Vegetation Conditions Research Articles

Integration of remote sensing and machine learning algorithm for agricultural drought early warning over Genale Dawa river basin, Ethiopia.

Drought remains a menace in the Horn of Africa; as a result, the Ethiopia's Genale Dawa River Basin is one of the most vulnerable to agricultural drought. Hence, this study integrates remote sensing and machine learning algorithm for early warning identification through assessment and prediction of index-based agricultural drought over the basin. To track the severity of the drought in the basin from 2003 to 2023, a range of high-resolution satellite imagery output indexes were used, including the Vegetation Condition Index (VCI), Thermal Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, the Artificial Neural Network machine learning technique was used to predict agricultural drought VHI for the period of 2028 and 2033. Results depict that during the 2023 period, 25% of severe drought and 18% of extreme drought countered at the lower part of the basin at Dolo ado and Chereti regions. A high TCI value was found that around 23.24% under extreme drought and low precipitation countered in areas of Moyale, Dolo ado, Dolobay, Afder, and Bure lower than 3.57mm per month. Similarly, increment of severe drought from 24.26% to 24.58% and 16.53% to 16.58% of extreme drought value of VHI might be experienced during the 2028 and 2033 period respectively in the area of Mada Wolabu, Dolo ado, Dodola, Gore, Gidir, and Rayitu. The findings of this study are significantly essential for the institutes located particularly in the basin as they will allow them to adapt drought-coping mechanisms and decision-making easily.

A Universal Framework for Near-Real-Time Detection of Vegetation Anomalies from Landsat Data

Vegetation anomalies are frequently occurring and may greatly affect ecological functions. Many near-real-time (NRT) detection methods have been developed to detect these anomalies in a timely manner whenever a new satellite observation is available. However, the undisturbed vegetation conditions captured by these methods are only applicable to a particular pixel or vegetation type, resulting in a lack of universality. Also, most methods that use single characteristic parameter may ignore the multi-spectral expression of vegetation anomalies. In this study, we developed a universal framework to simultaneously detect various vegetation anomalies in NRT from Landsat observations. Firstly, Landsat surface reflectance data from the Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites were selected as a reference vegetation dataset to calculate the normalized difference vegetation index (NDVI) and the normalized burn ratio (NBR), which describe vegetation conditions from the perspectives of greenness and moisture, respectively. After the elimination of cloud-contaminated pixels, the high-quality NDVI and NBR data over the BELMANIP sites were further normalized in order to remove the differences in the growth of the varying vegetation. Based on the normalized NDVI and NBR, kernel density estimation (KDE) was used to create a universal measure of undisturbed vegetation, which described the uniform spectral frequency distribution of different undisturbed vegetation with a series of accumulated probabilities on a monthly basis. Whenever a new Landsat observation is collected, the vegetation anomalies are determined according to the universal measure in NRT. To demonstrate the potential of this framework, three study areas with different anomaly types (deforestation, fire event, and insect outbreak) in distinct ecozones (rainforest, coniferous forest, and deciduous broad-leaf forest) were used. The quantitative analyses showed generally high overall accuracies (>90% with the kappa >0.82). The user accuracy for the fire event and the producer accuracy for the earlier insect infestation were relatively lower. The accuracies may be affected by the complexity of the land surface, the quality of the Landsat image, and the accumulated probability threshold.

