Drought Index Research Articles

An optimized network for drought prediction using satellite images

The change in climate and the hot temperature environment increased the risk of drought around the workplace. Predicting and forecasting the drought occurrence is essential for managing water resources and agricultural plans. Therefore, in this study, a novel Chimp-based Wide ResNet Prediction Framework (CWRPF) is designed to predict the drought. The key motive of the presented research is to predict the drought and no drought conditions derived from the satellite images. The satellite images are collected from the Bhuvan site. Initially, the satellite images are noise-filtered. The filtered images are then injected into the feature analysis phase to compute the drought indices of a specific area by the fitness function activated in the framework. After estimating the drought indices, the drought condition was categorized. Finally, the designed system is tested in the MATLAB platform and has gained more significant results by providing a 97.68% accuracy rate, R2 as 0.998, and lower RMSE and MAE values of 0.223 and 0.193. The accumulated results are compared with existing techniques to validate the improvement score. The accuracy of the CWRPF is more remarkable than that of other prediction models. Therefore, the system is efficient for drought prediction in satellite images.

Drought modelling: A comprehensive review

Droughts are a recurring natural hazard with significant social, economic, and environmental consequences. Accurate drought modelling is crucial for effective drought preparedness, mitigation, and response strategies. This review paper comprehensively overviews drought modelling approaches, including traditional statistical, physically-based and emerging machine learning techniques. We discuss the strengths and weaknesses of each approach, highlighting recent advancements and future research directions. Additionally, we examine the various drought indices employed for drought characterization and explore the integration of climate change scenarios into drought modelling frameworks. Finally, the review addresses the challenges and limitations of drought modelling and emphasizes the importance of data availability, model validation, and user-centric applications.

Impacts of hydrometeorological regime shifts on drought Propagation: The meteorological to hydrological perspective

Exploring drought propagation relationships and thresholds is crucial for drought control and mitigation. However, previous studies have overlooked the impacts of regime shifts in trends of hydrometeorological variables over time on drought propagation and its thresholds. This study proposes an integrated framework focusing on drought propagation from meteorological to hydrological drought. The regime shift periods, both increasing and decreasing trends, were identified using the cumulative difference curve-rank test on precipitation and streamflow at an annual time scale. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were used as meteorological and hydrological drought indices, respectively. Additionally, the propagation relationships and thresholds from meteorological drought to hydrological drought were identified and compared across various regime shift periods. The integrated framework’s applicability was verified by applying it to the Three-Rivers Headwaters region of China, which encompasses the Yellow River, Yangtze River, and Lancang River. This region is strategically important for China and has relatively little human activity. The framework revealed the variability in drought propagation and thresholds during different regime shift periods. The response sensitivity of SSI to SPI and the probabilities and thresholds for propagation from meteorological to hydrological drought differed between periods of increasing and decreasing trends in hydrometeorological variables. When regime shift characteristics were considered, the thresholds for meteorological drought propagating to hydrological drought were 2.81 %–16.90 % higher. These results indicated that, when examining drought propagation relationships and thresholds, accounting for regime shifts in hydrometeorological variables can improve early drought warning and enhance drought control and mitigation efforts.

Development of Ridge Ensemble Standardized Drought Index (RESDI) for improving drought characterization and future assessment.

Global warming upsets the environmental balance and leads to more frequent and severe climatic events. These extreme events include floods, droughts, and heatwaves. These widespread extreme events disrupt various sectors of ecosystems directly. However, among all these events, drought is one of the most prolonged climatic events that significantly destroys the ecosystem. Therefore, accurate and efficient assessment of droughts is necessary to mitigate their detrimental impacts. In recent years, several drought indices based on global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) have been proposed to quantify and monitor droughts. However, each index has its advantages and limitations. As each index ensembles different models by using different statistical approaches, it is well known that the margin of error is always a part of statistics. Therefore, this study proposed a new drought index to reduce the uncertainty involved in the assessment of droughts. The proposed index named the Ridge Ensemble Standardized Drought Index (RESDI) is based on the innovative ensemble approach termed ridge parameters and distance-based weighting (RDW) scheme. And the development of this RDW scheme is based on two types of methods i.e., ridge regression and divergence-based method. In this research, we ensemble 18 different GCMs of CMIP6 using the RDW scheme. A comparative analysis of the RDW scheme is performed against the simple model average (SMA) and Bayesian model averaging (BMA) schemes at 32 locations on the Tibetan plateau. The comparison revealed that RDW has less mean absolute error (MAE) and root-mean-square error (RMSE). Therefore, the developed RESDI based on RDW is used to project drought properties under three distinct shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, across seven different time scales (1, 3, 7, 9, 12, 24, and 48). The projected data is then standardized by using the K-components Gaussian mixture model (K-CGMM). In addition, the study employs steady-state probabilities (SSPs) to determine the long-term behavior of drought. The outcome of this research shows that "normal drought (ND)" has the highest probability of occurrence under all scenarios and time scales.

