How climate variability modes influenced the fires in South America during the extreme droughts of 2023-2024.

Dual-purpose wheat systems—where crops are grazed during vegetative stages and later harvested for grain—offer flexibility in mixed farming, particularly under variable rainfall. However, the effects of grazing-induced defoliation on root development, plant water status, stress response and final grain yield have been underexplored, especially under multi-season field conditions. We hypothesized that grazing-induced defoliation would temporarily suppress root growth but conserve subsoil water through reduced transpiration, potentially alleviating terminal drought stress to improve yield in dry seasons, although outcomes would be season-dependent. The study aimed to quantify the effects of grazing (defoliation) on root growth dynamics, soil water availability, canopy thermal stress responses, and grain yield of early-sown winter wheat across variable field seasons Field experiments were conducted over three growing seasons (2021–2023) in southern Australia using paired grazed and un-grazed treatments in early-sown winter wheat. Root depth progression and root length density were monitored throughout the season, together with soil moisture profiles, canopy temperature, and yield components. Seasonal conditions ranged from relatively wet (2021–2022) to dry (2023), enabling assessment across contrasting water availability scenarios. Grazing consistently delayed root descent by ∼200°C days but root depth had generally recovered by anthesis, with only transient reduction in root length density (0.4–0.8 m). In the dry 2023 season, grazed crops conserved subsoil water, lowered canopy temperatures, and reduced grain-filling stress, while effects were minimal in wetter years. Despite more rapid regrowth during the critical period, grazed crops had consistently lower yield (-0.6 t ha⁻¹), likely due to more biomass allocation to leaf and stem and delayed phenology. Grazing caused significant but transient changes in root growth and water use which had no effect on grain yield, which was primarily affected by post-grazing shoot regrowth dynamics. Grazing related stress reduction during spring drought did not translate into yield gains. These results improve mechanistic understanding of dual-purpose wheat systems and provide parameters to better represent post-defoliation root growth and water-use dynamics for use in crop models under variable climates. • Grazing delayed root descent by ∼200°C days, with full recovery by anthesis and no lasting rooting constraints. • Temporary reductions in root length density (0.4–0.8 m) had minimal long-term effects on water access. • In dry conditions, grazed crops conserved subsoil water and reduced canopy temperatures during grain filling. • A consistent yield penalty (∼0.6 t ha⁻¹) was linked to delayed phenology and shoot regrowth rather than root limitation.

ABSTRACT Maize ( Zea mays L.) is Uganda's most widely grown staple crop, yet productivity remains below potential due to nitrogen (N) deficiency and rainfall variability. Crop models are widely used to analyse these constraints but are typically calibrated at few sites, raising concerns about their spatial transferability. This study evaluated whether a single well‐calibrated point‐based CERES‐Maize model within the Decision Support System for Agrotechnology Transfer (DSSAT) framework, spatially implemented using the Geographic Support System for Agrotechnology Transfer (GSSAT2), can be extrapolated nationally to simulate maize response to planting dates and N rates. Using 40 years (1985–2024) of NASA POWER weather data and SoilGrids v2.6 data, simulations were conducted at 5‐km resolution to estimate potential yield (YP), water‐limited yield (YW) and N‐limited yield (YNL). National mean YP was 7.7 t ha −1 , YW 6.1 t ha −1 and YNL 1.1 t ha −1 . N limitation was the dominant constraint, with yield responses plateauing beyond 100–150 kg N ha −1 . March and July were the most favourable planting windows. These results demonstrate that spatial application of a calibrated crop model can provide national‐scale insights, while highlighting the need for multilocation field validation before field‐level recommendations.

