Drought Early Warning System Research Articles

Design of drought early warning system based on standardized precipitation index prediction using hybrid ARIMA-MLP in Banten province

Drought Early Warning System (DEWS) is an effort to disseminate early warning information based on climate and hydrology aspects. The DEWS design uses ARIMA, MLP, and hybrid ARIMA-MLP models to predict drought based on SPI for 1, 3, and 6 months. Predictions were made using ERA5 monthly rainfall data from 1981-2022 corrected based on observation data on 9 grids of observation rain gauges in Banten Province. The design of the ARIMA model is determined by selecting the combination of p and q parameters with the lowest AIC value, while the MLP architecture is determined by referring to the study literature and by trial and error testing. ARIMA models and hybrid models are not able to follow actual data fluctuations and have high error values in both SPI1, SPI3, and SPI6, so they are not recommended in this study. The MLP model has the best prediction ability, namely in SPI6 prediction with NSE value reaching >0.5 and RMSE value.

Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania

Study regionThis study refers to the Wami river sub-catchments in Eastern Tanzania. Study FocusThe five-machine learning (ML) algorithms, including long short-term memory (LSTM), multivariate adaptive regression spline (MARS), support vector machine (SVM), extreme learning machine (ELM), and M5 Tree, were used to predict the most widely used drought index, the standard precipitation index (SPI), at six and nine months. Algorithms were established using monthly rainfall data for the period from 1990 to 2022 at five meteorological stations distributed across the Wami River sub-catchment: Barega, Dakawa, Dodoma, Kongwa, and Mandera stations. New hydrological insights for the region. The predicted results of all five ML algorithms were evaluated using several statistical metrics, including Pearson’s correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and Nash Sutcliffe efficiency (NSE). The prediction results revealed that LSTM perform better in predicting drought conditions using SPI6 (6-month SPI) and SPI9 (9-month SPI) with the highest NSE of 0.99 in all five stations, and R of 0.99 in four stations except at Kongwa station, where R range from 0.75 to 0.99. These prediction results will aid decision-makers and planners to develop a drought monitoring and drought early warning system in order to strengthen the governance and resilience to the catchment and people on the impacts of water scarcity and climate change.

Characterize and analysis of meteorological and hydrological drought trends under future climate change conditions in South Wollo, North Wollo, and Oromia Zones, in Ethiopia

This research was conducted on North Wollo, South Wollo, and Oromia special zones, in Ethiopia. The study aimed to analyze the temporal and spatial variability of meteorological and hydrological drought trends using the selected drought indices and to predict its future trend in the selected areas. To achieve these objectives, meteorological and hydrological data were collected from the Ethiopian Meteorology Institute and the Ministry of Water and Energy respectively. The historical and future drought condition was analyzed by using the standardized precipitation index (SPI), reconnaissance drought index (RDI), and streamflow drought index (SDI) from the drought indicator calculator (DrinC) software. Based on the availability of the data, for historical drought analysis, ten meteorological stations with thirty-two years of daily data were selected. For the future scenario, RCP 4.5 was used to downscale the future climate data and to forecast SPI and RDI values. Also, an artificial neural network (ANN) was applied to forecast the future streamflow data using Python software, then the future hydrological drought was determined using the forecasted streamflow data. The result indicates that all zones were historically affected by severe to extreme droughts, especially 1984, 1986, 1987, 1989, 1991, 1992, 2003, 2007, 2010, 2013, and 2014 years. From 1984 to 1992 the probability of severe to extreme drought occurrence was on average of two years intervals and from 1992 to 2003 there is a huge gap. From the future drought analysis results, the probability of severe to extreme drought occurrence will be at five-year intervals on average. Based on the analyzed results, the frequency of severe to extreme drought occurrence of historical drought which was two and three years was increased to five years for the future conditions on average. But, these are short intervals and the magnitude of the event is very high. So, the regional water and energy office and other concerned bodies in the area have to plan a good drought mitigation mechanism and should develop a drought early warning system for the communities in and around the study area.