Integrated Remote Sensing for Enhanced Drought Assessment: A Multi‐Index Approach in Rajasthan, India

AbstractThis study investigates land use, land cover (LULC) changes, vegetation health, and drought severity in Rajasthan, India, from 1985 to 2020 using remote sensing techniques. By analyzing satellite imagery with the normalized difference vegetation index (NDVI), temperature condition index (TCI), vegetation condition index (VCI), and NDVI deviation (Dev_NDVI), we assess the spatial and temporal dynamics of the region's landscape and drought conditions. Our findings indicate significant LULC changes, including a decrease in water bodies from 6412.87 to 2248.51 km2 and dense forests by 61.37%, while built‐up areas expanded by 890.50%, reflecting substantial human impact and environmental change. Drought analysis revealed that nearly 49% of the study area experienced moderate to severe drought conditions, with VCI levels below 40%, indicating widespread drought impact across different regions and time periods. The study employs weighted sum analysis of Dev_NDVI, VCI, and TCI to create a detailed drought severity map, revealing areas of severe and extreme drought that necessitate immediate action for sustainable management. The novelty of this approach lies in its integrated multi‐index method for assessing drought over a 35 year period, providing a robust framework for analyzing environmental dynamics and the resilience of ecosystems to climatic stresses. This research emphasizes the value of remote sensing for continuous environmental monitoring and highlights future implications for integrating advanced satellite technologies to enhance drought management strategies, ultimately informing policy decisions for sustainable land and water resource management in Rajasthan and similar semi‐arid regions globally.

Drought Assessment in Kalaburgi District, North-eastern Dry Zone of Karnataka, India by Using Remote Sensing and Google Earth Engine

Drought is a significant natural disaster exacerbated by global warming, leading to severe environmental, economic and societal impacts. As one of the most complex phenomena, drought requires advanced methods for effective monitoring and assessment. Remote sensing indices have proven effective in analyzing drought's geographical and temporal distribution. In this study, the semi-arid nature of the North-Eastern Dry Zone of Karnataka, characterized by low rainfall and high temperature, was examined for its vulnerability to drought. The Google Earth Engine (GEE) platform was utilized, which provides cloud-based access to advanced computational resources for processing multi-temporal satellite data. This approach minimizes the need for extensive data downloads and complex software operations, enabling efficient drought monitoring. In this study, GEE was applied to create and execute customized scripts for drought assessment, thereby accelerating the procedure and minimizing the need for extensive data downloads and complex software operations. The study focused on the North Eastern Dry Zone of Karnataka, particularly Kalaburgi district, employing the Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI) derived from MODIS data. The analysis revealed severe drought conditions, particularly in 2001, with NDVI values as low as 0.07 at Afzalpur station and 0.06 at Chitapur station, indicating significant vegetation stress. The VCI analysis further supported these findings, with values as low as 0.05 at Afzalpur station and 0.03 at Chitapur station, highlighting the drought's intensity. This integrated approach provides a reliable evaluation of agricultural drought, essential for enhancing drought management and mitigation strategies in the region.

Estimation of Short-Term Vegetation Recovery in Post-Fire Siberian Dwarf Pine (Pinus pumila) Shrublands Based on Sentinel-2 Data

The frequency of wildfires ignited by lightning is increasing due to global climate change. Since the forest ecological recovery is influenced by numerous factors, the process of post-fire vegetation recovery in Siberian dwarf pine shrublands remains unclear and demands in-depth study. This paper explored the short-term recovery process of vegetation after two lightning-ignited fires in the Great Xing’an Mountains that occurred in 2017 and 2020, respectively. The study was aimed at presenting a monitoring approach for estimating the post-fire vegetation state and assessing the influence of various driving factors on vegetation recovery. Spectral indices were computed to evaluate forest vegetation recovery dynamics. The differences in vegetation recovery under various fire severity and topography conditions were also examined. Correlation analysis was employed to assess the influence of moisture content on the recovery of fire sites. The results show that fire severity, topographic features, and moisture content significantly impacted the rate of vegetation recovery. Specifically, regeneration takes place more rapidly on warm, high-altitude, and gentle slopes within highly and moderately burned areas. Additionally, areas marked by high moisture content demonstrate rapid recovery. Our study enriches the research cases of global wildfires and vegetation recovery and provides a scientific basis for forest management and the restoration of post-fire ecosystems.

