Search Results (1,248)

Achieving food security for a growing population under a changing climate is a key concern in Senegal, where agriculture employs 77% of the workforce with a majority of small farmers who rely on the production of crops for their subsistence and for income generation. Moreover, due to the underproductive soils and variable rainfall, Senegal depends on imports to fulfil 70% of its food requirements. In this research, we considered four crops that are crucial for Senegalese agriculture: millet, sorghum, peanuts and rice. We used crop simulation models to explore existing yield gaps and optimal agronomic practices. Improving the N fertilizer management in sorghum and millet resulted in 40–100% increases in grain yields. Improved N symbiotic fixation in peanuts resulted in yield increases of 20–100% with highest impact in wetter locations. Optimizing irrigation management and N fertilizer use resulted in 20–40% gains. The best N fertilizer strategy for sorghum and millet included applying low rates at sowing and in early development stages and adjusting a third application, considering the expected rainfall. Peanut yields of the variety 73-33 were higher than Fleur-11 in all locations, and irrigation showed no clear economic advantage. The best N fertilizer management for rainfed rice included applying 30 kg N/ha at sowing, 25 days after sowing (DAS) and 45 DAS. The best combination of sowing dates for a possible double rice crop depended on irrigation costs, with a first crop planted in January or March and a second crop planted in July. Our work confirmed results obtained in field research experiments and identified management practices for increasing productivity and reducing yield variability. Those crop management practices can be implemented in pilot experiments to further validate the results and to disseminate best management practices for farmers in Senegal. Full article

Typhoon Yagi (2024) was a rapidly moving storm that lasted for eight days and made landfall in three locations, producing heavy rainfall over Hainan and Vietnam. This study aims to investigate the dynamical processes contributing to the heavy rainfall, concentrating on enthalpy flux (EF) and moisture flux (MF). The results indicate that both EF and MF increased significantly during the typhoon’s intensification stage and were high at the time of landfall. Before landfalling at Hainan, latent heat flux (LHF) reached 600 W/m², while sensible heat flux (SHF) was recorded as 80 W/m². Landfall at Hainan resulted in a decrease in LHF and SHF. LHF and SHF subsequently increased to 700 W/m² and 100 W/m², respectively, as noted prior to the landfall in Vietnam. The increased LHF led to higher evaporation, which subsequently elevated moisture flux (MF) following the landfall in Vietnam, while the region’s topography further intensified the rainfall. The mean daily rainfall observed over Philippines is 75 mm on 2 September (landfall and passing through), 100 mm over Hainan (landfall and passing through) on 6 September, and 95 mm at over Vietnam on 7 September (landfall and after), respectively. Heavy rainfall was observed over the land while the typhoon was passing and during the landfall. This research reveals that Typhoon Yagi’s intensity was maintained by a well-organized and extensive circulation system, supported by favorable weather conditions, including high sea surface temperatures (SST) exceeding 30.5 °C, substantial low-level moisture convergence, and elevated EF during the landfall in Vietnam. Full article

While rainfall occurs for several reasons, climate change and urbanization influence its frequency and geographical disparities. Although recent research suggests that urbanization may lead to increased rainfall, insights into how urbanization can trigger rainfall remain limited. We selected the Dallas–Fort Worth (DFW) metroplex, which has minimal orographic and coastal influences, to analyze the urban impact on rainfall. DFW was divided into 256 equal grids (10 km × 10 km) and grouped into four clusters using K-means clustering based on the urbanization ratio. Using Multi-Sensor Precipitation Estimator data (with a spatial resolution of 4 km), we examined rainfall exceeding the 95th percentile (i.e., extreme rainfall) on low synoptic days to highlight localized effects. The urban heat island (UHI) effect was estimated based on the average temperature difference between the urban core and the other three non-urban clusters. Multiple rainfall events were monitored on an hourly basis. Potential linkages between urbanization, the UHI, extreme rainfall, wind speed, wind direction, convective inhibition, and convective available potential energy were evaluated. An intense UHI within the DFW area triggered a tornado, resulting in maximum rainfall in the urban core area under high wind speeds and a dominant wind direction. Our findings further clarify the role of urbanization in generating extreme rainfall events, which is essential for developing better policies for urban planning in response to intensifying extreme events due to climate change. Full article