Climate-driven variations in rainfall, infiltration, and temperature substantially alter matric suction and the hydro-mechanical response of geosynthetic-reinforced pile-supported (GRPS) embankments, yet no existing analytical model explicitly captures these coupled effects. This study first develops a Climate-Responsive Hydro-Mechanical (CRHM) framework that integrates an unsaturated soil arching formulation and a soil-geosynthetic interaction model into a single analytical framework for GRPS embankments. A new climate-suction interaction (CSI) index is introduced to capture how seasonal hydraulic forcing alters suction and, in turn, governs load transfer and system stability. Closed-form solutions integrating suction-dependent strength and stiffness are derived for arching efficacy, stress concentration ratio, geosynthetic tensile force, and differential settlement under transient climatic conditions. The framework is further extended to incorporate temperature effects through a temperature-dependent matric suction formulation, enabling the model to account for thermal-hydro-mechanical influences on soil strength and stiffness. Validation against full-scale field measurement shows excellent agreement between theoretical predictions and observed load redistribution and reinforcement tension. The results indicate that rainfall infiltration weakens soil arching, while evaporation-driven drying enhances suction. The proposed framework provides a physically consistent and computationally efficient analytical tool for climate-responsive design of GRPS embankments, bridging the gap between simplified analytical approaches and computationally intensive numerical simulations.

Abstract The spatio-temporal distribution of winter precipitation over the Arabian Peninsula (AP) is crucial for managing various socioeconomic sectors. Due to limited observational data, high-resolution atmospheric models are often used to investigate rainfall distribution across the region. However, uncertainties in current models are strongly influenced by the representation of clouds, moist convection, and complex topography. Reducing the grid spacing to a few kilometers using a cloud-resolving model (CRM) allows for improved treatment of clouds and associated hydro-climatological processes, which can help reduce uncertainties in model precipitation physics. To address this, we conducted multi-year CRM simulations using the WRF model at 2 km horizontal resolution, specifically targeting the winter season, to simulate seasonal precipitation patterns over the AP from 2006 to 2016. These CRM outputs are validated against available in-situ , gridded, remotely sensed observations and reanalyses fields. Furthermore, we used ERA5 reanalysis fields to evaluate circulation, thermodynamic and microphysical processes in the CRM. Our results demonstrate that CRM simulated rainfall agrees well with the observed rainfall patterns, albeit for some wet bias (ranging between 0.83–1.88) and root mean square error (0.81–1.40) over mountainous regions. Precipitation statistics confirm that the CRM adequately captures the spatial extent and variability of winter rainfall over the AP. The model also reveals that moisture transport from the Red Sea, Mediterranean Sea, and Arabian Sea substantially influences the regional precipitation patterns. Notably, the CRM enhances the simulation of extreme precipitation events, accurately capturing their spatial distribution, intensity, and frequency. We also examined the underlying physical mechanisms driving these precipitation dynamics using the CRM simulations. Overall, the findings demonstrate that the CRM provides a more realistic representation of fine-scale precipitation features and the associated physical processes across the AP. This study offers valuable insights into the application of high-resolution CRM frameworks for improving the prediction of precipitation extremes in this arid region.

The municipality of Za-Kpota, located in southern Benin, is highly vulnerable to climate variability due to its strong dependence on rain-fed agriculture, which constitutes the main source of livelihood for local populations. This study aims to analyze the spatio-temporal variability of key climatic parameters in this municipality over the period 1993–2022. This study presents a retrospective analysis of long-term climatic data (1993–2022) obtained from the Bohicon synoptic station. The methodological approach is based on the analysis of long-term climatic time series, particularly rainfall and temperature data. Statistical analyses, including trend detection methods and the computation of climatic indices, were applied to characterize the temporal evolution and variability of the studied parameters. The results reveal a bimodal rainfall regime, characterized by two rainy seasons with a cumulative duration of approximately six months. Mean monthly temperatures range from 25.8 °C in August to 29.5 °C in March, with the highest temperatures recorded between February and April. This period is associated with increased evaporation and evapotranspiration, potentially reducing water availability in the study area. In addition, the climatic water balance indicates an overall annual deficit of −171.3 mm, despite significant surpluses observed between May and June (+279.8 mm), and secondary positive balances during September and October. These findings highlight the pronounced variability of climatic conditions in Za-Kpota and their potential impacts on water resources and agricultural systems. The study provides a robust scientific basis for the development of locally adapted climate change strategies and emphasizes the need to integrate climate information into territorial planning and sustainable agricultural development policies.

