Drought Index Research Articles

Assessment of future drought sensitivity shift associated with crop yield loss risk in the Anthropocene

ABSTRACT This study proposed a framework using artificial neural networks (ANNs), the designed residual regression method, and probabilistic indexing, for drought sensitivity shift analysis, associated with future yield loss risk in Anthropocene. The temporal assessment of the drought index (standardized precipitation evapotranspiration index, SPEI) illustrated prevalent occurrence of mild to moderate drought with a fluctuating trend. Furthermore, the optimal ANN model, with R2 = 0.86 for training and 0.7 for testing, was used to project yield for future analysis. The projected spatial changes in drought sensitivity patterns suggest most of the area will experience an increased sensitivity to drought in the far future (2036–2050). The estimated probability loss under the different risk levels defined by the yield reduction rate was 6.2–55.5%. Furthermore, the spatial distribution of the designed risk index (IR) revealed the districts with the highest probability of vulnerability and showed a probable increase in future vulnerability.

Monitoring and forecasting desertification and land degradation using remote sensing and machine learning techniques in Sistan plain, Iran

Monitoring and predicting desertification in arid regions are crucial for addressing environmental and societal challenges. Remote sensing is vital for tracking land surfaces and ecosystems changes. The study aims to use remote sensing-based data to monitor and predict desertification in the Sistan Plain through a data screening approach. The study's satellite data consisted of Landsat 5 and 8 images taken in June each year over 10 years (1990–2020). Remote sensing-based indices, including land use and land cover (LULC) map, normalized differential vegetation index (NDVI), improved vegetation index (EVI), vegetation condition index (VCI), surface temperature condition index (TCI), modified normalized differential water level index (MNDWI) and salinity index (SI) were used in the study. In addition to satellite data, environmental indices, including standardized precipitation index (SPI) and streamflow drought index (SDI), were used. The study employed the random forest (RF) method and the mixed model of automated cells and Markov chain (CA-Markov) to monitor desertification and quantitatively predict its condition in 2030. Root-mean-square error (RMSE) and mean-square error (MSE) indicators were used to evaluate the error. Based on the findings, the RF correlation coefficient (R2) and RMSE were obtained about 0.97 and 0.08, respectively. High coefficient values and low RMSE values indicate that the random forest model is highly efficient in assessing desertification for the study period from 1990 to 2020. The change detection method revealed that desertification increased from 1990 to 2010 but decreased from 2010 to 2020. The decreasing trend is expected to continue until 2030. The Kappa coefficient for the prediction of desertification in 2030 was found to be 0.94, which indicates a correct classification based on the collected samples. In addition, the study identified the SI and SDI as effective indices in the desertification process in the study area. Overall, this study provides valuable insights into monitoring and predicting desertification, which could help develop appropriate strategies for managing and controlling desertification in the Sistan Plain through remote sensing and machine learning techniques.

Integration of machine learning and remote sensing for drought index prediction: A framework for water resource crisis management

Integration of machine learning and remote sensing for drought index prediction: A framework for water resource crisis management

Watering the trees for the forest: Drought alleviation in oaks and pines by ancestral ditches

Traditional ditches (“acequias” in Spanish) derive meltwater and infiltrate groundwater providing ecological services downstream in the semi-arid Sierra Nevada range (SE Spain). Therefore, they may act as a nature-based solution by alleviating drought stress in trees growing near ditches by enhancing growth and reducing their intrinsic water-use efficiency (iWUE). Such a mitigation role of acequias is critical given that some oak (Quercus pyrenaica) and pine (Pinus sylvestris) stands reach their xeric distribution limits in Europe. We compared tree-ring width data and wood δ13C, a proxy of iWUE, in oak and pine stands located near or far (control) from ditches with different infiltration capacity in two watersheds. We assessed how trees responded to climate data, drought stress, and vegetation greenness through correlations and resilience indices. Oak trees located near ditches grew more and responded less to precipitation, soil moisture, a drought index, and greenness than control trees. In pines, we did not find this pattern, and ditch trees grew more than control trees only during an extremely dry year (1995). Climate-growth correlations suggested a longer growing season in ditch pines. Growth of ditch oaks from the “Acequia Nueva” (AN), with high infiltration capacity, responded more to autumn soil moisture and showed the lowest δ13C. Growth was enhanced by cool-wet spring conditions in pines and also by warm-wet conditions in the prior winter in the case of oaks. Control trees showed lower resistance to drought. Control trees presented higher wood δ13C values except for old oaks from the “Acequia Grande” (AG) site which may show long-term acclimation. Traditional ditches alleviate drought stress in oak and pine stands subjected to regional xeric climate conditions.

