Drought Phenomenon Research Articles

Drought Analysis Using Various Drought Indices in Sub-District Ngemplak

The study namely Drought Analysis Using Various Drought Indices in District Ngemplak, Boyolali offers a comprehensive examination of drought phenomena in the Ngemplak, Boyolali district through the lens of var­ious drought indices, aiming to understand the impact on agricultural productivity and propose effective water management strategies. Using data from 2009 to 2023, this study used SPI and PDSI. Focusing on the Ngem­plak, Boyolali district, this research identifies the most relevant drought indices tailored to the local climatic and geographical contexts, analyzes the correlation between these indices and the productivity of primary crops, and suggests sustainable water management practices to mitigate drought risks. The study underscores the importance of adopting a multifaceted and adaptive water management framework, incorporating sophisticated irrigation technologies, and fostering policy reforms to enhance water use efficiency and resilience against climatic variability. It calls for collaborative efforts involving stakeholders across different sectors to implement these recommendations, ensuring the sustainability of water resources and agricultural productivity in the face of drought challenges.

SPEI and SPI correlation in the study of drought phenomena in Umbria region (central Italy)

This study aims to analyze the performances and correlation of the standardized precipitation index (SPI) and standardized precipitation evaporation index (SPEI) from the perspective of supplying effective indicators for drought risk management prevention. Indices have been evaluated using long time series of precipitation and temperature data (from 1961 to 2020) gauged and validated in the land monitoring system of the Umbria region (central Italy). Results show how SPEI can evaluate better the drought phenomena, both in terms of occurred events and in terms of trends. In particular, SPEI can appreciate the effects of the increase in temperatures, which in the next years could be predominant in c limate change. Currently, the high correlation between SPEI and SPI (R2 > 0.8 and r > 0.9) makes possible the use of SPI instead of SPEI in drought analysis; however, this correlation, evaluated on the two times series 1961–1990 and 1991–2020, shows a decreasing tendency; then, it could be no longer reliable in the future. These results should lead to increasingly synergistic monitoring of rainfall and temperature data, limiting as much as possible the lack of temporal overlap of the two sensors at the gauging stations. The possibility of using ERA 5 reanalysis data was also explored for the years that presented severe droughts by comparing them to the station-based observations. While the temperature data is reliable, the total precipitation parameter seems less affordable, and then, other available gridded datasets, e.g., CHIRPS, MERRA2, and Terraclimate, should have to be considered to improve the modeled precipitation’s suitability.Graphical abstract

Evaluation of meteorological drought indices using remote sensing

Drought indices are a suitable tool for management measures and dealing with drought and are widely used worldwide. One of the most important stages of assessing the drought situation in each region is determining the drought indices to analyze the intensity and duration of drought in that region. The drought index is actually a function of various environmental factors that affect the drought phenomenon. In this study, the drought indices of the standardized precipitation index (SPI), China-Z index (CZI), Z Score Index (ZSI), and modified CZI (MCZI) in different month time scales (1, 3, 6, 9, 12, 18, 24 and 48) and the present of normal index (PNI) in different monthly, seasonally and yearly time scales in Kurdistan Province (stations Saqqez, Qorveh, Bijar, Sanandaj) were evaluated. Global land data assimilation system (GLDAS), Climatic Research Unit (CRU) dataset, and Tropical Rainfall Measuring Mission (TRMM) precipitation data (2000-2020) and TRMM precipitation (2000-2019) were received, and the drought indices were calculated. Root mean square error (RMSE), maximum error (ME), Pearson, and Spearman correlation coefficients were used for evaluation. The results of the SPI index showed that there is a significant relationship between TRMM, CRU, and GLDAS at the Saqqez, Qorveh, and Sanandaj stations (at the 5% level), and there was no significant relationship for TRMM at Bijar station for the 24-month time scale. The correlation coefficient results for the Saqqez and Sanandaj stations in time scales of 1 to 9 months for the SPI, CZI, ZSI, and MCZI indices were better than those of the Bijar and Qorveh stations. In assessing the SPI and CZI drought indices, the highest RMSE was for GLDAS at Bijar station and for the 48-month time scale. In general, the results showed that the drought indices of the SPI, CZI, ZSI, MCZI, and PNI obtained from the TRMM satellite, CRU dataset, and GLDAS model have a good correlation with the drought indices of synoptic stations.

