Drought Monitoring Research Articles

A dataset of annual TCI, VCI, VHI and TVDI with the resolution of 1 km in the Yellow River Basin (2003–2022)

Primarily situated in arid and semi-arid climatic zones, characterized by naturally limited water resources, the Yellow River Basin is the most drought-affected among China's major basins. As global environmental and climate change continue to evolve, droughts in the Yellow River Basin have become increasingly frequent, making drought monitoring research a current focal point. Based on MODIS vegetation data and surface temperature products, this dataset undergoes quality control procedures such as cloud removal and data reconstruction to collect the data of annual Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI) and Temperature Vegetation Dryness Index (TVDI) in the Yellow River Basin from 2003 to 2022. The spatial scope of this dataset ranges from 32°10′N–41°50′N，95°53′E–119°05′E. The dataset is in GeoTiff format with a spatial resolution of 1km. It can provide foundational data and scientific support for drought disaster monitoring in the Yellow River Basin.

Projection of meteorological drought in Türkiye’s Mediterranean region based on multi-model ensemble from CMIP6

ABSTRACT In this study, drought projections in Türkiye’s Mediterranean region under future climate scenarios (Shared Socioeconomic Pathways SSP2-4.5 and SSP5-8.5) were investigated using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). A multi-model ensemble (MME) was created with the General Circulation Models (GCMs) that best predicted observed precipitation and temperature values using Artificial Neural Network (ANN)-based statistical downscaling. The MME outputs assessed changes in drought indices for 2021–2060 and 2061–2100, compared to the baseline period of 1979–2020. Results indicate that future drought indices calculated by SPEI are more severe than those using SPI, highlighting the significance of potential evapotranspiration (PET) in drought monitoring. There is a notable increase in PET, especially in coastal regions, projected to rise to 120 mm/year by the century’s end under SSP5-8.5. Due to significant temperature increases, the expected rise in drought severity and frequency is more pronounced in mountainous areas.

Comparison of Meteorological Drought Indices in Tanzania Using the Meteorological Drought Monitoring (MDM) Software Package

Comparison of Meteorological Drought Indices in Tanzania Using the Meteorological Drought Monitoring (MDM) Software Package

Deep Learning for Flash Drought Detection: A Case Study in Northeastern Brazil

Flash droughts (FDs) pose significant challenges for accurate detection due to their short duration. Conventional drought monitoring methods have difficultly capturing this rapidly intensifying phenomenon accurately. Machine learning models are increasingly useful for detecting droughts after training the models with data. Northeastern Brazil (NEB) has been a hot spot for FD events with significant ecological damage in recent years. This research introduces a novel 2D convolutional neural network (CNN) designed to identify spatial FDs in historical simulations based on multiple environmental factors and thresholds as inputs. Our model, trained with hydro-climatic data, provides a probabilistic drought detection map across northeastern Brazil (NEB) in 2012 as its output. Additionally, we examine future changes in FDs using the Coupled Model Intercomparison Project Phase 6 (CMIP6) driven by outputs from Shared Socioeconomic Pathways (SSPs) under the SSP5-8.5 scenario of 2024–2050. Our results demonstrate that the proposed spatial FD-detecting model based on 2D CNN architecture and the methodology for robust learning show promise for regional comprehensive FD monitoring. Finally, considerable spatial variability of FDs across NEB was observed during 2012 and 2024–2050, which was particularly evident in the São Francisco River Basin. This research significantly contributes to advancing our understanding of flash droughts, offering critical insights for informed water resource management and bolstering resilience against the impacts of flash droughts.