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.

Autoencoder-based composite drought indices

Depending on the type, drought events are described using different indices, such as meteorological, agricultural, and hydrological. The use of different indices often causes confusion for making water-related management decisions. One simple summarized index which can describe the different aspects of drought is desired. Several methods have therefore been proposed, especially with the linear combination method which does not adequately describe drought characteristics. Meanwhile, autoencoders, nonlinear transformation in dimensional reduction, have been applied in the deep learning literature. The objective of this study, therefore, was to derive autoencoder-based composite drought indices (ACDIs). First, a basic autoencoder was directly applied as ACDI, illustrating a negative relation with the observed drought indices which was further multiplied by a negative. Also, the hyperbolic tangent function was adopted instead of the sigmoid transfer function due to its higher sensitivity to drought conditions. For better expression of drought indices, positive and unity constraints were applied for weights, denoted as ACDI-C. Further simplification was made as sACDI by excluding the decoding module since it was not necessary. All applied weights of different sites over a country can be unified into one weight, and the same weights were made for all the sites, called as sACDI1. In the context of model evaluation, a comprehensive analysis was undertaken employing metrics as root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficients. The collective findings underscore the superior performance of both the sACDI and sACDI1 models over their counterparts. Notably, these simplified models manifestly diminished RMSE and MAE values, indicating their enhanced predictive capabilities. Of particular note, sACDI1 exhibited a discernibly lower MAE in comparison to alternative models. Further alarm performance metrics was conducted including the false alarm ratio, probability of detection, and accuracy (ACC). The investigations revealed superiority of the simplified models in terms of alarm ACC, especially in the case of SRSI(A). The developed ACDI can comprehensively summarize multiple drought aspects and provide summarized information about drought conditions.

Predicting Drought in East Nusa Tenggara: A Novel Approach Using Wavelet Fuzzy Logic and Support Vector Machines

The water crisis, or what is hereinafter referred to as drought, has become a systemic and crucial problem in several regions in Indonesia. Indonesia is an agricultural country, where the presence of water is very influential so that drought can become a natural disaster if it starts to cause an area to lose its source of income due to disturbances in agriculture and the ecosystem it causes. Drought forecasting can provide support solutions in preventing the impact of drought. In this paper, we compare the performance of wavelet fuzzy logic and the support vector machine (SVM) as a supervised learning method for drought forecasting in East Nusa Tenggara. This study examines the monthly rainfall data for 1999-2015 which is the basis for calculating the drought index based on the Standardized Precipitation Index (SPI). The SPI value used is SPI-3 at a station in East Nusa Tenggara. The performance of models is compareded on R2. The results showed that R2 of wavelet fuzzy logic is smaller than one of SVMVM is better than the wavelet fuzzy logic for forecasting SPI value of drought in East Nusa Tenggara.

Response of the water level of the Balkash Lake to the distribution of meteorological and hydrological droughts under the conditions of climate change

ABSTRACT Hydrological droughts occur due to a variety of hydrometeorological phenomena, such as lack of precipitation, reduced snow cover and high evaporation. The values of these factors vary depending on the climate and the severity of drought events. Droughts caused by a lack of precipitation and continuing in the warm season have a longer periodicity. This important statement raises the question of whether climate change may exacerbate the phenomenon of drought. Therefore, understanding changes in the formation of hydrological droughts is key to foreseeing possible changes in the future. This scientific study analyzes the spread of hydrometeorological droughts in the Ile-Balkash basin using standardized precipitation indices and the drought index of river runoff. Lake Balkash plays an important role in the hydrological cycle and is a valuable freshwater resource, especially in dry years. Prolonged droughts in the area have serious consequences, including deterioration of water quality and loss of wetlands, which are important to the ecological system and migratory birds. The analysis shows that during the period of instrumental observations, several extreme hydrological droughts were observed in this area, (1943–1946, 1973–1975 and 1983–1987), which emphasizes the relevance and importance of scientific research on the problem of drought.