Drought Dynamics in Sub-Saharan Africa: Impacts and Adaptation Strategies

The escalation in both frequency and severity of drought events has significantly amplified the vulnerability of numerous countries, particularly in developing ones, imposing substantial economic, environmental, and social pressures. This article presents a systematic review of drought occurrences in Sub-Saharan Africa (SSA), examining historical trends, current impacts, and projected future implications. Through this comprehensive assessment, a clear trend of intensifying drought phenomena emerges across SSA, leading to crop failures, drying of water sources, loss of pasture, food shortages, and an increase in food prices. This review also highlights the concerning potential for worsening conditions in certain regions, resulting in consequences such as migration, food insecurity, malnutrition, family disintegration, crop losses, and increased disease prevalence, notably HIV/AIDS. This study further reveals that current adaptation measures by governments and NGOs should be improved to effectively adapt to the diverse impacts of drought, and it contributes to a deeper understanding of drought dynamics in Sub-Saharan Africa and assesses its critical impacts on food security and social well-being. It also evaluates adaptation measures across different countries, highlighting their strengths and weaknesses and enabling quick identification of areas for improvement. Additionally, it informs resilience-building efforts in vulnerable communities.

An application of the remote sensing derived indices for drought monitoring in a dry zone district, in tropical island

Recent research has shown that droughts have intensified in South Asia over the past two decades. As a natural disaster, this has severely impacted people’s livelihoods, especially in the dry zones of Sri Lanka. Thus, to ensure human well-being, security of water resources, and ecosystem health, it is essential to minimize the impacts of droughts using reliable information. Remote sensing (RS) data and techniques help bridge the gap by enabling the analysis of drought phenomena through a diverse array of indices developed in the fields. However, until now, there has been a lack of systematic monitoring and reliable data for accurately characterizing droughts in the study area. As the first comprehensive analysis, we tried to evaluate the spatial–temporal dynamics of drought conditions in two Divisional Secretariat Divisions of the Anuradhapura district, Sri Lanka using eight standardized remote sensing-derived indices over a decade (2013–2023) including the Standardized Precipitation Index (SPI). SPI values indicated that the region has experienced notably below-average precipitation. According to SPI results, a significant portion of the Medawchchiya area experienced arid conditions in 2023. All other indices proved that 2018 was the driest year and 2013 was the wettest year among the three time points, as reflected by their low and high index values. However, according to NVSMI and LST, the wettest year is 2023, with only 1.78 % of areas experiencing severe drought and a maximum LST of 31.4 °C. LULC change detection revealed that 14.3 % of agricultural lands and 5.1 % of forest areas were converted into barren lands over a decade. Overall, this conversion may be another leading factor contributing to increased LST and dryness in the area during the concerned period while increasing the mean LST of barren land by around 4.7 °C per decade. Land surface-related and vegetation-related indices, such as NDWI, NDMI, LST, and NDVI, exhibited a more pronounced impact on short-term drought occurrences. The findings revealed that average precipitation coincides with short-term drought episodes in the area, with 2018 standing out as having the least rainfall and the driest year. The study’s findings may provide additional insights for planning authorities, supporting environmental protection and enhancing agricultural production by mitigating droughts’ impacts through short- and long-term strategies. Although, the study focused on a small area, a similar approach could be extended to other areas by incorporating advanced machine learning techniques and additional drought indices in the future.

Forecasting of meteorological drought using ensemble and machine learning models

This study highlights drought forecasting for understanding the semi-arid area in India, where drought phenomena play vital role in the irrigation, drinking water supplies, and sustaining the ecological with economic balance for every nation. Therefore, drought forecasting is important for the future drought planning based on the machine learning (ML) models. Hence, The Standardized Precipitation Index (SPI) at 3- and 6-month periods have been selected and used for future drought forecasting scenarios in area. The combinations of ten inputs SPI-1- and SPI-10 were used for predicting modeling for SPI-3 and SPI-6 timescales, that modeling developed based on the historical SPI datasets from 1989 to 2019 years. The SPI-3 and SPI-6 maximum and minimum values are shown SPI-3 (2.03 and -5.522) and SPI-6 (1.94 and -6.93). The SPI is a popular method for estimating the drought analysis and has been used everywhere at global level. The developed models have been compared with each other, with the best combination of input variables selected using subset regression models and sensitivity studies. After that, the active input parameters were used for forecasting of SPI-3 and SPI-6 values to understanding of drought in semi-arid area. The finest input variables combination have been used in the Ml models and established the novel five models such as robust linear regression, bagged trees, boosted trees, support vector regression (SVM-Linear), and Matern Gaussian Process Regression (Matern GPR) models. Such kind of models first time has been applied for the forecasting of future drought conditions. Whole models were fine and improved modeling by using hyperparameters tuning, bagging, and boosting models. Entire ML models’ accuracy was compared using different statistical metrics. Compared with five ML models accuracy, we have found that the Matern GPR model better accuracy than other ML models. The best model accuracy is R2 = 0.95 and 0.93, RMSE, MSE, MAE, MARE, and NSE values, respectively, for predicting SPI-3 and SPI-6 values in the area. Therefore, the Matern GPR model was identified as the finest ML algorithm for predicting SPI-3 and SPI-6 associated with other algorithms. This research demonstrates the Matern GPR model's efficacy in predicting multiscale SPI-3 and SPI-6 under climate variations. It can be helpful in soil and water resource conservation planning and management and understanding droughts in the entire basin areas of the country India.

