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Nature sports include pursuits such as paragliding, white-water kayaking, free diving, mountaineering, and surfing. Participants in nature sports interact with geographical features (e.g. mountains, rivers, oceans, snow fields, ice sheets, caves, rock faces) as well as the dynamic forces that produce them (e.g. gravity, waves, thermal currents, flowing water, wind, rain, sun). In this article, I engage a representational approach to analyze how participants in nature sports interact with nature. Anthropocentric representations privilege participants' interests, wants, desires, and ends; they typically refer to claims of conquest/achievement in nature, or praise nature for its therapeutic qualities. In contradistinction, ecocentric representations recognize humankind as one entity in an interdependent world that comprises all living organisms and the geological processes and geomorphological features that sustain them. Ecocentric representations of nature sports highlight networks of participants and landforms that help preserve a balance between people and the environment. Yet, notwithstanding the allure of ecocentric representations, especially in the wake of evidence that human-induced greenhouse gases are predisposing environmental calamities, there is a substantial gap between the ontological concepts and categories of ecocentrism and lived sporting experiences and practices in nature. [ABSTRACT FROM AUTHOR]

Image deraining, a crucial process in image restoration, finds wide-ranging applications in computer vision. Existing state-of-the-art deraining techniques, predominantly relying on image smoothing, dictionary learning, sparse coding, and deep neural networks, often fall short in delivering desirable outputs when faced with heavy rain. In this research article, we propose an advanced approach for image deraining, employing a multilayer decomposition strategy based on Empirical Wavelet Transform (EWT) and Dual Dictionary Learning (DDL). The proposed method introduces the Dark Channel Prior (DCP) in the preprocessing stage and utilizes Frequency Discrimination (FD), Empirical Wavelet Transform, and sparse-based methods with Dual Dictionary Learning to generate one low and three high frequency (HF) decomposed image components. The rain parts are subsequently removed from each HF image component through morphological decomposition in multiple layers. The non-rain outputs are combined with the lower frequency image obtained from the bilateral filter output to produce the rain-free image. The final output is further refined by adjusting the contrast, sharpness, and color balance of the de-rained image. To validate the efficacy of our proposed algorithm, we conducted a comprehensive evaluation using both subjective (visual quality) and objective (quantitative quality metrics) approaches. Comparative analysis with state-of-the-art methods confirms that our method outperforms existing techniques, demonstrating superior image-deraining capabilities. The proposed approach showcases promising results in addressing the challenges posed by heavy rain, establishing it as a robust and effective solution for image-deraining applications in various computer vision domains. [ABSTRACT FROM AUTHOR]

This paper describes the first assessment of ISRO's indigenously developed S-band Polarimetric Doppler Weather Radar (DWR) at Sriharikota High Altitude Range (SHAR), Sriharikota (13.66°N, 80.23°E). The assessment is done during the passage of a tropical cyclone 'Phethai' and four different cases of Mesoscale convective systems. A comparison of reflectivity measured from SHAR-DWR with India Meteorological Department (IMD) DWR located at Chennai (13.07°N, 80.28°E) and Ku-band space-borne radar of the Global Precipitation Mission (GPM) is done using the common volume matching method. A good comparison is observed between SHAR-DWR with IMD-DWR and GPM-Ku radar in both space and height. The vertical structure of the cyclone is provided using Contour Frequency by Altitude Diagrams (CFAD) which show the vertical extent up to an altitude of 16 km with a reflectivity between 25 and 30 dBZ. Maximum occurrence of 30 dBZ occurs below 6 km. Bright band signatures show ~ 25 dBZ between 4 and 5 km depicting the stratiform precipitation of the cyclone. Further rain rate is estimated using both reflectivity and the polarimetric product which compared well with rain estimated from IMD-DWR and rainfall observed from rain gauges. Rain rate is observed from 5 to 35 mm hr−1. Hydrometer classification during the passage of tropical cyclone 'Phethai' shows the presence of rain below 4 km and graupels, ice crystals, and large droplets above. [ABSTRACT FROM AUTHOR]

Environmental concerns are growing in parts of the rural United States as drinking water wells become contaminated with agrichemicals, and climate change impacts such as drought and extreme rain events disrupt farming practices. Analyzing in‐depth interviews with 29 U.S. rural journalists, we explore the role rural journalists play when reporting on the environment as it relates to agricultural issues. Findings show that reporting about the environment—such as weather, climate change, water shortages, and droughts—appear in rural journalists' stories given those topics' relevance to their rural communities and the social systems forces exerting influence on journalists, yet rural journalists often do not perceive themselves as doing environmental reporting. Rural journalists also report lacking training and resources to do environmental reporting. This study points to the need for more support for rural journalists' environmental reporting as one way to build social change and maintain local community. [ABSTRACT FROM AUTHOR]

Future projections indicate that Indian Summer Monsoon Rainfall (ISMR) faces a "wetter and more variable" climate. However, the reasons remain uncertain. The Last Interglacial (LIG) climate provides a potential analog for future warming. Investigating ISMR responses to these two warming scenarios could help understand the causes of ISMR changes. Using PMIP4 simulations, we find that ISMR became "wetter and more stable" during the LIG, contrasting the future climate. The opposing changes in ISMR variability are related to divergent changes in the El Niño‐Southern Oscillation (ENSO) amplitudes, ENSO‐ISMR relationships, and ENSO‐induced large‐scale atmospheric circulation anomalies. During the LIG, orbital forcing weakened ENSO variability and its impacts on ISMR. A westward positioning of ENSO shifted the atmospheric circulation anomalies westward, suppressing extreme ISMR anomalies. These processes are supported by atmospheric model simulations. Our results suggest that different warming patterns (dynamic effects) are more critical than moisture‐increasing effects in controlling regional climate variability. Plain Language Summary: The Last Interglacial (LIG), approximately 129,000 to 116,000 years before the present, is a potential analog for future warming. We found that the variability of Indian Summer Monsoon Rainfall (ISMR) decreased while its mean state increased during the LIG, which is a "wetter and more stable" climate. This contrasts with the simultaneous increase in both the mean state and variability of ISMR projected in future warming scenarios. The opposing changes in ISMR variability during these two warm periods can be attributed to reverse changes in El Niño‐Southern Oscillation (ENSO) variability and its associated large‐scale circulation. During the LIG, reduced ENSO variability weakened the ENSO‐ISMR relationship. Sea surface temperature anomalies associated with ENSO extended westward in LIG, shifting precipitation and associated heating‐induced atmospheric circulation anomalies westward, which weakened the extreme ISMR anomalies, thus making the ISMR variability stable. This process is further supported by atmospheric general circulation model (CAM5) experiments. Our findings suggest that different external forcing‐induced warming patterns (dynamic effects) can be more critical than moisture‐increasing effects in contributing to regional climate variability change. Key Points: Indian Summer Monsoon Rainfall (ISMR) experienced a more stable climate during the LIG opposite to the change under anthropogenic warmingRelationship between ISMR and ENSO significantly weakened due to the waning ENSO variability induced by orbital forcingA westward positioning of ENSO during the LIG shifted the anomalous large‐scale circulation westward, reducing the extreme ISMR anomalies [ABSTRACT FROM AUTHOR]