Rice cultivation continuously remains important for food and livelihood security. The forecasts of increasing water shortage under a changing climate and rising labour scarcities in agriculture have brought a paradigm swing in rice cultivation from the transplanting method (flooded) to direct-seeded rice (DSR). Despite all these returns, the potential yield losses through enormous weed threats under DSR remain a challenge and may reduce yield by up to 50 %. Several Southeast Asian nations have seen a transition in long-term DSR agriculture towards dominating grassy weeds, including Echinochloa crusgalli, Leptochloa chinensis and Ischaemum rugosum. DSR favours the proliferation of grasses and sedges over broadleaf weeds due to the absence of flooding, which is a natural weed suppressant in TPR. Early flooding in TPR can effectively suppress weed emergence, whereas DSR offers limited scope for early flooding since most rice cultivars are intolerant to anaerobic conditions during the germination process. However, anaerobic germination-tolerant rice varieties may enable early flooding and better weed control in DSR. Soil moisture, tillage techniques and application time all have a significant impact on their control efficacy. Crop phytotoxicity and decreased herbicide efficacy might result from improper soil moisture at the time of application. Herbicides may be washed away by excessive irrigation after application, while absorption may be hampered by inadequate moisture. Studies advise using herbicides soon after irrigation when the soil is sufficiently saturated. The efficacy of herbicides is also affected by variations in seasonal rainfall. For DSR systems to maintain productivity and reduce weed-related losses, integrated weed management, which includes prudent herbicide usage and water management, is essential.

Daily rainfall data plays an important role in hydrological and climatological analysis, especially in tropical regions characterised by high rainfall variability and sharp seasonal changes. However, observational data often has gaps, which can reduce model accuracy and obscure relevant climatological signals. This study addresses these issues by applying the Trend-Based Adaptive Window Imputation (TBaWI) method, an adaptive imputation approach that considers local temporal trends and seasonal dynamics in estimating missing rainfall values. This method was tested using CHIRPS data for the Makassar region for the period 2014–2023 with synthetic data loss scenarios of 10%, 15%, 20%, and 25%. The results show that TBaWI consistently provides a lower Mean Absolute Error (MAE) value, namely 6.14–7.65 mm, compared to linear interpolation, which produces 6.46–7.75 mm. The SMAPE value of TBaWI is also lower, for example 33.16% in the 15% data loss scenario, compared to interpolation at 35.06%. In addition, this method showed an improvement in the ability to identify dry days through the Zero Hit Rate (ZHR), which reached 60.08% in the 20% data loss scenario, higher than the interpolation of 58.32%, while the Rainy Hit Rate (RHR) remained in a stable range of 79–88%. These findings indicate that TBaWI is more effective in maintaining climatological consistency and numerical accuracy of tropical rainfall data. Further research is expected to integrate spatial aspects and optimise machine learning-based parameters to improve the generalisation of the method under various climatic conditions.