Hydrological drought forecasts using precipitation data depend on catchment properties and human activities

Hydrological Drought Early Warning Systems play a crucial role in effective drought planning and management, as the impacts of drought are more closely associated with hydrological droughts than meteorological ones. However, current systems primarily focus on meteorological drought forecasts due to the limited access to hydrological data. Here we assess the feasibility of forecasting drought in streamflow and groundwater by solely using precipitation data. The results demonstrate that meteorological drought forecasts derived from the Standardized Precipitation Index with 6-month accumulation periods and various lag times hold the potential to predict streamflow and groundwater droughts. This study also highlights the importance of catchment properties in hydrological drought predictions. Our findings present an opportunity for developing hydrological drought early warning system globally to reach the goal of the Sendai framework for disaster risk reduction by 2030 and support the initiative of early warnings for all.

Pairing monitoring datasets with probabilistic forecasts to provide early warning of drought in Australia

Droughts are a cyclical feature of Australia's climate that have compounding impacts on agricultural productivity and the wellbeing of drought-affected communities. Understanding anticipated drought conditions in context of antecedent observations is critical to providing early warning of drought. In this study we paired probabilistic seasonal rainfall forecasts with precipitation, soil moisture and evapotranspiration data that were objectively combined using Principal Component Analysis (PCA). The final Drought Early Warning System (DEWS) maps overlay forecasting information with the multi-variate PCA-weighted maps at 1-, 3- and 6-month timescales over the common period of overlap between all datasets (1982–2018). In this study period, the 1982–1983 Ash Wednesday tinder drought and the 1997–2001 Millennium drought were investigated. We validated PCA-weighted maps with satellite vegetation data and found performance was strongest over the Murray Darling Basin region (R = 0.63, p = 0.009) and poorest over Central interior Australia (insignificant correlations). We also validated PCA-weighted maps using agricultural commodity data from ABARES. Significant negative correlations at 95 %, 99 % and 99.9 % confidence intervals were found between %-Area in drought category and crop cultivation area; export volume/value; crop yield; and rural debt. Our findings indicate that early warning of drought can be categorised by concern – wherein dry antecedent conditions and dry forecasted conditions are of the highest concern. The developed proof-of-concept DEWS contributes to the growing body of proactive drought management research. In a drought vulnerable future, operationalising and communicating drought early warnings will be critical to reducing the harmful impacts of drought on communities, economies, and environments.

Analyzing the informative value of alternative hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale

Abstract. Streamflow drought hazard indicators (SDHIs) are mostly lacking in large-scale drought early warning systems (DEWSs). This paper presents a new systematic approach for selecting and computing SDHIs for monitoring drought for human water supply from surface water and for river ecosystems. We recommend considering the habituation of the system at risk (e.g., a drinking water supplier or small-scale farmers in a specific region) to the streamflow regime when selecting indicators; i.e., users of the DEWSs should determine which type of deviation from normal (e.g., a certain interannual variability or a certain relative reduction of streamflow) the risk system of interest has become used to and adapted to. Distinguishing four indicator types, we classify indicators of drought magnitude (water anomaly during a predefined period) and severity (cumulated magnitude since the onset of the drought event) and specify the many relevant decisions that need to be made when computing SDHIs. Using the global hydrological model WaterGAP 2.2d, we quantify eight existing and three new SDHIs globally. For large-scale DEWSs based on the output of hydrological models, we recommend specific SDHIs that are suitable for assessing the drought hazard for (1) river ecosystems, (2) water users without access to large reservoirs, and (3) water users with access to large reservoirs, as well as being suitable for informing reservoir managers. These SDHIs include both drought magnitude and severity indicators that differ by the temporal averaging period and the habituation of the risk system to reduced water availability. Depending on the habituation of the risk system, drought magnitude is best quantified either by the relative deviation from the mean or by the return period of the streamflow value that is based on the frequency of non-exceedance. To compute the return period, we favor empirical percentiles over the standardized streamflow indicator as the former do not entail uncertainties due to the fitting of a probability distribution and can be computed for all streamflow time series. Drought severity should be assessed with indicators that imply habituation to a certain degree of interannual variability, to a certain reduction from mean streamflow, and to the ability to fulfill human water demand and environmental flows. Reservoir managers are best informed by the SDHIs of the grid cell that represents inflow into the reservoir. The DEWSs must provide comprehensive and clear explanations about the suitability of the provided indicators for specific risk systems.