We propose the lag‐1 autocorrelation of daily precipitation as a simple diagnostic of tropical precipitation variability in climate models. This metric generally has a relatively uniform distribution of positive values across the tropics. However, selected land regions are characterized by exceptionally low autocorrelation values. Low values correspond to the dominance of high frequency variance in precipitation, and specifically of high frequency convectively coupled equatorial waves. Consistent with previous work, we show that CMIP6 climate models overestimate the autocorrelation. Global kilometer‐scale models capture the observed autocorrelation when deep convection is explicitly simulated. When a deep convection parameterization is used, though, the autocorrelation increases over land and ocean, suggesting that land surface‐atmosphere interactions are not responsible for the changes in autocorrelation. Furthermore, the metric also tracks the accuracy of the representation of the relative importance of high frequency and low frequency convectively coupled equatorial waves in the models. Plain Language Summary: Rainfall in the tropics is influenced by many atmospheric processes that depend on geographic location. We use the lag‐1 autocorrelation as a metric for the day‐to‐day persistence of rainfall. We find that rainfall is very persistent in most parts of the tropics with a few exceptions over land, for example, the Sahel, where high frequency rainfall events dominate. Our results show that models with a horizontal resolution of a few kilometers reproduce the autocorrelation, in contrast to coarser climate models. We also analyze atmospheric waves and find that they are important for the autocorrelation pattern in the observations and the simulations. Key Points: The lag‐1 autocorrelation pattern of daily precipitation in the tropics is robust across different observation‐based data setsThe lag‐1 autocorrelation reflects the relative variance of high frequency and low frequency convectively coupled equatorial wavesKilometer‐scale models capture the observed autocorrelation, but models with parameterized deep convection overestimate it [ABSTRACT FROM AUTHOR]

Extreme rainfall events (EREs) caused by convection are a major prediction challenge and can lead to deadly flash flooding. This study investigates EREs from 2003 to 2023 over the central and eastern United States using high‐resolution (4‐km) Stage IV precipitation analyses. EREs were defined where 12‐hr accumulations exceeded the 10‐year average recurrence interval thresholds. Summertime (June–August) EREs exhibited pronounced interannual variability, but with statistically significant increasing trends in annual frequency, particularly among those caused by mesoscale convective systems. Significant relationships were also found between summertime convective EREs over the central United States and enhanced poleward moisture transport, consistent with a westward expansion of the climatological North Atlantic Subtropical High. This relationship may aid in medium‐ and long‐range forecasting. However, the spatial scale of EREs often included localized or narrow bands of extreme accumulations, revealing significant challenges in the accurate representation of EREs in weather and climate models. Plain Language Summary: Extreme rainfall events often result from slow‐moving thunderstorms that drop heavy rain over the same area for several hours, which can cause deadly flash flooding. This study defines an extreme rainfall event where the 12‐hr accumulation would only exceed that amount once every 10 years on average, with a focus on the summer months over the central and eastern United States from 2003 to 2023. Our results show a significant increasing trend in the number of extreme events, especially those caused by larger, longer‐duration storms. Our use of a high‐resolution rainfall data set also revealed that extreme rainfall often occurs due to nighttime thunderstorms producing heavy rainfall over highly localized areas or narrow (20–40 km wide) swaths. These characteristics present a challenge for global weather and climate models. The frequency of these summertime extreme events also significantly varied from year to year. Summers with more extreme events were associated with a greater northward transport of moist air around the western edge of an area of high pressure that extends westward from the Atlantic Ocean. The relationship between this flow pattern and extreme rainfall suggests a possible path for improving forecasts of extreme rainfall. Key Points: High‐resolution rainfall data reveal an increasing trend in extreme summertime rainfall, especially from mesoscale convective systemsMore events occurred during summers with greater southerly moisture transport driven by differences in the North Atlantic Subtropical HighThe relatively small scale of extreme convective rainfall presents a challenge for representing these events in weather and climate models [ABSTRACT FROM AUTHOR]

A multichannel singular spectrum analysis of 121 years of daily rainfall over India and 43 years of outgoing longwave radiation and horizontal wind has revealed two dominant intraseasonal oscillations (ISOs) that describe the space–time variability of monsoon rainfall. The two nonlinear oscillations are not perfectly periodic but exhibit broadband spectra centered at 45 and 28 days. These modes have coherent and near-regular spatial structure and temporal variations. This study posits that the two nonlinear oscillations provide a consistent and comprehensive framework to study the variability and predictability of intraseasonal rainfall variations. The active and break cycles of monsoon rainfall over India have been traditionally defined by using an arbitrarily chosen duration of persistence of arbitrarily chosen amount of rainfall averaged over an arbitrarily chosen area. This study shows that the phases of the two objectively derived modes of variability provide an objective method for defining the active and break cycles. The two ISOs are further shown to make negligible contribution to the seasonal mean rainfall and its interannual variability. This study proposes that the investigations of the origins of these nonlinear modes, and modulations of their amplitudes and phases, provide a new paradigm for future research on validation and predictability of intraseasonal variations in climate models. [ABSTRACT FROM AUTHOR]

The traditional real-time load warning method for distribution transformers has problems such as low recall rate, low warning accuracy, and long warning time, which may lead to potential equipment failures or overload situations not being detected and dealt with in a timely manner, increasing the safety risk of transformer operation and potentially causing safety issues such as equipment damage, fire, or power outage. Therefore, a real-time early warning method of distribution transformer load considering meteorological factor data is designed. The meteorological factor data are collected by the light sensor, humidity sensor, temperature sensor and rainfall sensor, and the load data collection architecture is built by the load monitor, central master station and maintenance station to realize the load data collection of the distribution transformer. The K-nearest neighbor (KNN) method is used to process the missing values of the data, and the LOF algorithm is used to determine the local outliers and eliminate the outliers in the data set to achieve data cleaning. Considering the load loss, hot spot temperature and meteorological factors of the distribution transformer, an early warning model is built, and the cleaned data are input into the model to realize Real-time early warning of the distribution transformer load. The experimental results show that the recall rate of this method varies from 95% to 97%, the accuracy rate of early warning is always above 94%, and the maximum value of early warning time is 0.63s. Having good early warning ability. [ABSTRACT FROM AUTHOR]