Applicability of Remote Sensing Techniques in Drought Predicting: A Case Study in Ben Tre Province, Vietnam

Background: Drought poses significant threats to ecosystems, agriculture and the environment, including reduced crop yields, habitat degradation and increased risk of forest fires and soil erosion. In addressing this issue, remote sensing technology (RST) has emerged as a valuable tool for studying and monitoring drought phenomena, offering advantages over traditional research methods. Methods: This study focuses on the application of RST, specifically utilizing Landsat satellite images, to calculate drought indices and develop drought maps for the Ben Tre province in Vietnam. The research involved the analysis of Landsat satellite images collected between 2000 and 2020, using various drought indices such as TCI, VCI, SAVI and WSVI. Result: By employing these indices, the study was able to classify the levels of drought and generate a drought map for the study area. The findings revealed that the coastal regions encompassing Thanh Phu, Ba Tri and Binh Dai districts face a high risk of drought during the dry season.

A modification of normalized difference drought index to enhance drought assessment using remotely sensed imagery.

Drought is one of the common natural disasters with a wide range of occurrences in terms of space and time, and with varying levels of severity, that may result in economic damage and health issues to humans. This study focuses on assessing drought severity in the Central Highlands of Vietnam based on ground meteorological stations and multispectral remote sensing data. A Modification of the Normalized Difference Drought Index (MNDDI) was developed to enhance the effectiveness of remote sensing indices in the drought assessment. Results indicate that MNDDI outperforms Normalized Difference Drought Index and other investigated indicators, such as Normalized Difference Vegetation Index, Normalized Difference Latent Heat Index, and Normalized Difference Water Index, in representing the Earth's surface response to drought events. Correlations ranging from 0.85 to 0.63 were identified between MNDDI and various time scales of the commonly used meteorological drought indicator, namely the Standardized Precipitation Index, during the drought year of 2015. This work also reveals the superiority of MNDDI in portraying the response of land cover types to drought situations. The finding of a severe drought phenomenon in critical agricultural zones is highly consistent with the report from the Ministry of Agriculture and Rural Development of Vietnam. This study contributes valuable insights to the preliminary assessment of drought through remote sensing data, offering a foundation for precise drought outlooks and effective risk management strategies.

Drought prediction using artificial intelligence models based on climate data and soil moisture

Drought is deemed a major natural disaster that can lead to severe economic and social implications. Drought indices are utilized worldwide for drought management and monitoring. However, as a result of the inherent complexity of drought phenomena and hydroclimatic condition differences, no universal drought index is available for effectively monitoring drought across the world. Therefore, this study aimed to develop a new meteorological drought index to describe and forecast drought based on various artificial intelligence (AI) models: decision tree (DT), generalized linear model (GLM), support vector machine, artificial neural network, deep learning, and random forest. A comparative assessment was conducted between the developed AI-based indices and nine conventional drought indices based on their correlations with multiple drought indicators. Historical records of five drought indicators, namely runoff, along with deep, lower, root, and upper soil moisture, were utilized to evaluate the models’ performance. Different combinations of climatic datasets from Alice Springs, Australia, were utilized to develop and train the AI models. The results demonstrated that the rainfall anomaly drought index was the best conventional drought index, scoring the highest correlation (0.718) with the upper soil moisture. The highest correlation between the new and conventional indices was found between the DT-based index and the rainfall anomaly index at a value of 0.97, whereas the lowest correlation was 0.57 between the GLM and the Palmer drought severity index. The GLM-based index achieved the best performance according to its high correlations with conventional drought indicators, e.g., a correlation coefficient of 0.78 with the upper soil moisture. Overall, the developed AI-based drought indices outperformed the conventional indices, hence contributing effectively to more accurate drought forecasting and monitoring. The findings emphasized that AI can be a promising and reliable prediction approach for achieving better drought assessment and mitigation.

