Search Results (85)

Drought indices are important resources for monitoring and warning of drought impacts. However, regions like New Mexico, which are highly vulnerable to drought, as identified by the United States Drought Monitor (USDM), lack a comprehensive drought monitoring system that integrates multiple agrometeorological variables into a single indicator. The purpose of this study is to create a Combined Drought Indicator for New Mexico (CDI-NM) as an indicator tool for use in monitoring historical drought events and measuring its extent across the New Mexico. The CDI-NM was constructed using four key variables: the Vegetation Condition Index (VCI), temperature, Smoothed Normalized Difference Vegetation Index (SMN), and gridded rainfall data. A quantitative approach was used to assign weights to these variables employing Principal Component Analysis (PCA) to produce the CDI-NM. Unlike conventional indices, CDI-NM assigns weights to each variable based on their statistical contributions, allowing the index to adapt to local spatial and temporal drought dynamics. The performance of CDI-NM was evaluated against gridded rainfall data using the 3-month Standardized Precipitation Index (SPI3) over a 17-year period (2003–2019). The results revealed that CDI-NM reliably detected moderate and severe droughts with a strong correlation (R² > 0.8 and RMSE = 0.10) between both indices for the entire period of analysis. CDI-NM showed negative correlation (r < 0) with crop yield. While promising, the method assumes linear relationships among variables and consistent spatial resolution in the input datasets, which may affect its accuracy under certain local conditions. Based on the results, the CDI-NM stands out as a promising instrument that brings us closer to improved decision-making by stakeholders in the fight against agricultural droughts throughout New Mexico. Full article

This study focuses on Qilu Lake to study how to mitigate the impacts of seasonal droughts and provide technical support for drought resistance decision-making in low-latitude plateau lake basins. Using the Standardized Precipitation Index (SPI), the Vegetation Condition Index (VCI), and the Temperature Condition Index (TCI) as bases, in this study, the applicability of the vegetation health index (VHI) within the basin is investigated, and the optimal weight distribution between the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) in the VHI is determined. The VHI is then applied to analyze the correlation between drought frequency and severity within the basin. The results indicate that the method is most effective in assessing agricultural drought in the Qilu Lake Basin when the VCI and TCI are weighted at a 4:6 ratio, optimizing the VHI’s evaluative performance. The drought limit water levels of lakes are further divided into short- and long-term drought limit water levels. The short-term drought limit water level is divided into the drought warning water level and the drought emergency water level. The drought warning water level (corresponding to moderate drought conditions, with a frequency of P = 75%) ranges from 1794.53 m to 1795.11 m, while the drought emergency water level (corresponding to extreme drought conditions, with a frequency of P = 95%) ranges from 1793.94 m to 1794.31 m. These levels are set to meet the emergency water demand during droughts in the basin. The long-term drought limit water levels are calculated by accumulating the water deficits of various sectors within the watershed under different agricultural drought conditions, based on the short-term drought limit water levels. By setting the drought limit water level using this method, as well as considering the original water regulation capacity of the lake resources, when the watershed experiences drought, the scheduling method based on this drought limit water level can better alleviate the water supply pressure on various sectors in the local area. Full article

Agricultural drought poses a severe threat to food security in the North China Plain, necessitating accurate and timely monitoring approaches. This study presents a novel drought assessment framework that innovatively integrates multiple remote sensing indices through an optimized random forest algorithm, achieving unprecedented accuracy in regional drought monitoring. The framework introduces three key innovations: (1) a systematic integration of six drought-related factors including vegetation condition index (VCI), temperature condition index (TCI), precipitation condition index (PCI), land cover type (LC), aspect (ASPECT), and available water capacity (AWC); (2) an optimized random forest algorithm configuration with 100 decision trees and enhanced feature extraction capability; and (3) a robust triple-validation strategy combining standardized precipitation evapotranspiration index (SPEI), comprehensive meteorological drought index (CI), and soil moisture verification. The framework demonstrates exceptional performance with R² values consistently above 0.80 for monthly assessments, reaching 0.86 during autumn and 0.73 during summer seasons. Particularly, it achieves 87% accuracy in mild drought (−1.0 < SPEI ≤ −0.5) and 85% in moderate drought (−1.5 < SPEI ≤ −1.0) detection. The 20-year (2000–2019) spatiotemporal analysis reveals that moderate drought events dominated the region (23.7% of total occurrences), with significant intensification during the 2010–2012 and 2014–2016 periods. Summer drought frequency peaked at 12–15 months in south-central Shandong (37°N, 117°E) and eastern Henan (34°N, 114°E). The framework’s high spatial resolution (1 km) and comprehensive validation protocol establish a reliable foundation for agricultural drought monitoring and water resource management, offering a transferable methodology for regional drought assessment worldwide. Full article