The study on the characteristic and the parameterization of cloud droplet effective radius (re) is important for the optimization of warm cloud microphysical schemes in numerical models. Based on numerical simulations combined with the four aircraft observations of stratiform warm clouds in Jiangxi, China, this work investigated their microphysical properties, focusing on the evolutionary pattern and the parameterization of re, as well as the correlation between re and the number concentration (Nc) of cloud droplet and liquid water path (LWP). It is found that there is a negative correlation between re and Nc, and the rate of the decrease could be increased by 2–4 times with a strengthening of collision‐coalescence process. What's more, the direct introduction of collision‐coalescence is conducted based on the existing re parameterization schemes, and the results show that it can perform well in most of collision‐coalescence intensities. It is also found that LWP is positively correlated with re when re is less than 12 μm, after which the relationship changes to a negative correlation due to the impact of rainfall intensity. Plain Language Summary: The bin microphysical scheme can reproduce the microphysical structures of warm cloud, combined with aircraft observations to investigate the microphysics within warm clouds. This work analyzes the evolution characteristics and the parameterization of effective radius in warm clouds based on bin microphysics simulations. Thereafter, the impact of these cloud microphysical quantities to liquid water path (LWP) are also researched. The results show that the rate of effective radius decrease with number concentration can be intensified by collision‐coalescence intensity, and an effective radius parameterization scheme related to collision‐coalescence is proposed based on the existing parameterization schemes. The variation pattern of LWP with effective radius is linked to the critical radius for rainfall intensity. Above all, the effective radius variation pattern depends largely on collision‐coalescence intensity and can affect LWP a lot. These findings are important for understanding warm cloud microphysical parameterization and improving climate assessment. Key Points: The relation between the effective radius and cloud droplet number concentration greatly influenced by collision‐coalescence intensityAn effective radius parameterization scheme which is directly related to collision‐coalescence is considered based on the existing schemesLiquid water path has different relationship with number and effective radius of cloud droplets, also affected by the rainfall intensity [ABSTRACT FROM AUTHOR]

The occurrence of tropical cyclones (TCs) in the Horn of Africa and nearby areas is for the first time examined to document their contribution to local rainfall and their trends over the period 1990–2020. An average 1.5 TC (of any intensity) per year was observed over the Western Arabian Sea, with two asymmetrical seasons, namely May–June (30% of cyclonic days) and September–December (70%). Case studies reveal that in many instances, TC‐related rainfall extends beyond 500 km from the TC center, and that substantial rains occur one to 2 days after the lifecycle of the TC. Despite their rarity, in the otherwise arid to semi‐arid context characteristic of the region, TCs contribute in both seasons to a very high percentage of total rainfall (up to 30%–60%) over the northwestern Arabian Sea, the Gulf of Aden and their coastlines. Over inland northern Somalia, contributions are much lower. TCs disproportionately contribute to some of the most intense daily falls, which are often higher than the mean annual rainfall. A strong increase in the number of TCs is found from 1990 to 2020, hence their enhanced contribution to local rainfall. This increase is associated with a warmer eastern/southern Arabian Sea, a decrease in vertical wind shear, and a strong increase in tropospheric moisture content. Plain Language Summary: The role played by tropical cyclones in local rainfall in the Horn of Africa and nearby areas is for the first time examined. An average 1.5 cyclones (of any intensity) per year is observed over the Western Arabian Sea. They occur in two different seasons: May–June (30% of cyclonic days) and September–December (70%). Despite their rarity, these cyclones affect a region which displays arid to semi‐arid climates, hence they contribute to a very high percentage of total rainfall (30%–60%) over the northwestern Arabian Sea, the Gulf of Aden and their coastlines. Over inland northern Somalia, contributions are lower. Many of the most intense rainfall events are related to cyclones, with daily rainfall sometimes higher than the mean annual rainfall. There has been a strong increase in the number of cyclones from 1990 to 2020, and they contribute more to local rainfall. This increase is associated with a warmer Arabian Sea, a change in the wind flow and a higher moisture content. Key Points: An average 1.5 tropical cyclones occur annually in the Western Arabian Sea, 70% of them in September–December, the rest in May–JuneThey strongly contribute (30%–60%) to rainfall over the northwestern Arabian Sea, the Gulf of Aden and their coastlinesTheir frequency sharply increased from 1990 to 2020, resulting in a 11% rise of their contribution to the Gulf of Aden precipitation [ABSTRACT FROM AUTHOR]

The occurrence of natural disasters, accelerated by climate change, has become a continuous menace to the environment and consequently impacts the socioeconomic well-being of people. Flood events are natural disasters resulting from excessive rainfall duration, intensity, and snow melt. Flood disaster management systems that are machine learning-based have been increasingly suggested and applied to forestall the impacts of floods on the environment in terms of monitoring and warning. This study aims to critically review various studies conducted on flood management systems to identify applicable data sources and machine learning models. The study applied Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to source data from an academic database using some selected keywords, which were identified for the review process after filtering a total number of forty-two pertinent research papers was used. The review identified different combinations of flood data, flood management techniques, flood models, application of machine learning in flood predictions, optimization techniques, data processing techniques, and evaluation techniques. The study concluded that a standard approach should be applied in building robust and efficient flood disaster management systems. Lastly, informed future research directions on using machine learning for flood prediction and susceptibility mapping are provided. [ABSTRACT FROM AUTHOR]

In order to optimize the allocation of water resources in the Yellow River Basin and the irrigation regime for winter wheat crops in southeast Shanxi Province areas, based on the meteorological data from 1960 to 2019 in Yangcheng County, the annual changes of water requirement and rainfall at different growth stages of winter wheat were analyzed and calculated. In the typical irrigation year (25%, 50%, 75% and 95%), the coupling analysis of the water requirement and rainfall was carried out, and the irrigation water consumption of the winter wheat was calculated. The results show that in the past 60 years, the climate tendency rate of winter wheat water requirement increases with the change of 0.593 mm/(10a), and the annual average water requirement is 586.09 mm in southeast Shanxi Province areas. The climatic tendency rate of rainfall increases by 9.827 mm/(10 a), and the annual average value of rainfall is 213.66 mm. The climatic tendency rate of water requirement of winter wheat is the highest at jointing-heading stage. Under four typical irrigation years (25%, 50%, 75% and 95%), the coupling degree of rainfall and water demand in the whole growth period of winter wheat is 0.43, 0.33, 0.28 and 0.18 respectively. Natural rainfall cannot meet the water requirement of winter wheat growth. The amount of supplementary irrigation water is 261.74 mm, 427.45 mm, 554.34 mm and 674.66 mm respectively. Except typical irrigation years of 25%, the amount of supplementary irrigation water is the largest at heading to filling period. The supplementary irrigation water should be 116.87 mm, 142.55 mm and 171.54 mm respectively. [ABSTRACT FROM AUTHOR]

Background: Dengue outbreaks are common in tropical or temperate countries, and climate change can exacerbate the problem by creating conditions conducive to the spread of mosquitoes and prolonging the transmission season. Warmer temperatures can allow mosquitoes to mature faster and increase their ability to spread disease. Additionally, changes in rainfall patterns can create more standing water, providing a breeding ground for mosquitoes. Objective: The objective of this study is to investigate the correlation between environmental and demographic factors and the dissemination of dengue fever. The study will use yearly data from 2000 to 2021 from 11 countries highly affected by dengue, considering multiple factors such as dengue cases, temperatures, precipitation, and population to better understand the impact of these variables on dengue transmission. Methods: In this research, Poisson regression (PR) and negative binomial regression (NBR) models are used to model count data and estimate the effect of different predictor variables on the outcome. Also, histogram plots and pairwise correlation plots are used to provide an initial overview of the distribution and relationship between the variables. Moreover, Goodness-of-fit tests, t-test analysis, diagnostic plots, influence plots, and residual vs. leverage plots are used to check the assumptions and validity of the models and identify any outliers or influential observations that may be affecting the results. Results: The findings indicate that mean temperature and log(Urban) had a positive impact on dengue infection rates, while maximum temperature, log(Precipitation), and population density had a negative impact. However, minimum temperature, log(Rural), and log(Total population) did not demonstrate any significant effects on the incidence of dengue. Conclusion: The impact of demographic-environmental factors on dengue outbreaks in 11 Asian countries is illuminated by this study. The results highlight the significance of mean temperature (Tmean), maximum temperature (Tmax), log(Urban), log(Precipitation), and population density in influencing dengue incidence rates. However, further research is needed to gain a better understanding of the role of additional variables, such as immunity levels, awareness, and vector control measures, in the spread of dengue. [ABSTRACT FROM AUTHOR]

