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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]

Climate projections like UKCP18 predict that the UK will move towards a wetter and warmer climate with a consequent increased risk from surface water flooding (SWF). SWF is typically caused by localized convective rainfall, which is difficult to predict and requires high spatial and temporal resolution observations. The likelihood of SWF is also affected by the microtopographic configuration near buildings and the presence of resilience and resistance measures. To date, most research on SWF has focused on modelling and prediction, but these models have been limited to 2 m resolution for England to avoid excessive computational burdens. The lead time for predicting convective rainfall responsible for SWF can be as little as 30 minutes for a 1 km x 1 km part of the storm. Therefore, it is useful to identify the locations most vulnerable to SWF based on past rainfall data and microtopography to provide better risk management measures for properties. In this study, we present a framework that uses long-term gridded rainfall data to quantify SWF hazard at the 1 km x 1 km pixel level, thereby identifying localized areas vulnerable to SWF. We also use high-resolution photographic (10 cm) and LiDAR (25 cm) DEMs, as well as a property flood resistance and resilience (PFR) database, to quantify SWF exposure at property level. By adopting this methodology, locations and properties vulnerable to SWF can be identified, and appropriate SWF management strategies can be developed, such as installing PFR features for the properties at highest risk from SWF. [ABSTRACT FROM AUTHOR]

Late maturity α‐amylase (LMA) is known to reduce falling number (FN) in wheat (Triticum aestivum L.), similar to the effect of preharvest sprouting (PHS) and frost, which can result in grain parcels testing below trading thresholds. Hence, Grains Australia mandates that new Australian wheat cultivars must be at a low risk of LMA expression to receive a milling classification. The multi‐environment trial dataset contained 34 environments not affected by PHS or frost and was analyzed using a five‐factor analytic linear mixed model. Factor 1 accounted for 71.4% of the genetic variation in FN, factor 2 accounted for 8.8%, factor 3 accounted for 5.7%, factor 4 accounted for 3.5%, and factor 5 accounted for 3.1%. The interaction class (iClass) summary method was used to assist in the characterization of crossover genotype‐by‐environment interaction (G × E). Poorer FN performance was best observed in the “ppp” iClass, which indicated a minor, but significant, response to crossover G × E. The environment loadings for factor 1 were associated with mild ripening conditions, characterized by fewer days above 28°C, increased rainfall, and increased variation in daily maximum temperature and relative humidity. Factors 2 and 3 were associated with “cool shock” conditions, where the maximum temperature for 1 day was above 24°C followed by at least three consecutive days below 18°C during the grain fill period. This study provides further evidence of the crossover G × E present for FN associated with LMA, poor FN performance in genotypes that express higher levels of LMA, and the environmental conditions that contribute to LMA expression. [ABSTRACT FROM AUTHOR]

This editorial discusses the importance of preparing communities, cities, and nations for extreme flooding events. It highlights the need to adapt infrastructure to be more resilient, while acknowledging the tradeoff between efficiency and resilience. The editorial provides examples of infrastructure failures during past flooding events and discusses the challenges of adapting critical infrastructure systems to a changing climate. The document includes articles that explore different approaches to managing flood risk, the use of satellite data for monitoring and warning systems, and the vulnerability of industrial process facilities to flood damage. The authors emphasize the need for comprehensive solutions that consider uncertainties and involve collaboration between academia and the public and private sectors. The text also mentions the challenges of developing protective infrastructures, retrofitting existing infrastructures, and considering implications throughout the lifecycle. [Extracted from the article]

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]

Nutrient addition, particularly nitrogen, often increases plant aboveground biomass but causes species loss. Asymmetric competition for light is frequently assumed to explain the biomass-driven species loss. However, it remains unclear whether other factors such as water can also play a role. Increased aboveground leaf area following nitrogen addition and warming may increase transpiration and cause water limitation, leading to a decline in diversity. To test this, we conducted field measurements in a grassland community exposed to nitrogen and water addition, and warming. We found that warming and/or nitrogen addition significantly increased aboveground biomass but reduced species richness. Water addition prevented species loss in either nitrogen-enriched or warmed treatments, while it partially mitigated species loss in the treatment exposed to increases in both temperature and nitrogen. These findings thus strongly suggest that water limitation can be an important driver of species loss as biomass increases after nitrogen addition and warming when soil moisture is limiting. This result is further supported by a meta-analysis of published studies across grasslands worldwide. Our study indicates that loss of grassland species richness in the future may be greatest under a scenario of increasing temperature and nitrogen deposition, but decreasing precipitation. [ABSTRACT FROM AUTHOR]