Revealing the intricate relationship: Droughts and typhoons in Taiwan using the Standardized Precipitation Index (SPI)

Study regionTaiwan Study focusThis study examines the relationship between typhoon parameters and droughts in Taiwan, particularly following the severe drought of 2020–2021. Using tropical cyclone best-track and satellite-based precipitation datasets from 1981 to 2020, we analyzed anomalies, correlation matrices, and wavelet coherence. Seasonal variations and long-term trends were also detected. New hydrological insights for the regionWe found a positive correlation between typhoon characteristics (count, duration, length, wind speed) in Taiwan and drought occurrence and severity, especially over 2–4 year periods. Conversely, negative relationships were observed between typhoon duration and length in the Western North Pacific (WNP) and drought indices in Taiwan, influenced by large-scale atmospheric patterns. Typhoon duration and length in the WNP had a greater impact on Taiwan's drought than typhoon quantity, showing significant coherence with long-term drought over multi-year to decadal timescales. Seasonally, drought intensity peaked in central and southeastern Taiwan during late winter and early spring when typhoons were absent, in contrast to the rainy summer typhoon season. Spatially, increasing drought trends were identified in central and southern Taiwan, while northern regions exhibited decreasing dryness, potentially linked to the concentration of typhoon landing points in the north. This study underscores the complex relationships between drought severity in Taiwan and typhoon behavior in both the vicinity of Taiwan and the WNP.

Spruce bark beetle phenological modelling and drought risk within framework of TANABBO II model

Global warming plays a major role in the disruption of forest ecosystems by bark beetle outbreaks. Decreasing precipitation and more frequent droughts create the conditions for water stress in trees. The Eurasian spruce bark beetle (Ips typographus L.) is more dangerous for Norway spruce (Picea abies (L.) Karst.) forests in Central Europe under these conditions, because even healthy trees under water stress have a lower probability of survival during massive spruce bark beetle outbreaks. This critical issue highlights the importance of targeted research and intervention strategies in affected regions; hence, this study has a focus on the Horní Planá region in Central Europe, which is managed by the Military Forests and Farms of the Czech Republic (VLS).The study presents the results of the drought risk assessment of the operational part of TANABBO II and its validation. TANABBO is a high spatial resolution risk assessment tool for spruce bark beetle infestation in Central European forests, incorporating 30 m spatial resolution and extensive meteorological data, which outperforms existing models in its ability to accurately predict bark beetle activity based on the PHENIPS phenological model. The operational part integrates environmental factors such as global radiation, temperature, precipitation and, in particular, water stress, which is assessed by using a transpiration deficit. The drought risk assessment included the calculation of Drought Indices (DI) and Cumulative Transpiration Deficit Indices (CTDI) on a daily basis, and the estimation of spruce bark beetle swarming time using the PHENIPS model. The risk assessment was validated by comparing it with the number of bark beetles caught in traps and the value of the drought index. Generalised additive models (GAM) were used for validation, with the most effective models incorporating the CTDI. The model with the highest predictive accuracy achieved an adjusted R-squared value of 0.478. The study highlights the critical role of high-resolution environmental data in understanding and managing bark beetle outbreaks, particularly in the context of climate change.