Abstract Tropical South American summer precipitation is primarily controlled by the intensity and position of the South American monsoon and the intertropical convergence zone, both of which respond to sea surface temperature anomalies over the surrounding tropical oceans. Our analysis examines how well contemporary, high-complexity Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6) simulate the summer precipitation distribution and its interannual variability under preindustrial climate conditions. Specifically, we investigate how El Niño–Southern Oscillation (ENSO) and Atlantic Niño—two major zonal modes of variability in the tropical ocean—shape tropical South American precipitation through remote atmospheric teleconnections. The quality of the simulated climatological mean state and interannual variability across models is primarily evaluated using pattern correlations with the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) product. The three models with the highest and the three models with the lowest field correlation with the ERA5 reference are selected for a more detailed study of their representation of major modes of variability and associated teleconnection patterns. We show that models with large discrepancies in the location and abundance of core monsoon precipitation also typically fail to accurately represent atmospheric deep convection and teleconnections associated with the zonal modes. Differences in the ability to simulate South American summer precipitation, even under preindustrial forcings, emphasize the importance of selecting an appropriate model for studying the regional hydroclimate. Our study further calls for future research using high-resolution models that explicitly resolve deep convection to realistically capture South American monsoon rainfall. Significance Statement Tropical South America receives abundant rainfall from December through February, which is dominated by the South American monsoon system and the deep convection in the intertropical convergence zone. Naturally occurring climate modes in the surrounding tropical oceans, such as El Niño–Southern Oscillation and Atlantic Niño, also drive large variability in summer rainfall. Accurately representing how rainfall over tropical South America responds to modes of climate variability in preindustrial simulations is essential for evaluating the robustness and realism of climate models. Our study shows that models with more realistic atmospheric convection (upward vertical motion in the lower to midtroposphere) better depict the observed rainfall patterns and year-to-year changes over tropical South America, including the rainfall patterns shaped by the modes of variability. Models that better replicate these influences can be instrumental not only in understanding the future of South American rainfall but also in attribution studies of extreme events like droughts and floods.

Abstract This study evaluates the ability of the Coupled Model Intercomparison Project phase 6 (CMIP6) climate models to simulate the observed effects of tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTAs) on Indian summer monsoon rainfall (ISMR) variability. Using observational data and the large ensemble historical simulations of seven CMIP6 models from 1950 to 2014, we applied a cyclostationary linear inverse model (CS-LIM) to isolate the impacts of tropical Pacific SSTAs, Indian Ocean SSTAs, and their interaction on the interannual variability of ISMR. Overall, these CMIP6 models well reproduced the observed enhanced (reduced) ISMR variability from Pacific SSTAs (Indian Ocean SSTAs and the Indo-Pacific interaction), though with varying spatial patterns and magnitudes. Among them, CESM2 and Energy Exascale Earth System Model version 2.0 (E3SM-2-0) showed the best agreement with observations for the effects of Pacific SSTAs and the Indo-Pacific interaction, respectively. Composite analysis of ISMR anomalies during the developing phases of pure and co-occurring El Niño–Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) events revealed that the impacts from Pacific SSTAs were captured reasonably well by E3SM-2-0, CESM2, MIROC6, and MPI-ESM1-2-LR, while E3SM-2-0 also showed the best agreement with observations for the effects from the Indo-Pacific interaction. However, all seven models exhibited substantial biases in simulating the Indian Ocean SSTA impacts on ISMR, particularly during pure El Niño events. Overall, this study provides new insights into how individual CMIP6 models simulate the isolated impacts from the tropical Pacific and Indian Oceans, which have important applications for improving ISMR predictions and interpreting ISMR future projections.