Big Data for the Social Good: The Drought Early-Warning Experience Report

Data deluge is growing exponentially, but the consumption of the data is not growing at the same pace. DataOps is an emerging family of techniques and tools that harnesses the potential of data continuously whilst incrementally using complex cloud systems orchestration techniques. This paper offers a proof-of-concept implementation of a DataOps pipeline for the social good. Specifically, we prototype—using a combined field-lab Action Research and Design Science approach—a DataOps system to incrementally and iteratively mitigate the devastating effects of droughts for high-risk areas. The context of our study is a game reserve in the Waterberg area in the province Limpopo in South Africa. The objective of this paper is threefold: to (1) develop and study a proof of concept for DataOps, by (2) exploring the applicability of individual software components in a complex and large-scale continuous pipeline, and, finally (3) elaborate on the spatial classification of such components in a new-frontier Drought Early-Warning System (DEWS). As a result, we offer an overview of the challenges and opportunities laid bare by our experimentation in a complex societal scenario while combining Artificial Intelligence and DataOps technologies. We conclude that a combined model of local, regional, and global data performs best on all tests within a stakeholder-acceptable timeframe.

Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia

The objective of this study is to investigate and perform long–term forecasting of both streamflow and hydrological drought over Ethiopia. Observed streamflow and precipitation data are collected from 17 streamflow stations and 34 rainfall gauge stations to forecast future streamflow and hydrological drought from 2026 to 2099. Streamflow forecasting is performed using an artificial neural network (ANN) in conjunction with python software. Observed precipitation and streamflow data from 1973 to 2014 are used to train and test the ANN model by 70 and 30% ratios, respectively. After training the model, future downscaled precipitation data from regional climate models (RCM) have been used as input data to forecast future streamflow. Three RCM models were used to downscale historical and future climate data. RACMO is found a good downscaling model for all selected stations. The linear scaling bias correction technique results in less than 2% error compared to other alternative techniques. The result indicates that ANN is a good tool to forecast streamflow in areas having a good correlation between precipitation and streamflow such as Abbay, Awash, Baro, Omo Gibe, and Tekeze river basins. But in arid areas for example Genale Dawa, Wabishebele, and Rift Valley basins, the model is not suitable because the input data (precipitation) have high variation than the output variable (streamflow). In such areas, meteorological drought analysis and forecasting are better than hydrological drought analysis. Finally, future hydrological drought is analyzed using forecasted streamflow data as input to the streamflow drought index (SDI). The result indicates that 2028, 2036, 2042, 2044, 2062, and 2063 are the expected extreme drought years in most river basins of Ethiopia in the future. This shows that at least one extreme drought is expected in each decade in the future. Therefore, extensive research in drought analysis and forecasting is needed to develop an effective drought early warning system, and water resource management policy.

Getting ahead of Flash Drought: From Early Warning to Early Action

Abstract Flash droughts, characterized by their unusually rapid intensification, have garnered increasing attention within the weather, climate, agriculture, and ecological communities in recent years due to their large environmental and socioeconomic impacts. Because flash droughts intensify quickly, they require different early warning capabilities and management approaches than are typically used for slower-developing “conventional” droughts. In this essay, we describe an integrated research-and-applications agenda that emphasizes the need to reconceptualize our understanding of flash drought within existing drought early warning systems by focusing on opportunities to improve monitoring and prediction. We illustrate the need for engagement among physical scientists, social scientists, operational monitoring and forecast centers, practitioners, and policy-makers to inform how they view, monitor, predict, plan for, and respond to flash drought. We discuss five related topics that together constitute the pillars of a robust flash drought early warning system, including the development of 1) a physically based identification framework, 2) comprehensive drought monitoring capabilities, and 3) improved prediction over various time scales that together 4) aid impact assessments and 5) guide decision-making and policy. We provide specific recommendations to illustrate how this fivefold approach could be used to enhance decision-making capabilities of practitioners, develop new areas of research, and provide guidance to policy-makers attempting to account for flash drought in drought preparedness and response plans.

