacity Building and Knowledge underpin food security. Transfer • Conduct training workshops at the national and With the AF-DEWS Tool now functional, it needs to be provincial levels. embedded within short- and long-term drought mitigation plans developed as part of the Early Warning, Early • Invest more human and financial resources in research Finance and Early Action (ENETAWF) project. Useful next on drought monitoring and early warning methods, steps include the following: and geospatial technologies to enhance drought preparedness and mitigation capabilities. • Efforts to promote cooperation among Afghanistan’s technical agencies, so they can easily share data, • Build capacity to implement emergency response and technologies and knowledge. recovery measures that reinforce national drought management policy goals.7 • Use of the AF-DEWS Tool for weather forecasting, monitoring and forecasting drought events, and • Strengthen the capacity of national and provincial initiating early warning procedures will need to be agencies to disseminate information generated by the operationalized by the appropriate parties. Training AF-DEWS Tool in local languages. will be needed in GEE and relevant programming languages to facilitate the development and • Establish a dedicated national drought monitoring maintenance of the AF-DEWS Tool. center to promote comprehensive drought risk management strategies – ranging from compiling data • Make appropriate institutions aware of the potential to monitoring drought and reporting impacts through to integrate drought knowledge products in broader drought bulletins issued by GoIRA’s Drought Early agricultural and water management processes to Warning Committee (DEWC). 7 https://www.droughtmanagement.info/find/library/ Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 39 References Aghelpour, P.; Bahrami-Pichaghchi, H.; Varshavian, V. 2021. Hydrological drought forecasting using multi-scalar streamflow drought index, stochastic models and machine learning approaches, in northern Iran. 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Province SPI (December) PSC (January) PCC (February) VHI (March) IDSI (April) ED SD MD ED SD MD ED SD MD ED SD MD ED SD MD Badakhshan -1.75 -1.25 -0.94 55.44 61.04 63.22 1.91 3.30 4.48 0.35 0.37 0.38 0.35 0.35 0.36 Badghes -2.4 -1.04 -0.52 0.50 1.01 2.33 33.83 35.26 36.32 0.16 0.19 0.32 0.17 0.19 0.29 Baghlan -1.9 -1.27 -0.52 12.55 16.52 18.98 7.97 9.47 13.17 0.28 0.29 0.42 0.32 0.35 0.38 Balkh -1.91 -0.94 -0.57 0.87 1.04 1.37 11.40 12.57 19.12 0.19 0.20 0.34 0.17 0.18 0.29 Bamyan -1.89 -1.06 -0.52 30.71 35.90 45.62 0.05 0.14 0.20 0.38 0.42 0.44 0.32 0.36 0.39 Daykundi -1.45 -1.17 -1.04 7.08 10.14 17.22 0.32 0.97 1.95 0.43 0.43 0.45 0.37 0.40 0.41 Farah -1.81 -0.86 -0.28 0.01 0.06 0.12 2.47 2.57 2.76 0.19 0.22 0.25 0.17 0.21 0.26 Faryab -1.85 -1.15 -0.38 2.88 3.32 5.94 18.36 18.97 19.31 0.17 0.20 0.27 0.16 0.19 0.28 Ghazni -1.39 -1.16 -0.73 1.64 2.25 6.40 2.04 2.33 2.94 0.30 0.32 0.33 0.26 0.31 0.33 Ghor -1.51 -1.36 -0.58 12.15 14.61 27.48 0.25 0.45 0.71 0.35 0.36 0.38 0.30 0.31 0.36 Hilmand -1.49 -0.86 -0.66 0.08 0.09 0.14 3.94 4.32 4.83 0.22 0.23 0.30 0.16 0.21 0.29 Hirat -2.29 -0.92 -0.24 0.43 1.00 1.46 6.95 8.02 9.08 0.24 0.25 0.28 0.21 0.26 0.28 Jawzjan -2.15 -0.93 -0.57 0.02 0.03 0.07 12.91 13.98 15.64 0.19 0.22 0.27 0.07 0.11 0.20 Kabul -1.58 -1.48 -0.82 1.84 3.67 5.66 5.17 5.72 7.40 0.33 0.34 0.36 0.33 0.33 0.34 Kandahar -1.66 -1.25 -0.82 0.01 0.01 0.02 0.54 0.99 1.40 0.17 0.19 0.26 0.09 0.17 0.23 Kapisa -1.54 -1.43 -0.83 1.18 2.30 5.33 11.90 12.37 13.36 0.40 0.41 0.42 0.33 0.35 0.36 Khost -1.79 -1.29 -0.65 0.00 0.00 0.00 9.20 9.53 10.62 0.27 0.33 0.43 0.24 0.31 0.38 Kunarha -1.62 -1.49 -0.95 2.60 4.45 7.16 7.47 7.97 8.30 0.37 0.41 0.44 0.28 0.31 0.33 Kunduz -1.6 -1.01 -0.46 0.01 0.01 0.02 26.09 29.13 33.24 0.22 0.31 0.37 0.22 0.23 0.34 Laghman -1.65 -1.45 -0.87 6.32 7.25 9.62 6.20 6.73 9.04 0.43 0.47 0.49 0.33 0.34 0.36 Logar -1.67 -1.46 -0.73 0.27 0.69 1.26 2.35 2.77 3.79 0.31 0.32 0.36 0.34 0.35 