Search Results (312)

Understanding the impact of climate change and altered hydrologic cycles on regional water storage trends is crucial for predicting changes in recharge and streamflow and informing decisions regarding drought resilience and flood mitigation. While many regions have become drier under global climate change, the northeast United States has experienced an increased precipitation intensity, driving groundwater rise. This study integrates terrestrial water storage data from NASA’s Gravity Recovery and Climate Experiment (GRACE) satellites and soil moisture data from Soil Moisture Active Passive (SMAP), as well as long-term instrumental groundwater records from USGS groundwater monitoring wells, to understand the nature of storage trends. The results show that while aquifer-wide groundwater storage anomalies have stabilized in recent years, shallow groundwater and certain surface water bodies have accumulated about 0.6 cm of water annually, adding over 10 cm to the landscape, since 2005. These findings indicate that excess water from heavy rainfall is mainly stored in the shallow subsurface as perched aquifers and temporary wetlands rather than deep (5–30 m) aquifers. Understanding this change in storage is crucial for improving water resource management and adapting more effectively to a changing climate in the region. Full article

Gully erosion is a major driver of land degradation globally, particularly in semi-arid regions where it is fundamentally controlled by rainfall and runoff dynamics. Understanding how rainfall translates into runoff in gullied landscapes is crucial for predicting erosion processes and modelling runoff to inform land management strategies. In this study, rainfall-runoff analysis was conducted using high-resolution rainfall and runoff data from intensely monitored alluvial gullies in the semi-arid regions of northern Australia. Runoff responses were strongly seasonal, with flashy but low-volume flows during the early wet season (October–November) and prolonged, high-discharge events during peak rainfall months (December–March). Antecedent soil moisture had a limited influence on runoff generation, likely due to rapid wetting–drying cycles and shallow infiltration depths. Notably, rainfall-runoff behavior diverged with catchment-to-gully area ratio (A_ca): linear runoff to rainfall responses were observed where gullies were eroded to the catchment limit (A_ca ≈ 1) whereas high-A_ca systems (A_ca > 5) exhibited threshold, stepwise behavior with upslope contributions activating at ~26 mm event rainfall. Field infiltration tests showed upslope catchment infiltration capacity was ~70% higher than on gully floors (~36 vs. 21 mm h⁻¹). This indicates greater near-surface storage and delayed upslope runoff, consistent with an activation threshold for upslope contributions. Mean rainfall–runoff ratios were higher in low-A_ca gullies (≈0.52–0.68) than in high-A_ca systems (≈0.40–0.46). These findings have implications for rainfall-runoff modelling, process-based understanding of gully erosion and gully management in semi-arid environments. Full article

Plastic agrotextiles are increasingly used in modern agriculture to protect crops from adverse climatic events, such as excessive rainfall, wind, and solar radiation. Among these, anti-rain nets represent a promising solution to mitigate rain-induced disorders, such as fruit cracking, especially in crops sensitive to water excess. This study investigates the structural and functional properties of eight agrotextiles, including both anti-rain and anti-insect nets. The analysis focuses on geometric characteristics (porosity, thread diameter, mesh density) and on functional performance through experimental evaluation of air and rainwater permeability under different slope conditions. Air permeability was assessed using a wind tunnel, while rainwater permeability was tested via a rainfall simulation bench. The results demonstrate a stronger correlation between the air permeability index (K_a) and the rainwater permeability index Φ_rw (R² = 0.95–0.99), across different net slopes (10° and 30°), than between the net porosity and Φ_rw (R² = 0.86–0.92). These findings emphasize the greater explanatory power of the dynamic performance indicator K_a as a predictor of rainwater permeability, over purely geometric descriptors like porosity, since it inherently accounts for the dynamic performance of the air flow through the net. This contributes to the development of more effective and sustainable net-based crop protection systems tailored to specific environmental and agronomic needs. Full article

As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (Solanum tuberosum L.) and sweet potato (Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar’s leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90–98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90–96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Full article