On the joint use of SAR and meteo-climatological information to monitor the Po River under nominal and drought conditions

This study addresses the drought phenomenon that affected the Po River, north of Italy, by jointly exploiting methods based on measurements remotely sensed by the European Sentinel-1 C-band Synthetic Aperture Radar (SAR) and meteo-climatological information derived from both in situ and satellite datasets. The SAR methods consist of a baseline approach, which estimates the water-covered area from dual-polarized SAR imagery using a constant false alarm rate (CFAR) approach, augmented with ancillary vertical ground displacement information derived from SAR interferometry (InSAR). The meteo-climatological information includes hydrometric water level, rainfall data, and the Standard Precipitation Evapotranspiration Index (SPEI). Experimental results, obtained using a seven-year time series of SAR scenes collected over the Po River close to the city of Piacenza and contrasted with ancillary external information, demonstrate the soundness of the proposed SAR-based added-value products and their satisfactory accuracy in emphasizing both seasonal trends and the drought phenomena occurred in the area. This paves the way for an operational use of SAR measurements to monitor the water-covered area of the Po River and potentially other similar environments.

Meteorological Drought Analysis and Regional Frequency Analysis in the Kızılırmak Basin: Creating a Framework for Sustainable Water Resources Management

Drought research is needed to understand the complex nature of drought phenomena and to develop effective management and mitigation strategies accordingly. This study presents a comprehensive regional frequency analysis (RFA) of 12-month meteorological droughts in the Kızılırmak Basin of Turkey using the L-moments approach. For this purpose, monthly precipitation data from 1960 to 2020 obtained from 22 meteorological stations in the basin are used. In the drought analysis, the Standard Precipitation Index (SPI), Z-Score Index (ZSI), China-Z Index (CZI) and Modified China-Z Index (MCZI), which are widely used precipitation-based indices in the literature, are employed. Here, the main objectives of this study are (i) to determine homogeneous regions based on drought, (ii) to identify the best-fit regional frequency distributions, (iii) to estimate the maximum drought intensities for return periods ranging from 5 to 1000 years, and (iv) to obtain drought maps for the selected return periods. The homogeneity test results show that the basin consists of a single homogeneous region according to the drought indices considered here. The best-fit regional frequency distributions for the selected drought indices are identified using L-moment ratio diagrams and ZDIST goodness-of-fit tests. According to the results, the best-fit regional distributions are the Pearson-Type 3 (PE3) for the SPI and ZSI, generalized extreme value (GEV) for the CZI, and generalized logistic distribution (GLO) for the MCZI. The drought maps obtained here can be utilized as a useful tool for estimating the probability of drought at any location across the basin, even without enough data for hydrological research.

Analysis of Flash Drought and Its Impact on Forest Normalized Difference Vegetation Index (NDVI) in Northeast China from 2000 to 2020

Flash drought is characterized by rapid onset and short-duration drought conditions caused by a combination of factors, including high evaporation, high temperature, and prolonged periods of little to no precipitation, leading to a sudden and severe decrease in soil moisture levels. In comparison to conventional drought, it is more susceptible to the effects of global warming and has the potential to become a common drought phenomenon in the coming years, necessitating further research. In this paper, we focused on flash drought events, specifically in forest parts of northeastern China that are included within the Greater Khingan Mountains (GKM), Lesser Khingan Mountains (LKM), and Changbai Mountains (CM), using daily soil moisture data as well as SPOT- VEGETATION NDVI satellite data from 2000 to 2020 and determined their impact on the forest NDVI. Our major findings are as follows. (1) The forest within GKM had the maximum area being affected by flash drought events. (2) The frequency ranged from 1 to 2 times, whereas the total duration varied between 20 and 55 days over the study area in a 21-year period. (3) Flash drought was most common in the plant-growing seasons. (4) The flash drought events had a negative influence on the forest NDVI. Our study contributes to a deeper understanding of the flash drought dynamics in forest areas of northeast China for flash drought monitoring, prediction, and management strategies in this region.