Monitoring annual meteorological drought in arid and semi-arid watersheds by SPI12 drought index and spatial autocorrelation pattern analysis: a case study of the Khuzestan province, Southwest Iran

Monitoring annual meteorological drought in arid and semi-arid watersheds by SPI12 drought index and spatial autocorrelation pattern analysis: a case study of the Khuzestan province, Southwest Iran

Modulatory effects of selenium nanoparticles against drought stress in some grapevine rootstock/scion combinations

BackgroundDrought is a significant abiotic stress that adversely affects plant growth, development, and metabolic processes, thereby reducing plant yield, quality, and production, and threatening global food security. In recent years, nanotechnology has emerged as a promising strategy to overcome the existing environmental challenges and has been tested on some plant species. But it is still awaiting investigation for grapevines. The aim of this study was to investigate the potential of selenium nanoparticles (Se-NPs) to modulate some morphological, physiological, and biochemical parameters in grapevine saplings (5 BB/Crimson Seedless, 41 B/Crimson Seedless, and 1103 P/Crimson Seedless) under drought stress conditions.ResultsIn the study, Se-NP solutions at different concentrations (0 (control), 1, 10, and 100 ppm) were applied by the spray method to wet the entire green surface of grapevine saplings grown under well-irrigated (90–100% field capacity) and drought stress (40–50% field capacity) conditions. Our results showed that 10 ppm Se-NP concentration had the most positive effect, 1 ppm concentration showed limited effects, and 100 ppm concentration led to toxic effects, especially when combined with drought conditions. Se-NP applications at 10 ppm concentration improved the growth parameters (leaf number, leaf area, root fresh and dry weight, shoot fresh and dry weight, etc.) and increased the SPAD index of grapevine saplings under both normal and drought conditions. Additionally, 10 ppm Se-NP applications improved the relative water content (RWC) and stomatal conductance values, proportional to the increases in protein content. On the other hand, under drought conditions, the drought index, leaf temperature, membrane damage index, hydrogen peroxide (H2O2) content, and malondialdehyde (MDA) levels significantly decreased as a result of 10 ppm Se-NP applications, showing an opposite trend. Furthermore, the levels of proline, total phenolics, and antioxidant enzymes (CAT, SOD, and APX) that rose significantly due to drought stress were reduced by 10 ppm Se-NP applications, which also helped to lessen the oxidative stress caused by the drought.ConclusionThe study concluded that foliar application of Se-NPs at 10 ppm significantly enhances drought tolerance in grapevine saplings by improving antioxidant defense, proline and protein accumulation, and overall growth, while lower concentrations are less effective and higher concentrations can cause phytotoxicity. These findings indicate that Se-NPs applications may hold promise not only for grapevines but also for mitigating drought stress effects and improving productivity in other economically important fruit species, warranting further exploration across diverse crop systems.Graphical

Spatiotemporal analysis of tropical vegetation ecosystems and their responses to multifaceted droughts in Mainland Southeast Asia using satellite-based time series

ABSTRACT Drought ranks among the costliest of all climate-related phenomena and manifests in various forms, posing significant challenges in understanding its influence on agriculture and natural ecosystems. Mainland Southeast Asia (MSEA), a significant region of tropical agriculture and vegetation ecosystems, has become increasingly susceptible to drought hazards. In this study, we characterized and assessed vegetation dynamics and their drought impacts using correlation analysis and explainable machine learning methods under different vegetation types and elevation zones during the dry growing seasons from 2000 to 2022. Specifically, we characterized the vegetation dynamics and their trend in space and time. Next, we assessed vegetation-drought responses in consideration of meteorological, hydrological, and agricultural droughts under different land cover types and elevation characteristics. Lastly, we used an explainable machine learning method to quantify the drivers and impacts of multifaceted droughts on natural and undisturbed vegetation ecosystems. Our findings revealed that nearly 70% of the MSEA region experienced a greening trend despite large areas of vegetative damage during the drought years. Vietnam witnessed increasing vegetation condition in most observed years while the declining trend was mainly found in Cambodia and southern Laos. Vegetation-drought responses showed that tropical vegetation had a high sensitivity to drought conditions, and stronger responses were observed in rainfed crop, mixed forest, and deciduous forest at lower altitude areas. In natural and undisturbed ecosystems, short-term meteorological and agricultural drought disturbances accounted for nearly 93% of variations in tropical vegetation. Among the different examined drought indices, the 3-month Standardized Precipitation Evapotranspiration (SPEI-3) and Temperature Condition Index (TCI) were identified as the factors having the largest influence, together explaining about 55% of the observed natural undisturbed vegetation variations. These findings deepen our understanding of tropical vegetation-drought responses and the underlying drivers of natural and undisturbed ecosystems. Such insights could provide valuable information to assist national and local governments in the MSEA region in developing effective drought management and adaptation programs to safeguard tropical agricultural production and natural ecosystems amidst growing climate challenges.

Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta

Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta

Hydrological drought assessment of the Sava River basin in South-Eastern Europe

ABSTRACT Hydrological drought poses significant challenges to water resources, ecosystems, and human activities, necessitating comprehensive investigation. Monthly streamflow data from 12 monitoring stations across the Sava River basin were utilized to compute the streamflow drought index (SDI). The Mann–Kendall test evaluated trends, and the SDI's hydrological states were classified based on cumulative streamflow volumes. The study identified an alarming 83.3% of stations exhibiting statistically significant decreases in summer streamflow, indicating a widespread and concerning trend of declining water availability in the Sava River basin. Drought severity was particularly pronounced in tributaries such as the Vrbas and Bosna rivers, emphasizing the heterogeneous nature of hydrological changes. These findings underscore the urgent need for adaptive water resource management strategies in the face of escalating hydrological drought risks, especially given the far-reaching consequences on agriculture, industry, ecosystems, and social well-being. The study provides crucial insights for developing targeted resilience measures tailored to the specific challenges presented by the diverse hydrological conditions in the Sava River basin.

A Probabilistic Approach to Characterizing Drought Using Satellite Gravimetry

AbstractIn the recent past, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor GRACE Follow‐On (GRACE‐FO), have become invaluable tools for characterizing drought through measurements of Total Water Storage Anomaly (TWSA). However, the existing approaches have often overlooked the uncertainties in TWSA that stem from GRACE orbit configuration, background models, and intrinsic data errors. Here we introduce a fresh view on this problem which incorporates the uncertainties in the data: the Probabilistic Storage‐based Drought Index (PSDI). Our method leverages Monte Carlo simulations to yield realistic realizations for the stochastic process of the TWSA time series. These realizations depict a range of plausible drought scenarios that later on are used to characterize drought. This approach provides probability for each drought category instead of selecting a single final category at each epoch. We have compared PSDI with the deterministic approach (Storage‐based Drought Index, SDI) over major global basins. Our results show that the deterministic approach often leans toward an overestimation of storage‐based drought severity. Furthermore, we scrutinize the performance of PSDI across diverse hydrologic events, spanning continents from the United States to Europe, the Middle East, Southern Africa, South America, and Australia. In each case, PSDI emerges as a reliable indicator for characterizing drought conditions, providing a more comprehensive perspective than conventional deterministic indices. In contrast to the common deterministic view, our probabilistic approach provides a more realistic characterization of the TWS drought, making it more suited for adaptive strategies and realistic risk management.

Driving factors and trend prediction for annual runoff in the upper and middle reaches of the yellow river from 1990 to 2020