Domain knowledge-driven variational recurrent networks for drought monitoring

In the context of climate change, droughts, increasingly frequent and severe, necessitate effective monitoring. Existing methods, such as drought indices and data-driven models, face important limitations. Drought indices are built on prior expert knowledge but lack calibration based on actual drought events, while data-driven models prioritize goodness of fit over real event identification, undermining their credibility and generalization, and also struggling to generalize from regional to large-scale contexts. To address these challenges, here we introduce a hybrid machine learning framework for time series that combines domain knowledge and observational data in a variational recurrent neural network. The network models the joint distribution of total precipitation, air temperature, and real drought events, providing accurate predictions and uncertainty estimates. Extensive experiments focusing on a wide range of European drought events from 2011 to 2018 consistently show that our hybrid model surpasses both drought indices and data-driven models in terms of accuracy in drought detection, underlining its effectiveness, robustness, and stability. Our model achieves the best ROC-AUC (%) results in Afghanistan (79.7 ± 0.5), Italy (84.3 ± 0.6), Russia (89.4 ± 0.2), Europe-0 (84.3 ± 0.1), and Europe-1 (82.8 ± 0.4), effectively capturing the starting and ending times of drought events with lower uncertainty, and also generalizing better for unseen locations.

Drought monitoring in Burdur Lake, Turkey using multi-sensor remote sensing data sets

Drought monitoring in Burdur Lake, Turkey using multi-sensor remote sensing data sets

Drought Monitoring Using MODIS Derived Indices and Google Earth Engine Platform for Vadodara District, Gujarat

Drought Monitoring Using MODIS Derived Indices and Google Earth Engine Platform for Vadodara District, Gujarat

A hybrid ensemble learning merging approach for enhancing the super drought computation over Lake Victoria Basin

This study introduces a novel Hybrid Ensemble Machine-Learning (HEML) algorithm to merge long-term satellite-based reanalysis precipitation products (SRPPs), enabling the estimation of super drought events in the Lake Victoria Basin (LVB) during the period of 1984 to 2019. This study considers three widely used Machine learning (ML) models, including RF (Random Forest), GBM (Gradient Boosting Machine), and KNN (k-nearest Neighbors), for the emerging HEML approach. The three SRPPs, including CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station), ERA5-Land, and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record), were used to merge for developing new precipitation estimates from HEML model. Additionally, classification and regression models were employed as base learners in developing this algorithm. The newly developed HEML datasets were compared with other ML and SRPP products for super-drought monitoring. The Standardized precipitation evapotranspiration index (SPEI) was used to estimate super drought characteristics, including Drought frequency (DF), Drought Duration (DD), and Drought Intensity (DI) from machine learning and SRPPs products in LVB and compared with RG observation. The results revealed that the HEML algorithm shows excellent performance (CC = 0.93) compared to the single ML merging method and SRPPs against observation. Furthermore, the HEML merging product adeptly captures the spatiotemporal patterns of super drought characteristics during both training (1984–2009) and testing (2010–2019) periods. This research offers crucial insights for near-real-time drought monitoring, water resource management, and informed policy decisions.

A novel GNSS and precipitation-based integrated drought characterization framework incorporating both meteorological and hydrological indicators

The Global Navigation Satellite System (GNSS) has become instrumental in developing drought indices, particularly meteorological drought indicators derived from atmospheric precipitable water vapor and hydrological drought indicators based on inverted terrestrial water storage changes. However, these indices traditionally focus on individual aspects of droughts, either meteorological or hydrological droughts, and do not fully capture the integrated nature of drought phenomena. Addressing this gap, this study proposes a novel integrated drought characterization framework using the Gringorten plotting position to derive joint probabilities for GNSS-derived meteorological and hydrological drought indicators. This leads to the creation of a comprehensive multivariate drought severity index (GNSS-MDSI). The analysis across the western United States indicates significant spatial variability in multiyear average precipitation efficiency, ranging from 7.51% to 28.1%. This variability corresponds with marked differences in seasonal terrestrial water storage changes, which oscillate between 25 and 123 mm. Applying this framework in eight states, 9–13 comprehensive drought events from January 2006 to December 2021, with durations spanning from 3 to 54 months, were identified. The GNSS-MDSI not only captured these comprehensive drought periods across various temporal and spatial scales but also aligned closely with drought classifications provided by the US Drought Monitor. These results underscore the utility of this framework in providing a more nuanced and multifaceted perspective on drought conditions, surpassing the capabilities of single-indicator systems. Overall, this study presents an innovative framework for drought monitoring by integrating two GNSS-derived drought indicators, enabling precise and comprehensive delineation of drought characteristics, and offering a geodesy-based solution for integrated global and regional drought monitoring.