The Gezira Scheme—one of the world’s largest gravity‑fed irrigation systems—faces chronic water‑timing failures, seasonal credit shortages, and labour constraints that destabilize its official crop rotation and generate large fluctuations in total net return. Although the rotation is fixed on paper, farmers’ actual rotation has become dynamic, reactive, and constraint‑driven, especially after the 2005 Gezira Act. Using 50 years of data, this study applies a Vector Autoregression (VAR) with an Error Correction Model (ECM) to quantify the dynamic interactions among crop areas (cotton, wheat, sorghum, groundnuts) and total net return. The Error Correction Term captures how quickly the system returns to long‑run equilibrium after water shortages, rainfall variability, price changes, or labour constraints shocks. Results show that the production system is highly sensitive to water timing, with cotton and wheat acting as the main sources of volatility, sorghum functioning as a stabilizing buffer crop, and groundnuts responding opportunistically to liquidity stress. A central structural finding is that only about 25% of the scheme can be irrigated simultaneously due to long‑term deterioration of conveyance capacity, forcing staggered irrigation cycles and chronic timing failures. Small shocks to water, finance, or labour trigger large behavioural adjustments, causing short‑run volatility and long‑run disequilibrium. Shifting irrigation from push to pull system at the minor and field canal levels—while maintaining the 25% simultaneous‑irrigation constraint—creates a demand‑driven water delivery regime that dramatically reduces water stress and timing failures, stabilizes crop choices, and improves both short‑run and long‑run system performance. This intervention with digital transformation aligns directly with VAR-ECM findings showing water as the primary driver of volatility and offers a practical, resilience‑engineering pathway to restore stability and predictability in the Gezira Scheme. The study proposes a resilience‑engineering intervention framework—including downstream pull‑system irrigation at minor and field canal levels, Bt cotton adoption, reducing cotton area to save water, predictable seasonal finance, and mechanization—to restore system stability and improve water use efficiency. VAR-ECM stability tests confirm that these interventions directly address the structural drivers of volatility. This study provides the first dynamic econometric assessment of the Gezira Scheme’s production system, quantifies crop‑specific roles in system sensitivity and stability, identifies water‑timing constraints as the dominant behavioural driver of rotation deviations. Significant contribution of the study is using econometric model for analyzing short‑run adjustments and long‑run equilibrium relationships in Gezira production system and introduces a novel integration of resilience‑engineering interventions within a VAR-ECM framework to guide sustainable transformation of large‑scale irrigation systems.

Dryland farming systems in South Dakota face rainfall variability and rising water demand, which can reduce crop productivity and threaten long-term soil health. We combined field experiments across three dryland sites in South Dakota (Roscoe, Selby, Fort Pierre) with continuous soil moisture monitoring (0–15, 15–30, 30–45 cm) and HYDRUS-1D modeling to evaluate cover crops and soil amendments (biochar, manure) on water retention. During the active cover crop growth period, plots with cover crops consistently exhibited lower soil water content than plots without cover crops, likely due to increased transpiration. Plots with no cover crop (NCC) retained more water than cover crop (CC) plots (Roscoe: 26.27% vs. 24.16% at 0–15 cm). During the primary crop growing season, biochar consistently increased soil moisture (θ) compared with manure and unamended plots. Following a 43-day dry spell (1 July–13 August 2024), soil moisture declined by approximately 0.096 m3 m−3 in the biochar plots, compared with 0.125 m3 m−3 under manure and 0.216 m3 m−3 in the unamended control, exhibiting differences in water retention capacity among treatments. HYDRUS inverse modeling reproduced observed soil moisture dynamics (R2 ~ 0.91) and demonstrated higher water content under biochar. Scenario analysis using representative wet (2008) and dry (2012) years showed the cover crop + biochar combination maintained the highest average water content. Results support integrating biochar with cover cropping to buffer drought and improve soil water availability in dryland farming.