Drought may impact plant–soil biotic interactions in ways that modify aboveground herbivore performance, but the outcomes of such biotic interactions under future climate are not yet clear. We performed a growth chamber experiment to assess how long‐term, drought‐driven changes in belowground communities influence plant growth and herbivore performance using a plant–soil feedback experimental framework. We focussed on two common pasture legumes—lucerne, Medicago sativa L., and white clover, Trifolium repens L. (both Fabaceae)—and foliar herbivores—cotton bollworm, Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae), and two‐spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae). Soil was collected from a field facility where rainfall had been manipulated for 6 years, focussing on treatments representing ambient rainfall and prolonged drought (50% reduction relative to ambient), to consider the effects of biological legacies mediated by the prolonged drought. All soils were sterilized and re‐inoculated to establish the respective home (i.e. where a given plant is cultivated in its own soil) and away (i.e. where a given plant is cultivated in another species' soil) treatments in addition to a sterile control. We found that the relative growth rate (RGR) and relative consumption of larvae were significantly lower on lucerne grown in soil with ambient rainfall legacies conditioned by white clover. Conversely, the RGR of insect larvae was lower on white clover grown in soil with prolonged drought legacies conditioned by lucerne. Two‐spotted spider mite populations and area damage (mm2) were significantly reduced on white clover grown in lucerne‐conditioned soil in drought legacies. The higher number of nodules found on white clover in lucerne‐conditioned soil suggests that root–rhizobia associations may have reduced foliar herbivore performance. Our study provides evidence that foliar herbivores are affected by plant–soil biotic interactions and that prolonged drought may influence aboveground–belowground linkages with potential broader ecosystem impacts. [ABSTRACT FROM AUTHOR]

Background: Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks. Methods: The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions. Results: A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk. Conclusion: The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau has exhibited a discernible trend towards increased precipitation over the past 50 years. However, previous research predominantly focused on thermal stability of permafrost without the consideration of water flux boundary conditions, and therefore ignored the dynamics of water migration and its impacts on the embankment stability. To bridge this gap, a novel water-heat transfer model incorporating rainfall and water migration was developed and subsequently validated using monitored data. Comparative analyses were then conducted across three distinct rainfall intensities to investigate the variations in the moisture and temperature of superficial soil. Results indicate rainfall events exert a notable cooling effect during warm seasons but have little influence on cooling during cold seasons. By increasing the latent heat of evaporation, sensible heat and reducing the soil heat flux, rainfall results in embankment cooling, and the cooling effect correlates positively with rainfall intensity. Disregarding the water flux boundary conditions will overestimate the embankment temperature and underestimate the variation of water content, especially at the superficial soil. Rainfall results in a decline in water vapor flux and an increase in liquid water flux, which facilitates rapid downward transport and accumulation of liquid water. Despite the increased convective heat transfer of liquid water, the decrease in heat conduction, latent heat of evaporation and convective heat transfer of water vapor in the embankment is more pronounced. Rainfall changes the stability of permafrost embankment mainly by adjusting the energy distribution, which delays temperature increases in the underlying permafrost. When predicting the stability of permafrost, it is recommended to incorporate the water flux boundary conditions. [ABSTRACT FROM AUTHOR]

Background: For decades, dengue has posed a significant threat as a viral infectious disease, affecting numerous human lives globally, particularly in tropical regions, yet no cure has been discovered. The genetic trait of vector competence in Aedes mosquitoes, which facilitates dengue transmission, is difficult to measure and highly sensitive to environmental changes. Methods: In this study we attempt, for the first time in a non-laboratory setting, to quantify the vector competence of Aedes mosquitoes assuming its homogeneity across both species; aegypti and albopictus and across the four Dengue serotypes. Estimating vector competence in relation to varying rainfall patterns was focused in this study to showcase the changes in this vector trait with respect to environmental variables. We quantify it using an existing mathematical model originally developed for malaria in a Bayesian inferencing setup. We conducted this study in the Colombo district of Sri Lanka where the highest number of human populations are threatened with dengue. Colombo district experiences continuous favorable temperature and humidity levels throughout the year creating ideal conditions for Aedes mosquitoes to thrive and transmit the Dengue disease. Therefore we only used the highly variable and seasonal rainfall as the primary environmental variable as it significantly influences the number of breeding sites and thereby impacting the population dynamics of Aedes. Results: Our research successfully deduced vector competence values for the four identified seasons based on Monsoon rainfalls experienced in Colombo within a year. We used dengue data from 2009 - 2022 to infer the estimates. These estimated values have been corroborated through experimental studies documented in the literature, thereby validating the malaria model to estimate vector competence for dengue disease. Conclusion: Our research findings conclude that environmental conditions can amplify vector competence within specific seasons, categorized by their environmental attributes. Additionally, the deduced vector competence offers compelling evidence that it impacts disease transmission, irrespective of geographical location, climate, or environmental factors. [ABSTRACT FROM AUTHOR]

