Agricultural Drought Assessment Research Articles

Hydrological modelling for post-monsoon agricultural drought assessment and implications for the agro-economy

Reliable drought monitoring is a prerequisite for minimizing potential agricultural losses. Soil moisture is a key variable for monitoring agricultural drought assessment. The study conducted in the Bundelkhand region of Uttar Pradesh uses a macroscale variable infiltration capacity (VIC) hydrological model to simulate soil moisture and calculate soil moisture deficit index (SMDI) for agricultural drought assessment for the Rabi crop growing season from 1998 to 2016. Crop yield (CY) was linked with SMDI and other covariates using random forest (RF) machine learning (ML) based regression technique. The results show that the VIC model effectively simulated root zone soil moisture when compared with the reference data. Major droughts were identified in years 2000–01, 2007–08, and 2015–16 in the study region. The RF-based crop yield prediction accuracy improved when irrigational factors were added. The study provides a noteworthy reference for drought assessment and prevention, water resource management, and ensuring food security.

Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation

Abstract. Satellite-derived agricultural drought indices can provide a complementary perspective of terrestrial vegetation trends. In addition, their integration for drought assessments under future climates is beneficial for providing more comprehensive assessments. However, satellite-derived drought indices are only available for the Earth observation era. In this study, we aim to improve the agricultural drought assessments under future climate change by applying deep learning (DL) to predict satellite-derived vegetation indices from a regional climate simulation. The simulation is produced by the Terrestrial Systems Modeling Platform (TSMP) and performed in a free evolution mode over Europe. TSMP simulations incorporate variables from underground to the top of the atmosphere (ground-to-atmosphere; G2A) and are widely used for research studies related to water cycle and climate change. We leverage these simulations for long-term forecasting and DL to map the forecast variables into normalized difference vegetation index (NDVI) and brightness temperature (BT) images that are not part of the simulation model. These predicted images are then used to derive different vegetation and agricultural drought indices, namely NDVI anomaly, BT anomaly, vegetation condition index (VCI), thermal condition index (TCI), and vegetation health index (VHI). The developed DL model could be integrated with data assimilation and used for downstream tasks, i.e., for estimating the NDVI and BT for periods where no satellite data are available and for modeling the impact of extreme events on vegetation responses with different climate change scenarios. Moreover, our study could be used as a complementary evaluation framework for TSMP-based climate change simulations. To ensure reliability and to assess the model’s applicability to different seasons and regions, we provide an analysis of model biases and uncertainties across different regions over the pan-European domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation of the long-term TSMP simulation using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K with coefficient of determination (R2) scores of 0.88 and 0.92 for the NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT satellite images, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP, last access: 4 April 2024).

Improving remote sensing based agricultural drought characterization in Saurashtra, Gujarat : A region-specific threshold approach

Remote sensing technology has demonstrated its significant utility in the monitoring and mapping of agricultural drought on a global scale. This study focused on the assessment of agricultural drought in the Saurashtra region of Gujarat, India, utilizing a comprehensive dataset spanning 33 years from Landsat and Sentinel satellites. It employed various vegetation indices, including NDVI (Normalized Difference Vegetation Index), Anomaly Index (NAI), Vegetation Condition Index (VCI) and NDWI Anomaly index (NDWIA), to gauge drought conditions. The performance of these indices was evaluated through the generation of drought severity maps and their correlation analysis with major Kharif crops in the region, specifically cotton and groundnut. The analysis pinpointed major agricultural drought years, such as 1986, 1987, 1991, 2000, 2002 and 2012, which corresponded to substantial crop yield losses ranging from 37% to 76% for cotton and 66% to 95% for groundnut, varying by district. Despite VCI demonstrating equivalent or superior correlations with crop yields (ranging from 0.32 to 0.73 for cotton and 0.33 to 0.75 for groundnut) compared to NAI in various districts, it tended to underestimate drought severities, designating only 2 to 9 drought years for different districts. Consequently, this study recommends revised VCI drought severity thresholds, which enhance the categorization of agricultural drought in terms of severity levels and corresponding yield losses for cotton and groundnut in the Saurashtra region of Gujarat. Furthermore, it underscores the need to establish region-specific drought severity thresholds by identifying the most suitable vegetation index for effective quantification of agricultural drought, thereby facilitating informed drought mitigation measures.

Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery

Abstract Climate change poses an urgent global challenge in water resource management, with drought emerging as a pervasive threat worldwide. Over the past two decades, Iraq has faced severe droughts, categorized into meteorological, agricultural, hydrological, and socioeconomic types. Agricultural drought, marked by prolonged soil moisture deficits due to insufficient rainfall, often leads to crop failures. This comprehensive study delves into the impact of drought on Iraq’s vegetation cover from 2000 to 2022, utilizing advanced tools like remote sensing (RS) and geographic information systems. The normalized difference vegetation index (NDVI) facilitated the creation of multitemporal drought maps. Employing Landsat satellite imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, the study revealed 2000, 2008, 2009, 2010, 2012, and 2022 as the most drought-prone years. In specific years such as 2000, 2008, 2010, and 2009, Landsat imagery showcased the lowest vegetation cover percentages (3.356, 4.984, 5.112, and 5.409%, respectively), while MODIS data indicated the lowest percentages in 2008, 2000, 2009, 2012, and 2022 (11.122, 11.260, 12.580, 13.026, and 14.445%, respectively). This study underscores the pivotal role of RS, particularly NDVI, as a valuable tool for agricultural drought early warning systems. The findings highlight the effectiveness of NDVI as a simple and cost-efficient index for monitoring changes in vegetation conditions and assessing the impact of droughts on agriculture.

Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress

Soil water balance is an essential element to consider for the management of droughts and agricultural land use. It is important to evaluate the water consumption of a crop in each of its phenological phases and the status of water reserves during critical hydrologic periods. This study developed an agricultural drought index (Standardized Soil Moisture Deficit Index - SMODI) conceptualized with a water balance model considering the vegetation stress caused by soil moisture deficit. This contribution was based on meteorological information, soil moisture from satellite images, hydrophysical properties of the soil and crop evapotranspiration. Information from 61 weather stations located in the dry zone of Tolima was used for estimating the water balance. SMODI was compared with the most common drought indexes: Standardized Precipitation - Evapotranspiration Index (SPEI), the Palmer Self-Calibrated Drought Index (scPDSI), and other eleven macroclimatic indexes. Pearson's correlation coefficients (r), Tukey's test, and analysis of variance were applied to analyze the degree of association between SMODI and the contrasting indexes on a quarterly basis. SMODI considers factors influencing soil moisture distribution and retention and the water stress thresholds that plants have evolved to withstand during drought periods. Consequently, this integrated approach enhances the assessment of agricultural drought by relying on pertinent physical processes. SMODI identified extremely dry, severe, moderate and normal drought 5 %, 3 %, 20 % and 72 % respectively conditions in areas characterized by Entisols, Inceptisols, and Andisols, where rice and fruit crops and pasturelands are cultivated. The SMODI has a good correlation with macroclimatic indexes (0.70 < r < 0.74).

Spatio-temporal assessment of agricultural drought using remote sensing and ground-based data indices in the Northern Ethiopian Highland

Study regionNorthern Ethiopian highland, North Wollo, Ethiopia Study focusDrought is one of the most serious environmental and socioeconomic problems of Sub-Sahara Africa's countries. The study aims to assess and evaluate the agricultural drought occurrence and its severity by using remote sensing-based multivariate indices to provide real-time spatial-temporal information for drought monitoring. Satellite datasets from Landsat images with a spatial resolution of 30 m were processed and rescaled to compute vegetation and temperature indices and derive drought status indices. The temporal and spatial variability of drought were detected and mapped by using satellite-based drought indices and validated by available in-situ data. New hydrological insights for the regionThe study revealed that the area was exposed to extreme drought in 1984, 2009, and 2014. On the contrary, no longer extreme drought dominated in 1989, 1994, 1999, and 2004. Moreover, the relationships between various indices have been examined by using a linear regression model. It has been detected that there is a significant strong relationship between NDVI and LST (R2 /p = 0.93/0.00), VCI and TCI (R2 /p = 0.71/0.00), VHI and SPI (R2/p = 0.85/0.00). Therefore, the spatial extent and temporal assessment of drought-related information are important for decision-making in the field of agriculture.