Global warming is causing climate change (CC) characterized by increased frequency of heatwaves, droughts, erratic rains, hailstorms, cloudbursts, floods, landslides etc. The CC has already adversely affected ecosystems. In spite of efforts to mitigate greenhouse gas emissions, which lead to warming, the global temperature during 2011-2020 was 1.1C above that during pre-industrial era. The projections are that warming will continue to increase and adverse effects will intensify particularly in developing countries like India. In India a number of studies have recorded wide spatial variability in rainfall, though, many reported a general overall negative trend since mid-20th century. Further, varying pattern of rainfall has been recorded in three agroclimatic regions of Punjab state, the granary of India. Unseasonal rains followed by spiked temperature during rabi 2021-22 reduced wheat yield In Punjab by 651 kg/ha and by 301 kg/ha in Haryana compared to 2020-21. Further, the grain was of lower quality. During kharif 2022, Southern Rice Black-streaked Dwarf Virus, appeared for the first time in Punjab and Haryana. Some farmers ploughed the affected fields. Adverse weather during rabi 2022-23 also, reduced wheat yield (143-150 kg/ha) in these states. At the national level, erratic weather during rabi 2021-22 and kharif 2022 caused loss about 3 mt of grain of each of wheat and rice. The projected increased adverse effects due to intensified CC include food insecurity. Thus, there is an emergent need to accelerate implementation of adaptation and mitigation strategies in agriculture. Conservation agriculture conserves land and water resources, environment and biodiversity, reduces heat and drought stresses, captures carbon and improves soil health. The adaptation options include cultivar improvement, altering growing seasons, crop diversification, and sustainable soil and water resource management. In the process of adaptive management of crop production, adjusting sowing dates and breeding cultivars having varying duration in consonance with CC has been one of the central aspects. Shifting sowing dates to find appropriate crop cultivation season is a low-cost measure. However, cultivar development is time and resource consuming. Novel biotechnological tools enable fast cultivar development with precision, and facilitate mobilization of genes in wild-weedy relatives, which are rich in genes conferring resistance/tolerance to biotic and biotic stresses, required to combat CC challenge. In view of CC stress on water resources, improving water use efficiency has gained importance. Sensor-based micro-irrigation/fertigation has great potential to enhance water and fertilizer use efficiency. Similarly, the application of other smart technologies like nanotechnology, sensor-based pesticide application, bio-fertilizers and bio-pesticides, need to be mobilised. In view of agro-ecological diversity in India, right-sized region-specific technology packages have to be developed implying that crop research will expand exponentially. This needs strengthening of human resources and institutional infrastructure, expanding and linking basic and applied researches, and fortifying inter-disciplinary/inter-institutional collaborations to develop and diffuse technology innovations. Enabling factors include enhanced funding and international cooperation. All out efforts are needed to have more climate-resilient agriculture.

Mosquito control remains the cornerstone of the prevention and control of diseases caused by Aedes-borne pathogens, such as dengue, chikungunya and Zika viruses. An innovative vector control method adapted to Aedes albopictus mosquitoes is the Sterile Insect Technique (SIT), which consists of the mass-release of sterilized male mosquitoes. The impact of SIT and the optimization of release strategies can be studied through modelling. The objective of this study was to evaluate the ability of a mathematical model to simulate the impact of SIT releases by comparing the simulation outputs with entomological data collected during and after SIT trials in Mauritius. We modified a model of Ae. albopictus population dynamics (ARBOCARTO) that incorporates variations in temperature and rainfall, as well as the availability of breeding sites to introduce SIT. We then simulated SIT releases under the same conditions as the field trials and assessed the model's ability to realistically reproduce the impact of SIT releases by comparing the simulation outputs with entomological data observed in a trial site (where SIT releases were performed between May 2017 and February 2018) and a control site (without SIT releases). Four simulation scenarios were considered: without SIT, and with SIT applied on 50%, 75% and 100% of the trial area. Results showed that the ARBOCARTO model reproduced the major trends in the intra-annual Ae. albopictus population variations: simulated abundances of eggs, based on weather conditions, were highly and significantly correlated with the egg abundances observed at the SIT control site. The model also matched the trial site data for both the predicted number of newly produced eggs and the percentage of fertile eggs. The simulation results also revealed the importance of the percentage of the area covered by SIT releases as a key parameter for SIT impact, both for the reduction rate and for the resilience time, defined as the time required after the end of releases for the mosquito population to return to its initial state. Thanks to its user-friendly interface, the ARBOCARTO model can be used by vector control services and health stakeholders to simulate the impact of SIT releases and optimize release strategies, taking into account the operational capacity of sterile mosquito rearing facilities and the environmental conditions of the releases.