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period. Full article

High-resolution numerical weather prediction requires accurate cloud microphysical initial conditions to enhance forecasting capabilities for high-impact severe weather events such as convective storms. This study integrated Fengyun-2 (FY-2) geostationary satellite data (equivalent blackbody temperature and total cloud cover) and next-generation 3D weather radar reflectivity from the China Meteorological Administration (CMA) to construct cloud microphysical initial fields and evaluate their impact on the CMA-MESO 3 km regional model. An analysis of the catastrophic rainfall event in Henan on 20 July 2021, and a 92-day continuous experiment (May–July 2024) revealed that assimilating cloud microphysical variables significantly improved precipitation forecasting: the equitable threat scores (ETSs) for 1 h forecasts of light, moderate, and heavy rain increased from 0.083, 0.043, and 0.007 to 0.41, 0.36, and 0.217, respectively, with average hourly ETS improvements of 21–71% for 2–6 h forecasts and increases in ETSs for light, moderate, and heavy rain of 7.5%, 9.8%, and 24.9% at 7–12 h, with limited improvement beyond 12 h. Furthermore, the root mean square error (RMSE) of the 2 m temperature forecasts decreased across all 1–72 h lead times, with a 4.2% reduction during the 1–9 h period, while the geopotential height RMSE reductions reached 5.8%, 3.3%, and 2.0% at 24, 48, and 72 h, respectively. Additionally, synchronized enhancements were observed in 10 m wind prediction accuracy. These findings underscore the critical role of cloud microphysical initialization in advancing mesoscale numerical weather prediction systems. Full article

Mungbean is grown as a summer crop in subtropical climates globally. The global demand for mungbean is increasing, and opportunities exist to expand production regions to more marginal environments, such as southern Australia, as an opportunistic summer crop to help meet the growing global demand. Mungbean has the potential to be an opportunistic summer crop when an appropriate sowing window coincides with sufficient soil water. This expansion from subtropical to temperate climates will pose challenges, including low temperatures, a longer day length and a low and variable water supply. To assess mungbean suitability to temperate, southern Australian summer rainfall patterns and soil water availability, we conducted field experiments applying a range of water treatments across four locations with contrasting rainfall patterns within the state of Victoria (in southern Australia) in 2020–2021 and 2021–2022. The water treatments were applied prior to sowing (60 mm), the vegetative stage (40 mm) and the reproductive stage (40 mm) in a factorial combination at each location. Two commercial cultivars, Celera II-AU and Jade-AU, were used. Water scarcity during flowering and the pod-filling stages were important factors constraining yield. Analysis of yield components showed that increasing water availability at critical growth stages, viz. the vegetative and reproductive stages, of mungbean was associated with increases in total biomass, HI and grain number in addition to increased water use and water use efficiency (WUE). Average WUEs ranged from 1.3 to 7.6 kg·ha⁻¹·mm⁻¹. The maximum potential WUE values were 6.4 and 5.1 kg·ha⁻¹·mm⁻¹ for Celera II-AU and Jade-AU across the sites, with the estimated soil evaporation values (x-intercept) of 83 and 74 mm, respectively. Nitrogen fixation was variable, with %Ndfa values ranging from 9.6 to 76.8%, and was significantly affected by soil water availability. This study emphasises the importance of water availability during the reproductive phase for mungbean yield. The high rainfall zones within Victoria have the potential to grow mungbean as an opportunistic summer crop. Full article