Mediterranean‐climate shrublands are key biodiversity hotspots and carbon storage pools, but are increasingly threatened by climate change, non‐native species, and altered fire regimes. Fires are important to historic shrubland disturbance cycles but can also promote non‐native plants, which may limit post‐fire native shrub recovery. Increasing drought with climate change could also reduce post‐fire shrub regeneration. I developed a stochastic, individual‐based demographic model (IBM) for the native shrub Artemisia californica, parameterized from an experimental removal of non‐native annuals after a 2013 fire in southern California. The IBM simulated A. californica recovery for 7 years after fire under different rainfall conditions (drought or pre‐drought) and non‐native removal strategies (from no years to all 7 years). Drought lowered A. californica canopy volume 7 years after fire by 90% or more. Rainfall in the second year after fire, when most A. californica germination occurred, had particularly strong effects on final canopy cover. Non‐native removal in all 7 years increased canopy volume by three times under drought conditions and 3.5 times under pre‐drought conditions. Targeting non‐native removal in the first 2 years proved nearly as effective, achieving from 88% (drought) to 95% (pre‐drought) the benefits of removal in all 7 years. In sum, low rainfall may be the most important limitation on post‐fire shrub recovery, but removal of non‐natives in years of pulsed shrub recruitment can be an effective restoration strategy even under drought conditions. More generally, this study illustrates how demographic models can help optimize the targeting of scarce management and restoration resources. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSIONS ‘Xynisteri’ is considered as the reference white grape cultivar in Cyprus with remarkable adaptation to adverse edaphoclimatic conditions and appreciable oenological properties that renders it as an appropriate cultivar for studies within a global context due to climate change. To this aim, two distinct non‐irrigated plots with different climatic conditions, soil properties and levels of rainfall were selected; Koilani [KO, altitude 800 m, 76% calcium carbonate (CaCO3) content, pH 7.97, average temperature: 16.5 °C, rainfall: 229 mm] and Kyperounda (KY, altitude 1200 m, CaCO3‐free soil, pH 6.47, average temperature: 14.9 °C, rainfall: 658 mm). An array of physiological, biochemical and qualitative indices during successive developmental stages (BBCH 75–89) were determined. During the advanced on‐vine developmental stages (BBCH 85–89), the aromatic profile of grapes was assessed with the employment of gas chromatography–mass spectrometry (GC–MS). Such analysis was complemented with non‐destructive chemometric analyses.Berry ripening process substantially differed on the examined plots; BBCH 89 stage reached at 267 and 303 Julian days for KO and KY, respectively. Results indicated that berry weight, soluble solids content (SSC) and α‐amino nitrogen were higher in KO than in KY, with exception made for ammonium nitrogen content. A total of 75 compounds, including aliphatic alcohols, benzenic compounds, phenols, vanillins, monoterpenes and C13‐norisoprenoids were identified and quantified. The variations of mesoclimatic conditions affected the volatile organic compound (VOC) profiles at the fully‐ripe stage, showing a considerable rise in glycosylated aroma compounds, especially monoterpenes and benzenic compounds. In particular, the higher amount of glycosylated aroma compounds were obtained in KY berries up to mid‐ripe, whereas KO showed higher glycosylated aroma compounds at fully‐ripe stage. Results reported herein indicate that aroma profile of ‘Xynisteri’ grapes varied substantially in the examined terroirs. Interestingly, the limited rainfall in KΟ non‐irrigated vine did not compromise qualitative and aromatic properties of berries.The present study aimed at dissecting the impact of terroir on bush‐trained, non‐irrigated grapevines of a cultivar appropriate for extreme climate change scenarios. The volatilome fingerprint was highly variable among the examined plots; such results can be further exploited at vinification level towards production of single vineyard premium end products. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Societal Impact Statement Summary Long‐term phenology data is crucial to elucidate the effects of climate change on plants, but such efforts are lacking in the tropics. Historical records at the Singapore Botanic Gardens and ongoing phenological monitoring have allowed us to study leafing behaviour and its association with changing rainfall patterns over nine decades in equatorial Singapore. We found that the leafing behaviour of trees in our tropical site changed between 1927 and 2022. This study was made possible by assiduous past and present record‐keeping, and demonstrates the potential of botanical gardens as sentinels of environmental change, which is especially important in the tropics. Understanding tropical phenology and the potential impacts of a changing climate requires long‐term monitoring but such efforts are rare in the tropics. We took advantage of the availability of historical phenological and weather records at the Singapore Botanic Gardens and ongoing phenological monitoring to ask whether leafing behaviour has changed over an exceptionally long period of about nine decades, and whether these changes are associated with changing rainfall patterns in supposedly aseasonal Singapore. Records of leaf flush intervals from 1927 to 1939 in the Singapore Botanic Gardens were compared against leaf flush intervals calculated from present‐day phenological monitoring conducted since 2016. Daily rainfall was modelled as a predictor of present‐day leaf flushing events using moving‐window analyses. Leaf flush intervals have shortened considerably for most of the species that were monitored in the two periods. Mean daily rainfall over a window of 1 to 4 months before was negatively associated with probability of leaf flushing for five species and positively associated with four out of a total of 23 species analysed. Long‐term rainfall records from 1929 to 2022 show that February has become drier and May has become wetter since the 1960s. The results consistently support that more distinctive dry and wet periods today compared to nine decades ago have resulted in more frequent leaf flushing, which may have implications for plant‐herbivore interactions, nutrient cycling and consequently plant health and ecosystem resilience. We also demonstrate the potential and importance of supporting long‐term research in botanical gardens in the tropics. [ABSTRACT FROM AUTHOR]

Woody vegetation restoration projects are an important feature of landscape function in Indonesian karst savannas. Understanding the relationship between available moisture and vegetation condition can assist with the planning and implementation of revegetation efforts. Working at vegetation restoration sites in East Nusa Tenggara, Indonesia, we applied a windowed cross-correlation method to mean values of NDVI to examine the lag between moisture input and NDVI response for both rainfall and soil moisture between 1999 and 2018. To test for increasing or decreasing trends in NDVI and rainfall time series, we undertook Mann-Kendall trend analyses. We identified increasing trends in Landsat 7 NDVI at two of four restoration sites, with annual increases in NDVI of 2.7 and 3.74 × 10-4 respectively. We found that rainfall dependent sites had significant Pearson's correlations with NDVI ranging from 0.52 to 0.71, while NDVI was not correlated with rainfall at shallow groundwater sites. There was a clear negative effect of the very dry period on all sites, and this was less pronounced at shallow groundwater sites. Wet years resulted in a positive response to NDVI across all sites, while the response was lower in very wet years with annual rainfall above 1,200 mm. We found that between 2 and 4 months of antecedent rainfall gave the highest correlation with NDVI, while for soil moisture the closest relationship was found with no lag and 1 month lag. Through this study, we demonstrated the applicability of using NDVI, rainfall, and soil moisture trend analyses to identify groundwater-dependent vegetation patches and monitor the effectiveness of vegetation restoration. [ABSTRACT FROM AUTHOR]

In breeding programs, stress memory in plants can develop drought stress tolerance. Memory stress, as an approach, can keep stress data by activating tolerance mechanisms. This research was conducted to evaluate some physiologically effective mechanisms in inducing memory drought stress in the seeds that were exposed to water stress three times in four treatments including rainfed, 33%, 66%, and 100% of field capacity (FC). After the production of the seeds, the third-generation seeds were placed under different irrigation treatments, seed and seedling traits, starch to carbohydrate ratio in seed, protein concentration and glutathione reductase were investigatied in a factorial format based on a randomized complete block design with three replications. Results showed that percentage of changes from the lowest to the highest value for traits including seed vigor, seed endosperm weight, seed coat weight, accelerated aging, cold test, seedling biomass and seedling length were 25, 37, 65, 65, 55, 77, 55, 65 and 79, respectively and germination uniformity was 3.9 times higher than the lowest amount. According to the deterioration percentage, seed vigor and the percentage of seed germination in cold test data, it can be reported that seed production by 100% FC was not appropriate for rainfed plots. However, considering the the appropriate results in the percentage of germination for a cold test, germination uniformity percentage, and the lowest accelerated aging seeds, seed production under the rainfed conditions with 33% FC watering can be recommended. In-silico analysis was coducted on Glutathione reductase (GR) enzymes in Gossypium hirsutum. It is clear that GR has a Redox-active site and NADPH binding, and it interacts with Glutathione S transferase (GST). So, memory drought stress through inducing physiological drought tolerance mechanisms such as starch-to-carbohydrate ratio and GR can determine the suitable pattern for seed production for rainfed and low rainfall regions in a breeding program. Our study thus illustrated that seed reprduction under 33% FC equipped cotton with the tolerance against under draught stress from the seedling stage. This process is done through activating glutathione reductase and balancing the ratio of starch to carbohydrates concentration. [ABSTRACT FROM AUTHOR]