A multi-spectral and hyperspectral image dataset for evaluating chemical traits and the water status of avocado, olive and grape through leaf dehydration under laboratory conditions

Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of vegetation in laboratory settings, and also hold the potential of assessing vegetation of large portions of land. However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and grape trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.

Modelling Amazon fire regimes under climate change scenarios

Fire is one of the most important disturbances of the earth‐system, shaping the biodiversity of ecosystems and particularly forests. Climatic change and other anthropogenic drivers such as deforestation and land use change could produce abrupt changes in fire regimes, potentially triggering transition from forests to savannah or grasslands ecosystems with large accompanying biodiversity losses. The interplay between climate change and deforestation might intensify fire ignition and spread, potentially giving rise to more extensive, intense, and frequent fires, but this is highly uncertain. We use a simple forest‐fire model to analyze the possible changes in the Amazon region's fire regime that depend on climate change‐related variables. We first explored the model behavior and found that there are two possible regime changes: a critical regime that implies high variability in fire extent and mega‐fires, and an absorbing phase transition which would produce the extinction of the forest and transition to a different vegetation state. We parameterize the model using remote sensing data on fire extent and temperature, and show that it demonstrates proficiency in predicting past fires. Upon considering 21st‐century climate projections and deforestation scenarios, our findings suggest that the Amazon region is not currently nearing any of these regime changes but predict a consistent increase in fire extent mainly induced by deforestation. Therefore, stopping deforestation could be an important factor in reducing the potential for drastic alterations in tropical forests of the Amazon region.

УРОЖАЙНОСТЬ РАННЕСПЕЛЫХ СОРТОВ ЯРОВОЙ ПШЕНИЦЫ В РАЗЛИЧНЫХ ПРИРОДНО-КЛИМАТИЧЕСКИХ ЗОНАХ КРАСНОЯРСКОГО КРАЯ

The goal is to study the yield dynamics of mid-early varieties of spring wheat in different natural zones of the Krasnoyarsk Region. Tasks: to establish the features of the influence of contrasting natural and climatic zones on productivity, to identify areas with a higher rate of its growth. Elaboration of the results of variety testing of the yield of 35 mid-early varieties of spring wheat over the periods of years from 1999 to 2020 in six natural and climatic zones of the Krasnoyarsk Region showed a significant amplitude of its variability in almost all zones, except for VI (Prichulymye forest-steppe), in which for 22 years high yields are constantly got (from 3.75 to 4.43 t/ha). An increase in yield (from 1.86 to 2.84 t/ha) is evenly observed in zone VIII (foothill steppe on ordinary and southern chernozems). A significant variation in productivity over the periods of the studied years is characteristic of zones IV (foothill subtaiga), III (lowland subtaiga), and VII (southern forest-steppe) zones. The lowest yield (1.11–1.81 t/ha) was obtained in zone III. Significant discrepancies in yield within the same natural zone in different variety plots were revealed, which can be explained by the difference in weather conditions during the growing season and the level of agricultural technology. The phenotypic variability of the yield of mid-early varieties is mainly due to the natural and climatic conditions of the zones (26.5–64.1 %). The complex of natural conditions of each natural zone prevails in the formation of productivity. The influence of natural zones in the studied periods of years on the variation in yield is not the same. Their effect on productivity was least of all in 1999–2001, and most in 2012–2016. A significant contribution to the formation of productivity is made by the interaction of the factors “zone x variety plots” (4.5–33.7 %), “years × zones × variety plots” (6.6–20.7 %), vegetation conditions (2.0–26.3 %). In each natural-climatic zone, the features of the relationship between productivity and meteorological indicators were revealed. To realize the potential of mid-early varieties, it is necessary to take into account the possibilities of soil and climatic zones, to carry out agricultural technology in accordance with the biological characteristics of varieties, taking into account their responsiveness to the agricultural background and weather conditions.

Combining extraction and cultivation methods for soil seed bank analysis increases number of captured species and their similarity to above-ground vegetation.