The spatiotemporal changes and driving mechanisms of rain-on-snow (ROS) events and their intensities are crucial for responding to disasters triggered by such events. However, there is currently a lack of detailed assessment of the seasonal variations and driving mechanisms of ROS events and their intensities in China. Therefore, this study utilized daily meteorological data and daily snow depth data from 513 stations in China during 1978–2020 to investigate spatiotemporal variations of ROS events and their intensities. Also, based on the detrend and partial correlation analysis model, the driving factors of ROS events and their intensity were explored. The results showed that ROS events primarily occurred in northern Xinjiang, the Qinghai–Tibet Plateau, Northeast China, and central and eastern China. ROS events frequently occurred in the middle and lower Yangtze River Plain in winter but were easily overlooked. The number and intensity of ROS events increased significantly (p < 0.05) in the Changbai Mountains in spring and the Altay Mountains and the southeast part of the Qinghai–Tibet Plateau in winter, leading to heightened ROS flood risks. However, the number and intensity of ROS events decreased significantly (p < 0.05) in the middle and lower Yangtze River Plain in winter. The driving mechanisms of the changes for ROS events and their intensities were different. Changes in the number of ROS events and their intensities in snow-rich regions were driven by rainfall days and quantity of rainfall, respectively. In regions with more rainfall, these changes were driven by snow cover days and snow water equivalent, respectively. Air temperature had no direct impact on ROS events and their intensities. These findings provide reliable evidence for responding to disasters and changes triggered by ROS events. Full article

Agriculture accounts for 29% of the Gross Domestic Product of the Nusa Tenggara Islands (NTIs). However, recurring agricultural droughts pose a major threat to the sustainability of agriculture in this region. The interplay between precipitation, solar radiation, and surface temperature as meteorological factors plays a key role in affecting vegetation (Soil-Adjusted Vegetation Index) and agricultural drought (Temperature Vegetation Dryness Index) in the NTIs. Based on the analyses of interplay with temporal lag, this study investigates the effect of each factor on agricultural drought and attempts to provide early warnings regarding drought in the NTIs. We collected surface information data from Moderate-Resolution Imaging Spectroradiometer (MODIS). Meanwhile, rainfall was estimated from Himawari-8 based on the INSAT Multi-Spectral Rainfall Algorithm (IMSRA). The results showed reliable performance for 8-day and monthly scales against gauges. The drought analysis results reveal that the NTIs suffer from mild-to-moderate droughts, where cropland is the most vulnerable, causing shifts in the rice cropping season. The driving factors could also explain >60% of the vegetation and surface-dryness conditions. Furthermore, our monthly and 8-day TVDI estimation models could capture spatial drought patterns consistent with MODIS, with coefficient of determination (R²) values of more than 0.64. The low error rates and the ability to capture the spatial distribution of droughts, especially in open-land vegetation, highlight the potential of these models to provide an estimation of agricultural drought. Full article

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the nonlinear relationship between relative humidity and ET. Unlike conventional approaches such as the Penman–Monteith or Priestley–Taylor models, the Sigmoid-RH model requires fewer inputs and is better suited for large-scale applications where data availability is limited. In this study, we applied the Sigmoid-RH model to estimate ET over mainland China from 2001 to 2018 by using satellite remote sensing and meteorological reanalysis data. Key driving inputs included air temperature (Ta), net radiation (Rn), relative humidity (RH), and the normalized difference vegetation index (NDVI), all of which are readily available from public datasets. Validation at 20 flux tower sites showed strong performance, with R-square (R²) ranging from 0.26 to 0.93, Root Mean Squard Error (RMSE) from 0.5 to 1.3 mm/day, and Kling-Gupta efficiency (KGE) from 0.16 to 0.91. The model performed best in mixed forests (KGE = 0.90) and weakest in shrublands (KGE = 0.27). Spatially, ET shows a clear increasing trend from northwest to southeast, closely aligned with climatic zones, with national mean annual ET of 560 mm/yr, ranging from less than 200 mm/yr in arid zones to over 1100 mm/yr in the humid south. Seasonally, ET peaked in summer due to monsoonal rainfall and vegetation growth, and was lowest in winter. Temporally, ET declined from 2001 to 2009 but increased from 2009 to 2018, influenced by changes in precipitation and NDVI. These findings confirm the applicability of the Sigmoid-RH model and highlight the importance of hydrothermal conditions and vegetation dynamics in regulating ET. By improving the accuracy and scalability of ET estimation, this model can provide practical implications for drought early warning systems, forest ecosystem management, and agricultural irrigation planning under changing climate conditions. Full article