Different geographical zones have regional heterogeneity in underlying earth surface structure and microclimate which result in different evolution trends and their response to climate change varies in extreme rainfalls in these zones. In the Guizhou province of China, there are complex landforms, which lead to spatial redistribution of rainfall, frequent extreme rainfall, and disasters high risk of geologic disasters. Research on extreme climate in Guizhou mostly paid attention to its spatio-temporal characteristics and modeling, but lack of analysis on its characteristics of extreme rainfall variability and response to temperature changes under different subsurface conditions. This study investigated the characteristics of the extreme rainfall spatiotemporal and recurrence periods in Guizhou province and discussed the relationship between the response of extreme rainfall to temperature change. Daily rainfall data from 1990 to 2020 and 2021–2100 at 31 meteorological observation stations throughout the province were collected to calculate extreme precipitation. This research had the following results. (1) Both historical and future periods show an upward trend in extreme rainfall in Guizhou province, with a spatial distribution pattern of "high in the south and low in the north, high in the east and low in the west" and "high in the southeast and low in the northwest", respectively; the spatial distribution of extreme rainfall under each recurrence period is consistent with the non-recurrence period. (2) Both historical and future periods show an upward trend in temperature in Guizhou province, with a spatial distribution consistent with that of the extreme rainfall in the corresponding period. (3) The change in extreme rainfall intensity with increasing temperature is almost always greater than the C–C rate for different periods and underlying earth surface structure; Extreme rainfall has a Hook response structure to temperature change, and the climate response structure shifts to the right with climate warming. The results of the study can provide a basis for decision-making on regional disaster prevention and mitigation in the context of temperature change. [ABSTRACT FROM AUTHOR]

The issue of rainfall-induced slope failure has attracted more attention from geotechnical engineers as a consequence of global warming. Current cumulative waste disposal has generated scientific interest in the utilization of waste materials in geotechnical design for climate change adaptation measures. Taking into consideration the effect of slope height and angle, steel slag—a waste product derived from the production of steel—was investigated as a slope cover against rainfall. To assess the stability of the slope and the infiltration of water into the soil, numerical analyses were conducted using both SEEP/W and SLOPE/W software in conjunction with rainfall conditions. Based on the findings, it can be concluded that increasing the slope's elevation and inclination will have an adverse effect on its safety factor. Steel slag can nevertheless be utilized for minimizing rainwater infiltration into the slope, as indicated by the pore-water pressure variations and graphs of the safety factor versus time. For a 20-m slope height, steel slag slopes have demonstrated a lower factor of safety difference in comparison to the initial slope without remediation. Regardless of slope angle and slope height, the safety factor reduces marginally during rainfall. [ABSTRACT FROM AUTHOR]

Freshwater mussels preserve valuable information about hydrology, climate, and population dynamics, but developing seasonal chronologies can be problematic. Using clumped isotope thermometry, we produced high-resolution reconstructions of modern and historic (~ 1900) temperatures and δ18Owater from mussel shells collected from an impounded river, the Brazos in Texas, before and after damming. We also performed high-resolution growth band analyses to investigate relationships between mussel growth rate, rainfall, and seasonal temperature. Reconstructed δ18Owater and temperature vary little between the modern (3R5) and historic shell (H3R). However, a positive relationship between reconstructed δ18Owater and growth rate in H3R indicates that aside from diminished growth in winter, precipitation and flow rate are the strongest controls on mussel growth in both modern and pre-dam times. Overall, our results demonstrate (1) the impact, both positive and negative, of environmental factors such as flow alteration and temperature on mussel growth and (2) the potential for clumped isotopes in freshwater mussels as a paleohydrology and paleoclimate proxies in terrestrial environments. [ABSTRACT FROM AUTHOR]

The rapid temporal evolution of convective rainfall poses a challenge for rainfall nowcasting models. With the growing potential of machine learning models for precipitation nowcasting to produce realistic-looking nowcasts for long lead times, it is important to investigate whether the nowcasts also produce realistic development for convective rainfall. Common verification metrics traditionally used to validate nowcasting models are often dominated by large-scale stratiform rainfall, and averaging the metrics across entire precipitation fields obscures how accurately the models replicate individual convective cells, which makes it difficult to distinguish the model skill for the growth and decay of convective rainfall. In this study, we present a convective cell tracking-based framework to investigate how accurately nowcasting models reproduce the development of convective rainfall. The framework consists of first identifying and tracking the convective cells in the input observation rainfall fields, and then identifying and tracking the cells separately in the target observations and the nowcast rainfall fields by continuing the cell tracks identified in the observations. Features describing the cells and cell tracks, such as the cell volume rain rate and area, are then extracted. In addition to the errors in these feature values, the models' skill in reproducing the existence of convective cells is estimated by calculating several contingency-table metrics, such as the Critical Success Index. The results allow the analysis of how accurately the models reproduce the growth and decay of convective rainfall and quantify the differences between the models, for example, due to differences in how the models smooth the nowcasts, i.e., blurring. The framework also allows differentiation of the results based on the initial conditions of the cell tracks, demonstrated here by separating the tracks into decaying or growing cell tracks based on the cell status when the nowcast is created. The framework is demonstrated using four open-source advection-based models: the advection nowcast, S-PROG, and LINDA implemented in the pysteps library, and L-CNN, with data from the Swiss radar network. The results indicate that the L-CNN model reproduced the existence of convective cells best among the models and had smaller errors in the cell volume rain rate than LINDA and S-PROG. LINDA had the smallest underestimation in the cell mean rain rate, whereas S-PROG significantly overestimated the cell volume rain rate and area because of blurring. [ABSTRACT FROM AUTHOR]

Because of their high sensitivity to hydrometeors and their high vertical resolutions, space-borne radar observations are emerging as an undeniable asset for Numerical Weather Prediction (NWP) applications. The EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) NWP SAF (Satellite Application Facility) released an active sensor module within version 13 of the RTTOV (Radiative Transfer for TOVS) software with the goal of simulating both active and passive microwave instruments within a single framework using the same radiative transfer assumptions. This study provides an in-depth description of the radar simulator available within this software. In addition, this study proposes a revised version of the existing melting layer parametrization scheme of Bauer (2001) within the RTTOV-SCATT v13.1 model to provide a better fit to observations below the freezing level. Simulations are performed with and without melting layer schemes for the Dual precipitation radar (DPR) instrument onboard GPM using the ARPEGE (Action de Recherche Petite Echelle Grande Echelle) global NWP model running operationally at Météo-France for two different one-month periods (June, 2020 and January, 2021). Results for a case study over the Atlantic ocean show that the revised melting scheme produces more realistic simulations as compared to the default scheme both at Ku (13.5 GHz) and Ka (35.5 GHz) frequencies and these simulations are much closer to observations. A statistical assessment using more samples show significant improvement of the first-guess departure statistics with the revised scheme compared to the existing melting scheme. As a step further, this study showcases the use of melting layer simulations for the classification of precipitation (stratiform, convective and transition) using the Dual Frequency Ratio algorithm (DFR). The classification results also reveal a significant overestimation of the rain reflectivities in all hemispheres, which can either be due to a tendency of the ARPEGE model to produce a too large amount of convective precipitation, or to a mis-representation of the convective precipitation fraction within the forward operator. [ABSTRACT FROM AUTHOR]