Agricultural drought response to meteorological drought over different agro-climatic zones of the Ganga River basin

Abstract Drought directly impacts the agricultural ecosystem, thus causing significant threat to regional and global food security. Investigating the occurrence and propagation patterns of drought events is crucial for its better understanding and mitigation. The study investigates different agro-climatic regions of the Ganga River basin from 2001 to 2020 to quantify meteorological drought using the Standardized Precipitation Index (SPI). Additionally, the assessment of agricultural drought was conducted using the Standardized Soil Moisture Index (SSMI) and Normalized Difference Vegetation Index (NDVI). For understanding the propagation dynamics of drought, Pearson Correlation Coefficient (PCC)-based approach was employed to compute the propagation time between meteorological and agricultural drought types. Stronger correlations were observed between SPI and SSMI compared to SPI and NDVI anomaly, highlighting the direct connection between precipitation and soil moisture. The results of the present study show that the time for propagation ranges within 1–11 months across the Ganga basin as inferred from the maximum PCC values between the SPI and SSMI time series. The propagation rate from meteorological drought to agricultural drought varied from 29.03 to 73.33% among different agro-climatic regions. The insights gained from this analysis on propagation time and rate can inform policymakers in formulating appropriate measure.

A Bio-inspired and Deep Learning Based Hybrid Model for Agricultural Drought Assessment

Agricultural droughts can cause many serious hazards. Drought monitoring indices, namely Normalized Difference Vegetation Index (NDVI), Atmospherically Resistant Vegetation Index (ARVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI) have been used for an agricultural drought assessment. Satellite images from the Kolar region of Karnataka are used to calculate these indices. This paper proposes an integration model based on Convolutional Neural Networks (CNN) and a bio-inspired algorithm (Sparrow Search Algorithm (SSA) and Barnacles Mating Optimizer (BMO)) considering the indices as population. Performance is compared with the standalone CNN model in terms of efficiency. For the CNN, the accuracy, time taken for Epoch1, and time taken for Epoch2 is 91%, 16s (3s/step), and 2s (2s/step), respectively. For the CNN integrated with SSA, it is 94%, 3s (3s/step) and 0s (43ms/step), respectively. For the CNN integrated with BMO, it is 94%, 3s (2s/step) and 0s (46ms/step) respectively.

Assessing meteorological and agricultural drought characteristics and drought propagation in Guangdong, China

Study regionGuangdong, China Study focusDroughts have resulted in significant crop failure in Guangdong province, where annual crop yield exceed 12 million tons. However, there is a lack of a comprehensive assessment of both meteorological drought and agricultural drought in Guangdong, particularly regarding the influencing factors and drought propagation. This study aims to address this gap by utilizing the standardized precipitation evapotranspiration index (SPEI) and soil water deficit index (SWDI) to represent meteorological drought and agricultural drought, respectively. To examine the characteristics of drought in the past 50 years (1973–2022), the year is divided into flood and non-flood season based on the temporal distribution of precipitation. New hydrological insights for the regionOver the last five decades, the number of meteorological and agricultural drought days decreases from coastal to inland areas. Notably, agricultural droughts have become more pronounced in the last two decades. More agricultural drought days appears during the non-flood season than the flood season. Precipitation emerges as the most influential factor for meteorological drought, while potential evapotranspiration, actual evapotranspiration and temperature are identified as the primary factors for agricultural drought. The drought propagation time ranges from 10 to 45 days which is shorter in central Guangdong than other regions, and it is longer in non-flood season than in flood season.