The Yellow River Basin (YRB) plays a pivotal role in the water resources management of its region, significantly influenced by the interplay between climate change and human activities, particularly in its upper and middle reaches (UMRYR). This study aims to elucidate the evolving patterns and determinants of runoff within the UMRYR, a matter of considerable importance for the basin’s water resource management, strategy, and distribution. Utilizing the Google Earth Engine (GEE) platform, this research accessed comprehensive datasets including precipitation, drought index, and terrace area, among others, to examine their effects on runoff variations at five gauge stations across the YRB. Terrace data was extracted from Landsat imagery via the Random Forest Model, while annual runoff figures from 1990 to 2020 were sourced from the Sediment Bulletin of China River. Employing the Mann-Kendall test, we assessed the temporal changes in runoff over three decades. In addition, runoff drivers were analyzed by stepwise regression and redundancy analysis, leading to the construction of a multiple linear regression model. The accuracy of predicting annual runoff using the multiple linear model was verified through cross-validation and comparison with the ARIMA time series model. Our findings reveal the efficacy of the random forest algorithm in classifying terraces, achieving an accuracy rate exceeding 0.8. The period from 1990 to 2020 saw a general increase in annual runoff across the five gauging stations in the UMRYR, albeit with variations in the pattern, particularly at the Tangnaihai gauge station which presented the most complex changes. Crucially, three main drivers—summer precipitation (SP), terrace area (TR), and drought index (DI)—were identified as significant predictors in the regression models. The multiple linear regression model outperformed the ARIMA model in forecasting accuracy, underlining the significance of integrating these drivers into runoff prediction models for the UMRYR.

Assessment of dynamic hydrological drought risk from a non‐stationary perspective

AbstractThe stationarity hypothesis of hydrometeorological elements has been questioned in the context of global warming and intense human disturbance. The conventional drought index and methods of frequency analysis may no longer be applicable for hydrological drought risk evaluation under a changing environment. In this study, a new dynamic hydrological drought risk evaluation framework is proposed for application to the Hanjiang River basin (HRB), which simultaneously considers the non‐stationarity in the construction of drought index as well as in the frequency analysis. First, a non‐stationary standardized runoff index (NSRI) is developed using a generalized additive model for location, scale and shape (GAMLSS) framework. Then, hydrological drought characteristics including duration and severity are identified, and their marginal distributions are established. Finally, based on the dynamic copula, considering the non‐stationarity of the dependence structure, the dynamic joint probability distribution, conditional probability distribution and return period of the bivariate hydrological drought properties are analysed. Results showed that NSRI, which integrates the impacts of climate change and anthropogenic activities on the non‐stationarity of runoff series, had a better ability to capture runoff extremes than had SRI. In addition, it is indispensable to consider the non‐stationarity of the dependence structure between variables when discussing the multivariate joint risk of hydrological drought. The risk of hydrological drought in the study area has shown an increasing trend in the past 65 years, and the drought conditions from upstream to downstream have been alleviated first and then intensified. This study provides valuable information for regional drought risk estimation and water resources management from a non‐stationary perspective.

Drought Characterization With GPS: Insights Into Groundwater and Surface‐Reservoir Storage in California

AbstractDrought intensity is commonly characterized using meteorologically‐based metrics that do not provide insight into water deficits within deeper hydrologic systems. In contrast, global positioning system (GPS) displacements are sensitive to both local and regional hydrologic‐storage fluctuations. While a few studies have leveraged this sensitivity to produce geodetic drought indices, hydrologic drought characterization using GPS is not commonly accounted for in drought assessment and management. To motivate this application, we produce a new geodetic drought index (GDI) and quantify its ability to characterize hydrologic drought conditions in key surface and sub‐surface hydrologic reservoirs/pools across California. In northern California, the GDI exhibits a strong regional association with surface‐reservoir storage at the 1‐month time scale (correlation coefficient: 0.83) and groundwater levels at the 3‐month time scale (correlation coefficient: 0.87), along with moderate associations with stream discharge at the daily (instantaneous) time scale (correlation coefficient: 0.50). Groundwater in southern California is best characterized with a 12‐month GDI (correlation coefficient: 0.77), and surface‐reservoir storage is optimized with the 3‐month GDI (correlation coefficient: 0.72). Two sigma uncertainties are ±0.03. Differences between northern and southern California reveal that the GDI is sensitive to unique aquifer and drainage basin characteristics. In addition to capturing long‐term hydrologic trends, rapid changes in the GDI initiate during clusters of large atmospheric river events that closely mirror fluctuations in traditional hydrologic and meteorological observations. We show that GPS‐based hydrologic drought indices provide a significant opportunity to improve drought assessment, in California and beyond, by improving our understanding of the hydrologic cycle.