Establishing effective warning storage to derive optimal reservoir operation policy based on the drought condition

The conventional discrete hedging model is connected to a drought early warning system (DEWS) and a new concept called effective warning storage (ES). To prepare the desired model, an optimization model (genetic algorithm method) is connected to a simulation model to simultaneously search for threshold values (rule curves) and rationing coefficients as well as ES-related coefficients in the reservoir system of the Jarreh Dam located on the Zard River in southwestern Iran. The advantage of this method over conventional hedging models is that it responds earlier to drought periods and starts rationing earlier. With the application of the proposed rationing model, the objective function i.e., modified shortage index (MSI) in the historical period is better optimized about 6 % than the conventional rationing method, which shows the superiority of the new model in adjustment of large deficiencies and its distribution in other normal periods. The impact of the proposed method on the objective function in the future period is found to be small (about 1 %). It is mainly due to the drastic reduction of inflow to the reservoir in the future period (both in scenarios RCP4.5 and RCP8.5), so that the needs won't be fully met in most of the months. The results show that with the application of the proposed hedging policy, the objective function (MSI) in the historical period and also in the scenario of RCP4.5 and RCP8.5 is improved by 45 %, 21.3 % and 25.7 %, respectively, compared to the standard operation policy (SOP) method. Also, with the application of the hedging policy, the vulnerability of the system has been greatly improved in both the historical and future periods compared to the SOP method. System vulnerability decreases from 0.64 to 0.24 in the historical period while it changes from 0.62 to 0.44 and from 0.6 to 0.41 for the RCP4.5 and RCP8.5 scenarios.

Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation

The problem of drought in India is a major issue in terms of various adverse impacts on livelihood of society. Drought Early Warning System (DEWS), a real-time drought-monitoring tool, has reported that over a fifth of India's geographical area (21.06 %) is suffering drought-like situations. This is 62 % larger than the drought-affected area during the same period last year, which was 7.86 %. Drought affects 21.06 %, with conditions ranging from unusually dry to extremely dry. While 1.63 % and 1.73 % of the area are experiencing ‘extreme’ or ‘exceptional’ dry conditions, 2.17 % is experiencing ‘severe’ dry conditions. Under ‘moderate’ dry circumstances, up to 8.15 % is possible. In this perspective groundwater vulnerability assessment in the overall country is needed for implementing the sustainable and long-term strategies for escaping from this type of hazardous situation. The main objective of this study is to estimate the drought vulnerability in changing climate which eventually influences the food security of India. The groundwater overdraft is one of the crucial elements in agricultural drought vulnerability. Various related parameters have been selected for estimating the drought vulnerability and its impact to food security in India. Here, MaxEnt (maximum entropy) and ANN (analytical neural network) has been considered in this perspective. The AUC values for the training datasets in the ANN and MaxEnt model are 0.891 and 0.921, respectively. The AUC values in ANN and MaxEnt model for the validation datasets are 0.876 and 0.904, respectively. Here MaxEnt model is most optimal than ANN considering predictive accuracy. From this study analysis it is established that western, south and middle portion of country is very much prone to drought vulnerability. So, special emphases in terms of the regional planning have to be taken into consideration for sustainable planning.

Applying Machine Learning for Threshold Selection in Drought Early Warning System

This study investigates the relationship between the Normalized Difference Vegetation Index (NDVI) and meteorological drought category to identify NDVI thresholds that correspond to varying drought categories. The gridded evaluation was performed across a 34-year period from 1982 to 2016 on a monthly time scale for Grassland and Temperate regions in Australia. To label the drought category for each grid inside the climate zone, we use the Australian Gridded Climate Dataset (AGCD) across a 120-year period from 1900 to 2020 on a monthly scale and calculate percentiles corresponding to drought categories. The drought category classification model takes NDVI data as the input and outputs of drought categories. Then, we propose a threshold selection algorithm to distinguish the NDVI threshold to indicate the boundary between two adjacent drought categories. The performance of the drought category classification model is evaluated using the accuracy metric, and visual interpretation is performed using the heat map. The drought classification model provides a concept to evaluate drought severity, as well as the relationship between NDVI data and drought severity. The results of this study demonstrate the potential application of this concept toward early drought warning systems.

Towards effective drought monitoring in the Middle East and North Africa (MENA) region: implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco

Abstract. The Middle East and North Africa (MENA) region has experienced more frequent and severe drought events in recent decades, leading to increasingly pressing concerns over already strained food and water security. An effective drought monitoring and early warning system is thus critical to support risk mitigation and management by countries in the region. Here we investigate the potential for assimilation of leaf area index (LAI) and soil moisture observations to improve the representation of the overall hydrological and carbon cycles and drought by an advanced land surface model. The results reveal that assimilating soil moisture does not meaningfully improve model representation of the hydrological and biospheric processes for this region, but instead it degrades the simulation of the interannual variation in evapotranspiration (ET) and carbon fluxes, mainly due to model weaknesses in representing prognostic phenology. However, assimilating LAI leads to greater improvement, especially for transpiration and carbon fluxes, by constraining the timing of simulated vegetation growth response to evolving climate conditions. LAI assimilation also helps to correct for the erroneous interaction between the prognostic phenology and irrigation during summertime, effectively reducing a large positive bias in ET and carbon fluxes. Independently assimilating LAI or soil moisture alters the categorization of drought, with the differences being greater for more severe drought categories. We highlight the vegetation representation in response to changing land use and hydroclimate as one of the key processes to be captured for building a successful drought early warning system for the MENA region.