The traditional crop calendar for yam (Dioscorea spp.) in South-Kivu, eastern Democratic Republic of Congo (DRC), is becoming increasingly inadequate given the significant climatic variability observed over the last three decades. This study aimed at: (i) assessing trends in weather data across time and space to ascertain climate change, and (ii) optimizing the yam crop calendar for various South-Kivu agro-ecological zones (AEZs) to adapt to the changing climate. The 1990–2022 weather data series were downloaded from the NASA-MERRA platform, bias correction was carried out using local weather stations' records, and analyses were performed using RClimDex 1.9. Local knowledge and CROPWAT 8.0 were used to define planting dates for yam in different AEZs. Results showed the existence of four AEZs in the South-Kivu province, with contrasting altitudes, temperatures, and rainfall patterns. Climate change is real in all these South-Kivu's AEZs, resulting either in rainfall deficits in some areas, or extreme rainfall events in others, with significant temperature increases across all AEZs. Suitable yam planting dates varied with AEZs, September 15th and 20th were recommended for the AEZ 2 while October 15th was optimal for AEZ 1, AEZ 3, and AEZ 4. However, none of the planting date scenarios could meet the yam water requirements in AEZ1, AEZ3, and AEZ4, since the effective rainfall (Pmm) was always inferior to the plant water demand (ETc), meaning that soil water conservation practices are needed for optimum plant growth and yield in these AEZs. This study does not recommend planting yam during the short rainy season owing to prolonged droughts coinciding with critical growth phases of yam, unless supplemental irrigation is envisaged. This study provided insights on the nature of climate change across the past three decades and suggested a yam crop calendar that suits the changing climate of eastern DRC. [ABSTRACT FROM AUTHOR]

Nutrient enrichment impacts grassland plant diversity such as species richness, functional trait composition and diversity, but whether and how these changes affect ecosystem stability in the face of increasing climate extremes remains largely unknown. We quantified the direct and diversity‐mediated effects of nutrient addition (by nitrogen, phosphorus, and potassium) on the stability of above‐ground biomass production in 10 long‐term grassland experimental sites. We measured five facets of stability as the temporal invariability, resistance during and recovery after extreme dry and wet growing seasons. Leaf traits (leaf carbon, nitrogen, phosphorus, potassium, and specific leaf area) were measured under ambient and nutrient addition conditions in the field and were used to construct the leaf economic spectrum (LES). We calculated functional trait composition and diversity of LES and of single leaf traits. We quantified the contribution of intraspecific trait shifts and species replacement to change in functional trait composition as responses to nutrient addition and its implications for ecosystem stability. Nutrient addition decreased functional trait diversity and drove grassland communities to the faster end of the LES primarily through intraspecific trait shifts, suggesting that intraspecific trait shifts should be included for accurately predicting ecosystem stability. Moreover, the change in functional trait diversity of the LES in turn influenced different facets of stability. That said, these diversity‐mediated effects were overall weak and/or overwhelmed by the direct effects of nutrient addition on stability. As a result, nutrient addition did not strongly impact any of the stability facets. These results were generally consistent using individual leaf traits but the dominant pathways differed. Importantly, major influencing pathways differed using average trait values extracted from global trait databases (e.g. TRY). Synthesis. Investigating changes in multiple facets of plant diversity and their impacts on multidimensional stability under global changes such as nutrient enrichment can improve our understanding of the processes and mechanisms maintaining ecosystem stability. [ABSTRACT FROM AUTHOR]

For the expansion soil slope in the JiangHuai area before the disposal of the neglect of expansion of the weak defects and slope disposal after the poor long-term stability of the current situation. This study investigates the ability of ecological slope protection technology to cope with the destabilizing geohazard of expansive soil slopes. Analyzing the collapse reasons of weak expansive soil slopes in the JiangHuai region based on the reinforcement project of expansive soil slopes along highways in the JiangHuai region, combined with actual engineering research, a "storage-resistance" water regulation ecological prevention and control technology is proposed. The feasibility and sustainability of the ecological slope protection technology is discussed in terms of its principles and influencing factors, and the protection effect is verified by combining numerical simulation and field test methods. Research findings suggest that the "storage-resistance" technology effectively prevents rainwater infiltration, particularly under light rain conditions, with continuous blocking capability. Under rainstorm conditions, it can prevent infiltration for about 4 h, significantly enhancing slope stability. Slope rate variations show no significant impact on reinforced slope stability, with maximum deformation occurring at the slope's foot after rainfall. Reinforcement plans should prioritize strengthening support at the slope's base. Proper selection and optimization of technical parameters can lead to more economical and sustainable solutions while extending protection time. Field trials confirm the suitability of the "storage and blocking" water regulation ecological control technology for the JiangHuai region, particularly where light rain prevails. These findings suggest that ecological control techniques for expansive soil slopes can effectively regulate slope moisture changes and reduce the geohazard risk of expansive soil slope instability. [ABSTRACT FROM AUTHOR]

Escalating demands on urban water sources present a formidable challenge to practitioners in environmental and social change to more effectively promote water conservation strategies. This study was based on pilot project learnings and aimed to determine whether a sign displaying weekly local rainfall data and turfgrass water needs could effectively influence a Florida (USA) community's water conservation. Three core data sources/methods were used: pre- and post-surveys, water utility data compared between treatment and control communities, and qualitative post-intervention feedback. The findings illustrate nuanced outcomes including the adoption of the rainfall sign as a central information source and some knowledge increases, contrasted with decreased engagement in the target conservation practice, no significant changes in water usage, and unfavorable perceptions of project approaches. Successes and failures of this pilot can guide professionals leading future residential conservation initiatives, which should better engage the target community and incorporate their ideas and needs into the project design. [ABSTRACT FROM AUTHOR]