Droughts are a major driver of global environmental degradation, threatening lives and causing significant economic losses, with approximately 80% of these losses linked to agricultural drought, characterized by soil moisture deficits. Remote sensing technology offers high spatiotemporal resolution data for continuous monitoring of soil moisture and drought severity. However, the effectiveness of remote sensing drought indices across different soil depths remains unclear. This study assessed the performance of eight widely used drought indices—Perpendicular Drought Index (PDI), Modified Perpendicular Drought Index (MPDI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Temperature–Vegetation Dryness Index (TVDI), and Standardized Precipitation–Evapotranspiration Index (SPEI) at multiple timescales—in monitoring soil moisture at five depths (0–50 cm, at 10 cm intervals) across nine agricultural regions of China from 2001 to 2020. Results reveal that the monitoring performance of drought indices varies significantly across regions and soil depths, with a general decline in performance as soil depth increases. For soil depths between 10–40 cm, VCI and NVSWI exhibited the highest accuracy, while PDI, MPDI, and VHI performed optimally in the Northeast China Plain. At 50 cm depth, however, optical remote sensing indices struggled to accurately capture soil moisture conditions. Additionally, TCI and TVDI showed notable lag effects, with 4-month and 5-month delays, respectively, while SPEI exhibited cumulative effects over 3–6 months. These findings provide critical insights to guide the selection of appropriate drought indices for soil moisture monitoring, aiding agricultural drought management and decision-making. Full article

Drought, as an extreme climatic event, is considered one of the most severe natural disasters worldwide. In Northeast Asia, the frequency and intensity of drought have been exacerbated by climate change, causing significant negative impacts on the region’s socioeconomic conditions and agricultural production. This study analyzed the spatiotemporal evolution and trends in drought in transboundary river basins in Northeast Asia from 1990 to 2020, using meteorological station data and remote sensing data. The Standardized Precipitation Evapotranspiration Index (SPEI) and Vegetation Condition Index (VCI) were employed to assess drought characteristics, and a comprehensive analysis of the SPEI and VCI indices was conducted to evaluate drought severity under different land cover types. The results indicate that (1) in the past two decades, both the SPEI and VCI indices have shown an increasing trend in the basin, with moderate and mild droughts being predominant. (2) High and extreme droughts mainly occur in forest areas, accounting for 17.91% and 10.76%, respectively, followed by farmland. Full article

The rainfed cereal growing regions of Northern Kazakhstan experience significant yield fluctuations due to dependence on weather conditions. Early detection and monitoring of droughts is crucial for effective mitigation strategies in this region. This study emphasises the following objectives: (1) description of the current vegetation condition with a possible separation of short-term weather effects and (2) analysing trends of changes with their directionality and quantification. Terra MODIS satellite images from 2000 to 2023 are used. Differential indices—Normalised Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI)—are used to determine the characteristics of each current season. A key component is the comparison of the current NDVI values with historical maximum, minimum, and average values to identify early indicators of drought. NDVI deviations from multiyear norms and VCI values below 0.3 visually reflect changing vegetation conditions influenced by seasonal weather patterns. The results show that the algorithm effectively detects early signs of drought through observed deviations in NDVI values, showing a trend towards increasing drought frequency and intensity in Northern Kazakhstan. The algorithm was particularly effective in detecting severe drought seasons in advance, as was the case in June 2010 and May 2012, thus supporting early recognition of drought onset. The Integrated Vegetation Index (IVI) and Integrated Vegetation Condition Index (IVCI) time series are used for integrated multiyear assessments, in analysing temporal changes in vegetation cover, determining trends in these changes, and ranking the weather conditions of each growing season in the multiyear series. Areas with high probability of drought based on low IVCI values are mapped. The present study emphasises the value of remote sensing as a tool for drought monitoring, offering timely and spatially detailed information on vulnerable areas. This approach provides critical information for agricultural planning, environmental management and policy making, especially in arid and semi-arid regions. The study emphasises the importance of multiyear data series for accurate drought forecasting and suggests that this methodology can be adapted to other drought-sensitive regions. Emphasising the socio-economic benefits, this study suggests that the early detection of drought using satellite data can reduce material losses and facilitate targeted responses. Full article