Hydrological modeling is critical for assessing water availability and guiding sustainable resource management, particularly in monsoon-dependent, data-scarce basins such as the Chalakudy River Basin (CRB) in Kerala, India. This study integrated the Soil Conservation Service Curve Number (SCS-CN) method within the Google Earth Engine (GEE) platform, making novel use of multi-source, open access datasets (CHIRPS precipitation, MODIS land cover and evapotranspiration, and OpenLand soil data) to estimate spatially distributed long-term runoff (2001–2023). Model calibration against observed runoff showed strong performance (NSE = 0.86, KGE = 0.81, R² = 0.83, RMSE = 29.37 mm and ME = 13.48 mm), validating the approach. Over 75% of annual runoff occurs during the southwest monsoon (June–September), with July alone contributing 220.7 mm. Seasonal assessments highlighted monsoonal excesses and dry-season deficits, while water balance correlated strongly with rainfall (r = 0.93) and runoff (r = 0.94) but negatively with evapotranspiration (r = –0.87). Time-series analysis indicated a slight rise in rainfall, a decline in evapotranspiration, and a marginal improvement in water balance, implying gradual enhancement of regional water availability. Spatial analysis revealed a west–east gradient in precipitation, evapotranspiration, and water balance, producing surpluses in lowlands and deficits in highlands. These findings underscore the potential of cloud-based hydrological modeling to capture spatiotemporal dynamics of hydrological variables and support climate-resilient water management in monsoon-driven and data-scarce river basins. Full article

The primary objective of this study is to assess the techno-economic feasibility of an innovative solar energy generation system with a rainwater collection feature to generate electrical energy and meet irrigation needs in agriculture. The proposed system is designed for an agricultural area (Gonyeli, North Cyprus) with high solar potential and limited rainfall. In the present study, global rainfall datasets are utilized to assess the potential of rainwater harvesting at the selected site. Due to the lack of the measured rainfall data at the selected site, the accuracy of rainfall of nine global reanalysis and analysis datasets (CHIRPS, CFSR, ERA5-LAND, ERA5, ERA5-AG, MERRA2, NOAA CPC CMORPH, NOAA CPC DAILY GLOBAL, and TerraClimate) are evaluated by using data from ground-based observations collected from the Meteorological Department located in Lefkoşa, Northern Cyprus from 1981 to 2023. The results demonstrate that ERA5 outperformed the other datasets, yielding a high R-squared value along with a low mean absolute error (MAE) and root mean square error (RMSE). Based on the best dataset, the potential of the rainwater harvesting system is estimated by analyzing the monthly and seasonal rainfall patterns utilizing 65 different probability distribution functions for the first time. Three goodness-of-fit tests are utilized to identify the best-fit probability distribution. The results show that the Johnson and Wakeby SB distributions outperform the other models in terms of fitting accuracy. Additionally, the results indicate that the rainwater harvesting system could supply between 31% and 38% of the building’s annual irrigation water demand (204 m³/year) based on average daily rainfall and between 285% and 346% based on maximum daily rainfall. Accordingly, the system might be able to collect a lot more water than is needed for irrigation, possibly producing an excess that could be stored for non-potable uses during periods of heavy rainfall. Furthermore, the techno-economic feasibility of the proposed system is evaluated using RETScreen software (version 9.1, 2023). The results show that household energy needs can be met by the proposed photovoltaic system, and the excess energy is transferred to the grid. Furthermore, the cash flow indicates that the investor can expect a return on investment from the proposed PV system within 2.4 years. Consequently, the findings demonstrate the significance of this system for promoting resource sustainability and climate change adaptation. Besides, the developed system can also help reduce environmental impact and enhance resilience in areas that rely on water and electricity. Full article

Urban gardens play a vital role in enhancing the quality of the environment and biodiversity. However, irregular rainfall and poor soil drainage due to climate change have increased the exposure of garden plants to waterlogging stress. Pseudolysimachion linariifolium (Pall. ex Link) Holub, a perennial herbaceous plant native to Northeast Asia, is widely used for its ornamental value in urban landscaping. However, its physiological responses to excess moisture conditions remain understudied. In our study, we evaluated the stress responses of P. linariifolium to waterlogging by using non-destructive analysis with drone-based multispectral imagery. We used R (ver. 4.3.2) and the Quantum Geographical Information System (QGIS ver. 3.42.1) to calculate vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Green Leaf Index (GLI), Normalized Green Red Difference Index (NGRDI), Blue Green Pigment Index (BGI), and Visible Atmospherically Resistant Index (VARI). We analyzed the indices combined with the Cumulative volumetric Soil Moisture content (SM_Cum) measured by sensors. With waterlogging treatment, NDVI decreased by 21% and GNDVI by over 34% to indicate reduced photosynthetic activity and chlorophyll content. Correlation analysis, principal component analysis, and hierarchical clustering clearly distinguished stress responses over time. Regression models using NDVI and GNDVI explained 89.7% of the variance in SM_Cum. Our results demonstrate that drone-based vegetation index analysis can effectively quantify waterlogging stress in garden plants and can contribute to improved moisture management and growth monitoring in urban gardens. Full article