Evaluation of root zone soil moisture products over the Huai River basin

Abstract. Root zone soil moisture (RZSM) is critical for water resource management, drought monitoring and sub-seasonal flood climate prediction. While RZSM is not directly observable from space, several RZSM products are available and widely used at global and continental scales. This study conducts a comprehensive and quantitative evaluation of eight RZSM products using observations from 58 in situ soil moisture stations over the Huai River basin (HRB) in China. Attention is drawn to the potential factors that contribute to the uncertainties of model-based RZSM, including the errors in atmospheric forcing, vegetation parameterizations, soil properties and spatial scale mismatch. The results show that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) outperforms the other RZSM products with the highest correlation coefficient (R= 0.69) and the lowest unbiased root mean square error (ubRMSE = 0.018 m3 m−3), while SMOS Level 4 (L4) RZSM shows the worst performance among eight RZSM products. The RZSM products based on land surface models generally perform better in the wet season than in the dry season due to the enhanced ability to capture of the temporal dynamics of in situ observations in the wet season and the inertia of remaining high soil moisture values even in the dry season, while the SMOS L4 RZSM product, derived from SMOS L3 surface moisture (SSM) combined with an exponential filter method, performs better in the dry season due to the attenuated ground microwave radiation signal caused by the increased water vapour absorption and scattering in the wet season. The underestimated SMOS L3 SSM triggers the underestimation of RZSM in SMOS L4. The overestimated RZSM products based on land surface models could be associated with the overestimated precipitation amounts and frequency, the underestimated air temperature, and the underestimated ratio of transpiration to the total terrestrial evapotranspiration. In addition, the biased soil properties and flawed vegetation parameterizations affect the hydrothermal transport processes represented in different land surface models (LSMs) and lead to inaccurate soil moisture simulation. The scale mismatch between point and footprint also introduces representative errors. The comparison of frequency of normalized soil moisture between RZSM products and in situ observations indicates that the LSMs should focus on reducing the frequency of wet soil moisture, increasing the frequency of dry soil moisture and the ability to capture the frequency peak of soil moisture. The study provides some insights into how to improve the ability of land surface models to simulate the land surface states and fluxes by taking into account the issues mentioned above. Finally, these results can be extrapolated to other regions located in similar climate zones, as they share similar precipitation patterns that dominate the terrestrial water cycle.

Optimizing Drought Assessment for Soil Moisture Deficits

AbstractAccurate drought assessments are critical for mitigating the deleterious impacts of water scarcity on communities across the world. In many regions, deficits in soil moisture represent a key driver of drought conditions. However, relationships between soil moisture and widely used drought indicators have not been thoroughly evaluated. In addition, there has not been an in‐depth assessment of the accuracy of operational soil moisture models used for drought monitoring. Here, we used 2,405 observed time series of soil moisture from 637 long‐term monitoring stations across the conterminous United States to test the ability of meteorological drought indices and soil moisture models to accurately characterize soil moisture drought. The optimal timescales for meteorological drought indices varied substantially by depth, but were ∼30 days for depth averaged conditions; progressively longer timescales (∼10–80 days) represent progressively deeper soil moisture (2–36 in.). However, soil moisture models (including Short‐term Prediction Research and Transition Center, Soil Moisture Active Passive L4, and Topofire) significantly outperformed the meteorological drought indices for predicting standardized soil moisture anomalies and drought conditions. Additionally, soil moisture models represent near instantaneous conditions, implicitly aggregating antecedent data thereby eliminating the need for timescales, providing a more effective and convenient method for soil moisture drought monitoring. We conclude that soil moisture models provide a straightforward and favorable alternative to meteorological drought indices that better characterize soil moisture drought.