The ecological environment of the Yellow River Basin in China is characterized by drought, which has been exacerbated by global warming. It is critical to keep accurate track of the region’s agricultural drought conditions. To enhance the vegetation health index (VHI), the optimal time scale for the standardized precipitation evapotranspiration index (SPEI) was determined by using the maximum correlation coefficient method, and the calculation method for VHI was optimized. The contributions of the vegetation condition index (VCI) and the temperature condition index (TCI) to the VHI were scientifically optimized, leading to the development of the optimal VHI (VHIopt). Soil moisture anomaly (SMA) and the SPEI were employed for assessing the performance of VHIopt. Furthermore, the temporal and spatial evolution of agricultural drought in the Yellow River Basin (YRB) was analyzed using VHIopt. The results indicate the following: (1) In the YRB, the optimal contribution of the VCI to the VHI is lower than that of the TCI. (2) The drought monitoring accuracy of VHIopt in forests, grasslands, croplands, and other vegetation types exceeds that of the original VHI (VHIori). Additionally, it demonstrates a high level of consistency with the SMA and the SPEI03 regarding spatial and temporal characteristics. (3) Agricultural drought in the YRB is gradually diminishing; however, significant regional differences remain. Generally, the findings of this study highlight that VHIopt is better suited to the specific climate and vegetation conditions of the Yellow River Basin, enhancing its effectiveness for agricultural drought monitoring in this region. Full article

Analyzing agricultural and hydrological drought at different timescales is essential for designing adaptation strategies. This study aimed to assess agricultural and hydrological drought in the Abbay Basin of Ethiopia by using multiple indices, namely the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), and drought severity index (DSI). Climate extremes were assessed over the Abbay Basin between 1981 and 2022. The results indicate that the years 1982 and 2014 were the most drought-prone, while the year 1988 was the wettest year in the Abbay Basin. The results revealed the presence of extremely dry and severely dry conditions, potentially impacting agricultural output in the region. Agricultural drought was identified during the main crop seasons (June to September). The VCI results indicated the presence of extremely wet and severely wet conditions. In 2012, 65% of the area was affected by extreme drought conditions, while nearly half of the Basin experienced extreme drought in 2013 and 2022. The DSI results indicated the occurrence of agricultural drought, although the spatial coverage of extreme dry conditions was lower than that of the other indices. In 2003, 78.49% of the Basin experienced moderate drought conditions, whereas severe drought affected 20% of the region. In 2010, about 90% of the Basin experienced moderate drought. This study provides valuable insights for agricultural communities, enabling them to mitigate the impact of drought on crop yields by utilizing different adaptation strategies. An adequate knowledge of agricultural and hydrological drought is essential for policymakers to assess the potential effects of drought on socioeconomic activities and to recognize the significance of implementing climate change adaptation measures. Full article

As the global climate changes and droughts become more frequent, understanding the characteristics and propagation dynamics of drought is critical for monitoring and early warning. This study utilized the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), and Groundwater Drought Index (GDI) to identify meteorological drought (MD), agricultural drought (AD), and groundwater drought (GD), respectively. Sen’s slope method and Mann–Kendall trend analysis were used to examine drought trends. The Pearson correlation coefficient (PCC) and theory of run were utilized to identify the propagation times between different types of droughts. Cross-wavelet transform (XWT) and wavelet coherence (WTC) were applied to investigate the linkages among the three types of droughts. The results showed that, from 2004 to 2022, the average durations of MD, AD, and GD in Henan Province were 4.55, 8.70, and 29.03 months, respectively. MD and AD were gradually alleviated, while GD was exacerbated. The average propagation times for the different types of droughts were as follows: 6.1 months (MD-AD), 4.4 months (MD-GD), and 16.3 months (AD-GD). Drought propagation exhibited significant seasonality, being shorter in summer and autumn than in winter and spring, and there were close relationships among MD, AD, and GD. This study revealed the characteristics and propagation dynamics of different types of droughts in Henan Province, providing scientific references for alleviating regional droughts and promoting the sustainable development of agriculture and food production. Full article