Revolutionizing Agriculture with Satellite Technology for Farmers: A Review

Farmers in India have benefited considerably from the combination of satellite-enabled services and data obtained on the ground. The India Meteorological Department, Ministry of Earth Sciences, provides weather forecasting, agro-advisory services, agromet services, soil moisture monitoring, and agricultural extension initiatives to encourage agricultural operations in India. Indian Space Research Organization?s (ISRO) also partners with the Ministry of Agriculture and Farmers Welfare on several satellite data and geographic information systems-based agricultural applications. ISRO, in collaboration with the Ministry of Agriculture and Farmers Welfare, has developed applications including horticultural crop inventory and site suitability for expansion in unutilized places; crop assessment using medium- and high-resolution satellite data; field information gathering with field photos using a smartphone application; and crop cutting experiments based on satellite-derived crop vigor information. ISRO has provided technology for FASAL and the National Agricultural Drought Assessment and Surveillance System to the Department of Agriculture Cooperation and Farmer Welfare. ISRO has also incorporated the Central Water Commission?s monitoring of irrigation systems. Overall, satellite-enabled services have transformed agricultural operations in India by providing farmers with precise and timely data that enable them to make educated decisions about their crops, resulting in increased crop yields and financial returns.. KEYWORDS :Agriculture, Farming, Satellite technology, Meteorology, Space organization

Assessment of Different Satellite Image-Derived Drought Indices over the Contiguous United States: A Comparison in Different Climates, Vegetation Cover Types, and Soil Layers

Agricultural drought assessment is based on soil moisture deficit during the plant-growing season. The available long-term in situ soil moisture data can be used to evaluate the drought indices’ performance. Drought indices have different sensitivities to various climates and vegetation cover types. Moreover, little research has been carried out on the sensitivity to soil moisture in specific soil layers. Comparing the performance of drought indices in different environments enables us to use and construct an effective drought index for drought delineation. We used soil moisture data from 2000 to 2021 to compare the performance of satellite image-derived drought indices (VCI, NLSWI, TCI, VHI, and NVSWI) in different climates, vegetation cover types, and soil layers over the Contiguous United States at monthly intervals and a 1000 m spatial resolution. The performances of drought indices based on vegetation status showed little differences in the different soil layers. In contrast, drought indices based on single-temperature conditions performed better in the 0–20 cm soil layer than in the deeper soil layer. The integrated index might not improve the correlation in all soil layers compared with single-condition indices under some environments. In general, while the optimal drought index might vary in different environments, the VHI performed most efficiently in most cases. This research has the potential to improve our understanding of drought monitoring in large regions with different climates, vegetation cover types, and soil layers.

A Modified Temperature Vegetation Dryness Index (mTVDI) for Agricultural Drought Assessment Based on MODIS Data: A Case Study in Northeast China

Satellite-based drought indices have been shown to be effective and convenient in detecting drought conditions. The temperature vegetation dryness index (TVDI) is one of the most frequently used drought indices; however, it is not suitable for areas with high fractional vegetation cover (FVC). In this study, a modified temperature vegetation dryness index (mTVDI) was constructed by using the multispectral vegetation dryness index (MVDI) proposed by a PROSAIL simulation and water stress experiments which was based on the theory of the TVDI and utilized MODIS data. Compared with the TVDI, the mTVDI presents a more triangular feature space, as well as obviously increased R2 values for dry and wet edges (from 0.37–0.90 to 0.53–0.91 for dry edges and from 0.00–0.77 to 0.24–0.80 for wet edges). The mTVDI was evaluated using standardized precipitation evapotranspiration indices (SPEIs), precipitation, potential evapotranspiration (PET), and the crop water deficit index (CWDI), and the results confirmed that the mTVDI can better reflect the actual spatial changes, compared to the TVDI, under high FVC, as well as presenting an increased Pearson correlation coefficient (by 0.06–0.10) when compared with SPEIs. Moreover, the good performance of the mTVDI in major drought events indicates its reliability and accuracy for drought monitoring. Overall, the mTVDI is a reliable and accurate satellite-based dryness index suitable for high FVC conditions.