Semi-arid rather than arid regions of China deserve the priority in drought mitigation efforts

The integration of drought assessment, mitigation priority analysis and mitigation measures development is a forward-looking strategy to achieve water resources allocation, implement anti-drought techniques and formulate corresponding mitigation measures strategies. However, since most drought indices cannot be based on surface water-balance to estimate water demand for drought recovery, there is a gap between drought assessment and proactive drought mitigation. Here, we established the Standardized Water Availability Index (taking water yield as surface water deficit/surplus) to identify hydrological drought, and further use it to determine the water demand for hydrological drought mitigation. On that basis, a comprehensive framework, integrating with drought assessment-resilience-recovery and water scarcity stress, was proposed to investigate the hydrological drought mitigation priority for basins in different climate regions in China. The results show that non-humid river basins have greater water scarcity stress, lower resilience to hydrological drought, but less water demand for hydrological drought mitigation (3 mm ∼ 7 mm in mild drought, 5 mm ∼ 14 mm in moderate drought, 7 mm ∼ 21 mm in severe drought). In contrast, humid river basins have greater water demand for hydrological drought mitigation (15 mm ∼ 49 mm in mild drought, 27 mm ∼ 93 mm in moderate drought, 32 mm ∼ 119 mm in severe drought), but higher resilience to hydrological drought. The densely populated semi-arid river basins in China should be given priority attention in mitigating hydrological drought on account of their greatest water scarcity stress, lower drought resilience, and less water demand for drought mitigation. This work highlights the complexity of the hydrological drought mitigation, and our findings can help stakeholders to make precise drought mitigation measures and adapt drought-relative water management.

Impact of extreme seasonal drought on ecosystem carbon‒water coupling across China

Water use efficiency (WUE) is a critical evaluation indicator of ecosystem responses to climate change and its extremes. However, the influence of the timing of extreme drought on the variation of ecosystem WUE under severe water stress has not been studied extensively. In particular, the modulation of drought impacts on WUE under regional hydro-climatic conditions and biome types is poorly understood. Considering observation-based ecosystem flux and drought index datasets, this study examined the impact of extreme seasonal drought on WUE in China and attempted to reveal the underlying drivers of seasonal variations in drought impacts. Results showed that the direction and magnitude of drought impacts on WUE depend on the occurrence time of extreme drought and the seasonal dynamics of regional ecological and climatic conditions. Across the vegetated regions in China, the most widespread reduction in WUE under extreme drought conditions was observed in summer, whereas approximately 60% of the study area showed positive changes in WUE under extreme drought conditions in spring. Furthermore, the co-regulation of drought characteristics and background environmental conditions in determining the impacts of extreme seasonal drought on ecosystem WUE is highlighted. Classification and regression tree analysis results illustrate that leaf area index (LAI) and drought timing dominated ecosystem WUE variation in response to extreme drought in China. Regions with lower LAI experienced more serious reductions in WUE under extreme drought. These findings indicate the importance of accounting for the interaction between drought seasonality and biome features in assessing drought impacts, thus contributing to improving the modelling of terrestrial ecosystem responses to climate extremes under global warming.

Climate-conscious archaeology: contextualizing drought and history in the Chesapeake

This paper adopts a climate-conscious approach to archaeology, integrating environmental scientists’ definitions of extended droughts and megadroughts into the analysis of historical processes in the Chesapeake region of North America. We explore the relationship between drought conditions and historical processes through three case studies: Ancestral Monacans’ migration, the settlement dynamics in the Middle Potomac, and the emergence of the Powhatan chiefdom. Employing the Palmer Modified Drought Index as a paleoclimatic proxy, the research assesses how variations in rainfall and drought influenced migration, agriculture, and political formations. The findings underscore the complex interplay between Native history and environmental conditions, suggesting that the impact of climate on historical processes ranged from negligible to substantial, particularly with the adoption of maize-based agriculture. This study highlights the benefits of a climate-informed archaeological inquiry that recognizes the historically contingent ways in which climatic variability has shaped and is entangled with social change.