Countries face challenges of excess, scarcity, pollution, and uneven water distribution. This study highlights the benefits of advances in groundwater engineering that improve the understanding of utilizing local geological characteristics due to their crucial role in resisting drought in southern Lebanon. The type of drought in the region was determined using the Standardized Precipitation Index (SPI), Standardized Vegetation Index (NDVI), Vegetation Condition Index (VCI), and Soil Moisture Anomaly Index (SM). The dry aquifer and its characteristics were analyzed using mathematical equations and established hydrogeological principles, including Darcy’s law. Additionally, a morphometric assessment of the Litani River was performed to evaluate its suitability for artificial recharge, where the optimal placement of the water barrier and recharge tunnels was determined using Spearman’s rank correlation coefficient. This analysis involved excluding certain parameters based on the Shapiro–Wilk test for normality. Accordingly, using the Geographic Information System (GIS), we modeled and simulated the potential water table. The results showed the importance and validity of linking groundwater engineering and morphometric characteristics in combating the drought of groundwater layers. The Eocene layer showed a clearer trend for the possibility of being artificially recharged from the Litani River than any other layer. The results showed that the proposed method can enhance artificial recharge, raise the groundwater level to four levels, and transform it into a large, saturated thickness. On the other hand, it was noted that the groundwater levels near the surface will cover most of the area of the studied region and could potentially store more than one billion cubic meters of water, mitigating the effects of climate change for decades. Full article

The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of vegetation and soil moisture in semiarid rangelands using vegetation and soil moisture indices. We aim to study the feasibility of using soil moisture negative anomalies as a warning index for vegetation or agricultural drought. Two semiarid agricultural regions were selected in Spain for this study: Los Vélez (Almería) and Bajo Aragón (Teruel). MODIS images, with 250 m and 500 m spatial resolution, from 2002 to 2019, were acquired to calculate the Vegetation Condition Index (VCI) and the Water Condition Index (WCI) based on the Normalised Difference Vegetation Index (NDVI) and soil moisture component (W), respectively. The Optical Trapezoid Model (OPTRAM) estimated this latter W index. From them, the anomaly (Z-score) for each index was calculated, being $Z_{VCI}$ and $Z_{WCI}$, respectively. The probability of coincidence of their negative anomalies was calculated every 10 days (10-day periods). The results show that for specific months, the $Z_{WCI}$ had a strong probability of informing in advance, where the negative $Z_{VCI}$ will decrease. Soil moisture content and vegetation indices show more similar dynamics in the months with lower temperatures (from autumn to spring). In these months, given the low temperatures, precipitation leads to vegetation growth. In the following months, water availability depends on evapotranspiration and vegetation type as the temperature rises and the precipitation falls. The stronger relationship between vegetation and precipitation from autumn to the beginning of spring is reflected in the feasibility of $Z_{WCI}$ to aid the prediction of $Z_{VCI}$. During these months, using $Z_{WCI}$ as a warning index is possible for both areas studied. Notably, November to the beginning of February showed an average increase of 20–30% in the predictability of vegetation anomalies, knowing moisture soil anomalies four lags in advance. We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón. Full article

This research proposes an optimized method for identifying high-quality vegetation areas, with a focus on forest ecosystems, using an improved Vegetation Health Index (VHI). The study introduces the Land Cover Vegetation Health Index (LCVHI), which integrates the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) with land cover data. Utilizing MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery and Google Earth Engine (GEE), the study assesses the impact of land cover changes on vegetation health, with particular attention to forested areas. The application of the LCVHI demonstrates that forests exhibit a VHI approximately 25% higher than that of croplands, and wetlands show an 18% higher index compared to grasslands. Analysis of data from 2012 to 2022 in Hubei Province, China, reveals an overall upward trend in vegetation health, highlighting the effectiveness of environmental protection and forest management measures. Different land cover types, including forests, wetlands, and grasslands, significantly impact vegetation health, with forests and wetlands contributing most positively. These findings provide important scientific evidence for regional and global ecological management strategies, supporting the development of forest conservation policies and sustainable land use practices. The research results offer valuable insights into the effective management of regional ecological dynamics. Full article