Analysis of aboveground vegetation and soil seed bank is an important source of data on the state and dynamics of vegetation. It is especially important in landscapes exposed to disturbances, which have lost their functions. For our research, a post-mining area in the region of the Upper Silesian Black Coal Basin was selected, whose relief and ecosystems are strongly disturbed by underground mining and are currently also affected by ongoing climate change. Data collection for our research took place in the territory of two waterlogged subsidence basins in the Karvina region, Czech Republic. We evaluated 30 phytosociological releves using techniques of Zurich - Montpellier school and 540 soil cores using cultivation and extraction method. In the above-ground vegetation, 115 plant species were identified. By cultivating soil samples, we determined 60 species from 1,487 seedlings, by extraction method 66 species from 5,999 seeds. A statistically significant effect of the presence of the tree layer on the number of species obtained by the extraction method was demonstrated. There is also a statistically significant difference between the selected analysis methods in terms of the length of the captured seeds and their seed mass. The construction of a rarefaction curve demonstrated that the use of cultivation and extraction methods leads to a greater capture of soil seed bank species. The similarity between the species composition of aboveground vegetation and the soil seed bank correspond to similarities observed in other studies from degraded habitats. Very low similarity between the species of the soil seed bank from cultivation and extraction method is probably caused by the highly variable distribution of seeds in the soil in time and space.

Spatiotemporal analysis of agricultural drought variability in the uMkhanyakude District Municipality, KwaZulu-Natal

Exploring drought dynamics has become urgent due to unprecedented climate change. Projections indicate that drought events will become increasingly widespread globally, posing a significant threat to the sustainability of the agricultural sector. This growing challenge has resulted in heightened interest in understanding drought dynamics and their impacts on agriculture. uMkhanyakude District Municipality (UKDM) has experienced substantial drought occurrences, and 95% of rural dwellers within the district depend on small-scale agriculture, social security grants, and remittances for their livelihoods. Hence, there is a critical need for spatiotemporal assessments of drought within the district to fully comprehend the severity and spatial distribution of these events. This study addressed this need by assessing vegetation variability and agricultural drought occurrences across the UKDM from 2002 to 2023, by leveraging key vegetation health indices—namely, the Vegetation Condition Index, Temperature Condition Index, and Vegetation Health Index (VHI). The results identified major historical droughts, including episodes in 2002–2004 and 2015–2016 linked to El Niño events. Moreover, the findings revealed localised vulnerability to drought, although severe drought was limited at the district level. Moderate drought conditions characterised most months across Mtubatuba, Umhlabuyalingana, Jozini, and Big 5 Hlabisa local municipalities, ranging from 40.34% in Jozini to 59.75% in Umhlabuyalingana. No drought conditions occur less frequently across the district, ranging from 0.89% in Jozini to 7.33% in Mtubatuba, indicating limited periods of optimal vegetation health. This pattern suggests that certain areas within the district are particularly susceptible to drought, potentially threatening agricultural productivity and food security. The study highlights the efficacy of vegetation indices in capturing known drought events, underscoring their utility, especially in regions where ground-based data may be scarce. This spatiotemporal assessment provides an enhanced understanding of agricultural drought patterns to inform drought-related decision-making and adaptation in the agricultural sector. Given the consistent vulnerability identified, government support should be directed toward drought-prone areas, particularly through enhanced water resource management and infrastructure investment. Targeted measures are particularly recommended for areas with persistently low VHI values, such as the inland western regions of Jozini. Such efforts will strengthen resilience and sustainability in agricultural practices, safeguarding livelihoods and food security.