Seed purchasing enables farmers to respond to adverse events that may cause chronic and temporary seed insecurity by allowing them to exploit opportunities associated with accessing new seeds. However, as with other inputs, seed purchasing is complicated by pervasive market imperfections and climate risk common in Sub-Sahara Africa. This study uses balanced household panel data for Malawi (2010–2018) and Ethiopia (2012–2016) and applies dynamic random effects Probit and Tobit models to assess how seed purchase decisions are affected by earlier participation in the market, lagged rainfall shocks, and historical climate variables. Our findings show that there are nonlinear effects of lagged seed purchase decisions on subsequent decisions with strong initial effects (weakening over time). For instance, initial maize seed purchase decisions are associated with about 11 and 22% higher probability of purchase and 1 and 2% higher shares of seed volumes purchased in later rounds in Malawi and Ethiopia, respectively. Seed purchase decisions also respond to climate variability and shocks. For instance, lagged drought shocks enhance subsequent maize purchase decisions in both countries. Historical average rainfall and temperature enhance maize seed purchase decisions in both countries. Overall, results point to state dependency on the demand side of the seed market, leading to selective access to purchased seeds. Also, seed purchase in smallholder farming is a liquidity and risk-dependent input choice. Policy efforts need to continue targeting reducing transaction costs and other barriers to entry into seed markets to enhance access to off-farm seed and support adaptation to rainfall shocks. [ABSTRACT FROM AUTHOR]

Agriculture in Ethiopia is highly dictated by spatial patterns and temporal distributions of climate variables. The analysis of these climate variables is crucial for understanding the impacts on agricultural productivity. This study aimed to analyze spatiotemporal variability and trends of intra-seasonal rainfall and temperature using site-specific daily data from the Ethiopian Meteorology Institute (1992–2021). Standardized methods explore variability, while Mann–Kendall tests identify trends, using the Modified version for data with autocorrelation. Inverse Distance Weighted interpolation was employed for spatial analysis of rainfall, length of growing season, and temperature. The findings identified that Kiremt dominated the mono-modal rainfall pattern, contributing 72%-86% of total annual rainfall. The study found that the season typically begins early on June 13 in Adiszemen, and July 6 in Arbgebiya and ends between October 6 and October 26. The duration of the season varied across locations, averaging 95 days at Ebenat and 148 days at Adiszemen. The seasonal rainfall anomaly index shows identical patterns between ENSO episodes and seasonal rainfall. These findings inform decision-making and adaptation strategies for ENSO-driven rainfall variability. Temperatures showed predictable seasonal patterns, but have significantly increased over time, with maximum and minimum temperatures rising 0.014 °C to 0.421 °C and 0.027 °C to 0.485 °C per year respectively. This warming trend is negatively impacting water, crops, and livestock, requiring adaptation measures to build regional resilience. This study underscores the critical impact of climate variability on agriculture in the study area. The findings reveal shifts in rainfall patterns and temperature trends, providing essential insights for adapting agricultural practices. [ABSTRACT FROM AUTHOR]

This study investigates the germination requirements of 12 plant species native to the Argyle region of the east Kimberley, a biodiverse monsoonal tropical region characterized by high temperatures, high evaporation, and episodic seasonal rainfall. The research involved quality assessment of mature seeds, followed by dormancy alleviation and laboratory‐based germination to determine the responses of seeds to a range of temperatures (5–40°C) in terms of germination speed (T10), mean germination time, and maximum germination proportion. Data were then modeled to calculate the optimal temperature to support germination for each species. The results showed that germination rapidly commences in response to a wide range of temperatures typical of the wet season (November–February) in the east Kimberley, though germination for most species was still high (>50%) after exposure to temperatures as low as 15°C. Mean optimal temperature for germination across all species was 25.8 ± 1.5°C, with minimal variation between most species, the exception being Dodonaea physocarpa, which preferred cooler temperatures (Topt = 14.0°C). The speed of germination was also rapid (T10 = 1–3 days) across all species at the optimal germination temperature. The findings suggest that temperature is not a limiting factor for germination in this region and that the onset and intensity of the wet season are the most significant factors determining successful germination, emergence, and seedling establishment. The study underscores the importance of species‐specific understanding of environmental temperatures required for seed germination in seed‐based restoration efforts and informs the planning of direct seeding works, thus enhancing restoration outcomes. [ABSTRACT FROM AUTHOR]

In recent years, the safety issues of high-speed railways have remained severe. The intrusion of personnel or obstacles into the perimeter has often occurred in the past, causing derailment or parking, especially in the case of bad weather such as fog, haze, rain, etc. According to previous research, it is difficult for a single sensor to meet the application needs of all scenario, all weather, and all time domains. Due to the complementary advantages of multi-sensor data such as images and point clouds, multi-sensor fusion detection technology for high-speed railway perimeter intrusion is becoming a research hotspot. To the best of our knowledge, there has been no review of research on multi-sensor fusion detection technology for high-speed railway perimeter intrusion. To make up for this deficiency and stimulate future research, this article first analyzes the situation of high-speed railway technical defense measures and summarizes the research status of single sensor detection. Secondly, based on the analysis of typical intrusion scenarios in high-speed railways, we introduce the research status of multi-sensor data fusion detection algorithms and data. Then, we discuss risk assessment of railway safety. Finally, the trends and challenges of multi-sensor fusion detection algorithms in the railway field are discussed. This provides effective theoretical support and technical guidance for high-speed rail perimeter intrusion monitoring. [ABSTRACT FROM AUTHOR]

Sudden mountain landslides can pose substantial threats to human lives and property. On 4 June 2023, a landslide occurred in Jinkouhe District, Leshan City, Sichuan Province, resulting in 19 deaths and 5 injuries. This study, drawing on field investigations, geological data, and historical imagery, elucidates the characteristics and causes of the landslide and conducts a reverse analysis of the landslide movement process using Massflow V2.8 numerical simulation software. The results indicate that rainfall and human engineering activities are key factors that triggered this landslide. Numerical simulation shows that the landslide stopped after 60 s of sliding, with a movement distance of approximately 286 m, a maximum sliding speed of 17 m/s, and a maximum accumulation thickness of 7 m, eventually forming a loose landslide debris accumulation of approximately 5.25 × 103 m3. The findings of this study provide significant reference value for research on landslide movement characteristics and disaster prevention and mitigation in mountainous areas. [ABSTRACT FROM AUTHOR]

Transportation resilience, as a component of city sustainability, plays a crucial role in the daily management and emergency response of urban road systems. With coastal cities becoming increasingly vulnerable to typhoons, rainstorms, and other disasters, it is essential to assess the resilience of urban road transportation in a refined and differentiated approach. Existing resilience assessment methods often overlook significant biases, neglecting the dynamic response of road traffic and non-stationary characteristics of traffic systems. To address these limitations, we develop a quantitative resilience assessment method for urban road transportation during rainfall that is based on the improved Resilience Triangle. The method is applied to DiDi urban traffic speed and meteorological data of Shenzhen, China, from April to September 2018, with a focus on Typhoon Mangkhut as an extreme weather case. By analyzing transportation resilience variations across road densities, road hierarchies, and rainfall scenarios, we found that road densities and rainfall intensities explain resilience variations better than road hierarchies. Specifically, as accumulative precipitation exceeds 100 mm, a substantial surge in loss of performance is observed. Typhoon rainfalls result in a greater loss in urban road traffic compared to general rainfalls. The results offer valuable insights for urban road planning, traffic emergency management, and transportation resilience construction in the face of increasingly severe weather challenges. [ABSTRACT FROM AUTHOR]