Rainfall and temperature variability serve as crucial indicators of hydroclimatic hazards, including floods, droughts, heatwaves, and cold spells. While such events may arise from a single variable reaching extreme levels, they often result from the interplay of multiple climatic factors. This study examines the spatio-temporal variability of compound extreme events (CEEs) over the semi-arid Ken and Betwa River catchments in Central India. Although these regions primarily receive rainfall during the Indian Summer Monsoon (ISM) season, they have experienced a post-2000 drying trend along with rising temperatures. A significant negative correlation between rainfall and temperature indicates rainfall suppression under hotter conditions due to enhanced atmospheric stability and reduced moisture availability. The analysis further shows that extreme wet and dry events have declined in the Betwa basin, while the Ken basin exhibits an increase in extreme dry events and a decrease in wet extremes. Cold extremes (T10) have also shown a decreasing trend across both regions. Investigation of different combinations of rainfall and temperature extremes reveals that moderate and extreme warm-dry CEEs have intensified over the past four decades, emerging as the most dominant compound events. The persistence of these events is largely driven by wind patterns and convective inhibition energy (CIN) in the case of moderate events, and by moisture transport and divergence for extreme ones. The intensification of such CEEs poses substantial risks to regional agriculture, eco-hydrological systems, and socioeconomic stability. Composite Resilience Index (CRI) was developed at the district level, integrating indicators like the Human Development Index, Multidimensional Poverty Index, and literacy rates. Results reveal that Ashoknagar, Shivpuri, Lalitpur, and Chhatarpur are the relatively low-resilience districts, while Bhopal, Sagar, Jhansi, and Hamirpur exhibit higher resilience. Overall, the findings underscore the urgent need for climate-informed policies and adaptive strategies to ensure water sustainability and socio-economic stability in the Ken–Betwa river catchments under a warming climate.

Abstract Recurrent drought increasingly threatens almond production in Mediterranean and semi-arid regions, highlighting the need to exploit plant genetic resources with stable adaptive traits. This study reports a 3-year multi-genotypic evaluation of 41 almond genetic resources grown under rainfed conditions in a semi-arid environment characterized by interannual rainfall variability. Significant genotypic and interannual variability was observed across morphological, physiological and biochemical traits. Chlorophyll content ( r = 0.7 with PC1; CV < 12%) emerged as a stable primary discriminant trait. Leaf nitrogen content, wood density, yield and leaf area also contributed significantly to genotype differentiation in multivariate analyses, together explaining 60% of total variance in the first principal component. A two-level hierarchical classification consistently separated tolerant, intermediate and sensitive genotypes. Among the evaluated genetic resources, ‘Princesse n° 3’, ‘Ferragnes*princesse 23’, ‘F1 melange 68/2’, ‘L 158’, ‘II A 7’, ‘(486*217)16’ and ‘GN9’ were identified as high-performing and drought-tolerant genotypes, highlighting their potential value for almond breeding and conservation programmes. This integrative, multi-year phenotypic approach provides a robust framework for identifying and utilizing drought-resilient almond genetic resources.