Enhanced autumn phenology model incorporating agricultural drought

The impacts of various drought types on autumn phenology have yet to be extensively explored. We address the influence of pre-season agricultural and meteorological droughts on autumn phenology in the Northern Hemisphere. To this end, enhanced autumn phenology models incorporating drought factors was developed, contributing to a deeper understanding of these complex interactions. The study reveals that there was no significant trend of advancement or delay in the End of Season (EOS) across the Northern Hemisphere based on SIF estimates from 2001 to 2020. The cumulative and delayed impacts of pre-season agricultural drought on EOS were found to be more pronounced than those associated with meteorological drought. The analysis of various evaluation indexes shows that the performance of the Cooling Degree Days (CDD) model incorporating the Standardized Soil Moisture Drought Index (CDDSSMI) in simulating EOS in the Northern Hemisphere is >14 % higher than that of the standard CDD model. Additionally, the performance of the CDD model with the Standardized Precipitation Index (CDDSPI) in simulating EOS in the Northern Hemisphere is improved by >5.6 % compared to the standard CDD model. A comparison of future EOS projections across various models reveals that the CDD model significantly overestimates EOS in different scenarios (SSP245 and SSP585). The CDDSSMI model projects EOS approximately 7 days earlier than the CDD model, and the CDDSPI model projects EOS approximately 5 days earlier than the CDD model. This study highlights the diverse impacts of drought types on plant autumn phenology and underscores the significance of parameterizing drought impacts in autumn phenology models.

Correlation between Soil Moisture Change and Geological Disasters in E’bian Area (Sichuan, China)

E’bian Yi Autonomous County is a mineral-rich area located in a complex geological structure zone. The region experiences frequent geological disasters due to concentrated rainfall, steep terrain, and uneven vegetation cover. In particular, during the rainy season, large amounts of rainwater rapidly accumulate, increasing soil moisture and slope pressure, making landslides and debris flows more likely. Additionally, human activities such as mining, road construction, and building can alter the original geological structure, exacerbating the risk of geological disasters. According to publicly available data from the Leshan government, various types of geological disasters occurred in 2019, 2020, 2022, and 2023, resulting in economic losses and casualties. Although some studies have focused on geological disaster issues in E’bian, these studies are often limited to specific areas or types of disasters and lack comprehensive spatial and temporal analysis. Furthermore, due to constraints in technology, funding, and manpower, geophysical exploration, field geological exploration, and environmental ecological investigations have been challenging to carry out comprehensively, leading to insufficient and unsystematic data collection. To provide data support and monitoring for regional territorial spatial planning and geological disaster prevention and control, this paper proposes a new method to study the correlation between soil moisture changes and geological disasters. Six high-resolution Landsat remote sensing images were used as the main data sources to process the image band data, and terrain factors were extracted and classified using a digital elevation model (DEM). Meanwhile, a Normalized Difference Vegetation Index–Land Surface Temperature (NDVI-LST) feature space was constructed. The Temperature Vegetation Drought Index (TVDI) was calculated to analyze the variation trend and influencing factors of soil moisture in the study area. The research results showed that the variation in soil moisture in the study area was relatively stable, and the overall soil moisture content was high (0.18 < TVDI < 0.33). However, due to the large variation in topographic relief, it could provide power and be a source basis for geological disasters such as landslide and collapse, so the inversion value of TVDI was small. The minimum and maximum values of the correlation coefficient (R2) were 0.60 and 0.72, respectively, indicating that the surface water content was relatively large, which was in good agreement with the calculated results of vegetation coverage and conducive to the restoration of ecological stability. In general, based on the characteristics of remote sensing technology and the division of soil moisture critical values, the promoting and hindering effects of soil moisture on geological hazards can be accurately described, and the research results can provide effective guidance for the prevention and control of geological hazards in this region.