Drought Monitoring due to Climate Change in Urban and Sub Urban Area

Drought Monitoring due to Climate Change in Urban and Sub Urban Area

Spatiotemporal dynamics of meteorological and agricultural drought, part of Manbhum-Singhbhum Plateau (India): Four decades study using NASA POWER and MODIS data

Drought is an extremely hazardous situation that predominantly affects ecosystems, hydrological systems, and agricultural mechanisms in different pockets of the world. Management of water resources in dry and semi-arid environments has become a critical challenge in the last few decades. The principal objectives of this study are (i) to categorize the severity and frequency of drought events using the Reconnaissance Drought Index and Rainfall Deviation Index and ii) to evaluate the spatial extension of drought in particular drought times of severe and extreme droughts through Remote Sensing Drought Indices (RSDIs). This drought monitoring index is commonly used index for its high degree of resilience and sensitivity to droughts. The result shows that severe to extreme drought occurred in Bero, Tulin, Sirkabad, Manbazar and Bundwan which are 22, 28, 23, 14 and 25 months correspondingly based on RDI3 whereas RDI12 values indicated 26, 47, 42, 22 and 38 months in the study period (1981 to 2021). Bundwan area faced the most extreme drought occurrence in May month of 1996 with −3.58 values followed by Sirkabad, Bero, Manbazar and Tulin. Manbazar is going through severe to extreme drought months (for short time) whereas ‘extreme drought’ frequency (1.67 % to 1.05 %) is higher in the western part mainly in Jhalda I, Jhalda II, Bagmundi, and Arsha blocks of Purulia district (part of Manbhum Singhbhum Plateau). Additionally, the recurrence of ‘moderate drought’ is much greater in Balarampur along with some portions of Purulia I block. The whole study can be summarized as the northeastern portion being challenged with moderate or severe drought conditions while mild or severe drought is a considerable recurrence in the northern section. The outcome of this study will be helpful for local planners and management officials during implementation strategies related to water resource management.

Developing a Multivariate Agro‐Meteorological Index to Improve Capturing Onset and Persistence of Droughts Utilizing Vapor Pressure Deficit and Soil Moisture

AbstractDrought is associated with adverse environmental and societal impacts across various regions. Therefore, drought monitoring based on a single variable may lead to unreliable information, especially about the onset and persistence of drought. Previous studies show vapor pressure deficit (VPD) data can detect drought onset earlier than other drought indicators such as precipitation. On the other hand, soil moisture (SM) is a robust indicator for assessing drought persistence. This study introduces a nonparametric multivariate drought index Vapor Pressure Deficit Soil moisture standardized Drought Index (VPDSDI) which is developed by combining VPD with SM information. The performance of the multivariate index in terms of drought onset detection is compared with the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) for six major drought events across the United States including three rapidly developing drought events (this term refers to flash droughts that develop on monthly scales) and three conventional drought events. Additionally, the performance of the proposed index in detecting drought persistence is compared with the Standardized Soil moisture Index (SSI), which is an agricultural drought index. Results indicate the multivariate index detects drought onset always earlier than SPI for conventional events, but VPDSDI detects drought onset earlier than or about the same time as SPEI for rapidly developing droughts. In terms of persistence, VPDSDI detects persistence almost identical to SSI for both rapidly developing and conventional drought events. The results also show that combining VPD with SM reduces the high variability of VPD and produces a smoother index which improves the onset and persistence detection of drought events leveraging VPD and SM information.

Evaluating Performance of Multiple Machine Learning Models for Drought Monitoring: A Case Study of Typical Grassland in Inner Mongolia

Driven by continuously evolving precipitation shifts and temperature increases, the frequency and intensity of droughts have increased. There is an obvious need to accurately monitor drought. With the popularity of machine learning, many studies have attempted to use machine learning combined with multiple indicators to construct comprehensive drought monitoring models. This study tests four machine learning model frameworks, including random forest (RF), convolutional neural network (CNN), support vector regression (SVR), and BP neural network (BP), which were used to construct four comprehensive drought monitoring models. The accuracy and drought monitoring ability of the four models when simulating a well-documented Inner Mongolian grassland site were compared. The results show that the random forest model is the best among the four models. The R2 range of the test set is 0.44–0.79, the RMSE range is 0.44–0.72, and the fitting accuracy relationship could be described as RF > CNN > SVR ≈ BP. Correlation analysis between the fitting results of the four models and SPEI found that the correlation coefficient of RF from June to September was higher than that of the other three models, though we noted the correlation coefficient of CNN in May was slightly higher than that of RF (CNN = 0.79; RF = 0.78). Our results demonstrate that comprehensive drought monitoring indices developed from RF models are accurate, have high drought monitoring ability, and can achieve the same monitoring effect as SPEI. This study can provide new technical support for comprehensive regional drought monitoring.