0.40 Maydanwardag -1.74 -1.18 -0.65 11.89 15.36 18.05 0.35 0.37 0.38 0.35 0.37 0.38 0.29 0.32 0.39 Nangarhar -1.49 -1.34 -1.04 0.70 1.21 2.45 9.43 9.89 10.29 0.31 0.35 0.42 0.21 0.27 0.32 Nemroz -1.22 -0.92 -0.62 0.00 0.00 0.00 0.03 0.04 0.12 0.18 0.20 0.26 0.24 0.24 0.32 Noristan -1.68 -1.52 -1.03 39.81 51.62 55.09 1.06 1.16 1.25 0.37 0.38 0.42 0.28 0.29 0.32 Pakteka -1.6 -1 -0.64 0.00 0.01 0.06 0.65 0.79 1.10 0.24 0.26 0.30 0.22 0.25 0.27 Paktya -1.62 -1.6 -0.64 0.06 0.08 0.16 1.69 1.89 3.01 0.34 0.34 0.36 0.32 0.37 0.41 Panjsher -1.75 -1.45 -0.74 40.57 43.98 55.85 0.57 0.71 0.94 0.34 0.36 0.42 0.32 0.33 0.34 Parwan -1.62 -1.33 -0.93 12.41 16.86 19.81 8.60 8.98 9.56 0.35 0.39 0.42 0.32 0.35 0.36 Samangan -2.13 -0.96 -0.52 6.99 7.74 16.48 10.12 14.25 15.71 0.22 0.25 0.39 0.26 0.32 0.44 Sar-e-Pul -1.67 -1.04 -0.55 12.41 14.17 18.06 20.20 21.11 24.79 0.27 0.28 0.37 0.25 0.29 0.36 Takhar -1.89 -1.23 -0.64 8.15 10.08 11.74 12.14 16.73 25.25 0.19 0.28 0.32 0.24 0.25 0.41 Uruzgan -1.83 -1.06 -0.75 1.15 1.60 2.68 0.96 1.11 1.83 0.33 0.34 0.36 0.24 0.29 0.34 Zabul -1.61 -1.27 -0.72 0.39 0.50 0.60 0.21 0.36 0.42 0.18 0.20 0.27 0.13 0.22 0.28 IWMI - 44 Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool Annex 3. Glossary. Climate – The prevailing weather conditions in a particular area over a long period of time. Climatology – The study of climate. Composite Drought Index – Multiple indicators, such as the Vegetation Health Index, Integrated Drought Severity Index and Surface Water Supply Index, can be used in combination to indicate drought presence and severity. Drought – When less rainfall than the long-term average occurs over an extended period, usually several months or longer. Or, more formally, a deficiency of rainfall over a period of time, resulting in a water shortage for some activity, group or the environmental sector. Drought early warning system – Drought early warning systems typically aim to track, assess and deliver relevant information about climatic, hydrologic and water supply conditions and trends. Ideally, they incorporate a monitoring component (including impacts) and a forecasting component. The objective is to provide timely information in advance of, or during, the early onset of drought to instigate action (via threshold triggers) to implement a drought risk management plan as a means of reducing adverse impacts. Indicators – Indicators are variables used to describe drought conditions. They include precipitation, temperature, streamflow, groundwater and reservoir levels, soil moisture and snow cover. Indices – Indices are used to quantitatively assess the severity, timing and duration of drought events. Single index – A single indicator, such as the Standardized Precipitation Index (measuring rainfall or snowfall), can be used on its own to indicate drought condition and severity. Research Report 188 - Afghanistan Drought Early Warning Decision Support (AF-DEWS) Tool IWMI - 45 IWMI Research Report Series 188 Afghanistan Drought Early 187 Assessment of Farmers' 186 Towards the Harmonization Warning Decision Support Willingness to Pay for of Global Environmental Flow (AF-DEWS) Tool Bundled Climate Insurance Estimates: Comparing the Global https://doi.org/10.5337/2023.223 Solutions in Sri Lanka Environmental Flow Information https://doi.org/10.5337/2023.222 System (GEFIS) with Country Data https://doi.org/10.5337/2023.224 185 Institutional Gender 184 Land Cover Changes in the 183 Living Customary Water Mainstreaming in Small-Scale Upper Great Ruaha (Tanzania) Tenure in Rights-based Water Irrigation: Lessons from Ethiopia and the Upper Awash (Ethiopia) Management in Sub-Saharan Africa https://doi.org/10.5337/2023.218 River Basins and their Potential https://doi.org/10.5337/2022.214 Implications for Groundwater Resources https://doi.org/10.5337/2023.212 For access to all IWMI publications, visit www.iwmi.org/publications/ Headquarters 127 Sunil Mawatha Pelawatta Battaramulla Sri Lanka Mailing address P. 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