Forecasting the weather in an area characterized by erratic weather patterns and unpredictable climate change is a challenging endeavour. The weather is classified as a non-linear system since it is influenced by various factors that contribute to climate change, such as humidity, average temperature, sea level pressure, and rainfall. A reliable forecasting system is crucial in several industries, including transportation, agriculture, tourism, & development. This study showcases the effectiveness of data mining, meteorological analysis, and machine learning techniques such as RNN-LSTM, TensorFlow Decision Forest (TFDF), and model stacking (including ElasticNet, GradientBoost, KRR, and Lasso) in improving the precision and dependability of weather forecasting. The stacking model strategy entails aggregating multiple base models into a meta-model to address issues of overfitting and underfitting, hence improving the accuracy of the prediction model. To carry out the study, a comprehensive 60-year meteorological record from Bangladesh was gathered, encompassing data on rainfall, humidity, average temperature, and sea level pressure. The results of this study suggest that the stacking average model outperforms the TFDF and RNN-LSTM models in predicting average temperature. The stacking average model achieves an RMSLE of 1.3002, which is a 10.906% improvement compared to the TFDF model. It is worth noting that the TFDF model had previously outperformed the RNN-LSTM model. The performance of the individual stacking model is not as impressive as that of the average model, with the validation results being better in TFDF. [ABSTRACT FROM AUTHOR]

Under global warming, a notable intensification of precipitation extremes has posed significant threats to both natural ecosystems and human societies. However, there is still a lack of systematic studies on the variability of hourly precipitation extremes and a comparison with daily precipitation extremes. This study introduces a set of indices for hourly and daily precipitation extremes to systematically examine the changes across eastern China during the summers from 1980 to 2020. The results show that southern China is the sensitive regions with a notable large value of climatology in frequency and intensity for both of hourly and daily precipitation extremes. For the trends, the frequency of hourly precipitation extremes shows a rising trend in southern China, and intensity increased in northern China. Conversely, for daily precipitation extremes, the enhanced intensity and frequency are observed only in southeastern China. Additionally, the eastern coastal area experiences prolonged hourly rainfall with increasing persistence, while the region with an increased duration of daily precipitation is situated further south. Comparing the periods 1980–1999 and 2000–2020, precipitation extremes shift toward more intense and more frequent, with more pronounced variations at the hourly scale. Notably, northeastern China tend to be more intense and shorter‐lived hourly precipitation extremes after 1999, while south China experiences more frequent and longer‐lasting extremes. These changes are partly attributed to the increased unstable conditions, enhanced convective available potential energy and weakened convective inhibition. [ABSTRACT FROM AUTHOR]

Precipitation stable-isotope data are often used in hydroclimatic, hydrological and hydrogeological investigations, with measurements typically undertaken on integrated monthly samples. However, daily sampling reveals overlooked aspects of controls on precipitation isotope values, including synoptic meteorological conditions. We present a one-year record of stable isotopes in daily precipitation during 2021, from a site in SE England close to Greater London. We find marked daily variability over the course of the year (–15.62 to +0.92 ‰ for δ18O, –108.7 to +2.9 ‰ for δ2H and –6.5 to +23.1 ‰ for deuterium excess). Correlations with individual meteorological variables including precipitation amount, temperature and weather type are moderate to weak suggesting complex controls on the daily rainfall isotope values. The daily data are compared with three other daily datasets from England and, by conversion to monthly values, directly with data from three long-term collection stations across Britain and Ireland. The scale of variability in the daily data from our site is consistent with that seen in other English records despite them all coming from different time periods. The monthly data show broad consistency, although there are differences that also highlight geographical variability in precipitation values across the British Isles. [ABSTRACT FROM AUTHOR]

Accurate and precise KDP estimates are essential for radar-based applications, especially in quantitative precipitation estimation and radar data quality control routines. The accuracy of these estimates largely depends on the post-processing of the radar's measured Φ DP , which aims to reduce noise and backscattering effects while preserving fine-scale precipitation features. In this study, we evaluate the performance of several publicly available KDP estimation methods implemented in open-source libraries such as PyArt and Wradlib, and the method used in the Vaisala weather radars. To benchmark these methods, we employ a polarimetric self-consistency approach that relates KDP to reflectivity and differential reflectivity in rain, providing a reference self-consistency KDP (K DPSC) for comparison. This approach allows for the construction of the reference KDP observations that can be used to assess the accuracy and robustness of the studied KDP estimation methods. We assess each method by quantifying uncertainties using C-band weather radar observations where the reflectivity values ranged between 20 and 50 dBZ. Using the proposed evaluation framework we could define optimized parameter settings for the methods that have user-configurable parameters. Most of such methods showed significant reduction in the estimation errors after the optimization with respect to the default settings. We have found significant differences in the performances of the studied methods, where the best performing methods showed smaller normalized biases in the high reflectivity values (i.e., ≥ 40 dBZ) and overall smaller normalized root mean squared errors across the range of reflectivity values. [ABSTRACT FROM AUTHOR]