In many regions, the El Niño Southern Oscillation (ENSO) cycle is a key factor in modulating climate processes that can influence seasonal variability in the production and dispersal of allergy-triggering pollen. However, the impacts on allergy health are not well known. We compare grass pollen seasons between the major modes of the ENSO cycle in Auckland, New Zealand’s largest city, within a region that is highly sensitive to quasi-predictable meteorological oscillations of the ENSO cycle. We find no clear difference in the timing of onset of the pollen seasons, but season length was shorter, by >30 days, and less severe during the La Niña phase than for the other phases. The difference in pollen season length may be explained by the greater summer rainfall typically experienced in Auckland and elsewhere in northern New Zealand during La Niña phases, which tend to suppress grass pollen abundance when excessive. As grass pollen is the principal source of allergenic pollen in New Zealand and in many other countries, these results have wider implications for allergy management. With ENSO forecasting offering the prospect of several month’s lead time, there is potential for improving community preparedness and resilience to inter-annual dynamics of the grass pollen season. This work points to the need to better understand the influence of short-term climate cycles on seasonal variability in pollen allergy, while we also emphasise that the strong geographical heterogeneity in ENSO cycle climate impacts necessitates a region-specific approach. This work also further underscores the need for standardised, local–regional pollen monitoring in NZ and the risk of relying upon static, nationwide pollen calendars for informing allergy treatment. Full article

The incidence of infectious diseases in children may be affected by climate change-related disaster risks that increase as extreme weather events become more frequent. Therefore, this research aims to diagnose the impact of such disaster risks on the disease incidence, focusing on diarrhoea, dengue haemorrhagic fever (DHF), and acute respiratory infection (ARI), commonly experienced by children. To accomplish this task, we construct integer-valued autoregressive (INAR) models for the number of disease cases among children in several age groups, with an overdispersed distributional assumption to account for its variability that exceeds its central tendency. Additionally, we include the numbers of floods, landslides, and extreme weather events at previous times as explanatory variables. In particular, we consider a case study in Indonesia, a tropical country highly vulnerable to the aforementioned climate change-related diseases and disasters. Using monthly data from January 2010 to December 2024, we find that the incidence of diarrhoea in children is positively impacted by landslides (but negatively affected by floods and extreme weather events). Landslides, frequently caused by excessive rainfall, also increase DHF incidence. Furthermore, the increased incidence of ARI is driven by extreme weather conditions, which are more apparent during and after COVID-19. These findings offer insights into how climate scenarios may increase children’s future health risks. This helps shape health strategies and policy responses, highlighting the urgent need for preventive measures to protect future generations. Full article

Creep through mainly vertical displacement along en echelon ground ruptures within the creeping segment of the West Valley Fault (WVF) in the Luzon Island, Philippines, has been occurring since first documented in the 90s. It is believed to have been triggered by excessive groundwater withdrawal, mainly because of the high rates of slip recorded in the 90s. Near-field displacements measured by locally fabricated linear variable differential transformer (LVDT) and ultrasonic creepmeters are compared with near-field long-term displacements as measured by precise leveling surveys. Though the ultrasonic creepmeter is less accurate in measuring short-term displacement than the LVDT creepmeter, both are reliable in measuring longer-term displacements. Data from creepmeters can reveal association of displacement with seasonal precipitation and correlation between short-term displacement and episodic rainfall. In the case of the WVF’s creeping segment, rainfall episodes and wet seasons do not always result in immediate abrupt displacement changes. Nevertheless, the results of our monitoring with creepmeters underscores the contribution of precipitation in triggering creep, through its effect on the ground and by releasing stored tectonic strain, in the southern region of the WVF’s creeping zone where groundwater withdrawal remains largely unregulated. Continuous monitoring and periodic leveling surveys should continue as creep continues to cause damage and the potential for induced seismicity remains. Full article