Drought Monitoring and Prediction in Agriculture: Employing Earth Observation Data, Climate Scenarios and Data Driven Methods; a Case Study: Mango Orchard in Tamale, Ghana

The study focused on the prediction of the Temperature Vegetation Dryness Index (TVDI), an agricultural drought index, for a Mango orchard in Tamale, Ghana. It investigated the temporal relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and TVDI. The SPI was calculated based on utilizing precipitation data from the World Meteorological Organization (WMO) database (2010–2022) and CMIP6 projected precipitation data (2023–2050) from 35 climate models representing various Shared Socioeconomic Pathway (SSP) climate change scenarios. Concurrently, TVDI was derived from Landsat 8/9 satellite imagery, validated using thermal data obtained from unmanned aerial vehicle (UAV) surveys. A comprehensive cross-correlation analysis between TVDI and SPI was conducted to identify lag times between these indices. Building on this temporal relationship, the TVDI was modeled as a function of SPI, with varying lag times as inputs to the Wavelet-Adaptive Neuro-Fuzzy Inference System (Wavelet-ANFIS). This innovative approach facilitated robust predictions of TVDI as an agricultural drought index, specifically relying on SPI as a predictor of meteorological drought occurrences for the years 2023–2050. The research outcome provides practical insights into the dynamic nature of drought conditions in the Tamale mango orchard region. The results indicate significant water stress projected for different time frames: 186 months for SSP126, 183 months for SSP245, and 179 months for both SSP370 and SSP585. This corresponds to a range of 55–57% of the projected months. These insights are crucial for formulating proactive and sustainable strategies for agricultural practices. For instance, implementing supplemental irrigation systems or crop adaptations can be effective measures. The anticipated outcomes contribute to a nuanced understanding of drought impacts, facilitating informed decision-making for agricultural planning and resource allocation.

Construction and Validation of Surface Soil Moisture Inversion Model Based on Remote Sensing and Neural Network

Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation and smart agriculture construction. Based on Sentinel-1, Sentinel-2, and Landsat-8 images, the effect of vegetation was removed by the water cloud model (WCM), and SSM was retrieved and validated by a radial basis function (RBF) neural network model in bare soil and vegetated areas, respectively. The normalized difference vegetation index (NDVI) calculated by Landsat-8 (NDVI_Landsat-8) had a better effect on removing the influence the of vegetation layer than that of NDVI_Sentinel-2. The RBF network model, established in a bare area (R = 0.796; RMSE = 0.029 cm3/cm3), and the RBF neural network model, established in vegetated areas (R = 0.855; RMSE = 0.024 cm3/cm3), have better simulation effects on SSM than a linear SSM inversion model with single polarization. The introduction of surface parameters to the RBF neural network model can improve the accuracy of the model and realize the high-accuracy inversion of SSM in the study area.

Agricultural drought-driven mechanism of coupled climate and human activities in the karst basin of southern China.