Systematic assessment of the development and recovery characteristics of hydrological drought in a semi-arid area

Studies have documented the significant effect of various factors on hydrological drought events. However, few studies have quantified drought's development and recovery process under environmental changes. This study focused on identifying hydrological drought's development and recovery characteristics and their potential causes in a typical semi-arid area. The Standardized Streamflow Index (SSI) was used as a metric for hydrological droughts, while the run theory was applied to identify the development and recovery processes of droughts. Changes in observed (human disturbed scenario) and simulated (natural scenario) droughts by employing the SWAT (Soil and Water Assessment Tool) model were also investigated from 1970 to 2016. The “simulated-observed” approach was used to assess the impacts of human regulations on hydrological drought development and recovery characteristics. Results showed that hydrological droughts occurred mainly during 1980–1990 and 2000–2016. In the natural condition, drought duration and intensity were higher, while lower severity in the drought recovery stage than development stage was observed. The drainage characteristics of the basin played the most critical role in the development and recovery characteristics of drought, which were also influenced by climatic conditions. Human activities had exacerbated recent natural hydrological drought. When considering the contribution of human activities, the reservoir operation was the dominant anthropic factor affected the development and recovery process of drought in the study area. Under the effects of reservoir regulation, long-duration hydrological droughts became rare. Moreover, the recovery ability of drought had been weakened. The effects of the reservoir were progressively crucial gradually. Although the water got from the river by the reservoir had been reduced, the negative impact on aggravating drought remains stronger than the reservoir was initially constructed. The results of our study will help improve the optimal management of reservoirs in semi-arid areas and enhance drought early warning and forecasting system.

Catchment memory explains hydrological drought forecast performance

Hydrological drought forecasts outperform meteorological ones, which is anticipated coming from catchment memory. Yet, the importance of catchment memory in explaining hydrological drought forecast performance has not been studied. Here, we use the Baseflow Index (BFI) and the groundwater Recession Coefficient (gRC), which through the streamflow, give information on the catchment memory. Performance of streamflow drought forecasts was evaluated using the Brier Score (BS) for rivers across Europe. We found that BS is negatively correlated with BFI, meaning that rivers with high BFI (large memory) yield better drought prediction (low BS). A significant positive correlation between gRC and BS demonstrates that catchments slowly releasing groundwater to streams (low gRC), i.e. large memory, generates higher drought forecast performance. The higher performance of hydrological drought forecasts in catchments with relatively large memory (high BFI and low gRC) implies that Drought Early Warning Systems have more potential to be implemented there and will appear to be more useful.

A Bibliometric Analysis of Drought Indices, Risk, and Forecast as Components of Drought Early Warning Systems

In this study, we apply a bibliometric analysis to characterize publication data on droughts, mainly focusing on drought indices (DIs), drought risk (DR), and drought forecast (DF). Data on publications on these selected topics were obtained through the Scopus database, covering the period from 1963 to June 2021. The DI-related publications, based on meteorological, soil moisture, hydrological, remote sensing, and composite/modeled Dis, accounted for 57%, 8%, 4%, 29%, and 2% of the scientific sources, respectively. DI-related studies showed a notable increase since the 1990s, due perhaps to a higher number of major droughts during the last three decades. It was found that USA and China were the two leading countries in terms of publication count and academic influence on the DI, DR, and DF studies. A network analysis of the country of residence of co-authors on DR and DF research highlighted the top three countries, which were the USA, China, and the United Kingdom. The most productive journal for the DI studies was found to be the International Journal of Climatology, whereas Natural Hazards was identified as the first-ranked journal for the DR and DF studies. In relation to individual researchers, Singh VP from the USA was found to be the most prolific author, having the greatest academic influence on DF study, whereas Zhang Q from China was identified as the most productive author on DR study. This bibliometric analysis reveals that further research is needed on droughts in the areas of risk management, water management, and drought management. This review maps trends of previous research in drought science, covering several important aspects, such as drought indices, geographic regions, authors and their collaboration paths, and sub-topics of interest. This article is expected to serve as an index of the current state of knowledge on drought warning systems and as guidance for future research needs.