Seed germination is a key determinant of wheat seed quality, strongly affected by genetic potential, weather conditions during production, and storage duration. Although numerous studies have investigated seed viability, little is known about how the interaction between annual climatic variability and storage length affects long-term germination performance of winter wheat. The objective of this study was therefore to assess the influence of weather conditions and storage period on germination energy and germination of 50 Croatian winter wheat (Triticum aestivum L.) cultivars released between 1947 and 2010. The experiment was conducted over five consecutive production years (2013/2014–2017/2018). Seeds of each cultivar were reproduced under standardized field conditions, harvested annually, and stored under identical controlled conditions (5 °C, 30–35% RH). Germination energy (first count, day 4) and total germination (final count, day 8) were evaluated according to ISTA protocols. The results revealed significant effects of both production year and cultivar on germination performance. Seeds produced in 2016/2017 exhibited the highest germination (96.21%), which was ~15% higher than the lowest rate observed in 2013/2014 (80.48%). Germination energy of 2013/2014 seeds was 23% lower compared to 2015/2016 and 2016/2017. Unexpectedly, seeds stored for only one year (2017/2018 production) showed lower germination (90.92%) than those stored for two (96.21%) or three years (95.01%), likely due to excessive rainfall (>100% above average) during seed maturation in 2018 that impaired seed quality. Several cultivars, including Una, Tonka, Žitarka, and Kuna, consistently maintained high germination rates (>94%) even after five years of storage, demonstrating strong physiological stability and long-term viability. These findings underline the combined importance of weather conditions during seed production and storage duration for seed longevity. In practical terms, cultivars with proven stability may be recommended for long-term storage and reliable field performance. Future research should extend germination assessment to additional vigor indices (e.g., germination synchrony, vigor index, abnormal seedlings) and explore genetic mechanisms underlying superior seed longevity in modern wheat breeding. Full article

This study investigates the long-term variability and extremes of monthly precipitation during 150 years or more at 4 locations in Argentina: Corrientes, Córdoba, Buenos Aires, and Bahía Blanca. Annual and seasonal trends, extreme dry and wet months over the whole period, and the relationships between large-scale climate drivers and monthly rainfall are considered. Results show that, except for Córdoba, the complete anomaly timeseries trend analysis for all other stations yielded null trends over the centennial study period. Considerable month-to-month variability is observed for all locations together with the existence of low-frequency decadal to interdecadal variability, both for monthly precipitation anomalies and for statistically significant excess and deficit months. Linear fits considering oceanic climate indicators as drivers of variability yield significant differences between locations, while not between full records and seasonally sampled. Issues regarding the use of linear analysis to quantify variability, the dispersion along the timeline of record extreme rainy months at each location, together with the evidence of severe daily precipitation events not necessarily coinciding with the ranking of the rainiest months at each location, highlights the challenges of understanding the drivers of variability of both monthly and severe daily precipitation and the need of using extended centennial timeseries whenever possible. Full article

Field trials were carried out over two tobacco cropping seasons (2020 and 2021) to assess the effectiveness of soil (PRE-T) and post-transplant (POST-T (OT)) herbicides in a tobacco crop, depending on rainfall and the type of soil. The effectiveness of PRE-T and POST-T (OT) herbicides alternated according to the presence of weeds, treatments, the region, and years. Unpredictable meteorological conditions throughout the two study years likely influenced the control of weeds. An unusually moist May in 2020 with a precipitation of 29 mm in the first WA PRE-T before the emergence of weeds generated the leaching of the PRE-T herbicide from the surface of the soil, which was likely the most probable reason for the reduced effectiveness of PRE-T-applied herbicides (less than 77%) in comparison to the POST-T (OT) application treatment in 2020 in the Prilep region. Conversely, the restricted rainfall after PRE-T and POST-T (OT) application may have caused the unsatisfactory efficacy of both PRE-T and POST-T (OT) herbicide treatments in the Titov Veles region in 2021 (less than 78 and 80%, respectively) in comparison with 2020. Excessive rain immediately after PRE-T and POST-T (OT) application resulted in the injury of tobacco plants in the Prilep region in 2020 and 2021, which was between 8 and 25%, and 7 and 22%, respectively, after seven DAHAs across both treatments. The injuries caused by pendimethalin and metolachlor were more serious. The yields of tobacco after both PRE-T and POST-T treatment in each region typically reflect the overall effectiveness of weed control and the extent of tobacco crop injury. Full article