The theory of radiative transfer in the atmosphere is crucial in the study of climate, because radiative exchanges are at the origin of the atmospheric dynamics. It is therefore important to evaluate this phenomenon in order to be able to take effective measures to tackle climate change. The objective of this work is to evaluate the capability of the RegCM5 climate model to reproduce radiative transfer over Central Africa. The analysis is carried out over a 10‐year period, from January 2002 to December 2011 preceded by 1 year as spin‐up. RegCM5 model were evaluated using the ERA5 dataset for the radiative transfer parameters (the shortwave radiation [SWR], longwave radiation [LWR], cloud cover [CLT], surface albedo [ALB] and surface temperature), as well as CHIRPS dataset for precipitation. Three subregions were identified for more specific analysis of the model, namely the Sahel, Congo basin and Cameroon highlands. Two radiative schemes were used: the radiative scheme of the community climate model (CCM) and Rapid Radiative Transfer Model (RRTM). The assessment of radiative transfer parameters was carried out by examining their seasonal variability and annual cycles using data from two RegCM5 experiments, RegCM5‐CCM3 and RegCM5‐RRTM. Before this assessment, a sensibility analysis to convective schemes carried out with the default RegCM5 radiative scheme (CCM3) shows that Grell scheme with Arakawa and Shulber closure is the best scheme to represent key radiation parameters (LWR and SWR). This convective scheme is therefore used for assessing the two Radiative transfer schemes. Results show that both RegCM5 experiments simulate relatively well the variables linked to radiative transfer for the four seasons of the year. However, RegCM5 with RRTM as radiative scheme depicts better performance over all subregions and seasons, suggesting that the choice of this scheme does not depend on land cover, topography and rainfall regimes in a complex region such as Central Africa. [ABSTRACT FROM AUTHOR]

AbstractRainfall infiltration is a primary cause of slope failure. Analyzing the entire process of a landslide triggered by rainfall is crucial for the design of infrastructure. However, the complexity associated with simulating large deformations has often hindered previous studies from modeling the entire landslide process subsequent to rainfall infiltration. This study aims to provide a method to simulate the entire process of landslides considering rainfall and more insight into the mechanism of landslides. The discrete element method (DEM) is first applied to model the entire process of shallow landslides triggered by rainfall. The numerical results indicate that the intensity and duration of rainfall infiltration have significant impacts on the slope stability. The particle displacement field, resulting from landslides due to rainfall, corresponds with three distinct stages within the landslide process. The increased rainfall intensity causes the area of the particles within failure mass grows rapidly during the initial stage of the landslide. The closer to the slope surface the faster the particle velocity. The simulations demonstrate that the slope surface transitions from a linear to a concave downward geometry during the landslides. Larger particles display greater displacement and velocity compared to smaller particles. [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]

ABSTRACT Slopes in nature usually present layered characteristics, and its stability is susceptible to rainfall events. Considering that current analytical solutions are only suited to simulate the rainfall infiltration of double‐layered infinite unsaturated slopes, an analytical procedure is hence proposed in this study to tackle the consideration of multiple layers. The variable separation method and transfer matrix method are combined to derive the analytical solution of pore water pressure (PWP) for simulating rainfall infiltration in layered infinite unsaturated slopes. After having validated the proposed model and analytical solutions by comparing with existing literature and numerical simulation, the closed‐form solution of PWP is incorporated into the limit equilibrium for assessing slope stability. A three‐layer slope is selected as an example for further discussion. PWP distribution and factor of safety are calculated, considering the effects of saturated hydraulic conductivity and thickness of the upper layer, intensity of antecedent and subsequent rainfall, and varied soil unit weight along the depth. The slope stability subjected to rainfall effects is consistent with the variation of PWP. The proposed analytical solutions provide a simple and practical avenue for computing PWP distribution and evaluating the stability of multi‐layered slopes under rainfall conditions, which can also serve as a benchmark for numerical solutions. [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]