Timely and accurate agricultural drought monitoring and drought-driven mechanism analysis in karst basins in the context of global warming are highly important for drought disaster monitoring and sustainable ecological development in a basin. In this study, based on MODIS data, meteorological and topographic data and land use data from 2001 to 2020, we used the Sen slope, the Mann-Kendall test and a geographic detector to explore the driving mechanisms of agricultural drought caused by climate change and human activities in the karst basin of southern China from 2001 to 2020. The results showed that (1) the spatial distribution of the TVDI in the karst basin in southern China has obvious regional characteristics, showing a decreasing trend from west to east. (2) According to the interannual trend of drought, the degree of drought in the South China karst basin exhibited a weakening trend over the last 20 years, with the most severe drought occurring in 2003. Regarding the seasonal change in the TVDI, drought in spring, summer and autumn exhibited a decreasing trend, while that in winter exhibited an increasing trend, and the drought intensity decreased in the following order: spring (0.58) > autumn (0.53) > summer (0.5) > winter (0.48). (3) Single-factor detection the results showed that rainfall, temperature and elevation were the main factors driving aridification in the study area; multifactor coupling (mean) drove drought in descending order: rainfall (q = 0.424) > temperature (q = 0.340) > elevation (q = 0.219) > land use (q = 0.188) > population density (q = 0.061) > slope (q = 0.057). Therefore, revealing the mechanism of agricultural drought in karst basins through the study of this paper has important theoretical significance and provides technical guidance for drought relief in karst areas.

From drought hazard to risk: A spring wheat vulnerability assessment in the Canadian Prairies

The Canadian drought monitor (CDM) provides monthly assessments of drought severity but does not provide guidance on the potential agricultural impacts. This study extends the CDM to a comprehensive risk assessment framework by assessing the vulnerability of spring wheat to drought timing and severity in the Canadian Prairies. To achieve this, a bayesian neural network (BNN) model was developed that predicts high-resolution wheat yields and uncertainties using the CDM and normalized difference vegetation index (NDVI) data from 2010 to 2021. Model performance is comparable to previous studies, with errors ranging from 13% to 18% of mean yields and R2 values from 0.43 to 0.70 that peak in early August. The model was then applied under various drought conditions to evaluate how different drought timings and severities affect yield vulnerability. The results demonstrate that drought exposure from June to August reduces wheat yields in proportion to drought severity while abnormally dry conditions may have a positive impact of yield, particularly in August. Yield uncertainty decreases substantially from June to July while yield prediction accuracy increases, suggesting that mitigation efforts are most effective prior to July. The final component of the research involved creating operational risk maps that integrate the yield vulnerability assessment with the CDM. These maps were applied in a case study to demonstrate their utility in providing actionable, real-time information for agricultural stakeholders. Overall, this study contributes to the body of knowledge regarding crop sensitivities to varying drought conditions and represents a methodological advancement by enabling high-resolution, operational agricultural risk assessments at scale. By integrating detailed vulnerability data with drought hazard monitoring from the CDM, the information provided by these risk maps can be leveraged by agricultural stakeholders in near real-time to make well-informed decisions that mitigate the impacts of drought on wheat production, enhancing the resilience of agricultural systems to climatic variability.

The composite drought index incorporated solar-induced chlorophyll fluorescence enhances the monitoring capability of short-term drought

Understanding the response of vegetation to drought is significant for socio-economic development and biodiversity conservation. However, due to the complexity of drought, relying only on one approach, such as the surrounding physical environmental conditions or specific vegetation characteristics, does not yield precise results. To address this challenge, composite drought indices incorporating Normalized Difference Vegetation Index (NDVI) and other multiple variables, have been developed and shown promising effectiveness in drought detection. Compared to NDVI, solar-induced chlorophyll fluorescence (SIF) decreases due to the increased non-photochemical quenching and reduced photosynthesis during short-term drought stress. This indicates a direct association between SIF and vegetation photosynthesis, offering advantages over NDVI in capturing drought effects. Nevertheless, SIF has not yet been fully integrated into composite drought indices. Therefore, this study focused on assessing the advantages of SIF for short-term drought monitoring by constructing a new composite drought index (CPDI) using the Principal Component Analysis (PCA) method. Subsequently, CPDI was employed to forecast future drought conditions. Overall, CPDI performs exceptionally well as a composite drought index in drought trend analysis and drought event identification, indeed advancing the monitoring capability for short-term drought. Furthermore, the incorporation of SIF in CPDI, obtained from various data sources provides more timely monitoring of drought events, while the CPDI with NDVI reflects the cumulative effect of drought conditions over a longer period. In the future, it is potential to utilize the benefits of SIF in constructing drought indices or combine it with NDVI for comprehensive drought characterization.