Comparison of drought indices in the analysis of temporal and spatial changes of climatic drought events: a case study in the Egirdir Lake basin (Isparta/Turkey)

Comparison of drought indices in the analysis of temporal and spatial changes of climatic drought events: a case study in the Egirdir Lake basin (Isparta/Turkey)

Integrated approach for drought and saline intrusion severity assessment on the coastal Mekong Delta of Vietnam contextualizing physical change to risk management and policy development

Drought and saline intrusion are common hazards that typically occur during dry seasons in the Mekong Delta of Vietnam (MDV), leading to significant socio-economic impacts such as water shortages, crop failures, and social unrest. Climate change exacerbated the impacts of these hazards, which have become more complex and unpredictable, particularly in coastal provinces in MDV. This study aims to explore the intricate relationships between drought and saline intrusion by utilizing the Streamflow Drought Index (SSI), Standardized Precipitation Evapotranspiration Index (SPEI), saline boundary 4 g/l, Pearson correlation (R), and p-values. Additionally, the study analyzes policy developments between the 2016 and 2020 droughts using document review and comparative approach methods. Overall, the results indicate that the 2020 drought was more severe than the 2016 drought. Hydrological drought shows a stronger connection with saline intrusion than meteorological drought in the MDV. Governance in drought and saline intrusion management in 2020 showed improvement compared to 2016. The management of aquaculture remained deficient, leading to more severe impacts of drought and saline intrusion on aquaculture in 2020 than in 2016. The findings of this study can complement risk information and assist decision-makers in effectively managing drought and saline intrusion hazards.

Yield prediction models for some wheat varieties with satellite‐based drought indices and machine learning algorithms

AbstractIn recent years, frequent drought events in Konya, one of Türkiye's most important cereal production centres, have led to increased pressure on water and soil resources, resulting in yield losses, particularly in wheat production. Alternative yield prediction models, especially those that play a crucial role in agricultural import–export planning in the region, are important for economic contributions and the development of early warning systems. In this context, the aim of this study is to develop models that can be used in the yield prediction of wheat varieties widely grown in the Konya Altınova region. Agricultural drought indices obtained from Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) products of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were used to obtain model inputs. These indices are the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI) and Vegetation Supply Water Index (VSWI). In obtaining the input parameters for the models, the growth periods of the varieties in the region were also considered. Using various machine learning algorithms, 21 yield prediction models for Bayraktar‐2000, 12 for Kızıltan‐91 and 8 for Bezostaya‐1 were presented as alternatives, with model performances (coefficient of determination, R2) ranging between 0.74 and 0.97, 0.73 and 0.96, and 0.69 and 0.87, respectively.

Monitoring drought impacts on street trees using remote sensing - Disentangling temporal and species-specific response patterns with Sentinel-2 imagery

Healthy street trees provide important ecosystem services to cities, but are under increasing stress from urbanisation and climate change, including drought. Traditional field observations are limited in their ability to provide city-wide and regular monitoring of the drought response of street trees, which is essential for their effective management. To overcome this, we propose a novel workflow using freely available remote sensing imagery from Sentinel-2 to identify temporal and species-specific drought response patterns of street trees. We evaluate the workflow on a sample of 2514 mature street trees of the seven most common street tree species in Leipzig, Germany. For each tree, we generate time series of eight vegetation indices from 2017 to 2022, namely enhanced vegetation index (EVI), normalised burn ratio (NBR) and normalised difference vegetation index (NDVI), each using the 10 m and 20 m resolution near-infrared band (EVI-10, EVI-20, NBR-10, NBR-20, NDVI-10, NDVI-20), as well as red edge normalised difference vegetation index (RENDVI) and red-green vegetation index (RGVI). They form the basis for a correlation analysis with the meteorological drought indicator standardised precipitation evapotranspiration index (SPEI) and for annual growing season integrals, which we subtract from those of the base year 2017. We use boxplots and statistical hypothesis testing to examine differences between and within tree species and years. The results show positive relationships between the eight vegetation indices and the SPEI, with the NBR-20 having the highest correlation coefficients. We also find significant differences in the drought response of several tree species, with Quercus robur being the most drought responsive and Platanus x acerifolia being the least. While most tree species have significantly smaller growing season integrals in the drought years 2018 and 2020 than in the non-drought years 2017 and 2021, the effects are not as pronounced in the drought years 2019 and 2022. Uncertainties arise, for example, from spectral signal variations caused by adjacent land use. Nevertheless, the proposed workflow holds promise for incorporation into holistic urban green management solutions and mixed-method approaches for further research into the causes and consequences of drought-induced damage to street trees.

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.