Assessment of agricultural drought based on multi-source remote sensing data in a major grain producing area of Northwest China

Assessment of agricultural drought based on multi-source remote sensing data in a major grain producing area of Northwest China

The Suitability Assessment of Agricultural Drought Monitoring Indices: A Case Study in Inland River Basin

Drought monitoring is an important scientific basis for drought impact evaluation and the selection of mitigation strategies. Since the drivers of drought vary among regions, there is no universal drought index applicable to different regions. The Shiyang River Basin, an inland river basin, located in Gansu Province, China, has a closed water cycle system. Drought is a dominant nature disaster for the sustainable development of the region. Thus, this is an ideal area to explore the suitability of drought-monitoring indices. Here, we took the Shiyang River Basin as an example, in order to explore suitable indicators for agricultural drought monitoring in inland river basins. This study assessed the twelve different widely used drought indices used for monitoring the impact of drought on crop growth, represented by net primary production (NPP). The results showed that the vegetation status-based drought indices (VCI and NVSWI) had the highest significant correlation (0.6 ≤ |R| ≤ 1, p &lt; 0.05) on NPP, and the integrated drought indices (DSI and ISDI) had the strong significant correlation (0.4 ≤ |R| &lt; 0.6, p &lt; 0.1). These four indices are good indicators for agricultural drought monitoring. Studies based on these four indices showed that agricultural drought has a tendency to slow down from 1982 to 2020. This is inconsistent with the monitoring of drought indices based on the meteorological variables that show a trend of increasing drought. This is mainly due to the increased efficiency of water management and its use in inland river basins. This indicates that other water resource information, such as runoff, should be included to construct an integrated agricultural drought-monitoring indices in management intensive regions, such as in an inland river basin.

Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data

ABSTRACT Using a multivariate drought index that incorporates important environmental variables and is suitable for a specific geographical region is essential to fully understanding the pattern and impacts of drought severity. This study applied feature scaling algorithms to MODIS time-series imagery to develop an integrated Multivariate Drought Index (iMDI). The iMDI incorporates the vegetation condition index (VCI), the temperature condition index (TCI), and the evaporative stress index (ESI). The 54,474 km2 Vietnamese Central Highlands region, which has been significantly affected by drought severity for several decades, was selected as a test site to assess the feasibility of the iMDI. Spearman correlation between the iMDI and other commonly used spectral drought indices (i.e. the Drought Severity Index (DSI–12) and the annual Vegetation Health Index (VHI–12)) and ground-based drought indices (i.e. the Standardized Precipitation Index (SPI–12) and the Reconnaissance Drought Index (RDI–12)) was employed to evaluate performance of the proposed drought index. Pixel-based linear regression together with clustering models of the iMDI time-series was applied to characterize the spatiotemporal pattern of drought from 2001 to 2020. In addition, a persistent area of LULC types (i.e. forests, croplands, and shrubland) during the 2001–2020 period was used to understand drought variation in relation to LULC. Results suggested that the iMDI outperformed the other spectral drought indices (r > 0.6; p < 0.005). The analysis revealed an increase in drought risk in some provinces of the Central Highlands including Gia Lai, Kon Tum, and Dak Lak. It was also found that changes in LULC patterns could minimize (reforestation) or exacerbate (deforestation) the impacts of drought. Our study suggests that applying a multivariate drought index enables a better understanding of drought patterns at the local scale. This provides valuable information for the development of appropriate land and environmental management practices that can affect and mitigate climate change effects.

A review of agricultural drought assessment with remote sensing data: methods, issues, challenges and opportunities

A review of agricultural drought assessment with remote sensing data: methods, issues, challenges and opportunities

Agricultural Drought Assessment in Nine Agricultural Regions of China through Cross-Validation of Multiple Data Products and Multiple Drought Indices

Agricultural Drought Assessment in Nine Agricultural Regions of China through Cross-Validation of Multiple Data Products and Multiple Drought Indices

Development of PCA-based composite drought index for agricultural drought assessment using remote- sensing