From meteorological to agricultural drought: Propagation time and influencing factors over diverse underlying surfaces based on CNN-LSTM model

As global warming intensifies and extreme weather events become more frequent, the severity of drought conditions in China's Xinjiang region has escalated. This exacerbates socio-economic pressures in the area and presents increasingly formidable challenges for the future. In response to these challenges, researching drought phenomena in Xinjiang is imperative. This study employs Bayesian methods and copula functions to estimate drought propagation time. It utilizes a hybrid deep learning model (CNN-LSTM) to analyze the process of drought propagation and its influencing factors across four land cover types: crops, forest land, grassland, and unused land. The findings indicate that Cropland experiences the longest average time of drought propagation (5.27 months), while forests have the shortest duration (4.2 months). Unused land and grassland exhibit similar average durations of propagation (4.8 months). On an annual scale, drought propagation time for each land type manifests in two phases: from January to May and from June to December. The former phase shows propagation time ranging from 6 to 9 months, while the latter ranges from 1 to 5 months; both demonstrate an increasing trend over time. Seasonally, all Land Cover Types exhibit a pattern of shorter propagation times in summer and autumn compared with winter and spring. Moreover, a longer time of drought propagation correlates with a greater disparity between meteorological and resultant agricultural drought severity. In analyzing the influence of factors on drought propagation, soil moisture content and El Niño-Southern Oscillation(ENSO) were found to significantly impact all Land Cover Types, progressively strengthening their influence over the years.

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.

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.

The phenomenon of drought in Ethiopia: Historical evolution and climatic forcing

ABSTRACT This study examines drought patterns in Ethiopia's 12 major river basins from 1981 to 2018 using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Both indices reveal historical drought episodes with slight variations, with significant differences in 1984, 2009, and 2015. Except for the Wabi-Shebelle catchment in southern Ethiopia, all river basins show an increasing trend in SPI12 and SPEI12 indices. The eastern and central regions experience more drought according to SPEI3. Seasonal correlations show that during the March–May rainy season, precipitation is negatively correlated with the Indian Ocean Dipole (IOD) index, while in the June–September season, it negatively correlates with Nino 3.4 and positively with IOD. The study also found that El Niño leads to less rainfall in the Ethiopian highlands, while La Niña results in more rainfall in the central and northern highlands but less in the south.

Integration of geophysical methods and aerophotogrammetry applied to groundwater exploration in fissured environments in Cedro, Ceará (CE), Brazil

In Northeastern Brazil, the semi-arid region is known for its periodic natural drought phenomenon and the predominant fissured aquifer system, which is recognized for its low cost/benefit ratio concerning the construction of tubular wells for using groundwater. This work, therefore, aims at improving prospecting efficacy when exploring these natural resources using the available historical geological and structural data on the existing wells, integrated geophysical methods (resistivity and electromagnetic), and drone imaging. The proposed methodology was applied in a controlled environment in Cedro, Ceará/CE, using electromagnetism as a preliminary method followed by electroresistivity as a detailing technique. The analysis allowed us to recommend minimum values for the studied parameters in the research area. The dipole-dipole and Wenner-Schlumberger arrays with an electrode spacing of 10 m were the best suited for the study conditions. These results integrated with the structural geology can explain the failing wells observed in several areas as well as the successful wells associated with brittle structures found in the area. Thus, the proposed methodology allowed us to evaluate different responses and, consequently, define a set of basic steps to improve the accuracy of the studied procedures.

Global eight drought types: Spatio-temporal characteristics and vegetation response

The traditional classification of drought events into seasonal and flash types oversimplified the complexity and variability of global drought phenomena, limiting a deeper understanding of drought characteristics and their impacts on vegetation. To address this issue, soil moisture percentile methods and the Soil Moisture Anomaly Percentage Index (SMAPI) were employed to create time series for flash drought (FD) and seasonal drought (SD) events globally from 1981 to 2020. A novel categorization framework was proposed to subdivide the two basic drought categories into eight distinct drought types using a set relationship identification method. The results showed fluctuating trends in the frequencies of Independent FD and Inclusion FD, which declined rapidly after 2011 at rates of 0.05 and 0.04 times/year, respectively. Independent FD frequency was highest in humid areas and decreased with increasing aridity. The spatial distributions of Inclusion FD and SD were similar, with both frequencies highest in extremely arid areas and decreasing with increasing humidity. The frequency of Independent SD, which peaked in semi-arid areas, increased significantly after 2011 at a rate of 0.01 times/year. The occurrence of FD evolving into SD or emerging at the end of SD was rare, with a global average of 0.46 events/decade and little spatial variation. Between 1981 and 2020, FD showed a U-shaped trend in drought duration, while SD showed no clear pattern. The duration of FD showed little difference across arid and humid zones, but the duration of SD decreased significantly with increasing humidity. Vegetation responses to drought varied, with arid regions showing longer response time compared to humid regions. A positive correlation between temperature and solar-induced chlorophyll fluorescence (SIF) during droughts was observed, while precipitation generally showed a negative correlation with SIF. Radiation had a minimal effect on SIF during droughts. The study offered a comprehensive categorization of drought events, enhancing our understanding of their spatiotemporal characteristics and vegetation responses on a global scale.