The Performance of Drought Indices on Maize Production in Northern Nigeria Using Artificial Neural Network Model

Drought is widely known to put the ecosystem at risk. It ensues when there is a major rainfall shortage that causes hydrological discrepancies and alters the land productive structures. The degree of rainfall influences the growth and harvests of maize, particularly where irrigation is not practicable. In some parts of northern Nigeria, rainfall is unpredictable and often lower than the quantity needed for a viable crop. For the detection, classification, and control of drought conditions, drought indices are used. There has been notable progress in the last few years in terms of modelling droughts by utilizing statistical or physical models. Despite the successes documented by most of these approaches; a plain, effective, and well-built statistical model is the artificial neural network (ANN). The use of artificial neural networks (ANN) to evaluate the impact of drought indices on maize output in the 17 northern Nigerian states is presented in this research. For a 25-year period from 1993 to 2018, observed annual data of drought indices, RDI, and the Palmer drought indices, which comprise SCPDSI, SCPHDI, and SCWLPM, as well as maize yield (measured in tonnes) in Northern states of Nigeria. The ANN model was evaluated using several activation functions (sigmoid, hyperbolic tangent, and rectified linear unit), hidden layers (1, 2, and 3), and training sets (70%, 80%, and 90%). The Mean Square Error (MSE) was employed to evaluate each ANN model's performance. In summary, most of the states' lowest mean square errors (MSEs) were generated via RELU. Also, as the training percentage increases, the mean square error increases.

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.

Identification of sinkhole‐prone zones by successive coincidence deficit index analysis

AbstractHydrological data‐driven models require the time series of several hydrological events with different time resolutions. The interpretation of any time series event is generally difficult without some sort of filtering or converting it to a single index value. The simultaneous analysis of two or more hydrological events over a definite time span may be more informative about the region of interest. For this purpose, a new index, referred to as the successive coincidence deficit index (SCDI), was introduced to identify sinkhole‐prone regions using the persistent water deficit concept. In this study, monthly integrated multi‐satellite retrievals for GPM based precipitation (P) and gravity recovery and climate experiment‐based groundwater storage (GWS) datasets over Konya Closed Basin (KCB) in Türkiye were used to analyse the sinkhole occurrence. The main finding of this study is that SCDI distribution with high index values, concentrated on the southwestern part of KCB, is in line with the sinkholes occurred mainly after 2010. The proposed SCDI could also serve as a kind of drought index, which enables practitioners to quantify the relationship between drought and sinkhole occurrence. Moreover, the event coincidence analysis was utilized to detect deficiency in GWS over the KCB, which was associated with a rate of 0.8 for P deficiency, and this rate reaches up to 0.9 in the sinkhole region analysed in this study. As a conclusion, the proposed methodology can detect sinkhole‐prone regions to construct risk maps for stakeholders, policymakers, and end users.

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.

Evaluation of the Rayalseema Meteorological Subdivision’s Drought Using Multiple Drought Indices

Evaluation of the Rayalseema Meteorological Subdivision’s Drought Using Multiple Drought Indices

Leveraging unsupervised machine learning to examine women's vulnerability to climate change

AbstractWe provide an application of machine learning to identify the distributional consequences of climate change in Malawi. We compare climate impact estimates based on drought indicators established objectively from the k‐means algorithm to more traditional measures. Young women affected by drought were 5 percentage points more likely to be married by 18 than those living in nondrought areas. Our approach generates robust results when varying the number of clusters and definition of treatment status. In some cases, we find the design using k‐means to define treatment is more likely to satisfy the assumptions underlying the difference‐in‐differences strategy than when using arbitrary thresholds. Projections from the estimates indicate future drought risk may lead to larger declines in labor productivity due to women's engagement in early age marriage than other factors affecting their participation rates. Under the extreme representative concentration pathway scenario, drought exposure encourages the exit of 3.3 million women workers by 2100.

Quantifying the Regulation Capacity of the Three Gorges Reservoir on Extreme Hydrological Events and Its Impact on Flow Regime in a Changing Climate

AbstractThe Three Gorges Reservoir (TGR) is one of the world's largest hydropower projects and plays an important role in water resources management in the Yangtze River. For the sake of disaster prevention and catchment management, it is crucial to understand the regulation capacity of the TGR on extreme hydrological events and its impact on flow regime in a changing climate. This study obtains historical inflows of the TGR from 1961 to 2019 and uses a distributed hydrological model to simulate the future inflows from 2021 to 2070. These data are adopted to drive a machine learning‐based TGR operation model to obtain the simulated outflow with TGR operation, which are then compared with the natural flow without TGR operation to assess the impact of TGR. The results indicate that the average flood peaks and total flooding days in the historical period could have been reduced by 29.2% and 53.4% with the operation of TGR. The relative declines in drought indicators including duration and intensity were generally less than 10%. Faced with more severe extreme hydrological events in the future, the TGR is still expected to alleviate floods and droughts, but cannot bring them down to historical levels. The impact of TGR operation on flow regime will also evolve in a changing climate, potentially altering the habitats of river ecosystems. This study proposes feasible methods for simulating the operation of large reservoirs and quantifying the impact on flow regime, and provides insights for integrated watershed management in the upper Yangtze River basin.