Abstract. Increasingly variable rainfall patterns expose soils to more frequent waterlogging in humid climates. Yet, the effects of waterlogging on soil organic matter decomposition in mineral soils remain uncertain. We studied the impact of off-season waterlogging on carbon dioxide (CO2) production and dissolved carbon dynamics in controlled greenhouse conditions using 32 monolithic soil columns (hereafter monoliths) (h=63 cm, d=15.2 cm) sampled from two agricultural fields (silty clay, sandy loam) in southern Finland. The 1.5 year study comprised three growth cycles with alternating growing and off-seasons. Spring barley (Hordeum vulgare) was grown in all monoliths during the growing seasons. In turn, during all three off-seasons, half of the monoliths were subjected to waterlogging lasting seven weeks, while in the other half soil moisture was maintained at ∼70 % field capacity. Within these water treatment groups (waterlogged and control), the monoliths were further divided into two plant treatment groups: in half of the monoliths, an overwintering cover crop (Festuca arundinacea) was grown, while in the other half soil was left bare for the off-seasons. Soil temperature and moisture were continuously monitored, dissolved organic (DOC) and inorganic carbon (DIC) concentrations in pore water were analyzed at three depths and CO2 fluxes were measured at the surface. Contrary to our hypothesis, waterlogging did not increase soil DOC content. Instead, on-going microbial/rhizospheric activity promoted an increase in DIC content while CO2 fluxes declined, indicating an accumulation of respired CO2 in soil pore water. The sustained CO2 production could not be explained solely by mobilization of Fe-associated C, as initially hypothesized. After the onset of drainage of the waterlogged monoliths, CO2 fluxes from both soils increased more than predicted based on changes in soil moisture and temperature, likely due to the release of previously accumulated CO2. These post-waterlogging increases in CO2 fluxes roughly equaled the earlier decreases during waterlogging. Thus, although off-season waterlogging strongly influenced the temporal dynamics of CO2 fluxes, it did not alter total cumulative CO2 emissions from the studied agricultural soils.

The Climate Hazards Center Infrared Precipitation with Stations (CHIRPS) data stream combines: (1) a high-resolution climatology, (2) thermal infrared (TIR) geostationary satellite observations, and (3) station observations. In the past, CHIRPS version 2 (CHIRPS2) has proven to be valuable for drought monitoring, hydrologic modeling, scientific studies and agricultural decision making. Version 3 (CHIRPS3) improves each of these components. The new version, CHIRPS3 extends to 60°S/N, adopts an improved variance-preserving TIR-to-precipitation estimation method, uses many more stations and station sources than the original CHIRPS2 product, and implements gauge-undercatch correction. In this paper, we evaluate the performance of satellite-only CHIRP3, CHIRP2, IMERG, PERSIANN- CCS, and GPI using high quality interpolated data in twelve regions with dense station coverage. CHIRP3 represents both the observed mean and variance more accurately than CHIRP2. A usage section in Morocco shows that CHIRPS3 better captures the observed rainfall variability when compared to CHIRPS2. This section also demonstrates how station data should be gauge-undercatch-corrected when validating CHIRPS3.

Drought constitutes one of the most significant natural hazards worldwide, exacerbated by climate variability and change, with profound implications for ecosystems, agriculture, and livelihoods. In South Africa, particularly within the drought-prone uMkhanyakude District of KwaZulu-Natal, comprehending rainfall variability and enhancing drought prediction are imperative for sustainable water and food security planning. This study utilized daily rainfall records from six meteorological stations spanning the years 1980 to 2023 to calculate the Standardized Precipitation Index (SPI) at 6-, 9-, and 12-month time scales. Long-term drought trends were evaluated employing Innovative Trend Analysis (ITA) methods, which identified statistically significant decreasing trends at five stations and an increasing trend at Riverview. To augment drought forecasting, a novel hybrid model that integrates the Savitzky-Golay filter with a Temporal Convolutional Network and Long Short-Term Memory (SG-TCN-LSTM) was developed. Comparative assessments against ARIMA, LSTM, TCN, and other hybrid models demonstrated that the SG-TCN-LSTM consistently achieved the lowest Root Mean Square Error (RMSE) values (0.0349-0.1453) and the highest [Formula: see text] values (0.95-0.99) across all SPI scales, indicating superior predictive accuracy and stability. The integration of signal smoothing with deep learning methodologies enhanced the robustness of forecasts, providing critical insights for proactive drought risk management. This research underscores the potential of hybrid models as reliable early-warning instruments for meteorological drought and establishes a framework that can inform national adaptation strategies. Future research should aim to extend the model to incorporate additional climatic drivers, evaluate its transferability across diverse climatic regions, and investigate its application in operational drought early-warning systems.

Analyzing the combined drought index using geospatial technology in the Tigray Region, northern Ethiopia.