Drought, characterized by frequent occurrences, an extended duration, and a wide range of destruction, has become one of the natural disasters posing a significant threat to both socioeconomic progress and agricultural livelihoods. Large-scale geographical environments often exhibit obvious spatial heterogeneity, leading to significant spatial differences in drought’s development and outcomes. However, traditional drought monitoring models have not taken into account the impact of regional spatial heterogeneity on drought, resulting in evaluation results that do not match the actual situation. In response to the above-mentioned issues, this study proposes the establishment of ecological–geographic zoning to adapt to the spatially stratified heterogeneous characteristics of large-scale drought monitoring. First, based on the principles of ecological and geographical zoning, an appropriate index system was selected to carry out ecological and geographical zoning for Yunnan Province. Second, based on the zoning results and using data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and the Tropical Rainfall Measuring Mission (TRMM) 3B43, the vegetation condition index (VCI), the temperature condition index (TCI), the precipitation condition index (TRCI), and three topographic factors including the digital elevation model (DEM), slope (SLOPE), and aspect (ASPECT) were selected as model parameters. Multiple linear regression models were then used to establish integrated drought monitoring frameworks at different eco–geographical zoning scales. Finally, the standardized precipitation evapotranspiration index (SPEI) was used to evaluate the monitoring effects of the model, and the spatiotemporal variation patterns and characteristics of winter and spring droughts in Yunnan Province from 2008–2019 were further analyzed. The results show that (1) compared to the traditional non-zonal models, the drought monitoring model constructed based on ecological–geographic zoning has a higher correlation and greater accuracy with the SPEI and (2) Yunnan Province experiences periodic and seasonal drought patterns, with spring being the peak period of drought occurrence and moderate drought and light drought being the main types of drought in Yunnan Province. Therefore, we believe that ecological–geographic zoning can better adapt to geographical spatial heterogeneity characteristics, and the zonal drought monitoring model constructed can more effectively identify the actual occurrence of drought in large regions. This research finding can provide reference for the formulation of drought response policies in large-scale regions. Full article

Accurately evaluating drought and its effects on the natural environment is difficult in regions with limited climate monitoring stations, particularly in the hyper-arid region of the Sultanate of Oman. Rising global temperatures and increasing incidences of insufficient precipitation have turned drought into a major natural disaster worldwide. In Oman, drought constitutes a major threat to food security. In this study, drought indices (DIs), such as temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI), which integrate data on drought streamflow, were applied using moderate resolution imaging spectroradiometer (MODIS) data and the Google Earth Engine (GEE) platform to monitor agricultural drought and assess the drought risks using the drought hazard index (DHI) during the period of 2001–2023. This approach allowed us to explore the spatial and temporal complexities of drought patterns in the Najd region. As a result, the detailed analysis of the TCI values exhibited temporal variations over the study period, with notable minimum values observed in specific years (2001, 2005, 2009, 2010, 2014, 2015, 2016, 2017, 2019, 2020, and 2021), and there was a discernible trend of increasing temperatures from 2014 to 2023 compared to earlier years. According to the VCI index, several years, including 2001, 2003, 2006, 2008, 2009, 2013, 2015, 2016, 2017, 2018, 2020, 2021, 2022, and 2023, were characterized by mild drought conditions. Except for 2005 and 2007, all studied years were classified as moderate drought years based on the VHI index. The Pearson correlation coefficient analysis (PCA) was utilized to observe the correlation between DIs, and a high positive correlation between VHI and VCI (0.829, p < 0.01) was found. Based on DHI index spatial analysis, the northern regions of the study area faced the most severe drought hazards, with severity gradually diminishing towards the south and east, and approximately 44% of the total area fell under moderate drought risk, while the remaining 56% was classified as facing very severe drought risk. This study emphasizes the importance of continued monitoring, proactive measures, and effective adaptation strategies to address the heightened risk of drought and its impacts on local ecosystems and communities. Full article

Climate change has massive global impacts and affects a wide range of species. Threatened species such as the roan antelope (Hippotragus equinus) are particularly vulnerable to these changes because of their ecological requirements. Attempts to address concerns about the roan’s vulnerability have not been well documented in South African protected areas. This study identifies the landscape use and distribution of the roan as well as habitat and forage suitability changes to help inform management decisions for the conservation of roan. We used fine- and broad-scale data from Mokala National Park, South Africa that includes roan occurrence data, vegetation condition indices, vegetation (structure and plant species composition), elevation and temperature differences, and precipitation strata to construct a suitability framework using the Maximum Entropy (Maxent) and Random Forest statistical package. In Mokala National Park, roan occurred in the Schmidtia pappophoroides–Vachellia erioloba sparse woodland, Senegalia mellifera–Vachellia erioloba closed woodland, Senegalia mellifera–Vachellia tortilis open shrubland, Vachellia erioloba–V. tortilis closed woodland and Rhigozum obovatum–Senegalia mellifera open shrubland. The veld (vegetation) condition index (VCI) improved from 2019 (VCI < 50%) to 2021 (VCI > 60%), with the proportion of palatable grass species (Schmidtia pappophoroides and Eragrostis lehmanniana) also increasing. This study identified four key climatic conditions affecting roan distribution, namely annual mean daily temperature range, temperature seasonality, minimum temperatures of the coldest month, and precipitation of the wettest month. These results suggest that the conservation of roan antelope should consider these key variables that affect their survival in preferred habitats and foraging areas in anticipation of changing ecological conditions. Full article