Gross primary productivity is more sensitive to accelerated flash droughts

Flash droughts, characterized by their rapid onset, substantially affect terrestrial ecosystems. However, the sensitivity of ecosystem productivity to the rapid development of flash droughts under varying vegetation conditions remains poorly understood. Here we investigate the ecosystem response to the speed of flash drought onset for different plant functional types, considering the decline rate of root-zone soil moisture and standardized gross primary productivity anomaly. Our findings reveal a significant increase of approximately 10% in the proportion of 1- and 2-pentad (5 and 10 days) onset flash droughts leading to negative standardized gross primary productivity anomalies during 2001–2018. Furthermore, while standardized gross primary productivity anomalies decline at higher rates, they do not promptly respond on a shorter timescale to faster-onset flash droughts compared to slower-onset flash droughts. Vegetation types with shallower root systems exhibit higher sensitivities to faster-onset flash droughts, suggesting an escalating threat to terrestrial ecosystems in a changing climate.

Establishment and Application of Biomass Model for Vegetation Condition Assessment After Ecological Restoration—Yixing Quarry Case Study

Establishment and Application of Biomass Model for Vegetation Condition Assessment After Ecological Restoration—Yixing Quarry Case Study

Regional Hotspots of Change in Northern High Latitudes Informed by Observations From Space

AbstractThe high latitudes cover ∼20% of Earth's land surface. This region is facing many shifts in thermal, moisture and vegetation properties, driven by climate warming. Here we leverage remote sensing and climate reanalysis records to improve understanding of changes in ecosystem indicators. We applied non‐parametric trend detections and Getis‐Ord Gi* spatial hotspot assessments. We found substantial terrestrial warming trends across Siberia, portions of Greenland, Alaska, and western Canada. The same regions showed increases in vapor pressure deficit; changes in precipitation and soil moisture were variable. Vegetation greening and browning were widespread across both continents. Browning of the boreal zone was especially evident in autumn. Multivariate hotspot analysis indicated that Siberian ecoregions have experienced substantial, simultaneous, changes in thermal, moisture and vegetation status. Finally, we found that using regionally‐based trends alone, without local assessments, can yield largely incomplete views of high‐latitude change.

An Evaluation of Eco-Environmental Quality Based on the Fused Remote Sensing Ecological Index (FRSEI) in the Jinjie Coal Mine Area, Northwest China

This study focused on the Jinjie mining area by addressing the severe resource and environmental issues arising from excessive coal mining in Shenmu County, Northwest China. Multi-source remote sensing data from GF-1 and Landsat 8 OLI were utilized in this study. Specifically, the fused remote sensing ecological index (FRSEI) was constructed to conduct a detailed evaluation and analysis of the eco-environmental quality in the mining area from 2013 to 2023. The results indicated that the overall eco-environmental quality of the Jinjie mining area exhibited a trend of initial improvement, followed by degradation, and then improvement again over the decade. The eco-environmental quality of the mine pits and their surrounding areas was significantly lower than the overall level, confirming the destructive impact of coal mining on the eco-environment. Meanwhile, as the study area, Shenmu County is actively utilizing coal mining subsidence areas to develop the photovoltaic power generation industry, aiming to achieve green and low-carbon transformation. Although the construction of photovoltaic power plants initially led to the degradation of the condition of vegetation and the FRSEI, both gradually improved after the plants were operational. Furthermore, by comparing the FRSEI with the remote sensing ecological index (RSEI) calculated solely using Landsat 8 OLI data, we found a high degree of similarity between the two, thereby validating the stability and accuracy of the FRSEI. This study not only provides high-precision data support for eco-environmental monitoring in mining areas but also highlights the potential of multi-source remote sensing data fusion technology in improving monitoring accuracy, further providing a scientific basis for formulating sustainable development strategies specifically for the eco-environment in mining areas.