Development of a Drought Early Warning System based on the Prediction of Agricultural Productivity: A Data Science Approach

Development of a Drought Early Warning System based on the Prediction of Agricultural Productivity: A Data Science Approach

Drought propagation modification after the construction of the Three Gorges Dam in the Yangtze River Basin

The construction of Three Gorges Dam (TGD) has exerted substantial effects on local hydrological regime and eco-system since its operation in 2003. Due to this significant anthropogenic activity, drought propagation processes from Meteorological drought to Hydrological (M-H) and Soil moisture (M-S) drought are inevitably modified. However, current studies are still insufficient to answer whether the TGD operation mitigated or exacerbated the drought propagation process due to its complex impacts. To explore this key issue, this study proposed an integrated framework to quantify the impact of TGD construction on drought propagation process in the Yangtze River Basin (YRB). The drought characteristics propagation models and propagation time were calculated to identify the modification of drought propagation process between different TGD operation scenarios. Trend and attribution analyses were also utilized to explore potential influence factors. Results demonstrated that TGD construction did significantly aggravate the drought characteristics propagation. Specifically, the slopes of hydrological drought duration and severity in linear propagation models increased 23.98% and 29.23% in average. While for M-S propagation, the slopes of duration and severity models increased 28.72% and 21.91%, respectively. This research also found that, after TGD operation, the M-H propagation speed had been slowed down but the M-S lag time became shorter. Additionally, the variations and trend patterns of six potential influence factors at four separate periods were full analyzed to provide implications for the drought propagation modifications. And TGD operation was confirmed to become a major factor inducing the variations of annual runoff, which accounted for 56.75% for the entire YRB. The above quantitative findings are expected to reveal the potential changes of drought propagation process caused by the construction of TGD in YRB. The proposed integrated framework is also beneficial to the drought early warning system and understanding of drought development not only in YRB, but also in other basins at the global scale.

Determining the relationship of evapotranspiration with precipitation and temperature over Turkey

Evapotranspiration (ET), which is a combination of the words evaporation and transpiration, is expressed as the sum of water due to water consumption and evaporation in plants. In this study, the NASA Drought Early Warning Systems Network Land Data Assimilation System model (FLDAS NOAH) was used to determine evapotranspiration data for the 2018-2019 water years (October 2017-September 2019) on Turkey. In addition, NASA Global Land Data Assimilation System (GLDAS NOAH) was used for temperature data. Total precipitation data with corrected error rates are taken from MERRA-2 Model M2TMNXFLX. The relationship between the determined monthly total average evapotranspiration values and the monthly average precipitation and monthly average temperature values was determined by ArcGIS. It is important to examine the local evaporation and transpiration conditions in more detail in the regions where water resources planning will be made. The importance of water holding capacity in plants in determining agricultural and hydrological drought can be explained by evapotranspiration.

Drought Early Warning in Agri-Food Systems

Droughts will increase in frequency, intensity, duration, and spread under climate change. Drought affects numerous sectors in society and the natural environment, including short-term reduced crop production, social conflict over water allocation, severe outmigration, and eventual famine. Early action can prevent escalation of impacts, requiring drought early warning systems (DEWSs) that give current assessments and sufficient notice for active risk management. While most droughts are relatively slow in onset, often resulting in late responses, flash droughts are becoming more frequent, and their sudden onset poses challenging demands on DEWSs for timely communication. We examine several DEWSs at global, regional, and national scales, with a special emphasis on agri-food systems. Many of these have been successful, such as some of the responses to 2015–2017 droughts in Africa and Latin America. Successful examples show that early involvement of stakeholders, from DEWS development to implementation, is crucial. In addition, regional and global cooperation can cross-fertilize with new ideas, reduce reaction time, and raise efficiency. Broadening partnerships also includes recruiting citizen science and including seemingly subjective indigenous knowledge that can improve monitoring, data collection, and uptake of response measures. More precise and more useful DEWSs in agri-food systems will prove even more cost-effective in averting the need for emergency responses, improving global food security.