Extreme weather events such as heavy rainfall significantly reduce surface salinity in coastal waters, presenting considerable challenges to the aquaculture of Japanese scallops (Mizuhopecten yessoensis) in shallow cage systems. This study investigated the effects of chronic low-salinity stress on the growth performance, antioxidant capacity, and gene expression profile of M. yessoensis using a 60-day salinity gradient experiment. S33 represents the control treatment with normal seawater salinity (33‰), while S30, S28, and S26 represent experimental groups with progressively lower salinities of 30‰, 28‰, and 26‰, respectively. A decline in salinity was accompanied by an increase in oxygen consumption. The S26 group exhibited a higher ammonia excretion rate (2.73 μg/g·h) than other groups, indicating intensified nitrogen metabolism. Growth was inhibited under low-salinity conditions. The S33 group exhibited greater weight gain (16.7%) and shell growth (8.4%) compared to the S26 group (11.6% and 6%), which also showed a substantially higher mortality rate (46%) compared to the control (13%). At 28‰, antioxidant enzyme activities (T-AOC, SOD, CAT, POD) were elevated, indicating a moderate level of stress. However, at the lowest salinity (26‰), these indicators decreased, reflecting the exhaustion of the antioxidant systems and indicating that the mollusks’ adaptive capacity had been exceeded, leading to a state of stress fatigue. NAD-MDH activity was elevated in the S26 group, reflecting enhanced aerobic metabolism under stress. Transcriptome analysis revealed 564 differentially expressed genes (DEGs) between the S33 and S26 groups. Functional enrichment analysis indicated that these DEGs were mainly associated with immune and stress response pathways, including NF-κB, TNF, apoptosis, and Toll/Imd signaling. These genes are involved in key metabolic processes, such as alanine, aspartate, and glutamate metabolism. Genes such as GADD45, ATF4, TRAF3, and XBP1 were upregulated, contributing to stress repair and antioxidant responses. Conversely, the expressions of CASP3, IKBKA, BIRC2/3, and LBP were downregulated, potentially mitigating apoptosis and inflammatory responses. These findings suggest that M. yessoensis adapts to chronic low-salinity stress through the activation of antioxidant systems, modulation of immune responses, and suppression of excessive apoptosis. This study provides new insights into the molecular mechanisms underlying salinity adaptation in bivalves and offers valuable references for scallop aquaculture and selective breeding programs. Full article

Landslide susceptibility assessment (LSA) plays a pivotal role in regional disaster prevention, particularly in southeastern Tibet, where frequent landslides pose significant threats to human safety and critical infrastructure. However, current LSA approaches face two key challenges: (1) the absence of standardized guidelines for selecting appropriate slope unit scales, which may result in over-smoothing or excessive noise in spatial patterns; and (2) the limited interpretability of machine learning models, which hampers understanding of factor contributions. This study investigates the scale effects of slope unit delineation on LSA in the Yuqu River Basin. Using the r.slopeunits method, six datasets at varying scales were generated to capture terrain heterogeneity. An XGBoost-based framework was applied for susceptibility modeling, with SHAP (Shapley Additive Explanations) used to enhance model interpretability. Results indicate that slope unit scale substantially affects sample distribution, feature representation, and model performance. At the smallest scale (c = 0.05), excessive data redundancy and imbalanced class ratios reduced accuracy (AUC = 0.824). At the largest scale (c = 0.5), spatial heterogeneity was over-smoothed, also impairing performance (AUC = 0.832). The intermediate scale (c = 0.3) performed best, yielding a balanced representation and a mean AUC of 0.856. SHAP analysis highlighted freezing index, relative height, and rainfall as the most influential factors. Notably, susceptibility increased significantly when the freezing index ranged between 1500 and 3000 °C·d and relative height between 500 and 1500 m. Additionally, interactions—such as between the freezing index and slope gradient or fault density—further intensified landslide risk, underscoring the need to consider nonlinear dependencies. By integrating multi-scale modeling with SHAP-based interpretation, this study enhances both the predictive accuracy and transparency of LSA. Full article