The present study was conducted in the Saurashtra region of Gujarat to demonstrate the development and validation of location and crop-specific composite drought index (CDI) using a linear combination of three parameters including meteorological drought index, vegetation drought index and inverse of maximum consecutive dry days%for major Kharif crops of the region i.e. cotton and groundnut. The performance of nine drought indices including six meteorological and three remote sensing-based vegetation indices wasevaluated in terms of correlation with district scale crop yields.The district-wise expressions of CDI were developed by assigning principal component analysis (PCA) based weights to parameters.Standardized Precipitation Evapotranspiration Index (SPEI)/ Reconnaissance Drought Index (RDI) among meteorological indices and NDVI Anomaly Index (NDVIA)/ Vegetation Condition Index (VCI) among vegetation indices were found suitable for generating district specific CDI expressions. The developed CDI showed higher correlation with Kharif cotton and Groundnut crop yields as compared to various meteorological as well as vegetation indices used in the study and effectively quantified major historic agricultural droughts.The average correlation coefficients of developed CDI with cotton and groundnut yields were 0.71 and 0.77 respectively. The correlations of CDI and crop yields for all CDI expression were highly significant with p&lt;0.01. The method developed in the study will be useful to generate crop and region-specific multi-scalar drought indices by the amalgamation of multiple drought indices for assessing crop production losses.

Use of Remote-Sensing-Based Global Products for Agricultural Drought Assessment in the Narmada Basin, India

Droughts exert severe impacts on the environment, economy, and society. The south Asian region is vulnerable to droughts and the Indian sub-continent is one of the most vulnerable in the region to frequent drought disasters. This study assesses the agricultural droughts in the Narmada River Basin (NRB), India, where more than 50% of the area is utilized for agriculture, through freely available local and global remote-sensing-based data focusing on long-term rainfall trends (1989–2018) and recently weakened monsoons in 2017 and 2018. In this study, some of the widely used indices to characterize droughts (viz., Standardized Precipitation Index (SPI), simplified Rainfall Index (RIs), Normalized Difference Vegetation Index (NDVI)), soil moisture content, and reservoir surface areas were used to assess the drought conditions in the Narmada River Basin over the study period. Our analysis shows that the NRB has experienced a decreasing trend in monsoon rainfall over the past three decades. The SPI captured most of the basin’s historical droughts. The weakened monsoons during 2017–2018 show that different parts of the NRB have experienced severe or moderate drought conditions. A clear difference does not show in the NDVI and in the soil moisture contents of the basin over three hydrological years (2015/16, 2016/17, and 2017/18), except for July to September 2017/18. The estimated water area depletion using the Normalized Difference Water Index (NDWI) follows the actual water levels in three selected reservoirs in the basin, of which, two show a decline in the maximum surface area, likely due to the weakened monsoons in 2017 and 2018. This research indicates that the freely available data can be beneficial for local authorities to monitor and understand the drought conditions to support water resources management and planning for agricultural activities.

Agricultural drought assessment and monitoring using MODIS-based multiple indices: the case of North Wollo, Ethiopia.

Agriculture is the most sensitive sector which has largely been affected by the impacts of drought. The study aims to detect and characterize agricultural droughts using MODIS-based multiple indices in North Wollo, Ethiopia. Two Moderate Resolution Imaging Spectroradiometer (MODIS) datasets (MOD13Q1 and MOD11A2) for the period 2000 to 2019 were used to generate Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Accordingly, NDVI anomaly, Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI) were computed to characterize agricultural droughts during the crop growing season. Both the NDVI anomaly and VCI confirmed that there was no single drought-free year in the area throughout the study period. TCI showed relatively exaggerated drought stress than the other indices. However, VHI indicated lower area coverage and a lower level of stress than its aggregates (VCI and TCI). Specifically, 2002, 2004, 2009, 2010, and 2015 were all identified as severe drought years, where over 60% of the area was affected by droughts. Results of the regression analysis indicated that VCI, TCI, and VHI were having significant positive trends with precipitation in the majority of the districts. Using the aggregated drought frequency of each index, 13.5, 73.7, and 12.8% of the area were under moderate, high, and extremely high levels of agricultural drought occurrence, respectively, and the likelihood of implied risks. Therefore, all the districts of North Wollo were affected by persistent drought stress. Such drought recurrences have the potential to impose significant impacts on the agro-based livelihoods of the local community demanding ongoing drought monitoring and the application of effective early warning systems.