Cropping system support in downy mildew control in basil in organic farming: a two-year open field experiment

AbstractBasil Downy Mildew (BDM), caused by the oomycete Peronospora belbahrii, is a major issue for sweet basil (Ocimum basilicum L.) production worldwide. Currently, the disease is mainly controlled by chemical fungicides, but the development of populations of the pathogen which are resistant to the most widely used compounds is leading to the research of alternative crop protection strategies. Therefore, in this paper, some cropping variables were tested in a field trial conducted in two consecutive years (2021 and 2022) in Northern Italy in organic farming conditions, with the overall objective to optimize basil productivity and quality and limit BDM occurrence. These include two basil varieties, two sowing densities (dense, 30 kg/ha, and sparse, 15 kg/ha), and two irrigation systems (drip and sprinkler). A higher incidence and severity of BDM in 2022 compared to 2021 was observed, mainly due to the different climatic conditions that occurred in the two years. Year 2022 was characterized by high temperatures and repeated drought phenomena that led to basil stress and BDM severe outbreak. Moreover, variety 1 (considered resistant to P. belbahrii) was confirmed to be completely resistant in 2021 but it was found to be susceptible the following year, with disease incidence and severity comparable to variety 2 (medium susceptible). No differences were detected in terms of BDM occurrence and crop yield between the two sowing densities (mean of 58.4% and 26.6% of BDM incidence and severity, respectively; mean yield 1.4 kg/m2), while it emerged that drip irrigation can be useful in reducing BDM (−23.1% BDM severity). Therefore, this study suggests that the crop protection strategies tested, even if not definitive solutions, can significantly contribute to manage BDM more effectively, while preserving basil productivity and quality.

Spatiotemporal Variation in Water Deficit- and Heatwave-Driven Flash Droughts in Songnen Plain and Its Ecological Impact

The phenomenon of flash droughts, marked by their fast onset, limited predictability, and formidable capacity for devastation, has elicited escalating concern. Despite this growing interest, a comprehensive investigation of the spatiotemporal dynamics of flash drought events within zones of ecological sensitivity, alongside their consequential ecological ramifications, remains elusive. The Songnen Plain, distinguished as both an important granary for commodity crops and an ecological keystone within China, emerges as an indispensable locus for the inquiry into the dynamics of flash droughts and their repercussions on terrestrial biomes. Through the application of daily soil moisture raster datasets encompassing the years 2002 to 2022, this investigation delves into the spatiotemporal progression of two distinct categories of flash droughts—those instigated by heatwaves and those precipitated by water deficits—within the Songnen Plain. Moreover, the ecosystem’s response, with a particular focus on gross primary productivity (GPP), to these climatic variables was investigated. Flash drought phenomena have been observed to manifest with a relative frequency of approximately one event every three years within the Songnen Plain, predominantly lasting for periods of 28–30 days. The incidence of both heatwave-induced and water deficit-induced flash droughts was found to be comparable, with a pronounced prevalence during the summer and autumn. Nevertheless, droughts caused by water scarcity demonstrated a more extensive distribution and a heightened frequency of occurrence, whereas those rooted in heatwaves were less frequent but exhibited a propensity for localization in specific sectors. The sensitivity of GPP to these meteorological anomalies was pronounced, with an average response rate surpassing 70%. This spatial distribution of the response rate revealed elevated values in the northwestern segment of the Songnen Plain and diminished values towards the southeastern sector. Intriguingly, GPP’s reaction pace to the onset of heatwave-driven flash droughts was observed to be more rapid in comparison to that during periods of water scarcity. Additionally, the spatial distribution of water use efficiency during both the development and recovery periods of flash droughts largely deviated from that of base water use efficiency. The insights from this study hold profound implications for the advancement of regional drought surveillance and adaptive management.