Shallow Hydrostratigraphy Beneath Marsh Platforms: Insights from Electrical Resistivity Tomography

Salt marshes are ecologically and economically valuable ecosystems, yet are vulnerable to marsh dieback, the rapid death of marsh vegetation, which has affected coastal areas along the southeastern and Gulf coasts of the United States in recent decades. This study used multichannel electrical resistivity tomography (ERT) surveys to investigate the shallow hydrostratigraphy (up to 39.2 m depth) of three dieback-affected salt marshes along the Georgia coast to evaluate the influence of site location, vegetation status (dieback versus healthy), and tidal conditions on ERT profiles. ERT profiles revealed consistent subsurface resistivity patterns across the marsh platforms, with low resistivity (0.2 ohm-m) at shallow depths indicating saltwater saturation and a transition to higher resistivity (up to 8.1 ohm-m) at greater depths, potentially signifying a shift to brackish conditions and/or sandy strata. The ERT data indicated that the hydrostratigraphy is similar across all study sites. Furthermore, the ERT data remained consistent regardless of vegetation status, tidal variations, and seasonal changes, suggesting that the processes driving the recovery of marsh dieback are independent of the shallow marsh stratigraphy. These findings enhance our understanding of marsh subsurface conditions, supporting efforts to better understand marsh resilience and guide future research on salt marshes.

Effect of Different Renovation Methods on the Productivity of Mid-Forest Meadows as Foraging Areas for Free-Living Red Deer Population

Mid-forest meadows are integral to maintaining biodiversity and ecological services in forested landscapes but face degradation due to various reasons. This study evaluated the effectiveness of renovation methods on sward yield and herbage quality in two mid-forest meadows in northwestern Poland (54°10′ N, 16°78′ E), aiming to maintain their function as the foraging areas for the free-living red deer population. The results indicated that overdrilling was insufficient to significantly enhance sward quality or productivity (with no significant differences in DM yield between treatment and control), largely due to competition with existing vegetation and suboptimal habitat conditions. The full tillage method, in combination with sowing dedicated seed mixtures, substantially improved the sward yielding and forage quality, especially in terms of DM yield (av. 7% on object W; 18% on object TD). The efficacy of renovation methods varied between experimental sites, suggesting that the renovation strategy of mid-forest meadows should be tailored according to the habitat conditions.

The comparative analysis of the cytogenetic and morphometric characteristics of some mammals

5 species of the Meriones genus are known in Azerbaijan. In these species (Libyan Jird ( Meriones libycus Lichtenstein, 1842)), the studying of the chromosomal homology and intraspecies problems is one of the actual issues. Based on the literature data about the species of Libyan Jird (Meriones libycus Lichtenstein, 1842), it turns out that very few researches have been conducted in the physical-geographical region of Jeyranchol - Ajinohur. There is a need to the ecological, morphological and cytogenetic researches of this species in the research area and the researches are carried out by us. At the same time, there is a need of a comparative morphometric researching of the Common Pipistrelle (Pipistrellus pipistrellus Schreber, 1774) species in the research areas, because the comparative morphometric measurements of the species have not been researched in the research areas. In the areas of the Jeyranchol - Ajinohur geographical region, the comparative morphometric measurements of the Common Pipistrelle species are investigating by us. Both areas are located in the semi-desert zone and are characterized by relatively different landscape, vegetation, substrate, altitude and weather conditions (especially precipitation). The Gobustan region is the area with little precipitation and the most wind, and more characterized by the glasswort-wormwood-ephemeral plants. Jeyranchol has wormwood- caperbushes, sparsely shrubby xerophytic vegetation.

Monitoring of agricultural drought using remote sensing data in the Sebou watershed, Morocco

Drought is a climatic phenomenon that can adversely affect the environment and human activities. Currently, agricultural drought has become a concern in countries where agriculture largely depends on rainfall and is highly vulnerable to climatic variability. The study of drought requires the availability of hydroclimatic and remote sensing data. Based on these data, researchers have proposed several indices (SPI, SPEI, PDSI, VCI, VHI, etc.) that allow the monitoring of the spatiotemporal evolution of drought. This study focuses on remote sensing data to illustrate agricultural drought in the Sebou watershed from 2010 to 2023. We used the NDVI derived from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite images to calculate the Vegetation Condition Index (VCI) during the crop growth season. The analysis of the VCI over 14 years shows that the Sebou watershed has undergone droughts of varying severity, with a notable increase in drought events from 2016 to 2023. In 2016, approximately 50% of the basin experienced extreme agricultural drought. Thus, we found that the VCI is a valuable indicator that reflects the vegetation's response to the rainfall deficit recorded in recent years.