More than half of the world’s rivers are intermittent, and climate change is increasing their intermittency, affecting water resources and ecosystems. In Taiwan, steep topography and uneven rainfall have led to increased intermittency in some areas, reflecting changes in hydrological conditions. Using streamflow data, this study applied intermittency ratio (IR), modified 6-month dry period seasonality (SD6), and trend analysis, as well as watershed properties and climate indices. Results showed that 92% of stations had low flows for less than 20% of the time. The dry season was mainly from November to April, and intermittency was spatially affected mainly by upstream soil moisture, moderately by potential evapotranspiration and infiltration, and less by actual evapotranspiration and catchment area. Intermittency increased in the east and decreased in the west. It was negatively correlated with upstream soil moisture and strongly associated with rainfall frequency, especially the proportion of days with precipitation less than 1 mm. These patterns highlight regional differences in river responses to climate. Full article

Rain attenuation of radio waves with frequencies above 10 GHz causes a serious problem in wireless communications. For wireless systems design, highly accurate methods for estimating the magnitude of attenuation have long been studied. ITU-R recommends a calculation method for rain attenuation using R_0.01, the 1 min rainfall rate that is exceeded for 0.01% of an average year. Accordingly, an R_0.01 database suitable for this calculation has been constructed. In recent years, global warming has emerged as an important climatological issue. If the predicted rise in temperatures associated with global warming induces a significant effect on rainfall characteristics, the existing R_0.01 database will need to be revised. However, there is currently no information for quantitatively evaluating the likely long-term change in R_0.01. In our previous study, the long-term trend in annual maximum values for 1-day, 1 h, and 10 min rainfall in Japan was estimated from a large amount of meteorological data and a 95% confidence interval approach was used to identify an increasing trend of more than 10% over approximately 100 years. In this paper, we investigate the long-term trend in greater detail using non-linear approximations for three types of rainfall and adopt the Akaike Information Criterion to determine the optimal order of the non-linear approximation. The future trend of R_0.01 is then estimated based on the long-term change in annual maximum 1 h rainfall, exploiting the strong correlation between long-term average annual maximum 1 h rainfall and R_0.01. Full article

In recent years, satellite products have played an increasingly significant role in monitoring and estimating global extreme weather events, owing to their advantages of an excellent spatiotemporal continuity and broad coverage. This study systematically evaluates the Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for the GPM Late Run (IMERG-L) product for regional precipitation events based on the observations in Zhejiang Province from 2001 to 2020. In this study, seven typical precipitation indices with seven accuracy evaluation indexes are applied to analyze the performance of IMERG-L from multiple perspectives in terms of the precipitation intensity, frequency and spatial distribution dimensions. The results show that IMERG-L is capable of capturing the spatial distribution trends, especially in the frequency-based precipitation indices (CWD, R10mm and R20mm), which can depict the regional wetness and precipitation pattern. However, the product suffers from a systematic overestimation in capturing heavy precipitation and an extreme precipitation intensity, with a high false alarm rate and unstable accuracy, especially in heavy rainfall and above class events, where the Probability of Detection (POD) drops significantly, showing an obvious reduction in the recognition capability and risk of misclassification. Specifically, IMERG-L failed to reproduce the observed eastward-increasing trends in the annual maximum precipitation for both one-day (RX1day) and five-day (RX5day) durations, demonstrating its limitations in accurately capturing extreme precipitation patterns across Zhejiang Province. Overall, furthering the optimization and improvement of IMERG-L in reducing the intensity-dependent biases in heavy rainfall detection, increasing spatial inhomogeneity in trend representations and improving the false alarm suppression for extreme events are needed for the accurate monitoring and quantitative estimation of high-intensity extreme precipitation events. Full article