In recent years, heavy rainfall disasters linked to climate change have become more frequent, raising concerns about the release of chemicals stored in factories. Assessing chemical contamination during such emergencies therefore necessitates the development of a quick and easy method for evaluating hazardous contaminants in combination with toxicity testing. This study proposes a "toxicity screening" method that combines biological response testing and chemical analysis to systematically evaluate hazardous contaminants in emergency situations. The toxicity screening method evaluates the water quality in three steps, including water quality measurements and a delayed fluorescence (DF) assay, metal content measurements and a DF assay, and targeted screening analysis and a DF assay. The efficacy of this method was tested using industrial wastewater from 14 locations. Seven of the samples were non-toxic, while the other seven samples were toxic, displaying no observed effect concentration (NOEC) values ranging from 0.625 to 20%. Two toxic samples in the first phase possessed high total chlorine concentrations (0.4 mg L−1) and conductivities (2200 mS m−1), indicating that the main sources of toxicity were residual chlorine and a high salt concentration. In the second phase, metal content analysis identified metals as the toxicity cause in four samples. In the third phase, the organic contaminants were analyzed, and tri-n-octyl phosphate (TNOP) was detected at a concentration of 0.00027 mg L−1. The results of solid-phase extraction experiments and exposure tests with TNOP alone indicated that the contribution of TNOP to the toxicity was negligible and that chemicals not adsorbed on the solid-phase extraction cartridges were the cause of toxicity. The proposed method can therefore be considered effective for disaster-related water quality assessment, delivering results within 12 days. [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]

Rainfall intensity and land use changes and from natural vegetation to cultivated fields both impact on river streamflows and sediment dynamics. Assessing the magnitude of these flow and sediment changes is important for water management. However, many rivers are still under-studied/monitored and data sparse. Fully distributed rainfall–runoff models, such as the Jena Adaptable Modelling System (JAMS/J2000), allow for simulation of streamflow, its flow components and potential sediment yield given land use and hydrological changes. This study demonstrates the use of this method in the Bot River, a data-scarce catchment in South Africa. A hydrological model was implemented, and sediment yield was estimated under three land use and two hydrological flow component scenarios. The model reported Nash-Sutcliffe efficiencies (E2) of 0.64 and 0.32 (for streamflow) for model calibration and validation periods, respectively. Modelled mean annual sediment yield increased from 423 t/km2/year in 1990 to 490 t/km2/year in 2018, attributable to land use changes. [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]

The general view of trees is that they threaten heritage structures, their roots disrupt archaeological features, or that they create microclimates conducive to biodeterioration. The conservator's experience in assessing the impact of trees on various cultural assets highlights the positive role they can play in modifying the outdoor environment to reduce stress mechanisms. Trees intercept sunlight and rain, absorb ground water and stabilise soils. This is particularly beneficial at rock art sites where infrastructure must be kept to a minimum to maintain the spiritual and aesthetic ambience. While focussing on studies demonstrating the protective role of trees, it is critical to make balanced assessments that recognise both positive and negative implications; a tree can both shade an object and increase the risk of mechanical damage from roots and falling branches. Objective assessments consider all implications rather than through intellectual and operational bias. Laboratory studies demonstrate hydrothermal stress to be greater than heating or wetting alone, and freeze–thaw that has been traditionally considered one of the most destructive mechanisms disrupting outdoor stone. Interventive approaches for reducing hydrothermal stresses in stone rely upon chemical treatments to repel water, and consolidation to better resist such stresses. A well-designed tree canopy can substantially reduce thermal expansion and almost completely remove rain from the object through interception, funnelling to the trunk and uptake of groundwater. [ABSTRACT FROM AUTHOR]

Land degradation is a significant global environmental issue, and its prevention has become a serious concern of the twenty-first century, particularly in the context of anthropogenic impact and climate change. It is crucial to map and assess the grade of land degradation to determine vulnerable areas. The Indian Himalayan region faces heightened risks with its steep terrain, active tectonics, heavy precipitation, and human interventions. This study focuses on the Nandakini Watershed. Employing AHP technique, the study calculates the Land Degradation Vulnerability Index (LDVI) using slope, terrain ruggedness, rainfall, temperature, LULC, soil depth, and organic carbon content. The results indicate that the upper region is highly vulnerable, and a higher slope influences the runoff in the area, followed by factors like NDVI and rainfall, collectively contributing to 62% of the overall index. In this context, the upstream regions of the watershed display a high LDVI, while most of the area falls within the low LDVI category. This indicates that approximately 2.10% of the study area is highly susceptible to soil erosion. RUSLE-based estimates have been used to further validate the LDVI result. The integrated LDVI offers a valuable tool for prioritising soil conservation and disaster mitigation. [ABSTRACT FROM AUTHOR]