Analysis of the factors that influences pastoral resources dynamic in the Zou waterdhed at dome Outlet (Benin)

Livestock the main economic sector in West Africa is threatened in the Zou Watershed at the Domé outlet (BVZD) by climatic changes, land degradation and agricultural's growing pressure expansion due to demographic growth. The aim of this study is to analyze the factors that influences pastoral resources dynamics in the basin. This study relies on 30 years climatic's data (1991–2020), Sentinel-2 satellite images from 2016 and 2020 and demographic data from INStaD. Indices such as the Vegetation Condition Index (VCI) and the Standardized Precipitation Index (SPI) were used to characterize droughts and climate conditions. The results show significant precipitation variability, with an increase in severe droughts between 2016 and 2020. In January 2016, 29.2% of the BVZD area was affected by severe drought, compared to 36.84% in January 2020. Moreover, areas without drought decreased from 8.63% in January 2016 to 7.78% in 2020. Demographic's growth in the basin, which increased from 576,727 inhabitants in 1992 to 1,367,389 in 2020 increased a pressure on pastoral carrying capacity and accelerating ecosystem degradation. This study highlights the urgency of sustainable strategies to protect livestock in the face of climate and demographic challenges in Zou Watershed.

Changes in climate extreme indices and agricultural drought monitoring in the semi-arid areas of Borana zone, southern Ethiopia.

Climate variability and extremes negatively impact on agricultural productivity and production in arid and semi-arid areas of Ethiopia. This study evaluated changes in climate extreme indices in the semi-arid areas of Borana Zone. Data of daily rainfall, maximum and minimum temperature from 1993 to 2022 were acquired from the National Meteorological Institute of Ethiopia and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). R-package for climate extreme indices (RClimDex), Xlstat 2019, OriginPro, and ArcGIS software were used as data analysis tools to determine climate extreme indices, trends, and drought anomalies in the study area. Spatial and temporal variations in rainfall, temperature, and vegetation indices in the semi-arid Borana zone were analyzed using Google Earth Engine (GEE). The results revealed a decrease in the frequency of consecutive wet days (CWD), maximum 1-day precipitation amount (Rx1days), maximum 5-day precipitation amount (Rx5days), and very wet days (R95p) in Dire, Guchi, Wachele, and Moyale districts of Borana zone. Annual and monthly total precipitation (PRCPTOT) also decreased in Dhaso, Dilo, Dubluk, Guchi, Moyale, and Tatlate districts of the semi-arid areas of the zone over the study years. Warm spell duration index (WSDI), monthly or annual maximum of daily minimum temperature (TNx), warm nights (TN90p), and warm days (TX90p) showed an increase in the study area. However, standardized precipitation indices of R10, R20, R95p, and R99p indicated moderate drought across the studied districts of Borana zone. The Vegetation Condition Index (VCI) showed higher drought severity and spatial distribution in Dilo and Yabelo, whereas Temperature Condition Index (TCI) detected in Dire, Dubluk and Yabelo areas indicated drought stress due to moisture and thermal stress. Moisture-stress and thermal-stress play role in drought formation, whereas, drought severity is determined by moisture availability, which is dependent on rainfall. We demonstrate that the study area experienced moderate to severe drought frequent over the study years, and multiple satellite-based drought indices are necessary for better early warning and assessment of agricultural drought in the study area. Further studies that consider socioeconomic aspects of the farmers in the area are necessary to better understand the impacts of climate extremes on smallholder farmers, and devise location-specific adaptation strategies.