Accelerating climate change poses significant risks to water security and ecological stability in arid regions due to the increasing frequency and intensity of extreme precipitation events. As a climate-sensitive area, the inland river basin (IRB) of Northwest China—a critical water source for local ecosystems and socioeconomic activities—remains insufficiently studied in terms of future extreme precipitation dynamics. This study evaluated the spatiotemporal evolution of extreme precipitation in the IRB under a new low radiative forcing scenario (SSP1-1.9) by employing four global climate models (GCMs: GFDL-ESM4, MRI-ESM2, MIROC6, and IPSL-CM6A-LR). Eight core extreme precipitation indices were analyzed to quantify changes during the near future (NF: 2021–2050) and far future (FF: 2071–2100) periods. Our research demonstrated that all four models were capable of capturing seasonal patterns and exhibited inherent uncertainty. The annual total precipitation (PRCPTOT) in mountainous regions showed minimal variation, while desert areas were projected to experience a 2-6-fold increase in precipitation in the NF and FF. The Precipitation Intensity Index (SDII) weakened by approximately −10% in mountainous areas but strengthened by around +10% in desert regions. Most mountainous areas showed an increase in the maximum consecutive dry days (CDD), whereas desert regions exhibited extended maximum consecutive wet days (CWD). Moderate rainfall (P1025) variations primarily ranged between −5% and +20%, with greater fluctuations in desert areas. Heavy rainfall (PG25) fluctuated between −40% and +40%, reflecting stark contrasts in extreme precipitation between arid basins and mountainous zones. The maximum 1-day precipitation (Rx1day) and maximum 5-day precipitation (Rx5day) both showed significant increases, which indicated heightened risks from extreme rainfall events in the future. Moreover, the IRB region experienced increased total precipitation, enhanced rainfall intensity, more frequent alternations between drought and precipitation, more frequent moderate-to-heavy rainfall days, and higher daily precipitation extremes in both the NF and FF periods. These findings provide critical data for regional development planning and emergency response strategy formulation. Full article

Many studies relate to long-term changes in annual precipitation in Poland, yet most of them were statistically insignificant. The primary objective of this research was to investigate the precipitation regime during the year in the context of climate change, which is more crucial than annual averages from the perspectives of agriculture and plant growth, as well as for the industrial sector and human access to clean water. For this reason, we used daily precipitation data from the Institute of Meteorology and Water Management—National Research Institute from 1966 to 2024. Each month of the study was examined for changes in monthly totals, the number of dry and wet days, precipitation intensities, and extremes. In the cold season, a considerable shift in precipitation patterns was found between November and December, which became drier, and January and February, which became wetter with more intense and extreme precipitation. Pronounced changes were also noticed in April and June, when not only the monthly totals but also the number of wet days and precipitation intensity decreased. These two months, together with winter, are essential for plant growth. On the contrary, July became slightly wetter. Interesting changes were also observed in September, including an increase in dry days and more intense rainfall. With the increase in temperature and changes in the advection of air masses, September became more similar to summer than to autumn months. The key factors driving shifts in precipitation regimes during the year were a warmer atmosphere and changes in circulation patterns. Full article

Floods and droughts are natural disasters that disrupt livelihoods and destroy the environment, with floods constituting up to 40% of all natural disasters globally. South Sudan has experienced severe, recurrent flooding for decades, with two-thirds of the country affected. An integrated flood management system is urgently needed to mitigate impacts and improve community resilience. This requires understanding the inundation process and analyzing flood causes and characteristics. This research leverages data from the Climate Hazards Center InfraRed Precipitation with Station (CHIRPS v2.0) to examine rainfall patterns and analyze trends in annual total precipitation (PRCPTOT), days with precipitation ≥ 20 mm (R20 mm), and simple precipitation intensity (SDII) at the basin scale. It also incorporates Nile River flow data from the Mangala station and Lake Victoria water levels from satellite altimetry. Findings indicate decreasing trends in PRCPTOT, R20 mm, and SDII in Jonglei and Unity States, but increasing trends in river flows and Lake Victoria levels. The Global Surface Water dataset reveals increased water surface areas in these states. These findings suggest that river flow trends oppose rainfall patterns, indicating that local rainfall is not the primary contributor to the recurrent flooding in the area. Full article